From d6e40349689c4a6086bbe39b9bd2bd772f6cb72e Mon Sep 17 00:00:00 2001 From: Karan Sampath <176953591+karanataryn@users.noreply.github.com> Date: Mon, 27 Jan 2025 12:45:42 -0800 Subject: [PATCH 1/5] delete --- apps/crawler/README.md | 37 - apps/crawler/crawler/http/Dockerfile | 39 - apps/crawler/crawler/http/crawler/__init__.py | 0 apps/crawler/crawler/http/crawler/items.py | 12 - .../crawler/http/crawler/middlewares.py | 103 - .../crawler/crawler/http/crawler/pipelines.py | 13 - apps/crawler/crawler/http/crawler/settings.py | 36 - .../crawler/http/crawler/spiders/__init__.py | 4 - .../crawler/http/crawler/spiders/sycamore.py | 112 - apps/crawler/crawler/http/run-crawler.sh | 31 - apps/crawler/crawler/http/scrapy.cfg | 11 - .../http/tests/http_crawler_integration.sh | 124 - .../crawler/http/tests/test_http_server.py | 21 - .../crawler/crawler/http/tests/visit_aryn.pdf | Bin 8702 -> 0 bytes apps/crawler/crawler/s3/Dockerfile | 44 - apps/crawler/crawler/s3/__init__.py | 0 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--- a/apps/crawler/README.md +++ /dev/null @@ -1,37 +0,0 @@ -# crawler - -Crawler component - -## Overview - -### HTTP crawler - -The Sycamore HTTP crawler is based on [scrapy](http://scrapy.org), a framework for writing web -crawlers. We override the crawl class to follow links within a single domain starting from a -single root and store those files locally to match with how the Sycamore importer wants them. The -crawler uses scrapy's RFC2616 caching in order to reduce the load from crawling. - -## Branches - -* main: releaseable quality work -* dev-_username_*: checkpoints for sharing interim work - -## Build - -### To create the volume -docker volume create crawl_data - -### To update the http crawler -docker build -t crawler_http crawler/http/Dockerfile - -### To update the S3 crawler, from root directory run -docker build -t crawler_s3 -f crawler/s3/Dockerfile . - -### To use the http crawler -docker run -v crawl_data:/app/.data/.scrapy crawler_http - -### To use s3 crawler -docker run -v crawl_data:/app/.data/.s3 -e AWS_ACCESS_KEY_ID -e AWS_SECRET_ACCESS_KEY -e AWS_SESSION_TOKEN crawler_s3 - -### To look at the data -docker run -it -v crawl_data:/app/.data ubuntu bash diff --git a/apps/crawler/crawler/http/Dockerfile b/apps/crawler/crawler/http/Dockerfile deleted file mode 100644 index 05fe38c92..000000000 --- a/apps/crawler/crawler/http/Dockerfile +++ /dev/null @@ -1,39 +0,0 @@ -# Repo name: arynai/sycamore-crawler-http - -# Match Python version UI uses. PyTorch not compatible with 3.12 yet. -FROM python:3.11 - -WORKDIR /app -COPY docker-app-user.sh . -RUN /bin/bash /app/docker-app-user.sh - -RUN pip install poetry - -USER app -ENV POETRY_NO_INTERACTION=1 \ - POETRY_VIRTUALENVS_IN_PROJECT=1 \ - POETRY_VIRTUALENVS_CREATE=1 \ - POETRY_CACHE_DIR=/tmp/poetry_cache - -COPY apps/crawler/pyproject.toml apps/crawler/poetry.lock apps/crawler/README.md ./ -RUN poetry install --only crawler_http --no-root && rm -rf $POETRY_CACHE_DIR - -COPY apps/crawler/crawler/http . -# Hack because pyproject.toml expects a sycamore directory -RUN mkdir sycamore && touch sycamore/__init__.py -RUN poetry install --only-root && rm -rf $POETRY_CACHE_DIR - -ARG GIT_BRANCH="main" -ARG GIT_COMMIT="unknown" -ARG GIT_DIFF="unknown" - -ENV GIT_BRANCH=${GIT_BRANCH} -ENV GIT_COMMIT=${GIT_COMMIT} -ENV GIT_DIFF=${GIT_DIFF} - -LABEL org.opencontainers.image.authors="opensource@aryn.ai" -LABEL git_branch=${GIT_BRANCH} -LABEL git_commit=${GIT_COMMIT} -LABEL git_diff=${GIT_DIFF} - -ENTRYPOINT [ "./run-crawler.sh" ] diff --git a/apps/crawler/crawler/http/crawler/__init__.py b/apps/crawler/crawler/http/crawler/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/crawler/crawler/http/crawler/items.py b/apps/crawler/crawler/http/crawler/items.py deleted file mode 100644 index b3d39d6db..000000000 --- a/apps/crawler/crawler/http/crawler/items.py +++ /dev/null @@ -1,12 +0,0 @@ -# Define here the models for your scraped items -# -# See documentation in: -# https://docs.scrapy.org/en/latest/topics/items.html - -import scrapy - - -class CrawlerItem(scrapy.Item): - # define the fields for your item here like: - # name = scrapy.Field() - pass diff --git a/apps/crawler/crawler/http/crawler/middlewares.py b/apps/crawler/crawler/http/crawler/middlewares.py deleted file mode 100644 index c843d64ca..000000000 --- a/apps/crawler/crawler/http/crawler/middlewares.py +++ /dev/null @@ -1,103 +0,0 @@ -# Define here the models for your spider middleware -# -# See documentation in: -# https://docs.scrapy.org/en/latest/topics/spider-middleware.html - -from scrapy import signals - -# useful for handling different item types with a single interface -# from itemadapter import is_item, ItemAdapter - - -class CrawlerSpiderMiddleware: - # Not all methods need to be defined. If a method is not defined, - # scrapy acts as if the spider middleware does not modify the - # passed objects. - - @classmethod - def from_crawler(cls, crawler): - # This method is used by Scrapy to create your spiders. - s = cls() - crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) - return s - - def process_spider_input(self, response, spider): - # Called for each response that goes through the spider - # middleware and into the spider. - - # Should return None or raise an exception. - return None - - def process_spider_output(self, response, result, spider): - # Called with the results returned from the Spider, after - # it has processed the response. - - # Must return an iterable of Request, or item objects. - for i in result: - yield i - - def process_spider_exception(self, response, exception, spider): - # Called when a spider or process_spider_input() method - # (from other spider middleware) raises an exception. - - # Should return either None or an iterable of Request or item objects. - pass - - def process_start_requests(self, start_requests, spider): - # Called with the start requests of the spider, and works - # similarly to the process_spider_output() method, except - # that it doesn’t have a response associated. - - # Must return only requests (not items). - for r in start_requests: - yield r - - def spider_opened(self, spider): - spider.logger.info("Spider opened: %s" % spider.name) - - -class CrawlerDownloaderMiddleware: - # Not all methods need to be defined. If a method is not defined, - # scrapy acts as if the downloader middleware does not modify the - # passed objects. - - @classmethod - def from_crawler(cls, crawler): - # This method is used by Scrapy to create your spiders. - s = cls() - crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) - return s - - def process_request(self, request, spider): - # Called for each request that goes through the downloader - # middleware. - - # Must either: - # - return None: continue processing this request - # - or return a Response object - # - or return a Request object - # - or raise IgnoreRequest: process_exception() methods of - # installed downloader middleware will be called - return None - - def process_response(self, request, response, spider): - # Called with the response returned from the downloader. - - # Must either; - # - return a Response object - # - return a Request object - # - or raise IgnoreRequest - return response - - def process_exception(self, request, exception, spider): - # Called when a download handler or a process_request() - # (from other downloader middleware) raises an exception. - - # Must either: - # - return None: continue processing this exception - # - return a Response object: stops process_exception() chain - # - return a Request object: stops process_exception() chain - pass - - def spider_opened(self, spider): - spider.logger.info("Spider opened: %s" % spider.name) diff --git a/apps/crawler/crawler/http/crawler/pipelines.py b/apps/crawler/crawler/http/crawler/pipelines.py deleted file mode 100644 index 2826acb5f..000000000 --- a/apps/crawler/crawler/http/crawler/pipelines.py +++ /dev/null @@ -1,13 +0,0 @@ -# Define your item pipelines here -# -# Don't forget to add your pipeline to the ITEM_PIPELINES setting -# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html - - -# useful for handling different item types with a single interface -# from itemadapter import ItemAdapter - - -class CrawlerPipeline: - def process_item(self, item, spider): - return item diff --git a/apps/crawler/crawler/http/crawler/settings.py b/apps/crawler/crawler/http/crawler/settings.py deleted file mode 100644 index c347a01f7..000000000 --- a/apps/crawler/crawler/http/crawler/settings.py +++ /dev/null @@ -1,36 +0,0 @@ -# Scrapy settings for crawler project -# -# For simplicity, this file contains only settings considered important or -# commonly used. You can find more settings consulting the documentation: -# -# https://docs.scrapy.org/en/latest/topics/settings.html -# https://docs.scrapy.org/en/latest/topics/downloader-middleware.html -# https://docs.scrapy.org/en/latest/topics/spider-middleware.html - -BOT_NAME = "crawler" - -SPIDER_MODULES = ["crawler.spiders"] -NEWSPIDER_MODULE = "crawler.spiders" - - -# Crawl responsibly by identifying yourself (and your website) on the user-agent -USER_AGENT = "sycamore crawler (+https://github.com/aryn-ai/sycamore)" - -# Crawl at about 1 QPS -DOWNLOAD_DELAY = 1 - -# Obey robots.txt rules -ROBOTSTXT_OBEY = True - -# Enable and configure HTTP caching (disabled by default) -# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings -HTTPCACHE_ENABLED = True -HTTPCACHE_EXPIRATION_SECS = 0 -HTTPCACHE_DIR = "httpcache" -HTTPCACHE_STORAGE = "scrapy.extensions.httpcache.FilesystemCacheStorage" -HTTPCACHE_POLICY = "scrapy.extensions.httpcache.RFC2616Policy" - -# Set settings whose default value is deprecated to a future-proof value -REQUEST_FINGERPRINTER_IMPLEMENTATION = "2.7" -TWISTED_REACTOR = "twisted.internet.asyncioreactor.AsyncioSelectorReactor" -FEED_EXPORT_ENCODING = "utf-8" diff --git a/apps/crawler/crawler/http/crawler/spiders/__init__.py b/apps/crawler/crawler/http/crawler/spiders/__init__.py deleted file mode 100644 index ebd689ac5..000000000 --- a/apps/crawler/crawler/http/crawler/spiders/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# This package will contain the spiders of your Scrapy project -# -# Please refer to the documentation for information on how to create and manage -# your spiders. diff --git a/apps/crawler/crawler/http/crawler/spiders/sycamore.py b/apps/crawler/crawler/http/crawler/spiders/sycamore.py deleted file mode 100644 index a5c3feed1..000000000 --- a/apps/crawler/crawler/http/crawler/spiders/sycamore.py +++ /dev/null @@ -1,112 +0,0 @@ -from pathlib import Path - -import os -import re -import scrapy -import time -import urllib - - -class SycamoreSpider(scrapy.Spider): - name = "sycamore" - - def __init__(self, category=None, *args, **kwargs): - self.dest_dir = kwargs.get("dest_dir", ".scrapy/downloads") - self.prefix = None - if "preset" in kwargs: - self._setup_by_preset(kwargs["preset"]) - elif "url" in kwargs: - self.start_urls = [kwargs["url"]] - if "prefix" in kwargs: - self.prefix = kwargs["prefix"] - if "domain" in kwargs: - self.allowed_domains = [kwargs["domain"]] - else: - u = urllib.parse.urlparse(kwargs["url"]) - d = u.hostname.removeprefix("www.") - print("Using inferred domain", d) - self.allowed_domains = [d] - else: - print( - "Neither -a preset= or -a url=... and an optional -a prefix=... or -a domain=... set.\n" - + "Using default, tiny single document crawl" - ) - self._setup_by_preset("sort_single") - - def _setup_by_preset(self, preset): - presets = { - "sort_single": ["sortbenchmark.org", "http://sortbenchmark.org/2004_Nsort_Minutesort.pdf"], - "sort": ["sortbenchmark.org", "http://sortbenchmark.org/"], - "aryn": ["aryn.ai", "http://aryn.ai"], - "integration_test": ["localhost", "http://localhost:13756"], - } - if preset in presets: - v = presets[preset] - self.allowed_domains = [v[0]] - self.start_urls = [v[1]] - else: - v = list(presets.keys()) - v.sort() - raise RuntimeError("Unknown preset " + preset + ". Expected one of: " + " ".join(v)) - - def parse(self, response): - lm = self._last_modified_time_unix(response) - ct = self._content_type(response) - name = os.path.join(ct, re.sub("/", "_", response.url)) - - file = os.path.join(self.dest_dir, name) - if ct == "pdf" and not file.endswith(".pdf"): - file = file + ".pdf" - - if self._possibly_modified(lm, file): - print("Store", response.url, "as", file) - os.makedirs(os.path.dirname(file), exist_ok=True) - Path(file).write_bytes(response.body) - os.utime(file, (time.time(), lm)) - - if ct == "html": - print("Processing", response.url) - # Scrapy docs imply this should work, but it doesn't. - # It misses pdf links on sortbenchmark.org - # links = LinkExtractor(allow = '^http').extract_links(response) - links = response.css("a::attr(href)").getall() - for i in links: - i = urllib.parse.urljoin(response.url, i) - if not i.startswith("http"): - continue - - if self.prefix is not None and not i.startswith(self.prefix): - print(" Skipping", i, "as it does not start with", self.prefix) - continue - - print(" Following", i) - yield scrapy.Request(i, callback=self.parse) - - def _content_type(self, response): - ctk = "Content-Type" - if ctk not in response.headers: - print("No Content-Type ", response.url) - return "unknown" - ct = response.headers[ctk] - if ct.startswith(b"text/html"): - return "html" - if ct == b"application/pdf": - return "pdf" - print('Unknown content type "', ct, '" in ', response.url) - return "unknown" - - def _last_modified_time_unix(self, response): - lm = "Last-Modified" - if lm not in response.headers: - print("WARNING: " + response.url + " is missing last-modified header") - return True - date = str(response.headers[lm], "UTF-8") - t = time.strptime(date, "%a, %d %b %Y %H:%M:%S %Z") - return time.mktime(t) - - def _possibly_modified(self, last, file): - try: - m = os.path.getmtime(file) - return m < last - except FileNotFoundError: - return True diff --git a/apps/crawler/crawler/http/run-crawler.sh b/apps/crawler/crawler/http/run-crawler.sh deleted file mode 100755 index a6c57e7b2..000000000 --- a/apps/crawler/crawler/http/run-crawler.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash -echo "Version-Info, Sycamore Crawler HTTP Branch: ${GIT_BRANCH}" -echo "Version-Info, Sycamore Crawler HTTP Commit: ${GIT_COMMIT}" -echo "Version-Info, Sycamore Crawler HTTP Diff: ${GIT_DIFF}" - -if [[ -O /app && -O /app/.scrapy ]]; then - : # ok, proper ownership -else - echo "ERROR: /app or /app/.scrapy has incorrect ownership" - echo "TO FIX: docker compose run fixuser" - exit 1 -fi -case "$#-$1" in - 1-http*) - poetry run scrapy crawl sycamore -a url="$1" - exit 0 - ;; - 1-help|1---help) - echo - echo "Usage:" - echo "# crawl starting at a url to the domain associated with that url (stripping www)" - echo "docker compose up sycamore_crawler_http " - echo "# crawl starting at a url with a specified domain" - echo "or docker compose up sycamore_crawler_http -a url= -a domain=" - echo "# crawl starting at a url ignoring links that don't match the prefix" - echo "or docker compose up sycamore_crawler_http -a url= -a prefix=" - exit 0 - ;; -esac - -poetry run scrapy crawl sycamore "$@" diff --git a/apps/crawler/crawler/http/scrapy.cfg b/apps/crawler/crawler/http/scrapy.cfg deleted file mode 100644 index 83a4eef6a..000000000 --- a/apps/crawler/crawler/http/scrapy.cfg +++ /dev/null @@ -1,11 +0,0 @@ -# Automatically created by: scrapy startproject -# -# For more information about the [deploy] section see: -# https://scrapyd.readthedocs.io/en/latest/deploy.html - -[settings] -default = crawler.settings - -[deploy] -#url = http://localhost:6800/ -project = crawler diff --git a/apps/crawler/crawler/http/tests/http_crawler_integration.sh b/apps/crawler/crawler/http/tests/http_crawler_integration.sh deleted file mode 100755 index bb5108f2d..000000000 --- a/apps/crawler/crawler/http/tests/http_crawler_integration.sh +++ /dev/null @@ -1,124 +0,0 @@ -#!/bin/bash -set -e - -cleanup() { - echo "Starting cleanup" - if [[ -z "${WORK_DIR}" ]]; then - echo " No WORK_DIR, no files to cleanup" - else - echo " Delete ${WORK_DIR}" - rm -rf "${WORK_DIR}" || true - fi - if [[ -z "${HTTP_PID}" ]]; then - echo " No HTTP_PID, missing http server" - else - echo " Kill http server ${HTTP_PID}" - kill -TERM "${HTTP_PID}" || true - fi - echo "Finished cleanup" -} - -trap "cleanup" EXIT - -main() { - if curl localhost:13756 >/dev/null 2>&1; then - echo "ERROR: You already have something running on localhost:13756" - echo "ERROR: It could be a previous run of this test." - echo "ERROR: To find leftovers: ps -efl | grep test_http_server" - ps -efl | grep test_http_server - echo "ERROR: Clean this up so that curl localhost:13756 returns an error" - exit 1 - fi - # run test in the http directory - cd "$(dirname "$0")"/.. - setup - - test_simple - test_update - test_prefix - check_only_localhost - - # put this after phases to keep the working directory around for debugging - # pause - exit 0 -} - -setup() { - if [ "$(uname -s)" = "Darwin" ]; then - WORK_DIR=$(mktemp -d -t crawler_test.XXXXXXXXX) - else - WORK_DIR=$(mktemp -d --tmpdir crawler_test.XXXXXXXXX) - fi - echo "Using workdir ${WORK_DIR}" - SRC_DIR="${PWD}" - cd "${WORK_DIR}" - pwd - mkdir http_serve work - cat >http_serve/robots.txt <<'EOF' -User-agent: * -Allow: / -EOF - echo 'Go to http://aryn.ai/' >http_serve/example1.txt - echo 'Visit Aryn' >http_serve/example2.html - cp "${SRC_DIR}/tests/visit_aryn.pdf" http_serve/example3.pdf - cp "${SRC_DIR}/tests/visit_aryn.pdf" http_serve/example4.qdf - python3 "${SRC_DIR}/tests/test_http_server.py" >http_server.log 2>&1 & - HTTP_PID=$! -} - -scrape() { - scrape_complex "$1" -a dest_dir="${WORK_DIR}/work" -a preset=integration_test -} - -scrape_complex() { - what="$1" - shift - local log="${WORK_DIR}/scrape.log.$what" - echo "Scraping for $what" - (cd "${SRC_DIR}" && poetry run scrapy crawl sycamore "$@" >"${log}" 2>&1) \ - || die "scrape failed" -} - -die() { - echo "ERROR: $@" - pause - exit 1 -} - -test_simple() { - scrape simple - [[ -f work/unknown/http:__localhost:13756_example1.txt ]] || die "missing example1" - [[ -f work/html/http:__localhost:13756_example2.html ]] || die "missing example2" - [[ -f work/pdf/http:__localhost:13756_example3.pdf ]] || die "missing example3" - [[ -f work/pdf/http:__localhost:13756_example4.qdf.pdf ]] || die "missing example3" -} - -test_update() { - # make sure last modified seconds changes - sleep 1 - echo 'updated' >http_serve/example1.txt - scrape update - cmp http_serve/example1.txt work/unknown/http:__localhost:13756_example1.txt \ - || die "failed to update" -} - -test_prefix() { - scrape_complex "prefix" -a dest_dir="${WORK_DIR}/prefix" -a url=http://localhost:13756 -a prefix=http://localhost:13756/example3 - [[ -f prefix/pdf/http:__localhost:13756_example3.pdf ]] || die "missing example3" - [[ $(find prefix -type f -print | grep -c localhost) == 2 ]] || die "Prefix download did not download exactly 2 files" -} - -pause() { - echo -n "return to continue, ctrl-c to abort: " - read foo -} - -check_only_localhost() { - # only files matching localhost should have been downloaded even though there is a link to - # aryn.ai in the html sample - [[ $(find work -type f -print | grep -c -v localhost) == 0 ]] || die "Found non localhost files" - # expect to have downloaded exactly 6 files (root, robots.txt, 4 examples) - [[ $(find work -type f -print | grep -c localhost) == 6 ]] || die "Did not download exactly 5 files" -} - -main diff --git a/apps/crawler/crawler/http/tests/test_http_server.py b/apps/crawler/crawler/http/tests/test_http_server.py deleted file mode 100644 index 9e76a039d..000000000 --- a/apps/crawler/crawler/http/tests/test_http_server.py +++ /dev/null @@ -1,21 +0,0 @@ -import http.server -import os -import socketserver - -IPADDR = "127.0.0.1" -PORT = 13756 - -os.chdir("http_serve") -Handler = http.server.SimpleHTTPRequestHandler - 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s.st_gid != 1000: - raise RuntimeError( - f"Incorrect ownership on /app/.data/.s3 {s.st_uid},{s.st_gid}" - "\nTo fix: docker compose run fixuser" - ) - - def crawl(self) -> None: - try: - self._s3_client = self._get_s3_client() - self._find_and_download_new_objects() - return - except NoCredentialsError: - if self._anon: - raise - print("Automatically retrying in anonymous mode") - self._anon = True - self._s3_client = self._get_s3_client() - self._find_and_download_new_objects() - - def _find_and_download_new_objects(self) -> None: - paginator = self._s3_client.get_paginator("list_objects_v2") - page_iterator = paginator.paginate(Bucket=self._bucket_location, Prefix=self._prefix) - - for page in page_iterator: - for s3_object in page["Contents"]: - object_key = s3_object["Key"] - if not object_key.endswith("/"): - self._download_if_new_object(s3_object) # type: ignore - else: - print("WARNING, ignoring directory-like", object_key) - - # TODO: Parth - Rewrite this using s3.get_object API instead and handle the content type. - def _download_if_new_object(self, object_metadata: dict[str, Any]) -> None: - object_key = object_metadata["Key"] - file_path = os.path.join( - self._file_storage_location, self._get_file_extension(object_key), self._get_file_name(object_key) - ) - try: - if self._is_new_object(object_metadata, file_path): - print("Downloading", object_key, "as", file_path) - os.makedirs(os.path.dirname(file_path), exist_ok=True) - self._s3_client.download_file(self._bucket_location, object_key, file_path) - else: - # print("Skipping up-to-date", object_key) - pass - except Exception: - raise - - def _get_file_name(self, object_key: str) -> str: - return object_key.replace("/", "_") - - def _get_file_extension(self, object_key: str) -> str: - extension = os.path.splitext(object_key)[1][1:] - if extension == "pdf": - return "pdf" - elif extension == "html": - return "html" - else: - return "unknown" - - def _is_new_object(self, object_metadata: dict[str, Any], file_path: str) -> bool: - try: - last_modified_os = datetime.datetime.fromtimestamp(os.path.getmtime(file_path), datetime.timezone.utc) - last_modified_s3 = object_metadata.get("LastModified") - if last_modified_s3 is None: - print("WARNING: missing LastModified on object metadata, assuming newer", object_metadata["Key"]) - return False - - return last_modified_os < last_modified_s3 - except FileNotFoundError: - # TODO: parth - if I change this to return False, no test fails - return True - - def _get_s3_client(self) -> S3Client: - if self._anon: - cfg = Config(signature_version=UNSIGNED) - return self._session.client("s3", config=cfg) - else: - return self._session.client("s3") - - -if __name__ == "__main__": - print("Version-Info, Sycamore Crawler S3 Branch:", os.environ.get("GIT_BRANCH", "unset")) - print("Version-Info, Sycamore Crawler S3 Commit:", os.environ.get("GIT_COMMIT", "unset")) - print("Version-Info, Sycamore Crawler S3 Diff:", os.environ.get("GIT_DIFF", "unset")) - - parser = argparse.ArgumentParser( - description="The Sycamore crawler for Amazon AWS S3, from Aryn.ai", - ) - parser.add_argument("bucket", nargs="?", default="", help="The AWS S3 bucket to crawl") - parser.add_argument("prefix", nargs="?", default="", help="The prefix within the bucket to crawl") - parser.add_argument("--anon", action="store_true", help="For accessing public buckets without credentials") - args = parser.parse_args() - - # We'd like to auto-detect when no credentials are present, instead of - # using --anon, but boto3 is clever finding creds and does so very late. - - if not args.bucket: - args.bucket = "aryn-public" - args.prefix = "sort-benchmark" - - s3 = S3Crawler(args.bucket, args.prefix, args.anon) - s3.crawl() diff --git a/apps/crawler/crawler/s3/tests/__init__.py b/apps/crawler/crawler/s3/tests/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/crawler/crawler/s3/tests/unit/test_s3_crawler.py b/apps/crawler/crawler/s3/tests/unit/test_s3_crawler.py deleted file mode 100644 index 0137bc171..000000000 --- a/apps/crawler/crawler/s3/tests/unit/test_s3_crawler.py +++ /dev/null @@ -1,110 +0,0 @@ -from datetime import datetime, timedelta, timezone -from pathlib import Path -from typing import Any - -import botocore.paginate - -from crawler.s3.crawler import s3_crawler -from crawler.s3.crawler.s3_crawler import S3Crawler - - -# TODO : Parth - investigate doing an integration test similar to the http one. Maybe using minio or localstack -# TODO: Test anonymous mode; may need to be an integration test. -class TestS3Crawler: - def setup_mocks( - self, - mocker, - crawler: S3Crawler, - mock_page_iterator: list[dict[str, Any]], - downloaded_files: list[str], - ): - client = mocker.patch.object(crawler, "_get_s3_client") - mock_client = crawler._session.client("s3") - client.return_value = mock_client - - paginator = mocker.patch.object(mock_client, "get_paginator") - mock_paginator = mocker.Mock(spec=botocore.paginate.Paginator) - paginator.return_value = mock_paginator - - paginate = mocker.patch.object(mock_paginator, "paginate") - paginate.return_value = mock_page_iterator - - mock_os = mocker.patch.object(s3_crawler.os.path, "getmtime") - mock_os.return_value = (datetime.now(timezone.utc) - timedelta(1)).timestamp() - - def mock_download_func(bucket, key, file): - downloaded_files.append(key) - - download = mocker.patch.object(mock_client, "download_file") - download.side_effect = mock_download_func - - def test_s3_crawler_first_crawl(self, mocker, tmp_path: Path): - s3_crawler = S3Crawler("bucket", "prefix", False) - mock_page_iterator = [ - { - "Contents": [ - {"Key": "folder/key1", "Etag": "etag1", "LastModified": datetime.now(timezone.utc), "Size": "0"} - ] - } - ] - - downloaded_objects: list[str] = [] - self.setup_mocks(mocker, s3_crawler, mock_page_iterator, downloaded_objects) - - s3_crawler.crawl() - assert len(mock_page_iterator[0]["Contents"]) == len(downloaded_objects) - - def test_s3_crawler_finds_new_object(self, mocker, tmp_path: Path): - s3_crawler = S3Crawler("bucket", "prefix", False) - mock_page_iterator = [ - { - "Contents": [ - { - "Key": "folder/key1.pdf", - "Etag": "etag1", - "LastModified": datetime.now(timezone.utc) + timedelta(1), - "Size": "0", - }, - { - "Key": "folder/key2.pdf", - "Etag": "etag2", - "LastModified": datetime.now(timezone.utc) - timedelta(2), - "Size": "0", - }, - ] - } - ] - - downloaded_objects: list[str] = [] - self.setup_mocks(mocker, s3_crawler, mock_page_iterator, downloaded_objects) - - s3_crawler.crawl() - assert len(mock_page_iterator[0]["Contents"]) == len(downloaded_objects) + 1 - - def test_s3_crawler_nothing_to_crawl(self, mocker, tmp_path: Path): - s3_crawler = S3Crawler("bucket", "prefix", True) - - mock_page_iterator = [ - { - "Contents": [ - { - "Key": "folder/key1", - "Etag": "etag1", - "LastModified": datetime.now(timezone.utc) - timedelta(2), - "Size": "0", - }, - { - "Key": "folder/key2", - "Etag": "etag2", - "LastModified": datetime.now(timezone.utc) - timedelta(2), - "Size": "0", - }, - ] - } - ] - - downloaded_objects: list[str] = [] - self.setup_mocks(mocker, s3_crawler, mock_page_iterator, downloaded_objects) - - s3_crawler.crawl() - assert len(downloaded_objects) == 0 diff --git a/apps/crawler/poetry.lock b/apps/crawler/poetry.lock deleted file mode 100644 index 61849c74d..000000000 --- a/apps/crawler/poetry.lock +++ /dev/null @@ -1,3467 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - -[[package]] -name = "aiosignal" -version = "1.3.1" -description = "aiosignal: a list of registered asynchronous callbacks" -optional = false -python-versions = ">=3.7" -files = [ - 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-[tool.poetry.dependencies] -python = ">=3.9,<3.13" - -# s3 deps; make sure this is consistent with lib/sycamore pyproject.toml -[tool.poetry.group.crawler_s3.dependencies] -boto3 = "^1.28.70" - -# http deps; make sure these are present in root pyproject.toml -[tool.poetry.group.crawler_http.dependencies] -scrapy = "^2.11" -cryptography = "^43.0.1" - -[tool.poetry.group.test.dependencies] -flake8 = "4.0.1" -pytest = "^7.4" -pytest-mock = "^3.11" - -[tool.poetry.group.dev.dependencies] -sycamore-poetry-lock = { path = "../../lib/poetry-lock", develop = true } - -[tool.ruff] -line-length = 120 - -[tool.black] -line-length = 120 - -[mypy] -mypy_path = "crawler/s3:crawler/http" -explicit_package_bases = true - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" - -[tool.pytest.ini_options] -pythonpath = [ "." ] diff --git a/apps/demo-ui/.dockerignore b/apps/demo-ui/.dockerignore deleted file mode 100644 index 3c3629e64..000000000 --- a/apps/demo-ui/.dockerignore +++ /dev/null @@ -1 +0,0 @@ -node_modules diff --git a/apps/demo-ui/.gitignore b/apps/demo-ui/.gitignore deleted file mode 100644 index 1f8018aaa..000000000 --- a/apps/demo-ui/.gitignore +++ /dev/null @@ -1,25 +0,0 @@ -*#*# -*.#* -*.iml -*.ipr -*.iws -*.pyc -*.pyo -*.pyd -*.swp -*~ -.DS_Store -.cache -.idea/ -.idea_modules/ -.mypy_cache -.project -.pydevproject -.pytest_cache -.ruff_cache -.scrapy -.settings -.vscode -venv/ -python/dist/ -__pycache__/ diff --git a/apps/demo-ui/Dockerfile.build b/apps/demo-ui/Dockerfile.build deleted file mode 100644 index bfdfeaba8..000000000 --- a/apps/demo-ui/Dockerfile.build +++ /dev/null @@ -1,53 +0,0 @@ -# Repo name: arynai/sycamore-demo-ui - -# For a slower docker build you can use: docker build -f Dockerfile.build . -# You can re-derive the Dockerfile.build file using ./dockerfile-buildx-to-build.sh - -FROM nikolaik/python-nodejs:python3.11-nodejs20 - -# TODO: run as non-root user -ENV DEBIAN_FRONTEND=noninteractive -RUN apt update; apt -y install sudo -COPY apps/demo-ui/sudoers /etc/sudoers - -WORKDIR /home/pn/js-ui -COPY ui/package.json ui/package-lock.json ui/npm-install.sh ui/pdf.worker.js.patch ./ -RUN \ - /bin/bash npm-install.sh - -ENV POETRY_NO_INTERACTION=1 \ - POETRY_VIRTUALENVS_IN_PROJECT=1 \ - POETRY_VIRTUALENVS_CREATE=1 \ - POETRY_CACHE_DIR=/tmp/poetry_cache - -WORKDIR /home/pn/py-proxy -COPY openai-proxy/pyproject.toml openai-proxy/poetry.lock openai-proxy/README.md ./ -RUN \ - poetry install --only main --no-root - -WORKDIR / -COPY ui /home/pn/js-ui -COPY openai-proxy /home/pn/py-proxy -COPY run-ui.sh /home/pn - -WORKDIR /home/pn/py-proxy -RUN poetry config virtualenvs.in-project true -RUN poetry install --only-root && rm -rf $POETRY_CACHE_DIR - -WORKDIR /home/pn - -ARG GIT_BRANCH="main" -ARG GIT_COMMIT="unknown" -ARG GIT_DIFF="unknown" - -ENV GIT_BRANCH=${GIT_BRANCH} -ENV GIT_COMMIT=${GIT_COMMIT} -ENV GIT_DIFF=${GIT_DIFF} -ENV SSL=0 - -LABEL org.opencontainers.image.authors="opensource@aryn.ai" -LABEL git_branch=${GIT_BRANCH} -LABEL git_commit=${GIT_COMMIT} -LABEL git_diff=${GIT_DIFF} - -CMD /bin/bash run-ui.sh diff --git a/apps/demo-ui/Dockerfile.buildx b/apps/demo-ui/Dockerfile.buildx deleted file mode 100644 index ce08aee50..000000000 --- a/apps/demo-ui/Dockerfile.buildx +++ /dev/null @@ -1,56 +0,0 @@ -# Repo name: arynai/sycamore-demo-ui - -# Note: This dockerfile is intended to work with docker buildx build -f Dockerfile.buildx . -# For a slower docker build you can use: docker build -f Dockerfile.build . -# You can re-derive the Dockerfile.build file using ./dockerfile-buildx-to-build.sh - -FROM nikolaik/python-nodejs:python3.11-nodejs20 - -# TODO: run as non-root user -ENV DEBIAN_FRONTEND=noninteractive -RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \ - --mount=type=cache,target=/var/lib/apt,sharing=locked \ - apt update; apt -y install sudo -COPY apps/demo-ui/sudoers /etc/sudoers - -WORKDIR /home/pn/js-ui -COPY apps/demo-ui/ui/package.json apps/demo-ui/ui/package-lock.json apps/demo-ui/ui/npm-install.sh apps/demo-ui/ui/pdf.worker.js.patch ./ -RUN --mount=type=cache,target=/root/.npm/_cacache,sharing=locked \ - /bin/bash npm-install.sh - -ENV POETRY_NO_INTERACTION=1 \ - POETRY_VIRTUALENVS_IN_PROJECT=1 \ - POETRY_VIRTUALENVS_CREATE=1 \ - POETRY_CACHE_DIR=/tmp/poetry_cache - -WORKDIR /home/pn/py-proxy -COPY apps/demo-ui/openai-proxy/pyproject.toml apps/demo-ui/openai-proxy/poetry.lock apps/demo-ui/openai-proxy/README.md ./ -RUN poetry config virtualenvs.in-project true -RUN --mount=type=cache,target=/tmp/poetry_cache,sharing=locked \ - poetry install --only main --no-root - -WORKDIR / -COPY apps/demo-ui/ui /home/pn/js-ui -COPY apps/demo-ui/openai-proxy /home/pn/py-proxy -COPY apps/demo-ui/run-ui.sh /home/pn - -WORKDIR /home/pn/py-proxy -RUN poetry install --only-root && rm -rf $POETRY_CACHE_DIR - -WORKDIR /home/pn - -ARG GIT_BRANCH="unknown" -ARG GIT_COMMIT="unknown" -ARG GIT_DIFF="unknown" - -ENV GIT_BRANCH=${GIT_BRANCH} -ENV GIT_COMMIT=${GIT_COMMIT} -ENV GIT_DIFF=${GIT_DIFF} -ENV SSL=0 - -LABEL org.opencontainers.image.authors="opensource@aryn.ai" -LABEL git_branch=${GIT_BRANCH} -LABEL git_commit=${GIT_COMMIT} -LABEL git_diff=${GIT_DIFF} - -CMD /bin/bash run-ui.sh diff --git a/apps/demo-ui/LICENSE b/apps/demo-ui/LICENSE deleted file mode 100644 index 261eeb9e9..000000000 --- a/apps/demo-ui/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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Created using create-react-app. - -Checkout the other .md files for some instructions. - -## How to Run - -1. Download NODE > 16(npm 9.8.1) from the [website](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) or by running `brew to install npm` -2. Download Python -3. Run `npm install` from the project -4. Export OPEN_API_KEY by running `export OPENAI_API_KEY #####` -5. Run `python openai-proxy/py-back/proxy.py` to bring up the proxy for openAI to use your api key - - The proxy currently uses 3000, pretty quick switch if you need another port change the port in proxy.py, and then change the port in App.js -6. Run `npm run build` to build the project -7. Bring up the UI by running `PORT=3001 BROWSER=none npm start` and navigate to http://localhost:3000/ - - If you get a react-scripts not found error, that means your npm isn't updated or you did not run npm install. - -Note : This UI is intended to be run for demo purposes only. diff --git a/apps/demo-ui/dockerfile-buildx-to-build.sh b/apps/demo-ui/dockerfile-buildx-to-build.sh deleted file mode 100755 index c22c503fe..000000000 --- a/apps/demo-ui/dockerfile-buildx-to-build.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash -# If there are other buildx/build incompatibilities this script will need to be extended. -for i in $(find . -name Dockerfile.buildx -print); do - out=$(echo "${i}" | sed 's/x$//') - perl -ne 'next if /^# Note:.*intended.*buildx/;s/--mount\S+//;print' <"${i}" >"${out}" - echo "Made ${out} from ${i}" -done diff --git a/apps/demo-ui/openai-proxy/README.md b/apps/demo-ui/openai-proxy/README.md deleted file mode 100644 index 2ae92b3f0..000000000 --- a/apps/demo-ui/openai-proxy/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Proxy for openai - -Probably not secure, since this is an endpoint that tacks an openai key onto any request and sends it to openai. -Someone malicious could throttle us by ddosing out openai acount. diff --git a/apps/demo-ui/openai-proxy/package-lock.json b/apps/demo-ui/openai-proxy/package-lock.json deleted file mode 100644 index 62eac8e36..000000000 --- a/apps/demo-ui/openai-proxy/package-lock.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "name": "openai-proxy", - "lockfileVersion": 3, - "requires": true, - "packages": {} -} diff --git a/apps/demo-ui/openai-proxy/poetry.lock b/apps/demo-ui/openai-proxy/poetry.lock deleted file mode 100644 index 0c8d8e84f..000000000 --- a/apps/demo-ui/openai-proxy/poetry.lock +++ /dev/null @@ -1,1721 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - -[[package]] -name = "aiohttp" -version = "3.9.3" -description = "Async http client/server framework (asyncio)" -optional = false -python-versions = ">=3.8" -files = [ - {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"}, - 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-[tool.poetry.dependencies] -python = "^3.11" -Flask = "^3.0.0" -gevent = "^23.9.1" -Flask-Cors = "^4.0.0" -Werkzeug = "^3.0.0" -requests = "^2.31" -boto3 = "^1.28.70" -openai = "0.28.1" -anthropic = "^0.25.8" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" diff --git a/apps/demo-ui/package-lock.json b/apps/demo-ui/package-lock.json deleted file mode 100644 index d707304be..000000000 --- a/apps/demo-ui/package-lock.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "name": "demo-ui", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "dependencies": { - "@types/node": "^20.11.25" - } - }, - "node_modules/@types/node": { - "version": "20.11.25", - "resolved": "https://registry.npmjs.org/@types/node/-/node-20.11.25.tgz", - "integrity": "sha512-TBHyJxk2b7HceLVGFcpAUjsa5zIdsPWlR6XHfyGzd0SFu+/NFgQgMAl96MSDZgQDvJAvV6BKsFOrt6zIL09JDw==", - "dependencies": { - "undici-types": "~5.26.4" - } - }, - "node_modules/undici-types": { - "version": "5.26.5", - "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz", - "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==" - } - } -} diff --git a/apps/demo-ui/package.json b/apps/demo-ui/package.json deleted file mode 100644 index 3471643ae..000000000 --- a/apps/demo-ui/package.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "dependencies": { - "@types/node": "^20.11.25" - } -} diff --git a/apps/demo-ui/run-ui.sh b/apps/demo-ui/run-ui.sh deleted file mode 100755 index caa576440..000000000 --- a/apps/demo-ui/run-ui.sh +++ /dev/null @@ -1,116 +0,0 @@ -#!/bin/bash - -echo "Version-Info, Demo UI Branch: ${GIT_BRANCH}" -echo "Version-Info, Demo UI Commit: ${GIT_COMMIT}" -echo "Version-Info, Demo UI Diff: ${GIT_DIFF}" - -# run python proxy -if [[ -z "${OPENAI_API_KEY}" ]]; then - echo "ERROR: Missing OPENAI_API_KEY; will not work" - exit 1 -fi - -# FIXME: If we unify Dockerfile and git dirs, this will become simpler. -if [[ -f /.dockerenv ]]; then - PROXYDIR=/home/pn/py-proxy - UIDIR=/home/pn/js-ui -else - DIR=$(cd "$(dirname "$0")"; pwd) - PROXYDIR="${DIR}/openai-proxy" - UIDIR="${DIR}/ui" -fi - -function cleanup { - kill ${KILLPIDS} -} - -function first { - echo $1 -} - -cd "${PROXYDIR}" -: ${HOST:=localhost} -SELFSIGNED="${HOST}_ss" -SSLNAME="${SELFSIGNED}" -ARYN_ETC=/etc/opt/aryn - -if [[ (! -f ${SELFSIGNED}-key.pem) || (! -f ${SELFSIGNED}-cert.pem) ]]; then - openssl req -batch -x509 -newkey rsa:4096 -days 10000 \ - -subj "/C=US/ST=California/O=Aryn.ai/CN=${HOST}" \ - -extensions v3_req -addext "subjectAltName=DNS:${HOST}" \ - -noenc -keyout "${SELFSIGNED}-key.pem" \ - -out "${SELFSIGNED}-cert.pem" 2> /dev/null - echo "Created ${HOST} certificate" -fi - -if [[ -f ${ARYN_ETC}/hostcert.pem && -f ${ARYN_ETC}/hostkey.pem ]]; then - cp -p "${ARYN_ETC}/hostcert.pem" "${HOST}-cert.pem" - cp -p "${ARYN_ETC}/hostkey.pem" "${HOST}-key.pem" - SSLNAME="${HOST}" - echo "Copied certificate from ${ARYN_ETC}" -fi - -if [[ ${SSL} == 0 ]]; then - BASEURL="http://${HOST}:3000" -else - BASEURL="https://${HOST}:3000" -fi - -poetry run python py_proxy/proxy.py "${SSLNAME}" & -KILLPIDS=$! -trap cleanup EXIT - -tries=0 -while ! curl -k "${BASEURL}/healthz" >/dev/null 2>&1; do - echo "Demo-UI proxy not running after $tries seconds" - if [[ $tries -ge 30 ]]; then - echo "Demo UI probably broken, report a bug on slack" - fi - tries=$(expr ${tries} + 1) - sleep 1 -done - -# Only in certain environments, run token-protected UI proxy... -if [[ ${SSL} != 0 && -f ${ARYN_ETC}/hostcert.pem ]]; then - TOKEN=$(openssl rand -hex 24) - # we should be able to sudo without password here... - sudo -b poetry run python py_proxy/token_proxy.py "${TOKEN}" "${SSLNAME}" - # Getting the process ID isn't hard, as the token is new and unique... - OUT=$(ps -eo pid,cmd | grep "${TOKEN}" | grep -vE 'grep|sudo') - PID=$(first ${OUT}) - KILLPIDS="${KILLPIDS} ${PID}" - secs=0 - while ! curl -ks -o /dev/null https://localhost/healthz; do - echo "Waited ${secs} seconds for token proxy to respond" - (( ${secs} > 30 )) && echo "Token proxy not working; report on slack" - sleep 1 - ((++secs)) - done - URL="https://${HOST}/tok/${TOKEN}" - echo "${URL}" > token_proxy_url - ( - for i in {1..10}; do - echo "token_proxy: ${URL}" - sleep 5 - done - ) & - KILLPIDS="${KILLPIDS} $!" -fi - -# run node ui -cd "${UIDIR}" -# Running the UI this way means that the html is unminified and hence easy to read -# Since the UI is open source, there's little downside of doing this and it helps with -# debugging. -# See this link for why we're using RSA self-signed certificates for npm: -# https://community.letsencrypt.org/t/ecdsa-certificates-not-supported-by-create-react-app/201387/9 -BROWSER=none PORT=3001 WDS_SOCKET_PORT=3000 HTTPS=true \ -SSL_CRT_FILE="${PROXYDIR}/${SELFSIGNED}-cert.pem" \ -SSL_KEY_FILE="${PROXYDIR}/${SELFSIGNED}-key.pem" \ -npm start | cat - -# These are the steps that would build the UI and serve it minified. We -# should move the build back into the Dockerfile now that we no longer need -# to patch 'localhost' in the source. -#npm run build -#npx serve -n -s build diff --git a/apps/demo-ui/sudoers b/apps/demo-ui/sudoers deleted file mode 100644 index bdce3e760..000000000 --- a/apps/demo-ui/sudoers +++ /dev/null @@ -1,27 +0,0 @@ -# -# This file MUST be edited with the 'visudo' command as root. -# -# Please consider adding local content in /etc/sudoers.d/ instead of -# directly modifying this file. -# -# See the man page for details on how to write a sudoers file. -# -Defaults env_reset -Defaults mail_badpass -Defaults secure_path="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin" - -# This fixes CVE-2005-4890 and possibly breaks some versions of kdesu -# (#1011624, https://bugs.kde.org/show_bug.cgi?id=452532) -Defaults use_pty - -# User privilege specification -root ALL=(ALL:ALL) ALL - -# Members of the admin group may gain root privileges -%admin ALL=(ALL) ALL - -# Allow members of group sudo to execute any command -%sudo ALL=(ALL:ALL) ALL - -# Since you can do docker run -u root, there is little point in restricting what can be sudoed -pn ALL = (ALL:ALL) NOPASSWD:SETENV: ALL diff --git a/apps/demo-ui/ui/.gitignore b/apps/demo-ui/ui/.gitignore deleted file mode 100644 index 4d29575de..000000000 --- a/apps/demo-ui/ui/.gitignore +++ /dev/null @@ -1,23 +0,0 @@ -# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. - -# dependencies -/node_modules -/.pnp -.pnp.js - -# testing -/coverage - -# production -/build - -# misc -.DS_Store -.env.local -.env.development.local -.env.test.local -.env.production.local - -npm-debug.log* -yarn-debug.log* -yarn-error.log* diff --git a/apps/demo-ui/ui/.prettierrc.json b/apps/demo-ui/ui/.prettierrc.json deleted file mode 100644 index bf357fbbc..000000000 --- a/apps/demo-ui/ui/.prettierrc.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "trailingComma": "all" -} diff --git a/apps/demo-ui/ui/README.md b/apps/demo-ui/ui/README.md deleted file mode 100644 index 25984fc3a..000000000 --- a/apps/demo-ui/ui/README.md +++ /dev/null @@ -1,52 +0,0 @@ -# Getting Started with Create React App - -This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app). - -## Available Scripts - -In the `openai-proxy` directory, you must run: - -### `poetry run python py_proxy/proxy.py` - -This runs the proxy that handles the UI, OpenSearch, OpenAI, PdfViewer, etc. - -In the `ui` directory, you can run: - -### `PORT=3001 BROWSER=none npm start` - -Runs the React app in the development mode.\ -Open [http://localhost:3000](http://localhost:3000) to view it in the browser via the proxy. - -The page will reload if you make edits.\ -You will also see any lint errors in the console. - -### `npm test` - -Launches the test runner in the interactive watch mode.\ -See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information. - -### `npm run build` - -Builds the app for production to the `build` folder.\ -It correctly bundles React in production mode and optimizes the build for the best performance. - -The build is minified and the filenames include the hashes.\ -Your app is ready to be deployed! - -See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information. - -### `npm run eject` - -**Note: this is a one-way operation. Once you `eject`, you can’t go back!** - -If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project. - -Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own. - -You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it. - -## Learn More - -You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started). - -To learn React, check out the [React documentation](https://reactjs.org/). diff --git a/apps/demo-ui/ui/eslint.config.mjs b/apps/demo-ui/ui/eslint.config.mjs deleted file mode 100644 index 1eb1b43f4..000000000 --- a/apps/demo-ui/ui/eslint.config.mjs +++ /dev/null @@ -1,9 +0,0 @@ -// @ts-check - -import eslint from "@eslint/js"; -import tseslint from "typescript-eslint"; - -export default tseslint.config( - eslint.configs.recommended, - ...tseslint.configs.recommended, -); diff --git a/apps/demo-ui/ui/npm-install.sh b/apps/demo-ui/ui/npm-install.sh deleted file mode 100755 index 2d95c45fa..000000000 --- a/apps/demo-ui/ui/npm-install.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash - -apt -y install --no-install-recommends patch -apt clean -rm -rf /var/lib/apt/lists/* - -npm install --loglevel verbose -err=$? - -if [[ ${err} != 0 ]]; then - ls /root/.npm/_logs - cat /root/.npm/_logs/* - exit 1 -fi - -# Cleanup files we don't want to keep around. We want to preserve the downloaded file cache - -# Don't use npm cache clean --force here because the whole point of having the docker cache is to -# not re-download the files every time and cleaning the cache would defeat that. -rm -rf /root/.npm/_logs /root/.npm/_update-notifier-last-checked - -# TODO: https://github.com/aryn-ai/demo-ui/issues/9 - figure out proper fix for worker was terminated. -patch -p0 =0.10.0" - } - }, - "node_modules/@adobe/css-tools": { - "version": "4.3.1", - "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.3.1.tgz", - "integrity": "sha512-/62yikz7NLScCGAAST5SHdnjaDJQBDq0M2muyRTpf2VQhw6StBg2ALiu73zSJQ4fMVLA+0uBhBHAle7Wg+2kSg==" - }, - "node_modules/@alloc/quick-lru": { - "version": "5.2.0", - "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", - "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", - "engines": { - "node": ">=10" - }, - "funding": { - 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ABK4z)!{8PdM@98ztDU^Wcl`t+-iALL;o;(9FwRl` zt{aeGU|2(<*`J;UA#gB3m0;GL_MBjs{OMB9w zo@W~G=z+2LctO)l6(ku3UFsBQ&qZU=_#js~020;1glyrn92xxO`LlxyX9$clV5YB7 rU;Z88@Z#-~HUSif2y$QydV_B#W%f?`%qRHoLW``Vl0=P|N$~#%qU%V) diff --git a/apps/demo-ui/ui/public/manifest.json b/apps/demo-ui/ui/public/manifest.json deleted file mode 100644 index 25db5fce3..000000000 --- a/apps/demo-ui/ui/public/manifest.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "short_name": "React App", - "name": "Create React App Sample", - "icons": [ - { - "src": "favicon.ico", - "sizes": "64x64 32x32 24x24 16x16", - "type": "image/x-icon" - }, - { - "src": "logo_only.png", - "type": "image/png", - "sizes": "192x192" - }, - { - "src": "logo_only.png", - "type": "image/png", - "sizes": "512x512" - } - ], - "start_url": ".", - "display": "standalone", - "theme_color": "#000000", - "background_color": "#ffffff" -} diff --git a/apps/demo-ui/ui/public/robots.txt b/apps/demo-ui/ui/public/robots.txt deleted file mode 100644 index e9e57dc4d..000000000 --- a/apps/demo-ui/ui/public/robots.txt +++ /dev/null @@ -1,3 +0,0 @@ -# https://www.robotstxt.org/robotstxt.html -User-agent: * -Disallow: diff --git a/apps/demo-ui/ui/src/.env b/apps/demo-ui/ui/src/.env deleted file mode 100644 index e15d1c7de..000000000 --- a/apps/demo-ui/ui/src/.env +++ /dev/null @@ -1 +0,0 @@ -SYCAMORE_DEFAULT_FILTERS="_product_code" diff --git a/apps/demo-ui/ui/src/.eslintrc b/apps/demo-ui/ui/src/.eslintrc deleted file mode 100644 index 13dbaef54..000000000 --- a/apps/demo-ui/ui/src/.eslintrc +++ /dev/null @@ -1,7 +0,0 @@ -{ - "extends": ["prettier"], - "plugins": ["prettier"], - "rules": { - "prettier/prettier": ["error"], - }, -} diff --git a/apps/demo-ui/ui/src/App.css b/apps/demo-ui/ui/src/App.css deleted file mode 100644 index 10e4996c0..000000000 --- a/apps/demo-ui/ui/src/App.css +++ /dev/null @@ -1,39 +0,0 @@ -.App { - text-align: center; -} - -.App-logo { - height: 40vmin; - pointer-events: none; -} - -@media (prefers-reduced-motion: no-preference) { - .App-logo { - animation: App-logo-spin infinite 20s linear; - } -} - -.App-header { - background-color: #282c34; - min-height: 100vh; - display: flex; - flex-direction: column; - align-items: center; - justify-content: center; - font-size: calc(10px + 2vmin); - color: white; -} - -.App-link { - color: #61dafb; -} - -@keyframes App-logo-spin { - from { - transform: rotate(0deg); - } - - to { - transform: rotate(360deg); - } -} diff --git a/apps/demo-ui/ui/src/App.tsx b/apps/demo-ui/ui/src/App.tsx deleted file mode 100644 index c91a39b0b..000000000 --- a/apps/demo-ui/ui/src/App.tsx +++ /dev/null @@ -1,17 +0,0 @@ -import React from "react"; -import { BrowserRouter, Route, Routes } from "react-router-dom"; -import PdfViewer from "./pages/PDFViewer"; -import HomePage from "./pages/HomePage"; - -function App() { - return ( - - - } /> - } /> - - - ); -} - -export default App; diff --git a/apps/demo-ui/ui/src/Types.tsx b/apps/demo-ui/ui/src/Types.tsx deleted file mode 100644 index d7fe1ce2f..000000000 --- a/apps/demo-ui/ui/src/Types.tsx +++ /dev/null @@ -1,94 +0,0 @@ -export class SearchResultDocument { - id: string = ""; - index: number = -1; - title: string = ""; - description: string = ""; - url: string = ""; - relevanceScore: string = ""; - properties: any; - bbox: any; - - public constructor(init?: Partial) { - Object.assign(this, init); - } - - isPdf() { - if (this.properties.filetype === "application/pdf") { - return true; - } - if (this.url.endsWith(".pdf")) { - // legacy test - return true; - } - return false; - } - - hasAbsoluteUrl() { - return this.url.search(/^[a-z]+:\/\//i) >= 0; - } -} -export class UserChat { - id: string = ""; - interaction_id: string = ""; - query: string = ""; - rephrasedQuery: string | null = ""; - - public constructor(init?: Partial) { - Object.assign(this, init); - } -} -export class SystemChat { - id: string = ""; - interaction_id: string = ""; - ragPassageCount?: number = 0; - modelName: string | null = ""; - response: string = ""; - hits: SearchResultDocument[] = []; - queryUsed: string = ""; - originalQuery: string = ""; - rawQueryUsed: string = ""; - rawResults: any = null; - queryUrl: string = ""; - feedback: boolean | null = null; - fromAdhoc: boolean = false; - editing: boolean = false; - comment: string = ""; - filterContent: FilterValues = {}; - aggregationContent: AggregationValues = {}; - - public constructor(init?: Partial) { - Object.assign(this, init); - } -} -export class Settings { - openSearchIndex: string = ""; - embeddingModel: string = ""; - ragPassageCount: number = 5; - modelName: string = "gpt-4o"; - modelId: string = "abScoYoBAwYohYvwjxcP"; - availableModels: string[] = [ - "gpt-3.5-turbo", - "gpt-4", - "gpt-4o", - "gpt-4-turbo-preview", - ]; - activeConversation: string = ""; - simplify: boolean = false; - auto_filter: boolean = false; - required_filters: string[] = []; - - public constructor(init?: Partial) { - Object.assign(this, init); - } -} - -export interface FilterValues { - [key: string]: - | string - | { gte?: string; lte?: string } - | { gte?: number; lte?: number }; -} - -export interface AggregationValues { - [key: string]: string; -} diff --git a/apps/demo-ui/ui/src/index.css b/apps/demo-ui/ui/src/index.css deleted file mode 100644 index 4a1df4db7..000000000 --- a/apps/demo-ui/ui/src/index.css +++ /dev/null @@ -1,13 +0,0 @@ -body { - margin: 0; - font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Roboto", "Oxygen", - "Ubuntu", "Cantarell", "Fira Sans", "Droid Sans", "Helvetica Neue", - sans-serif; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} - -code { - font-family: source-code-pro, Menlo, Monaco, Consolas, "Courier New", - monospace; -} diff --git a/apps/demo-ui/ui/src/index.tsx b/apps/demo-ui/ui/src/index.tsx deleted file mode 100644 index 4fa2a47f5..000000000 --- a/apps/demo-ui/ui/src/index.tsx +++ /dev/null @@ -1,19 +0,0 @@ -import React from "react"; -import ReactDOM from "react-dom/client"; -import "./index.css"; -import App from "./App"; -import reportWebVitals from "./reportWebVitals"; - -const root = ReactDOM.createRoot( - document.getElementById("root") as HTMLElement, -); -root.render( - - - , -); - -// If you want to start measuring performance in your app, pass a function -// to log results (for example: reportWebVitals(console.log)) -// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals -reportWebVitals(); diff --git a/apps/demo-ui/ui/src/logo.svg b/apps/demo-ui/ui/src/logo.svg deleted file mode 100644 index 716947603..000000000 --- a/apps/demo-ui/ui/src/logo.svg +++ /dev/null @@ -1 +0,0 @@ - diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/AddAggregationModal.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/AddAggregationModal.tsx deleted file mode 100644 index 017aa84fe..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/AddAggregationModal.tsx +++ /dev/null @@ -1,103 +0,0 @@ -import { - Button, - Group, - Modal, - NativeSelect, - Select, - Stack, -} from "@mantine/core"; -import { Dispatch, SetStateAction, useEffect, useState } from "react"; -import { documentLevelFields } from "../../../../utils/ChatboxUtils"; -import { AggregationValues } from "../../../../Types"; - -export const AddAggregationModal = ({ - addAggregationsModalOpened, - addAggregationsModalhandlers, - aggregations, - setAggregations, - aggregationFields, -}: { - addAggregationsModalOpened: boolean; - addAggregationsModalhandlers: any; - aggregations: AggregationValues; - setAggregations: Dispatch>; - aggregationFields: string[]; -}) => { - const [aggregationType, setAggregationType] = useState("terms"); - const [aggregationValue, setAggregationValue] = useState(""); - const [fieldsData, setFieldsData] = useState< - { value: string; label: string; group: string }[] - >([]); - - useEffect(() => { - const retrievedFieldsData = aggregationFields.map((field) => ({ - value: field, - label: field, - })); - const originalFieldsData = [ - { value: "location", label: "Location" }, - { value: "aircraftType", label: "Aircraft type" }, - { value: "accidentNumber", label: "Accident number" }, - ]; - const AllFields = [...originalFieldsData, ...retrievedFieldsData]; - const groupedFieldsData = AllFields.map((field) => ({ - ...field, - group: documentLevelFields.includes(field.value) - ? "Document Level Field" - : "Entity Specific Field", - })); - - setFieldsData(groupedFieldsData); - console.log(retrievedFieldsData); - console.log(); - }, [aggregationFields]); - - const handleSubmit = (e: React.FormEvent) => { - if (aggregationValue && aggregationValue.length !== 0) { - setAggregations((prevAggs: any) => ({ - [aggregationType]: aggregationValue, - })); - setAggregationValue(""); - setAggregationType("terms"); - addAggregationsModalhandlers.close(); - } - }; - - return ( - - - setAggregationType(event.currentTarget.value)} - data={[ - { label: "Terms", value: "terms" }, - { label: "Cardinality", value: "cardinality" }, - ]} - /> - `+ Filter on ${query}`} - withinPortal - data={fieldsData} - /> - {newFilterType === "day" ? ( - - - - - ) : newFilterType === "windSpeedInKnots" || - (newFilterType && rangeFilterFields.includes(newFilterType)) ? ( - - setMinFilterValue(e.currentTarget.value)} - onChange={(e) => setMinFilterValue(e.currentTarget.value)} - autoFocus - size="sm" - fz="xs" - placeholder="Enter min value" - /> - setMaxFilterValue(e.currentTarget.value)} - autoFocus - size="sm" - fz="xs" - placeholder="Enter max value" - /> - - ) : ( - - )} - {Object.keys(newFilterContent).length !== 0 && ( - - Filters : - {Object.entries(newFilterContent).map( - ([key, value]) => { - if (typeof value === "object") { - const start = value.gte ?? ""; - const end = value.lte ?? ""; - const rangeText = - start && end - ? `${start} - ${end}` - : start - ? `>= ${start}` - : `<= ${end}`; - return ( - - ); - } else { - return ( - - ); - } - }, - // - )} - - )} - - - - - - - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Citation.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Citation.tsx deleted file mode 100644 index b331e7db6..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Citation.tsx +++ /dev/null @@ -1,73 +0,0 @@ -import { useState } from "react"; -import { SearchResultDocument } from "../../../../Types"; -import { - IconFileTypeHtml, - IconFileTypePdf, - IconLink, -} from "@tabler/icons-react"; -import { - Anchor, - Badge, - Group, - HoverCard, - Text, - useMantineTheme, -} from "@mantine/core"; - -export const Citation = ({ - document, - citationNumber, -}: { - document: SearchResultDocument; - citationNumber: number; -}) => { - const [doc, setDoc] = useState(document); - const [docId, setDocId] = useState(document.id); - const [docUrl, setDocUrl] = useState(document.url); - const [citNum, setCitNum] = useState(citationNumber); - const theme = useMantineTheme(); - function icon() { - if (document.isPdf()) { - return ; - } else if (document.url.endsWith("htm") || document.url.endsWith("html")) { - return ; - } - return ; - } - return ( - - - { - event.preventDefault(); - if (doc.isPdf()) { - const dataString = JSON.stringify(doc); - localStorage.setItem("pdfDocumentMetadata", dataString); - window.open("/viewPdf"); - } else { - window.open(docUrl); - } - }} - > - - {citNum} - - - - - - {doc.title} - {icon()} - - - {" "} - {doc.url} - - - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Controlpanel.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Controlpanel.tsx deleted file mode 100644 index 47ee52ab6..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Controlpanel.tsx +++ /dev/null @@ -1,237 +0,0 @@ -import React, { - Dispatch, - FormEvent, - SetStateAction, - useEffect, - useState, -} from "react"; -import { - Button, - Checkbox, - Divider, - Group, - Modal, - NativeSelect, - Stack, - Text, -} from "@mantine/core"; -import { Settings } from "../../../../Types"; -import { - getIndices, - getEmbeddingModels, - FEEDBACK_INDEX_NAME, - createFeedbackIndex, -} from "../../../../utils/OpenSearch"; - -export const ControlPanel = ({ - settings, - setSettings, - controlPanelOpened, - onControlPanelClose, - openErrorDialog, -}: { - settings: Settings; - setSettings: Dispatch>; - controlPanelOpened: boolean; - onControlPanelClose: any; - openErrorDialog: any; -}) => { - const [availableIndices, setAvailableIndices] = useState(new Array()); - const [availableEmbeddings, setAvailableEmbeddings] = useState( - new Array(), - ); - const [formValues, setFormValues] = useState({ - ragPassageCount: settings.ragPassageCount, - modelName: settings.modelName, - openSearchIndex: settings.openSearchIndex, - embeddingModel: settings.embeddingModel, - }); - const [isDefault, setIsDefault] = useState(false); - - const getIndicesAndEmbeddings = async () => { - const [getIndicesResponse, getEmbeddingsResponse] = await Promise.all([ - getIndices(), - getEmbeddingModels(), - ]); - const newIndiciesMaybeWFeedback = Object.keys(getIndicesResponse).filter( - (key) => !key.startsWith("."), - ); - let newIndicies; - if (newIndiciesMaybeWFeedback.includes(FEEDBACK_INDEX_NAME)) { - newIndicies = newIndiciesMaybeWFeedback.filter( - (name) => name !== FEEDBACK_INDEX_NAME, - ); - } else { - newIndicies = newIndiciesMaybeWFeedback; - createFeedbackIndex(); - } - - const hits = getEmbeddingsResponse.hits.hits; - const models = []; - for (const idx in hits) { - models.push(hits[idx]._id); - } - - return [newIndicies, models]; - }; - const doit = async () => { - try { - const [indexNames, modelIds] = await getIndicesAndEmbeddings(); - setSettings((settings) => ({ - ...settings, - openSearchIndex: indexNames[0], - embeddingModel: modelIds[0], - })); - setFormValues((prev) => ({ - ...prev, - openSearchIndex: indexNames[0], - embeddingModel: modelIds[0], - })); - const storedSettings = localStorage.getItem("defaultSettings"); - if (storedSettings) { - const storedSettingsJSON = JSON.parse(storedSettings); - setSettings((settings) => ({ - ...settings, - ragPassageCount: storedSettingsJSON.ragPassageCount, - modelName: storedSettingsJSON.modelName, - openSearchIndex: indexNames.includes( - storedSettingsJSON.openSearchIndex, - ) - ? storedSettingsJSON.openSearchIndex - : settings.openSearchIndex, - embeddingModel: modelIds.includes(storedSettingsJSON.embeddingModel) - ? storedSettingsJSON.embeddingModel - : settings.embeddingModel, - })); - setFormValues((prev) => ({ - ...prev, - ragPassageCount: storedSettingsJSON.ragPassageCount, - modelName: storedSettingsJSON.modelName, - openSearchIndex: indexNames.includes( - storedSettingsJSON.openSearchIndex, - ) - ? storedSettingsJSON.openSearchIndex - : prev.openSearchIndex, - embeddingModel: modelIds.includes(storedSettingsJSON.embeddingModel) - ? storedSettingsJSON.embeddingModel - : prev.embeddingModel, - })); - } - - setAvailableIndices(indexNames); - setAvailableEmbeddings(modelIds); - } catch (error: any) { - openErrorDialog("Error loading settings: " + error.message); - console.error("Error loading settings:", error); - } - }; - useEffect(() => { - doit(); - }, []); - - useEffect(() => { - const reloadIndicesAndEmbeddings = async () => { - try { - const [indexNames, modelIds] = await getIndicesAndEmbeddings(); - setAvailableIndices(indexNames); - setAvailableEmbeddings(modelIds); - } catch (error: any) { - openErrorDialog("Error loading settings: " + error.message); - console.error("Error loading settings:", error); - } - }; - if (controlPanelOpened) { - reloadIndicesAndEmbeddings(); - } - }, [controlPanelOpened]); - - const handleSubmit = (event: FormEvent) => { - event.preventDefault(); - if (isDefault) { - localStorage.setItem("defaultSettings", JSON.stringify(formValues)); - } - setIsDefault(false); - setSettings((prevSettings) => ({ ...prevSettings, ...formValues })); - onControlPanelClose(); - }; - - return ( - -

- - - - RAG passage count: - { - const newCount = +event.currentTarget.value; - setFormValues((prev) => ({ - ...prev, - ragPassageCount: newCount, - })); - }} - /> - - - AI model: - { - const newModal = event.currentTarget.value; - setFormValues((prev) => ({ - ...prev, - modelName: newModal, - })); - }} - /> - - - OpenSearch index: - { - const newIndex = event.currentTarget.value; - setFormValues((prev) => ({ - ...prev, - openSearchIndex: newIndex, - })); - }} - /> - - - Embedding Model: - { - const newEmbeddingModal = event.currentTarget.value; - setFormValues((prev) => ({ - ...prev, - embeddingModel: newEmbeddingModal, - })); - }} - /> - - - setIsDefault(e.target.checked)} - /> - - - -
- - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Doclist.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Doclist.tsx deleted file mode 100644 index fe266592a..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/Doclist.tsx +++ /dev/null @@ -1,170 +0,0 @@ -import React from "react"; -import { - Card, - Container, - Group, - LoadingOverlay, - Badge, - ScrollArea, - Text, - Title, - useMantineTheme, - HoverCard, - Anchor, -} from "@mantine/core"; -import { useHover } from "@mantine/hooks"; -import { - IconInfoCircle, - IconFileTypeHtml, - IconLink, - IconFileTypePdf, -} from "@tabler/icons-react"; -import { SearchResultDocument, Settings } from "../../../../Types"; - -const DocumentItem = ({ document }: { document: SearchResultDocument }) => { - const theme = useMantineTheme(); - const { hovered, ref } = useHover(); - const openDocument = () => { - if (document.isPdf()) { - const dataString = JSON.stringify(document); - console.log("You clicked: ", document); - localStorage.setItem("pdfDocumentMetadata", dataString); - window.open("/viewPdf"); - } else if (document.hasAbsoluteUrl()) { - window.open(document.url); - } else if (document.url.startsWith("/")) { - window.open("/viewLocal" + document.url); - } else { - window.open(document.url); - } - }; - function icon() { - if (document.isPdf()) { - return ( - - ); - } else if (document.url.endsWith("htm") || document.url.endsWith("html")) { - return ( - - ); - } - return ; - } - - let snippet: string; - try { - snippet = document.description.substring(0, 750); - } catch (err) { - snippet = ""; - } - const parts: string[] = document.url.split("/"); - const filename: string = parts[parts.length - 1]; - - return ( -
- - - { - openDocument(); - }} - target="_blank" - mb="sm" - > - - - {" "} - {document.index} - - - {document.title != "Untitled" - ? document.title - : document.properties.entity - ? document.properties.entity.accidentNumber ?? "Untitled" - : "Untitled"} - - - - {icon()} - - {filename} - - - - - - - {document.title} - openDocument()}> - {document.url} - - - {"entity" in document.properties - ? ["location", "aircraftType", "day"].map((key) => { - if (document.properties.entity[key] != "None") - return ( - - {key} {document.properties.entity[key]} - - ); - }) - : null} - - {document.description} - - - -
- ); -}; - -export const DocList = ({ - documents, - settings, - docsLoading, -}: { - documents: SearchResultDocument[]; - settings: Settings; - docsLoading: boolean; -}) => { - return ( - - - - - - {documents.map((document) => ( - - ))} - - - - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/FeedbackButtons.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/FeedbackButtons.tsx deleted file mode 100644 index 8a4cbf710..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/FeedbackButtons.tsx +++ /dev/null @@ -1,99 +0,0 @@ -import { useState } from "react"; -import { Settings, SystemChat } from "../../../../Types"; -import { updateFeedback } from "../../../../utils/OpenSearch"; -import { ActionIcon, Group, TextInput } from "@mantine/core"; -import { - IconThumbDown, - IconThumbDownFilled, - IconThumbUp, - IconThumbUpFilled, -} from "@tabler/icons-react"; - -export const FeedbackButtons = ({ - systemChat, - settings, -}: { - systemChat: SystemChat; - settings: Settings; -}) => { - const [thumbUpState, setThumbUp] = useState(systemChat.feedback); - const [comment, setComment] = useState(systemChat.comment); - const handleSubmit = async (thumb: boolean | null) => { - updateFeedback( - settings.activeConversation, - systemChat.interaction_id, - thumb, - comment, - ); - }; - const handleInputKeyPress = (e: React.KeyboardEvent) => { - if (e.key === "Enter") { - e.preventDefault(); - handleSubmit(thumbUpState); - } - }; - const handleInputChange = (e: React.ChangeEvent) => { - setComment(e.target.value); - }; - return ( - - - { - if (thumbUpState == null || !thumbUpState) { - setThumbUp(true); - systemChat.feedback = true; - handleSubmit(true); - } else { - setThumbUp(null); - systemChat.feedback = null; - handleSubmit(null); - } - }} - > - {thumbUpState == null || !thumbUpState ? ( - - ) : ( - - )} - - { - if (thumbUpState == null || thumbUpState) { - setThumbUp(false); - systemChat.feedback = false; - handleSubmit(false); - } else { - setThumbUp(null); - systemChat.feedback = null; - handleSubmit(null); - } - }} - > - {thumbUpState == null || thumbUpState ? ( - - ) : ( - - )} - - - - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/FilterInput.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/FilterInput.tsx deleted file mode 100644 index 6255c58c6..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/FilterInput.tsx +++ /dev/null @@ -1,46 +0,0 @@ -import { Group, Text, TextInput } from "@mantine/core"; -import { Settings } from "../../../../Types"; - -export const FilterInput = ({ - settings, - filtersInput, - setFiltersInput, - filterError, - setFilterError, -}: { - settings: Settings; - filtersInput: any; - setFiltersInput: any; - filterError: boolean; - setFilterError: any; -}) => { - const handleInputChange = (filterName: string, value: string) => { - if (filterError) { - setFilterError(false); - } - setFiltersInput((prevValues: any) => ({ - ...prevValues, - [filterName]: value, - })); - }; - - return ( - - {settings.required_filters.map((required_filter) => ( - - {required_filter} - handleInputChange(required_filter, e.target.value)} - value={filtersInput[required_filter] || ""} - autoFocus - required - error={filterError} - size="xs" - fz="xs" - pl="sm" - /> - - ))} - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/OpenSearchQueryEditor.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/OpenSearchQueryEditor.tsx deleted file mode 100644 index a6880b7e2..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/OpenSearchQueryEditor.tsx +++ /dev/null @@ -1,145 +0,0 @@ -import { - Button, - Group, - JsonInput, - Modal, - ScrollArea, - Text, - TextInput, -} from "@mantine/core"; -import { SystemChat } from "../../../../Types"; -import { interpretOsResult } from "../../../../utils/ChatboxUtils"; -import { openSearchCall } from "../../../../utils/OpenSearch"; -import { IconPlayerPlayFilled } from "@tabler/icons-react"; - -export const OpenSearchQueryEditor = ({ - openSearchQueryEditorOpened, - openSearchQueryEditorOpenedHandlers, - currentOsQuery, - currentOsUrl, - setCurrentOsQuery, - setCurrentOsUrl, - setLoadingMessage, - chatHistory, - setChatHistory, -}: { - openSearchQueryEditorOpened: boolean; - openSearchQueryEditorOpenedHandlers: any; - currentOsQuery: string; - currentOsUrl: string; - setCurrentOsQuery: any; - setCurrentOsUrl: any; - setLoadingMessage: any; - chatHistory: any; - setChatHistory: any; -}) => { - const handleOsSubmit = (e: React.MouseEvent) => { - runJsonQuery(currentOsQuery, currentOsUrl); - }; - - // json query run - const runJsonQuery = async ( - newOsJsonQuery: string, - currentOsQueryUrl: string, - ) => { - try { - openSearchQueryEditorOpenedHandlers.close(); - setLoadingMessage("Processing query..."); - - const query = JSON.parse(newOsJsonQuery); - const populateChatFromRawOsQuery = async (openSearchResults: any) => { - const openSearchResponse = await openSearchResults; - // send question and OS results to LLM - const response = await interpretOsResult( - newOsJsonQuery, - JSON.stringify(openSearchResponse, null, 4), - ); - const length = 10; - const newSystemChat = new SystemChat({ - id: Math.random() - .toString(36) - .substring(2, length + 2), - response: response, - queryUsed: "User provided OpenSearch query", - rawQueryUsed: newOsJsonQuery, - queryUrl: currentOsQueryUrl, - rawResults: openSearchResponse, - interaction_id: "Adhoc, not stored in memory", - editing: false, - hits: [], - }); - setChatHistory([...chatHistory, newSystemChat]); - }; - await Promise.all([ - openSearchCall(query, currentOsQueryUrl).then( - populateChatFromRawOsQuery, - ), - ]); - } finally { - setLoadingMessage(null); - } - }; - - const handleInputChange = (e: React.ChangeEvent) => { - setCurrentOsUrl(e.target.value); - }; - - const jsonPlaceholder = { - query: { - match_all: {}, - }, - size: 300, - }; - return ( - - - Note: If you want a RAG answer, make sure the search pipeline is being - used. Ensure it's configured in the URL - (search_pipeline=hybrid_rag_pipeline), and also in the query itself - (ext.generative_qa_parameters parameters) - - - - - - - setCurrentOsQuery(newValue)} - validationError="Invalid JSON" - placeholder={"e.g.\n" + JSON.stringify(jsonPlaceholder, null, 4)} - formatOnBlur - autosize - minRows={4} - /> - - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/SearchControlPanel.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/SearchControlPanel.tsx deleted file mode 100644 index 864d0dbb4..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/SearchControlPanel.tsx +++ /dev/null @@ -1,75 +0,0 @@ -import { Button, Chip, Group, Switch, createStyles } from "@mantine/core"; -import { Settings } from "../../../../Types"; - -const useStyles = createStyles((theme) => ({ - track: { - width: "30px", - }, -})); - -export const SearchControlPanel = ({ - disableFilters, - setDisableFilters, - questionRewriting, - setQuestionRewriting, - queryPlanner, - setQueryPlanner, - chatHistory, - setChatHistory, - openSearchQueryEditorOpenedHandlers, - settings, - queryAnaylzerSwitchRef, -}: { - disableFilters: any; - setDisableFilters: any; - questionRewriting: any; - setQuestionRewriting: any; - queryPlanner: boolean; - setQueryPlanner: any; - chatHistory: any; - setChatHistory: any; - openSearchQueryEditorOpenedHandlers: any; - settings: Settings; - queryAnaylzerSwitchRef: any; -}) => { - const { classes } = useStyles(); - - return ( - - {settings.required_filters.length > 0 ? ( - setDisableFilters((v: any) => !v)} - variant="light" - > - Filters - - ) : null} - {settings.auto_filter ? ( - setQueryPlanner(!queryPlanner)} - variant="light" - label="Query analyzer" - ref={queryAnaylzerSwitchRef} - /> - ) : null} - - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/SystemChatbox.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/SystemChatbox.tsx deleted file mode 100644 index e2ce9d275..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/SystemChatbox.tsx +++ /dev/null @@ -1,731 +0,0 @@ -import { - ActionIcon, - Badge, - Button, - Card, - Collapse, - Container, - Flex, - Group, - Modal, - NativeSelect, - Paper, - ScrollArea, - Stack, - Text, - TextInput, - Title, - UnstyledButton, - useMantineTheme, -} from "@mantine/core"; -import { FilterValues, Settings, SystemChat } from "../../../../Types"; -import { - getHybridConversationSearchQuery, - hybridConversationSearch, - hybridSearchNoRag, - updateInteractionAnswer, -} from "../../../../utils/OpenSearch"; -import { useState } from "react"; -import { useDisclosure } from "@mantine/hooks"; -import { - IconChevronDown, - IconChevronRight, - IconCopy, - IconEdit, - IconInfoCircle, - IconPlayerPlayFilled, - IconPlus, - IconX, -} from "@tabler/icons-react"; -import { Prism } from "@mantine/prism"; -import { DocList } from "./Doclist"; -import { Citation } from "./Citation"; -import { - anthropicRag, - parseFilters, - parseFiltersForDisplay, - parseOpenSearchResults, - simplifyAnswer, - streamingAnthropicRag, -} from "../../../../utils/ChatboxUtils"; -import { FeedbackButtons } from "./FeedbackButtons"; - -/** - * This component manages an interaction effectively. It shows the question/answer/hits, and also supports the edit/resubmit functionality. - * All context here is lost when switching a conversation or refreshing the page. - */ -export const SystemChatBox = ({ - systemChat, - chatHistory, - settings, - handleSubmit, - setChatHistory, - setSearchResults, - setErrorMessage, - setLoadingMessage, - setCurrentOsQuery, - setCurrentOsUrl, - openSearchQueryEditorOpenedHandlers, - disableFilters, - anthropicRagFlag, - streamingRagResponse, - setStreamingRagResponse, - openErrorDialog, - setManualFilters, - setManualAggregations, -}: { - systemChat: SystemChat; - chatHistory: any; - settings: Settings; - handleSubmit: any; - setChatHistory: any; - setSearchResults: any; - setErrorMessage: any; - setLoadingMessage: any; - setCurrentOsQuery: any; - setCurrentOsUrl: any; - openSearchQueryEditorOpenedHandlers: any; - disableFilters: boolean; - anthropicRagFlag: boolean; - streamingRagResponse: any; - setStreamingRagResponse: any; - openErrorDialog: any; - setManualFilters: any; - setManualAggregations: any; -}) => { - const citationRegex = /\[(\d+)\]/g; - const theme = useMantineTheme(); - const [openSearchResultOpened, openSearchResultHandler] = - useDisclosure(false); - const [openSearchQueryModalOpened, openSearchQueryModalHandler] = - useDisclosure(false); - - const replaceCitationsWithLinks = (text: string) => { - const cleanedText = text - .replace(/\[\${(\d+)}\]/g, "[$1]") - .replace(/\\n/g, "\n"); //handle escaped strings - const elements: React.ReactNode[] = []; - let lastIndex = 0; - if (text == null) return elements; - cleanedText.replace( - citationRegex, - (substring: string, citationNumberRaw: any, index: number) => { - elements.push(cleanedText.slice(lastIndex, index)); - const citationNumber = parseInt(citationNumberRaw); - if (citationNumber >= 1 && citationNumber <= systemChat.hits.length) { - elements.push( - , - ); - } else { - elements.push(substring); - } - lastIndex = index + substring.length; - return substring; - }, - ); - elements.push(cleanedText.slice(lastIndex)); - return elements; - }; - - // for editing - const { query, url } = getHybridConversationSearchQuery( - systemChat.queryUsed, - systemChat.queryUsed, - parseFilters(systemChat.filterContent ?? {}, setErrorMessage), - settings.openSearchIndex, - settings.embeddingModel, - settings.modelName, - settings.ragPassageCount, - ); - const queryUrl = systemChat.queryUrl != "" ? systemChat.queryUrl : url; - const currentOsQuery = - systemChat.rawQueryUsed != null && systemChat.rawQueryUsed != "" - ? systemChat.rawQueryUsed - : JSON.stringify(query, null, 4); - const [editing, setEditing] = useState(systemChat.editing); - const [newQuestion, setNewQuestion] = useState(systemChat.queryUsed); - const [newFilterContent, setNewFilterContent] = useState( - systemChat.filterContent ?? {}, - ); - const [newFilterInputDialog, newFilterInputDialoghHandlers] = - useDisclosure(false); - - const [newFilterType, setNewFilterType] = useState("location"); - const [newFilterValue, setNewFilterValue] = useState(""); - - const filters = () => { - if (systemChat.filterContent == null && !editing) { - return null; - } - - const removeFilter = (filterToRemove: any) => { - console.log("Removing filter: ", filterToRemove); - const updatedNewFilterContent = { ...newFilterContent }; - delete updatedNewFilterContent[filterToRemove]; - setNewFilterContent(updatedNewFilterContent); - }; - - const editFilter = (filterToEdit: any) => { - setNewFilterType(filterToEdit); - setNewFilterValue(newFilterContent[filterToEdit] as string); - newFilterInputDialoghHandlers.open(); - }; - const FilterBadge = ({ - filterKey, - filterValue, - }: { - filterKey: string; - filterValue: any; - }) => { - return ( - - {filterKey}: {filterValue} - - ); - }; - - const addFilter = () => { - const handleSubmit = (e: React.FormEvent) => { - console.log("Adding filter", newFilterType + " " + newFilterValue); - const updatedNewFilterContent = { ...newFilterContent }; - updatedNewFilterContent[newFilterType] = newFilterValue; - setNewFilterContent(updatedNewFilterContent); - newFilterInputDialoghHandlers.close(); - }; - - const handleInputChange = (e: React.ChangeEvent) => { - setNewFilterValue(e.target.value); - }; - - const handleInputKeyPress = ( - e: React.KeyboardEvent, - ) => { - if (e.key === "Enter") { - e.preventDefault(); - handleSubmit(e); - } - }; - - return ( - - - setNewFilterType(event.currentTarget.value)} - data={[ - { value: "location", label: "Location" }, - { value: "airplane_name", label: "Airplane type" }, - { value: "day_start", label: "Before" }, - { value: "day_end", label: "After" }, - ]} - /> - - - - - ); - }; - - const editFiltersButtons = (filter: any) => ( - - editFilter(filter)} - > - - - removeFilter(filter)} - > - - - - ); - - if (!editing) { - return ( - - {systemChat.filterContent && - Object.keys(systemChat.filterContent).length !== 0 && ( - - Filters : - - {Object.entries(systemChat.filterContent as FilterValues).map( - ([key, value]) => { - if (typeof value === "object") { - const start = value.gte ?? ""; - const end = value.lte ?? ""; - const rangeText = - start && end - ? `${start} - ${end}` - : start - ? `>= ${start}` - : `<= ${end}`; - return ( - - ); - } else { - return ( - - ); - } - }, - // - )} - - { - setManualFilters(systemChat.filterContent); - }} - size="xs" - > - - - - )} - {systemChat.aggregationContent && - Object.keys(systemChat.aggregationContent).length !== 0 && ( - - Aggregations : - {Object.keys(systemChat.aggregationContent).map((aggs: any) => { - return ( - - {aggs}: {systemChat.aggregationContent[aggs]} - - ); - })} - - { - setManualAggregations(systemChat.aggregationContent); - }} - size="xs" - > - - - - )} - - ); - } else { - return ( - - {addFilter()} - {Object.keys(newFilterContent).map((filter: any) => { - return ( - - {filter}: {newFilterContent[filter]} - - ); - })} - newFilterInputDialoghHandlers.open()}> - { - setEditing(false); - newFilterInputDialoghHandlers.open(); - }} - > - - New filter - - - - - - - - ); - } - }; - const handleInputChange = (e: any) => { - setNewQuestion(e.target.value); - }; - - const handleInputKeyPress = (e: React.KeyboardEvent) => { - if (e.key === "Enter") { - e.preventDefault(); - rerunQuery(); - } - }; - - // rerun mechanism - const rerunQuery = async () => { - try { - setEditing(false); - if (disableFilters) { - setLoadingMessage("Processing query..."); - } else { - setLoadingMessage("Processing query with filters..."); - } - const populateChatFromOs = ({ - openSearchResponse, - query, - }: { - openSearchResponse: any; - query: any; - }) => { - console.log("New filter content is", newFilterContent); - console.log("Main processor ", openSearchResponse); - console.log("Main processor: OS results ", openSearchResponse); - const endTime = new Date(Date.now()); - const elpased = endTime.getTime() - startTime.getTime(); - console.log("Main processor: OS took seconds: ", elpased); - const parsedOpenSearchResults = parseOpenSearchResults( - openSearchResponse, - setErrorMessage, - ); - const newSystemChat = new SystemChat({ - id: parsedOpenSearchResults.interactionId + "_system", - interaction_id: parsedOpenSearchResults.interactionId, - response: parsedOpenSearchResults.chatResponse, - ragPassageCount: settings.ragPassageCount, - modelName: settings.modelName, - rawQueryUsed: JSON.stringify(query, null, 4), - queryUrl: queryUrl, - queryUsed: newQuestion, - hits: parsedOpenSearchResults.documents, - filterContent: newFilterContent, - }); - setChatHistory([...chatHistory, newSystemChat]); - }; - - const clean = async (result: any) => { - const openSearchResponseAsync = result[0]; - const query = result[1]; - const openSearchResponse = await openSearchResponseAsync; - let generatedAnswer = - openSearchResponse.ext.retrieval_augmented_generation.answer; - if (settings.simplify && openSearchResponse.hits.hits.length > 0) { - console.log("Simplifying answer: ", generatedAnswer); - generatedAnswer = await simplifyAnswer(newQuestion, generatedAnswer); - } - await updateInteractionAnswer( - openSearchResponse.ext.retrieval_augmented_generation.interaction_id, - generatedAnswer, - query, - ); - openSearchResponse.ext.retrieval_augmented_generation.answer = - generatedAnswer; - return { openSearchResponse, query }; - }; - - const clean_rag = async ({ - openSearchResponse, - query, - }: { - openSearchResponse: any; - query: any; - }) => { - let generatedAnswer = - openSearchResponse.ext.retrieval_augmented_generation.answer; - if (settings.simplify && openSearchResponse.hits.hits.length > 0) { - console.log("Simplifying answer: ", generatedAnswer); - generatedAnswer = await simplifyAnswer(newQuestion, generatedAnswer); - } - await updateInteractionAnswer( - openSearchResponse.ext.retrieval_augmented_generation.interaction_id, - generatedAnswer, - query, - ); - openSearchResponse.ext.retrieval_augmented_generation.answer = - generatedAnswer; - return { openSearchResponse, query }; - }; - - const anthropic_rag_og = async (result: any) => { - const openSearchResponseAsync = result[0]; - const query = result[1]; - const openSearchResponse = await openSearchResponseAsync; - let generatedAnswer = "Error"; - if (openSearchResponse.hits.hits.length > 0) { - console.log("Anthropic RAG time..."); - generatedAnswer = await anthropicRag(newQuestion, openSearchResponse); - } - openSearchResponse["ext"] = { - retrieval_augmented_generation: { - answer: generatedAnswer, - }, - }; - return { openSearchResponse, query }; - }; - - const anthropic_rag = async (result: any) => { - const openSearchResponseAsync = result[0]; - const openSearchResponse = await openSearchResponseAsync; - - console.log("Anthropic processor ", openSearchResponse); - console.log("Anthropic processor: OS results ", openSearchResponse); - const endTime = new Date(Date.now()); - const elpased = endTime.getTime() - startTime.getTime(); - console.log("Anthropic processor: OS took seconds: ", elpased); - const parsedOpenSearchResults = parseOpenSearchResults( - openSearchResponse, - setErrorMessage, - ); - const newSystemChat = new SystemChat({ - id: parsedOpenSearchResults.interactionId + "_system", - ragPassageCount: settings.ragPassageCount, - modelName: settings.modelName, - queryUsed: newQuestion, - hits: parsedOpenSearchResults.documents, - filterContent: newFilterContent, - }); - setChatHistory([newSystemChat, ...chatHistory]); - if (openSearchResponse.hits.hits.length > 0) { - await streamingAnthropicRag( - newQuestion, - openSearchResponse, - newSystemChat, - setStreamingRagResponse, - ); - } else { - newSystemChat.response = "No search results"; - } - }; - - const startTime = new Date(Date.now()); - if (!anthropicRagFlag) { - await Promise.all([ - hybridConversationSearch( - newQuestion, - newQuestion, - parseFilters(newFilterContent, setErrorMessage), - settings.activeConversation, - settings.openSearchIndex, - settings.embeddingModel, - settings.modelName, - settings.ragPassageCount, - ) - .then(clean) - .then(populateChatFromOs) - .catch((error: any) => { - openErrorDialog( - "Error sending opensearch query:" + error.message, - ); - console.error("Error sending opensearch query:", error); - }), - ]); - } else { - await Promise.all([ - hybridSearchNoRag( - newQuestion, - parseFilters(filters, setErrorMessage), - settings.openSearchIndex, - settings.embeddingModel, - true, - ) - .then(anthropic_rag_og) - .then(clean_rag) - .then(populateChatFromOs) - .catch((error: any) => { - openErrorDialog("Error sending query:" + error.message); - console.error("Error sending query:" + error); - }), - ]); - } - } finally { - setLoadingMessage(null); - } - }; - - return ( - - - - - - {JSON.stringify(systemChat.rawQueryUsed, null, 4)} - - - - - {systemChat.rawQueryUsed && ( - - - - )} - - {editing ? ( - - - { - setEditing(false); - setNewQuestion(systemChat.queryUsed); - }} - /> - - - ) : ( - - setEditing(true)} /> - - )} - {editing ? ( - - ) : ( - - {systemChat.queryUsed} - - )} - - - {settings.auto_filter ? filters() : null} - - {editing ? ( - - - - - ) : null} - - - {/* {textNodes} */} - {replaceCitationsWithLinks(systemChat.response)} - {systemChat.rawResults != null ? ( - - - {openSearchResultOpened ? ( - - ) : ( - - )} - OpenSearch results - - - - - - {JSON.stringify(systemChat.rawResults, null, 4)} - - - - - - ) : null} - - - - {systemChat.originalQuery !== "" && - systemChat.originalQuery !== systemChat.queryUsed ? ( - - Original Query: {systemChat.originalQuery} - - ) : null} - - Interaction id:{" "} - {systemChat.interaction_id ? systemChat.interaction_id : ""} - - - - - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/index.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/index.tsx deleted file mode 100644 index 90ef2fda1..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Chatbox/index.tsx +++ /dev/null @@ -1,1018 +0,0 @@ -import React, { useEffect } from "react"; -import { Dispatch, SetStateAction, useRef, useState } from "react"; -import { - ActionIcon, - Badge, - Button, - Center, - Container, - Flex, - Group, - Image, - Loader, - ScrollArea, - Skeleton, - Stack, - Text, - TextInput, - Tooltip, - createStyles, - rem, - useMantineTheme, -} from "@mantine/core"; -import { - IconChevronRight, - IconClearAll, - IconSettings, - IconWriting, - IconX, -} from "@tabler/icons-react"; -import { getFilters, rephraseQuestion } from "../../../../utils/Llm"; -import { - AggregationValues, - FilterValues, - SearchResultDocument, - Settings, - SystemChat, -} from "../../../../Types"; -import { - hybridConversationSearch, - updateInteractionAnswer, - createConversation, - hybridSearchNoRag, - openSearchCall, -} from "../../../../utils/OpenSearch"; -import { - useDisclosure, - useMediaQuery, - useResizeObserver, -} from "@mantine/hooks"; -import { ControlPanel } from "./Controlpanel"; -import { - anthropicRag, - buildOpenSearchQueryFromManualFiltersAggs, - interpretOsResult, - parseAggregationsForDisplay, - parseFilters, - parseFiltersForDisplay, - buildOpensearchQueryFromLlmResponse, - parseManualFilters, - parseOpenSearchResults, - parseOpenSearchResultsOg, - simplifyAnswer, - streamingAnthropicRag, - excludeFields, -} from "../../../../utils/ChatboxUtils"; -import { SystemChatBox } from "./SystemChatbox"; -import { SearchControlPanel } from "./SearchControlPanel"; -import { FilterInput } from "./FilterInput"; -import { OpenSearchQueryEditor } from "./OpenSearchQueryEditor"; -import { AddFilterModal } from "./AddFilterModal"; -import { AddAggregationModal } from "./AddAggregationModal"; - -const useStyles = createStyles((theme) => ({ - inputBar: { - width: "50vw", - [theme.fn.smallerThan("sm")]: { - width: "100%", - }, - }, - fixedBottomContainer: { - margin: 0, - maxWidth: "none", - bottom: 0, - borderTop: "1px solid lightgrey", - flex: 1, - }, - settingsIcon: { - zIndex: 1, - }, - settingsStack: { - position: "absolute", - top: 10, - right: 0, - alignItems: "end", - }, - chatFlex: { - paddingBottom: 0, - }, -})); - -const LoadingChatBox = ({ - loadingMessage, -}: { - loadingMessage: string | null; -}) => { - const theme = useMantineTheme(); - return ( - - {/* */} - - {loadingMessage ? loadingMessage : null} - - - - - - ); -}; - -export const ChatBox = ({ - chatHistory, - searchResults, - setChatHistory, - setSearchResults, - streaming, - setStreaming, - setDocsLoading, - setErrorMessage, - settings, - setSettings, - refreshConversations, - chatInputRef, - openErrorDialog, -}: { - chatHistory: SystemChat[]; - searchResults: SearchResultDocument[]; - setChatHistory: Dispatch>; - setSearchResults: Dispatch>; - streaming: boolean; - setStreaming: Dispatch>; - setDocsLoading: Dispatch>; - setErrorMessage: Dispatch>; - settings: Settings; - setSettings: Dispatch>; - refreshConversations: any; - chatInputRef: any; - openErrorDialog: any; -}) => { - const theme = useMantineTheme(); - const [chatInput, setChatInput] = useState(""); - const [anthropicRagFlag, setAnthropicRagFlag] = useState(false); - const [disableFilters, setDisableFilters] = useState(settings.auto_filter); - const [queryPlanner, setQueryPlanner] = useState(settings.auto_filter); - const [questionRewriting, setQuestionRewriting] = useState(false); - const [filtersInput, setFiltersInput] = useState<{ [key: string]: string }>( - {}, - ); - const [loadingMessage, setLoadingMessage] = useState(null); - const [currentOsQuery, setCurrentOsQuery] = useState(""); - const [currentOsUrl, setCurrentOsUrl] = useState( - "/opensearch/" + settings.openSearchIndex + "/_search?", - ); - const [openSearchQueryEditorOpened, openSearchQueryEditorOpenedHandlers] = - useDisclosure(false); - const { classes } = useStyles(); - const mobileScreen = useMediaQuery(`(max-width: ${theme.breakpoints.sm})`); - const [settingsOpened, settingsHandler] = useDisclosure(false); - const [filterError, setFilterError] = useState(false); - const scrollAreaRef = useRef(null); - const [streamingRagResponse, setStreamingRagResponse] = useState(""); - const [bottomContainerRef, bottomContainerRect] = useResizeObserver(); - const [manualFilters, setManualFilters] = useState({}); - const [addFilterModalOpened, addFilterModalHandlers] = useDisclosure(false); - const [manualAggregations, setManualAggregations] = - useState({}); - const [addAggregationsModalOpened, addAggregationsModalhandlers] = - useDisclosure(false); - const [filterFields, setFilterFields] = useState([]); - const queryAnaylzerSwitchRef = useRef(null); - - useEffect(() => { - setCurrentOsUrl("/opensearch/" + settings.openSearchIndex + "/_search?"); - }, [settings.openSearchIndex]); - - const scrollToBottom = () => { - scrollAreaRef.current?.scrollTo({ - top: scrollAreaRef.current.scrollHeight, - }); - }; - - useEffect(() => { - if ( - Object.keys(manualAggregations).length === 0 && - Object.keys(manualFilters).length === 0 - ) { - setQueryPlanner(true); - if (queryAnaylzerSwitchRef.current) { - queryAnaylzerSwitchRef.current.disabled = false; - } - } else { - setQueryPlanner(false); - if (queryAnaylzerSwitchRef.current) { - queryAnaylzerSwitchRef.current.disabled = true; - } - } - }, [manualAggregations, manualFilters]); - - useEffect(() => { - const apiUrl = `https://localhost:9200/${settings.openSearchIndex}/_mapping`; - - const fetchData = async () => { - try { - const response = await fetch(apiUrl); - - if (!response.ok) { - throw new Error("Network response was not ok."); - } - - const data = await response.json(); - const retrievedFields = Object.keys( - data[settings.openSearchIndex].mappings.properties.properties - .properties, - ).filter((field) => !excludeFields.includes(field)); - setFilterFields(Array.from(new Set(retrievedFields))); - console.log( - "Fields from mappings: ", - Object.keys( - data[settings.openSearchIndex].mappings.properties.properties - .properties, - ), - ); - } catch (error) { - setErrorMessage("Error fetching filter fields"); - } - }; - - fetchData(); - }, [settings.openSearchIndex]); - - useEffect(() => { - scrollToBottom(); - }, [chatHistory, streaming, loadingMessage]); - - useEffect(() => { - setManualFilters({}); - setManualAggregations({}); - }, [settings.activeConversation]); - - // This method does all the search workflow execution - const handleSubmitParallelDocLoad = async (e: React.FormEvent) => { - try { - e.preventDefault(); - if (chatInputRef.current != null) { - chatInputRef.current.disabled = true; - } - - setStreaming(true); - setDocsLoading(true); - console.log("Rephrasing question: ", chatInput); - // Generate conversation text list - const chatHistoryInteractions = chatHistory.map((chat) => { - if ("query" in chat) { - return { role: "user", content: chat.query }; - } else { - return { role: "system", content: chat.response ?? "" }; - } - }); - let filterResponse; - let filters: any; - let filterContent: any = null; - let rawOSQueryFlag: boolean = false; - let osJsonQuery: any = null; - let aggregations: any = null; - if (disableFilters) { - if (Object.keys(manualAggregations).length !== 0) { - osJsonQuery = buildOpenSearchQueryFromManualFiltersAggs( - manualFilters, - manualAggregations, - ); - aggregations = manualAggregations; - filterContent = manualFilters; - rawOSQueryFlag = true; - } else if (Object.keys(manualFilters).length !== 0) { - filters = parseManualFilters(manualFilters); - filterContent = manualFilters; - console.log("Only manual filters", JSON.stringify(filters, null, 2)); - } else if (queryPlanner) { - filterResponse = await getFilters(chatInput, settings.modelName); - console.log("filterResponse", filterResponse); - if (filterResponse.ok) { - const filterData = await filterResponse.json(); - console.log("filterData", filterData); - const autoFilterRawResult = filterData.choices[0].message.content; - filters = autoFilterRawResult; - if ( - autoFilterRawResult.error !== undefined && - autoFilterRawResult.error.type === "timeout_exception" - ) { - const documents = new Array(); - const chatResponse = "Timeout from OpenAI"; - const interactionId = ""; - setErrorMessage(chatResponse); - return null; - } - try { - let filterContentGenerated = JSON.parse(autoFilterRawResult); - if ( - filterContentGenerated?.cardinalityAggregations?.length === 0 && - filterContentGenerated?.termsAggregations?.length === 0 - ) { - filters = parseFilters(filterContentGenerated, setErrorMessage); - filterContent = parseFiltersForDisplay(filterContentGenerated); - } else { - osJsonQuery = buildOpensearchQueryFromLlmResponse( - filterContentGenerated, - ); - filterContent = parseFiltersForDisplay(filterContentGenerated); - aggregations = parseAggregationsForDisplay( - filterContentGenerated, - ); - console.log( - "from buildOpensearchQueryFromLlmResponse", - JSON.stringify(osJsonQuery, null, 2), - "aggregations", - aggregations, - "filterContent", - filterContent, - ); - rawOSQueryFlag = true; - } - } catch (error) { - console.error("Error parsing JSON:", error); - } - } - } else if (settings.required_filters.length > 0) { - filters = parseFilters(filtersInput, setErrorMessage); - filterContent = filtersInput; - if ( - filters["keyword"]["bool"]["filter"].length != - settings.required_filters.length - ) { - throw new Error("All required filters not populated"); - } - } - } else { - filters = null; - } - console.log("Filters are: ", filters); - let question: string = chatInput; - const originalQuestion: string = question; - if (questionRewriting) { - setLoadingMessage("Rephrasing question with conversation context"); - const rephraseQuestionResponse = await rephraseQuestion( - chatInput, - chatHistoryInteractions, - settings.modelName, - ); - const responseData = await rephraseQuestionResponse.json(); - const rephrasedQuestion = responseData.choices[0].message.content; - console.log("Rephrased question to ", rephrasedQuestion); - question = rephrasedQuestion; - } - console.log("Question is: ", question); - if (question.length === 0) { - setLoadingMessage("Running Opensearch Query"); - } else { - setLoadingMessage( - 'Querying knowledge database with question: "' + question + '"', - ); - } - - const clean = async (result: any) => { - const openSearchResponseAsync = result[0]; - const query = result[1]; - const openSearchResponse = await openSearchResponseAsync; - let generatedAnswer = - openSearchResponse.ext.retrieval_augmented_generation.answer; - if (settings.simplify && openSearchResponse.hits.hits.length > 0) { - console.log("Simplifying answer: ", generatedAnswer); - setLoadingMessage("Simplifying answer.."); - generatedAnswer = await simplifyAnswer(question, generatedAnswer); - } - await updateInteractionAnswer( - openSearchResponse.ext.retrieval_augmented_generation.interaction_id, - generatedAnswer, - query, - ); - openSearchResponse.ext.retrieval_augmented_generation.answer = - generatedAnswer; - return { openSearchResponse, query }; - }; - - const clean_rag = async ({ - openSearchResponse, - query, - }: { - openSearchResponse: any; - query: any; - }) => { - let generatedAnswer = - openSearchResponse.ext.retrieval_augmented_generation.answer; - if (settings.simplify && openSearchResponse.hits.hits.length > 0) { - console.log("Simplifying answer: ", generatedAnswer); - generatedAnswer = await simplifyAnswer(question, generatedAnswer); - } - await updateInteractionAnswer( - openSearchResponse.ext.retrieval_augmented_generation.interaction_id, - generatedAnswer, - query, - ); - openSearchResponse.ext.retrieval_augmented_generation.answer = - generatedAnswer; - return { openSearchResponse, query }; - }; - - const anthropic_rag_og = async (result: any) => { - const openSearchResponseAsync = result[0]; - const query = result[1]; - const openSearchResponse = await openSearchResponseAsync; - let generatedAnswer = "Error"; - if (openSearchResponse.hits.hits.length > 0) { - console.log("Anthropic RAG time..."); - generatedAnswer = await anthropicRag(question, openSearchResponse); - } - openSearchResponse["ext"] = { - retrieval_augmented_generation: { - answer: generatedAnswer, - }, - }; - return { openSearchResponse, query }; - }; - - const anthropic_rag = async (result: any) => { - const openSearchResponseAsync = result[0]; - const openSearchResponse = await openSearchResponseAsync; - - console.log("Anthropic processor ", openSearchResponse); - const endTime = new Date(Date.now()); - const elpased = endTime.getTime() - startTime.getTime(); - console.log("Anthropic processor: OS took seconds: ", elpased); - const parsedOpenSearchResults = parseOpenSearchResults( - openSearchResponse, - setErrorMessage, - ); - const newSystemChat = new SystemChat({ - id: parsedOpenSearchResults.interactionId + "_system", - ragPassageCount: settings.ragPassageCount, - modelName: settings.modelName, - queryUsed: question, - originalQuery: originalQuestion, - hits: parsedOpenSearchResults.documents, - filterContent: filterContent, - }); - setChatHistory([newSystemChat, ...chatHistory]); - if (openSearchResponse.hits.hits.length > 0) { - await streamingAnthropicRag( - question, - openSearchResponse, - newSystemChat, - setStreamingRagResponse, - ); - } else { - newSystemChat.response = "No search results"; - } - }; - - const populateChatFromOs = ({ - openSearchResponse, - query, - }: { - openSearchResponse: any; - query: any; - }) => { - console.log("Main processor ", openSearchResponse); - console.log("Main processor: OS results ", openSearchResponse); - const endTime = new Date(Date.now()); - const elpased = endTime.getTime() - startTime.getTime(); - console.log("Main processor: OS took seconds: ", elpased); - const parsedOpenSearchResults = parseOpenSearchResults( - openSearchResponse, - setErrorMessage, - ); - const newSystemChat = new SystemChat({ - id: parsedOpenSearchResults.interactionId + "_system", - interaction_id: parsedOpenSearchResults.interactionId, - response: parsedOpenSearchResults.chatResponse, - ragPassageCount: settings.ragPassageCount, - rawQueryUsed: query, - modelName: settings.modelName, - queryUsed: question, - originalQuery: originalQuestion, - hits: parsedOpenSearchResults.documents, - filterContent: filterContent, - }); - setChatHistory([...chatHistory, newSystemChat]); - }; - const populateDocsFromOs = (openSearchResults: any) => { - console.log("Info separate processor ", openSearchResults); - console.log("Info separate processor : OS results ", openSearchResults); - const endTime = new Date(Date.now()); - const elpased = endTime.getTime() - startTime.getTime(); - console.log("Info separate processor : OS took seconds: ", elpased); - const parsedOpenSearchResults = - parseOpenSearchResultsOg(openSearchResults); - setSearchResults(parsedOpenSearchResults); - console.log( - "Info separate processor : set docs in independent thread to: ", - parsedOpenSearchResults, - ); - setDocsLoading(false); - }; - const startTime = new Date(Date.now()); - if (!settings.activeConversation) { - const conversationId = await createConversation(chatInput); - settings.activeConversation = conversationId.memory_id; - setSettings(settings); - refreshConversations(); - } - - if (rawOSQueryFlag) { - const populateChatFromRawOsQuery = async (openSearchResults: any) => { - const openSearchResponse = await openSearchResults; - const response = await interpretOsResult( - question, - JSON.stringify(openSearchResponse, null, 4), - ); - const length = 10; - const newSystemChat = new SystemChat({ - id: Math.random() - .toString(36) - .substring(2, length + 2), - response: response, - queryUsed: - question.length === 0 ? "Opensearch Query Response" : question, - rawQueryUsed: osJsonQuery, - queryUrl: currentOsUrl, - rawResults: openSearchResponse, - interaction_id: "Adhoc, not stored in memory", - editing: false, - hits: [], - filterContent: filterContent, - aggregationContent: aggregations, - }); - setChatHistory([...chatHistory, newSystemChat]); - }; - console.log("OsJsonQuery: ", osJsonQuery); - const query = osJsonQuery; - await Promise.all([ - openSearchCall(query, currentOsUrl).then(populateChatFromRawOsQuery), - ]); - } else if (!anthropicRagFlag) { - await Promise.all([ - hybridConversationSearch( - chatInput, - question, - filters, - settings.activeConversation, - settings.openSearchIndex, - settings.embeddingModel, - settings.modelName, - settings.ragPassageCount, - ) - .then(clean) - .then(populateChatFromOs) - .catch((error: any) => { - openErrorDialog( - "Error sending opensearch query:" + error.message, - ); - console.error("Error sending opensearch query:" + error); - }), - ]); - } else { - await Promise.all([ - hybridSearchNoRag( - question, - parseFilters(filters, setErrorMessage), - settings.openSearchIndex, - settings.embeddingModel, - true, - ) - .then(anthropic_rag_og) - .then(clean_rag) - .then(populateChatFromOs) - .catch((error: any) => { - openErrorDialog("Error sending query:" + error.message); - console.error("Error sending query:" + error); - }), - ]); - } - } catch (e) { - console.log(e); - if (typeof e === "string") { - setErrorMessage(e.toUpperCase()); - } else if (e instanceof Error) { - setErrorMessage(e.message); - } - } finally { - setStreaming(false); - setChatInput(""); - setDocsLoading(false); - setLoadingMessage(null); - if (chatInputRef.current != null) { - chatInputRef.current.disabled = false; - } - } - }; - - // This method does all the search workflow execution - const handleSubmit = async (e: React.FormEvent) => { - if (Object.keys(manualAggregations).length > 0) { - return handleSubmitParallelDocLoad(e); - } - if (chatInput.length === 0) { - return; - } - if (!disableFilters && settings.required_filters.length > 0) { - const someNonEmptyValues = - Object.keys(filtersInput).length === 0 || - Object.keys(filtersInput).some((key) => filtersInput[key] === ""); - if (someNonEmptyValues) { - setFilterError(true); - return; - } - } - return handleSubmitParallelDocLoad(e); - }; - - const handleInputChange = (e: React.ChangeEvent) => { - setChatInput(e.target.value); - }; - - const handleInputKeyPress = (e: React.KeyboardEvent) => { - if (e.key === "Enter") { - e.preventDefault(); - handleSubmit(e); - } - }; - - const handleRemoveFilter = (filterKeyToRemove: string) => { - setManualFilters((prevFilters) => { - const updatedFilters = { ...prevFilters }; - delete updatedFilters[filterKeyToRemove]; - return updatedFilters; - }); - }; - - const handleRemoveAgg = (AggKeyToRemove: string) => { - setManualAggregations((prevAggs) => { - const updatedAggs = { ...prevAggs }; - delete updatedAggs[AggKeyToRemove]; - return updatedAggs; - }); - }; - - React.useEffect(() => { - chatInputRef.current?.focus(); - }, [streaming]); - - const FilterBadge = ({ - filterKey, - filterValue, - }: { - filterKey: string; - filterValue: any; - }) => { - return ( - { - handleRemoveFilter(filterKey); - }} - > - - - } - > - {filterKey}: {filterValue} - - ); - }; - - const AggregationBadge = ({ - aggregationType, - aggregationValue, - }: { - aggregationType: string; - aggregationValue: string; - }) => { - return ( - { - handleRemoveAgg(aggregationType); - }} - > - - - } - > - {aggregationType}: {aggregationValue} - - ); - }; - - return ( - <> - - - - - - - - - - - - - {chatHistory.length === 0 && !loadingMessage && !streaming ? ( - - - How can I help you today? - - ) : ( - - - - {chatHistory.map((chat, index) => { - return ( - - ); - })} - {loadingMessage ? ( - - ) : null} - -
- {streaming ? : ""} -
- - - - )} - - - 0) || - settings.auto_filter - ? "apart" - : "right" - } - ml="auto" - mr="auto" - p="xs" - w={mobileScreen ? "90vw" : "70vw"} - noWrap - > - {settings.auto_filter && ( - - - Filters : - {Object.entries(manualFilters).length !== 0 && ( - - setManualFilters({})} size="xs"> - - - - )} - {Object.entries(manualFilters).map( - ([key, value]) => { - if (typeof value === "object") { - const start = value.gte ?? ""; - const end = value.lte ?? ""; - const rangeText = - start && end - ? `${start} - ${end}` - : start - ? `>= ${start}` - : `<= ${end}`; - return ( - - ); - } else { - return ( - - ); - } - }, - // - )} - - - - - Aggregations : - {Object.entries(manualAggregations).map(([key, value]) => ( - - ))} - - - - )} - {!disableFilters && settings.required_filters.length > 0 ? ( - - ) : null} - - -
- - - ({ - color: questionRewriting ? "white" : "#5688b0", - backgroundColor: questionRewriting ? "#5688b0" : "white", - "&:hover": { - backgroundColor: questionRewriting - ? "#5688b0" - : theme.colors.gray[2], - }, - })} - > - { - setQuestionRewriting((o) => !o); - chatInputRef?.current?.focus(); - }} - /> - - - {(chatInput.length !== 0 || - Object.keys(manualAggregations).length !== 0) && ( - ({ - "&:hover": { - backgroundColor: theme.colors.gray[2], - }, - })} - > - - - )} - - } - placeholder="Ask me anything" - disabled={settings.activeConversation == null} - /> -
- - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Header/index.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Header/index.tsx deleted file mode 100644 index 5eb6bcfdf..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Header/index.tsx +++ /dev/null @@ -1,67 +0,0 @@ -import { - Burger, - Container, - Group, - Header, - Image, - MediaQuery, - Text, - useMantineTheme, -} from "@mantine/core"; -import { Dispatch, SetStateAction } from "react"; -import { Settings } from "../../../../Types"; -import { useMediaQuery } from "@mantine/hooks"; - -export const HeaderComponent = ({ - navBarOpened, - setNavBarOpened, - settings, -}: { - navBarOpened: boolean; - setNavBarOpened: Dispatch>; - settings: Settings; -}) => { - const theme = useMantineTheme(); - const mobileScreen = useMediaQuery(`(max-width: ${theme.breakpoints.sm})`); - - return ( -
({ - display: "flex", - alignItems: "center", - paddingLeft: theme.spacing.md, - paddingRight: theme.spacing.md, - })} - > - - - setNavBarOpened((o) => !o)} - size="sm" - maw="0.5rem" - mr="xl" - /> - - - {!mobileScreen && ( - - - index: {settings.openSearchIndex} - - - llm model: {settings.modelName} - - - llm model id: {settings.modelId} - - - )} - -
- ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/NavBarConversationItem.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/NavBarConversationItem.tsx deleted file mode 100644 index a8b38ca40..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/NavBarConversationItem.tsx +++ /dev/null @@ -1,189 +0,0 @@ -import { Dispatch, SetStateAction } from "react"; -import { Settings, SystemChat } from "../../../../Types"; -import { deleteConversation } from "../../../../utils/OpenSearch"; -import { - ActionIcon, - Anchor, - Group, - Loader, - Menu, - Text, - createStyles, - rem, -} from "@mantine/core"; -import { IconDotsVertical, IconMessage, IconTrash } from "@tabler/icons-react"; - -const useStyles = createStyles((theme) => ({ - link: { - boxSizing: "border-box", - display: "inline-block", - textDecoration: "none", - borderRadius: theme.radius.md, - color: - theme.colorScheme === "dark" - ? theme.colors.dark[0] - : theme.colors.gray[7], - fontSize: theme.fontSizes.sm, - fontWeight: 500, - padding: rem(5), - overflow: "hidden", - textOverflow: "ellipsis", - - [theme.fn.smallerThan("sm")]: { - fontSize: theme.fontSizes.lg, - paddingLeft: rem(15), - borderRadius: theme.radius.xl, - }, - - "&:hover": { - backgroundColor: - theme.colorScheme === "dark" - ? theme.colors.dark[5] - : theme.colors.gray[2], - color: theme.colorScheme === "dark" ? theme.white : theme.black, - }, - }, - linkRow: { - width: "100%", - marginTop: rem(10), - marginBottom: rem(10), - }, - - linkActive: { - "&, &:hover": { - borderLeftColor: theme.fn.variant({ - variant: "filled", - color: theme.primaryColor, - }).background, - backgroundColor: "#5688b0", - color: theme.white, - }, - }, - conversationName: { - [theme.fn.smallerThan("sm")]: { - width: "55vw", - }, - [theme.fn.largerThan("sm")]: { - width: "calc(175px - 7rem)", - }, - [theme.fn.largerThan("lg")]: { - width: "calc(275px - 7rem)", - }, - }, -})); - -export const NavBarConversationItem = ({ - conversation, - conversations, - setConversations, - selectConversation, - loading, - settings, - setSettings, - setChatHistory, - setNavBarOpened, - openErrorDialog, -}: { - conversation: any; - conversations: any[]; - setConversations: any; - selectConversation: any; - loading: any; - settings: any; - setChatHistory: any; - setSettings: Dispatch>; - setNavBarOpened: any; - openErrorDialog: any; -}) => { - const { classes, cx } = useStyles(); - - const handleDelete = (event: React.MouseEvent) => { - try { - event.stopPropagation(); - console.log("Removing ", conversation.id); - deleteConversation(conversation.id); - if (conversation.id === settings.activeConversation) { - setChatHistory(new Array()); - settings.activeConversation = ""; - setSettings(new Settings(settings)); - } - const newConversations = conversations.filter( - (c) => c.id !== conversation.id, - ); - - console.log("newLinks", newConversations); - setConversations(newConversations); - } catch (error: any) { - openErrorDialog("Error deleting conversation: " + error.message); - console.error("Error deleting conversation: " + error.message); - } - }; - return ( - <> - - { - event.preventDefault(); - selectConversation(conversation.id); - setNavBarOpened(false); - }} - w="100%" - > - - - - - {conversation.name} - - - - - e.stopPropagation()} - sx={(theme) => ({ - borderRadius: 100, - "&:hover": { - backgroundColor: - conversation.id === settings.activeConversation - ? "#4f779b" - : theme.colors.gray[4], - }, - })} - > - - - - - } - onClick={handleDelete} - > - Delete - - - - - - {loading ? : ""} - - - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/NewConversationInput.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/NewConversationInput.tsx deleted file mode 100644 index 600aec042..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/NewConversationInput.tsx +++ /dev/null @@ -1,107 +0,0 @@ -import { Dispatch, SetStateAction, useRef, useState } from "react"; -import { Settings, SystemChat } from "../../../../Types"; -import { ActionIcon, TextInput, useMantineTheme } from "@mantine/core"; -import { useMediaQuery } from "@mantine/hooks"; -import { createConversation } from "../../../../utils/OpenSearch"; -import { setActiveConversation } from "."; -import { IconMessagePlus } from "@tabler/icons-react"; - -export const NewConversationInput = ({ - refreshConversations, - setErrorMessage, - chatInputRef, - settings, - setSettings, - setChatHistory, - setNavBarOpened, - loadActiveConversation, - navBarOpened, -}: { - refreshConversations: any; - setErrorMessage: Dispatch>; - chatInputRef: any; - settings: Settings; - setSettings: Dispatch>; - setChatHistory: Dispatch>>; - setNavBarOpened: any; - loadActiveConversation: any; - navBarOpened: boolean; -}) => { - const [newConversationName, setNewConversationName] = useState(""); - const [error, setError] = useState(false); - const newConversationInputRef = useRef(null); - const theme = useMantineTheme(); - const mobileScreen = useMediaQuery(`(max-width: ${theme.breakpoints.sm})`); - - const handleInputChange = (e: React.ChangeEvent) => { - setError(false); - setNewConversationName(e.target.value); - }; - async function handleSubmit() { - try { - if (newConversationName === "") { - setError(true); - return; - } - setError(false); - const createConversationResponse = - await createConversation(newConversationName); - const conversationId = createConversationResponse.memory_id; - setActiveConversation( - conversationId, - settings, - setSettings, - loadActiveConversation, - ); - setNavBarOpened(false); - refreshConversations(); - } catch (e) { - console.log("Error creating conversation: ", e); - if (typeof e === "string") { - setErrorMessage(e.toUpperCase()); - } else if (e instanceof Error) { - setErrorMessage(e.message); - } - } finally { - setNewConversationName(""); - } - } - const handleInputKeyPress = (e: React.KeyboardEvent) => { - if (e.key === "Enter") { - e.preventDefault(); - handleSubmit(); - } - }; - const handleBlur = () => { - if (error) { - setError(false); - } - }; - - return ( - <> - - - - ) : ( - "" - ) - } - placeholder="New conversation" - error={error ? "Conversation name cannot be empty" : ""} - onBlur={handleBlur} - /> - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/index.tsx b/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/index.tsx deleted file mode 100644 index 63b53e25e..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/components/Navbar/index.tsx +++ /dev/null @@ -1,160 +0,0 @@ -import React, { Dispatch, SetStateAction, useState, useEffect } from "react"; -import { - createStyles, - Loader, - Navbar, - useMantineTheme, - rem, - Center, - Container, -} from "@mantine/core"; -import { Settings } from "../../../../Types"; -import { NavBarConversationItem } from "./NavBarConversationItem"; -import { NewConversationInput } from "./NewConversationInput"; - -const useStyles = createStyles((theme) => ({ - wrapper: { - display: "flex", - padding: "1rem", - }, - main: { - flex: 1, - width: "100%", - backgroundColor: - theme.colorScheme === "dark" - ? theme.colors.dark[6] - : theme.colors.gray[0], - }, - title: { - textTransform: "uppercase", - letterSpacing: rem(-0.25), - }, -})); - -export function setActiveConversation( - conversationId: string, - settings: Settings, - setSettings: Dispatch>, - loadActiveConversation: any, -) { - settings.activeConversation = conversationId; - setSettings(settings); - loadActiveConversation(conversationId); -} - -export const ConversationListNavbar = ({ - navBarOpened, - settings, - setSettings, - setErrorMessage, - loadingConversation, - loadActiveConversation, - conversations, - refreshConversations, - setConversations, - setChatHistory, - chatInputRef, - setNavBarOpened, - openErrorDialog, -}: { - navBarOpened: boolean; - settings: Settings; - setSettings: Dispatch>; - setErrorMessage: Dispatch>; - loadingConversation: boolean; - loadActiveConversation: any; - conversations: any; - refreshConversations: any; - setConversations: any; - setChatHistory: any; - chatInputRef: any; - setNavBarOpened: any; - openErrorDialog: any; -}) => { - const theme = useMantineTheme(); - const { classes, cx } = useStyles(); - const [loading, setLoading] = useState(false); - - const selectConversation = (conversationId: string) => { - console.info("Set active conversation to ", conversationId); - setActiveConversation( - conversationId, - settings, - setSettings, - loadActiveConversation, - ); - refreshConversations(); - }; - - useEffect(() => { - setLoading(true); - refreshConversations(); - setLoading(false); - }, []); - return ( - - ); -}; diff --git a/apps/demo-ui/ui/src/pages/HomePage/index.tsx b/apps/demo-ui/ui/src/pages/HomePage/index.tsx deleted file mode 100644 index 5f5b4b700..000000000 --- a/apps/demo-ui/ui/src/pages/HomePage/index.tsx +++ /dev/null @@ -1,233 +0,0 @@ -import React, { useRef, useState } from "react"; -import { ChatBox } from "./components/Chatbox"; -import { - ConversationListNavbar, - setActiveConversation, -} from "./components/Navbar"; -import { - Alert, - AppShell, - Dialog, - MantineProvider, - useMantineTheme, -} from "@mantine/core"; -import { SearchResultDocument, Settings, SystemChat } from "../../Types"; -import { IconAlertCircle } from "@tabler/icons-react"; -import { - getConversations, - getFeedback, - getInteractions, -} from "../../utils/OpenSearch"; -import { useDisclosure, useMediaQuery } from "@mantine/hooks"; -import { thumbToBool } from "../../utils/ChatboxUtils"; -import { HeaderComponent } from "./components/Header"; - -export default function HomePage() { - const [settings, setSettings] = useState(new Settings()); - const [searchResults, setSearchResults] = useState( - new Array(), - ); - const [streaming, setStreaming] = useState(false); - const [docsLoading, setDocsLoading] = useState(false); - const [chatHistory, setChatHistory] = useState(new Array()); - const [errorMessage, setErrorMessage] = useState(null); - const [navBarOpened, setNavBarOpened] = useState(false); - const [loadingConversation, setLoadingConversation] = useState(false); - const [conversations, setConversations] = useState([]); - const chatInputRef = useRef(null); - const theme = useMantineTheme(); - const mobileScreen = useMediaQuery(`(max-width: ${theme.breakpoints.sm})`); - const [errorDialogMessage, setErrorDialogMessage] = useState(""); - const [errorDialogOpened, errorDialogHandler] = useDisclosure(false); - - const openErrorDialog = (message: string, timeout: number = 3000) => { - setErrorDialogMessage(message); - errorDialogHandler.open(); - setTimeout(() => { - errorDialogHandler.close(); - }, 3000); - }; - - const reset = () => { - setStreaming(false); - setDocsLoading(false); - setSearchResults([]); - setChatHistory([]); - setErrorMessage(null); - }; - const loadActiveConversation = () => { - const populateConversationMessages = async () => { - try { - setLoadingConversation(true); - setStreaming(true); - console.log("Loading convos"); - const interactionsResponse = await getInteractions( - settings.activeConversation, - ); - let previousInteractions = new Array(); - let interactionsData = await interactionsResponse; - if ("hits" in interactionsData) { - interactionsData = interactionsData.hits.hits; - } else { - interactionsData = interactionsData.interactions; - } - console.info("interactionsData: ", interactionsData); - const previousInteractionsUnFlattened = await Promise.all( - interactionsData.map(async (interaction_raw: any) => { - const interaction = interaction_raw._source ?? interaction_raw; - const interaction_id = - interaction_raw._id ?? interaction_raw.interaction_id; - const feedback = await getFeedback(interaction_id); - const systemChat = new SystemChat({ - id: interaction_id + "_response", - response: interaction.response, - interaction_id: interaction_id, - modelName: null, - queryUsed: interaction.input, - feedback: feedback.found - ? thumbToBool(feedback._source.thumb) - : null, - comment: feedback.found ? feedback._source.comment : "", - }); - return systemChat; - }), - ); - previousInteractionsUnFlattened.forEach((chat) => { - previousInteractions = [chat, ...previousInteractions]; - }); - console.log("Setting previous interactions", previousInteractions); - setChatHistory(previousInteractions); - setLoadingConversation(false); - setStreaming(false); - } catch (error: any) { - openErrorDialog("Error loading messages: " + error.message); - console.error("Error loading messages: " + error.message); - } - }; - populateConversationMessages(); - }; - - async function refreshConversations() { - try { - let result: any = []; - const getConversationsResult = await getConversations(); - const retrievedConversations: { conversations: any } = { - conversations: null, - }; - if ("conversations" in getConversationsResult) { - retrievedConversations.conversations = - getConversationsResult.conversations; - } else { - retrievedConversations.conversations = getConversationsResult.memories; - } - retrievedConversations.conversations.forEach((conversation: any) => { - result = [ - { - id: conversation.conversation_id ?? conversation.memory_id, - name: conversation.name, - created_at: conversation.create_time, - }, - ...result, - ]; - }); - setConversations(result); - if (result.length > 0 && settings.activeConversation == "") { - setActiveConversation( - result[0].id, - settings, - setSettings, - loadActiveConversation, - ); - } - } catch (error: any) { - openErrorDialog("Error fetching conversation: " + error.message); - console.error("Error fetching conversation: " + error.message); - } - } - - return ( - - - } - navbarOffsetBreakpoint="sm" - navbar={ - - } - styles={() => ({ - main: { backgroundColor: "white", paddingBottom: 0 }, - })} - > - - } - title="Error" - color="red" - withCloseButton - onClose={errorDialogHandler.close} - > - {errorDialogMessage} - - - - - - ); -} diff --git a/apps/demo-ui/ui/src/pages/PDFViewer/PdfViewer.css b/apps/demo-ui/ui/src/pages/PDFViewer/PdfViewer.css deleted file mode 100644 index 8adb9a6c6..000000000 --- a/apps/demo-ui/ui/src/pages/PDFViewer/PdfViewer.css +++ /dev/null @@ -1,9 +0,0 @@ -.document { - background-color: lightgray; - display: flex; - justify-content: center; -} - -.page { - width: min-content !important; -} diff --git a/apps/demo-ui/ui/src/pages/PDFViewer/index.tsx b/apps/demo-ui/ui/src/pages/PDFViewer/index.tsx deleted file mode 100644 index 7ceeda955..000000000 --- a/apps/demo-ui/ui/src/pages/PDFViewer/index.tsx +++ /dev/null @@ -1,337 +0,0 @@ -import React, { useRef } from "react"; -import { - ActionIcon, - Anchor, - AppShell, - Box, - Button, - Center, - Container, - Flex, - Group, - Header, - Loader, - Popover, - SimpleGrid, - Stack, - Text, - useMantineTheme, -} from "@mantine/core"; -import { useLocation, useSearchParams } from "react-router-dom"; -import { useState, useEffect } from "react"; -import { Document, Page, pdfjs } from "react-pdf"; -import { StyleSheet } from "@react-pdf/renderer"; -import "react-pdf/dist/Page/TextLayer.css"; -import "react-pdf/dist/Page/AnnotationLayer.css"; -import "pdfjs-dist/build/pdf.worker.entry"; -import { - IconCircleArrowLeft, - IconCircleArrowRight, - IconCircleMinus, - IconCirclePlus, - IconInfoCircle, - IconMinus, - IconPlus, - IconZoomIn, - IconZoomOut, -} from "@tabler/icons-react"; -import "./PdfViewer.css"; -import { useDisclosure } from "@mantine/hooks"; - -pdfjs.GlobalWorkerOptions.workerSrc = new URL( - "pdfjs-dist/build/pdf.worker.js", - import.meta.url, -).toString(); - -function isValid(left: string, top: string, ranges = []) { - const l = parseFloat(left.replace("%", "")) * 0.01; - const t = parseFloat(top.replace("%", "")) * 0.01; - for (const range of ranges) { - console.log(range[0], range[1], range[2], range[3]); - if (range[0] < l && l < range[1] && range[2] < t && t < range[3]) { - return true; - } - } - return false; -} - -const fetchPDFThroughProxy = async (url: string) => { - try { - console.log("Getting pdf for: ", url); - const response = await fetch("/v1/pdf", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - url: url, - }), - }); - - if (!response.ok) { - throw new Error("Network response was not ok"); - } - - const pdfBlob = await response.blob(); - return URL.createObjectURL(pdfBlob); - } catch (error) { - console.error("Error fetching PDF through proxy:", error); - throw error; - } -}; - -export default function PdfViewer() { - const [numPages, setNumPages] = useState(-1); - const [pageNumber, setPageNumber] = useState(1); - const [url, setUrl] = useState(""); - const [originalUrl, setOriginalUrl] = useState(""); - const [title, setTitle] = useState("Untitled"); - const [entity, setEntity] = useState<{ [key: string]: string }>({}); - const [boxes, setBoxes] = useState(); - const [loading, setLoading] = useState(true); - const [infoOpened, setInfoOpened] = useState(false); - const theme = useMantineTheme(); - const [scale, setScale] = useState(1.5); - const initialized = useRef(false); - - useEffect(() => { - const loadPdf = async () => { - console.log("Loading metadata"); - const dataString: string = - localStorage.getItem("pdfDocumentMetadata") ?? ""; - const pdfDocumentMetadata: any = JSON.parse(dataString); - const props: any = {}; - props["Document id"] = pdfDocumentMetadata.id; - setTitle(pdfDocumentMetadata.title); - const pdfURl = pdfDocumentMetadata.url - ? pdfDocumentMetadata.url - : pdfDocumentMetadata.properties.path; - setOriginalUrl(pdfURl); - const response = await fetchPDFThroughProxy(pdfURl); - setUrl(response); - - let pageNum: number = 1; - - if (pdfDocumentMetadata.properties.hasOwnProperty("page_number")) { - pageNum = pdfDocumentMetadata.properties.page_number; - } else if ( - pdfDocumentMetadata.properties.hasOwnProperty("page_numbers") && - Array.isArray(pdfDocumentMetadata.properties.page_numbers) - ) { - pageNum = pdfDocumentMetadata.properties.page_numbers[0]; - } else if (pdfDocumentMetadata.properties.hasOwnProperty("box")) { - pageNum = pdfDocumentMetadata.properties.box; - } else if ( - pdfDocumentMetadata.properties.hasOwnProperty("boxes") && - Array.isArray(pdfDocumentMetadata.properties.boxes) - ) { - pageNum = pdfDocumentMetadata.properties.boxes[0]; - } - - let boxObj: any = null; - const bbox: number[] = pdfDocumentMetadata.bbox; - if (bbox) { - // This is overly complex. We can simplify when we - // drop the backward compatibility stuff below. - boxObj = { [pageNum]: [bbox] }; - } else { - if ("boxes" in pdfDocumentMetadata.properties) - boxObj = pdfDocumentMetadata.properties.boxes; - else if ("coordinates" in pdfDocumentMetadata.properties) - boxObj = pdfDocumentMetadata.properties.points; - } - if ("entity" in pdfDocumentMetadata.properties) { - Object.keys(pdfDocumentMetadata.properties.entity).forEach((key) => { - props[key] = pdfDocumentMetadata.properties.entity[key]; - }); - } - if (boxObj) { - setBoxes(boxObj); - let firstPage = Math.min(...Object.keys(boxObj).map(Number)); - if (!firstPage) { - firstPage = pageNum; - if (!firstPage) { - firstPage = 1; - } - } - setPageNumber(firstPage); - } else { - setPageNumber(pageNum); // i guess - } - setEntity(props); - console.log("pdfDocumentMetadata is ", pdfDocumentMetadata); - setLoading(false); - }; - if (!initialized.current) { - initialized.current = true; - loadPdf(); - } - }, []); - - function onDocumentLoadSuccess({ numPages }: { numPages: number }): void { - setNumPages(numPages); - } - const goToPrevPage = () => - setPageNumber(pageNumber - 1 <= 1 ? 1 : pageNumber - 1); - - const goToNextPage = () => - setPageNumber(pageNumber + 1 >= numPages ? numPages : pageNumber + 1); - - const handleZoomIn = () => { - setScale((prevScale) => prevScale + 0.2); - }; - - const handleZoomOut = () => { - setScale((prevScale) => Math.max(0.2, prevScale - 0.2)); - }; - - return ( - - - {!loading && ( - - - - ({ - "&:hover, &:active": { backgroundColor: "transparent" }, - })} - > - - - ({ - "&:hover, &:active": { backgroundColor: "transparent" }, - })} - > - - - - - {" "} - {pageNumber} / {numPages} - - - - )} - - - {title} - - - {!loading && ( - - - - setInfoOpened((o) => !o)} - sx={(theme) => ({ - "&:hover, &:active": { backgroundColor: "transparent" }, - })} - > - - - - - - {originalUrl} - - {Object.entries(entity).map(([key, value]) => ( - - - {key}:{" "} - - {value} - - ))} - - - - )} - - - } - styles={() => ({ - main: { padding: 0, paddingTop: 50, backgroundColor: "lightgray" }, - })} - > - {loading ? ( - - - - ) : ( - <> - - - {boxes - ? boxes[pageNumber] && - boxes[pageNumber].map((box: any, index: number) => ( -
- )) - : ""} - - - - - ({ - border: "1px solid darkgray", - backgroundColor: "white", - "&:hover, &:active": { backgroundColor: "white" }, - })} - > - - - ({ - border: "1px solid darkgray", - backgroundColor: "white", - "&:hover, &:active": { backgroundColor: "white" }, - })} - > - - - - - )} - - ); -} diff --git a/apps/demo-ui/ui/src/react-app-env.d.ts b/apps/demo-ui/ui/src/react-app-env.d.ts deleted file mode 100644 index 6431bc5fc..000000000 --- a/apps/demo-ui/ui/src/react-app-env.d.ts +++ /dev/null @@ -1 +0,0 @@ -/// diff --git a/apps/demo-ui/ui/src/reportWebVitals.ts b/apps/demo-ui/ui/src/reportWebVitals.ts deleted file mode 100644 index 5fa3583b7..000000000 --- a/apps/demo-ui/ui/src/reportWebVitals.ts +++ /dev/null @@ -1,15 +0,0 @@ -import { ReportHandler } from "web-vitals"; - -const reportWebVitals = (onPerfEntry?: ReportHandler) => { - if (onPerfEntry && onPerfEntry instanceof Function) { - import("web-vitals").then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => { - getCLS(onPerfEntry); - getFID(onPerfEntry); - getFCP(onPerfEntry); - getLCP(onPerfEntry); - getTTFB(onPerfEntry); - }); - } -}; - -export default reportWebVitals; diff --git a/apps/demo-ui/ui/src/setupTests.ts b/apps/demo-ui/ui/src/setupTests.ts deleted file mode 100644 index 1dd407a63..000000000 --- a/apps/demo-ui/ui/src/setupTests.ts +++ /dev/null @@ -1,5 +0,0 @@ -// jest-dom adds custom jest matchers for asserting on DOM nodes. -// allows you to do things like: -// expect(element).toHaveTextContent(/react/i) -// learn more: https://github.com/testing-library/jest-dom -import "@testing-library/jest-dom"; diff --git a/apps/demo-ui/ui/src/utils/ChatboxUtils.tsx b/apps/demo-ui/ui/src/utils/ChatboxUtils.tsx deleted file mode 100644 index df0ae0d2c..000000000 --- a/apps/demo-ui/ui/src/utils/ChatboxUtils.tsx +++ /dev/null @@ -1,506 +0,0 @@ -import { Dispatch, SetStateAction } from "react"; -import { FilterValues, SearchResultDocument } from "../Types"; - -export const documentLevelFields = [ - "dateTime", - "injuries", - "aircraftType", - "location", - "registration", - "accidentNumber", - "definingEvent", - "day", -]; - -export const excludeFields = [ - "_schema", - "_schema_class", - "3-point", - "3point", - "3Point", - "5Point", - "4-point", - "5-point", - "columns", - "entity", - "filetype", - "image_mode", - "image_size", - "page_number", - "page_numbers", - "path", - "rows", - "score", - "location", - "day", - "aircraftType", - "accidentNumber", - "lowestCloudCondition", - "windSpeedInKnots", - "injuries", - "yearOfManufacture", - "temperatureInC", - "aircraftMake", -]; - -export function parseManualFilters(filterInputs: FilterValues) { - if (filterInputs == null) return null; - const resultNeural: any = { - bool: { - filter: [], - }, - }; - const resultKeyword: any = { - bool: { - filter: [], - }, - }; - - for (const [field, value] of Object.entries(filterInputs)) { - if (value) { - let fieldName = `properties.${field}`; - if (documentLevelFields.includes(field)) { - fieldName = `properties.entity.${field}`; - } - if (typeof value === "object") { - const rangeFilterNeural: any = { - range: { [fieldName]: {} }, - }; - const rangeFilterKeyword: any = { - range: { [`${fieldName}.keyword`]: {} }, - }; - if (value.gte) { - rangeFilterNeural.range[fieldName]["gte"] = value.gte; - rangeFilterKeyword.range[`${fieldName}.keyword`]["gte"] = value.gte; - } - if (value.lte) { - rangeFilterNeural.range[fieldName]["lte"] = value.lte; - rangeFilterKeyword.range[`${fieldName}.keyword`]["lte"] = value.lte; - } - resultNeural.bool.filter.push(rangeFilterNeural); - resultKeyword.bool.filter.push(rangeFilterKeyword); - } else { - let matchFilter; - matchFilter = { - match: { - [fieldName]: value, - }, - }; - resultNeural.bool.filter.push(matchFilter); - resultKeyword.bool.filter.push(matchFilter); - } - } - } - - const result = { - keyword: resultKeyword, - neural: resultNeural, - }; - return result; -} - -export function parseFilters( - filterInputs: any, - setErrorMessage: Dispatch>, -) { - if (filterInputs == null) return null; - const resultNeural: any = { - bool: { - filter: [], - }, - }; - const resultKeyword: any = { - bool: { - filter: [], - }, - }; - - if (filterInputs.matchFilters == null) return null; - filterInputs.matchFilters.forEach((filter: any) => { - if (filter == null || filter.fieldName === "") return; - const matchFilter = { - match: { - [filter.fieldName]: filter.fieldValue, - }, - }; - resultNeural["bool"]["filter"].push(matchFilter); - resultKeyword["bool"]["filter"].push(matchFilter); - }); - - filterInputs.rangeFilters.forEach((filter: any) => { - if (filter == null || filter.fieldName === "") return; - const rangeFilterNeural = { - range: { - [filter.fieldName]: { - gte: filter.gte, - lte: filter.lte, - }, - }, - }; - - const rangeFilterKeyword = { - range: { - [`${filter.fieldName}.keyword`]: { - gte: filter.gte, - lte: filter.lte, - }, - }, - }; - resultNeural.bool.filter.push(rangeFilterNeural); - resultKeyword.bool.filter.push(rangeFilterKeyword); - }); - const result = { - keyword: resultKeyword, - neural: resultNeural, - }; - return result; -} - -export function parseFiltersForDisplay(filterInputs: any) { - console.log("inside parseFiltersForDisplay", filterInputs); - - const parsedFilter: any = {}; - filterInputs.matchFilters.forEach((filter: any) => { - const fieldName = filter.fieldName.split(".").pop(); - parsedFilter[fieldName] = filter.fieldValue; - }); - filterInputs.rangeFilters.forEach((filter: any) => { - const fieldName = filter.fieldName.split(".").pop(); - parsedFilter[fieldName] = {}; - if (filter.gte) { - parsedFilter[fieldName].gte = filter.gte; - } - if (filter.lte) { - parsedFilter[fieldName].lte = filter.lte; - } - }); - return parsedFilter; -} - -export function parseAggregationsForDisplay(filterInputs: any) { - console.log("inside parseAggregationsForDisplay", filterInputs); - - const parsedAggs: any = {}; - filterInputs.termsAggregations.forEach((aggs: any) => { - const fieldName = aggs.fieldName.split(".").slice(-2).reverse().pop(); - if (fieldName === "") return; - parsedAggs["terms"] = fieldName; - }); - filterInputs.cardinalityAggregations.forEach((aggs: any) => { - const fieldName = aggs.fieldName.split(".").slice(-2).reverse().pop(); - if (fieldName === "") return; - parsedAggs["cardinality"] = fieldName; - }); - return parsedAggs; -} - -const camelToSnakeCase = (str: string) => - str.replace(/[A-Z]/g, (letter) => `_${letter.toLowerCase()}`); - -export function buildOpensearchQueryFromLlmResponse(filterInputs: any) { - const openSearchQuery: any = { size: 0 }; - openSearchQuery.query = { - bool: { - must: [{ match_all: {} }], - filter: [], - }, - }; - openSearchQuery.aggs = {}; - filterInputs.matchFilters.forEach((filter: any) => { - openSearchQuery.query.bool.filter.push({ - match_phrase: { [filter.fieldName]: filter.fieldValue }, - }); - }); - filterInputs.rangeFilters.forEach((filter: any) => { - openSearchQuery.query.bool.filter.push({ - range: { [filter.fieldName]: { gte: filter.gte, lte: filter.lte } }, - }); - }); - filterInputs.cardinalityAggregations.forEach((agg: any) => { - let aggsKeyword = - camelToSnakeCase(agg.fieldName.split(".").slice(-2).reverse().pop()) + - "s"; - openSearchQuery.aggs[`unique_${aggsKeyword}`] = { - cardinality: { field: agg.fieldName }, - }; - }); - filterInputs.termsAggregations.forEach((agg: any) => { - let aggsKeyword = - camelToSnakeCase(agg.fieldName.split(".").slice(-2).reverse().pop()) + - "s"; - openSearchQuery.aggs[`unique_${aggsKeyword}`] = { - terms: { field: agg.fieldName, size: 1000 }, - aggs: { - unique_pdf_documents: { - cardinality: { - field: "parent_id.keyword", - }, - }, - }, - }; - }); - return openSearchQuery; -} - -export function buildOpenSearchQueryFromManualFiltersAggs( - filters: FilterValues, - aggregations: { [key: string]: string }, -) { - const openSearchQuery: any = { size: 0 }; - openSearchQuery.query = { - bool: { - must: [{ match_all: {} }], - filter: [], - }, - }; - openSearchQuery.aggs = {}; - - if (filters) { - Object.keys(filters).forEach((key) => { - const value = filters[key]; - let fieldName = `properties.${key}`; - if (documentLevelFields.includes(key)) { - fieldName = `properties.entity.${key}`; - } - if (typeof value === "object") { - openSearchQuery.query.bool.filter.push({ - range: { [fieldName]: value }, - }); - } else { - openSearchQuery.query.bool.filter.push({ - match_phrase: { [fieldName]: value }, - }); - } - }); - } - if (aggregations) { - Object.entries(aggregations).forEach(([aggType, fieldName]) => { - let openSearchFieldName = `properties.${fieldName}.keyword`; - if (documentLevelFields.includes(fieldName)) { - openSearchFieldName = `properties.entity.${fieldName}.keyword`; - } - const aggregationName = `unique_${camelToSnakeCase(fieldName)}s`; - - if (aggType === "cardinality") { - openSearchQuery.aggs[aggregationName] = { - [aggType]: { field: openSearchFieldName }, - }; - } else { - openSearchQuery.aggs[aggregationName] = { - [aggType]: { field: openSearchFieldName, size: 1000 }, - aggs: { - unique_pdf_documents: { - cardinality: { - field: "parent_id.keyword", - }, - }, - }, - }; - } - }); - } - return openSearchQuery; -} - -export function parseOpenSearchResults( - openSearchResponse: any, - setErrorMessage: Dispatch>, -) { - if ( - openSearchResponse.error !== undefined && - openSearchResponse.error.type === "timeout_exception" - ) { - const documents = new Array(); - const chatResponse = "Timeout from OpenAI"; - const interactionId = ""; - setErrorMessage(chatResponse); - return { - documents: documents, - chatResponse: chatResponse, - interactionId: interactionId, - }; - } - const documents = openSearchResponse.hits.hits.map( - (result: any, idx: number) => { - const doc = result._source; - return new SearchResultDocument({ - id: result._id, - index: idx + 1, - title: doc.properties.title ?? "Untitled", - description: doc.text_representation, - url: doc.properties._location ?? doc.properties.path, - relevanceScore: "" + result._score, - properties: doc.properties, - bbox: doc.bbox, - }); - }, - ); - const chatResponse = - openSearchResponse.ext.retrieval_augmented_generation.answer; - const interactionId = - openSearchResponse.ext.retrieval_augmented_generation.interaction_id; - return { - documents: documents, - chatResponse: chatResponse, - interactionId: interactionId, - }; -} - -export function parseOpenSearchResultsOg(openSearchResponse: any) { - const documents = openSearchResponse.hits.hits.map( - (result: any, idx: number) => { - const doc = result._source; - return new SearchResultDocument({ - id: result._id, - index: idx + 1, - title: doc.properties.title ?? "Untitled", - description: doc.text_representation, - url: doc.properties._location ?? doc.properties.path, - relevanceScore: "" + result._score, - properties: doc.properties, - bbox: doc.bbox, - }); - }, - ); - return documents; -} - -export const simplifyAnswer = async (question: string, answer: string) => { - try { - const response = await fetch("/aryn/simplify", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - question: question, - answer: answer, - }), - }); - - if (!response.ok) { - throw new Error("Network response was not ok"); - } - console.log("Simplify response is:", response); - return response.text(); - } catch (error) { - console.error("Error simplifying through proxy:", error); - throw error; - } -}; - -export const anthropicRag = async (question: string, os_result: any) => { - try { - const response = await fetch("/aryn/anthropic_rag", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - question: question, - os_result: os_result, - }), - }); - - if (!response.ok) { - throw new Error("Network response was not ok"); - } - console.log("AnthropicRAG response is:", response); - return response.text(); - } catch (error) { - console.error("Error in AnthropicRAG through proxy:", error); - throw error; - } -}; - -export const streamingAnthropicRag = async ( - question: string, - os_result: any, - newSystemChat: any, - setStreamingRagResponse: any, -) => { - try { - let result = ""; - const response = await fetch("/aryn/anthropic_rag_streaming", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - question: question, - os_result: os_result, - }), - }); - if (response.body != null) { - const reader = response.body.getReader(); - const decoder = new TextDecoder("utf-8"); - while (true) { - const startTime = new Date(Date.now()); - console.log("Waiting for streaming response..."); - const { done, value } = await reader.read(); - const elapsed = new Date(Date.now()).getTime() - startTime.getTime(); - console.log("Time for chunk: " + elapsed); - if (done) break; - result += decoder.decode(value); - console.log("Received: " + result); - setStreamingRagResponse(result); - } - newSystemChat.response = result; - setStreamingRagResponse(""); - } - - if (!response.ok) { - throw new Error("Network response was not ok"); - } - console.log("Finished AnthropicRAG with result:", result); - console.log("AnthropicRAG response is:", response); - } catch (error) { - console.error("Error in AnthropicRAG through proxy:", error); - throw error; - } -}; - -export const interpretOsResult = async ( - question: string, - os_result: string, -) => { - try { - const response = await fetch("/aryn/interpret_os_result", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - question: question, - os_result: os_result, - }), - }); - - if (!response.ok) { - throw new Error("Network response was not ok"); - } - console.log("Simplify response is:", response); - return response.text(); - } catch (error) { - console.error("Error interpret_os_result through proxy:", error); - throw error; - } -}; - -export const thumbToBool = (thumbValue: string) => { - switch (thumbValue) { - case "null": { - return null; - } - case "up": { - return true; - } - case "down": { - return false; - } - default: { - console.log("received unexpected feedback thumb value: " + thumbValue); - return null; - } - } -}; diff --git a/apps/demo-ui/ui/src/utils/Llm.tsx b/apps/demo-ui/ui/src/utils/Llm.tsx deleted file mode 100644 index 4e63bb3f5..000000000 --- a/apps/demo-ui/ui/src/utils/Llm.tsx +++ /dev/null @@ -1,114 +0,0 @@ -import { SearchResultDocument } from "../Types"; -import { queryPlannerPrompt } from "./queryPlannerPrompt"; - -export const rephrase_question_prompt = (text: string, conversation: any[]) => { - // const sys = "You are a helpful assistant that takes the conversation into context and rephrases sentences by request correcting grammar and spelling mistakes. This question will be fed into a query search engine. \n" - // const prompt = "Rephrase this question: \n" + text - const sys = - "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\ - If there is no conversation, return the question unchanged. Only correct grammar and resolve references, do not change anything else. \n"; - const prompt = "Follow Up Input: " + text + "\nStandalone question: "; - const openAiPrompt = [ - { role: "system", content: sys }, - ...conversation, - { role: "user", content: prompt }, - ]; - return openAiPrompt; -}; -export const rephraseQuestion = async ( - question: string, - conversation: any[], - modelName: string, -) => { - const prompt = rephrase_question_prompt(question, conversation); - const chatJson = JSON.stringify({ - stream: false, - model: modelName, - messages: prompt, - max_tokens: question.length, - temperature: 0.0, - }); - console.log("sending rephrase prompt: ", chatJson); - try { - return fetch("/v1/chat/completions", { - body: chatJson, - method: "POST", - headers: { "Content-Type": "application/json" }, - }); - } catch (e: any) { - throw new Error("Error making OpenAI summarize call: " + e.message); - } -}; - -export const get_filters_prompt = (text: string) => { - const sys = queryPlannerPrompt; - const prompt = text; - const openAiPrompt = [ - { role: "system", content: sys }, - { role: "user", content: prompt }, - ]; - return openAiPrompt; -}; -export const getFilters = async ( - question: string, - modelName: string = "gpt-3.5-turbo-1106", -) => { - const prompt = get_filters_prompt(question); - const chatJson = JSON.stringify({ - stream: false, - model: modelName, - messages: prompt, - temperature: 0.0, - response_format: { type: "json_object" }, - }); - console.log("sending filtering prompt: ", chatJson); - try { - return fetch("/v1/chat/completions", { - body: chatJson, - method: "POST", - headers: { "Content-Type": "application/json" }, - }); - } catch (e: any) { - throw new Error("Error making OpenAI filtering call: " + e.message); - } -}; - -// Deprecated, for local hybrid search pipeline only -export const qa_prompt = (question: string, docs: SearchResultDocument[]) => { - const sys = - "You are a helpful assistant that answers the users question as accurately taking into context an enumerated list of search results. You always cite search results using [${number}] notation. Do not cite anything that isn't in the search results. Do not repeat yourself. SEARCH RESULTS: \n[SEAC_R]"; - - let searchResultString = ""; - docs.forEach((doc) => { - searchResultString += - doc.index + " Title: " + doc.title + "\n" + doc.description; - }); - const openAiPrompt = [ - { role: "system", content: sys.replace("[SEAC_R]", searchResultString) }, - { role: "user", content: question }, - ]; - return openAiPrompt; -}; - -export const getAnswer = async ( - question: string, - docs: SearchResultDocument[], -) => { - const prompt = qa_prompt(question, docs); - console.log("sending qa prompt: ", prompt); - const chatJson = JSON.stringify({ - stream: false, - model: "gpt-4", - messages: prompt, - temperature: 0.0, - }); - try { - return fetch("/v1/chat/completions", { - body: chatJson, - method: "POST", - headers: { "Content-Type": "application/json" }, - }); - } catch (e: any) { - throw new Error("Error making OpenAI RAG call: " + e.message); - } -}; diff --git a/apps/demo-ui/ui/src/utils/OpenSearch.tsx b/apps/demo-ui/ui/src/utils/OpenSearch.tsx deleted file mode 100644 index 6c09c2669..000000000 --- a/apps/demo-ui/ui/src/utils/OpenSearch.tsx +++ /dev/null @@ -1,431 +0,0 @@ -const SOURCES = [ - "type", - "_id", - "doc_id", - "properties", - "title", - "text_representation", - "bbox", - "shingles", -]; -const SEARCH_PIPELINE = "hybrid_rag_pipeline"; -const NO_RAG_SEARCH_PIPELINE = "hybrid_pipeline"; -export const FEEDBACK_INDEX_NAME = "feedback"; - -export const hybridSearch = ( - rephrasedQuestion: string, - filters: any, - index_name: string, - model_id: string, - rerank: boolean = false, -) => { - const query: any = { - _source: SOURCES, - query: { - hybrid: { - queries: [ - { - bool: { - must: [ - { - exists: { - field: "text_representation", - }, - }, - { - match: { - text_representation: rephrasedQuestion, - }, - }, - ], - filter: [ - { - match_all: {}, - }, - ], - }, - }, - { - neural: { - embedding: { - query_text: rephrasedQuestion, - k: 100, - model_id: model_id, - filter: { - match_all: {}, - }, - }, - }, - }, - ], - }, - }, - size: 20, - }; - if (rerank) { - query.ext = { - rerank: { - query_context: { - query_text: rephrasedQuestion, - }, - }, - }; - } - if (filters != null) { - console.log("OS setting filters"); - if ( - query.query.hybrid.queries && - query.query.hybrid.queries.length > 0 && - query.query.hybrid.queries[0].bool - ) { - query.query.hybrid.queries[0].bool.filter = filters["keyword"]; - } - if ( - query.query.hybrid.queries && - query.query.hybrid.queries.length > 0 && - query.query.hybrid.queries[1].neural - ) { - query.query.hybrid.queries[1].neural.embedding.filter = filters["neural"]; - } - } - return query; -}; - -export const getHybridConversationSearchQuery = ( - question: string, - rephrasedQuestion: string, - filters: any, - index_name: string, - embeddingModel: string, - llmModel: string, - numDocs: number = 7, - rerank: boolean = false, -) => { - const query: any = hybridSearch( - rephrasedQuestion, - filters, - index_name, - embeddingModel, - rerank, - ); - query.ext = { - generative_qa_parameters: { - llm_question: question, - context_size: numDocs, - llm_model: llmModel, - }, - }; - const url = - "/opensearch/" + index_name + "/_search?search_pipeline=" + SEARCH_PIPELINE; - return { query, url }; -}; - -export const hybridSearchNoRag = async ( - rephrasedQuestion: string, - filters: any, - index_name: string, - embeddingModel: string, - rerank: boolean, -) => { - const query: any = hybridSearch( - rephrasedQuestion, - filters, - index_name, - embeddingModel, - rerank, - ); - const url = - "/opensearch/" + - index_name + - "/_search?search_pipeline=" + - NO_RAG_SEARCH_PIPELINE; - return [openSearchCall(query, url), query]; -}; - -export const hybridConversationSearch = async ( - question: string, - rephrasedQuestion: string, - filters: any, - conversationId: string, - index_name: string, - embeddingModel: string, - llmModel: string, - numDocs: number = 7, -) => { - const { query, url } = getHybridConversationSearchQuery( - question, - rephrasedQuestion, - filters, - index_name, - embeddingModel, - llmModel, - numDocs, - ); - - query.ext.generative_qa_parameters.memory_id = conversationId; - - return [openSearchCall(query, url), query]; -}; - -export const updateInteractionAnswer = async ( - interactionId: any, - answer: string, - query: any, -) => { - console.log("Updating interaction with new answer", interactionId); - const url = "/opensearch/_plugins/_ml/memory/message/" + interactionId; - const data = { - response: answer, - additional_info: { - queryUsed: query, - }, - }; - return openSearchCall(data, url, "PUT"); -}; - -export const getIndices = async () => { - const url = "/opensearch/_aliases?pretty"; - return openSearchNoBodyCall(url); -}; -export const getEmbeddingModels = async () => { - const body = { - query: { - bool: { - must_not: { - exists: { - field: "chunk_number", - }, - }, - minimum_should_match: 1, - should: [ - { term: { algorithm: "TEXT_EMBEDDING" } }, - { match_phrase: { description: "embedding" } }, - ], - must: [{ term: { model_state: "DEPLOYED" } }], - }, - }, - }; - const url = "/opensearch/_plugins/_ml/models/_search"; - return openSearchCall(body, url); -}; -export const createConversation = async (conversationId: string) => { - const body = { - name: conversationId, - }; - let url; - url = "/opensearch/_plugins/_ml/memory/"; - return openSearchCall(body, url); -}; -export const getInteractions = async (conversationId: any) => { - const url = "/opensearch/_plugins/_ml/memory/" + conversationId + "/_search"; - const body = { - query: { - match_all: {}, - }, - sort: [ - { - create_time: { - order: "DESC", - }, - }, - ], - }; - return openSearchCall(body, url); -}; -export const getConversations = async () => { - const url = "/opensearch/_plugins/_ml/memory/"; - return openSearchNoBodyCall(url); -}; -export const deleteConversation = async (conversation_id: string) => { - console.log("Going to delete", conversation_id); - const url = "/opensearch/_plugins/_ml/memory/" + conversation_id; - console.log("Now deleting"); - return openSearchNoBodyCall(url, "DELETE"); -}; -export const openSearchNoBodyCall = async ( - url: string, - http_method: string = "GET", -) => { - try { - console.log("sending ", http_method + " " + url); - const response = await fetch(url, { - method: http_method, - headers: { - "Content-Type": "application/json", - }, - }); - if (!response.ok) { - return response.text().then((text) => { - throw new Error( - `OpenSearchRequest rejected with status ${response.status} and message ${text}`, - ); - }); - } - - const data = await response.json(); - console.log("Response data:", data); - return data; - } catch (error: any) { - console.error("Error sending query:", error); - throw new Error( - "Error making OpenSearch query to " + - url + - " without body: " + - error.message, - ); - } -}; -export const openSearchCall = async ( - query: any, - url: string, - http_method: string = "POST", -) => { - try { - console.log("sending request", url, JSON.stringify(query)); - const response = await fetch(url, { - method: http_method, - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify(query), - }); - if (!response.ok) { - return response.text().then((text) => { - console.error( - `Request to ${url}:\n` + - JSON.stringify(query) + - `\nrejected with status ${response.status} and message ${text}`, - ); - throw new Error(`Request to ${url}:\n rejected with message ${text}`); - }); - } - - const data = await response.json(); - console.log("Response data:", data); - return data; - } catch (error: any) { - console.error("Error sending query:", error); - throw new Error( - "Error making OpenSearch to " + - url + - " query with body: " + - error.message, - ); - } -}; - -// Legacy for local RAG -export const sendQuery = async (query: any, index_name: string) => { - const url = "/opensearch/" + index_name + `/_search/`; - return openSearchCall(query, url); -}; - -export async function queryOpenSearch( - question: string, - index_name: string, - model_id: string, -) { - const query = { - query: { - bool: { - should: [ - { - function_score: { - query: { - match: { - text_representation: question, - }, - }, - weight: 0.925, - }, - }, - { - neural: { - embedding: { - query_text: question, - model_id: model_id, - k: 20, - }, - }, - }, - ], - }, - }, - size: 10, - _source: SOURCES, - }; - console.log("OS question ", question); - console.log("OS query ", JSON.stringify(query)); - const response = sendQuery(query, index_name); - return response; -} - -export const createFeedbackIndex = async () => { - const indexMappings = { - mappings: { - properties: { - interaction_id: { - type: "keyword", - }, - thumb: { - type: "keyword", - }, - conversation_id: { - type: "keyword", - }, - comment: { - type: "text", - }, - }, - }, - }; - openSearchCall(indexMappings, "/opensearch/" + FEEDBACK_INDEX_NAME, "PUT"); -}; - -export const updateFeedback = async ( - conversationId: string, - interactionId: string, - thumb: boolean | null, - comment: string | null, -) => { - console.log(thumb); - const conversationSnapshot = await getInteractions(conversationId); - const feedbackDoc: any = { - doc: { - interaction_id: interactionId, - conversation_id: conversationId, - thumb: thumb === null ? "null" : thumb ? "up" : "down", - comment: comment === null || comment == "" ? "null" : comment, - conversation_snapshot: conversationSnapshot, - }, - doc_as_upsert: true, - }; - const url = - "/opensearch/" + FEEDBACK_INDEX_NAME + "/_update/" + interactionId; - openSearchCall(feedbackDoc, url, "POST"); -}; - -export const addNoConversationFeedback = async ( - systemChat: any, - thumb: boolean | null, - comment: string | null, -) => { - console.log("Feedback for ", systemChat); - const formattedDate: string = new Date().toISOString(); - const feedbackDoc: any = { - doc: { - timestamp: formattedDate, - chat: systemChat, - thumb: thumb === null ? "null" : thumb ? "up" : "down", - comment: comment === null || comment == "" ? "null" : comment, - }, - doc_as_upsert: true, - }; - const url = "/opensearch/" + FEEDBACK_INDEX_NAME + "/_doc/" + formattedDate; - openSearchCall(feedbackDoc, url, "POST"); -}; - -export const getFeedback = async (interactionId: string) => { - const url = "/opensearch/" + FEEDBACK_INDEX_NAME + "/_doc/" + interactionId; - return openSearchNoBodyCall(url); -}; diff --git a/apps/demo-ui/ui/src/utils/queryPlannerPrompt.ts b/apps/demo-ui/ui/src/utils/queryPlannerPrompt.ts deleted file mode 100644 index cc58e8cea..000000000 --- a/apps/demo-ui/ui/src/utils/queryPlannerPrompt.ts +++ /dev/null @@ -1,117 +0,0 @@ -export const queryPlannerPrompt = `You are simple query planner whose job is to generate filter and aggregate components of an opensearch query that can answer a user-provided question. - -1. properties.entity.location (string) -2. properties.entity.day (YYYY-MM-DD format) -3. properties.entity.aircraftType (string) -4. properties.entity.accidentNumber (string) -5. properties.lowestCloudCondition (string) -6. properties.windSpeedInKnots (number) -7. properties.entity.injuries (string) -8. properties.yearOfManufacture (number) -9. properties.temperatureInC (number) -10. properties.aircraftMake (string) - -You can use these types of filters: -1. match - contains a field name, and an expected value to fuzzy match on -2. range - contains a field name, and a start and end value - -You can use these types of aggregations: -1. terms - contains a field name, and returns a list of counts per unique field value -2. cardinality - contains a field name, and return the number of unique values for that field -In aggregations, the field name but always be suffixed with ".keyword". - -Return your response as a json in the following format: - -{ - "matchFilters": [ - { - "fieldName": "name", - "fieldValue": "value" - } - ], - "rangeFilters": [ - { - "fieldName": "name", - "gte": "value", - "lte": "value" - } - ], - "cardinalityAggregations": [ - { - "fieldName": "name" - } - ], - "termsAggregations": [ - { - "fieldName": "name" - } - ] -} - - -example 1: -user question: What types of planes were involved in incidents in California? -answer: - -{ - "matchFilters": [ - { - "fieldName": "properties.entity.location", - "fieldValue": "California" - } - ], - "rangeFilters": [], - "cardinalityAggregations": [], - "termsAggregations": [ - { - "fieldName": "properties.entity.aircraftType.keyword" - } - ] -} - -example 2: -user question: How many types of planes were involved in crashes in 2022? -answer: - -{ - "matchFilters": [], - "rangeFilters": [ - { - "fieldName": "properties.entity.day", - "gte": "2022-01-01", - "lte": "2022-12-31" - } - ], - "cardinalityAggregations": [ - { - "fieldName": "properties.entity.aircraftType.keyword" - } - ], - "termsAggregations": [] -} - -example 3: -user question: What types of planes were involved in incidents in California occurred when the wind was stronger than 4 knots? -answer: - -{ - "matchFilters": [ - { - "fieldName": "properties.entity.location", - "fieldValue": "California" - } - ], - "rangeFilters": [ - { - "fieldName": "properties.windSpeedInKnots", - "gte": 4, - } - ], - "cardinalityAggregations": [], - "termsAggregations": [ - { - "fieldName": "properties.entity.aircraftType.keyword" - } - ] -} -`; diff --git a/apps/demo-ui/ui/tsconfig.json b/apps/demo-ui/ui/tsconfig.json deleted file mode 100644 index d26ce47e1..000000000 --- a/apps/demo-ui/ui/tsconfig.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "compilerOptions": { - "target": "es5", - "lib": ["dom", "dom.iterable", "esnext"], - "allowJs": true, - "skipLibCheck": true, - "esModuleInterop": true, - "allowSyntheticDefaultImports": true, - "strict": true, - "forceConsistentCasingInFileNames": true, - "noFallthroughCasesInSwitch": true, - "module": "esnext", - "moduleResolution": "node", - "resolveJsonModule": true, - "isolatedModules": true, - "noEmit": true, - "jsx": "preserve" - }, - "include": ["src"] -} diff --git a/apps/importer/Dockerfile.build b/apps/importer/Dockerfile.build deleted file mode 100644 index ef68a01e0..000000000 --- a/apps/importer/Dockerfile.build +++ /dev/null @@ -1,53 +0,0 @@ -# Repo name: arynai/sycamore-importer - -# Doesn't work with 3.12 -# depends on pyarrow==12.0.1 and ray[default]<3.0.0 and >=2.7.0 -FROM python:3.11 - -WORKDIR /app -COPY docker-app-user.sh . -RUN /bin/bash /app/docker-app-user.sh - -ARG POETRY_NO_INTERACTION=1 \ - POETRY_VIRTUALENVS_IN_PROJECT=1 \ - POETRY_VIRTUALENVS_CREATE=1 \ - POETRY_CACHE_DIR=/tmp/poetry_cache - -RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache -COPY apps/importer/importer/docker/importer-root-setup.sh . -RUN /bin/bash /app/importer-root-setup.sh - -USER app -COPY lib/sycamore lib/sycamore -COPY lib/poetry-lock lib/poetry-lock - -COPY apps/importer/pyproject.toml apps/importer/poetry.lock apps/importer/README.md \ - ./ -RUN mkdir -p importer/docker -COPY apps/importer/importer/docker/docker-poetry-packages.sh \ - importer/docker/docker-poetry-packages.sh - -RUN /bin/bash ./importer/docker/docker-poetry-packages.sh \ - install --only main,sycamore_library,docker --no-root -v - -# syntax=docker/dockerfile:1.7-labs -COPY apps/importer ./ -COPY examples/simple_config.py ./ - -RUN /bin/bash ./importer/docker/docker-poetry-packages.sh install --only-root -v -v -v - -ARG GIT_BRANCH="unknown" -ARG GIT_COMMIT="unknown" -ARG GIT_DIFF="unknown" - -ENV GIT_BRANCH=${GIT_BRANCH} -ENV GIT_COMMIT=${GIT_COMMIT} -ENV GIT_DIFF=${GIT_DIFF} - -LABEL org.opencontainers.image.authors="opensource@aryn.ai" -LABEL git_branch=${GIT_BRANCH} -LABEL git_commit=${GIT_COMMIT} -LABEL git_diff=${GIT_DIFF} - -RUN touch .git.commit.${GIT_COMMIT} -CMD [ "poetry", "run", "python", "importer/docker/docker_local_import.py", "/app/.scrapy" ] diff --git a/apps/importer/Dockerfile.buildx b/apps/importer/Dockerfile.buildx deleted file mode 100644 index 61ee0bcec..000000000 --- a/apps/importer/Dockerfile.buildx +++ /dev/null @@ -1,63 +0,0 @@ -# Repo name: arynai/sycamore-importer - -# Note: This dockerfile is intended to work with docker buildx build -f Dockerfile.buildx . - -# Doesn't work with 3.12 -# depends on pyarrow==12.0.1 and ray[default]<3.0.0 and >=2.7.0 -FROM python:3.11 - -WORKDIR /app -COPY docker-app-user.sh . -RUN /bin/bash /app/docker-app-user.sh - -ARG POETRY_NO_INTERACTION=1 \ - POETRY_VIRTUALENVS_IN_PROJECT=1 \ - POETRY_VIRTUALENVS_CREATE=1 \ - POETRY_CACHE_DIR=/tmp/poetry_cache - -RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache -COPY apps/importer/importer/docker/importer-root-setup.sh . -RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \ - --mount=type=cache,target=/var/lib/apt,sharing=locked \ - /bin/bash /app/importer-root-setup.sh - -USER app -COPY --chown=app:app lib/sycamore lib/sycamore -COPY --chown=app:app lib/poetry-lock lib/poetry-lock - -COPY --chown=app:app apps/importer/pyproject.toml apps/importer/poetry.lock apps/importer/README.md \ - ./ -RUN mkdir -p importer/docker -COPY --chown=app:app apps/importer/importer/docker/docker-poetry-packages.sh \ - importer/docker/docker-poetry-packages.sh - -RUN --mount=type=cache,id=cache_poetry_1000,target=/tmp/poetry_cache,uid=1000,gid=1000,sharing=locked \ - /bin/bash ./importer/docker/docker-poetry-packages.sh \ - install --only main,sycamore_library,docker --no-root -v - -# syntax=docker/dockerfile:1.7-labs -COPY --chown=app:app apps/importer ./ -COPY --chown=app:app examples/simple_config.py ./ - -RUN /bin/bash ./importer/docker/docker-poetry-packages.sh install --only-root -v -v -v - -# Make sure we don't get more unexpected files owned by root -RUN find . -uid 0 -ls -RUN find . -uid 0 -print | wc -w -RUN /bin/bash -c '[[ $(find . -uid 0 -print | wc -w) = 1 ]]' - -ARG GIT_BRANCH="unknown" -ARG GIT_COMMIT="unknown" -ARG GIT_DIFF="unknown" - -ENV GIT_BRANCH=${GIT_BRANCH} -ENV GIT_COMMIT=${GIT_COMMIT} -ENV GIT_DIFF=${GIT_DIFF} - -LABEL org.opencontainers.image.authors="opensource@aryn.ai" -LABEL git_branch=${GIT_BRANCH} -LABEL git_commit=${GIT_COMMIT} -LABEL git_diff=${GIT_DIFF} - -RUN touch .git.commit.${GIT_COMMIT} -CMD [ "poetry", "run", "python", "importer/docker/docker_local_import.py", "/app/.scrapy" ] diff --git a/apps/importer/README.md b/apps/importer/README.md deleted file mode 100644 index d3dc30b6a..000000000 --- a/apps/importer/README.md +++ /dev/null @@ -1,63 +0,0 @@ -![SycamoreLogoFinal.svg](https://raw.githubusercontent.com/aryn-ai/sycamore/main/docs/source/images/sycamore_logo.svg) - -[![PyPI](https://img.shields.io/pypi/v/sycamore-ai)](https://pypi.org/project/sycamore-ai/) -[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sycamore-ai)](https://pypi.org/project/sycamore-ai/) -[![Slack](https://img.shields.io/badge/slack-sycamore-brightgreen.svg?logo=slack)](https://join.slack.com/t/sycamore-ulj8912/shared_invite/zt-23sv0yhgy-MywV5dkVQ~F98Aoejo48Jg) -[![Docs](https://readthedocs.org/projects/sycamore/badge/?version=stable)](https://sycamore.readthedocs.io/en/stable/?badge=stable) -![License](https://img.shields.io/github/license/aryn-ai/sycamore) - -Sycamore is a conversational search and analytics platform for complex unstructured data, such as documents, presentations, transcripts, embedded tables, and internal knowledge repositories. It retrieves and synthesizes high-quality answers through bringing AI to data preparation, indexing, and retrieval. Sycamore makes it easy to prepare unstructured data for search and analytics, providing a toolkit for data cleaning, information extraction, enrichment, summarization, and generation of vector embeddings that encapsulate the semantics of data. Sycamore uses your choice of generative AI models to make these operations simple and effective, and it enables quick experimentation and iteration. Additionally, Sycamore uses OpenSearch for indexing, enabling hybrid (vector + keyword) search, retrieval-augmented generation (RAG) pipelining, filtering, analytical functions, conversational memory, and other features to improve information retrieval. - -![Untitled](docs/source/images/SycamoreDiagram2.png) - -## Features - -- Natural language, conversational interface to ask complex questions on unstructured data. Includes citations to source passages and conversational memory. -- Includes a variety of query operations over unstructured data, including hybrid search, retrieval augmented generation (RAG), and analytical functions. -- Prepares and enriches complex unstructured data for search and analytics through advanced data segmentation, LLM-powered UDFs for data enrichment, performant data manipulation with Python, and vector embeddings using a variety of AI models. -- Helpful features like automatic data crawlers (Amazon S3 and HTTP) and Jupyter notebook support to create and iterate on data preparation scripts. -- Scalable, secure, and customizable OpenSearch backend for indexing and data retrieval. - -## Demo - -[Hosted on Loom](https://www.loom.com/share/53e68b0eb5ab49948111a3fcf6286b7f?sid=8627ff2a-db36-46ef-9762-a01b37e20ced) - -## Get Started - -You can easily deploy Sycamore locally or on a virtual machine using Docker. - -With Docker installed: - -1. Clone the Sycamore repo: - -```git clone https://github.com/aryn-ai/sycamore``` - -2. Set OpenAI Key: - -```export OPENAI_API_KEY=YOUR-KEY``` - -3. Go to: - -```/sycamore``` - -4. Launch Sycamore. Conatainers will be pulled from DockerHub: - -```docker compose up --pull=always``` - -5. The Sycamore demo query UI will be at localhost:3000 - -You can next choose to run a demo that [prepares and ingests data from the Sort Benchmark website](docs/source/welcome_to_sycamore/get_started.md#demo-ingest-and-query-sort-benchmark-dataset), [crawl data from a public website](docs/source/welcome_to_sycamore/get_started.md#demo-ingest-and-query-data-from-an-arbitrary-website), or write your own data preparation script. - -For more info about Sycamore’s data ingestion and preparation feature set, visit the [Sycamore documentation](docs/source/data_ingestion_and_preparation/data_preparation_concepts.md). - - -## Resources - -- Documentation: https://sycamore.readthedocs.io -- Slack: https://join.slack.com/t/sycamore-ulj8912/shared_invite/zt-23sv0yhgy-MywV5dkVQ~F98Aoejo48Jg -- Data preparation libraries (PyPi): https://pypi.org/project/sycamore-ai/ -- Contact us: info@aryn.ai - -## Contributing - -Check out our [Contributing Guide](https://github.com/aryn-ai/sycamore/blob/main/CONTRIBUTING.md) for more information about how to contribute to Sycamore and set up your environment for development. diff --git a/apps/importer/importer/__init__.py b/apps/importer/importer/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/importer/importer/docker/__init__.py b/apps/importer/importer/docker/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/importer/importer/docker/docker-poetry-packages.sh b/apps/importer/importer/docker/docker-poetry-packages.sh deleted file mode 100644 index 674b44f89..000000000 --- a/apps/importer/importer/docker/docker-poetry-packages.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -set -e -set -x -[[ -f pyproject.toml ]] -[[ -f poetry.lock ]] -if [[ ! -d /app/.venv ]]; then - echo "Missing /app/.venv; assuming no poetry runs so far" -elif [[ $(grep -c VIRTUAL_ENV=./app/.venv .venv/bin/activate) != 1 ]]; then - echo "ERROR: Broken venv" - grep VIRTUAL_ENV .venv/bin/activate - exit 1 -fi -export POETRY_CACHE_DIR=/tmp/poetry_cache -# Edit the pyproject.toml so that the poetry setup is under /app. This lets us re-use the -# sycamore installation in the jupyter notebook with reduced confusion on the paths in that -# container. -sed -i 's,../../lib,lib,' pyproject.toml poetry.lock -if [[ $# = 0 ]]; then - echo "Usage: $0 " - exit 1 -fi -poetry "$@" diff --git a/apps/importer/importer/docker/docker_local_import.py b/apps/importer/importer/docker/docker_local_import.py deleted file mode 100755 index 43eae8eae..000000000 --- a/apps/importer/importer/docker/docker_local_import.py +++ /dev/null @@ -1,488 +0,0 @@ -from pathlib import Path -import datetime -import math -import numpy -import os -import requests -import stat -import sys -import time - -# ruff: noqa: E402 -# sys.path.append("../sycamore") -sys.path.append("/app") - -import sycamore -from sycamore.functions import HuggingFaceTokenizer, TextOverlapChunker -from sycamore.llms import OpenAI, OpenAIModels -from sycamore.transforms.embed import SentenceTransformerEmbedder -from sycamore.transforms.extract_entity import OpenAIEntityExtractor -from sycamore.transforms.extract_table import TextractTableExtractor -from sycamore.transforms.merge_elements import GreedyTextElementMerger -from sycamore.transforms.partition import UnstructuredPdfPartitioner, HtmlPartitioner - -# from simple_config import idx_settings, osrch_args, title_template - -running_in_container = False -enable_textract = "SYCAMORE_TEXTRACT_PREFIX" in os.environ and os.environ["SYCAMORE_TEXTRACT_PREFIX"] -index = "demoindex0" - - -# TODO: https://github.com/aryn-ai/sycamore/issues/155 - detect that the opensearch cluster -# has dropped documents and reload them -# TODO: https://github.com/aryn-ai/sycamore/issues/156 - figure out how to deal with documents -# that are deleted -# TODO: https://github.com/aryn-ai/sycamore/issues/157 - adjust the way we do importing so -# that the files have a permanent name so the viewPdf UI option works -# TODO: https://github.com/aryn-ai/sycamore/issues/158 - handle importing problems in a more -# clever way than blind retry. -def main(): - print("Version-Info, Sycamore Importer Branch:", os.environ.get("GIT_BRANCH", "unspecified")) - print("Version-Info, Sycamore Importer Commit:", os.environ.get("GIT_COMMIT", "unspecified")) - print("Version-Info, Sycamore Importer Diff:", os.environ.get("GIT_DIFF", "unspecified")) - print(flush=True) - root_path = "/app/.scrapy" - if len(sys.argv) <= 1: - if not os.path.isdir(root_path): - raise RuntimeError("Missing " + root_path + "; run with single path argument if not in container") - elif len(sys.argv) != 2: - raise RuntimeError("Usage: docker_local_injest.py [directory_root]") - else: - root_path = sys.argv[1] - if not os.path.isdir(root_path): - raise RuntimeError("Missing specified path " + root_path + "; correct argument or remove if in container") - - global running_in_container - if root_path == "/app/.scrapy": - print("Assuming execution is in container, using adjusted host") - running_in_container = True - - # check for user change in Mar 2024; code removable after Mar 2025 when - # everyone should have run their containers and fixed the problem by then - stat = os.stat(root_path) - fix = "To fix in containers: docker compose run fixuser" - if stat.st_uid != os.getuid(): - raise RuntimeError( - f"unsupported mismatch between {root_path} uid {stat.st_uid} and current uid {os.getuid()}\n{fix}" - ) - - if stat.st_gid != os.getgid(): - raise RuntimeError( - f"unsupported mismatch between {root_path} gid {stat.st_gid} and current gid {os.getgid()}\n{fix}" - ) - - if root_path == "/app/.scrapy" and "OPENAI_API_KEY" in os.environ: - print("WARNING: OPENAI_API_KEY in environment is potentially insecure.") - # TODO: https://github.com/aryn-ai/sycamore/issues/159 - use docker secrets, - # if necessary via docker swarm. - # Then enable the sleep since we shouldn't get the env var any more. - - # print "sleep(300)" - # time.sleep(300) - - if "OPENAI_API_KEY" not in os.environ: - raise RuntimeError("Missing OPENAI_API_KEY") - - if enable_textract: - if "AWS_ACCESS_KEY_ID" not in os.environ: - raise RuntimeError("Missing AWS_ACCESS_KEY_ID; or you can export ENABLE_TEXTRACT=false") - - if "AWS_SECRET_ACCESS_KEY" not in os.environ: - raise RuntimeError("missing AWS_SECRET_ACCESS_KEY; or you can export ENABLE_TEXTRACT=false") - - if "AWS_SESSION_TOKEN" not in os.environ: - print("WARNING: AWS_SESSION_TOKEN not present; secret key may not work if it is a short term sso token") - # TODO: https://github.com/aryn-ai/quickstart/issues/1 - check for AWS_CREDENTIAL_EXIRATION - - root = Path(root_path) - - failures = -1 # special marker for first try - ray_tasks = get_ray_task_count() - max_files_per_run = 1 - - # Make sure that we spend ~75% of the time running at full parallelism 75% because the tasks - # should finish in 4 units, and the first 3 of those will be at full parallelism. - files_per_run_limit = math.floor(ray_tasks * 4) - if ray_tasks == 1: # If we only have 1 tasks in parallel, no point in doing more than 1 task/run - files_per_run_limit = 1 - - while True: - files = find_files(root) - if len(files) > 0: - try: - if len(files) > max_files_per_run: - print("Have {} remaining files, too many to process in one run.".format(len(files))) - print("Limiting number of files in single run to {}".format(max_files_per_run)) - numpy.random.shuffle(files) - files = files[0:max_files_per_run] - print("Files:", [str(f["path"]) for f in files], flush=True) - import_files(root, files) - time.sleep(1) - if failures == -1: - max_files_per_run = min(ray_tasks * 2, files_per_run_limit) - failures = 0 - elif failures > 0: - failures = failures - 1 - else: - max_files_per_run = min(max_files_per_run * 2, files_per_run_limit) - print( - "Successfully imported:", - [str(f["path"]) for f in files], - flush=True, - ) - - print( - "Successful run adjusted failure count to {} and max_files_per_run to {}".format( - failures, max_files_per_run - ) - ) - except Exception as e: - if failures == -1: - failures = 0 - if max_files_per_run > 1: - max_files_per_run = max(1, math.floor(max_files_per_run / 2)) - else: - failures = failures + 1 - print( - "Failed run adjusted failure count to {} and max_files_per_run to {}".format( - failures, max_files_per_run - ) - ) - print("WARNING: caught and tolerating exception") - print("WARNING: exception type:", type(e)) - print("WARNING: exception args:", e.args) - print("WARNING: exception message:", e) - sleep_time = min(10**failures, 300) - print("WARNING: sleep(" + str(sleep_time) + ") in case this is persistent") - time.sleep(sleep_time) - else: - print("No changes at", datetime.datetime.now(), "sleeping", flush=True) - time.sleep(5) - - -def get_ray_task_count(): - mem_bytes = os.sysconf("SC_PAGE_SIZE") * os.sysconf("SC_PHYS_PAGES") - usable_mem_bytes = 0.5 * mem_bytes # use at most half of hosts RAM - gib = 1024 * 1024 * 1024 - bytes_per_task = 2 * gib # Importing sort benchmark varies between 1-2GB while running - ray_tasks = math.floor(usable_mem_bytes / bytes_per_task) - if ray_tasks <= 0: - ray_tasks = 1 - print("WARNING: Want 2GiB of RAM/ray-task.") - print("WARNING: Available memory (50% of total) is only {:.2f} GiB".format(usable_mem_bytes / gib)) - print("WARNING: Will run on single core and hope to not use too much swap") - - if ray_tasks <= 1: - ray_tasks = 2 - print("WARNING: import_pdf_sort_benchmark requires 2 simultaneous ray tasks to not hang.") - print("WARNING: on low-memory containers, the logic to adjust the number of files to process") - print("WARNING: in a batch will clamp the number of files to 1 which will allow for importing") - - return ray_tasks - - -def find_files(root): - downloads = root.joinpath("downloads") - pdf_files = find_recursive(downloads, "pdf") - html_files = find_recursive(downloads, "html") - ret = [] - for i in pdf_files + html_files: - t = reimport_timestamp(root, i["suffix"]) - if t > 0: - i["timestamp"] = t - ret.append(i) - else: - # print('File unchanged', i['suffix']) - pass - - return ret - - -def find_recursive(root, file_type): - prefix = Path(root) - type_root = os.path.join(prefix, file_type) - ret = [] - for path in Path(type_root).rglob("*"): - ret.append({"path": path, "suffix": path.relative_to(prefix), "type": file_type}) - - return ret - - -def reimport_timestamp(root, suffix): - s = os.lstat(root.joinpath("downloads", suffix)) - if not stat.S_ISREG(s.st_mode): - return -1 - - file_timestamp = s.st_mtime - - try: - s = os.lstat(root.joinpath("imported", suffix)) - except FileNotFoundError: - return file_timestamp - - if s.st_mtime < file_timestamp: - return file_timestamp - - if s.st_mtime > file_timestamp: - return file_timestamp - - return 0 - - -def import_files(root, files): - pending_paths = {} - for i in files: - # textractor uses PIL.Image.open to identify images, and it fails with an - # UnidentifiedImageError on a pdf file that doesn't end in .pdf. - if i["type"] == "pdf" and not str(i["path"]).endswith(".pdf"): - print( - "ERROR: Unable to import", - str(i["path"]), - "-- pdf files must end in .pdf; fix the crawler", - ) - continue - - if i["type"] not in pending_paths: - pending_paths[i["type"]] = [] - - pending_paths[i["type"]].append(str(i["path"])) - - if running_in_container: - wait_for_opensearch_ready() - - if "pdf" in pending_paths: - import_pdf(pending_paths["pdf"]) - - if "html" in pending_paths: - import_html(pending_paths["html"]) - - for i in files: - imported = root.joinpath("imported", i["suffix"]) - os.makedirs(os.path.dirname(imported), exist_ok=True) - if not os.path.isfile(imported): - imported.touch() - os.utime(imported, (time.time(), i["timestamp"])) - - -def wait_for_opensearch_ready(): - while True: - print("Waiting for opensearch to become ready...", flush=True) - try: - r = requests.get("https://opensearch:9200/_cluster/settings", verify=False) - if r.status_code == 200 and "aryn_deploy_complete" in r.text: - print("Ready") - return True - except requests.exceptions.ConnectionError: - pass - - time.sleep(1) - - -def import_pdf(paths): - if len(paths) == 0: - print("WARNING: import_html called with empty paths") - return - - openai_llm = OpenAI(OpenAIModels.GPT_3_5_TURBO_INSTRUCT.value) - tokenizer = HuggingFaceTokenizer("sentence-transformers/all-MiniLM-L6-v2") - merger = GreedyTextElementMerger(tokenizer, 256) - - ctx = sycamore_init() - if enable_textract: - table_extractor = TextractTableExtractor( - region_name="us-east-1", - s3_upload_root=os.environ["SYCAMORE_TEXTRACT_PREFIX"], - ) - else: - table_extractor = None - print("WARNING: Textract disabled, results on sort benchmark website will be mediocre") - - ( - # TODO: https://github.com/aryn-ai/quickstart/issues/2 - implement manifest generation - # so like s3://aryn-datasets-us-east-1/sort_benchmark/manifest.json read by - # JsonManifestMetadataProvider; same below for HTML importing - ctx.read.binary(paths, binary_format="pdf", filter_paths_by_extension=False) - # TODO: https://github.com/aryn-ai/quickstart/issues/3 - cache textract results - # so that we can - # 1) speed up testing; and 2) let people try out sycamore without also having to set up S3 - .partition( - partitioner=UnstructuredPdfPartitioner(), - table_extractor=table_extractor, - ) - .merge(merger) - .extract_entity( - entity_extractor=OpenAIEntityExtractor( - "title", llm=openai_llm, prompt_template=get_title_context_template() - ) - ) - .extract_entity( - entity_extractor=OpenAIEntityExtractor( - "authors", llm=openai_llm, prompt_template=get_author_context_template() - ) - ) - .spread_properties(["path", "title"]) - .explode() - .sketch() - .embed( - embedder=SentenceTransformerEmbedder(batch_size=100, model_name="sentence-transformers/all-MiniLM-L6-v2") - ) - .write.opensearch( - os_client_args=get_os_client_args(), - index_name=index, - index_settings=get_index_settings(), - ) - ) - - -def sycamore_init(): - ray_tasks = get_ray_task_count() - print("Using", ray_tasks, "CPUs for execution") - return sycamore.init(ray_args={"num_cpus": ray_tasks}) - - -def import_html(paths): - if len(paths) == 0: - print("WARNING: import_html called with empty paths") - return - - ctx = sycamore_init() - ( - ctx.read.binary(paths, binary_format="html", filter_paths_by_extension=False) - .partition( - partitioner=HtmlPartitioner( - extract_tables=True, - text_chunker=TextOverlapChunker(chunk_token_count=4000, chunk_overlap_token_count=400), - ) - ) - .spread_properties(["path", "title"]) - .explode() - .sketch() - .embed( - embedder=SentenceTransformerEmbedder(batch_size=100, model_name="sentence-transformers/all-MiniLM-L6-v2") - ) - .write.opensearch( - os_client_args=get_os_client_args(), - index_name=index, - index_settings=get_index_settings(), - ) - ) - - # TODO: https://github.com/aryn-ai/sycamore/issues/160 - implement HTML import - pass - - -def get_index_settings(): - return { - "body": { - "settings": { - "index.knn": True, - "number_of_shards": 5, - "number_of_replicas": 1, - }, - "mappings": { - "properties": { - "text": {"type": "text"}, - "embedding": { - "dimension": 384, - "method": { - "engine": "faiss", - "space_type": "l2", - "name": "hnsw", - "parameters": {}, - }, - "type": "knn_vector", - }, - "title": {"type": "text"}, - "searchable_text": {"type": "text"}, - "title_embedding": { - "dimension": 384, - "method": { - "engine": "faiss", - "space_type": "l2", - "name": "hnsw", - "parameters": {}, - }, - "type": "knn_vector", - }, - "url": {"type": "text"}, - } - }, - } - } - - -def get_os_client_args(): - args = { - "hosts": [{"host": "localhost", "port": 9200}], - "http_compress": True, - "http_auth": ("admin", "admin"), - "use_ssl": True, - "verify_certs": False, # because we're using self-signed certificates - "ssl_assert_hostname": False, - "ssl_show_warn": False, - "timeout": 120, - } - if running_in_container: - args["hosts"][0]["host"] = "opensearch" - - return args - - -def get_title_context_template(): - # ruff: noqa: E501 - return """ - ELEMENT 1: Jupiter's Moons - ELEMENT 2: Ganymede 2020 - ELEMENT 3: by Audi Lauper and Serena K. Goldberg. 2011 - ELEMENT 4: From Wikipedia, the free encyclopedia - ELEMENT 5: Ganymede, or Jupiter III, is the largest and most massive natural satellite of Jupiter as well as in the Solar System, being a planetary-mass moon. It is the largest Solar System object without an atmosphere, despite being the only moon of the Solar System with a magnetic field. Like Titan, it is larger than the planet Mercury, but has somewhat less surface gravity than Mercury, Io or the Moon. - ========= - "Ganymede 2020" - - ELEMENT 1: FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation - ELEMENT 2: Tarun Kalluri * UCSD - ELEMENT 3: Deepak Pathak CMU - ELEMENT 4: Manmohan Chandraker UCSD - ELEMENT 5: Du Tran Facebook AI - ELEMENT 6: https://tarun005.github.io/FLAVR/ - ELEMENT 7: 2 2 0 2 - ELEMENT 8: b e F 4 2 - ELEMENT 9: ] - ELEMENT 10: V C . s c [ - ======== - "FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation" - - """ - - -def get_author_context_template(): - # ruff: noqa: E501 - return """ - ELEMENT 1: Jupiter's Moons - ELEMENT 2: Ganymede 2020 - ELEMENT 3: by Audi Lauper and Serena K. Goldberg. 2011 - ELEMENT 4: From Wikipedia, the free encyclopedia - ELEMENT 5: Ganymede, or Jupiter III, is the largest and most massive natural satellite of Jupiter as well as in the Solar System, being a planetary-mass moon. It is the largest Solar System object without an atmosphere, despite being the only moon of the Solar System with a magnetic field. Like Titan, it is larger than the planet Mercury, but has somewhat less surface gravity than Mercury, Io or the Moon. - ========= - Audi Laupe, Serena K. Goldberg - - ELEMENT 1: FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation - ELEMENT 2: Tarun Kalluri * UCSD - ELEMENT 3: Deepak Pathak CMU - ELEMENT 4: Manmohan Chandraker UCSD - ELEMENT 5: Du Tran Facebook AI - ELEMENT 6: https://tarun005.github.io/FLAVR/ - ELEMENT 7: 2 2 0 2 - ELEMENT 8: b e F 4 2 - ELEMENT 9: ] - ELEMENT 10: V C . s c [ - ======== - Tarun Kalluri, Deepak Pathak, Manmohan Chandraker, Du Tran - - """ - - -#################################### - -main() -sys.exit(0) diff --git a/apps/importer/importer/docker/importer-root-setup.sh b/apps/importer/importer/docker/importer-root-setup.sh deleted file mode 100755 index f5d549ffd..000000000 --- a/apps/importer/importer/docker/importer-root-setup.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash -set -eu -set -x - -export DEBIAN_FRONTEND=noninteractive - -apt update -apt -y install --no-install-recommends poppler-utils tesseract-ocr python3-poetry gcc python3-dev diff --git a/apps/importer/poetry.lock 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"9e9df9a2b42788afc4703f8fc90b0bbe58346a34dc1e0f1bbc6afd220730eb8e" diff --git a/apps/importer/pyproject.toml b/apps/importer/pyproject.toml deleted file mode 100644 index 4565a540d..000000000 --- a/apps/importer/pyproject.toml +++ /dev/null @@ -1,19 +0,0 @@ -[tool.poetry] -name = "sycamore-importer" -version = "0.0.1" -description = "Sycamore is an LLM-powered semantic data preparation system for building search applications." -authors = ["aryn.ai "] -readme = "README.md" -repository = "https://github.com/aryn-ai/sycamore.git" -packages = [{ include = "importer" }] - -[tool.poetry.dependencies] -python = ">=3.9,<3.13" -sycamore-ai = "^0.1.13" - - -[tool.poetry.group.dev.dependencies] -sycamore-ai = { path = "../../lib/sycamore", develop = true } - -[tool.poetry.group.sycamore_poetry_lock.dependencies] -sycamore-poetry-lock = { path = "../../lib/poetry-lock", develop = true } diff --git a/apps/weaviate/compose.yml b/apps/weaviate/compose.yml deleted file mode 100644 index 67ae37cce..000000000 --- a/apps/weaviate/compose.yml +++ /dev/null @@ -1,32 +0,0 @@ ---- -version: "3.4" -services: - weaviate: - command: - - --host - - 0.0.0.0 - - --port - - "8080" - - --scheme - - http - image: cr.weaviate.io/semitechnologies/weaviate:1.25.0 - ports: - - 8080:8080 - - 50051:50051 - volumes: - - weaviate_data:/var/lib/weaviate - restart: on-failure:0 - environment: - QUERY_DEFAULTS_LIMIT: 25 - AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: "true" - PERSISTENCE_DATA_PATH: "/var/lib/weaviate" - DEFAULT_VECTORIZER_MODULE: "text2vec-transformers" - ENABLE_MODULES: "text2vec-transformers" - TRANSFORMERS_INFERENCE_API: http://t2v-transformers:8080 - CLUSTER_HOSTNAME: "node1" - t2v-transformers: - image: cr.weaviate.io/semitechnologies/transformers-inference:sentence-transformers-all-MiniLM-L6-v2 - environment: - ENABLE_CUDA: 0 -volumes: - weaviate_data: From 8b44b5a10f17caeb6fb7851e9ff5ab312edd9593 Mon Sep 17 00:00:00 2001 From: Karan Sampath <176953591+karanataryn@users.noreply.github.com> Date: Mon, 27 Jan 2025 12:47:21 -0800 Subject: [PATCH 2/5] remove rps unit tests --- .github/workflows/testing.yml | 29 ----------------------------- 1 file changed, 29 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index 24147942b..f6296447c 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -82,35 +82,6 @@ jobs: - name: DF-6 run: df - rps-unit-test: - runs-on: blacksmith-4vcpu-ubuntu-2204 - strategy: - matrix: - python-version: ["3.9", "3.10", "3.11"] - - steps: - - name: Checkout - uses: actions/checkout@v4 - with: - submodules: recursive - - name: Install Poetry - run: pipx install poetry - - name: Set up Python ${{ matrix.python-version }} - uses: useblacksmith/setup-python@v6 - with: - python-version: ${{ matrix.python-version }} - cache: "poetry" - - name: Install Protoc - uses: arduino/setup-protoc@v3 - with: - repo-token: ${{ secrets.GITHUB_TOKEN }} - - name: Install RPS - run: make install_rps - working-directory: lib/remote-processors - - name: Run Tests - run: poetry run pytest remote_processors/test/unit/ - working-directory: lib/remote-processors - notebook-tests-fast: runs-on: blacksmith-8vcpu-ubuntu-2204 strategy: From da94283f9b9b1c410aba9e384a23b08d99ad7fa9 Mon Sep 17 00:00:00 2001 From: Karan Sampath <176953591+karanataryn@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:10:32 -0800 Subject: [PATCH 3/5] deletion --- apps/query-eval/README.md | 108 - apps/query-eval/data/ntsb-full.yaml | 604 -- apps/query-eval/data/ntsb-mini.yaml | 516 - apps/query-eval/poetry.lock | 8678 ----------------- apps/query-eval/pyproject.toml | 26 - apps/query-eval/queryeval/__init__.py | 0 apps/query-eval/queryeval/driver.py | 625 -- apps/query-eval/queryeval/main.py | 136 - apps/query-eval/queryeval/queryeval_types.py | 108 - apps/query-eval/queryeval/test-results.yaml | 74 - apps/query-eval/queryeval/test_driver.py | 109 - apps/query-server/README.md | 13 - apps/query-server/poetry.lock | 7783 --------------- apps/query-server/pyproject.toml | 24 - apps/query-server/queryserver/__init__.py | 0 apps/query-server/queryserver/main.py | 259 - .../queryserver/tests/test_queryserver.py | 182 - apps/query-ui/.gitignore | 1 - apps/query-ui/Dockerfile.buildx | 44 - apps/query-ui/Makefile.query-ui | 8 - apps/query-ui/README.md | 24 - apps/query-ui/external_ray_wrapper.py | 24 - apps/query-ui/poetry.lock | 7608 --------------- apps/query-ui/pyproject.toml | 27 - apps/query-ui/queryui/Sycamore_Query.py | 173 - apps/query-ui/queryui/__init__.py | 0 apps/query-ui/queryui/chat.py | 329 - apps/query-ui/queryui/configuration.py | 32 - apps/query-ui/queryui/fetchdata.py | 61 - apps/query-ui/queryui/loaddata.py | 184 - apps/query-ui/queryui/main.py | 106 - apps/query-ui/queryui/ntsb.py | 357 - apps/query-ui/queryui/pages/Chat.py | 383 - .../queryui/tests/integration/Chat_test.py | 274 - apps/query-ui/queryui/util.py | 306 - apps/query-ui/run-query-ui.sh | 43 - lib/sycamore/pyproject.toml | 4 - poetry.lock | 813 +- pyproject.toml | 13 - 39 files changed, 2 insertions(+), 30057 deletions(-) delete mode 100644 apps/query-eval/README.md delete mode 100644 apps/query-eval/data/ntsb-full.yaml delete mode 100644 apps/query-eval/data/ntsb-mini.yaml delete mode 100644 apps/query-eval/poetry.lock delete mode 100644 apps/query-eval/pyproject.toml delete mode 100644 apps/query-eval/queryeval/__init__.py delete mode 100644 apps/query-eval/queryeval/driver.py delete mode 100644 apps/query-eval/queryeval/main.py delete mode 100644 apps/query-eval/queryeval/queryeval_types.py delete mode 100644 apps/query-eval/queryeval/test-results.yaml delete mode 100644 apps/query-eval/queryeval/test_driver.py delete mode 100644 apps/query-server/README.md delete mode 100644 apps/query-server/poetry.lock delete mode 100644 apps/query-server/pyproject.toml delete mode 100644 apps/query-server/queryserver/__init__.py delete mode 100644 apps/query-server/queryserver/main.py delete mode 100644 apps/query-server/queryserver/tests/test_queryserver.py delete mode 100644 apps/query-ui/.gitignore delete mode 100644 apps/query-ui/Dockerfile.buildx delete mode 100644 apps/query-ui/Makefile.query-ui delete mode 100644 apps/query-ui/README.md delete mode 100644 apps/query-ui/external_ray_wrapper.py delete mode 100644 apps/query-ui/poetry.lock delete mode 100644 apps/query-ui/pyproject.toml delete mode 100644 apps/query-ui/queryui/Sycamore_Query.py delete mode 100644 apps/query-ui/queryui/__init__.py delete mode 100644 apps/query-ui/queryui/chat.py delete mode 100644 apps/query-ui/queryui/configuration.py delete mode 100644 apps/query-ui/queryui/fetchdata.py delete mode 100644 apps/query-ui/queryui/loaddata.py delete mode 100755 apps/query-ui/queryui/main.py delete mode 100644 apps/query-ui/queryui/ntsb.py delete mode 100644 apps/query-ui/queryui/pages/Chat.py delete mode 100644 apps/query-ui/queryui/tests/integration/Chat_test.py delete mode 100644 apps/query-ui/queryui/util.py delete mode 100755 apps/query-ui/run-query-ui.sh diff --git a/apps/query-eval/README.md b/apps/query-eval/README.md deleted file mode 100644 index 05c77f8a2..000000000 --- a/apps/query-eval/README.md +++ /dev/null @@ -1,108 +0,0 @@ -# Sycamore Query Evaluation tool - -This tool can be used to evaluate the query planning and answering capabilities -of Sycamore Query against a given dataset and set of queries. This is a wrapper -around the `sycamore.query.client.SycamoreQueryClient` class that reads a -configuration from an input YAML file, and writes results to an output YAML file. - -## Input file format - -The input file format is YAML and is defined by the `queryeval.types.QueryEvalInputFile` -class. The following is a minimal example of the input file format: - -```yaml -# General configuration options. Each of these can be specified -# on the command line as well. -config: - # The OpenSearch index to use. - index: const_ntsb - -# The list of queries to run. Each has a query and an expected -# result, which can be either a string, or a list of dictionaries, -# with each element of the list representing a Sycamore Document -# expected to be returned by the query. -queries: - - query: "How many incidents were there in 2023?" - expected: "There were 10 incidents in 2023." - - query: "How many incidents occurred in bad weather?" - expected: "7 incidents occurred in bad weather." -``` - -Examples of input files can be found in the `data/` directory. - -## Output file format - -The output file format is YAML and is defined by the `queryeval.types.QueryEvalResultsFile` -type. Depending on the configuration options used when run, the output file may -contain one or more of the following: - * Query plans generated by the Sycamore Query Planner. - * Query results produced by running these query plans. - * Accuracy and quality metrics calculated from the query results. - -The idea is that each of these stages of the evaluation can be run independently, and -the results from previous stages can be used as input to the next stage. - -## Running the tool - -First, run `poetry install` in this directory to install all dependencies. - -You can get a full list of options by running: - -```bash -$ poetry run python queryeval/main.py --help -``` - -To generate query plans and run all of the resulting queries: - -```bash -$ poetry run python queryeval/main.py --outfile results.yaml data/ntsb-mini.yaml run -``` - -To only generate query plans: - -```bash -$ poetry run python queryeval/main.py --outfile results.yaml data/ntsb-mini.yaml plan -``` - -Note that the query plans generated during the `plan` phase are saved to the results -file, so if you use `run` after `plan` with the same `--outfile` option set, the query plans -will be reused. You can force regeneration of the query plans using the `--overwrite` option. - -## Specifying the schema - -By default, the data schema will be fetched from the provided OpenSearch index. -However, the schema can be specified manually by setting the `data_schema` field in the -input file. Each field in the schema has two entries: the type of the field, and -a list of example values. For example: - -```yaml -data_schema: - - properties.entity.accidentNumber: - # The type of the field. - - str - # A list of example values. - - ["CEN23LAO80", "DCA23LA133", "CEN23LA086", "ERA23LA168", "CEN23LA097"] - - properties.entity.aircraftDamage: - - str - # You can also specify individual examples as list entries on their own line. - - - Destroyed - - None - - Substantial -``` - -## Useful flags - -Use the `--query-cache-path` and `--llm-cache-path` flags to specify caches for intermediate -query results and LLM results, respectively. This can save a substantial amount of time and -LLM cost if you are doing repeated evaluations, however, be aware that stale cache entries -may affect your results. - -Use `--dry-run` to avoid performing any planning, queries, or writing results. This is useful -to test if your config file format is correct. - -Use `--logfile` to write detailed logs of the evaluation process to a file. - - - diff --git a/apps/query-eval/data/ntsb-full.yaml b/apps/query-eval/data/ntsb-full.yaml deleted file mode 100644 index f92be1c20..000000000 --- a/apps/query-eval/data/ntsb-full.yaml +++ /dev/null @@ -1,604 +0,0 @@ -# This file contains a query-eval config for evaluating Sycamore Query against -# the NTSB incident dataset. -# -# The queries are from the 100 NTSB reports in s3://aryn-public/ntsb_reports as of -# 2024-11-11, which cover portions of June, July, August, and September 2024. - -config: - index: const_ntsb - -########################################################################################## -# Examples -########################################################################################## - -examples: - - schema: &ntsb_example_schema - fields: - text_representation: - field_type: str - description: Can be assumed to have all other details - properties.entity.dateTime: - field_type: str - examples: - - "2023-01-12T11:00:00" - - "2023-01-11T18:09:00" - - "2023-01-10T16:43:00" - - "2023-01-28T19:02:00" - - "2023-01-12T13:00:00" - properties.entity.dateAndTime: - field_type: str - examples: - - "January 28, 2023 19:02:00" - - "January 10, 2023 16:43:00" - - "January 11, 2023 18:09:00" - - "January 12, 2023 13:00:00" - - "January 12, 2023 11:00:00" - properties.entity.lowestCeiling: - field_type: str - examples: - - "Broken 3800 ft AGL" - - "Broken 6500 ft AGL" - - "Overcast 500 ft AGL" - - "Overcast 1800 ft AGL" - properties.entity.aircraftDamage: - field_type: str - examples: - - "Substantial" - - "None" - - "Destroyed" - properties.entity.conditions: - field_type: str - examples: - - "Instrument (IMC)" - - "IMC" - - "VMC" - - "Visual (VMC)" - properties.entity.departureAirport: - field_type: str - examples: - - "Somerville, Tennessee" - - "Colorado Springs, Colorado (FLY)" - - "Yelm; Washington" - - "Winchester, Virginia (OKV)" - - "San Diego, California (KMYF)" - properties.entity.accidentNumber: - field_type: str - examples: - - "CEN23FA095" - - "ERA2BLAT1I" - - "WPR23LA088" - - "ERA23FA108" - - "WPR23LA089" - properties.entity.windSpeed: - field_type: str - examples: - - "" - - "10 knots" - - "7 knots" - - "knots" - - "19 knots gusting to 22 knots" - properties.entity.day: - field_type: str - examples: - - "2023-01-12" - - "2023-01-10" - - "2023-01-20" - - "2023-01-11" - - "2023-01-28" - properties.entity.destinationAirport: - field_type: str - examples: - - "Somerville, Tennessee" - - "Yelm; Washington" - - "Agua Caliente Springs, California" - - "Liberal, Kansas (LBL)" - - "Alabaster, Alabama (EET)" - properties.entity.location: - field_type: str - examples: - - "Hooker, Oklahoma" - - "Somerville, Tennessee" - - "Yelm; Washington" - - "Agua Caliente Springs, California" - - "Dayton, Virginia" - properties.entity.operator: - field_type: str - examples: - - "On file" - - "First Team Pilot Training LLC" - - "file On" - - "Anderson Aviation LLC" - - "Flying W Ranch" - properties.entity.temperature: - field_type: str - examples: - - "18'C /-2'C" - - "15.8C" - - "13'C" - - "2C / -3C" - properties.entity.visibility: - field_type: str - examples: - - "" - - "miles" - - "0.5 miles" - - "7 miles" - - "10 miles" - properties.entity.aircraft: - field_type: str - examples: - - "Piper PA-32R-301" - - "Beech 95-C55" - - "Cessna 172" - - "Piper PA-28-160" - - "Cessna 180K" - properties.entity.conditionOfLight: - field_type: str - examples: - - "" - - "Night/dark" - - "Night" - - "Day" - - "Dusk" - properties.entity.windDirection: - field_type: str - examples: - - "" - - "190°" - - "200" - - "2005" - - "040°" - properties.entity.lowestCloudCondition: - field_type: str - examples: - - "" - - "Broken 3800 ft AGL" - - "Overcast 500 ft AGL" - - "Clear" - - "Overcast 200 ft AGL" - properties.entity.injuries: - field_type: str - examples: - - "Minor" - - "Fatal" - - "None" - - "3 None" - - "2 None" - properties.entity.flightConductedUnder: - field_type: str - examples: - - "Part 91: General aviation Instructional" - - "Part 135: Air taxi & commuter Non-scheduled" - - "Part 91: General aviation Personal" - - "Part 135: Air taxi & commuter Scheduled" - - "Part 91: General aviation Business" - plan: - query: "List the incidents in Georgia in 2023." - result_node: 0 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports" - index: const_ntsb - node_id: 0 - query: - bool: - must: - - range: - properties.entity.dateTime: - gte: "2023-01-01T00:00:00" - lte: "2023-12-31T23:59:59" - format: strict_date_optional_time - - match: - properties.entity.location: Georgia - - - schema: *ntsb_example_schema - plan: - query: "Show the incidents involving Piper aircraft." - result_node: 0 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports" - index: const_ntsb - node_id: 0 - query: - match: - properties.entity.aircraft: Piper - - - schema: *ntsb_example_schema - plan: - query: "How many incidents happened in clear weather?" - result_node: 1 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports in clear weather" - index: const_ntsb - node_id: 0 - query: - match: - properties.entity.conditions: VMC - 1: - node_type: Count - description: "Count the number of incidents" - distinct_field: properties.entity.accidentNumber - inputs: [0] - node_id: 1 - - - schema: *ntsb_example_schema - plan: - query: "What types of aircrafts were involved in accidents in California?" - result_node: 1 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports in California" - index: const_ntsb - query: - match: - properties.entity.location: California - node_id: 0 - 1: - node_type: TopK - description: "Get the types of aircraft involved in incidents in California" - field: properties.entity.aircraft - primary_field: properties.entity.accidentNumber - K: 100 - descending: false - llm_cluster: false - llm_cluster_instruction: null - inputs: [0] - node_id: 1 - - - schema: *ntsb_example_schema - plan: - query: "Which aircraft accidents in California in 2023 occurred when the wind was stronger than 4 knots?" - result_node: 1 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports in California in 2023" - index: const_ntsb - query: - bool: - must: - - range: - properties.entity.dateTime: - gte: "2023-01-01T00:00:00" - lte: "2023-12-31T23:59:59" - format: strict_date_optional_time - - match: - properties.entity.location: California - node_id: 0 - 1: - node_type: LlmFilter - description: "Filter to reports with wind speed greater than 4 knots" - question: "Is the wind speed greater than 4 knots?" - field: properties.entity.windSpeed - inputs: [0] - node_id: 1 - - - schema: *ntsb_example_schema - plan: - query: "Which three aircraft types were involved in the most accidents?" - result_node: 1 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports" - index: const_ntsb - node_id: 0 - query: - match_all: {} - 1: - node_type: TopK - description: "Get the top three aircraft types involved in accidents" - field: properties.entity.aircraft - primary_field: properties.entity.accidentNumber - K: 3 - descending: true - llm_cluster: false - llm_cluster_instruction: null - inputs: [0] - node_id: 1 - - - schema: *ntsb_example_schema - plan: - query: "Show some incidents where pilot training was mentioned as a cause" - result_node: 0 - nodes: - 0: - node_type: QueryVectorDatabase - description: "Get incident reports mentioning pilot training" - index: const_ntsb - query_phrase: "pilot training" - node_id: 0 - - - schema: *ntsb_example_schema - plan: - query: "Show all incidents involving a Cessna 172 aircraft" - result_node: 0 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports involving a Cessna 172 aircraft" - index: const_ntsb - query: - match: - properties.entity.aircraft: "Cessna 172" - node_id: 0 - -########################################################################################## -# Queries -# -########################################################################################## - -queries: - - - query: "How many incidents were there?" - expected: "100" - tags: - - verified_ground_truth - - - query: "How many incidents were there in Illinois?" - expected: "7" - tags: - - verified_ground_truth - - - query: "How many incidents were there in Hawaii?" - expected: "0" - tags: - - verified_ground_truth - - - query: "How many incidents were there by state?" - expected: | - Florida: 10 - Wisconsin: 8 - Texas: 8 - Illinois: 7 - New York: 6 - Colorado: 5 - Nebraska: 4 - North Carolina: 4 - Kansas: 3 - Missouri: 3 - Michigan: 3 - Oklahoma: 3 - Minnesota: 3 - Louisiana: 3 - South Carolina: 3 - Georgia: 3 - Alabama: 2 - New Hampshire: 2 - Arkansas: 2 - Mississippi: 2 - Virginia: 2 - Indiana: 2 - Massachusetts: 1 - Delaware: 1 - Maine: 1 - West Virginia: 1 - Kentucky: 1 - Maryland: 1 - Washington: 1 - New Jersey: 1 - Ohio: 1 - South Dakota: 1 - Utah: 1 - Tennessee: 1 - tags: - - verified_ground_truth - - - query: "How many incidents involved student pilots?" - # Need ground truth answer. - expected: "XXX" - tags: - - need_ground_truth - - - query: "How many incidents involved a wind speed over 10 knots?" - expected: "16" - tags: - - verified_ground_truth - - - query: "What types of aircraft were involved in accidents in Texas?" - expected: | - FLIGHT DESIGN GMBH CTSW - Zenith Aircraft Company STOL CH 750 - Piper PA-38-112 - Cessna 172R - AMERICAN AIR RACING INC LANCAIR IVP - AIR TRACTOR INC AT-802 - Stinson VULTEE V-77 - AIR TRACTOR INC AT-402 - tags: - - verified_ground_truth - - - query: "What was the breakdown of incidents by month?" - expected: | - June 2024: 42 - July 2024: 36 - August 2024: 17 - September 2024: 5 - tags: - - verified_ground_truth - - - query: "What was the aircraft type with the most incidents?" - expected: "Cessna 172" - tags: - - verified_ground_truth - - - query: "What was the breakdown of incident types by aircraft manufacturer?" - expected: | - Aero Commander: 1 - Aeronca: 2 - Air tractor Inc: 4 - Airbus: 2 - American Air Racing Inc: 1 - AARBC Inc DBA Lindstrand: 2 - Aviat Aircraft Inc: 1 - Avipro: 1 - Beech: 5 - Bell: 1 - Boeing: 2 - Bucker: 1 - BURKHART GROB FLUGZEUGBAU: 1 - Canadian Light Acft Sales & Svc: 1 - Cirrus Design Corp: 2 - Construzzioni Aeronautiche Tecn: 1 - Corella Neil: 1 - Cessna: 24 - Diamond Aircraft Ind Inc: 1 - Evektor-Aerotechnik: 1 - Evolution Aircraft Inc: 1 - Esqual: 1 - Flight Design GmbH: 1 - Grumman Acft Eng Corp: 1 - Gulfstream American Corp: 1 - Hughes: 1 - Jonker Sailplanes (Pty) Ltd: 1 - Just Aircraft: 1 - Lake Aircraft Company: 1 - LET Aircraft Industries: 1 - Mooney: 1 - Pipisteel Italia Srl: 1 - Piper: 14 - Progressive Aerodyne: 3 - Quicksilver: 1 - Rans: 1 - Raytheon Aircraft Company: 1 - Robert L Staton Javron Inc: 1 - Robinson Helicopter Company: 2 - Schweizer: 1 - Skystar: 1 - Stinson: 1 - Textron Aviation Inc: 1 - Unknown: 1 - Vans: 3 - Zenith Aircraft Company: 2 - tags: - - verified_ground_truth - - - query: "Which incidents occurred in July involving birds?" - expected: | - 194630.pdf - 194715.pdf - tags: - - verified_ground_truth - - - query: "How many Piper aircrafts were involved in accidents?" - expected: "14" - tags: - - verified_ground_truth - - - query: "How many incidents involved substantial damage?" - expected: "94" - tags: - - verified_ground_truth - - - query: "What fraction of incidents involved substantial damage?" - expected: "0.94" - tags: - - verified_ground_truth - - - query: "How many incidents were due to engine problems?" - expected: "63" - tags: - - verified_ground_truth - - - query: "What fraction of incidents that resulted in substantial damage were due to engine problems?" - expected: "0.52" - tags: - - verified_ground_truth - - - query: "How many environmentally caused incidents were there?" - expected: "XXX - Could be 21" - tags: - - need_ground_truth - - - query: "How many incidents were caused by human error?" - expected: "XXX - Could be 90" - tags: - - need_ground_truth - - - query: "How many incidents were caused by human error that did not involve environmental factors?" - expected: "XXX - Could be 84" - tags: - - need_ground_truth - - - query: "How many more incidents were caused by human error compared to environmental causes?" - expected: "XXX - Could be 69" - tags: - - need_ground_truth - - - query: "How many incidents had 2+ serious injuries?" - expected: "3" - tags: - - verified_ground_truth - - - query: "What were the top three causes of incidents with serious injuries?" - expected: | - passenger non-compliance: 1 - pilot error: 10 - environmental factors: 3 - tags: - - need_ground_truth - - - query: "Which states in the Midwest were most affected by aviation incidents in July 2024?" - expected: | - Illinois: 4 - Nebraska: 3 - Wisconsin: 3 - Missouri: 2 - Michigan: 2 - Minnesota: 2 - Kansas: 1 - tags: - - need_ground_truth - - - query: "How many incidents occurred in the first 10 days of July 2024?" - expected: "11" - tags: - - verified_ground_truth - - - query: "How many states did incidents in the first 10 days of July 2024 occur in?" - expected: "9" - tags: - - verified_ground_truth - - - query: "How many more incidents happened in Florida compared to Oklahoma?" - expected: "7" - tags: - - verified_ground_truth - - - query: "How many incidents occurred that involved serious injuries when the wind speed was greater than four knots?" - expected: "12" - tags: - - need_ground_truth - - - query: "In incidents involving Piper aircraft, what was the most commonly damaged part of the aircraft?" - expected: "Wing, with 7 incidents in which it was damaged." - tags: - - need_ground_truth - - - query: "In incidents involving damage to the fuselage, what are the number of incidents by aircraft type?" - expected: | - Cessna 172: 3 - AIR TRACTOR INC AT-402: 2 - CIRRUS DESIGN CORP SR20: 2 - Cessna 172R: 2 - Cessna 180: 2 are 2 incidents reported. - Cessna 305: 2 - Cessna A185F: 2 - Piper PA28: 2 - Others: 1 each - tags: - - need_ground_truth - - - query: "How many incidents were there, broken down by number of engines?" - expected: | - 0 engines: 5 - 1 Engine: 87 - 2 Engines: 9 - tags: - - need_ground_truth \ No newline at end of file diff --git a/apps/query-eval/data/ntsb-mini.yaml b/apps/query-eval/data/ntsb-mini.yaml deleted file mode 100644 index f50802b45..000000000 --- a/apps/query-eval/data/ntsb-mini.yaml +++ /dev/null @@ -1,516 +0,0 @@ -# This file contains a query-eval config for evaluating Sycamore Query against -# the NTSB incident dataset. This version only contains a few queries for quick -# testing. - -config: - index: const_ntsb - -########################################################################################## -# Examples -########################################################################################## - -examples: - - schema: &ntsb_example_schema - fields: - text_representation: - field_type: str - description: Can be assumed to have all other details - properties.entity.dateTime: - field_type: str - examples: - - "2023-01-12T11:00:00" - - "2023-01-11T18:09:00" - - "2023-01-10T16:43:00" - - "2023-01-28T19:02:00" - - "2023-01-12T13:00:00" - properties.entity.dateAndTime: - field_type: str - examples: - - "January 28, 2023 19:02:00" - - "January 10, 2023 16:43:00" - - "January 11, 2023 18:09:00" - - "January 12, 2023 13:00:00" - - "January 12, 2023 11:00:00" - properties.entity.lowestCeiling: - field_type: str - examples: - - "Broken 3800 ft AGL" - - "Broken 6500 ft AGL" - - "Overcast 500 ft AGL" - - "Overcast 1800 ft AGL" - properties.entity.aircraftDamage: - field_type: str - examples: - - "Substantial" - - "None" - - "Destroyed" - properties.entity.conditions: - field_type: str - examples: - - "Instrument (IMC)" - - "IMC" - - "VMC" - - "Visual (VMC)" - properties.entity.departureAirport: - field_type: str - examples: - - "Somerville, Tennessee" - - "Colorado Springs, Colorado (FLY)" - - "Yelm; Washington" - - "Winchester, Virginia (OKV)" - - "San Diego, California (KMYF)" - properties.entity.accidentNumber: - field_type: str - examples: - - "CEN23FA095" - - "ERA2BLAT1I" - - "WPR23LA088" - - "ERA23FA108" - - "WPR23LA089" - properties.entity.windSpeed: - field_type: str - examples: - - "" - - "10 knots" - - "7 knots" - - "knots" - - "19 knots gusting to 22 knots" - properties.entity.day: - field_type: str - examples: - - "2023-01-12" - - "2023-01-10" - - "2023-01-20" - - "2023-01-11" - - "2023-01-28" - properties.entity.destinationAirport: - field_type: str - examples: - - "Somerville, Tennessee" - - "Yelm; Washington" - - "Agua Caliente Springs, California" - - "Liberal, Kansas (LBL)" - - "Alabaster, Alabama (EET)" - properties.entity.location: - field_type: str - examples: - - "Hooker, Oklahoma" - - "Somerville, Tennessee" - - "Yelm; Washington" - - "Agua Caliente Springs, California" - - "Dayton, Virginia" - properties.entity.operator: - field_type: str - examples: - - "On file" - - "First Team Pilot Training LLC" - - "file On" - - "Anderson Aviation LLC" - - "Flying W Ranch" - properties.entity.temperature: - field_type: str - examples: - - "18'C /-2'C" - - "15.8C" - - "13'C" - - "2C / -3C" - properties.entity.visibility: - field_type: str - examples: - - "" - - "miles" - - "0.5 miles" - - "7 miles" - - "10 miles" - properties.entity.aircraft: - field_type: str - examples: - - "Piper PA-32R-301" - - "Beech 95-C55" - - "Cessna 172" - - "Piper PA-28-160" - - "Cessna 180K" - properties.entity.conditionOfLight: - field_type: str - examples: - - "" - - "Night/dark" - - "Night" - - "Day" - - "Dusk" - properties.entity.windDirection: - field_type: str - examples: - - "" - - "190°" - - "200" - - "2005" - - "040°" - properties.entity.lowestCloudCondition: - field_type: str - examples: - - "" - - "Broken 3800 ft AGL" - - "Overcast 500 ft AGL" - - "Clear" - - "Overcast 200 ft AGL" - properties.entity.injuries: - field_type: str - examples: - - "Minor" - - "Fatal" - - "None" - - "3 None" - - "2 None" - properties.entity.flightConductedUnder: - field_type: str - examples: - - "Part 91: General aviation Instructional" - - "Part 135: Air taxi & commuter Non-scheduled" - - "Part 91: General aviation Personal" - - "Part 135: Air taxi & commuter Scheduled" - - "Part 91: General aviation Business" - plan: - query: "List the incidents in Georgia in 2023." - result_node: 0 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports" - index: const_ntsb - node_id: 0 - query: - bool: - must: - - range: - properties.entity.dateTime: - gte: "2023-01-01T00:00:00" - lte: "2023-12-31T23:59:59" - format: strict_date_optional_time - - match: - properties.entity.location: Georgia - - - schema: *ntsb_example_schema - plan: - query: "Show the incidents involving Piper aircraft." - result_node: 0 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports" - index: const_ntsb - node_id: 0 - query: - match: - properties.entity.aircraft: Piper - - - schema: *ntsb_example_schema - plan: - query: "How many incidents happened in clear weather?" - result_node: 1 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports in clear weather" - index: const_ntsb - node_id: 0 - query: - match: - properties.entity.conditions: VMC - 1: - node_type: Count - description: "Count the number of incidents" - distinct_field: properties.entity.accidentNumber - inputs: [0] - node_id: 1 - - - schema: *ntsb_example_schema - plan: - query: "What types of aircrafts were involved in accidents in California?" - result_node: 1 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports in California" - index: const_ntsb - query: - match: - properties.entity.location: California - node_id: 0 - 1: - node_type: TopK - description: "Get the types of aircraft involved in incidents in California" - field: properties.entity.aircraft - primary_field: properties.entity.accidentNumber - K: 100 - descending: false - llm_cluster: false - llm_cluster_instruction: null - inputs: [0] - node_id: 1 - - - schema: *ntsb_example_schema - plan: - query: "Which aircraft accidents in California in 2023 occurred when the wind was stronger than 4 knots?" - result_node: 1 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports in California in 2023" - index: const_ntsb - query: - bool: - must: - - range: - properties.entity.dateTime: - gte: "2023-01-01T00:00:00" - lte: "2023-12-31T23:59:59" - format: strict_date_optional_time - - match: - properties.entity.location: California - node_id: 0 - 1: - node_type: LlmFilter - description: "Filter to reports with wind speed greater than 4 knots" - question: "Is the wind speed greater than 4 knots?" - field: properties.entity.windSpeed - inputs: [0] - node_id: 1 - - - schema: *ntsb_example_schema - plan: - query: "Which three aircraft types were involved in the most accidents?" - result_node: 1 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports" - index: const_ntsb - node_id: 0 - query: - match_all: {} - 1: - node_type: TopK - description: "Get the top three aircraft types involved in accidents" - field: properties.entity.aircraft - primary_field: properties.entity.accidentNumber - K: 3 - descending: true - llm_cluster: false - llm_cluster_instruction: null - inputs: [0] - node_id: 1 - - - schema: *ntsb_example_schema - plan: - query: "Show some incidents where pilot training was mentioned as a cause" - result_node: 0 - nodes: - 0: - node_type: QueryVectorDatabase - description: "Get incident reports mentioning pilot training" - index: const_ntsb - query_phrase: "pilot training" - node_id: 0 - - - schema: *ntsb_example_schema - plan: - query: "Show all incidents involving a Cessna 172 aircraft" - result_node: 0 - nodes: - 0: - node_type: QueryDatabase - description: "Get all the incident reports involving a Cessna 172 aircraft" - index: const_ntsb - query: - match: - properties.entity.aircraft: "Cessna 172" - node_id: 0 - -########################################################################################## -# Queries -########################################################################################## - -queries: - - query: "List all incidents in California in 2023" - expected: "Here are the incidents in California in 2023:" - expected_docs: - - s3://aryn-public/ntsb/12.pdf - - s3://aryn-public/ntsb/38.pdf - - s3://aryn-public/ntsb/42.pdf - - s3://aryn-public/ntsb/45.pdf - - s3://aryn-public/ntsb/56.pdf - - s3://aryn-public/ntsb/67.pdf - - s3://aryn-public/ntsb/103.pdf - tags: - - easy - - - query: "How many incidents were there in Washington in 2023?" - expected: "3" - expected_docs: - - s3://aryn-public/ntsb/6.pdf - - s3://aryn-public/ntsb/66.pdf - - s3://aryn-public/ntsb/80.pdf - tags: - - easy - - - query: "How many Piper aircrafts were involved in accidents?" - expected: "21" - expected_docs: - - s3://aryn-public/ntsb/81.pdf - - s3://aryn-public/ntsb/101.pdf - - s3://aryn-public/ntsb/86.pdf - - s3://aryn-public/ntsb/90.pdf - - s3://aryn-public/ntsb/21.pdf - - s3://aryn-public/ntsb/39.pdf - - s3://aryn-public/ntsb/96.pdf - - s3://aryn-public/ntsb/25.pdf - - s3://aryn-public/ntsb/42.pdf - - s3://aryn-public/ntsb/28.pdf - - s3://aryn-public/ntsb/43.pdf - - s3://aryn-public/ntsb/47.pdf - - s3://aryn-public/ntsb/48.pdf - - s3://aryn-public/ntsb/5.pdf - - s3://aryn-public/ntsb/50.pdf - - s3://aryn-public/ntsb/54.pdf - - s3://aryn-public/ntsb/56.pdf - - s3://aryn-public/ntsb/63.pdf - - s3://aryn-public/ntsb/64.pdf - - s3://aryn-public/ntsb/70.pdf - - s3://aryn-public/ntsb/72.pdf - tags: - - medium - - - query: "List all incidents that resulted in substantial damage" - expected: "Here are the incidents that resulted in substantial damage:" - expected_docs: &substantial_damage_docs - - s3://aryn-public/ntsb/10.pdf - - s3://aryn-public/ntsb/32.pdf - - s3://aryn-public/ntsb/80.pdf - - s3://aryn-public/ntsb/85.pdf - - s3://aryn-public/ntsb/103.pdf - - s3://aryn-public/ntsb/34.pdf - - s3://aryn-public/ntsb/35.pdf - - s3://aryn-public/ntsb/12.pdf - - s3://aryn-public/ntsb/88.pdf - - s3://aryn-public/ntsb/36.pdf - - s3://aryn-public/ntsb/9.pdf - - s3://aryn-public/ntsb/37.pdf - - s3://aryn-public/ntsb/38.pdf - - s3://aryn-public/ntsb/20.pdf - - s3://aryn-public/ntsb/21.pdf - - s3://aryn-public/ntsb/92.pdf - - s3://aryn-public/ntsb/39.pdf - - s3://aryn-public/ntsb/23.pdf - - s3://aryn-public/ntsb/96.pdf - - s3://aryn-public/ntsb/40.pdf - - s3://aryn-public/ntsb/25.pdf - - s3://aryn-public/ntsb/42.pdf - - s3://aryn-public/ntsb/28.pdf - - s3://aryn-public/ntsb/43.pdf - - s3://aryn-public/ntsb/31.pdf - - s3://aryn-public/ntsb/44.pdf - - s3://aryn-public/ntsb/47.pdf - - s3://aryn-public/ntsb/5.pdf - - s3://aryn-public/ntsb/52.pdf - - s3://aryn-public/ntsb/53.pdf - - s3://aryn-public/ntsb/57.pdf - - s3://aryn-public/ntsb/50.pdf - - s3://aryn-public/ntsb/59.pdf - - s3://aryn-public/ntsb/60.pdf - - s3://aryn-public/ntsb/104.pdf - - s3://aryn-public/ntsb/62.pdf - - s3://aryn-public/ntsb/66.pdf - - s3://aryn-public/ntsb/63.pdf - - s3://aryn-public/ntsb/67.pdf - - s3://aryn-public/ntsb/73.pdf - - s3://aryn-public/ntsb/8.pdf - - s3://aryn-public/ntsb/101.pdf - - s3://aryn-public/ntsb/86.pdf - - s3://aryn-public/ntsb/87.pdf - - s3://aryn-public/ntsb/16.pdf - - s3://aryn-public/ntsb/18.pdf - - s3://aryn-public/ntsb/90.pdf - - s3://aryn-public/ntsb/91.pdf - - s3://aryn-public/ntsb/94.pdf - - s3://aryn-public/ntsb/26.pdf - - s3://aryn-public/ntsb/98.pdf - - s3://aryn-public/ntsb/41.pdf - - s3://aryn-public/ntsb/99.pdf - - s3://aryn-public/ntsb/3.pdf - - s3://aryn-public/ntsb/45.pdf - - s3://aryn-public/ntsb/48.pdf - - s3://aryn-public/ntsb/56.pdf - - s3://aryn-public/ntsb/0.pdf - - s3://aryn-public/ntsb/1.pdf - - s3://aryn-public/ntsb/6.pdf - - s3://aryn-public/ntsb/69.pdf - - s3://aryn-public/ntsb/72.pdf - - s3://aryn-public/ntsb/74.pdf - - s3://aryn-public/ntsb/78.pdf - tags: - - medium - - - query: "List all incidents that resulted in substantial damage due to engine problems" - expected: "Here are the incidents that resulted in substantial damage due to engine problems:" - expected_docs: - - s3://aryn-public/ntsb/10.pdf - - s3://aryn-public/ntsb/32.pdf - - s3://aryn-public/ntsb/80.pdf - - s3://aryn-public/ntsb/85.pdf - - s3://aryn-public/ntsb/103.pdf - - s3://aryn-public/ntsb/34.pdf - - s3://aryn-public/ntsb/35.pdf - - s3://aryn-public/ntsb/12.pdf - - s3://aryn-public/ntsb/88.pdf - - s3://aryn-public/ntsb/36.pdf - - s3://aryn-public/ntsb/9.pdf - - s3://aryn-public/ntsb/37.pdf - - s3://aryn-public/ntsb/38.pdf - - s3://aryn-public/ntsb/21.pdf - - s3://aryn-public/ntsb/92.pdf - - s3://aryn-public/ntsb/39.pdf - - s3://aryn-public/ntsb/23.pdf - - s3://aryn-public/ntsb/96.pdf - - s3://aryn-public/ntsb/40.pdf - - s3://aryn-public/ntsb/25.pdf - - s3://aryn-public/ntsb/42.pdf - - s3://aryn-public/ntsb/28.pdf - - s3://aryn-public/ntsb/43.pdf - - s3://aryn-public/ntsb/31.pdf - - s3://aryn-public/ntsb/44.pdf - - s3://aryn-public/ntsb/47.pdf - - s3://aryn-public/ntsb/5.pdf - - s3://aryn-public/ntsb/52.pdf - - s3://aryn-public/ntsb/53.pdf - - s3://aryn-public/ntsb/57.pdf - - s3://aryn-public/ntsb/50.pdf - - s3://aryn-public/ntsb/59.pdf - - s3://aryn-public/ntsb/60.pdf - - s3://aryn-public/ntsb/104.pdf - - s3://aryn-public/ntsb/62.pdf - - s3://aryn-public/ntsb/66.pdf - - s3://aryn-public/ntsb/63.pdf - - s3://aryn-public/ntsb/67.pdf - - s3://aryn-public/ntsb/73.pdf - - s3://aryn-public/ntsb/8.pdf - - s3://aryn-public/ntsb/86.pdf - - s3://aryn-public/ntsb/87.pdf - - s3://aryn-public/ntsb/18.pdf - - s3://aryn-public/ntsb/90.pdf - - s3://aryn-public/ntsb/26.pdf - - s3://aryn-public/ntsb/3.pdf - - s3://aryn-public/ntsb/48.pdf - - s3://aryn-public/ntsb/0.pdf - - s3://aryn-public/ntsb/6.pdf - - s3://aryn-public/ntsb/72.pdf - - s3://aryn-public/ntsb/74.pdf - - s3://aryn-public/ntsb/78.pdf - tags: - - medium - - - query: "What fraction of incidents that resulted in substantial damage were due to engine problems?" - expected: "0.81, or 52 out of 64" - expected_docs: *substantial_damage_docs - tags: - - hard diff --git a/apps/query-eval/poetry.lock b/apps/query-eval/poetry.lock deleted file mode 100644 index ace90c027..000000000 --- a/apps/query-eval/poetry.lock +++ /dev/null @@ -1,8678 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - -[[package]] -name = "absl-py" -version = "2.1.0" -description = "Abseil 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89fb9971e..000000000 --- a/apps/query-eval/pyproject.toml +++ /dev/null @@ -1,26 +0,0 @@ -[tool.poetry] -name = "queryeval" -version = "0.1.0" -description = "" -authors = ["Matt Welsh "] -packages = [{include = "queryeval"}] - -[tool.poetry.dependencies] -python = ">=3.9,<3.9.7 || >3.9.7,<3.13" -sycamore-ai = { path = "../../lib/sycamore", develop = true, extras = ["opensearch", "local-inference"] } -ragas = { version = "^0.2.6", extras = ["all"] } -pydantic-yaml = "^1.3.0" -pydantic = "^2.8.2" -rich = "^13.7.1" -pyyaml = "^6.0.1" - -[tool.poetry.group.dev.dependencies] -black = "^24.4" -sycamore-poetry-lock = { path = "../../lib/poetry-lock", develop = true } - -[tool.black] -line-length = 120 - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" diff --git a/apps/query-eval/queryeval/__init__.py b/apps/query-eval/queryeval/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/query-eval/queryeval/driver.py b/apps/query-eval/queryeval/driver.py deleted file mode 100644 index b4d7a317f..000000000 --- a/apps/query-eval/queryeval/driver.py +++ /dev/null @@ -1,625 +0,0 @@ -import datetime -import logging -import os -import time -import traceback -from typing import List, Optional - -from pydantic_yaml import to_yaml_str -from rich.console import Console -from sycamore.data import Document -from sycamore.docset import DocSet -from sycamore.query.client import SycamoreQueryClient, configure_logging -from yaml import safe_load - -from queryeval.queryeval_types import ( - QueryEvalConfig, - QueryEvalInputFile, - QueryEvalMetrics, - QueryEvalQuery, - QueryEvalResult, - QueryEvalResultsFile, - DocumentSummary, - DocSetSummary, -) - -import asyncio -from ragas.dataset_schema import SingleTurnSample -from ragas.metrics import BleuScore, RougeScore, SemanticSimilarity -from ragas.embeddings.base import ( - OpenAIEmbeddings, - LangchainEmbeddingsWrapper, -) -from ragas.metrics._factual_correctness import FactualCorrectness -from langchain_openai.chat_models import ChatOpenAI -from ragas.llms import LangchainLLMWrapper - -console = Console() - - -def compute_text_metrics( - sample: SingleTurnSample, - rouge_scorer: RougeScore, - bleu_scorer: BleuScore, - semantic_similarity_scorer: SemanticSimilarity, - correctness_scorer: FactualCorrectness, -): - d = {} - try: - d["rouge"] = asyncio.run(rouge_scorer.single_turn_ascore(sample)) - except Exception: - tb = traceback.format_exc() - console.print(f"[red]Error computing ROUGE score: {tb}") - try: - d["bleu"] = asyncio.run(bleu_scorer.single_turn_ascore(sample)) - except Exception: - tb = traceback.format_exc() - console.print(f"[red]Error computing BLEU score: {tb}") - try: - d["semantic_similarity"] = asyncio.run(semantic_similarity_scorer.single_turn_ascore(sample)) - except Exception: - tb = traceback.format_exc() - console.print(f"[red]Error computing semantic similarity score: {tb}") - try: - d["correctness"] = asyncio.run(correctness_scorer.single_turn_ascore(sample)) - except Exception: - tb = traceback.format_exc() - console.print(f"[red]Error computing correctness score: {tb}") - return d - - -class QueryEvalDriver: - """Class to run Sycamore Query evaluations. - - Args: - input_file_path: Path to the input configuration file. Any options not specified in the - configuration file can be passed as parameters. - index: OpenSearch index name. - results_file: Path to the file where the results will be written. - log_file: Path to the file where detailed logging output will be written. - query_cache_path: Path to the query cache directory. If not specified, no query result caching - will be performed. - llm_cache_path: Path to the LLM cache directory. If not specified, no LLM result caching will be - performed. - dry_run: If True, do not perform any actions. This is useful for testing your configuration. - natural_language_response: If True, return the response in natural language format. Otherwise, - return the raw DocSet results. - doc_limit: Limit the number of documents in each result set to this number. - llm: LLM model name to use. - overwrite: If True, overwrite the results file if it already exists. - tags: List of tags to filter queries by. If empty, all queries will be run. - """ - - def __init__( - self, - input_file_path: str, - index: Optional[str] = None, - results_file: Optional[str] = None, - log_file: Optional[str] = None, - query_cache_path: Optional[str] = None, - llm_cache_path: Optional[str] = None, - dry_run: bool = False, - natural_language_response: bool = True, - doc_limit: Optional[int] = None, - llm: Optional[str] = None, - overwrite: bool = False, - tags: Optional[List[str]] = None, - ): - console.print(":moon: Sycamore Query Eval Driver starting") - console.print(f"Reading input file: [green]{input_file_path}") - self.input_file_path = os.path.abspath(input_file_path) - self.config = QueryEvalDriver.read_input_file(self.input_file_path) - - # Override any config options passed as parameters. - self.config.config = self.config.config or QueryEvalConfig() - self.config.config.config_file = self.input_file_path - self.config.config.results_file = self.config.config.results_file or ( - os.path.abspath(results_file) if results_file else None - ) - self.config.config.log_file = self.config.config.log_file or (os.path.abspath(log_file) if log_file else None) - self.config.config.index = self.config.config.index or index - self.config.config.query_cache_path = self.config.config.query_cache_path or query_cache_path - self.config.config.llm_cache_path = self.config.config.llm_cache_path or llm_cache_path - self.config.config.dry_run = self.config.config.dry_run or dry_run - self.config.config.natural_language_response = ( - self.config.config.natural_language_response or natural_language_response - ) - self.config.config.doc_limit = self.config.config.doc_limit or doc_limit - self.config.config.llm = self.config.config.llm or llm - self.config.config.overwrite = self.config.config.overwrite or overwrite - self.config.config.tags = self.config.config.tags or tags - if self.config.config.tags: - console.print(f":label: Filtering queries by tags: {self.config.config.tags}") - - # Configure logging. - if self.config.config.log_file: - os.makedirs( - os.path.dirname(os.path.abspath(self.config.config.log_file)), - exist_ok=True, - ) - configure_logging(logfile=self.config.config.log_file, log_level=logging.INFO) - - if not self.config.config.index: - raise ValueError("Index must be specified") - if not self.config.config.results_file: - raise ValueError("Results file must be specified") - - if self.config.config.results_file: - console.print(f"Writing results to: {self.config.config.results_file}") - os.makedirs( - os.path.dirname(os.path.abspath(self.config.config.results_file)), - exist_ok=True, - ) - - # Read results file if it exists. - if ( - not self.config.config.overwrite - and self.config.config.results_file - and os.path.exists(self.config.config.results_file) - ): - results = self.read_results_file(self.config.config.results_file) - console.print( - f":white_check_mark: Read {len(results.results or [])} " - + f"existing results from {self.config.config.results_file}" - ) - else: - results = QueryEvalResultsFile(config=self.config.config, results=[]) - - # Build lookup from query string to result object. - results.results = results.results or [] - self.results_map = {r.query.query: r for r in results.results} - - # Set up Sycamore Query Client. - self.client = SycamoreQueryClient( - llm_cache_dir=self.config.config.llm_cache_path, - cache_dir=self.config.config.query_cache_path, - llm=self.config.config.llm, - ) - - # Use schema from the results file, input file, or OpenSearch, in that order. - if results.data_schema: - self.data_schema = results.data_schema - if self.config.data_schema: - self.data_schema = self.config.data_schema - else: - self.data_schema = self.client.get_opensearch_schema(self.config.config.index) - - console.print(f"Data schema:\n{self.data_schema}") - - # Use examples from the results file, or input file. Priority is given to the input file. - self.examples = None - if results.examples: - self.examples = results.examples - if self.config.examples: - self.examples = self.config.examples - - # Define scorers. - self.bleu_scorer = BleuScore() - self.rouge_scorer = RougeScore() - self.semantic_similarity_scorer = SemanticSimilarity() - self.semantic_similarity_scorer.embeddings = LangchainEmbeddingsWrapper( - OpenAIEmbeddings(model="text-embedding-3-small") - ) - self.correctness_scorer = FactualCorrectness() - self.correctness_scorer.llm = LangchainLLMWrapper(ChatOpenAI(model_name="gpt-4o")) - - @staticmethod - def read_input_file(input_file_path: str) -> QueryEvalInputFile: - """Read the given input file.""" - with open(input_file_path, "r", encoding="utf8") as input_file: - retval = safe_load(input_file) - return QueryEvalInputFile(**retval) - - @staticmethod - def read_results_file(results_file_path: str) -> QueryEvalResultsFile: - """Read the given results file.""" - with open(results_file_path, "r", encoding="utf8") as results_file: - retval = safe_load(results_file) - if isinstance(retval, dict): - return QueryEvalResultsFile(**retval) - else: - return QueryEvalResultsFile(config=QueryEvalConfig(), results=[]) - - def write_results_file(self): - """Write the results to the results file.""" - if self.config.config.dry_run: - console.print("[yellow]:point_right: Dry run: skipping writing results file") - return - - assert self.config.config and self.config.config.results_file - - results_file_obj = QueryEvalResultsFile( - config=self.config.config, - data_schema=self.data_schema, - results=list(self.results_map.values()), - examples=self.examples, - ) - - with open(self.config.config.results_file, "w", encoding="utf8") as results_file: - results_file.write(to_yaml_str(results_file_obj)) - console.print(f":white_check_mark: Wrote {len(self.results_map)} results to {self.config.config.results_file}") - - def format_doclist(self, doclist: List[Document]) -> DocSetSummary: - """Convert a document list query result to a list of dicts.""" - results = [] - for doc in doclist: - if hasattr(doc, "data"): - if hasattr(doc.data, "model_dump"): - results.append(doc.data.model_dump()) - else: - BASE_PROPS = [ - "filename", - "filetype", - "page_number", - "page_numbers", - "links", - "element_id", - "parent_id", - "_schema", - "_schema_class", - "entity", - ] - props_dict = doc.properties.get("entity", {}) - props_dict.update({p: doc.properties[p] for p in set(doc.properties) - set(BASE_PROPS)}) - results.append( - DocumentSummary( - doc_id=doc.doc_id, - path=doc.properties.get("path"), - text_representation=doc.text_representation, - properties=props_dict, - ) - ) - return DocSetSummary(docs=results) - - def get_result(self, query: QueryEvalQuery) -> Optional[QueryEvalResult]: - """Get the existing result for the query, or return a new result object.""" - if query.query in self.results_map: - return self.results_map.get(query.query) - - result = QueryEvalResult(query=query) - result.timestamp = datetime.datetime.now(datetime.timezone.utc).isoformat() - result.metrics = result.metrics or QueryEvalMetrics() - self.results_map[query.query] = result - return result - - def _check_tags_match(self, query): - if not self.config.config or not self.config.config.tags: - return True - if not query.tags: - return False - return len(set.intersection(set(self.config.config.tags), set(query.tags or []))) > 0 - - def do_plan(self, query: QueryEvalQuery, result: QueryEvalResult) -> QueryEvalResult: - """Generate or return an existing query plan.""" - if self.config.config: - if self.config.config.dry_run: - console.print("[yellow]:point_right: Dry run: skipping plan generation") - return result - elif not self._check_tags_match(query): - console.print("[yellow]:point_right: Skipping query due to tag mismatch") - return result - - if result.plan: - # Use existing result plan. - console.print("[blue]:point_right: Using existing query plan from results file") - elif query.plan: - # Use plan from input file. - result.plan = query.plan - console.print("[blue]:point_right: Using existing query plan from input file") - else: - # Generate a plan. - assert self.config.config - assert self.config.config.index - t1 = time.time() - plan = self.client.generate_plan( - query.query, - self.config.config.index, - self.data_schema, - examples=self.examples or None, - natural_language_response=self.config.config.natural_language_response or True, - ) - t2 = time.time() - assert result.metrics - result.metrics.plan_generation_time = t2 - t1 - - # For now, remove these fields from the output plan, since they are verbose - # and not particularly useful for the results file. - plan.llm_plan = None - plan.llm_prompt = None - result.plan = plan - result.error = None - console.print(f"[green]:clock1: Generated query plan in {result.metrics.plan_generation_time:.2f} seconds") - console.print(result.plan) - - return result - - def do_query(self, query: QueryEvalQuery, result: QueryEvalResult) -> QueryEvalResult: - """Run query plan.""" - if self.config.config: - if self.config.config.dry_run: - console.print("[yellow]:point_right: Dry run: skipping query execution") - return result - elif not self._check_tags_match(query): - console.print("[yellow]:point_right: Skipping query due to tag mismatch") - return result - - if not result.plan: - console.print("[red]:heavy_exclamation_mark: No plan available - skipping query execution") - return result - - t1 = time.time() - result.error = None - query_result = self.client.run_plan(result.plan) - if isinstance(query_result.result, str): - result.result = query_result.result - t2 = time.time() - elif isinstance(query_result.result, DocSet): - assert self.config.config - if self.config.config.doc_limit: - query_result.result = query_result.result.take(self.config.config.doc_limit) - else: - query_result.result = query_result.result.take_all() - t2 = time.time() - result.result = self.format_doclist(query_result.result) - else: - result.result = str(query_result.result) - t2 = time.time() - assert result.metrics - result.metrics.query_time = t2 - t1 - try: - result.retrieved_docs = query_result.retrieved_docs() - except Exception: - result.retrieved_docs = None - - console.print(f"[green]:clock9: Executed query in {result.metrics.query_time:.2f} seconds") - console.print(f":white_check_mark: Result: {result.result}") - return result - - def get_query_plan_metrics( - self, query: QueryEvalQuery, result: QueryEvalResult, metrics: QueryEvalMetrics - ) -> QueryEvalMetrics: - if not query.expected_plan: - console.print("[yellow]:construction: No expected query plan found, skipping.. ") - elif not result.plan: - console.print("[yellow]:construction: No computed query plan found, skipping.. ") - else: - plan_diff = query.expected_plan.compare(result.plan) - if len(plan_diff) == 0: - console.print("[green]✔ Plan match") - metrics.plan_similarity = 1.0 - metrics.plan_diff_count = 0 - else: - console.print("[red]:x: Plan mismatch") - for i, diff in enumerate(plan_diff): - console.print(f"[{i}]. Diff type: {diff.diff_type.value}") - - if diff.message: - console.print(f"Info: {diff.message}") - console.print(f"Expected node: {diff.node_a!r}") - console.print(f"Actual node: [red]{diff.node_b!r}") - console.print() - metrics.plan_similarity = max( - 0.0, - (len(query.expected_plan.nodes) - len(plan_diff)) / len(query.expected_plan.nodes), - ) - metrics.plan_diff_count = len(plan_diff) - return metrics - - def get_retrieval_metrics( - self, query: QueryEvalQuery, result: QueryEvalResult, metrics: QueryEvalMetrics - ) -> QueryEvalMetrics: - if not query.expected_docs: - console.print("[yellow]:construction: No expected document list found, skipping.. ") - elif not result.retrieved_docs: - console.print("[yellow]:construction: No computed document list found, skipping.. ") - else: - expected_doc_set = set(query.expected_docs) - retrieved_doc_set = set(result.retrieved_docs) - if expected_doc_set.issubset(retrieved_doc_set): - console.print("[green]✔ Documents retrieved include expected docs") - else: - console.print("[red]:x: Documents retrieved don't include all expected docs") - console.print(f"Missing docs: {expected_doc_set - retrieved_doc_set})") - metrics.doc_retrieval_recall = len(retrieved_doc_set & expected_doc_set) / len(expected_doc_set) - metrics.doc_retrieval_precision = len(retrieved_doc_set & expected_doc_set) / len(retrieved_doc_set) - return metrics - - def get_answer_metrics( - self, query: QueryEvalQuery, result: QueryEvalResult, metrics: QueryEvalMetrics - ) -> QueryEvalMetrics: - if not query.expected: - console.print("[yellow]:construction: No expected response found, skipping.. ") - elif not result.result: - console.print( - "[yellow] No query execution result available, skipping..", - style="italic", - ) - else: - if isinstance(query.expected, str) and isinstance(result.result, str): - sample = SingleTurnSample( - response=result.result, - reference=query.expected, - ) - scores = compute_text_metrics( - sample, - self.rouge_scorer, - self.bleu_scorer, - self.semantic_similarity_scorer, - self.correctness_scorer, - ) - metrics.bleu_score = scores.get("bleu", None) - metrics.rouge_score = scores.get("rouge", None) - metrics.similarity_score = scores.get("semantic_similarity", None) - metrics.correctness_score = scores.get("correctness", None) - console.print("[green]✔ Text metrics computed.") - elif isinstance(query.expected, str) and isinstance(result.result, DocSetSummary): - pass - else: - console.print("[red]:x: Unsupported expected/response type, skipping.. ") - return metrics - - def do_eval( - self, - query: QueryEvalQuery, - result: QueryEvalResult, - ) -> QueryEvalResult: - """Run query evaluation.""" - if self.config.config: - if self.config.config.dry_run: - console.print("[yellow]:point_right: Dry run: skipping eval") - return result - elif not self._check_tags_match(query): - console.print("[yellow]:point_right: Skipping query due to tag mismatch") - return result - - metrics = result.metrics or QueryEvalMetrics() - - # Evalute query plans - metrics = self.get_query_plan_metrics(query, result, metrics) - - # Evaluate doc retrieval - metrics = self.get_retrieval_metrics(query, result, metrics) - - # Evaluate text metrics - metrics = self.get_answer_metrics(query, result, metrics) - - result.metrics = metrics - - return result - - def plan_all(self): - """Run the plan stage. All queries without existing plans will have new plans generated.""" - for index, query in enumerate(self.config.queries): - try: - if not self._check_tags_match(query): - console.print("[yellow]:point_right: Skipping query due to tag mismatch") - continue - console.rule(f"Planning query [{index+1}/{len(self.config.queries)}]: {query.query}") - result = self.get_result(query) - result = self.do_plan(query, result) - except Exception: - tb = traceback.format_exc() - console.print(f"[red]Error generating plan: {tb}") - result.error = f"Error generating plan: {tb}" - self.write_results_file() - console.print(":tada: Done!") - - def query_all(self): - """Run the query stage.""" - for index, query in enumerate(self.config.queries): - try: - if not self._check_tags_match(query): - console.print("[yellow]:point_right: Skipping query due to tag mismatch") - continue - console.rule(f"Running query [{index+1}/{len(self.config.queries)}]: {query.query}") - result = self.get_result(query) - result = self.do_query(query, result) - except Exception: - tb = traceback.format_exc() - console.print(f"[red]Error running query: {tb}") - result.error = f"Error running query: {tb}" - self.write_results_file() - console.print(":tada: Done!") - - def print_metrics_summary(self): - """Summarize metrics.""" - console.rule("Evaluation summary") - - # Plan metrics - plan_correct = sum(1 for result in self.results_map.values() if result.metrics.plan_similarity == 1.0) - console.print(f"Plans correct: {plan_correct}/{len(self.results_map)}") - average_plan_correctness = sum( - result.metrics.plan_similarity for result in self.results_map.values() if result.metrics.plan_similarity - ) / len(self.results_map) - console.print(f"Avg. plan correctness: {average_plan_correctness}") - console.print( - "Avg. plan diff count: " - + str( - sum( - result.metrics.plan_diff_count - for result in self.results_map.values() - if result.metrics.plan_diff_count - ) - / len(self.results_map) - ) - ) - - # Evaluate doc retrieval - correct_retrievals = sum( - 1 for result in self.results_map.values() if result.metrics.doc_retrieval_recall == 1.0 - ) - expected_retrievals = sum(1 for result in self.results_map.values() if result.query.expected_docs) - average_precision = ( - sum( - result.metrics.doc_retrieval_precision - for result in self.results_map.values() - if result.metrics.doc_retrieval_precision - ) - / expected_retrievals - if expected_retrievals - else 0 - ) - console.print(f"Successful doc retrievals: {correct_retrievals}/{expected_retrievals}") - console.print(f"Average precision: {average_precision}") - - # Text metrics - bleu_scores = [result.metrics.bleu_score for result in self.results_map.values() if result.metrics.bleu_score] - rouge_scores = [ - result.metrics.rouge_score for result in self.results_map.values() if result.metrics.rouge_score - ] - similarity_scores = [ - result.metrics.similarity_score for result in self.results_map.values() if result.metrics.similarity_score - ] - correctness_scores = [ - result.metrics.correctness_score for result in self.results_map.values() if result.metrics.correctness_score - ] - avg_bleu_score = sum(bleu_scores) / len(bleu_scores) if bleu_scores else 0 - avg_rouge_score = sum(rouge_scores) / len(rouge_scores) if rouge_scores else 0 - avg_similarity_score = sum(similarity_scores) / len(similarity_scores) if similarity_scores else 0 - avg_correctness_score = sum(correctness_scores) / len(correctness_scores) if correctness_scores else 0 - console.print(f"Avg. BLEU score: {avg_bleu_score}") - console.print(f"Avg. ROUGE score: {avg_rouge_score}") - console.print(f"Avg. Semantic similarity score: {avg_similarity_score}") - console.print(f"Avg. Correctness score: {avg_correctness_score}") - - # TODO: Query execution metrics - console.print("Query result correctness: not implemented") - - def eval_all(self): - """Run the eval stage.""" - for index, query in enumerate(self.config.queries): - try: - if not self._check_tags_match(query): - console.print("[yellow]:point_right: Skipping query due to tag mismatch") - continue - console.rule(f"Evaluating query [{index+1}/{len(self.config.queries)}]: {query.query}") - result = self.get_result(query) - result = self.do_eval(query, result) - except Exception: - tb = traceback.format_exc() - console.print(f"[red]Error running eval: {tb}") - result.error = f"Error running eval: {tb}" - self.write_results_file() - self.print_metrics_summary() - - console.print(":tada: Done!") - - def run(self): - """Run all stages.""" - for index, query in enumerate(self.config.queries): - try: - if not self._check_tags_match(query): - console.print("[yellow]:point_right: Skipping query due to tag mismatch") - continue - console.rule(f"Running [{index+1}/{len(self.config.queries)}]: {query.query}") - result = self.get_result(query) - result = self.do_plan(query, result) - result = self.do_query(query, result) - result = self.do_eval(query, result) - except Exception: - tb = traceback.format_exc() - console.print(f"[red]Error: {tb}") - result.error = f"Error: {tb}" - self.write_results_file() - self.print_metrics_summary() - console.print(":tada: Done!") diff --git a/apps/query-eval/queryeval/main.py b/apps/query-eval/queryeval/main.py deleted file mode 100644 index c8fd618ed..000000000 --- a/apps/query-eval/queryeval/main.py +++ /dev/null @@ -1,136 +0,0 @@ -#!/usr/bin/env python - -# This is the CLI for the Sycamore Query Evaluation app, which benchmarks the -# performance of Sycamore Query for generating query plans and/or answering analytical questions -# from a dataset. -# -# Usage: -# poetry run python queryeval/main.py \ -# --outfile results.yaml \ -# --index const_ntsb \ -# data/ntsb-queries.yaml \ -# run - -import tempfile -from typing import Optional, Tuple - -import click -from rich.console import Console - - -from sycamore.llms import MODELS -from queryeval.driver import QueryEvalDriver -import nltk - - -console = Console() - -try: - nltk.data.find("tokenizers/punkt_tab") - console.print("The 'punkt_tab' tokenizer data is already downloaded.") -except LookupError: - console.print("The 'punkt_tab' tokenizer data is not found. Downloading now...") - nltk.download("punkt_tab") - console.print("The 'punkt_tab' tokenizer data has been downloaded.") - - -@click.group() -@click.argument("config-file", type=click.Path(exists=True)) -@click.option("--index", help="OpenSearch index name") -@click.option("--outfile", help="Output file", required=True) -@click.option("--logfile", help="Detailed log file") -@click.option("--query-cache-path", help="Query cache path") -@click.option("--llm-cache-path", help="LLM cache path") -@click.option("--dry-run", help="Dry run - do not run any stages", is_flag=True) -@click.option("--doc-limit", help="Limit number of docs in result set", type=int) -@click.option("--overwrite", help="Overwrite existing results file", is_flag=True) -@click.option("--llm", help="LLM model name", type=click.Choice(list(MODELS.keys()))) -@click.option("--tags", help="Filter queries by the given tags", multiple=True) -@click.option( - "--raw-output", - help="Output should be a raw DocSet, rather than natural language", - is_flag=True, - default=False, -) -@click.pass_context -def cli( - ctx, - config_file: str, - index: str, - outfile: str, - logfile: str, - query_cache_path: Optional[str], - llm_cache_path: Optional[str], - dry_run: bool, - doc_limit: Optional[int], - overwrite: bool, - llm: Optional[str], - tags: Optional[Tuple[str]], - raw_output: bool, -): - ctx.ensure_object(dict) - - if not query_cache_path: - query_cache_path = tempfile.mkdtemp() - console.print(f"[yellow]Using query cache path: {query_cache_path}") - - driver = QueryEvalDriver( - input_file_path=config_file, - index=index, - results_file=outfile, - query_cache_path=query_cache_path, - llm_cache_path=llm_cache_path, - dry_run=dry_run, - natural_language_response=not raw_output, - log_file=logfile, - doc_limit=doc_limit, - llm=llm, - overwrite=overwrite, - tags=list(tags) if tags else None, - ) - ctx.obj["driver"] = driver - - -@cli.command() -@click.pass_context -def run( - ctx, -): - """Run all query eval stages: plan, query, and eval.""" - driver = ctx.obj["driver"] - driver.run() - - -@cli.command() -@click.pass_context -def plan( - ctx, -): - """Run the plan stage.""" - driver = ctx.obj["driver"] - driver.plan_all() - - -@cli.command() -@click.pass_context -def query( - ctx, -): - """Run the query stage.""" - driver = ctx.obj["driver"] - driver.query_all() - - -@cli.command() -@click.pass_context -def eval( # pylint: disable=redefined-builtin - ctx, -): - """Run the eval stage.""" - driver = ctx.obj["driver"] - driver.eval_all() - - -if __name__ == "__main__": - # pylint: disable=no-value-for-parameter - cli() diff --git a/apps/query-eval/queryeval/queryeval_types.py b/apps/query-eval/queryeval/queryeval_types.py deleted file mode 100644 index 6a4cc4971..000000000 --- a/apps/query-eval/queryeval/queryeval_types.py +++ /dev/null @@ -1,108 +0,0 @@ -# This module defines types used for the config, input, and output -# files for the Sycamore Query evaluator. - -from typing import Any, Dict, List, Optional, Union - -from pydantic import BaseModel - -from sycamore.query.logical_plan import LogicalPlan -from sycamore.query.schema import OpenSearchSchema -from sycamore.query.planner import PlannerExample -from sycamore.schema import Schema - - -class QueryEvalConfig(BaseModel): - """Represents the configuration for a Query Eval run.""" - - config_file: Optional[str] = None - results_file: Optional[str] = None - log_file: Optional[str] = None - llm: Optional[str] = None - index: Optional[str] = None - query_cache_path: Optional[str] = None - llm_cache_path: Optional[str] = None - dry_run: Optional[bool] = False - natural_language_response: Optional[bool] = True - doc_limit: Optional[int] = None - overwrite: Optional[bool] = False - tags: Optional[List[str]] = None - - -class QueryEvalQuery(BaseModel): - """Represents a single query and expected response.""" - - query: str - expected: Optional[Union[str, List[Dict[str, Any]]]] = None - expected_plan: Optional[LogicalPlan] = None - expected_docs: Optional[list[str]] = None - plan: Optional[LogicalPlan] = None - tags: Optional[List[str]] = None - notes: Optional[str] = None - - -class QueryEvalInputFile(BaseModel): - """Represents the format of the query eval input file.""" - - config: Optional[QueryEvalConfig] = None - data_schema: Optional[OpenSearchSchema] = None - examples: Optional[List[PlannerExample]] = None - queries: List[QueryEvalQuery] - - -class QueryEvalMetrics(BaseModel): - """Represents metrics associated with a result.""" - - # Plan metrics - plan_generation_time: Optional[float] = None - plan_similarity: Optional[float] = None - plan_diff_count: Optional[int] = None - - # Documenet retrieval metrics - doc_retrieval_recall: Optional[float] = None - doc_retrieval_precision: Optional[float] = None - - # Performance metrics - query_time: Optional[float] = None - - # String answer metrics - bleu_score: Optional[float] = None - rouge_score: Optional[float] = None - similarity_score: Optional[float] = None - correctness_score: Optional[float] = None - - -class DocumentSummary(BaseModel): - """Represents a serializable Document summary.""" - - doc_id: Optional[str] = None - text_representation: Optional[str] = None - path: Optional[str] = None - properties: Optional[Dict[str, Any]] = None - - -class DocSetSummary(BaseModel): - """Represents a serializable DocSet.""" - - docs: list[Any] = [] - - -class QueryEvalResult(BaseModel): - """Represents a single result for running a query.""" - - timestamp: Optional[str] = None - query: QueryEvalQuery - plan: Optional[LogicalPlan] = None - result: Optional[Union[str, DocSetSummary]] = None - error: Optional[str] = None - metrics: Optional[QueryEvalMetrics] = None - notes: Optional[str] = None - retrieved_docs: Optional[list[str]] = None - - -class QueryEvalResultsFile(BaseModel): - """Represents the format of the query eval results file.""" - - config: QueryEvalConfig - data_schema: Optional[Union[Schema, OpenSearchSchema]] = None - examples: Optional[List[PlannerExample]] = None - results: Optional[List[QueryEvalResult]] = None diff --git a/apps/query-eval/queryeval/test-results.yaml b/apps/query-eval/queryeval/test-results.yaml deleted file mode 100644 index 232e4cb07..000000000 --- a/apps/query-eval/queryeval/test-results.yaml +++ /dev/null @@ -1,74 +0,0 @@ -config: - config_file: /private/var/folders/rd/5lfl86jj2yb3gn2zwwgqjgrc0000gn/T/pytest-of-vinayakthapliyal/pytest-110/test_driver_do_query0/test_input.yaml - doc_limit: null - dry_run: false - index: test-index - llm: null - llm_cache_path: null - log_file: null - natural_language_response: true - overwrite: false - query_cache_path: null - results_file: test-results.yaml - tags: null -data_schema: - fields: - field1: - description: text - examples: null - field_type: - field2: - description: keyword - examples: null - field_type: -examples: null -results: -- error: null - metrics: - correctness_score: null - doc_retrieval_precision: null - doc_retrieval_recall: null - plan_diff_count: null - plan_generation_time: null - plan_similarity: null - query_time: null - similarity_score: null - notes: null - plan: null - query: - expected: null - expected_docs: - - doc1.pdf - - doc2.pdf - expected_plan: null - notes: null - plan: null - query: test query 1 - tags: - - test - result: null - retrieved_docs: null - timestamp: '2024-11-05T02:46:16.861230+00:00' -- error: null - metrics: - correctness_score: null - doc_retrieval_precision: null - doc_retrieval_recall: null - plan_diff_count: null - plan_generation_time: null - plan_similarity: null - query_time: null - similarity_score: null - notes: null - plan: null - query: - expected: null - expected_docs: null - expected_plan: null - notes: null - plan: null - query: test query 2 - tags: null - result: null - retrieved_docs: null - timestamp: '2024-11-05T02:46:16.861896+00:00' diff --git a/apps/query-eval/queryeval/test_driver.py b/apps/query-eval/queryeval/test_driver.py deleted file mode 100644 index 383d094a3..000000000 --- a/apps/query-eval/queryeval/test_driver.py +++ /dev/null @@ -1,109 +0,0 @@ -import pytest -from unittest.mock import MagicMock, patch - -from sycamore.query.schema import OpenSearchSchema, OpenSearchSchemaField - -from queryeval.driver import QueryEvalDriver -from queryeval.queryeval_types import ( - QueryEvalQuery, - QueryEvalResult, - QueryEvalMetrics, -) -from sycamore.query.result import SycamoreQueryResult -from sycamore.query.logical_plan import LogicalPlan, Node - - -@pytest.fixture -def mock_client(): - client = MagicMock() - schema = OpenSearchSchema( - fields={ - "field1": OpenSearchSchemaField(field_type="", description="text"), - "field2": OpenSearchSchemaField(field_type="", description="keyword"), - } - ) - client.get_opensearch_schema.return_value = schema - return client - - -@pytest.fixture -def mock_plan(): - return LogicalPlan(query="test query", nodes={0: Node(node_id=0, node_type="test_op")}, result_node=0) - - -@pytest.fixture -def test_input_file(tmp_path): - input_file = tmp_path / "test_input.yaml" - input_file.write_text( - """ -config: - - index: test-index - results_file: test-results.yaml -queries: - - query: "test query 1" - tags: ["test"] - expected_docs: ["doc1.pdf", "doc2.pdf"] - - query: "test query 2" -""" - ) - return str(input_file) - - -def test_driver_init(test_input_file, mock_client): - with patch("queryeval.driver.SycamoreQueryClient", return_value=mock_client): - driver = QueryEvalDriver(input_file_path=test_input_file, index="test-index", doc_limit=10, tags=["test"]) - - assert driver.config.config.index == "test-index" - assert driver.config.config.doc_limit == 10 - assert driver.config.queries[0].tags == ["test"] - assert driver.config.queries[1].tags is None - assert driver.config.queries[0].expected_docs == ["doc1.pdf", "doc2.pdf"] - assert driver.config.queries[1].expected_docs is None - - -def test_driver_do_plan(test_input_file, mock_client, mock_plan): - with patch("queryeval.driver.SycamoreQueryClient", return_value=mock_client): - driver = QueryEvalDriver(input_file_path=test_input_file) - - query = QueryEvalQuery(query="test query") - result = QueryEvalResult(query=query, metrics=QueryEvalMetrics()) - - mock_client.generate_plan.return_value = mock_plan - - result = driver.do_plan(query, result) - assert result.plan is not None - mock_client.generate_plan.assert_called_once() - - -def test_driver_do_query(test_input_file, mock_client, mock_plan): - with patch("queryeval.driver.SycamoreQueryClient", return_value=mock_client): - driver = QueryEvalDriver(input_file_path=test_input_file) - - query = QueryEvalQuery(query="test query") - result = QueryEvalResult(query=query, plan=mock_plan, metrics=QueryEvalMetrics()) - - mock_query_result = SycamoreQueryResult(query_id="test", plan=result.plan, result="test result") - mock_client.run_plan.return_value = mock_query_result - - result = driver.do_query(query, result) - driver.eval_all() # we are only asserting that this runs - assert result.result == "test result" - - -def test_driver_do_eval(test_input_file, mock_client, mock_plan): - with patch("queryeval.driver.SycamoreQueryClient", return_value=mock_client): - driver = QueryEvalDriver(input_file_path=test_input_file) - expected_docs = {"doc1.pdf", "doc2.pdf"} - query = QueryEvalQuery(query="test query", expected_plan=mock_plan, expected_docs=expected_docs) - result = QueryEvalResult(query=query, plan=mock_plan, retrieved_docs=expected_docs) - - result = driver.do_eval(query, result) - assert result.metrics.plan_similarity == 1.0 - assert result.metrics.doc_retrieval_recall == 1.0 - - result = QueryEvalResult(query=query, plan=mock_plan, retrieved_docs={"doc1.pdf"}) - - result = driver.do_eval(query, result) - assert result.metrics.plan_similarity == 1.0 - assert result.metrics.doc_retrieval_recall == 0.5 diff --git a/apps/query-server/README.md b/apps/query-server/README.md deleted file mode 100644 index 2e3e9f969..000000000 --- a/apps/query-server/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Sycamore Query Server - -This is a simple server that provides a REST API wrapper to the Sycamore Query -functionality found in the `sycamore.query` package. - -It currently assumes that OpenSearch is running locally. This code performs no authentication, -so any OpenSearch index available for local querying will be accessible through this server. - -To build and run the server: -```bash -$ poetry install -$ poetry run fastapi dev queryserver/main.py -``` diff --git a/apps/query-server/poetry.lock b/apps/query-server/poetry.lock deleted file mode 100644 index ce7ed2172..000000000 --- a/apps/query-server/poetry.lock +++ /dev/null @@ -1,7783 +0,0 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. - -[[package]] -name = "aiohappyeyeballs" -version = "2.4.3" -description = "Happy Eyeballs for asyncio" -optional = false -python-versions = ">=3.8" -files = [ - {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"}, - {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"}, -] - -[[package]] -name = "aiohttp" -version = "3.10.10" -description = "Async http client/server framework (asyncio)" -optional = false -python-versions = ">=3.8" -files = [ - 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-[tool.poetry.dependencies] -python = ">=3.9,<3.13" -sycamore-ai = { path = "../../lib/sycamore", develop = true, extras = ["opensearch", "local-inference"] } -fastapi = { version = "^0.115.0", extras = ["standard"]} -pydantic = "^2.9.2" -sse-starlette = "^2.1.3" - -[tool.poetry.group.dev.dependencies] -black = "^24.4" -sycamore-poetry-lock = { path = "../../lib/poetry-lock", develop = true } - -[tool.black] -line-length = 120 - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" diff --git a/apps/query-server/queryserver/__init__.py b/apps/query-server/queryserver/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/query-server/queryserver/main.py b/apps/query-server/queryserver/main.py deleted file mode 100644 index 48a62a37a..000000000 --- a/apps/query-server/queryserver/main.py +++ /dev/null @@ -1,259 +0,0 @@ -# This is a FastAPI server that wraps Sycamore Query in a REST service. -# -# Run with: -# poetry run fastapi dev queryserver/main.py - -import asyncio -import logging -import os -import tempfile -import time -from typing import Annotated, Any, List, Optional, Union - -from fastapi import FastAPI, Path -from pydantic import BaseModel, model_validator -from sse_starlette.sse import EventSourceResponse -from sycamore import DocSet -from sycamore.data import Document, MetadataDocument -from sycamore.query.client import SycamoreQueryClient -from sycamore.query.logical_plan import LogicalPlan -from sycamore.schema import Schema - -# This is the uvicorn general logger, the error name is misleading. -# https://github.com/encode/uvicorn/issues/562 -logger = logging.getLogger("uvicorn.error") - - -app = FastAPI() - -# The query and LLM cache paths. -CACHE_PATH = os.getenv("QUERYSERVER_CACHE_PATH", os.path.join(tempfile.gettempdir(), "queryserver_cache")) -LLM_CACHE_PATH = os.getenv("QUERYSERVER_LLM_CACHE_PATH", os.path.join(tempfile.gettempdir(), "queryserver_llm_cache")) - -sqclient = SycamoreQueryClient(llm_cache_dir=LLM_CACHE_PATH, cache_dir=CACHE_PATH) - - -class Index(BaseModel): - """Represents an index that can be queried.""" - - index: str - """The index name.""" - - description: Optional[str] = None - """Description of the index.""" - - index_schema: Schema - """The schema for this index.""" - - -class Query(BaseModel): - """Query an index with a given natural language query string. One of 'query' or 'plan' must be provided.""" - - index: str - """The index to query.""" - - query: Optional[str] = None - """The natural language query to run. if specified, `plan` must not be set.""" - - plan: Optional[LogicalPlan] = None - """The logical query plan to run. If specified, `query` must not be set.""" - - stream: bool = False - """If true, query results will be streamed back to the client as they are generated.""" - - @model_validator(mode="after") - def check_not_both_query_and_plan(self): - if self.query is not None and self.plan is not None: - raise ValueError("query and plan cannot both be specified") - if self.query is None and self.plan is None: - raise ValueError("one of query or plan is required") - return self - - -class QueryResult(BaseModel): - """The result of a non-streaming query.""" - - query_id: str - """The unique ID of the query operation.""" - - plan: LogicalPlan - """The logical query plan that was executed.""" - - result: Any - """The result of the query operation. Depending on the query, this could be a list of documents, - a single document, a string, an integer, etc. - """ - - retrieved_docs: List[str] - """A list of document paths for the documents retrieved by the query.""" - - -@app.get("/v1/indices") -async def list_indices() -> List[Index]: - """List all available indices.""" - - retval = [] - # Exclude the 'internal' indices that start with a dot. - indices = {x for x in sqclient.get_opensearch_indices() if not x.startswith(".")} - for index in indices: - index_schema = sqclient.get_opensearch_schema(index) - retval.append(Index(index=index, index_schema=index_schema)) - return retval - - -@app.get("/v1/index/{index}") -async def get_index( - index: Annotated[str, Path(title="The ID of the index to get")], -) -> Index: - """Return details on the given index.""" - - schema = sqclient.get_opensearch_schema(index) - return Index(index=index, index_schema=schema) - - -@app.post("/v1/plan") -async def generate_plan(query: Query) -> LogicalPlan: - """Generate a query plan for the given query, but do not run it.""" - - if query.query is None: - raise ValueError("query is required") - if query.plan is not None: - raise ValueError("plan must not be specified") - - plan = sqclient.generate_plan(query.query, query.index, sqclient.get_opensearch_schema(query.index)) - return plan - - -def doc_to_json(doc: Document) -> Optional[dict[str, Any]]: - """Render a Document as a JSON object. Only external properties and truncated text_representation - are included. Returns None for MetadataDocuments.""" - - NUM_TEXT_CHARS_GENERATE = 1024 - - if isinstance(doc, MetadataDocument): - return None - - props_dict = {} - props_dict.update(doc.properties) - props_dict.pop("_schema", None) - props_dict.pop("_schema_class", None) - props_dict.pop("_doc_source", None) - props_dict["text_representation"] = ( - doc.text_representation[:NUM_TEXT_CHARS_GENERATE] if doc.text_representation is not None else None - ) - return props_dict - - -async def run_query_stream(query: Query) -> EventSourceResponse: - """Streaming version of run_query. Returns a stream of results as they are generated.""" - - async def query_runner(): - try: - plan = query.plan - if plan is None: - logger.info(f"Generating plan for {query.index}: {query.query}") - yield { - "event": "status", - "data": "Generating plan", - } - await asyncio.sleep(0.1) - plan = sqclient.generate_plan(query.query, query.index, sqclient.get_opensearch_schema(query.index)) - logger.info(f"Generated plan: {plan}") - # Don't want to return these through the API. - plan.llm_plan = None - plan.llm_prompt = None - yield { - "event": "plan", - "data": plan.model_dump_json(), - } - await asyncio.sleep(0.1) - logger.info("Running plan") - yield { - "event": "status", - "data": "Running plan", - } - await asyncio.sleep(0.1) - sqresult = sqclient.run_plan(plan) - t1 = time.time() - num_results = 0 - if isinstance(sqresult.result, DocSet): - logger.info("Got DocSet result") - for doc in sqresult.result.take_all(): - rendered = doc_to_json(doc) - logger.debug(f"Doc: {rendered}") - if rendered is not None: - num_results += 1 - yield { - "event": "result_doc", - "data": rendered, - } - await asyncio.sleep(0.1) - else: - num_results += 1 - yield { - "event": "result", - "data": sqresult.result, - } - await asyncio.sleep(0.1) - - for doc in sqresult.retrieved_docs(): - yield { - "event": "retrieved_doc", - "data": doc, - } - await asyncio.sleep(0.1) - - t2 = time.time() - logger.info(f"Finished query in {t2 - t1:.2f} seconds with {num_results} results") - yield { - "event": "status", - "data": f"Query complete - {num_results} results in {t2 - t1:.2f} seconds", - } - await asyncio.sleep(0.1) - except asyncio.CancelledError: - logger.info("Disconnected from client") - - return EventSourceResponse(query_runner()) - - -@app.post("/v1/query", response_model=None) -async def run_query(query: Query) -> Union[EventSourceResponse, QueryResult]: - """Run the given query. - - If the `stream` parameter is set to true, the result will be streamed back to the client as a series of SSE events. - Otherwise, the result will be returned as a QueryResult object. - """ - - logger.info(f"Running query: {query}") - - if query.stream: - return await run_query_stream(query) - - if query.plan is None: - assert query.query is not None - logger.info(f"Generating plan for {query.index}: {query.query}") - plan = sqclient.generate_plan(query.query, query.index, sqclient.get_opensearch_schema(query.index)) - logger.info(f"Generated plan: {plan}") - else: - plan = query.plan - - sqresult = sqclient.run_plan(plan) - returned_plan = sqresult.plan - - # Don't want to return these through the API. - returned_plan.llm_plan = None - returned_plan.llm_prompt = None - - query_result = QueryResult(query_id=sqresult.query_id, plan=returned_plan, result=[], retrieved_docs=[]) - - if isinstance(sqresult.result, DocSet): - for doc in sqresult.result.take_all(): - rendered = doc_to_json(doc) - logger.debug(f"Doc: {rendered}") - if rendered is not None: - query_result.result.append(rendered) - else: - query_result.result = sqresult.result - - query_result.retrieved_docs = sqresult.retrieved_docs() - return query_result diff --git a/apps/query-server/queryserver/tests/test_queryserver.py b/apps/query-server/queryserver/tests/test_queryserver.py deleted file mode 100644 index d0fcd6e25..000000000 --- a/apps/query-server/queryserver/tests/test_queryserver.py +++ /dev/null @@ -1,182 +0,0 @@ -import json -from unittest.mock import MagicMock, patch - -import pytest -from fastapi.testclient import TestClient - -from queryserver.main import app, Query -from sycamore.query.logical_plan import LogicalPlan -from sycamore.query.result import SycamoreQueryResult -from sycamore.query.schema import OpenSearchSchema, OpenSearchSchemaField - - -@pytest.fixture -def test_client(): - return TestClient(app) - - -@pytest.fixture -def mock_schema(): - return OpenSearchSchema(fields={"text": OpenSearchSchemaField(field_type="string")}) - - -@pytest.fixture -def mock_indices(): - return ["test_index1", "test_index2"] - - -def test_list_indices(test_client, mock_schema, mock_indices): - with patch("queryserver.main.sqclient.get_opensearch_indices", return_value=mock_indices), patch( - "queryserver.main.sqclient.get_opensearch_schema", return_value=mock_schema - ): - response = test_client.get("/v1/indices") - assert response.status_code == 200 - - indices = response.json() - assert len(indices) == 2 - assert {index["index"] for index in indices} == {"test_index1", "test_index2"} - assert indices[0]["index_schema"] == mock_schema.model_dump() - assert indices[1]["index_schema"] == mock_schema.model_dump() - - -def test_get_index(test_client, mock_schema): - with patch("queryserver.main.sqclient.get_opensearch_schema", return_value=mock_schema): - response = test_client.get("/v1/index/test_index1") - assert response.status_code == 200 - - index = response.json() - assert index["index"] == "test_index1" - assert index["index_schema"] == mock_schema.model_dump() - - -def test_generate_plan(test_client, mock_schema): - mock_plan = LogicalPlan(query="test query", result_node=1, nodes={}) - with patch("queryserver.main.sqclient.generate_plan", return_value=mock_plan): - with patch("queryserver.main.sqclient.get_opensearch_schema", return_value=mock_schema): - query = Query(query="test query", index="test_index") - response = test_client.post("/v1/plan", json=query.model_dump()) - - assert response.status_code == 200 - assert response.json() == mock_plan.model_dump() - - -def test_generate_plan_with_no_query(test_client): - response = test_client.post("/v1/plan", json={}) - assert response.status_code == 422 - - -def test_generate_plan_with_plan(test_client): - response = test_client.post("/v1/plan", json={"query": "test query", "plan": "test plan"}) - assert response.status_code == 422 - - -def test_query_with_no_query_or_plan(test_client): - response = test_client.post("/v1/query", json={}) - assert response.status_code == 422 - response = test_client.post("/v1/query", json={"stream": True}) - assert response.status_code == 422 - - -def test_query_with_query_and_plan(test_client): - response = test_client.post("/v1/query", json={"query": "test query", "plan": "test plan"}) - assert response.status_code == 422 - response = test_client.post("/v1/query", json={"query": "test query", "plan": "test plan", "stream": True}) - assert response.status_code == 422 - - -def test_run_plan(test_client): - mock_plan = LogicalPlan(query="test query", result_node=1, nodes={}) - - mock_result = MagicMock(spec=SycamoreQueryResult) - mock_result.query_id = "test_id" - mock_result.plan = mock_plan - mock_result.result = "test result" - mock_result.retrieved_docs.return_value = ["doc1.txt", "doc2.txt"] - - with patch("queryserver.main.sqclient.run_plan", return_value=mock_result): - query = Query(plan=mock_plan, index="test_index1") - response = test_client.post("/v1/query", json=query.model_dump()) - - assert response.status_code == 200 - result = response.json() - assert result["query_id"] == "test_id" - assert result["plan"] == mock_plan.model_dump() - assert result["result"] == "test result" - assert set(result["retrieved_docs"]) == {"doc1.txt", "doc2.txt"} - - -def test_run_query(test_client, mock_schema): - mock_plan = LogicalPlan(query="test query", result_node=1, nodes={}) - - class MockSycamoreQueryResult(SycamoreQueryResult): - def retrieved_docs(self): - return {"doc1.txt", "doc2.txt"} - - mock_result = MagicMock(spec=SycamoreQueryResult) - mock_result.query_id = "test_id" - mock_result.plan = mock_plan - mock_result.result = "test result" - mock_result.retrieved_docs.return_value = ["doc1.txt", "doc2.txt"] - - with patch("queryserver.main.sqclient.generate_plan", return_value=mock_plan), patch( - "queryserver.main.sqclient.run_plan", return_value=mock_result - ), patch( - "queryserver.main.sqclient.get_opensearch_schema", - return_value=mock_schema, - ): - - query = Query(query="test query", index="test_index") - response = test_client.post("/v1/query", json=query.model_dump()) - - assert response.status_code == 200 - result = response.json() - assert result["plan"] == mock_plan.model_dump() - assert result["result"] == "test result" - assert set(result["retrieved_docs"]) == {"doc1.txt", "doc2.txt"} - - -def test_run_query_stream(test_client, mock_schema): - mock_plan = LogicalPlan(query="test query", result_node=1, nodes={}) - - class MockSycamoreQueryResult(SycamoreQueryResult): - def retrieved_docs(self): - return {"doc1.txt", "doc2.txt"} - - mock_result = MagicMock(spec=SycamoreQueryResult) - mock_result.query_id = "test_id" - mock_result.plan = mock_plan - mock_result.result = "test result" - mock_result.retrieved_docs.return_value = ["doc1.txt", "doc2.txt"] - - with patch("queryserver.main.sqclient.generate_plan", return_value=mock_plan), patch( - "queryserver.main.sqclient.run_plan", return_value=mock_result - ), patch( - "queryserver.main.sqclient.get_opensearch_schema", - return_value=mock_schema, - ): - - query = Query(query="test query", index="test_index", stream=True) - response = test_client.post("/v1/query", json=query.model_dump()) - - assert response.status_code == 200 - retrieved_docs = [] - event = None - data = None - for line in response.iter_lines(): - if line.startswith("event: "): - event = line[7:] - elif line.startswith("data: "): - data = line[6:] - if event is not None and data is not None: - if event == "plan": - assert json.loads(data) == mock_plan.model_dump() - elif event == "result": - assert data == "test result" - elif event == "retrieved_doc": - retrieved_docs.append(data) - elif event != "status": - raise ValueError(f"Unknown event: {event}") - event = None - data = None - - assert set(retrieved_docs) == {"doc1.txt", "doc2.txt"} diff --git a/apps/query-ui/.gitignore b/apps/query-ui/.gitignore deleted file mode 100644 index 69222117b..000000000 --- a/apps/query-ui/.gitignore +++ /dev/null @@ -1 +0,0 @@ -traces* \ No newline at end of file diff --git a/apps/query-ui/Dockerfile.buildx b/apps/query-ui/Dockerfile.buildx deleted file mode 100644 index 60748649e..000000000 --- a/apps/query-ui/Dockerfile.buildx +++ /dev/null @@ -1,44 +0,0 @@ -# Repo name: arynai/sycamore-query-ui - -# Note: This dockerfile is intended to work with docker buildx build -f Dockerfile.buildx . - -ARG TAG=stable - -FROM arynai/sycamore-base:$TAG - -ARG GIT_BRANCH="unknown" -ARG GIT_COMMIT="unknown" -ARG GIT_DIFF="unknown" - -ENV GIT_BRANCH=${GIT_BRANCH} -ENV GIT_COMMIT=${GIT_COMMIT} -ENV GIT_DIFF=${GIT_DIFF} -ENV SSL=0 - -WORKDIR /app -USER app - -RUN make -f Makefile.docker-base check-version-compatibility - -# https://github.com/docker/buildx/issues/1408 app:app + --link isn't supported -COPY --link --chown=1000:1000 apps/query-ui/Makefile.query-ui /app/ - -USER root -RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \ - --mount=type=cache,target=/var/lib/apt,sharing=locked \ - make -f Makefile.query-ui apt-install - -USER app -RUN --mount=type=cache,id=cache_poetry_1000,target=/tmp/poetry_cache,uid=1000,gid=1000,sharing=locked \ - ./poetry-install.sh query-ui - -COPY --link --chown=1000:1000 apps/query-ui apps/query-ui - -RUN make -f Makefile.docker-base non-root-files-check - -LABEL org.opencontainers.image.authors="opensource@aryn.ai" -LABEL git_branch=${GIT_BRANCH} -LABEL git_commit=${GIT_COMMIT} -LABEL git_diff=${GIT_DIFF} - -ENTRYPOINT ["./apps/query-ui/run-query-ui.sh"] diff --git a/apps/query-ui/Makefile.query-ui b/apps/query-ui/Makefile.query-ui deleted file mode 100644 index 649454480..000000000 --- a/apps/query-ui/Makefile.query-ui +++ /dev/null @@ -1,8 +0,0 @@ -# -*- makefile -*- -help: - @echo "This should be run as part of the Dockerfile" - false - -apt-install: - echo "Nothing needed so far" - diff --git a/apps/query-ui/README.md b/apps/query-ui/README.md deleted file mode 100644 index 204a05772..000000000 --- a/apps/query-ui/README.md +++ /dev/null @@ -1,24 +0,0 @@ -# Sycamore Query Web UI - -This is a simple web-based UI for Sycamore Query. - -## Setup - -First, run `poetry install` to install all dependencies. - -Be sure to have OpenSearch running locally on port 9200. You can do this with: - -```bash -$ cd ../.. && docker compose up -``` - -## Using the web interface - -Run it with: - -```bash -$ poetry run python -m streamlit run ./queryui/Sycamore_Query.py -``` - -You can use this to issue a query against an index in the locally-running OpenSearch cluster. - diff --git a/apps/query-ui/external_ray_wrapper.py b/apps/query-ui/external_ray_wrapper.py deleted file mode 100644 index cc23e6865..000000000 --- a/apps/query-ui/external_ray_wrapper.py +++ /dev/null @@ -1,24 +0,0 @@ -import os -import subprocess -import time - -import ray - -from sycamore.executor import sycamore_ray_init - -sycamore_ray_init() - -while not ray.is_initialized(): - print("Waiting for ray to initialize...", flush=True) - time.sleep(1) - -os.environ["EXTERNAL_RAY"] = "1" -os.environ["PYTHONPATH"] = "apps/query-ui/queryui" - -while True: - print("Starting streamlit process...", flush=True) - # Hardcode the port so you can't accidentally be using a previous run of the app - ret = subprocess.run( - ["python", "-m", "streamlit", "run", "apps/query-ui/queryui/Sycamore_Query.py", "--server.port", "8501"] - ) - print(f"Subprocess exited {ret}", flush=True) diff --git a/apps/query-ui/poetry.lock b/apps/query-ui/poetry.lock deleted file mode 100644 index 9a79ed15d..000000000 --- a/apps/query-ui/poetry.lock +++ /dev/null @@ -1,7608 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - -[[package]] -name = "aiohappyeyeballs" -version = "2.4.3" -description = "Happy Eyeballs for asyncio" -optional = false -python-versions = ">=3.8" -files = [ - {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"}, - {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"}, -] - -[[package]] -name = "aiohttp" -version = "3.10.10" -description = "Async http client/server framework (asyncio)" -optional = false -python-versions = ">=3.8" -files = [ - 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diff --git a/apps/query-ui/queryui/Sycamore_Query.py b/apps/query-ui/queryui/Sycamore_Query.py deleted file mode 100644 index 1068ef8ab..000000000 --- a/apps/query-ui/queryui/Sycamore_Query.py +++ /dev/null @@ -1,173 +0,0 @@ -# This is the main page of the Sycamore Query UI. - -import argparse -import time -import io - -import queryui.util as util -from queryui.configuration import get_sycamore_query_client -import queryui.ntsb as ntsb - -from contextlib import redirect_stdout - -import streamlit as st -from streamlit_ace import st_ace - -from sycamore import ExecMode -from sycamore.executor import sycamore_ray_init -from sycamore.query.client import SycamoreQueryClient -from sycamore.query.logical_plan import LogicalPlan - -from typing import Any - -PLANNER_EXAMPLES = ntsb.PLANNER_EXAMPLES - - -def generate_code(client: SycamoreQueryClient, plan: LogicalPlan) -> str: - result = client.run_plan(plan, dry_run=True) - assert result.code is not None - return result.code - - -def show_schema(_client: SycamoreQueryClient, index: str): - schema = util.get_schema(_client, index) - table_data = [] - for field in schema.fields: - table_data.append([field.name] + list(field.examples or [])) - with st.expander(f"Schema for index `[{index}]`"): - st.dataframe(table_data) - - -@st.fragment -def show_code(client: SycamoreQueryClient, code: str): - st.session_state.code = code - - with st.expander("View code"): - code = st_ace( - value=st.session_state.code, - key=f"python_{hash(st.session_state.query)}", - language="python", - min_lines=20, - ) - - execute_button = st.button("Execute Code") - if execute_button: - code_locals: dict = {} - try: - with st.spinner("Executing code..."): - global_context: dict[str, Any] = {"context": client.context} - f = io.StringIO() - with redirect_stdout(f): - exec(code, global_context) - result_str = f.getvalue() - print("Printing out the results...") - print(result_str) - print("Done printing out the results...") - st.subheader("Result", divider="rainbow") - st.markdown(result_str, unsafe_allow_html=True) - # exec(code, global_context) - except Exception as e: - st.exception(e) - if code_locals and "result" in code_locals: - st.subheader("Result", divider="rainbow") - st.success(code_locals["result"]) - - -def run_query(): - """Run the given query.""" - client = get_sycamore_query_client( - llm_cache_dir=st.session_state.llm_cache_dir, - cache_dir=st.session_state.cache_dir, - exec_mode=ExecMode.LOCAL if st.session_state.local_mode else ExecMode.RAY, - ) - with st.spinner("Generating plan..."): - t1 = time.time() - plan = util.generate_plan(client, st.session_state.query, st.session_state.index, examples=PLANNER_EXAMPLES) - t2 = time.time() - with st.expander("Query plan"): - st.write(f"Generated plan in :blue[{t2 - t1:.2f}] seconds.") - st.write(plan.model_dump(serialize_as_any=True)) - - code = generate_code(client, plan) - show_code(client, code) - - if not st.session_state.plan_only: - with st.spinner("Running query..."): - t1 = time.time() - result = util.run_plan(client, plan) - t2 = time.time() - st.write(f"Ran query in :blue[{t2 - t1:.2f}] seconds.") - - result_str = util.result_to_string(result.result) - st.write(f"Query ID: `{result.query_id}`\n") - st.subheader("Result", divider="rainbow") - st.markdown(result_str, unsafe_allow_html=True) - - with st.expander("Query trace"): - util.show_query_traces(result) - - -def main(): - argparser = argparse.ArgumentParser() - argparser.add_argument("--local-mode", action="store_true", help="Enable Sycamore local execution mode.") - argparser.add_argument( - "--index", help="OpenSearch index name to use. If specified, only this index will be queried." - ) - argparser.add_argument("--cache-dir", type=str, help="Query execution cache dir.") - argparser.add_argument("--llm-cache-dir", type=str, help="LLM query cache dir.") - argparser.add_argument("--trace-dir", type=str, help="Directory to store query traces.") - args = argparser.parse_args() - - if "index" not in st.session_state: - st.session_state.index = args.index - - if "cache_dir" not in st.session_state: - st.session_state.cache_dir = args.cache_dir - - if "llm_cache_dir" not in st.session_state: - st.session_state.llm_cache_dir = args.llm_cache_dir - - if "local_mode" not in st.session_state: - st.session_state.local_mode = args.local_mode - - if not args.local_mode: - sycamore_ray_init(address="auto") - client = get_sycamore_query_client(exec_mode=ExecMode.LOCAL if args.local_mode else ExecMode.RAY) - - st.title("Sycamore Query") - st.write(f"Query cache dir: `{st.session_state.cache_dir}`") - st.write(f"LLM cache dir: `{st.session_state.llm_cache_dir}`") - - if not args.index: - with st.spinner("Loading indices..."): - try: - st.session_state.indices = util.get_opensearch_indices() - except Exception as e: - st.error(f"Unable to load OpenSearch indices. Is OpenSearch running?\n\n{e}") - return - st.selectbox( - "Index", - util.get_opensearch_indices(), - key="index", - placeholder="Select an index", - label_visibility="collapsed", - ) - - if st.session_state.index: - show_schema(client, st.session_state.index) - with st.form("query_form"): - st.text_input("Query", key="query") - col1, col2, col3 = st.columns(3) - with col1: - submitted = st.form_submit_button("Run query") - with col2: - st.toggle("Plan only", key="plan_only", value=False) - with col3: - st.toggle("Use Ray", key="use_ray", value=True) - - if submitted: - run_query() - - -if __name__ == "__main__": - main() diff --git a/apps/query-ui/queryui/__init__.py b/apps/query-ui/queryui/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/apps/query-ui/queryui/chat.py b/apps/query-ui/queryui/chat.py deleted file mode 100644 index c96f481c6..000000000 --- a/apps/query-ui/queryui/chat.py +++ /dev/null @@ -1,329 +0,0 @@ -# This file contains utility functions enabling agentic chat with the Sycamore Query UI. - -import datetime -from html.parser import HTMLParser -import re -from typing import Any, Dict, List, Optional, Tuple - -import pandas as pd -import streamlit as st - - -import queryui.util as util - -from sycamore.query.result import SycamoreQueryResult - - -# The agent LLM needs a prompt that understands how to render MDX output with the appropriate -# set of JSX components. This prompt should be appended to the system prompt to ensure -# that the agent knows how to render the output. -MDX_SYSTEM_PROMPT = """All your responses should be in MDX, which is Markdown augmented with JSX -components. As an example, your response could include a table of data, a link to a document, or a -component such as a button. Below is an example of MDX output that you might generate: - -```Here are the latest incidents referring to bad weather: - - - - Incident ID - Date - Location - Aircraft Type - - - ERA23LA153 - 2023-01-15 - Seattle, WA - Cessna 172 - -
-``` - -Additional markdown text can follow the use of a JSX component, and JSX components can be -inlined in the markdown text as follows: - -```For more information about this incident, please see the following document: - . -``` - -Do not include a starting ``` and closing ``` line in your reply. Just respond with the MDX itself. -Do not include extra whitespace that is not needed for the markdown interpretation. For instance, -if a JSX component has a property that's a JSON object, encode it into a single line, like so: - - - -The following JSX components are available for your use: - * - - Column name 1 - Column name 2 - - - Value 1 - Value 2 - - - Value 3 - Value 4 - -
- Displays a table showing the provided data. - - * - Displays an inline preview of the provided document. You may provide an S3 path or a URL. - ALWAYS use a instead of a regular link whenever a document is mentioned. - - * - Displays a button showing a query that the user might wish to consider asking next. - - * - - - - Displays a map with markers at the provided coordinates. - -Multiple JSX components can be used in your reply. You may ONLY use these specific JSX components -in your responses. Other than these components, you may ONLY use standard Markdown syntax. - -Please use any time a specific document or incident is mentioned, and any time -there is an opportunity to refer to a location. - -Please suggest 1-3 follow-on queries (using the component) that the user might -ask, based on the response to the user's question. -""" - - -class ChatMessageExtra: - """Extra content to show before or after a ChatMessage.""" - - def __init__(self, name: str, content: Optional[Any] = None): - self.name = name - self.content = content - - def show(self): - """Show this ChatMessageExtra.""" - st.write(self.content) - - -class ChatMessageTraces(ChatMessageExtra): - """A ChatMessageExtra subclass for showing query traces.""" - - def __init__(self, name: str, result: SycamoreQueryResult): - super().__init__(name) - self.result = result - - def show(self): - util.show_query_traces(self.result) - - -class ChatMessage: - """Represents a single message in a chat session with the agent. - - Args: - message: The content of the message. - before_extras: A list of ChatMessageExtra objects to display before the message content. - after_extras: A list of ChatMessageExtra objects to display after the message content. - """ - - def __init__( - self, - message: Optional[Dict[str, Any]] = None, - before_extras: Optional[List[ChatMessageExtra]] = None, - after_extras: Optional[List[ChatMessageExtra]] = None, - ): - assert "next_message_id" in st.session_state and isinstance(st.session_state.next_message_id, int) - self.message_id = st.session_state.next_message_id - st.session_state.next_message_id += 1 - self.timestamp = datetime.datetime.now() - self.message = message or {} - self.before_extras = before_extras or [] - self.after_extras = after_extras or [] - self.widget_key = 0 - - def next_key(self) -> str: - """Streamlit requires widgets to have unique keys. Used to generate a new key for each widget.""" - key = f"{self.message_id}-{self.widget_key}" - self.widget_key += 1 - return key - - def show(self): - """Show this ChatMessage.""" - self.widget_key = 0 - with st.chat_message(self.message.get("role", "assistant")): - self.show_content() - - def show_content(self): - """Show just the contents of this ChatMessage, without the surrounding role container.""" - for extra in self.before_extras: - with st.expander(extra.name): - extra.show() - if self.message.get("content"): - content = self.message.get("content") - self.render_mdx(content) - for extra in self.after_extras: - with st.expander(extra.name): - extra.show() - - def button(self, label: str, **kwargs): - """Return a button widget.""" - return st.button(label, key=self.next_key(), **kwargs) - - def render_mdx(self, text: str): - """Render the given text as MDX.""" - print(f"Rendering MDX:\n{text}\n") - renderer = MDXRenderer(self) - renderer.feed(text) - - def render_markdown(self, text: str): - """Render markdown text.""" - st.markdown(text) - - def render_table(self, columns: List[str], rows: List[List[str]]): - """Render a table with the given columns and rows.""" - if not rows: - return - num_cols = max([len(row) for row in rows]) - if not columns: - columns = [f"Column {i}" for i in range(1, num_cols + 1)] - if len(columns) < num_cols: - columns += [f"Column {i}" for i in range(len(columns) + 1, num_cols + 1)] - if len(columns) > num_cols: - columns = columns[:num_cols] - - data: Dict[str, List[Any]] = {col: [] for col in columns} - for row in rows: - for index, col in enumerate(columns): - try: - data[col].append(row[index]) - except IndexError: - # This can happen if we end up with missing cells in the MDX. - data[col].append(None) - - df = pd.DataFrame(data) - st.dataframe(df) - - def render_preview(self, path: str): - """Render a preview of the PDF document at the given path.""" - with st.container(border=True): - col1, col2 = st.columns([0.75, 0.25]) - with col1: - st.text("📄 " + path) - with col2: - if st.button("View document", key=self.next_key()): - util.show_pdf_preview(path) - - def render_suggested_query(self, query: str): - """Render a suggested query.""" - if self.button(query): - st.session_state.user_query = query - - def render_map(self, data: List[Tuple[float, float]]): - """Render a map with the given (latitude, longitude) points labeled.""" - df = pd.DataFrame(data, columns=["lat", "lon"]) - st.map(df, color="#00ff00", size=100) - - def to_dict(self) -> Optional[Dict[str, Any]]: - """Return a dict that can be sent to an LLM representing the content of this message.""" - return self.message - - def __str__(self): - return f"{self.message.get('role', 'assistant')}: {self.message.get('content')}" - - -class MDXRenderer(HTMLParser): - """Render MDX, which is Markdown with embedded JSX components. - - We instruct the LLM to generate MDX output, allowing us to embed tables, maps, and other - components in the rendered output. This class is used to parse the MDX and invoke the appropriate - ChatMessage methods to render the embedded components. - """ - - def __init__(self, chat_message: ChatMessage): - super().__init__() - self.chat_message = chat_message - self.in_table = False - self.in_header = False - self.in_header_cell = False - self.in_row = False - self.in_cell = False - self.table_data: List[List[Any]] = [] - self.row_data: List[Any] = [] - self.header_data: List[Any] = [] - self.in_map = False - self.map_data: List[Tuple[float, float]] = [] - - def handle_starttag(self, tag, attrs): - if tag == "table": - self.in_table = True - self.table_data = [] - elif tag == "tableheader" and self.in_table: - self.in_header = True - self.header_data = [] - elif tag == "tablerow" and self.in_table: - self.in_row = True - self.row_data = [] - elif tag == "tablecell": - if self.in_header: - self.in_header_cell = True - elif self.in_row: - self.in_cell = True - elif tag == "map": - self.in_map = True - self.map_data = [] - elif tag == "mapmarker" and self.in_map: - lat = float(dict(attrs).get("lat")) - lon = float(dict(attrs).get("lon")) - self.map_data.append((lat, lon)) - - def handle_endtag(self, tag): - if tag == "tablecell": - if self.in_header_cell: - self.in_header_cell = False - elif self.in_cell: - self.in_cell = False - elif tag == "tableheader" and self.in_header: - self.in_header = False - elif tag == "tablerow" and self.in_row: - self.in_row = False - self.table_data.append(self.row_data) - elif tag == "table" and self.in_table: - self.in_table = False - self.chat_message.render_table(self.header_data, self.table_data) - elif tag == "map" and self.in_map: - self.in_map = False - self.chat_message.render_map(self.map_data) - - def handle_startendtag(self, tag, attrs): - if tag == "preview": - path = dict(attrs).get("path") - if path: - self.chat_message.render_preview(path) - else: - self.chat_message.render_markdown("No path provided for preview.") - elif tag == "suggestedquery": - query = dict(attrs).get("query") - if query: - self.chat_message.render_suggested_query(query) - else: - self.chat_message.render_markdown("No query provided for suggested query") - elif tag == "mapmarker" and self.in_map: - lat = float(dict(attrs).get("lat")) - lon = float(dict(attrs).get("lon")) - self.map_data.append((lat, lon)) - - def handle_data(self, data): - data = data.strip() - if not data: - return - if self.in_header_cell: - self.header_data.append(data) - elif self.in_cell: - self.row_data.append(data) - else: - self.chat_message.render_markdown(data) - # Scan for previewable markdown links and add previews. - markdown_link_regexp = r"!?\[([^\]]+)\]\(([^\)]+)\)" - for m in re.finditer(markdown_link_regexp, data): - if m and m.group(2).startswith("s3://"): - self.chat_message.render_preview(m.group(2)) - data = re.sub(markdown_link_regexp, "\1", data) diff --git a/apps/query-ui/queryui/configuration.py b/apps/query-ui/queryui/configuration.py deleted file mode 100644 index 1a9719891..000000000 --- a/apps/query-ui/queryui/configuration.py +++ /dev/null @@ -1,32 +0,0 @@ -from typing import Optional - -from sycamore import ExecMode -from sycamore.query.client import SycamoreQueryClient - -""" -If you want to use custom LLMs or a custom context to override opensearch parameters, edit the -get_sycamore_query_client function below. - -For example, you can write: - context = sycamore.init(params={ - "default": {"llm": OpenAI(OpenAIModels.GPT_4O.value, cache=cache_from_path(llm_cache_dir))}, - "opensearch": {"os_client_args": get_opensearch_client_args(), - "text_embedder": SycamoreQueryClient.default_text_embedder()} - }) - return SycamoreQueryClient(context=context, trace_dir=trace_dir) - -Note that if you include keys in your configuration, it is safer to write: - import mycompany_configuration - return mycompany_configuration.get_sycamore_query_client(llm_cache_dir, trace_dir) - -We require you to edit this file so that if the arguments to get_sycamore_query_client change you -will get a merge conflict. -""" - - -def get_sycamore_query_client( - llm_cache_dir: Optional[str] = None, - cache_dir: Optional[str] = None, - exec_mode: ExecMode = ExecMode.RAY, -) -> SycamoreQueryClient: - return SycamoreQueryClient(llm_cache_dir=llm_cache_dir, cache_dir=cache_dir, sycamore_exec_mode=exec_mode) diff --git a/apps/query-ui/queryui/fetchdata.py b/apps/query-ui/queryui/fetchdata.py deleted file mode 100644 index 9f7115362..000000000 --- a/apps/query-ui/queryui/fetchdata.py +++ /dev/null @@ -1,61 +0,0 @@ -#!/usr/bin/env python - -# This script reads a CSV file from an NTSB CAROL query and fetches the PDFs for each report -# listed in the CSV file to the output directory. -# -# To use: -# 1. Run a CAROL Aviation Search: https://www.ntsb.gov/Pages/AviationQueryv2.aspx -# It's recommended you select "United States" in the "Country" dropdown and select "Completed" -# in the "Report Status" dropdown. -# 2. Click the "CSV" link under the search results to download a CSV of the results. -# 3. Run this script, supplying the path to the CSV file and the output directory. -# poetry run python queryui/fetchdata.py --destination output ntsb-results.csv - -import argparse -import csv -import logging -import os - -import requests - -logging.basicConfig(level=logging.INFO) - - -def main(): - argparser = argparse.ArgumentParser(prog="fetchdata") - argparser.add_argument("--destination", default="output", help="Output directory") - argparser.add_argument("--limit", default=None, type=int, help="Limit the number of documents to fetch") - argparser.add_argument("input", help="Input CSV file", type=argparse.FileType("r")) - args = argparser.parse_args() - - if not os.path.exists(args.destination): - os.makedirs(args.destination) - - reader = csv.DictReader(args.input) - count = len([name for name in os.listdir(args.destination) if os.path.isfile(os.path.join(args.destination, name))]) - print(f"Starting with {count} existing reports") - for row in reader: - try: - if args.limit and count >= args.limit: - break - if not row.get("DocketUrl"): - print(f"Skipping {row['Mkey']} because it has no DocketUrl") - continue - mkey = row["Mkey"] - if os.path.exists(os.path.join(args.destination, f"{mkey}.pdf")): - print(f"Skipping {args.destination}/{mkey}.pdf because it already exists") - continue - url = f"https://data.ntsb.gov/carol-repgen/api/Aviation/ReportMain/GenerateNewestReport/{mkey}/pdf" - pdf_data = requests.get(url, timeout=60).content - with open(os.path.join(args.destination, f"{mkey}.pdf"), "wb") as f: - f.write(pdf_data) - print(f"Wrote {len(pdf_data)} bytes to {args.destination}/{mkey}.pdf") - count += 1 - except Exception as e: - print(f"Error fetching report {mkey}: {e}") - - print(f"Wrote {count} reports to {args.destination}") - - -if __name__ == "__main__": - main() diff --git a/apps/query-ui/queryui/loaddata.py b/apps/query-ui/queryui/loaddata.py deleted file mode 100644 index b15040538..000000000 --- a/apps/query-ui/queryui/loaddata.py +++ /dev/null @@ -1,184 +0,0 @@ -#!/usr/bin/env python - -# This script will populate a local OpenSearch index, using Sycamore, with data -# from NTSB incident reports. This is useful for testing the Sycamore Query UI -# with a real dataset. -# -# Run with: poetry run python queryui/loader.py [--delete] - -import argparse -import json -import logging -import os -import tempfile - -import sycamore -from sycamore.data import Document -from sycamore.transforms.partition import ArynPartitioner -from sycamore.functions import HuggingFaceTokenizer -from sycamore.transforms import AssignDocProperties, DateTimeStandardizer, USStateStandardizer -from sycamore.transforms.extract_schema import ( - OpenAIPropertyExtractor, -) -from sycamore.transforms.merge_elements import GreedyTextElementMerger -from sycamore.llms import OpenAI, OpenAIModels -from sycamore.transforms.embed import SentenceTransformerEmbedder -from opensearchpy import OpenSearch - -logging.basicConfig(level=logging.INFO) - - -def add_schema_property(doc: Document) -> Document: - """Add a _schema and _schema_class property to the document with the NTSB data schema.""" - schema_json = { - "type": "object", - "properties": { - "location": {"type": "string"}, - "dateAndTime": {"type": "string"}, - "aircraft": {"type": "string"}, - "flightConductedUnder": {"type": "string"}, - "accidentNumber": {"type": "string"}, - "registration": {"type": "string"}, - "injuries": {"type": "string"}, - "aircraftDamage": {"type": "string"}, - "operator": {"type": "string"}, - "conditions": {"type": "string"}, - "lowestCloudCondition": {"type": "string"}, - "lowestCeiling": {"type": "string"}, - "conditionOfLight": {"type": "string"}, - "temperature": {"type": "string"}, - "windSpeed": {"type": "string"}, - "windDirection": {"type": "string"}, - "visibility": {"type": "string"}, - "departureAirport": {"type": "string"}, - "destinationAirport": {"type": "string"}, - }, - } - doc.properties.update({"_schema": schema_json, "_schema_class": "Flight Accident Report"}) - return doc - - -def standardize_location(doc: Document, key_path: list[str]) -> Document: - try: - doc = USStateStandardizer.standardize(doc, key_path=key_path) - except KeyError: - logging.warning(f"Key {key_path} not found in document: {doc}") - return doc - - -def main(): - argparser = argparse.ArgumentParser(prog="loaddata") - argparser.add_argument("--dump", action="store_true", help="Dump contents of existing index and exit") - argparser.add_argument("--delete", action="store_true", help="Delete the index if it already exists") - argparser.add_argument( - "--tempdir", default=tempfile.gettempdir(), help="Temporary directory for materialize output" - ) - argparser.add_argument("--oshost", default=None, help="OpenSearch host") - argparser.add_argument("--osport", default=9200, help="OpenSearch port") - argparser.add_argument("--index", default="const_ntsb", help="OpenSearch index name") - argparser.add_argument("--source", default="s3://aryn-public/ntsb_reports/", help="Source data path") - argparser.add_argument("--limit", default=None, type=int, help="Limit the number of documents to process") - args = argparser.parse_args() - - if args.oshost is not None: - opensearch_host = args.oshost - elif os.path.exists("/.dockerenv"): - opensearch_host = "opensearch" - print("Assuming we are in a Sycamore Jupyter container, using opensearch for OpenSearch host") - else: - opensearch_host = "localhost" - print("Assuming we are running outside of a container, using localhost for OpenSearch host") - - opensearch_port = args.osport - - os_client_args = { - "hosts": [{"host": opensearch_host, "port": opensearch_port}], - "http_compress": True, - "http_auth": ("admin", "admin"), - "use_ssl": True, - "verify_certs": False, - "ssl_assert_hostname": False, - "ssl_show_warn": False, - "timeout": 120, - } - os_client = OpenSearch(**os_client_args) # type: ignore - - if args.dump: - # This will dump up to the first 10000 documents in the index. - # TODO: Implement pagination to handle larger indices. - contents = os_client.search(index=args.index, body={"query": {"match_all": {}}}, size=args.limit or 10000) - hits = contents["hits"]["hits"] - for hit in hits: - print(json.dumps(hit, indent=2)) - return - - if os_client.indices.exists(index=args.index): - if args.delete: - print(f"Index {args.index} already exists, deleting as requested") - os_client.indices.delete(index=args.index) - else: - raise Exception(f"Index {args.index} already exists. Run with --delete to delete it.") - - index_settings = { - "body": { - "settings": {"index.knn": True, "number_of_shards": 5, "number_of_replicas": 1}, - "mappings": { - "properties": { - "embedding": { - "dimension": 384, - "method": { - "engine": "faiss", - "space_type": "l2", - "name": "hnsw", - "parameters": {}, - }, - "type": "knn_vector", - } - } - }, - } - } - - context = sycamore.init() - docset = context.read.binary(args.source, binary_format="pdf") - if args.limit: - docset = docset.limit(args.limit) - tokenizer = HuggingFaceTokenizer("thenlper/gte-small") - llm = OpenAI(OpenAIModels.GPT_4O.value) - - partitioned_docset = ( - docset.partition(partitioner=ArynPartitioner(extract_table_structure=True, use_ocr=True, extract_images=True)) - .materialize(path=f"{args.tempdir}/ntsb-loader-stage-0", source_mode=sycamore.MATERIALIZE_USE_STORED) - .map(add_schema_property) - .materialize(path=f"{args.tempdir}/ntsb-loader-stage-1", source_mode=sycamore.MATERIALIZE_USE_STORED) - .map( - lambda doc: AssignDocProperties.assign_doc_properties( - doc, element_type="table", property_name="table_props" - ) - ) - .extract_properties(property_extractor=OpenAIPropertyExtractor(llm=llm, num_of_elements=35)) - .merge(GreedyTextElementMerger(tokenizer, 300)) - .map(lambda doc: standardize_location(doc, key_path=["properties", "entity", "location"])) - .map(lambda doc: standardize_location(doc, key_path=["properties", "entity", "departureAirport"])) - .map(lambda doc: standardize_location(doc, key_path=["properties", "entity", "destinationAirport"])) - .map(lambda doc: DateTimeStandardizer.standardize(doc, key_path=["properties", "entity", "dateAndTime"])) - .spread_properties(["entity", "path"]) - .explode() - .sketch() - .embed( - embedder=SentenceTransformerEmbedder(batch_size=100, model_name="sentence-transformers/all-MiniLM-L6-v2") - ) - ) - - for doc in partitioned_docset.take_all(): - print(str(doc)) - - partitioned_docset.write.opensearch( - os_client_args=os_client_args, - index_name=args.index, - index_settings=index_settings, - ) - - -if __name__ == "__main__": - main() diff --git a/apps/query-ui/queryui/main.py b/apps/query-ui/queryui/main.py deleted file mode 100755 index 91a32fc01..000000000 --- a/apps/query-ui/queryui/main.py +++ /dev/null @@ -1,106 +0,0 @@ -#!/usr/bin/env python - -# This script is used to run the Streamlit-based Luna Query UI. -# Because of issues with Ray, we need to initialize Ray externally -# to the demo process, and then run the Streamlit UI in a subprocess. - -import argparse -import logging -import os -import subprocess -import sys -import time - -import ray - -from sycamore.executor import _ray_logging_setup - - -def ray_init(**ray_args): - """Used to initialize Ray before running the Streamlit app.""" - - os.environ["RAY_SCHEDULER_EVENTS"] = "0" - if ray.is_initialized(): - return - - if "logging_level" not in ray_args: - ray_args.update({"logging_level": logging.WARNING}) - if "runtime_env" not in ray_args: - ray_args["runtime_env"] = {} - if "worker_process_setup_hook" not in ray_args["runtime_env"]: - ray_args["runtime_env"]["worker_process_setup_hook"] = _ray_logging_setup - ray.init(**ray_args) - - while not ray.is_initialized(): - print("Waiting for ray to initialize...", flush=True) - time.sleep(1) - - -def main(): - argparser = argparse.ArgumentParser() - argparser.add_argument( - "--exec-mode", type=str, choices=["ray", "local"], default="ray", help="Configure Sycamore execution mode." - ) - argparser.add_argument("--chat", action="store_true", help="Only show the chat demo pane.") - argparser.add_argument("--test", action="store_true", help="Only run the integration test.") - argparser.add_argument( - "--index", help="OpenSearch index name to use. If specified, only this index will be queried." - ) - argparser.add_argument( - "--cache-dir", type=str, default="cache_dir", help="Query execution cache dir. Defaults to ./query_cache." - ) - argparser.add_argument( - "--llm-cache-dir", type=str, default="llm_cache", help="LLM query cache dir. Defaults to ./llm_cache." - ) - args, pytest_args = argparser.parse_known_args() - - if args.chat: - if args.test: - raise ValueError("Cannot use --test and --chat at the same time.") - cmdline = ["python", "-m", "streamlit", "run", "queryui/pages/Chat.py", "--"] - else: - cmdline = ["python", "-m", "streamlit", "run", "queryui/Sycamore_Query.py", "--"] - - cmdline_args = [] - - if args.index: - cmdline_args.extend(["--index", args.index]) - - if args.cache_dir: - if not args.cache_dir.startswith("s3://"): - cache_dir = os.path.abspath(args.cache_dir) - os.makedirs(cache_dir, exist_ok=True) - else: - cache_dir = args.cache_dir - cmdline_args.extend(["--cache-dir", cache_dir]) - - if args.llm_cache_dir: - if not args.llm_cache_dir.startswith("s3://"): - llm_cache_dir = os.path.abspath(args.llm_cache_dir) - os.makedirs(llm_cache_dir, exist_ok=True) - else: - llm_cache_dir = args.llm_cache_dir - cmdline_args.extend(["--llm-cache-dir", llm_cache_dir]) - - if args.exec_mode == "ray": - ray_init() - - elif args.exec_mode == "local": - cmdline_args.extend(["--local-mode"]) - - if args.test: - cmdline = ["pytest"] + pytest_args - ret = subprocess.run(cmdline, check=True) - sys.exit(ret.returncode) - - while True: - print("Starting streamlit process...", flush=True) - # Streamlit requires the -- separator to separate streamlit arguments from script arguments. - ret = subprocess.run(cmdline + cmdline_args, check=True) - print(f"Subprocess exited {ret}", flush=True) - if args.test: - break - - -if __name__ == "__main__": - main() diff --git a/apps/query-ui/queryui/ntsb.py b/apps/query-ui/queryui/ntsb.py deleted file mode 100644 index 316eea9c4..000000000 --- a/apps/query-ui/queryui/ntsb.py +++ /dev/null @@ -1,357 +0,0 @@ -# This file contains Sycamore Query UI configuration for use with NTSB Incident Report data. -# Different datasets and applications will need to customize these settings. - -from typing import List - -from sycamore.query.planner import PlannerExample -from sycamore.query.logical_plan import LogicalPlan -from sycamore.query.operators.query_database import QueryDatabase, QueryVectorDatabase -from sycamore.query.operators.llm_filter import LlmFilter -from sycamore.query.operators.count import Count -from sycamore.query.operators.top_k import TopK -from sycamore.query.schema import OpenSearchSchema, OpenSearchSchemaField - - -# Example queries shown as part of the welcome message. -EXAMPLE_QUERIES = [ - "How many incidents were there in Washington in 2023?", - "What was the breakdown of aircraft types for incidents with substantial damage?", - "Show me incidents involving Piper aircraft", - "Show the details on accident ERA23LA153", -] - -# The welcome message shown at the top of a chat session. -WELCOME_MESSAGE = f"""Welcome to the NTSB incident query demo! You can ask me questions about -[NTSB incident reports](https://carol.ntsb.gov/), and I'll do my best to answer them. Feel free -to ask about specific incidents, aggregate statistics, or anything else you're curious about. -If you're not sure what to ask, you can try one of the following example queries: - -{"".join([f"" for query in EXAMPLE_QUERIES])} -""" - -# The top-level system prompt provided to the chat agent LLM. -SYSTEM_PROMPT = """You are a helpful agent that answers questions about NTSB -(National Transportation Safety Board) incidents. You have access to a database of incident -reports, each of which has an associated PDF document, as well as metadata about the incident -including the location, date, aircraft type, and more. You can answer questions about the -contents of individual reports, as well as aggregate statistics about the incidents in the -database. You can perform actions such as filtering, sorting, and aggregating the data to -answer questions. You can also provide links to relevant documents and data sources.""" - -# The index name used for Planner query examples. -PLANNER_EXAMPLE_INDEX = "const_ntsb" - -# The example schema used for the Planner query examples. -PLANNER_EXAMPLE_SCHEMA = OpenSearchSchema( - fields={ - "text_representation": OpenSearchSchemaField( - field_type="str", description="Can be assumed to have all other details" - ), - "properties.entity.dateTime": OpenSearchSchemaField( - field_type="str", - examples=[ - "2023-01-12T11:00:00", - "2023-01-11T18:09:00", - "2023-01-10T16:43:00", - "2023-01-28T19:02:00", - "2023-01-12T13:00:00", - ], - ), - "properties.entity.dateAndTime": OpenSearchSchemaField( - field_type="str", - examples=[ - "January 28, 2023 19:02:00", - "January 10, 2023 16:43:00", - "January 11, 2023 18:09:00", - "January 12, 2023 13:00:00", - "January 12, 2023 11:00:00", - ], - ), - "properties.entity.lowestCeiling": OpenSearchSchemaField( - field_type="str", - examples=["Broken 3800 ft AGL", "Broken 6500 ft AGL", "Overcast 500 ft AGL", "Overcast 1800 ft AGL"], - ), - "properties.entity.aircraftDamage": OpenSearchSchemaField( - field_type="str", - examples=["Substantial", "None", "Destroyed"], - ), - "properties.entity.conditions": OpenSearchSchemaField( - field_type="str", - examples=["Instrument (IMC)", "IMC", "VMC", "Visual (VMC)"], - ), - "properties.entity.departureAirport": OpenSearchSchemaField( - field_type="str", - examples=[ - "Somerville, Tennessee", - "Colorado Springs, Colorado (FLY)", - "Yelm; Washington", - "Winchester, Virginia (OKV)", - "San Diego, California (KMYF)", - ], - ), - "properties.entity.accidentNumber": OpenSearchSchemaField( - field_type="str", - examples=["CEN23FA095", "ERA2BLAT1I", "WPR23LA088", "ERA23FA108", "WPR23LA089"], - ), - "properties.entity.windSpeed": OpenSearchSchemaField( - field_type="str", - examples=["", "10 knots", "7 knots", "knots", "19 knots gusting to 22 knots"], - ), - "properties.entity.day": OpenSearchSchemaField( - field_type="str", - examples=["2023-01-12", "2023-01-10", "2023-01-20", "2023-01-11", "2023-01-28"], - ), - "properties.entity.destinationAirport": OpenSearchSchemaField( - field_type="str", - examples=[ - "Somerville, Tennessee", - "Yelm; Washington", - "Agua Caliente Springs, California", - "Liberal, Kansas (LBL)", - "Alabaster, Alabama (EET)", - ], - ), - "properties.entity.location": OpenSearchSchemaField( - field_type="str", - examples=[ - "Hooker, Oklahoma", - "Somerville, Tennessee", - "Yelm; Washington", - "Agua Caliente Springs, California", - "Dayton, Virginia", - ], - ), - "properties.entity.operator": OpenSearchSchemaField( - field_type="str", - examples=["On file", "First Team Pilot Training LLC", "file On", "Anderson Aviation LLC", "Flying W Ranch"], - ), - "properties.entity.temperature": OpenSearchSchemaField( - field_type="str", - examples=["18'C /-2'C", "15.8C", "13'C", "2C / -3C"], - ), - "properties.entity.visibility": OpenSearchSchemaField( - field_type="str", - examples=["", "miles", "0.5 miles", "7 miles", "10 miles"], - ), - "properties.entity.aircraft": OpenSearchSchemaField( - field_type="str", - examples=["Piper PA-32R-301", "Beech 95-C55", "Cessna 172", "Piper PA-28-160", "Cessna 180K"], - ), - "properties.entity.conditionOfLight": OpenSearchSchemaField( - field_type="str", - examples=["", "Night/dark", "Night", "Day", "Dusk"], - ), - "properties.entity.windDirection": OpenSearchSchemaField( - field_type="str", - examples=["", "190°", "200", "2005", "040°"], - ), - "properties.entity.lowestCloudCondition": OpenSearchSchemaField( - field_type="str", - examples=["", "Broken 3800 ft AGL", "Overcast 500 ft AGL", "Clear", "Overcast 200 ft AGL"], - ), - "properties.entity.injuries": OpenSearchSchemaField( - field_type="str", - examples=["Minor", "Fatal", "None", "3 None", "2 None"], - ), - "properties.entity.flightConductedUnder": OpenSearchSchemaField( - field_type="str", - examples=[ - "Part 91: General aviation Instructional", - "Part 135: Air taxi & commuter Non-scheduled", - "Part 91: General aviation Personal", - "Part 135: Air taxi & commuter Scheduled", - "Part 91: General aviation Business", - ], - ), - } -) - -# The following to be replaced by config file. -# https://github.com/aryn-ai/sycamore/pull/843 -PLANNER_EXAMPLES: List[PlannerExample] = [ - PlannerExample( - schema=PLANNER_EXAMPLE_SCHEMA, - plan=LogicalPlan( - query="List the incidents in Georgia in 2023.", - result_node=0, - nodes={ - 0: QueryDatabase( - description="Get all the incident reports", - index=PLANNER_EXAMPLE_INDEX, - node_id=0, - query={ - "bool": { - "must": [ - { - "range": { - "properties.entity.dateTime": { - "gte": "2023-01-01T00:00:00", - "lte": "2023-12-31T23:59:59", - "format": "strict_date_optional_time", - } - } - }, - {"match": {"properties.entity.location": "Georgia"}}, - ] - } - }, - ), - }, - ), - ), - PlannerExample( - schema=PLANNER_EXAMPLE_SCHEMA, - plan=LogicalPlan( - query="Show the incidents involving Piper aircraft.", - result_node=0, - nodes={ - 0: QueryDatabase( - description="Get all the incident reports", - index=PLANNER_EXAMPLE_INDEX, - node_id=0, - query={"match": {"properties.entity.aircraft": "Piper"}}, - ), - }, - ), - ), - PlannerExample( - schema=PLANNER_EXAMPLE_SCHEMA, - plan=LogicalPlan( - query="How many incidents happened in clear weather?", - result_node=1, - nodes={ - 0: QueryDatabase( - description="Get all the incident reports in clear weather", - index=PLANNER_EXAMPLE_INDEX, - node_id=0, - query={"match": {"properties.entity.conditions": "VMC"}}, - ), - 1: Count( - description="Count the number of incidents", - distinct_field="properties.entity.accidentNumber", - inputs=[0], - node_id=1, - ), - }, - ), - ), - PlannerExample( - schema=PLANNER_EXAMPLE_SCHEMA, - plan=LogicalPlan( - query="What types of aircrafts were involved in accidents in California?", - result_node=1, - nodes={ - 0: QueryDatabase( - description="Get all the incident reports in California", - index=PLANNER_EXAMPLE_INDEX, - query={"match": {"properties.entity.location": "California"}}, - node_id=0, - ), - 1: TopK( - description="Get the types of aircraft involved in incidents in California", - field="properties.entity.aircraft", - primary_field="properties.entity.accidentNumber", - K=100, - descending=False, - llm_cluster=False, - llm_cluster_instruction=None, - inputs=[0], - node_id=1, - ), - }, - ), - ), - PlannerExample( - schema=PLANNER_EXAMPLE_SCHEMA, - plan=LogicalPlan( - query="Which aircraft accidents in California in 2023 occurred when the wind was stronger than 4 knots?", - result_node=1, - nodes={ - 0: QueryDatabase( - description="Get all the incident reports in California in 2023", - index=PLANNER_EXAMPLE_INDEX, - query={ - "bool": { - "must": [ - { - "range": { - "properties.entity.dateTime": { - "gte": "2023-01-01T00:00:00", - "lte": "2023-12-31T23:59:59", - "format": "strict_date_optional_time", - } - } - }, - {"match": {"properties.entity.location": "California"}}, - ] - } - }, - node_id=0, - ), - 1: LlmFilter( - description="Filter to reports with wind speed greater than 4 knots", - question="Is the wind speed greater than 4 knots?", - field="properties.entity.windSpeed", - inputs=[0], - node_id=1, - ), - }, - ), - ), - PlannerExample( - schema=PLANNER_EXAMPLE_SCHEMA, - plan=LogicalPlan( - query="Which three aircraft types were involved in the most accidents?", - result_node=1, - nodes={ - 0: QueryDatabase( - description="Get all the incident reports", - index=PLANNER_EXAMPLE_INDEX, - node_id=0, - query={"match_all": {}}, - ), - 1: TopK( - description="Get the top three aircraft types involved in accidents", - field="properties.entity.aircraft", - primary_field="properties.entity.accidentNumber", - K=3, - descending=True, - llm_cluster=False, - llm_cluster_instruction=None, - inputs=[0], - node_id=1, - ), - }, - ), - ), - PlannerExample( - schema=PLANNER_EXAMPLE_SCHEMA, - plan=LogicalPlan( - query="Show some incidents where pilot training was mentioned as a cause", - result_node=0, - nodes={ - 0: QueryVectorDatabase( - description="Get incident reports mentioning pilot training", - index=PLANNER_EXAMPLE_INDEX, - query_phrase="pilot training", - node_id=0, - ), - }, - ), - ), - PlannerExample( - schema=PLANNER_EXAMPLE_SCHEMA, - plan=LogicalPlan( - query="Show all incidents involving a Cessna 172 aircraft", - result_node=0, - nodes={ - 0: QueryDatabase( - description="Get all the incident reports involving a Cessna 172 aircraft", - index=PLANNER_EXAMPLE_INDEX, - query={"match": {"properties.entity.aircraft": "Cessna 172"}}, - node_id=0, - ), - }, - ), - ), -] diff --git a/apps/query-ui/queryui/pages/Chat.py b/apps/query-ui/queryui/pages/Chat.py deleted file mode 100644 index 17fb379c9..000000000 --- a/apps/query-ui/queryui/pages/Chat.py +++ /dev/null @@ -1,383 +0,0 @@ -# This file contains the Chat demo portion of the Sycamore Query UI, which uses an LLM-based -# agent to formulate and render responses from Sycamore queries. - -import argparse -import json -import os -import traceback -from typing import List, Union - -from queryui.chat import ChatMessage, ChatMessageExtra, ChatMessageTraces, MDX_SYSTEM_PROMPT -import queryui.util as util -import queryui.ntsb as ntsb - -from openai import OpenAI -from openai.types.chat import ChatCompletionToolParam -import requests -import streamlit as st -import sycamore -from sycamore.data import OpenSearchQuery -from sycamore import ExecMode -from sycamore.executor import sycamore_ray_init -from sycamore.transforms.query import OpenSearchQueryExecutor -from sycamore.query.client import SycamoreQueryClient -from sycamore.query.result import SycamoreQueryResult - -# The OpenAI model used for the chat agent. -openai_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) -OPENAI_MODEL = "gpt-4o" - -# We assume that OpenSearch is running locally on port 9200. -OS_CLIENT_ARGS = { - "hosts": [{"host": "localhost", "port": 9200}], - "http_compress": True, - "http_auth": ("admin", "admin"), - "use_ssl": True, - "verify_certs": False, - "ssl_assert_hostname": False, - "ssl_show_warn": False, - "timeout": 120, -} - -# These are application- and dataset-specific parameters that can be adjusted as needed. -# The defaults are in ntsb.py. -SYSTEM_PROMPT = ntsb.SYSTEM_PROMPT -PLANNER_EXAMPLES = ntsb.PLANNER_EXAMPLES -WELCOME_MESSAGE = ntsb.WELCOME_MESSAGE - -# The set of tools passed to the LLM. In this case, we only pass a queryDataSource tool, which is used -# to run a Sycamore query. -TOOLS: List[ChatCompletionToolParam] = [ - { - "type": "function", - "function": { - "name": "queryDataSource", - "description": """Run a query against the backend data source. The query should be a natural language - query and can represent operations such as filters, aggregations, sorting, formatting, - and more. This function should only be called when new data is needed; if the data - is already available in the message history, it should be used directly.""", - "parameters": { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "The user's query", - }, - }, - "required": ["query"], - }, - }, - }, -] - - -# The set of tools passed to the LLM when "RAG only" mode is chosen. In this case, we only pass -# a queryDataSource tool, which performs a vector search lookup, leaving the rest of the RAG -# processing to the agent itself. -TOOLS_RAG: List[ChatCompletionToolParam] = [ - { - "type": "function", - "function": { - "name": "queryDataSource", - "description": """Run a query against the backend data source. The query should be a - natural language query. This function should only be called when new data is needed; - if the data is already available in the message history, it should be used directly.""", - "parameters": { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "The user's query", - }, - }, - "required": ["query"], - }, - }, - }, -] - - -def get_embedding_model_id() -> str: - """Get the embedding model ID from the OpenSearch instance. Used for RAG queries.""" - query = { - "query": { - "bool": { - "must": [ - { - "match": {"name": "all-MiniLM-L6-v2"}, - }, - { - "term": {"model_config.model_type": "bert"}, - }, - ], - }, - }, - } - with requests.get( - "https://localhost:9200/_plugins/_ml/models/_search", json=query, verify=False, timeout=60 - ) as resp: - res = json.loads(resp.text) - return res["hits"]["hits"][0]["_id"] - - -def do_rag_query(query: str, index: str) -> str: - """Uses OpenSearch to perform a RAG query.""" - embedding_model_id = get_embedding_model_id() - search_pipeline = "hybrid_rag_pipeline" - llm = "gpt-4o" - rag_query = OpenSearchQuery() - rag_query["index"] = index - rag_query["query"] = { - "_source": {"excludes": ["embedding"]}, - "query": { - "hybrid": { - "queries": [ - {"match": {"text_representation": query}}, - { - "neural": { - "embedding": { - "query_text": query, - "model_id": embedding_model_id, - "k": 100, - } - } - }, - ] - } - }, - "size": 20, - } - - # RAG params - rag_query["params"] = {"search_pipeline": search_pipeline} - rag_query["query"]["ext"] = { - "generative_qa_parameters": { - "llm_question": query, - "context_size": 10, - "llm_model": llm, - } - } - - with st.expander("RAG query"): - st.write(rag_query) - - with st.spinner("Running RAG query..."): - osq = OpenSearchQueryExecutor(OS_CLIENT_ARGS) - rag_result = osq.query(rag_query)["result"] - return rag_result - - -def query_data_source(query: str, index: str) -> Union[SycamoreQueryResult, str]: - """Run a Sycamore or RAG query. - - Returns a tuple of (query_result, query_plan, query_id). - """ - - if st.session_state.rag_only: - return do_rag_query(query, index) - else: - sqclient = SycamoreQueryClient( - llm_cache_dir=st.session_state.llm_cache_dir, - cache_dir=st.session_state.cache_dir, - sycamore_exec_mode=ExecMode.LOCAL if st.session_state.local_mode else ExecMode.RAY, - ) - with st.spinner("Generating plan..."): - plan = util.generate_plan(sqclient, query, index, examples=PLANNER_EXAMPLES) - print(f"Generated plan:\n{plan}\n") - # No need to show the prompt used in the demo. - plan.llm_prompt = None - with st.expander("Query plan"): - st.write(plan) - with st.spinner("Running Sycamore query..."): - print("Running plan...") - result = util.run_plan(sqclient, plan) - print(f"Ran query ID: {result.query_id}") - return result - - -def show_messages(): - """Show all user and assistant messages.""" - for msg in st.session_state.messages: - if msg.message.get("role") not in ["user", "assistant"]: - continue - if msg.message.get("content") is None: - continue - msg.show() - - -def do_query(): - """Run a query based on the user's input.""" - - prompt = st.session_state.user_query - st.session_state.user_query = None - print(f"User query: {prompt}") - user_message = ChatMessage({"role": "user", "content": prompt}) - st.session_state.messages.append(user_message) - user_message.show() - - assistant_message = None - query_result = None - tool_response_str = None - with st.chat_message("assistant"): - # We loop here because tool calls require re-invoking the LLM. - while True: - system_prompt = { - "role": "system", - "content": SYSTEM_PROMPT + MDX_SYSTEM_PROMPT, - } - messages = [system_prompt] + [m.to_dict() for m in st.session_state.messages] - with st.spinner("Running LLM query..."): - response = openai_client.chat.completions.create( - model=OPENAI_MODEL, - messages=messages, - tools=TOOLS_RAG if st.session_state.rag_only else TOOLS, - tool_choice="auto", - ) - response_dict = response.choices[0].message.to_dict() - assistant_message = ChatMessage(response_dict) - st.session_state.messages.append(assistant_message) - - tool_calls = response.choices[0].message.tool_calls - if tool_calls: - tool_call_id = tool_calls[0].id - tool_function_name = tool_calls[0].function.name - tool_response_str = "" - query_result = None - # Try to catch any errors that might corrupt the message history here. - try: - tool_args = json.loads(tool_calls[0].function.arguments) - if tool_function_name == "queryDataSource": - tool_query = tool_args["query"] - raw_result = query_data_source(tool_query, st.session_state.index) - if isinstance(raw_result, SycamoreQueryResult): - query_result = raw_result - tool_response = query_result.result - else: - tool_response = raw_result - else: - tool_response = f"Unknown tool: {tool_function_name}" - - with st.spinner("Running Sycamore query..."): - if isinstance(tool_response, str): - # We got a straight string response from the query plan, which means we can - # feed it back to the LLM directly. - tool_response_str = tool_response - elif isinstance(tool_response, sycamore.DocSet): - # We got a DocSet result. - tool_response_str = util.docset_to_string(tool_response, html=False) - else: - # Fall back to string representation. - tool_response_str = str(tool_response) - if not tool_response_str: - tool_response_str = "No results found for your query." - except Exception as e: # pylint: disable=broad-except - st.error(f"Error running Sycamore query: {e}") - print(f"Error running Sycamore query: {e}") - # Print stack trace. - - traceback.print_exc() - tool_response_str = f"There was an error running your query: {e}" - finally: - print(f"\nTool response: {tool_response_str}") - tool_response_message = ChatMessage( - { - "role": "tool", - "content": tool_response_str, - "tool_call_id": tool_call_id, - "name": tool_function_name, - } - ) - st.session_state.messages.append(tool_response_message) - with st.expander("Sycamore query result"): - st.write(f"```{tool_response_str}```") - - else: - # No function call was made. - assistant_message.show_content() - if query_result: - assistant_message.before_extras.append(ChatMessageExtra("Query plan", query_result.plan)) - if tool_response_str: - assistant_message.before_extras.append( - ChatMessageExtra("Sycamore query result", f"```{tool_response_str}```") - ) - if query_result: - cmt = ChatMessageTraces("Query trace", query_result) - with st.expander("Query trace"): - cmt.show() - assistant_message.after_extras.append(cmt) - break - - -def main(): - argparser = argparse.ArgumentParser() - argparser.add_argument( - "--index", help="OpenSearch index name to use. If specified, only this index will be queried." - ) - argparser.add_argument("--local-mode", action="store_true", help="Enable Sycamore local execution mode.") - argparser.add_argument("--title", type=str, help="Title text.") - argparser.add_argument("--cache-dir", type=str, help="Query execution cache dir.") - argparser.add_argument("--llm-cache-dir", type=str, help="LLM query cache dir.") - argparser.add_argument("--trace-dir", type=str, help="Directory to store query traces.") - args = argparser.parse_args() - - if "index" not in st.session_state: - st.session_state.index = args.index - - if "cache_dir" not in st.session_state: - st.session_state.cache_dir = args.cache_dir - - if "llm_cache_dir" not in st.session_state: - st.session_state.llm_cache_dir = args.llm_cache_dir - - if "trace_dir" not in st.session_state: - st.session_state.trace_dir = args.trace_dir - - if "use_cache" not in st.session_state: - st.session_state.use_cache = True - - if "local_mode" not in st.session_state: - st.session_state.local_mode = args.local_mode - - if "next_message_id" not in st.session_state: - st.session_state.next_message_id = 0 - - if "user_query" not in st.session_state: - st.session_state.user_query = None - - if "messages" not in st.session_state: - st.session_state.messages = [ChatMessage({"role": "assistant", "content": WELCOME_MESSAGE})] - - if "trace_dir" not in st.session_state: - st.session_state.trace_dir = os.path.join(os.getcwd(), "traces") - - if not args.local_mode: - sycamore_ray_init(address="auto") - st.title("Sycamore Query Chat") - st.toggle("Use RAG only", key="rag_only") - - if not args.index: - with st.spinner("Loading indices..."): - try: - st.session_state.indices = util.get_opensearch_indices() - except Exception as e: - st.error(f"Unable to load OpenSearch indices. Is OpenSearch running?\n\n{e}") - return - st.selectbox( - "Index", - util.get_opensearch_indices(), - key="index", - placeholder="Select an index", - label_visibility="collapsed", - ) - - if st.session_state.index: - show_messages() - if prompt := st.chat_input("Ask me anything"): - st.session_state.user_query = prompt - - while st.session_state.user_query is not None: - do_query() - - -if __name__ == "__main__": - main() diff --git a/apps/query-ui/queryui/tests/integration/Chat_test.py b/apps/query-ui/queryui/tests/integration/Chat_test.py deleted file mode 100644 index 73bdf904c..000000000 --- a/apps/query-ui/queryui/tests/integration/Chat_test.py +++ /dev/null @@ -1,274 +0,0 @@ -#!/usr/bin/env python - -# This is a set of tests for Chat.py. -# Run with: -# poetry run queryui/main.py --test [pytest-args] -# where [pytest-args] are any additional arguments you want to pass to pytest. -# -# Recommended usage: -# poetry run python queryui/main.py --test --show-capture=stdout - -import json -import tempfile -from unittest.mock import patch -from typing import Any, Callable, Dict, Generator, List, Optional, Tuple - -import pytest -from streamlit.testing.v1 import AppTest -from streamlit.testing.v1.element_tree import Block, Expander, Json, Node, ChatMessage, ElementTree - - -# This is the index that must exist locally that is used for testing. -# It is assumed that this index contains the NTSB dataset populated by -# `loaddata.py` in the `queryui` directory. -TEST_INDEX = "const_ntsb" - - -@pytest.fixture -def app() -> Generator[AppTest, None, None]: - """Fixture to start the app.""" - with tempfile.TemporaryDirectory() as cache_dir, tempfile.TemporaryDirectory() as llm_cache_dir: - print(f"Test cache dir: {cache_dir}") - print(f"Test LLM cache dir: {llm_cache_dir}") - with patch( - "sys.argv", ["Chat.py", "--index", TEST_INDEX, "--cache-dir", cache_dir, "--llm-cache-dir", llm_cache_dir] - ): - at = AppTest.from_file("../../pages/Chat.py").run() - yield at - - -def get_last_message(at: AppTest) -> ChatMessage: - """Return the last message in the chat.""" - return at.chat_message[-1] - - -def do_query(at: AppTest, query: str, timeout: int = 120) -> ChatMessage: - """Run a query and return the last message result.""" - at.chat_input[0].set_value(query).run(timeout=timeout) - return get_last_message(at) - - -def walk(node: Node, fn: Callable[[Node, Any], Any], initial: Optional[Any] = None) -> Any: - """Walk the node tree, running the given function on each node with the current value of the accumulator.""" - retval = fn(node, initial) - if hasattr(node, "children"): - for child in node.children.values(): - retval = walk(child, fn, retval) - return retval - - -def expect_any(node: Node, fn: Callable[[Node], bool]): - """Expect that at least one node in the tree satisfies the given function.""" - - def collect(node, retval): - retval.append(fn(node)) - return retval - - assert any(walk(node, collect, [])) - - -def expect_all(node: Node, fn: Callable[[Node], bool]): - """Expect that all nodes in the tree satisfy the given function.""" - - def collect(node, retval): - retval.append(fn(node)) - return retval - - assert all(walk(node, collect, [])) - - -def expect_none(node: Node, fn: Callable[[Node], bool]): - """Expect that no nodes in the tree satisfy the given function.""" - - def collect(node, retval): - retval.append(fn(node) is False) - return retval - - assert all(walk(node, collect, [])) - - -def get_markdown(node: Node) -> str: - """Return the markdown content of the given node tree.""" - - def markdown(node: Node, retval: str) -> str: - if node.type == "markdown": - assert hasattr(node, "value") - retval += node.value - elif node.type == "arrow_data_frame": - assert hasattr(node, "value") - retval += node.value.to_markdown() - elif node.type == "header": - assert hasattr(node, "value") - retval += node.value - elif node.type == "subheader": - assert hasattr(node, "value") - retval += node.value - return retval + "\n\n" - - return walk(node, markdown, "") - - -@pytest.fixture -def block_tree() -> Block: - """Fixture to create a test block tree.""" - - class TestBlock(Block): - def __init__(self, label: str, root: ElementTree, children: Dict[int, Node] = {}): - super().__init__(proto=None, root=root) - self.label = label - self.children = children - - def __str__(self): - return f"" - - root = ElementTree() - return TestBlock( - "top", - root, - { - 0: TestBlock("a", root), - 1: TestBlock( - "b", - root, - { - 3: TestBlock("b1", root), - 4: TestBlock("b2", root), - }, - ), - 2: TestBlock("c", root), - }, - ) - - -def test_walk(block_tree): - """Test the walk function.""" - result = [] - - def print_node(node, count): - nonlocal result - result.append(str(node) + ":" + str(count)) - return count + 1 - - assert walk(block_tree, print_node, 0) == 6 - assert result == [ - ":0", - ":1", - ":2", - ":3", - ":4", - ":5", - ] - - -def test_expect_any(block_tree): - """Test the expect_any function.""" - top = block_tree - expect_any(top, lambda n: n.label == "a") - expect_any(top, lambda n: n.label == "b") - expect_any(top, lambda n: n.label == "c") - expect_any(top, lambda n: n.label == "a" or n.label == "b2") - with pytest.raises(AssertionError): - expect_any(top, lambda n: n.label == "d") - - -def test_expect_all(block_tree): - """Test the expect_all function.""" - top = block_tree - expect_all(top, lambda n: n.label) - with pytest.raises(AssertionError): - expect_all(top, lambda n: n.label == "a") - - -def test_expect_none(block_tree): - """Test the expect_none function.""" - top = block_tree - expect_none(top, lambda n: n.label == "d") - with pytest.raises(AssertionError): - expect_none(top, lambda n: n.label == "a") - - -def test_app_starts(app): - """Test that the app starts without raising an exception.""" - assert app.title[0].value == "Sycamore Query Chat" - assert not app.exception - - -def check_query_result(result: ChatMessage) -> Tuple[str, str]: - """Check that the query result satisfies the expected format. - - Returns the markdown content of the result text and the query traces. - """ - - # Should be no exception nodes. - expect_none(result, lambda x: x.type == "exception") - - # Query plan should be present. - expanders: List[Expander] = [ - x for x in result.children.values() if x.type == "expander" and isinstance(x, Expander) - ] - # assert all([isinstance(x, Expander) for x in expanders]) - assert expanders[0].label == "Query plan" - assert expanders[0].children[0].type == "json" - assert isinstance(expanders[0].children[0], Json) - query_plan_json = json.loads(expanders[0].children[0].value) - assert isinstance(query_plan_json, dict) - assert "query" in query_plan_json - assert set(query_plan_json.keys()) == {"query", "nodes", "result_node", "llm_prompt", "llm_plan"} - - # Sycamore query result should be present. - assert expanders[1].label == "Sycamore query result" - assert expanders[1].children[0].type == "markdown" - - # Query traces should be present. - assert expanders[2].label == "Query trace" - print(expanders[2]) - - assert expanders[2].children[0].type == "vertical" - assert expanders[2].children[0].children[0].children[0].label == "Node data" # type: ignore - assert expanders[2].children[0].children[0].children[1].label == "Query plan" # type: ignore - assert expanders[2].children[0].children[0].children[0].children[1].type == "markdown" - return get_markdown(result), get_markdown(expanders[2].children[0].children[0].children[0]) - - -def test_count_query(app): - """Test a basic count query.""" - result = do_query(app, "How many incidents were there in Washington?") - md, traces = check_query_result(result) - - md = get_markdown(result) - assert "3" in md or "three" in md - - assert "Node 0 - QueryDatabase" in traces - assert "Yelm" in traces - assert "Dallesport" in traces - assert "Kent" in traces - assert "WPR23LA086" in traces - assert "WPR23LA089" in traces - assert "WPR2BLA101" in traces - - assert "Node 1 - Count" in traces - assert "Node type Count does not produce document traces" in traces - - -def test_aircraft_types_query(app): - """Test a query for aircraft types.""" - result = do_query(app, "What types of aircrafts were involved in accidents in California?") - md, _ = check_query_result(result) - - cessnas = ["Cessna 172", "Cessna 180K", "Cessna 195A", "Cessna 414", "Cessna T21ON"] - piper = ["Piper PA-28-180", "Piper PAZ8R"] - - assert sum(1 for c in cessnas if c in md) >= 3 - assert any(p in md for p in piper) - - -def test_rag_count_query(app): - """Test a RAG count query.""" - app.toggle[0].set_value(True).run() - result = do_query(app, "How many incidents were there in Washington?") - expanders = [x for x in result.children.values() if x.type == "expander"] - assert expanders[0].label == "RAG query" - assert expanders[1].label == "Sycamore query result" - - md = get_markdown(result) - assert "incidents" in md diff --git a/apps/query-ui/queryui/util.py b/apps/query-ui/queryui/util.py deleted file mode 100644 index 080e0c199..000000000 --- a/apps/query-ui/queryui/util.py +++ /dev/null @@ -1,306 +0,0 @@ -import base64 -import json -import os -import pickle -from typing import Any, List, Set, Tuple - -import boto3 -import requests -import pandas as pd -import streamlit as st - -from sycamore.docset import DocSet -from sycamore.data import MetadataDocument -from sycamore.query.client import SycamoreQueryClient -from sycamore.query.logical_plan import LogicalPlan -from sycamore.query.planner import PlannerExample -from sycamore.query.result import SycamoreQueryResult -from sycamore.schema import Schema - -from queryui.configuration import get_sycamore_query_client - - -def get_schema(_client: SycamoreQueryClient, index: str) -> Schema: - """Return the OpenSearch schema for the given index.""" - return _client.get_opensearch_schema(index) - - -def generate_plan(_client: SycamoreQueryClient, query: str, index: str, examples: List[PlannerExample]) -> LogicalPlan: - """Generate a query plan for the given query and index.""" - return _client.generate_plan(query, index, get_schema(_client, index), examples=examples) - - -def run_plan(_client: SycamoreQueryClient, plan: LogicalPlan) -> SycamoreQueryResult: - """Run the given plan.""" - return _client.run_plan(plan) - - -def get_opensearch_indices() -> Set[str]: - """Return a list of OpenSearch indices.""" - return {x for x in get_sycamore_query_client().get_opensearch_indices() if not x.startswith(".")} - - -def result_to_string(result: Any) -> str: - """Convert the given query result to a string.""" - if isinstance(result, str): - # We got a straight string response from the query plan, which means we can - # return it directly. - return result - elif isinstance(result, DocSet): - # We got a DocSet. - return docset_to_string(result) - else: - # Fall back to string representation. - return str(result) - - -NUM_DOCS_GENERATE = 60 -NUM_TEXT_CHARS_GENERATE = 2500 - - -def docset_to_string(docset: DocSet, html: bool = True) -> str: - """Render the given DocSet as a string. - - Args: - docset: The DocSet to render. - html: Whether to render as HTML. Otherwise, JSON is used. - """ - - BASE_PROPS = [ - "filename", - "filetype", - "page_number", - "page_numbers", - "links", - "element_id", - "parent_id", - "_schema", - "_schema_class", - "entity", - ] - retval = "" - for doc in docset.take(NUM_DOCS_GENERATE): - if isinstance(doc, MetadataDocument): - continue - if html: - retval += f"**{doc.properties.get('path')}** \n" - - retval += "| Property | Value |\n" - retval += "|----------|-------|\n" - - props_dict = doc.properties.get("entity", {}) - props_dict.update({p: doc.properties[p] for p in set(doc.properties) - set(BASE_PROPS)}) - - for k, v in props_dict.items(): - retval += f"| {k} | {v} |\n" - - retval += "\n\n" - text_content = ( - doc.text_representation[:NUM_TEXT_CHARS_GENERATE] if doc.text_representation is not None else None - ) - if text_content: - retval += f'*..."{text_content}"...*

' - else: - props_dict = doc.properties.get("entity", {}) - props_dict.update({p: doc.properties[p] for p in set(doc.properties) - set(BASE_PROPS)}) - props_dict["text_representation"] = ( - doc.text_representation[:NUM_TEXT_CHARS_GENERATE] if doc.text_representation is not None else None - ) - # The DocumentSource is not JSON serializable. - if "_doc_source" in props_dict: - props_dict["_doc_source"] = None - - retval += json.dumps(props_dict, indent=2) + "\n" - return retval - - -def parse_s3_path(s3_path: str) -> Tuple[str, str]: - """Parse an S3 path into a bucket and key.""" - s3_path = s3_path.replace("s3://", "") - bucket, key = s3_path.split("/", 1) - return bucket, key - - -class PDFPreview: - """Display a preview of the given PDF file.""" - - def __init__(self, path: str): - self.path = path - - def show(self): - if self.path.startswith("s3://"): - bucket, key = parse_s3_path(self.path) - s3 = boto3.client("s3") - response = s3.get_object(Bucket=bucket, Key=key) - content = response["Body"].read() - elif self.path.startswith("http"): - content = requests.get(self.path, timeout=30).content - else: - st.write(f"Unknown path format: {self.path}") - return - - st.text(self.path) - encoded = base64.b64encode(content).decode("utf-8") - pdf_display = ( - f'' - ) - st.markdown(pdf_display, unsafe_allow_html=True) - - -@st.dialog("Document preview", width="large") -def show_pdf_preview(path: str): - """Display a preview of the given document in a dialog.""" - PDFPreview(path).show() - - -class QueryNodeTrace: - """Helper class to read and display the trace of a single query node.""" - - # The order here is chosen to ensure that the most important columns are shown first. - # The logic below ensure that additional columns are also shown, and that empty columns - # are omitted. - COLUMNS = [ - "properties.path", - "properties.key", - "properties.count", - "doc_id", - "parent_id", - "text_representation", - ] - - def __init__(self, result: SycamoreQueryResult, node_id: int): - self.result = result - self.node_id = node_id - self.df = None - self.num_files = 0 - self.readdata() - - def readfile(self, f): - """Read the given trace file.""" - with open(f, "rb") as file: - try: - doc = pickle.load(file) - except EOFError: - return None - - # For now, skip over MetadataDocuments. - if "metadata" in doc.keys(): - return None - - # Flatten properties. - if "properties" in doc: - for prop in doc["properties"]: - if isinstance(doc["properties"][prop], dict): - for nested_property in doc["properties"][prop]: - doc[".".join(["properties", prop, nested_property])] = doc["properties"][prop][ - nested_property - ] - else: - doc[".".join(["properties", prop])] = doc["properties"][prop] - doc.pop("properties") - - # Keep only the columns we care about. - # Limit size of each to avoid blowing out memory. - def format_value(value): - """Format the given value for display.""" - MAX_CHARS = 1024 - if isinstance(value, str): - if len(value) > MAX_CHARS: - return value[:MAX_CHARS] + "..." - else: - return value - if isinstance(value, dict): - return format_value(str(value)) - if isinstance(value, list): - return format_value(str(value)) - return value - - row = {k: format_value(doc.get(k)) for k in doc.keys() if k in self.COLUMNS or k.startswith("properties.")} - return row - - def readdata(self): - """Read the trace data for this node.""" - - if not self.result.execution: - return - - if self.node_id not in self.result.execution: - return - - directory = self.result.execution[self.node_id].trace_dir - if not directory: - return - - # We need to read all of the individual docs to ensure we get all of the parent - # docs. With a very large number of docs, we are likely to blow out memory doing - # this. Unfortunately there's no easy way to tell up-front that a given stage in - # the pipeline has a mix of parent and child docs, unless we do two passes. - # Just a heads up that with a larger number of docs, we may need to revisit this. - docs = [] - for filename in os.listdir(directory): - f = os.path.join(directory, filename) - if os.path.isfile(f): - self.num_files += 1 - newdoc = self.readfile(f) - if newdoc: - docs.append(newdoc) - - # Only keep parent docs if there are child docs in the list. - parent_docs = [x for x in docs if x.get("parent_id") is None] - if len(parent_docs) > 0: - docs = parent_docs - - # Transpose data to a dict where each key is a column, and each value is a list of rows. - if len(docs) > 0: - all_keys = {k for d in docs for k in d.keys()} - data = {col: [] for col in all_keys} - for row in docs: - for col in all_keys: - data[col].append(row.get(col)) - self.df = pd.DataFrame(data) - - def show(self, node): - """Render the trace data.""" - st.subheader(f"Node {self.node_id} - {node.node_type if node else 'n/a'}") - st.markdown(f"*Description: {node.description if node else 'n/a'}*") - if self.df is None or not len(self.df): - if self.num_files > 0: - st.write(":green[Cache hit - node execution skipped!]") - else: - st.write(f":green[Node type {node.node_type} does not produce document traces]") - return - - all_columns = list(self.df.columns) - column_order = [c for c in self.COLUMNS if c in all_columns] - column_order += [c for c in all_columns if c not in column_order] - st.write(f"**{len(self.df)} documents**") - st.dataframe(self.df, column_order=column_order) - - -class QueryTrace: - """Helper class used to read and display query traces.""" - - def __init__(self, result: SycamoreQueryResult): - self.node_traces = [] - self.query_plan = result.plan - for node_id in sorted(result.plan.nodes.keys()): - self.node_traces += [QueryNodeTrace(result, node_id)] - - def show(self): - tab1, tab2 = st.tabs(["Node data", "Query plan"]) - with tab1: - for node_trace in self.node_traces: - node_trace.show(self.query_plan.nodes.get(int(node_trace.node_id), None)) - with tab2: - if self.query_plan is not None: - st.write(f"Query: {self.query_plan.query}") - st.write(self.query_plan) - else: - st.write("No query plan found") - - -@st.fragment -def show_query_traces(result: SycamoreQueryResult): - """Show the query traces for the given query result.""" - QueryTrace(result).show() diff --git a/apps/query-ui/run-query-ui.sh b/apps/query-ui/run-query-ui.sh deleted file mode 100755 index c02b6d429..000000000 --- a/apps/query-ui/run-query-ui.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -if [[ ! -f apps/query-ui/queryui/Sycamore_Query.py ]]; then - echo "Run me as ./apps/query-ui/run-query-ui.sh" - exit 1 -fi -[[ "${QUERY_CACHE}" == "" ]] && export QUERY_CACHE=/app/work/cache_dir -if [[ "${QUERY_CACHE}" == s3://* ]]; then - # e.g. s3://aryn-temp/llm_cache/luna/ntsb - : -elif [[ ! -d "${QUERY_CACHE}" ]]; then - echo "Error: ${QUERY_CACHE} not present." - echo "Override default cache with QUERY_CACHE environment variable, or if running via docker, either:" - echo "a) use docker compose up query-ui" - echo "b) incude:" - echo ' -mount type=bind,source=\"$(pwd)/apps/query-ui/cache_dir",target=/app/work/cache_dir' - echo " in your docker run command line" - exit 1 -fi - -i=0 -while [[ "${OPENSEARCH_HOST}" == "" ]]; do - i=$(expr $i + 1) - if [[ $i -gt 120 ]]; then - echo "Waiting 120 seconds to find opensearch. Giving up." - exit 1 - fi - echo "Searching for opensearch try $i/120..." - if curl -k https://localhost:9200 >/dev/null 2>&1; then - export OPENSEARCH_HOST=localhost - elif curl -k https://opensearch:9200 >/dev/null 2>&1; then - export OPENSEARCH_HOST=opensearch - else - echo " unable to find opensearch at either localhost:9200 or opensearch:9200" - fi - sleep 1 -done - -echo "Inferred opensearch host ${OPENSEARCH_HOST}" - -# poetry run ray start --head -# poetry run python -m streamlit run apps/query-ui/queryui/Sycamore_Query.py -# might also work. Then move the restart logic in here. -poetry run python apps/query-ui/external_ray_wrapper.py diff --git a/lib/sycamore/pyproject.toml b/lib/sycamore/pyproject.toml index 6773cd7cb..60088f56e 100644 --- a/lib/sycamore/pyproject.toml +++ b/lib/sycamore/pyproject.toml @@ -150,10 +150,6 @@ line-length = 120 [tool.black] line-length = 120 -[mypy] -mypy_path = "crawler/s3:crawler/http" -explicit_package_bases = true - [tool.mypy] exclude = ["notebooks", "docs"] ignore_missing_imports = true diff --git a/poetry.lock b/poetry.lock index f6b7f6728..c9099e58b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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[tool.mypy] exclude = ["notebooks", "docs"] ignore_missing_imports = true @@ -89,12 +82,6 @@ integration = "apps/integration" ### be installed and uninstalled when moving between directories. Not symlinking the virtual envs ### causes duplication. -[tool.poetry.group.query-ui.dependencies] -sycamore-ai = { path = "lib/sycamore", develop = true, extras = ["opensearch"] } -streamlit = "^1.36.0" -streamlit-ace = "^0.1.1" -streamlit-agraph = "^0.0.45" - ### BEGIN Auto-generated by autogen-groups.py [tool.poetry.group.docs.dependencies] sycamore-ai = { path = "lib/sycamore", develop = true, extras = ["docs"] } From 44b24ec76f114e0c623ea5fa444010c6ea4ffe1f Mon Sep 17 00:00:00 2001 From: Karan Sampath <176953591+karanataryn@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:11:20 -0800 Subject: [PATCH 4/5] query-ui change --- .gitignore | 1 - .pre-commit-config.yaml | 22 ---------------------- 2 files changed, 23 deletions(-) diff --git a/.gitignore b/.gitignore index bc7c2a6bd..55a2ef38a 100644 --- a/.gitignore +++ b/.gitignore @@ -40,7 +40,6 @@ lib/sycamore/dist/ luna_traces/ traces/ apps/jupyter/bind_dir/poetry_cache -apps/query-ui/cache_dir docs/build cache notebooks/default-prep-data diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ed5ffcbb9..755b17988 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -32,28 +32,6 @@ repos: args: [--install-types, --explicit-package-bases, --non-interactive] exclude: docs/ - - repo: local - hooks: - - id: pytest-check - name: pytest-check - # Needed because otherwise it seems to always run pytest in the git root. - entry: poetry run pytest lib/sycamore/sycamore/tests/unit apps/crawler/crawler/s3/tests/unit - # lib/remote-processors/remote_processors/test/unit - # not run here but via github actions because it requires a make step to happen first - # and it requires a poetry env setup also. - language: system - pass_filenames: false - always_run: true - - - repo: local - hooks: - - id: http-integration-test - name: http-integration-test - entry: bash apps/crawler/crawler/http/tests/http_crawler_integration.sh - language: system - pass_filenames: false - always_run: true - - repo: https://github.com/kynan/nbstripout rev: 0.7.1 hooks: From 415d35547c2b60dc90c4006226764c39e505531b Mon Sep 17 00:00:00 2001 From: Karan Sampath <176953591+karanataryn@users.noreply.github.com> Date: Fri, 31 Jan 2025 10:24:10 -0800 Subject: [PATCH 5/5] comments and add caching --- .github/workflows/testing.yml | 10 +++++----- .pre-commit-config.yaml | 9 +++++++++ 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index f6296447c..1dfa8a393 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -53,7 +53,7 @@ jobs: - name: Install poetry run: pipx install poetry - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: useblacksmith/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: "poetry" @@ -101,7 +101,7 @@ jobs: - name: Install poetry run: pipx install poetry - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: useblacksmith/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: "poetry" @@ -170,7 +170,7 @@ jobs: - name: Install poetry run: pipx install poetry - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: useblacksmith/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: "poetry" @@ -211,7 +211,7 @@ jobs: - name: Install poetry run: pipx install poetry - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: useblacksmith/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: "poetry" @@ -250,7 +250,7 @@ jobs: - name: Install poetry run: pipx install poetry - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: useblacksmith/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: "poetry" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 755b17988..453fff5cb 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -31,6 +31,15 @@ repos: # same args as used in .github/workflows/linting.yml args: [--install-types, --explicit-package-bases, --non-interactive] exclude: docs/ + - repo: local + hooks: + - id: pytest-check + name: pytest-check + # Needed because otherwise it seems to always run pytest in the git root. + entry: poetry run pytest lib/sycamore/sycamore/tests/unit + language: system + pass_filenames: false + always_run: true - repo: https://github.com/kynan/nbstripout rev: 0.7.1