diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..1c48dd6 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,25 @@ +[run] +source = llm_agents +omit = + */gradio/* + */tests/* + */__pycache__/* + */.* + */venv/* + */.venv/* + +[report] +exclude_lines = + pragma: no cover + def __repr__ + if self.debug: + if settings.DEBUG + raise AssertionError + raise NotImplementedError + if 0: + if __name__ == .__main__.: + class .*\bProtocol\): + @(abc\.)?abstractmethod + +[html] +directory = htmlcov \ No newline at end of file diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..f07ed7e --- /dev/null +++ b/.dockerignore @@ -0,0 +1,46 @@ +# Python +__pycache__ +*.py[cod] +*$py.class +*.so +.Python +*.egg-info +dist +build +.eggs + +# Virtual environments +.venv +venv +ENV +env + +# IDE +.vscode +.idea +*.swp +*.swo +*~ + +# Version control +.git +.gitignore + +# OS +.DS_Store +Thumbs.db + +# Testing +.pytest_cache +.coverage +htmlcov + +# Documentation +docs/_build + +# Logs +*.log + +# Local development files +.env.local +*.local \ No newline at end of file diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml new file mode 100644 index 0000000..8ddb3f3 --- /dev/null +++ b/.github/workflows/tests.yaml @@ -0,0 +1,35 @@ +name: Tests + +on: + push: + branches: [ main, master, dev ] + pull_request: + branches: [ main, master, dev ] + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ['3.10'] + + steps: + - uses: actions/checkout@v4 + with: + submodules: recursive + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Install uv + uses: astral-sh/setup-uv@v3 + + - name: Install dependencies + run: uv sync + + - name: Run tests with coverage + run: | + uv run pytest --cov=llm_agents --cov-report=term --cov-report=json tests/ + diff --git a/.gitignore b/.gitignore index bfaba38..7c91ed3 100644 --- a/.gitignore +++ b/.gitignore @@ -143,6 +143,7 @@ venv/ ENV/ env.bak/ venv.bak/ +.idea/ # Spyder project settings .spyderproject @@ -209,4 +210,6 @@ __marimo__/ data/*.json .vscode/ -rag_index_json/ \ No newline at end of file +rag_index_json/ +*.pkl +*.json \ No newline at end of file diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..e69de29 diff --git a/.python-version b/.python-version index 6324d40..7c7a975 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.14 +3.10 \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..d5e5989 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,165 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Project Overview + +This is LLMAgents, a Python package for AI research analysis agents. The system helps researchers browse papers, find similar papers, write summaries, and plan conference schedules using Neo4j knowledge graphs and LLM agents. + +## Key Commands + +### Environment Setup +```bash +# Install dependencies +uv sync + +# Setup environment variables (required before running) +export $(grep -v '^#' .env | xargs) + +# Prepare gradio from source (for Python 3.14 compatibility) +bash scripts/prepare_gradio.sh +``` + +### Running the Application +```bash +# CLI interface +uv run agentic-nav-cli -t 0.4 --max-tokens 6000 -c 131072 --max-num-papers 10 + +# Web interface +agentic-nav-web +``` + +### Database and Knowledge Graph +```bash +# Start required services +docker compose up neo4j_db ollama_embed ollama_agent -d + +# Build knowledge graph from NeurIPS 2025 data +uv run agentic_nav/tools/knowledge_graph/graph_generator.py \ + --input-json-file data/neurips-2025-orals-posters.json \ + --embedding-model $EMBEDDING_MODEL_NAME \ + --ollama-server-url $EMBEDDING_MODEL_API_BASE \ + --embedding-gen-batch-size 32 \ + --max-parallel-workers 28 \ + --similarity-threshold 0.8 \ + --output-file graphs/knowledge_graph.pkl + +# Import knowledge graph to Neo4j +uv run agentic_nav/tools/knowledge_graph/neo4j_db_importer.py \ + --graph-path graphs/knowledge_graph.pkl \ + --neo4j-uri bolt://localhost:7687 \ + --batch-size 100 \ + --embedding-dimension 768 +``` + +### Testing +```bash +# Run all tests (recommended - avoids gradio conflicts) +uv run pytest tests/ + +# Run tests with coverage report +uv run pytest tests/ --cov=agentic_nav --cov-report=term-missing + +# Alternative: Use the custom test runner +python run_tests.py + +# Run specific test categories +uv run pytest tests/ -m unit # Unit tests only +uv run pytest tests/ -m integration # Integration tests only +uv run pytest tests/ -m "not slow" # Skip slow tests + +# Run tests for specific module +uv run pytest tests/agents/ +uv run pytest tests/tools/ +uv run pytest tests/utils/ +uv run pytest tests/frontend/ + +# Run single test file +uv run pytest tests/agents/test_base.py + +# Run with verbose output +uv run pytest tests/ -v + +# Generate HTML coverage report +uv run pytest tests/ --cov=agentic_nav --cov-report=html +# View coverage report at htmlcov/index.html + +# Note: Always specify tests/ directory to avoid conflicts with gradio workspace +``` + +### Development +```bash +# Run full system with Docker +docker compose up --build -d + +# Import pre-generated NeurIPS 2025 knowledge graph +bash scripts/import_neurips2025_kg.sh +``` + +## Architecture + +### Core Components + +1. **Agent System (`agentic_nav/agents/`)** + - `base.py`: Core LLMAgent class with streaming support and tool execution + - `neurips2025_conference.py`: Specialized agent for NeurIPS 2025 conference data + - Uses LiteLLM for model abstraction, supports Ollama models + +2. **Tools System (`agentic_nav/tools/`)** + - Knowledge graph tools: `search_similar_papers`, `find_neighboring_papers`, `traverse_graph` + - Graph traversal strategies: breadth-first, depth-first, neo4j builtin + - Tool registry automatically discovers callable functions + +3. **Frontend (`agentic_nav/frontend/`)** + - `cli.py`: Rich terminal interface with streaming, command history, auto-completion + - `browser_ui.py`: Gradio web interface for browser-based interactions + - Both interfaces support the same agent functionality + +4. **Knowledge Graph (`agentic_nav/tools/knowledge_graph/`)** + - Neo4j-based paper similarity and relationship storage + - Embedding-based vector search for paper discovery + - Support for graph traversal algorithms + +### Key Data Flow + +1. User input → Frontend (CLI/Web) +2. Frontend → Agent (stateless interaction with streaming) +3. Agent → LLM (via LiteLLM) + Tools (knowledge graph queries) +4. Tools → Neo4j database for paper retrieval +5. Results streamed back to user with live markdown rendering + +## Configuration + +### Required Environment Variables +```bash +NEO4J_USERNAME=neo4j +NEO4J_PASSWORD= +EMBEDDING_MODEL_NAME=nomic-embed-text +EMBEDDING_MODEL_API_BASE=http://localhost:11435 +AGENT_MODEL_NAME=gpt-oss:20b +AGENT_MODEL_API_BASE=http://localhost:11436 +OLLAMA_API_KEY= +POPULATE_DATABASE_NIPS2025=false +AGENTIC_NAV_LOG_LEVEL=INFO +``` + +### Model Support +- Primary: Ollama models (local and remote) +- Remote Ollama models via https://ollama.com with API key +- Uses LiteLLM for provider abstraction + +## Dependencies + +- **Python**: 3.14+ required +- **uv**: For dependency management +- **Neo4j**: Graph database for knowledge storage +- **Ollama**: LLM inference (supports GPU acceleration with Nvidia Container Toolkit) +- **Gradio**: Built from source for Python 3.14 compatibility + +## Development Notes + +- The system is designed for multi-user sessions via stateless agent interactions +- Streaming responses are supported in both CLI and web interfaces +- Tool calls are automatically executed and results fed back to the LLM +- Chat history can be saved/loaded in JSON format +- Logging is configured per environment with structured output to `logs/` directory \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..da83d71 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.10-slim + +WORKDIR /app + +RUN apt-get update && apt-get install -y \ + build-essential \ + git \ + bash \ + wget \ + curl \ + && rm -rf /var/lib/apt/lists/* + +# Install uv first (before copying files) +RUN pip install --no-cache-dir uv + +# Copy all necessary files +COPY pyproject.toml uv.lock* ./ +COPY .python-version* ./ +COPY README.md ./ +COPY LICENSE ./ +COPY agentic_nav/ ./agentic_nav/ +COPY scripts/ ./scripts/ +COPY graphs/ ./graphs/ + +# Use uv sync to install dependencies +RUN uv sync + +EXPOSE 7860 + +# Set entrypoint +# Download and initialize the NeurIPS 2025 conference knowledge graph +RUN chmod +x /app/scripts/docker-entrypoint.sh +RUN chmod +x /app/scripts/import_neurips2025_kg.sh +ENTRYPOINT ["scripts/docker-entrypoint.sh"] + +CMD ["uv", "run", "agentic_nav/frontend/browser_ui.py"] diff --git a/README.md b/README.md index 8c8b74d..a65ff7f 100644 --- a/README.md +++ b/README.md @@ -1,55 +1,192 @@ -# llm-agents +# AgenticNAV - Your AI conference companion +[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv) +![Coverage](https://github.com/core-aix/agentic-nav/workflows/Tests/badge.svg) + +This repository contains code for an agent that can help you do related work for your next research project. +Given the sheer amount of new publications that are being published at major machine learning conferences, this agent +can help browse papers, find similar papers, and help you write summaries to quickly get an overview of what is currently +going on. + +The agent can also support you in planning your next conference trip by providing a schedule around one or more topics +that you are interested in. + +## Installation & usage of the web-based interface +The agent is conveniently packaged as a docker image. You can spin up the entire system by using the commands below. +Make sure to have the [Nvidia Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation) installed. +At the moment we only support `ollama` models. + +Instead of a local agent model, you can also make use of remote ollama models. A full list is available here: +https://docs.ollama.com/cloud. +To make use of these large models, set `AGENT_MODEL_NAME=` and +`AGENT_MODEL_API_BASE=https://ollama.com`. +Don't forget to set your `OLLAMA_API_KEY` either directly via the environment or in the browser. + +**Important note:** The ollama docker containers cannot use GPU acceleration on MacOS. If you want to use your Mac's GPU, +you need to run ollama without containerization (i.e., you need to manually spin up the ollama server). +With `NEO4J_DB_NODE_RETURN_LIMIT`, we set a strict return limit of 200 nodes per query to avoid overstraining the database. +You can set it as needed for your use case. -## Installation -Install Ollama and pull `nomic-embed-text` model for embedding computation used in RAG. ```commandline -ollama pull nomic-embed-text +# Database config +echo "NEO4J_USERNAME=neo4j" >> .env +echo "NEO4J_PASSWORD=" >> .env +echo "NEO4J_DB_URI=bolt://neo4j_db:7687" >> .env +echo "NEO4J_DB_NODE_RETURN_LIMIT=200" >> .env + +echo "EMBEDDING_MODEL_NAME=ollama/nomic-embed-text" >> .env +echo "EMBEDDING_MODEL_API_BASE=http://ollama_agent:11434" >> .env + +echo "AGENT_MODEL_NAME=ollama_chat/gpt-oss:20b" >> .env +echo "AGENT_MODEL_API_BASE=http://ollama_agent:11434" >> .env + +# Optional: set your OLLAMA_API_KEY when using remote models +echo "OLLAMA_API_KEY=" >> .env + +# Set the following to true if you would like to import our pre-generated knowledge graph for the NeurIPS 2025 conference +# Warning (!): Setting the parameter below to 'true' will clear any existing data inside the docker-based neo4j database +echo "POPULATE_DATABASE_NIPS2025=false" >> .env + +git clone https://github.com/core-aix/agentic-nav.git +cd agentic-nav +docker compose up --build -d ``` -To use local model, also pull +This will launch the agent and its web interface, available via `http://localhost:7860`, along with a neo4j database +(community edition). +**It will also populate the database with all accepted papers of the NeurIPS 2025 machine learning conference (if you set `POPULATE_DATABASE_NIPS2025=true`).** +We include pair-wise similarity scores to enable graph traversals and the search for broadly related papers. + +After the docker containers are up and running, you can interact with the agent. Have fun! + + +## Development & contributing to the agent +If you are interested in understanding the system in detail, you may want to run all setup steps manually and avoid a +containerized runtime. Run the following steps to setup a development environment. + +We use [uv](https://docs.astral.sh/uv/) for dependency management. +Our docker containers for serving LLMs use Ollama and GPU acceleration. For that, you need the [Nvidia Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation) +Make sure to have both installed before you proceed. + +### Installation +After you cloned the repository, you need to setup the database. We use neo4j to manage the knowledge graph data we need +for the agent to work properly. +**Note:** We are using gradio built from source as the latest release (as of Nov. 12, 2025) does not yet support python 3.14. + +First, export all necessary environment variables: ```commandline -ollama pull gpt-oss:20b +echo "NEO4J_USERNAME=neo4j" >> .env +echo "NEO4J_PASSWORD=" >> .env +echo "NEO4J_DB_URI=bolt://localhost:7687" >> .env +echo "NEO4J_DB_NODE_RETURN_LIMIT=200" >> .env + +echo "EMBEDDING_MODEL_NAME=ollama/nomic-embed-text" >> .env +echo "EMBEDDING_MODEL_API_BASE=http://localhost:11435" >> .env + +echo "AGENT_MODEL_NAME=ollama_chat/gpt-oss:20b" >> .env +echo "AGENT_MODEL_API_BASE=http://localhost:11436" >> .env + +# Optional: set your OLLAMA_API_KEY when using remote models +echo "OLLAMA_API_KEY=" >> .env + +# Set the following to true if you would like to import our pre-generated knowledge graph for the NeurIPS 2025 conference +# Warning (!): Setting the parameter below to 'true' will clear any existing data inside the docker-based neo4j database +echo "POPULATE_DATABASE_NIPS2025=false" >> .env + +# Make sure you also have those values in your commandline environment +export $(grep -v '^#' .env | xargs) ``` -The chat model can be configured to use `gpt-oss:120b-cloud`. To use it, get an Ollama API key and pass it in the command line args (see below). +Then get the project files: +```commandline +git clone https://github.com/core-aix/agentic-nav.git +cd agentic-nav +docker compose up neo4j_db ollama_embed ollama_agent -d +# The following command is only needed if you'd like to use the gradio-based GUI +# This will eventually go away once gradio bumps their release version to support python 3.14 +bash scripts/prepare_gradio.sh +uv sync +``` -## Agent for NeurIPS 2025 papers +### Building the NeurIPS 2025 knowledge graph locally +You can also build the knowledge graph yourself and, for example, swap the embedding model we use by default. +Follow the steps below to do so. Note, that you still need to setup the project as described in the `Installation` +subsection above. Make sure to set `POPULATE_DATABASE_NIPS2025=false` in your .env file. -### Setup +#### Get the data Download https://neurips.cc/static/virtual/data/neurips-2025-orals-posters.json and put the file in the `./data` folder. ```commandline wget -O data/neurips-2025-orals-posters.json https://neurips.cc/static/virtual/data/neurips-2025-orals-posters.json ``` -Run `uv run build_json_rag.py` to build the vector database for RAG. -### Agent interaction -Run the following command. +#### Build the knowledge graph +You can build the knowledge graph per your needs by running the following script: +```commandline +uv run llm_agents/tools/knowledge_graph/graph_generator.py \ + --input-json-file data/neurips-2025-orals-posters.json \ + --embedding-model $EMBEDDING_MODEL_NAME \ + --ollama-server-url $EMBEDDING_MODEL_API_BASE \ + --embedding-gen-batch-size 32 \ + --max-parallel-workers 28 \ + --similarity-threshold 0.6 \ + --output-file graphs/knowledge_graph.pkl \ + # --limit-num-papers # Optional +``` +**Important note:** Generating the full graph for over 6k papers can take more than 1 hour. You can find a set of pre-generated +knowledge graphs here (the "thresh" in the file name indicates the `similarity-threshold` for which we create a `similar_to` relationship between papers): [LRZ Sync+Share](https://syncandshare.lrz.de/getlink/fiFMhMLLH7FaQ3Jipqqsye/) + -For local model: +#### Importing the knowledge graph to a neo4j database +We provide an importer to move the knowledge graph into a graph database that supports vector-based similarity search. ```commandline -uv run main_ui.py \ - -a http://localhost:11434 \ - -m ollama_chat/gpt-oss:20b \ - -t 0.4 \ - --max-tokens 6000 \ - -c 131072 \ - --max-num-papers 10 +uv run llm_agents/tools/knowledge_graph/neo4j_db_importer.py \ + --graph-path graphs/knowledge_graph.pkl \ + --neo4j-uri $NEO4J_DB_URI \ + --batch-size 100 \ + --embedding-dimension 768 # This must match the vector dims generated by the embedding model. ``` +**Note:** Depending on what your graph looks like this can also take a while (> 20min for 6K papers). Also, beware that +running this script will first clear any existing entries before the new graph is written to the database. -For Ollama cloud model: + +### Agent interactions +We offer two ways of interacting with agents, via the command line and via the browser. +The backend uses LiteLLM, which allows you to use a variety of LLM inference endpoints. +Find details on the various providers [here](https://docs.litellm.ai/docs/providers). + +#### Commandline interface +The agent can also be used via a versatile CLI. +Below are two examples how to run a local and a remote model. +We are using LiteLLM to abstract away from individual inference API providers. +Note, that we currently only test with Ollama models. ```commandline -uv run main_ui.py \ - -a https://ollama.com \ - -k $OLLAMA_API_KEY \ - -m ollama_chat/gpt-oss:120b-cloud \ +uv run agentic-nav-cli \ -t 0.4 \ --max-tokens 6000 \ -c 131072 \ --max-num-papers 10 ``` -The backend uses LiteLLM, which allows you to use a variety of LLM inference endpoints. -Find details on the various providers [here](https://docs.litellm.ai/docs/providers). \ No newline at end of file +#### Web-based interface (beginner friendly) +We use gradio to provide a chat interface with the same functionalities as the commandline-based interface. +You can launch the web app by running: +```commandline +agentic-nav-web +``` +All the hyperparameters you need to set can be configured in the web interface and will be used in you individual session. +Once you close the browser window, your session will terminate and all custom configuration will be removed. +At the moment, the web UI only supports Ollama models. + +### Debugging agent interactions +The agent involves a set of asynchronous operations. We provide a built-in logging instance to capture all relevant logs +for debugging. To set the right debugging level for your application, you can use the environment variable `AGENTIC_NAV_LOG_LEVEL`. +By default, it is set to `INFO`. + +#### Running tests +We try to cover all tools and agent functionalities in thorough unit tests. +You can run them via: +```commandline +uv run pytest tests/ +``` diff --git a/agent_neurips_2025.py b/agent_neurips_2025.py deleted file mode 100644 index 0f64299..0000000 --- a/agent_neurips_2025.py +++ /dev/null @@ -1,44 +0,0 @@ -from tools.rag_search import search_papers # <- the tool we expose -from utils.tool_chat import ToolChat -import sys - -system = {"role": "system", "content": "You are an assistant who can help browsing NeurIPS 2025 papers. " -"You are provided with a search tool that can search all accepted papers of NeurIPS 2025. " -"However, note that the search tool only takes paper titles and abstracts as input keywords; it cannot take anything else as the input keywords. " -"However, the output of the search includes various metadata fields such as authors, affiliations, and session times. " -"If your answer includes a list of papers, cite titles, abstracts, and OpenReview URLs in your answers."} - -msgs = [system] - -def agent_respond( - messages, - model_name: str = None, - api_base: str = None, - api_key: str = None, - llm_args: dict = {}, - tool_args: dict = {} -): - chat = ToolChat(model=model_name, api_base=api_base, api_key=api_key, default_params=llm_args) - return chat.tool_loop(messages, tool_funcs=[search_papers], tool_args=tool_args) - -# if __name__ == "__main__": -# chat = ToolChat() - -# while True: -# user = {"role": "user", "content": None} - -# if len(sys.argv) > 1: -# user["content"] = " ".join(sys.argv[1:]) -# multiturn = False -# else: -# user["content"] = input("User (please enter): ") -# multiturn = True - -# msgs.append(user) - -# msgs = chat.tool_loop(msgs, tool_funcs=[search_paper_titles_and_abstracts]) - -# if not multiturn: -# break - - diff --git a/agentic_nav/__init__.py b/agentic_nav/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/agentic_nav/agents/__init__.py b/agentic_nav/agents/__init__.py new file mode 100644 index 0000000..06f3daa --- /dev/null +++ b/agentic_nav/agents/__init__.py @@ -0,0 +1 @@ +from agentic_nav.agents.neurips2025_conference import NeurIPS2025Agent, DEFAULT_NEURIPS2025_AGENT_ARGS, AGENT_INTRODUCTION_PROMPT \ No newline at end of file diff --git a/agentic_nav/agents/base.py b/agentic_nav/agents/base.py new file mode 100644 index 0000000..d89b306 --- /dev/null +++ b/agentic_nav/agents/base.py @@ -0,0 +1,327 @@ +import json + +import litellm +import logging + +from dataclasses import dataclass, field +from typing import List, Dict + +from agentic_nav.tools import get_all_tools +from agentic_nav.utils.tooling import infer_tool + +try: + from datetime import datetime, UTC +except ImportError: + from datetime import datetime, timezone + UTC = timezone.utc + +LOGGER = logging.getLogger(__name__) + + +@dataclass +class LLMAgent: + model: str = "ollama_chat/gpt-oss:20b" + api_base: str = "http://localhost:11434" + api_key: str = None + llm_args: dict = field(default_factory=lambda: {"temperature": 0.2, "max_tokens": 6000, "num_ctx": 131072}) + tools: List[callable] = field(default_factory=lambda: get_all_tools()) + global_tool_args: dict = field(default_factory=lambda: {"max_num_papers": 10}) + max_interaction_rounds: int = 10 + messages: List[Dict] = field(default_factory=lambda: []) + tool_registry: Dict = None + tool_descriptions: List = None + default_system_prompt: Dict[str, str] = None + + def __remove_model_key_from_llm_args(self, stateful: bool = True): + if stateful: + self.model = self.llm_args["model"] + self.api_base = self.llm_args["api_base"] + + if "model" in self.llm_args.keys(): + del self.llm_args["model"] + + if "api_base" in self.llm_args.keys(): + del self.llm_args["api_base"] + + def test_llm_connection(self): + self.__remove_model_key_from_llm_args(stateful=True) + try: + response = litellm.completion( + model=self.model, + messages=[{"role": "user", "content": "test", "_ts": str(datetime.now(UTC))}], + tool_choice="auto", + api_base=self.api_base, + api_key=self.api_key, + stream=True, + **self.llm_args, + ) + + LOGGER.info(f"Model is available! Response: {response.choices[0].message.content}") + except Exception as e: + LOGGER.error(f"Model not available or connection failed: {str(e)}") + + def setup_session(self, tool_funcs: List[callable] = None): + self.tool_registry = {fn.__name__: fn for fn in self.tools} if tool_funcs is None else {fn.__name__: fn for fn in tool_funcs} + self.tool_descriptions = [infer_tool(fn, tool_args=self.global_tool_args) for fn in self.tool_registry.values()] + LOGGER.info(f"Agent setup and tools ready to use.") + LOGGER.debug(f"Available tools: {self.tools}") + + def remove_session(self): + """De-registers tools and resets messages to the initial state.""" + self.tool_registry = None + self.tool_descriptions = None + self.messages = [self.default_system_prompt if not None else {"role": "system", "content": "You are a helpful assistant."}] + + def interact(self, message: Dict): + assert self.tool_registry is not None, "Make sure to call 'setup_session()' before the first interaction." + assert self.tool_descriptions is not None, "Make sure to call 'setup_session()' before the first interaction." + + assert type(message) == dict, "Make sure to pass a dictionary as next message for the agent." + assert "role" in message.keys(), "The message must contain a 'role' key." + assert "content" in message.keys(), "The message must contain a 'content' key." + + self.__remove_model_key_from_llm_args(stateful=True) + if "_ts" not in message.keys(): + message["_ts"] = str(datetime.now(UTC)) + + self.messages.append(message) + for _ in range(self.max_interaction_rounds): + collected, calls = self._send_to_llm( + messages=self.messages, + model=self.model, + api_base=self.api_base, + api_key=self.api_key + ) + # append the assembled assistant message so tool execution sees the assistant's follow-up + self.messages.append({"role": "assistant", "content": collected, "_ts": str(datetime.now(UTC))}) + LOGGER.debug(f"Agent response: {collected}") + + if not calls: + return self.messages + else: + self.messages[-1]["tool_calls"] = calls + LOGGER.debug(f"Agent requested tool calls: {calls}") + + # execute tools and append results + for call in calls: + self.messages.append( + self.call_tool( + tool_call=call + ) + ) + + LOGGER.debug(f"Interaction complete. Total messages: {len(self.messages)}") + return self.messages + + def interact_stateless( + self, + messages: List[Dict], + model: str, + api_base: str, + api_key: str, + llm_args: Dict = None + ): + """ + This method is designed to support multi-user sessions and requires state management outside the agent class. + """ + assert self.tool_registry is not None, "Make sure to call 'setup_session()' before the first interaction." + assert self.tool_descriptions is not None, "Make sure to call 'setup_session()' before the first interaction." + self.__remove_model_key_from_llm_args(stateful=False) + + # Sanity check for all messages + for message in messages: + if "_ts" not in message.keys(): + message["_ts"] = str(datetime.now(UTC)) + + for round_num in range(self.max_interaction_rounds): + # Stream the LLM response + collected = "" + calls = [] + + # Create initial assistant message + assistant_msg_idx = len(messages) + messages.append({"role": "assistant", "content": "", "_ts": str(datetime.now(UTC))}) + + stream_iter = litellm.completion( + model=model if model is not None else self.model, + messages=messages[:assistant_msg_idx], # Don't include the empty assistant message + tools=self.tool_descriptions, + tool_choice="auto", + api_base=api_base if api_base is not None else self.api_base, + api_key=api_key if api_key is not None else self.api_key, + stream=True, + **llm_args if llm_args is not None else self.llm_args, + ) + + for chunk in stream_iter: + choices = chunk.get("choices", []) or [] + if not choices: + continue + choice = choices[0] + + # Extract content from chunk + content = None + delta = choice.get("delta") + + if delta and "content" in delta: + content = delta["content"] + elif delta and "message" in delta and isinstance(delta["message"], dict): + content = delta["message"].get("content") + + if delta and "tool_calls" in delta: + calls.extend(delta["tool_calls"] or []) + + if content is None: + msg = choice.get("message") + if isinstance(msg, dict): + content = msg.get("content") + + if content is None: + content = choice.get("text") + + if content: + if not isinstance(content, str): + try: + content = json.dumps(content, ensure_ascii=False) + except Exception: + content = str(content) + + collected += content + # Update the assistant message with accumulated content + messages[assistant_msg_idx]["content"] = collected + + # Yield the updated messages for streaming display + yield messages.copy() + + # After streaming is complete, update with final content + messages[assistant_msg_idx]["content"] = collected + LOGGER.debug(f"Agent response: {collected}") + + if not calls: + yield messages + return + else: + messages[assistant_msg_idx]["tool_calls"] = calls + LOGGER.debug(f"Agent requested tool calls: {calls}") + yield messages.copy() + + # Execute tools and append results + for call in calls: + messages.append(self.call_tool(tool_call=call)) + yield messages.copy() + + yield messages + + def _send_to_llm( + self, + messages: List[Dict], + model: str, + api_base: str, + api_key: str, + llm_args: Dict = None + ): + stream_iter = litellm.completion( + model=model if model is not None else self.model, + messages=messages, + tools=self.tool_descriptions, + tool_choice="auto", + api_base=api_base if api_base is not None else self.api_base, + api_key=api_key if api_key is not None else self.api_key, + stream=True, + **llm_args if llm_args is not None else self.llm_args, + ) + + collected = "" + calls = [] + + for chunk in stream_iter: + choices = chunk.get("choices", []) or [] + if not choices: + continue + choice = choices[0] + + # try several places where partial content may appear + content = None + delta = choice.get("delta") + + if "content" in delta: + content = delta["content"] + elif "message" in delta and isinstance(delta["message"], dict): + content = delta["message"].get("content") + + if "tool_calls" in delta: + calls.extend(delta["tool_calls"] or []) + + if content is None: + msg = choice.get("message") + if isinstance(msg, dict): + content = msg.get("content") + + if content is None: + content = choice.get("text") + + if content: + if not isinstance(content, str): + try: + content = json.dumps(content, ensure_ascii=False) + except Exception as e: + LOGGER.error(f"JSON encoding error encountered. {e}. Treating agent response as regular text.") + content = str(content) + + collected += content + + return collected, calls + + def call_tool(self, tool_call: Dict): + name = tool_call["function"]["name"] + args = tool_call["function"].get("arguments", "{}") + LOGGER.debug(f"Preparing tool call: {name}") + LOGGER.debug(f"Expected tool arguments: {args}") + try: + parsed = json.loads(args) if isinstance(args, str) else (args or {}) + LOGGER.debug(f"Parsed tool call arguments: {parsed}") + except json.JSONDecodeError: + parsed = {} + LOGGER.warning(f"Tool call arguments: COULD NOT BE PARSED") + out = self.tool_registry[name](**parsed) + LOGGER.debug(f"Tool call output: {parsed}") + + return { + "role": "tool", + "tool_call_id": tool_call.get("id"), + "name": name, + "content": json.dumps(out, ensure_ascii=False), + "_ts": str(datetime.now(UTC)) + } + + def set_history(self, messages): + self.messages = messages + LOGGER.info(f"Set new message history.") + + def get_history(self): + return self.messages + + @staticmethod + def set_system_prompt(new_system_prompt: str, messages: List[Dict]): + messages = [m for m in messages if m.get("role") != "system"] + messages.insert(0, { + "role": "system", + "content": new_system_prompt, + "_ts": str(datetime.now(UTC)) + }) + LOGGER.info(f"New system prompt set and configured.") + LOGGER.debug(f"New system prompt: {new_system_prompt}") + return messages + + def get_system_prompt(self): + for message in self.messages: + if "role" in message.keys() and message["role"] == "system": + return message + + return None + + def get_most_recent_assistant_message(self): + for message in reversed(self.messages): + if message.get("role") == "assistant": + return message + return None diff --git a/agentic_nav/agents/neurips2025_conference.py b/agentic_nav/agents/neurips2025_conference.py new file mode 100644 index 0000000..c074af6 --- /dev/null +++ b/agentic_nav/agents/neurips2025_conference.py @@ -0,0 +1,74 @@ +import os + +from dataclasses import dataclass +from agentic_nav.agents.base import LLMAgent +from agentic_nav.tools import search_similar_papers, find_neighboring_papers, traverse_graph, build_visit_schedule # <- the tools we expose +from zoneinfo import ZoneInfo + +try: + from datetime import datetime, UTC +except ImportError: + from datetime import datetime, timezone + UTC = timezone.utc + + +DEFAULT_NEURIPS2025_AGENT_ARGS = { + "model": os.environ.get("AGENT_MODEL_NAME", "ollama_chat/gpt-oss:120b-cloud"), + "api_base": os.environ.get("AGENT_MODEL_API_BASE", "https://ollama.com"), + "api_key": os.environ.get("OLLAMA_API_KEY"), + "llm_args": {"temperature": 0.2, "max_tokens": 16384, "num_ctx": 131072}, + "global_tool_args": {"max_num_papers": 10} +} + + +system = { + "role": "system", + "content": f""" + You are AgenticNAV, an assistant to navigate accepted papers at the NeurIPS 2025 conference. + You can assist users in finding papers based on their research interests, preferred dates, and time slots. + You can also build schedules for them to visit posters that they are interested in. + + **Here are some guidelines**: + - When searching for similar papers, the search tool only takes paper titles and abstracts as input keywords; it cannot take anything else as the input keywords. + - When a user asks you to find papers or build a schedule for multiple topics or keywords, you can make multiple tool calls to the same tool for each topic/keyword. + - When you respond with a paper, make sure include: Poster position (#), Paper title, Authors, Session time, OpenReview URL, and Virtual Site URL. + - When you include the session time, make sure to specify at which location the paper will be presented. + - When listing papers, make sure to order them by session details (i.e., date, time, location). Keep San Diego and Mexico City separate. + - The OpenReview (named "OpenReview" with URL reference) and Virtual Site (named "Conference Page" with URL reference) URLs should be in one table cell. The column name should be "Links". + - The paper title and author names should be in one table cell. If possible, make the author names smaller. + - If there is a Virtual Site available, you need to prepend https://neurips.cc for the link to be usable (never mention this to the user). + - Make sure to present papers in a Markdown table. Do not wrap it inside html code. + - When building a schedule, do not specify the name of the day. + - The attribute `poster_position` (starting with #) is the physical location of a poster in the conference venue at a given session. It is unique per session but may appear multiple times across sessions. + - If you find the same paper title multiple times, remove the duplicate titles and do not mention it in your response. + - When a user asks for a conference map, respond with the link: https://media.neurips.cc/Conferences/NeurIPS2025/sdconvctr-ground-level.svg. You don't know any specifics about the venue. + + **Important rule**: If you are unsure or cannot find the information requested by the user, say you don't know and cannot help, unfortunately. + + **Here is the current timestamp**: {datetime.now(ZoneInfo('America/Los_Angeles'))}. The conference is happening in San Diego, California. + """ +} + + +AGENT_INTRODUCTION_PROMPT = { + "role": "assistant", + "content": f""" + Welcome to AgenticNAV! + I am happy to assist you navigating NeurIPS 2025. You can ask things like: + - "Please show me paper on LLM request routing" + - "Please build a schedule for me to visit posters on federated learning on Tuesday afternoon" + + Feel free to start anytime you are ready! + """ +} + + + +@dataclass +class NeurIPS2025Agent(LLMAgent): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.messages = [{**system}] + self.tools = [search_similar_papers, find_neighboring_papers, traverse_graph, build_visit_schedule] + self.default_system_prompt = system diff --git a/agentic_nav/frontend/__init__.py b/agentic_nav/frontend/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/agentic_nav/frontend/browser_ui.py b/agentic_nav/frontend/browser_ui.py new file mode 100644 index 0000000..f62a3bb --- /dev/null +++ b/agentic_nav/frontend/browser_ui.py @@ -0,0 +1,561 @@ +""" +Gradio web UI that interacts with an agent implementation. + +Features matching terminal UI: +- Multi-turn chat with Markdown rendering +- System prompt editing +- View conversation history +- Save chat history to file +- All model configuration options +- Clear chat functionality +- **Per-user conversation state management with stateless agent** +""" +import gradio as gr +import os +import datetime +import logging +import json + +from pathlib import Path +from typing import List, Tuple, Optional, Dict + +from agentic_nav.agents import NeurIPS2025Agent, DEFAULT_NEURIPS2025_AGENT_ARGS, AGENT_INTRODUCTION_PROMPT +from agentic_nav.utils.logger import setup_logging +from agentic_nav.utils.file_handlers import save_chat_history + + +LOGGER = logging.getLogger(__name__) + +EMBEDDING_MODEL_NAME = os.environ.get("EMBEDDING_MODEL_NAME", "ollama/nomic-embed-text") +EMBEDDING_MODEL_API_BASE = os.environ.get("EMBEDDING_MODEL_API_BASE", "http://localhost:11435") + +AGENT_MODEL_NAME = os.environ.get("AGENT_MODEL_NAME", "ollama_chat/gpt-oss:20b") +AGENT_MODEL_API_BASE = os.environ.get("AGENT_MODEL_API_BASE", "http://localhost:11436") +OLLAMA_API_KEY = os.environ.get("OLLAMA_API_KEY", DEFAULT_NEURIPS2025_AGENT_ARGS["api_key"]) + + +def initialize_agent(): + """Initialize the AGENT instance.""" + agent = NeurIPS2025Agent( + model=f"ollama_chat/{AGENT_MODEL_NAME}", + api_base=AGENT_MODEL_API_BASE, + api_key=OLLAMA_API_KEY, + llm_args=DEFAULT_NEURIPS2025_AGENT_ARGS["llm_args"], + global_tool_args=DEFAULT_NEURIPS2025_AGENT_ARGS["global_tool_args"], + ) + agent.setup_session() + return agent + + +def configure_agent( + api_base: str, + api_key: str, + model: str, + temperature: float, + max_tokens: int, + num_ctx: int, + max_num_papers: int, + current_config: Dict +): + """Initialize the agent with a given configuration.""" + LOGGER.info(f"Agent runtime started via Gradio UI for session") + current_config.update({ + "model": model, + "api_base": api_base, + "api_key": api_key, + "llm_args": { + "temperature": temperature, + "max_tokens": max_tokens, + "num_ctx": num_ctx + }, + "global_tool_args": {"max_num_papers": max_num_papers} + }) + + current_config_to_print = current_config.copy() + if "api_key" in current_config_to_print: + del current_config_to_print["api_key"] + LOGGER.info(f"User-defined configuration saved. Config: {current_config_to_print}") + + return current_config, "✓ Agent configuration updated successfully!" + + +def chat_fn( + new_message: str, + history: List[Dict], + config: Optional[Dict], + messages: Optional[List[Dict]], + agent: NeurIPS2025Agent, +) -> Tuple[List[Dict], Optional[List[Dict]]]: + """ + Handle chat interaction using stateless agent. + + Args: + new_message: User's input message + history: Chat history as list of message dictionaries with role/content + config: Configuration dict with model, api_base, api_key, llm_args + messages: Current conversation messages list + agent: Agent instance + + Returns: + Tuple of (updated_history, messages) + """ + if not new_message.strip(): + yield history, messages + return + + LOGGER.debug(f"USER PROMPT: {new_message}") + + # Safety check: ensure messages is a list + if messages is None or not isinstance(messages, list): + LOGGER.warning("Messages state was not properly initialized, resetting...") + messages = [agent.get_system_prompt()] + + # Create a copy of history and messages to avoid mutation issues + history = history.copy() if history else [] + messages = messages.copy() + + # Add user message to history immediately with empty assistant response + user_msg_dict = {"role": "user", "content": new_message} + assistant_msg_dict = {"role": "assistant", "content": ""} + history.extend([user_msg_dict, assistant_msg_dict]) + + try: + # Create user message with timestamp + user_message = { + "role": "user", + "content": new_message, + "_ts": str(datetime.datetime.now(datetime.timezone.utc)) + } + + # Add user message to conversation + messages.append(user_message) + + # Stream the response + accumulated_response = "" + partial_messages = "" + for partial_messages in agent.interact_stateless( + messages=messages, + model=config["model"], + api_base=config["api_base"], + api_key=config["api_key"], + llm_args=config["llm_args"] + ): + # Get the latest assistant message content + for msg in reversed(partial_messages): + if msg.get("role") == "assistant": + accumulated_response = msg["content"] + break + + # Update the last assistant message in history with accumulated response + if len(accumulated_response) == 0: + history[-1]["content"] = "Working on it. Hang tight..." + else: + history[-1]["content"] = accumulated_response + + yield history, partial_messages + + # Final update with complete messages + messages = partial_messages + LOGGER.info("Agent response generated successfully") + + except Exception as e: + LOGGER.error(f"Agent encountered an error: {e}", exc_info=True) + error_msg = f"❌ Error: {str(e)}" + history[-1]["content"] = error_msg + yield history, messages + + +def update_system_prompt( + new_prompt: str, + messages: Optional[List[Dict]], + agent: NeurIPS2025Agent +) -> Tuple[str, Optional[List[Dict]]]: + """Update the system prompt in the message history. + + Args: + new_prompt: New system prompt + messages: Current message history + agent: Agent instance + + Returns: + Tuple of (status_message, agent_instance, config, updated_messages) + """ + if not new_prompt.strip(): + return "System prompt cannot be empty.", messages + + try: + # Initialize messages if None + if messages is None: + messages = [] + + # Use the static method to update system prompt + messages = agent.set_system_prompt(new_system_prompt=new_prompt, messages=messages) + + LOGGER.info("System prompt updated") + LOGGER.info(f"New system prompt: {messages[0]}") + return "✓ System prompt updated successfully!", messages + except Exception as e: + LOGGER.error(f"Error updating system prompt: {e}") + return f"Error: {str(e)}", messages + + +def view_history(messages: Optional[List[Dict]]) -> str: + """View the full conversation history in JSON format. + + Args: + messages: Current message history + + Returns: + JSON formatted history string + """ + if messages is None: + return "⚠️ No conversation history yet." + + try: + # Format as pretty JSON + return json.dumps(messages, indent=2, ensure_ascii=False) + except Exception as e: + LOGGER.error(f"Error viewing history: {e}") + return f"❌ Error: {str(e)}" + + +def save_history(filename: str, messages: Optional[List[Dict]]) -> str: + """Save chat history to a JSON file. + + Args: + filename: Optional filename + messages: Current message history + + Returns: + Status message + """ + if messages is None or len(messages) == 0: + return "⚠️ No conversation history to save." + + try: + # Create directory if it doesn't exist + Path("chat_histories/").mkdir(exist_ok=True, parents=True) + + # Generate filename if not provided + if not filename.strip(): + time_now = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + # Add session identifier to prevent conflicts + import uuid + session_id = str(uuid.uuid4())[:8] + filename = f"chat_histories/{time_now}_session_{session_id}_chat_history.json" + else: + filename = filename.strip() + # Ensure it's in chat_histories directory + if not filename.startswith("chat_histories/"): + filename = f"chat_histories/{filename}" + if not filename.endswith(".json"): + filename += ".json" + + # Save the history + save_chat_history(messages, filename) + + LOGGER.info(f"Chat history saved to {filename}") + return f"✓ Chat history saved to: {filename}" + + except Exception as e: + LOGGER.error(f"Error saving history: {e}") + return f"❌ Error: {str(e)}" + + +def clear_chat( + config: Optional[Dict], + messages: Optional[List[Dict]], + agent: NeurIPS2025Agent +) -> Tuple[List, Optional[List[Dict]]]: + """Clear the chat history in the UI and reset message list. + + Args: + config: Current configuration + messages: Current message history + agent: Agent instance + + Returns: + Tuple of (status_message, empty_history, reset_messages) + """ + system_prompt = agent.get_system_prompt() + if isinstance(system_prompt, dict): + reset_messages = [system_prompt] + else: + reset_messages = [] + + LOGGER.info("Chat cleared and reset") + return [], reset_messages + + +def submit_message(message, history, config, messages, agent): + """Wrapper to clear input and process message""" + yield from chat_fn(message, history, config, messages, agent) + + +def main(): + + # Setup the agent instance + agent = initialize_agent() + + with gr.Blocks( + title="AgenticNAV", + theme=gr.themes.Default( + spacing_size=gr.themes.sizes.spacing_md, + font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"], + radius_size=gr.themes.sizes.radius_sm, + primary_hue=gr.themes.colors.slate, + secondary_hue=gr.themes.colors.blue + ), + css=""" + #submit_textbox button { + background-color: #ff6b6b !important; + border-color: #ff6b6b !important; + } + #submit_textbox button:hover { + background-color: #ee5a52 !important; + border-color: #ee5a52 !important; + } + + .scrollable-div { + display: flex; + overflow-x: auto; + min-width: 200px; + } + + """ + ) as webapp: + + gr.Markdown(""" + # 🤖 AgenticNAV - Explore NeurIPS 2025 papers and build your personalized schedule, effortlessly! + + This agent can help you explore the more than 5000 papers at this year's NeurIPS conference. + You can start chatting right away but see below for more specific instructions on how to use the agent with your + favorite model and inference config. You can also set a custom system prompt. + + **Note:** This is an experimental deployment and LLMs can make mistakes. This can mean that the agent may not + discover your paper even though it is presented at the conference. Also, note that the ordering of authors may + not be correct. Check the paper links for more details. + + """) + + # Session state for agent instance, config, and messages + config_state = gr.State(value=DEFAULT_NEURIPS2025_AGENT_ARGS) + messages_state = gr.State(value=[agent.get_system_prompt(), AGENT_INTRODUCTION_PROMPT]) + + with gr.Row(): + with gr.Column(): + # Main chat interface + chatbot = gr.Chatbot( + value=messages_state.value, + label="Conversation", + height=750, + type="messages", + show_copy_button=True, + sanitize_html=True + ) + + with gr.Row(): + msg_input = gr.Textbox( + label="Your message", + placeholder="Type your message here...", + # lines=1, + scale=4, + show_label=False, + interactive=True, + submit_btn=True, + autofocus=True, + elem_id="submit_textbox", + # stop_btn=True TODO: Allow users to stop the conversation! + ) + # submit_btn = gr.Button("Send", variant="primary", scale=1) + + with gr.Row(): + clear_btn = gr.Button("🗑️ Clear Chat", size="sm") + # save_btn = gr.Button("💾 Save History", size="sm") + + with gr.Row(): + with gr.Column(scale=1): + gr.Markdown(""" + ### 📖 Usage Guide + + **You can start chatting with AgenticNAV right away.** + Note that we provide a default key for Ollama that may reach its quota quickly (depending on demand). + If you see that the agent cannot respond, please provide your own Ollama API key (see below for details). + + #### Updating the client config + 1. Open the "Configuration" tab in the Settings column + 2. Add your API key and make any other changes you'd like + 3. Click "Update Config" to save the changes + + #### Setting a custom system prompt + You can set a custom system prompt to customize the behavior of the agent in the "System Prompt" tab + + #### Viewing the complete history + Open the "History & Save" tab to see the full details on your conversation and what gets passed between + user and agent. + + ### _Note on Ollama API Keys_ + In case you are experiencing an error calling the agent model (usually indicated by a message + containing the word "unauthorized"), you may go to https://ollama.com and generate your own key. + You can provide it in the Agent Settings. It will not be stored on our system and gets deleted + when you end your session (i.e., close your browser window). + + **Note**: Each browser session maintains its own independent conversation state that will be deleted as you close the browser window. It will never be stored on our server. + """ + ) + + with gr.Column(scale=2): + # Settings panel + gr.Markdown("### ⚙️ Agent Settings") + + with gr.Accordion("Configuration", open=False): + api_base_input = gr.Textbox( + label="API Base URL (leave empty when using OpenAI, Anthropic, etc.)", + value=AGENT_MODEL_API_BASE, + placeholder="http://localhost:11434" + ) + + api_key_input = gr.Textbox( + label="API Key", + value="", + type="password", + placeholder="Please provide one if our quote is exceeded." + ) + + model_input = gr.Textbox( + label="Model", + value=AGENT_MODEL_NAME, + placeholder="ollama_chat/gpt-oss:120b-cloud" + ) + + temperature_input = gr.Slider( + label="Temperature", + minimum=0.0, + maximum=1.0, + value=DEFAULT_NEURIPS2025_AGENT_ARGS["llm_args"]["temperature"], + step=0.1 + ) + + max_tokens_input = gr.Slider( + label="Max Tokens", + minimum=100, + maximum=32768, + value=DEFAULT_NEURIPS2025_AGENT_ARGS["llm_args"]["max_tokens"], + step=10 + ) + + num_ctx_input = gr.Slider( + label="Context Window (may not have an effect on some models and providers)", + minimum=1024, + maximum=DEFAULT_NEURIPS2025_AGENT_ARGS["llm_args"]["num_ctx"], + value=DEFAULT_NEURIPS2025_AGENT_ARGS["llm_args"]["num_ctx"], + step=128 + ) + + max_papers_input = gr.Slider( + label="Max Papers to Retrieve", + minimum=0, + maximum=100, + value=50, + step=1 + ) + + init_btn = gr.Button("Update Config", variant="primary") + init_status = gr.Textbox(label="Status", interactive=False) + + with gr.Accordion("System Prompt", open=False): + system_prompt_input = gr.Textbox( + label="System Prompt", + value=agent.get_system_prompt()["content"] if type(agent.get_system_prompt()) is dict else None, + placeholder="Enter custom system prompt here...", + lines=12 + ) + update_system_btn = gr.Button("Update System Prompt") + system_status = gr.Textbox(label="Status", interactive=False) + + with gr.Accordion("History & Save", open=False): + view_history_btn = gr.Button("📜 View Full History") + gr.Markdown(f"**Note:** If you'd like to download the conversation, use the download button below (upper right corner).") + history_output = gr.Code( + label="Conversation History (JSON)", + language="json", + lines=10 + ) + + # save_filename_input = gr.Textbox( + # label="Filename (optional)", + # placeholder="Leave empty for auto-generated name", + # value="" + # ) + # save_status = gr.Textbox(label="Save Status", interactive=False) + + # Event handlers + init_btn.click( + fn=configure_agent, + inputs=[ + api_base_input, + api_key_input, + model_input, + temperature_input, + max_tokens_input, + num_ctx_input, + max_papers_input, + config_state + ], + outputs=[config_state, init_status] + ) + + msg_input.submit( + fn=lambda msg_input, chatbot, config_state, messages_state: (yield from submit_message(msg_input, chatbot, config_state, messages_state, agent)), + inputs=[msg_input, chatbot, config_state, messages_state], + outputs=[chatbot, messages_state] + ).then( + fn=lambda: "", + inputs=None, + outputs=msg_input + ) + + # System prompt update + update_system_btn.click( + fn=lambda system_prompt_input, messages_state: update_system_prompt(system_prompt_input, messages_state, agent), + inputs=[system_prompt_input, messages_state], + outputs=[system_status, messages_state] + ) + + # History viewing + view_history_btn.click( + fn=view_history, + inputs=messages_state, + outputs=history_output + ) + + # Save history + # save_btn.click( + # fn=save_history, + # inputs=[save_filename_input, messages_state], + # outputs=save_status + # ) + + # Clear chat + clear_btn.click( + fn=lambda config_state, messages_state: clear_chat(config_state, messages_state, agent), + inputs=[config_state, messages_state], + outputs=[chatbot, messages_state] + ) + + webapp.launch( + server_name="0.0.0.0", # Allow external connections + server_port=7860, # Default Gradio port + share=False, # Set to True to create a public link + show_error=True, + debug=True + ) + +if __name__ == "__main__": + # Setup logging (only needs to be done once globally) + setup_logging( + log_dir="logs", + level=os.environ.get("AGENTIC_NAV_LOG_LEVEL", "INFO") + ) + + main() diff --git a/agentic_nav/frontend/cli.py b/agentic_nav/frontend/cli.py new file mode 100644 index 0000000..6c8f6c8 --- /dev/null +++ b/agentic_nav/frontend/cli.py @@ -0,0 +1,371 @@ +""" +Enhanced terminal chat UI with async streaming and full terminal functionality. + +Features: +- Async streaming output as LLM generates tokens +- Rich prompt with command history and auto-completion +- Live markdown rendering during streaming +- Multi-line input via Ctrl+O or /edit command +- Commands: /help, /exit, /system, /edit, /history, /save , /clear +- Keyboard shortcuts: Ctrl+C to cancel, Ctrl+D to exit +""" +import asyncio +import click +import os +import logging +import litellm +from pathlib import Path +from typing import Optional + +from rich.console import Console +from rich.markdown import Markdown +from rich.live import Live +from rich.panel import Panel +from rich.text import Text +from prompt_toolkit import PromptSession +from prompt_toolkit.history import FileHistory +from prompt_toolkit.auto_suggest import AutoSuggestFromHistory +from prompt_toolkit.completion import WordCompleter +from prompt_toolkit.key_binding import KeyBindings +from prompt_toolkit.formatted_text import HTML + +from agentic_nav.agents import NeurIPS2025Agent +from agentic_nav.utils.logger import setup_logging +from agentic_nav.utils.file_handlers import save_chat_history +from agentic_nav.utils.cli import open_editor, show_history, print_help + +try: + from datetime import datetime, UTC +except ImportError: + from datetime import datetime, timezone + UTC = timezone.utc + + +LOGGER = logging.getLogger(__name__) + +EMBEDDING_MODEL_NAME = os.environ.get("EMBEDDING_MODEL_NAME", "ollama/nomic-embed-text") +EMBEDDING_MODEL_API_BASE = os.environ.get("EMBEDDING_MODEL_API_BASE", "http://localhost:11435") + +AGENT_MODEL_NAME = os.environ.get("AGENT_MODEL_NAME", "gpt-oss:20b") +AGENT_MODEL_API_BASE = os.environ.get("AGENT_MODEL_API_BASE", "http://localhost:11436") +OLLAMA_API_KEY = os.environ.get("OLLAMA_API_KEY") + +litellm._logging._disable_debugging() +console = Console(soft_wrap=True) + +# Command completer for auto-complete +command_completer = WordCompleter( + ['/help', '/exit', '/system', '/edit', '/history', '/save', '/clear'], + ignore_case=True, + sentence=True +) + +bindings = KeyBindings() + +@bindings.add('c-o') +def _(event): + """Multi-line input with Ctrl+O""" + event.current_buffer.insert_text('\n') + + +def create_prompt_session(): + """Create a prompt_toolkit session with history and auto-suggest""" + history_file = Path.home() / ".llm_agents_history" + + return PromptSession( + history=FileHistory(str(history_file)), + auto_suggest=AutoSuggestFromHistory(), + completer=command_completer, + complete_while_typing=True, + key_bindings=bindings, + enable_open_in_editor=True, + multiline=False, + ) + + +def render_markdown(text: str, title: Optional[str] = None): + """Render markdown with optional panel title""" + if title: + console.print(Panel(Markdown(text), title=title, border_style="blue")) + else: + console.print(Markdown(text)) + + +def stream_agent_response_sync(agent, message: dict): + """ + Stream agent response with live markdown rendering. + + This function: + 1. Copies the agent's current history and appends the new message + 2. Streams the response using interact_stateless generator + 3. Updates the live display with markdown content and tool execution status + 4. Updates the agent's history with the final message list + + Note: KeyboardInterrupt is caught to allow graceful cancellation, + then re-raised so the caller can handle cleanup. + + Args: + agent: The agent instance with interact_stateless support + message: User message dict with 'role', 'content', and optional '_ts' + """ + # Get current history and add the new message + messages = agent.get_history().copy() + messages.append(message) + + accumulated_text = "" + tool_calls_made = [] + final_messages = None + + with Live(console=console, refresh_per_second=10) as live: + try: + # Use interact_stateless for streaming (it's a generator) + for updated_messages in agent.interact_stateless( + messages=messages, + model=agent.model, + api_base=agent.api_base, + api_key=agent.api_key, + llm_args=agent.llm_args + ): + final_messages = updated_messages + + # Extract the last assistant message + for msg in reversed(updated_messages): + if msg.get("role") == "assistant": + content = msg.get("content", "") + if content != accumulated_text: + accumulated_text = content + + # Show streaming content + if accumulated_text: + live.update(Markdown(accumulated_text)) + + # Check for tool calls + if "tool_calls" in msg and msg["tool_calls"] != tool_calls_made: + tool_calls_made = msg["tool_calls"] + # Show tool execution + tool_names = [tc["function"]["name"] for tc in tool_calls_made] + tool_info = Text(f"\n🔧 Executing tools: {', '.join(tool_names)}", style="yellow") + live.update(tool_info) + break + + # Update agent's history with final messages + if final_messages: + agent.set_history(final_messages) + + except KeyboardInterrupt: + live.stop() + console.print("\n[yellow]⚠ Response cancelled by user[/yellow]") + raise + except Exception as e: + live.stop() + console.print(f"\n[red]❌ Error: {e}[/red]") + LOGGER.error(f"Streaming error: {e}", exc_info=True) + raise + + +async def async_interact(agent, message: dict): + """ + Async wrapper for agent interaction with streaming. + + Note: KeyboardInterrupt from stream_agent_response_sync is caught here + to prevent it from propagating up. The actual interrupt handling and + user feedback happens in stream_agent_response_sync. + """ + try: + # Run the synchronous streaming function in a thread pool + await asyncio.to_thread(stream_agent_response_sync, agent, message) + except KeyboardInterrupt: + # Already handled in stream_agent_response_sync with user feedback + LOGGER.info("Agent interaction cancelled by user") + except Exception as e: + LOGGER.error(f"Agent interaction failed: {e}") + console.print(f"[red]Error: {e}[/red]") + + +def print_welcome(): + """Print welcome message""" + welcome = Text() + welcome.append("╔═══════════════════════════════════════╗\n", style="bold blue") + welcome.append("║ ", style="bold blue") + welcome.append("LLM Agent Chat Interface", style="bold white") + welcome.append(" ║\n", style="bold blue") + welcome.append("╚═══════════════════════════════════════╝\n", style="bold blue") + welcome.append("\nCommands:\n", style="bold yellow") + welcome.append(" /help - Show help\n", style="cyan") + welcome.append(" /edit - Multi-line input\n", style="cyan") + welcome.append(" /history - Show conversation history\n", style="cyan") + welcome.append(" /system - Set system prompt\n", style="cyan") + welcome.append(" /save - Save conversation\n", style="cyan") + welcome.append(" /clear - Clear screen\n", style="cyan") + welcome.append(" /exit - Exit (or Ctrl+D)\n", style="cyan") + welcome.append("\nShortcuts:\n", style="bold yellow") + welcome.append(" Ctrl+O - New line in input\n", style="cyan") + welcome.append(" Ctrl+C - Cancel current response\n", style="cyan") + welcome.append(" Ctrl+D - Exit\n", style="cyan") + welcome.append(" ↑/↓ - Navigate history\n", style="cyan") + welcome.append(" Tab - Auto-complete commands\n", style="cyan") + + console.print(welcome) + + +@click.command() +@click.option("-t", "--temperature", default=0.2, type=float, + help="Specify the model temperature.") +@click.option("--max-tokens", default=6000, type=int, + help="Specify the max. number of model output tokens.") +@click.option("-c", "--num-ctx", default=131072, type=int, + help="Specify the model context window.") +@click.option("-l", "--max-num-papers", default=50, type=int, + help="Specify the maximum number of papers to retrieve.") +def main(temperature, max_tokens, num_ctx, max_num_papers): + """Enhanced LLM Agent CLI with async streaming and rich terminal features""" + + # Setup logging + setup_logging( + log_dir="logs", + level=os.environ.get("AGENTIC_NAV_LOG_LEVEL", "INFO") + ) + + print_welcome() + LOGGER.info("Agent runtime started") + + # Config for the LLM messages + llm_config = { + "model": f"ollama_chat/{AGENT_MODEL_NAME}", + "api_base": AGENT_MODEL_API_BASE, + "temperature": temperature, + "max_tokens": max_tokens, + "num_ctx": num_ctx + } + LOGGER.info(f"LLM configuration: {llm_config}") + + # Parameters to limit the tool calling scope + tool_args = { + "num_records": max_num_papers + } + LOGGER.info(f"Global tool arguments: {tool_args}") + + # Initialize agent (model is passed via llm_config/llm_args) + agent = NeurIPS2025Agent( + api_base=AGENT_MODEL_API_BASE, + api_key=OLLAMA_API_KEY, + llm_args=llm_config, + global_tool_args=tool_args, + ) + + agent.setup_session() + console.print("[green]✓ Agent initialized successfully[/green]\n") + + # Create prompt session + session = create_prompt_session() + + # Main interaction loop + while True: + try: + # Get user input with rich prompt + line = session.prompt( + HTML('You> '), + multiline=False, + ).strip() + + LOGGER.debug(f"USER PROMPT: {line}") + + except (EOFError, KeyboardInterrupt): + console.print("\n[yellow]Goodbye! 👋[/yellow]") + LOGGER.info("User exited") + break + + if not line: + continue + + # Handle commands + if line.startswith("/"): + parts = line.split(maxsplit=1) + cmd = parts[0].lower() + arg = parts[1] if len(parts) > 1 else "" + + if cmd == "/help": + print_help() + continue + + elif cmd == "/exit": + console.print("[yellow]Goodbye! 👋[/yellow]") + LOGGER.info("User exited via /exit command") + break + + elif cmd == "/clear": + console.clear() + print_welcome() + continue + + elif cmd == "/edit": + content = open_editor() + if content: + next_message = { + "role": "user", + "content": content, + "_ts": str(datetime.now(UTC)) + } + else: + console.print("[yellow]⚠ No content provided[/yellow]") + continue + + elif cmd == "/system": + content = open_editor() + if content: + messages = agent.set_system_prompt( + messages=agent.get_history(), + new_system_prompt=content + ) + agent.set_history(messages=messages) + console.print("[green]✓ System prompt updated[/green]") + continue + else: + console.print("[yellow]⚠ No content provided[/yellow]") + continue + + elif cmd == "/history": + show_history(agent.get_history()) + continue + + elif cmd == "/save": + Path("chat_histories/").mkdir(exist_ok=True, parents=True) + time_now = datetime.now().strftime("%Y-%m-%d_%H-%M") + path = arg.strip() or f"chat_histories/{time_now}_chat_history.json" + + try: + save_chat_history(agent.get_history(), path) + console.print(f"[green]✓ Chat saved to {path}[/green]") + except Exception as e: + console.print(f"[red]❌ Failed to save: {e}[/red]") + LOGGER.error(f"Save failed: {e}") + continue + + else: + console.print(f"[red]❌ Unknown command: {cmd}[/red]") + console.print("[yellow]Type /help for available commands[/yellow]") + continue + else: + # Regular single-line user message + next_message = { + "role": "user", + "content": line, + "_ts": str(datetime.now(UTC)) + } + + try: + console.print() + asyncio.run(async_interact(agent, next_message)) + console.print() + + except KeyboardInterrupt: + console.print("\n[yellow]⚠ Interrupted[/yellow]") + continue + except Exception as e: + console.print(f"\n[red]❌ Error: {e}[/red]") + LOGGER.error(f"Interaction error: {e}", exc_info=True) + continue + + +if __name__ == "__main__": + main() diff --git a/agentic_nav/tools/__init__.py b/agentic_nav/tools/__init__.py new file mode 100644 index 0000000..c62566c --- /dev/null +++ b/agentic_nav/tools/__init__.py @@ -0,0 +1,15 @@ +from agentic_nav.tools.knowledge_graph import search_similar_papers, find_neighboring_papers, traverse_graph +from agentic_nav.tools.session_routing import build_visit_schedule + + +__all__ = [ + 'search_similar_papers', + 'find_neighboring_papers', + 'traverse_graph', + 'build_visit_schedule', +] + + +def get_all_tools(): + """Get all tools as a dictionary.""" + return [globals()[name] for name in __all__] \ No newline at end of file diff --git a/agentic_nav/tools/knowledge_graph/__init__.py b/agentic_nav/tools/knowledge_graph/__init__.py new file mode 100644 index 0000000..4725cc1 --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/__init__.py @@ -0,0 +1,326 @@ +""" +This file defines the tools that can be made available to an agent. +The idea is to put the actual functions into wrappers that provide LLM-friendly and token efficient outputs. +""" +import os +import random + +from toon_format import encode as toon_encode +from typing import List, Optional, Union + +from agentic_nav.tools.knowledge_graph.retriever import Neo4jGraphWorker, LOGGER + +NEO4J_DB_URI = os.environ.get("NEO4J_DB_URI", "bolt://neo4j_db:7687") +NEO4J_USERNAME = os.environ.get("NEO4J_USERNAME", "neo4j") +NEO4J_PASSWORD = os.environ.get("NEO4J_PASSWORD") + + +def search_similar_papers( + user_query: str, + num_papers_to_return: int = 50, + min_similarity: float = None, + day: str = None, + timeslots: List[str] = None +) -> str: + """ + Search for research papers semantically similar to a user's natural language query. + + This function performs vector similarity search against a Neo4j knowledge graph database + to find papers that match the semantic meaning of the user's query. It serves as the + entry point for paper discovery workflows and is typically followed by neighborhood + or graph traversal searches for deeper exploration. + + Args: + user_query (str): Natural language query describing the research topic or interest. + The query is embedded and compared against paper embeddings in the database. + num_papers_to_return (int, optional): Maximum number of papers to return, ranked by + similarity score. Defaults to 50. + min_similarity (float, optional): Minimum similarity threshold for returned papers. + Defaults to None (no filtering). Should be a value between 0.0 and 1.0, where + higher values indicate stricter similarity requirements. + day (str, optional): Conference day as a date string in ISO format (e.g., "2024-12-10"). + When provided, only papers scheduled on this day will be searched. Defaults to None + (no day filtering). + timeslots (List[str], optional): List of time ranges to filter papers by their session + times. Each timeslot should be formatted as "HH:MM:SS-HH:MM:SS" (e.g., + ["09:00:00-12:00:00", "14:00:00-17:00:00"]). Papers with session start times + falling within any of these ranges will be included. Defaults to None (no time filtering). + + Returns: + str: A token-efficient formatted string representation of papers matching the query, + encoded using the toon_encode function. Papers are typically ordered by + descending similarity score. + + Restrictions: + - Requires a running Neo4j database instance at bolt://localhost:7687 with credentials + (username: "neo4j", password: "llm_agents") + - The database must have pre-computed embeddings for papers to enable similarity search + - The database must have a vector index configured for efficient similarity queries + - Currently creates a new database connection for each function call, which may not be + optimal for concurrent usage (see TODO note) + + Notes: + - This function is designed as the initial step in a multi-stage paper discovery workflow + - Results can be further explored using find_neighboring_papers() or traverse_graph() + - When day and/or timeslots are provided, the database filters papers by their session + times BEFORE performing vector similarity search for better performance + - TODO: The Neo4jGraphWorker should be wrapped in a session to better handle + concurrent connections and connection pooling + + Raises: + Connection errors if Neo4j database is not accessible + ValueError if min_similarity is outside the valid range [0.0, 1.0] + ValueError if day is not in valid ISO date format (YYYY-MM-DD) + ValueError if timeslots are not properly formatted + Embedding errors if the query cannot be properly embedded + + Example: + >>> # Basic similarity search + >>> papers = search_similar_papers( + ... user_query="federated learning for privacy-preserving machine learning", + ... num_papers_to_return=15 + ... ) + >>> + >>> # Search with similarity threshold + >>> highly_relevant_papers = search_similar_papers( + ... user_query="transformer architectures for NLP", + ... num_papers_to_return=20, + ... min_similarity=0.75 + ... ) + >>> + >>> # Search for papers on a specific day and time + >>> morning_papers = search_similar_papers( + ... user_query="computer vision applications", + ... num_papers_to_return=50, + ... day="2024-12-10", + ... timeslots=["09:00:00-12:00:00"] + ... ) + >>> + >>> # Search across multiple timeslots on a specific day + >>> daytime_papers = search_similar_papers( + ... user_query="reinforcement learning", + ... num_papers_to_return=25, + ... day="2024-12-11", + ... timeslots=["09:00:00-12:00:00", "14:00:00-17:00:00"] + ... ) + """ + # Type coercion for parameters that may come as strings from LLM tool calls + if num_papers_to_return is not None and not isinstance(num_papers_to_return, int): + num_papers_to_return = int(num_papers_to_return) + if min_similarity is not None and not isinstance(min_similarity, float): + min_similarity = float(min_similarity) + + # Handle timeslots - ensure it's a list or None + if timeslots is not None and isinstance(timeslots, str): + # If a single string is provided, wrap it in a list + timeslots = [timeslots] + + worker = Neo4jGraphWorker( + uri=NEO4J_DB_URI, + username=NEO4J_USERNAME, + password=NEO4J_PASSWORD + ) + + # Fetch papers with optional day and time filtering + papers = worker.similarity_search( + user_query=user_query, + top_k=num_papers_to_return, + min_similarity=min_similarity, + day=day, + timeslots=timeslots + ) + + # Format outputs to be more token efficient + formatted_papers = toon_encode(papers) + + return formatted_papers + + +def find_neighboring_papers( + paper_id: str, + relationship_types: Union[List[str], str] = ["SIMILAR_TO"], + num_neighbors_to_return: int = 10, + min_similarity: float = 0.75 +) -> str: + """ + Retrieve immediate neighboring entities of a specific paper from the Neo4j knowledge graph. + + This function performs a one-hop neighborhood search to find entities directly connected to + a target paper. It is designed to be used after an initial similarity search when users want + to explore specific relationships (similar papers, authors, or topics) for a paper of interest. + + Args: + paper_id (str): The unique identifier of the target paper node in the graph. neo4j UUID. + relationship_types (List[str], str): Types of relationships to query. + Defaults to ["SIMILAR_TO"]. + Valid options: ["SIMILAR_TO", "IS_AUTHOR_OF", "BELONGS_TO_TOPIC"] + neighbor_entity (str, optional): The type of neighboring entity to return. + Defaults to "similar_papers". + Valid options: ["similar_papers", "authors", "topics", "raw_results"] + num_neighbors_to_return (int, optional): Maximum number of neighbors to return. + Defaults to 10. Results are randomly shuffled before truncation to provide diversity. + min_similarity (float, optional): Minimum similarity threshold for returned neighbors. + + Returns: + str: A token-efficient formatted string representation of neighboring entities, + encoded using the toon_encode function. + + Restrictions: + - Requires a running Neo4j database instance at bolt://localhost:7687 with credentials + (username: "neo4j", password: "llm_agents") + - Should be used after an initial similarity search as part of a focused exploration workflow + - The paper_id must exist in the Neo4j graph database + - Only performs one-hop searches (direct neighbors only) + - Only the three specified relationship types are supported + - Only the four specified neighbor entity types are supported + - The neighbor_entity parameter must match the relationship_types used + (e.g., "similar_papers" with "SIMILAR_TO", "authors" with "IS_AUTHOR_OF") + + Notes: + - Results are randomly shuffled to provide diverse recommendations across multiple calls + - The function extracts only the "neighbor" data from the returned results + - There is a potential bug: the type check `type(relevant_neighbors) is int` should likely be + `type(num_neighbors_to_return) is int` for proper list truncation + + Raises: + Connection errors if Neo4j database is not accessible + KeyError if neighbor_entity doesn't exist in the returned neighbors dictionary + ValueError if invalid relationship_types or neighbor_entity are provided + + Example: + >>> similar_papers = find_neighboring_papers( + ... paper_id="", + ... relationship_types=["SIMILAR_TO"], + ... neighbor_entity="similar_papers", + ... num_neighbors_to_return=5 + ... ) + >>> + >>> authors = find_neighboring_papers( + ... paper_id="", + ... relationship_types=["IS_AUTHOR_OF"], + ... neighbor_entity="authors", + ... num_neighbors_to_return=3 + ... ) + """ + # Type coercion for parameters that may come as strings from LLM tool calls + if num_neighbors_to_return is not None and not isinstance(num_neighbors_to_return, int): + num_neighbors_to_return = int(num_neighbors_to_return) + + if type(relationship_types) is str: + relationship_types = [relationship_types] + + worker = Neo4jGraphWorker( + uri=NEO4J_DB_URI, + username=NEO4J_USERNAME, + password=NEO4J_PASSWORD + ) + + neighbors = worker.neighborhood_search( + paper_id=paper_id, + relationship_types=relationship_types, + min_similarity=min_similarity, + ) + + relevant_neighbors = [] + for rel_type, neighbor in neighbors.items(): + if rel_type != relationship_types: + relevant_neighbors.append(neighbor) + + # Constrain and shuffle neighbors for more diverse responses + random.shuffle(relevant_neighbors) + + # FIX: Changed type(relevant_neighbors) to type(num_neighbors_to_return) + if num_neighbors_to_return is not None and isinstance(num_neighbors_to_return, int): + relevant_neighbors = relevant_neighbors[:num_neighbors_to_return] + + # Format outputs to be more token efficient + formatted_neighbors = toon_encode(relevant_neighbors) + + return formatted_neighbors + + +def traverse_graph( + start_paper_id: str, + n_hops: int = 2, + relationship_type: Optional[str] = "BELONGS_TO_TOPIC", + max_results: Optional[int] = 30, + strategy: str = "breadth_first_random", + max_branches: Optional[int] = 2, + random_seed: Optional[int] = 42 +) -> str: + """ + Traverse a Neo4j knowledge graph to discover related research papers through various relationship types. + + This function performs exploratory graph traversal starting from a seed paper to find potentially + interesting related papers. It is designed to be used after an initial similarity search, allowing + users to discover papers through different connection paths (topics, authors, similarity). + + Args: + start_paper_id (str): The unique identifier of the starting paper node in the graph. neo4j UUID. + n_hops (int, optional): Number of relationship hops to traverse from the starting paper. + Defaults to 2. Higher values explore further but may return less relevant results. + relationship_type (str, optional): Types of relationships to follow during traversal. + Defaults to "BELONGS_TO_TOPIC". + Valid options: ["SIMILAR_TO", "AUTHORED_BY", "BELONGS_TO_TOPIC"] + max_results (int, optional): Maximum number of papers to return. Defaults to 30. + strategy (str, optional): Graph traversal strategy to use. Defaults to "breadth_first_random". + Valid options: ["breadth_first", "depth_first", "breadth_first_random", "depth_first_random"] + max_branches (int, optional): Maximum number of branches to follow at each node during traversal. + Defaults to 2. Controls the breadth of exploration at each step. + random_seed (int, optional): Seed for random number generation in randomized strategies. + Defaults to 42. Ensures reproducible results when using random strategies. + + Returns: + str: A formatted string representation of discovered papers, encoded using the toon_encode function. + + Restrictions: + - Requires a running Neo4j database instance at bolt://localhost:7687 with credentials + (username: "neo4j", password: "llm_agents") + - Should be used after an initial similarity search as part of an exploratory workflow + - The start_paper_id must exist in the Neo4j graph database + - Only the three specified relationship types are supported + - Only the four specified traversal strategies are supported + - Random strategies require random_seed for reproducibility + + Raises: + Connection errors if Neo4j database is not accessible + ValueError if invalid relationship_types or strategy are provided + + Example: + >>> related_papers = traverse_graph( + ... start_paper_id="paper_12345", + ... n_hops=3, + ... relationship_type="SIMILAR_TO", + ... max_results=50, + ... strategy="breadth_first_random" + ... ) + """ + # Type coercion for parameters that may come as strings from LLM tool calls + if n_hops is not None and not isinstance(n_hops, int): + n_hops = int(n_hops) + if max_results is not None and not isinstance(max_results, int): + max_results = int(max_results) + if max_branches is not None and not isinstance(max_branches, int): + max_branches = int(max_branches) + if random_seed is not None and not isinstance(random_seed, int): + random_seed = int(random_seed) + + worker = Neo4jGraphWorker( + uri=NEO4J_DB_URI, + username=NEO4J_USERNAME, + password=NEO4J_PASSWORD + ) + + papers = worker.graph_traversal( + start_paper_id=start_paper_id, + n_hops=n_hops, + relationship_type=relationship_type, + max_results=max_results, + strategy=strategy, + max_branches=max_branches, + random_seed=random_seed + ) + + formatted_neighbors = toon_encode(papers) + + return formatted_neighbors diff --git a/agentic_nav/tools/knowledge_graph/file_handler.py b/agentic_nav/tools/knowledge_graph/file_handler.py new file mode 100644 index 0000000..e18a140 --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/file_handler.py @@ -0,0 +1,29 @@ +import pickle +import networkx as nx + + +def save_graph(graph: nx.Graph, output_path: str): + """ + Save the graph to a file using pickle. + + Args: + output_path: Path to save the graph + """ + with open(output_path, 'wb') as f: + pickle.dump(graph, f) + f.close() + print(f"Graph saved to {output_path}") + + +def load_graph(input_path: str) -> nx.Graph: + """ + Load a graph from a pickle file. + + Args: + input_path: Path to the saved graph + """ + with open(input_path, 'rb') as f: + graph = pickle.load(f) + f.close() + print(f"Graph loaded from {input_path}") + return graph diff --git a/agentic_nav/tools/knowledge_graph/graph_generator.py b/agentic_nav/tools/knowledge_graph/graph_generator.py new file mode 100644 index 0000000..24a1325 --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/graph_generator.py @@ -0,0 +1,446 @@ +import json +import logging +import os + +import click +import networkx as nx +import numpy as np +import litellm +from typing import List, Dict, Any, Union +from litellm import embedding +from concurrent.futures import ThreadPoolExecutor, as_completed +from tqdm import tqdm + +from pathlib import Path + +from agentic_nav.utils.embedding_generator import batch_embed_documents +from agentic_nav.utils.logging import setup_logging +from agentic_nav.tools.knowledge_graph.file_handler import save_graph + + +# Setup logging +setup_logging( + log_dir="logs", + level=os.environ.get("AGENTIC_NAV_LOG_LEVEL", "INFO") +) +LOGGER = logging.getLogger(__name__) +litellm._logging._disable_debugging() +PROJECT_ROOT = Path(__file__).parent.parent.parent.parent +EMBEDDING_MODEL_NAME = os.environ.get("EMBEDDING_MODEL_NAME", "ollama/nomic-embed-text") +EMBEDDING_MODEL_API_BASE = os.environ.get("EMBEDDING_MODEL_API_BASE", "http://localhost:11435") + + +class PaperKnowledgeGraph: + """ + A knowledge graph builder for academic papers focusing on: + - Paper names (nodes) + - Topics (nodes) + - Abstract embeddings (stored as node attributes) + Uses litellm with ollama for local embedding generation with parallel processing. + """ + def __init__( + self, + embedding_model: str = EMBEDDING_MODEL_NAME, + ollama_base_url: str = EMBEDDING_MODEL_API_BASE, + embedding_gen_batch_size: int = 32, + max_parallel_workers: int = 8, + limit_num_papers: Union[int, None] = None + ): + """ + Initialize the knowledge graph builder. + + Args: + embedding_model: Name of the ollama embedding model (e.g., 'nomic-embed-text') + ollama_base_url: Base URL for the ollama server + embedding_gen_batch_size: Batch size for generating text embeddings + max_parallel_workers: Number of parallel workers for embedding generation + """ + self.graph = nx.Graph() + self.embedding_model = embedding_model + self.ollama_base_url = ollama_base_url + self.batch_size = embedding_gen_batch_size + self.max_workers = max_parallel_workers + self.papers_data = [] + self.limit_num_papers = limit_num_papers + + # Test connection + LOGGER.info(f"Initializing with model: {embedding_model}") + LOGGER.info(f"Ollama server: {ollama_base_url}") + self._test_embedding_connection() + + def _test_embedding_connection(self): + """Test connection to ollama server.""" + try: + response = embedding( + model=self.embedding_model, + input=["test connection"], + api_base=self.ollama_base_url + ) + LOGGER.info(f"Successfully connected to ollama server") + LOGGER.info(f"Embedding dimension: {len(response.data[0]['embedding'])}") + except Exception as e: + LOGGER.error(f"❌ Error connecting to ollama server: {e}") + LOGGER.error(f"Please ensure ollama is running and the model '{self.embedding_model}' is available") + LOGGER.error(f"Run: ollama pull nomic-embed-text") + raise + + def load_papers_from_json(self, json_file_path: str, paper_dict_key: str = "results"): + """ + Load papers from a JSON file or JSONL file. + + Args: + json_file_path: Path to the JSON/JSONL file + """ + self.papers_data = [] + + with open(json_file_path, 'r') as f: + # Try to parse as regular JSON first + try: + content = f.read() + # Try parsing as a single JSON object + try: + data = json.loads(content) + if isinstance(data[paper_dict_key], list): + self.papers_data = data[paper_dict_key] + else: + raise TypeError("File importer expects a list of papers.") + except json.JSONDecodeError: + # Try parsing as JSONL (one JSON object per line) + f.seek(0) + for line in f: + line = line.strip() + if line: + self.papers_data.append(json.loads(line)) + except Exception as e: + raise ValueError(f"Error parsing JSON file: {e}") + + if self.limit_num_papers is not None and self.limit_num_papers > 0: + LOGGER.warning(f"WARNING: Number of papers limited to {self.limit_num_papers} items. Set to 'None' for all papers") + self.papers_data = self.papers_data[:self.limit_num_papers] + + LOGGER.info(f"Loaded {len(self.papers_data)} papers from {json_file_path}") + + def build_graph(self): + """ + Build the knowledge graph from loaded papers. + Creates nodes for papers and topics, and edges between them. + Computes embeddings for abstracts in parallel. + """ + topic_nodes = set() + author_nodes = set() + + LOGGER.info(f"\nPreparing to process {len(self.papers_data)} papers...") + + # Extract all abstracts and paper info + paper_info = [] + abstracts = [] + + for paper in self.papers_data: + paper_id = paper.get('uid', paper.get('id')) + paper_name = paper.get('name', 'Unnamed Paper') + abstract = paper.get('abstract', '') + topic = paper.get('topic', 'Unknown') + authors = paper.get('authors', []) + keywords = paper.get("keywords", []) + decision = paper.get("decision", "") + session = paper.get("session", "") + session_start_time = paper.get("starttime", "") + session_end_time = paper.get("endtime", "") + presentation_type = paper.get("eventtype", "") + room_name = paper.get("room_name", "") + project_url = paper.get("url", "") + poster_position = paper.get("poster_position", "") + paper_url = paper.get("paper_url", "") + sourceid = paper.get("sourceid", "") + virtualsite_url = paper.get("virtualsite_url", "") + + paper_info.append({ + "id": paper_id, + "name": paper_name, + "abstract": abstract, + "topic": topic, + "authors": authors, + "keywords": keywords, + "decisions": decision, + "session": session, + "session_start_time": session_start_time, + "session_end_time": session_end_time, + "presentation_type": presentation_type, + "room_name": room_name, + "project_url": project_url, + "poster_position": poster_position, + "paper_url": paper_url, + "sourceid": sourceid, + "virtualsite_url": virtualsite_url + + }) + abstracts.append(abstract) + + # Generate all embeddings in parallel + LOGGER.info(f"\nGenerating embeddings with batch size {self.batch_size}...") + embeddings = batch_embed_documents( + abstracts, + batch_size=self.batch_size, + embedding_model=self.embedding_model, + api_base=self.ollama_base_url + ) + + # Convert to list so that embeddings can be mapped to samples properly + embeddings = embeddings.tolist() + + # Add nodes to graph + LOGGER.info("\nBuilding graph structure...") + with tqdm(total=len(paper_info), desc="Adding nodes") as pbar: + for info, embedding in zip(paper_info, embeddings): + + # Extract author information (store as list of dicts) + author_list = [] + if info['authors']: + for author in info['authors']: + author_info = { + 'id': author.get('id'), + 'fullname': author.get('fullname', ''), + 'institution': author.get('institution', ''), + 'url': author.get('url', '') + } + + author_uid = f"{author_info['id']} - {author_info['fullname']}" + if author_uid not in author_nodes: + self.graph.add_node( + author_uid, + **author_info + ) + author_nodes.add(author_uid) + + author_list.append(author_info) + + # Add paper node with attributes + paper_attrs = info.copy() + del paper_attrs["authors"] + + self.graph.add_node( + info["id"], + **paper_attrs, + embedding=embedding, + authors=author_list, + node_type="paper" + ) + + for author in author_list: + self.graph.add_edge(f"{author['id']} - {author['fullname']}", info["id"], relationship="is_author_of") + + # Add topic node if it doesn't exist + if info['topic'] and info['topic'] not in topic_nodes: + self.graph.add_node( + info['topic'], + node_type='topic', + name=info['topic'] + ) + topic_nodes.add(info['topic']) + + # Add edge between paper and topic + if info['topic']: + self.graph.add_edge(info['id'], info['topic'], relationship='belongs_to_topic') + + pbar.update(1) + + LOGGER.info(f"Built graph with {self.graph.number_of_nodes()} nodes and {self.graph.number_of_edges()} edges") + LOGGER.info(f" Papers: {len([n for n, d in self.graph.nodes(data=True) if d.get('node_type') == 'paper'])}") + LOGGER.info(f" Topics: {len([n for n, d in self.graph.nodes(data=True) if d.get('node_type') == 'topic'])}") + + def connect_similar_papers(self, similarity_threshold: float = 0.7): + """ + Connect papers based on abstract embedding similarity using parallel processing. + Args: + similarity_threshold: Minimum cosine similarity to create an edge (0-1) + """ + paper_nodes = [(n, d) for n, d in self.graph.nodes(data=True) if d.get('node_type') == 'paper'] + LOGGER.info(f"\nComputing similarities for {len(paper_nodes)} papers...") + + # Create pairs to compare (fast!) + pairs = [(i, j) for i in range(len(paper_nodes)) for j in range(i + 1, len(paper_nodes))] + LOGGER.info(f"Created {len(pairs)} pairs to compare") + + connections_added = 0 + + def compute_similarity(pair_idx): + """Compute similarity for a pair of papers.""" + i, j = pair_idx + node1, data1 = paper_nodes[i] + node2, data2 = paper_nodes[j] + emb1 = data1['embedding'] + emb2 = data2['embedding'] + similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2)) + if similarity >= similarity_threshold: + return (node1, node2, float(similarity)) + return None + + # Compute similarities in parallel + edges_to_add = [] + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + futures = {executor.submit(compute_similarity, pair): pair for pair in pairs} + with tqdm(total=len(pairs), desc="Computing similarities", unit="pair") as pbar: + for future in as_completed(futures): + result = future.result() + if result is not None: + edges_to_add.append(result) + pbar.update(1) + + # Add edges to graph + for node1, node2, similarity in edges_to_add: + self.graph.add_edge( + node1, + node2, + relationship='similar_to', + similarity=similarity + ) + connections_added += 1 + + LOGGER.info(f"Added {connections_added} similarity edges with threshold {similarity_threshold}") + + def get_papers_by_topic(self, topic: str) -> List[Dict[str, Any]]: + """ + Get all papers belonging to a specific topic. + + Args: + topic: Topic name + + Returns: + List of paper information dictionaries + """ + if topic not in self.graph: + return [] + + papers = [] + for neighbor in self.graph.neighbors(topic): + node_data = self.graph.nodes[neighbor] + if node_data.get('node_type') == 'paper': + papers.append({ + 'id': neighbor, + 'name': node_data.get('name'), + 'abstract': node_data.get('abstract'), + 'embedding': node_data.get('embedding') + }) + return papers + + def find_similar_papers(self, paper_id: str, top_k: int = 5) -> List[tuple]: + """ + Find the most similar papers to a given paper. + + Args: + paper_id: ID of the paper + top_k: Number of similar papers to return + + Returns: + List of (paper_id, similarity_score) tuples + """ + if paper_id not in self.graph: + return [] + + paper_data = self.graph.nodes[paper_id] + if paper_data.get('node_type') != 'paper': + return [] + + target_embedding = paper_data['embedding'] + similarities = [] + + for node, data in self.graph.nodes(data=True): + if data.get('node_type') == 'paper' and node != paper_id: + similarity = np.dot(target_embedding, data['embedding']) / \ + (np.linalg.norm(target_embedding) * np.linalg.norm(data['embedding'])) + similarities.append((node, float(similarity), data.get('name'))) + + # Sort by similarity and return top_k + similarities.sort(key=lambda x: x[1], reverse=True) + return similarities[:top_k] + + def get_graph_statistics(self) -> Dict[str, Any]: + """ + Get statistics about the knowledge graph. + + Returns: + Dictionary with graph statistics + """ + paper_nodes = [n for n, d in self.graph.nodes(data=True) + if d.get('node_type') == 'paper'] + topic_nodes = [n for n, d in self.graph.nodes(data=True) + if d.get('node_type') == 'topic'] + + stats = { + 'total_nodes': self.graph.number_of_nodes(), + 'total_edges': self.graph.number_of_edges(), + 'paper_nodes': len(paper_nodes), + 'topic_nodes': len(topic_nodes), + 'average_degree': sum(dict(self.graph.degree()).values()) / self.graph.number_of_nodes(), + 'density': nx.density(self.graph), + 'is_connected': nx.is_connected(self.graph), + } + + if nx.is_connected(self.graph): + stats['diameter'] = nx.diameter(self.graph) + stats['average_shortest_path'] = nx.average_shortest_path_length(self.graph) + + return stats + + +@click.command() +@click.option("-m", "--embedding-model", default="nomic-embed-text") +@click.option("-l", "--ollama-server-url", default="http://localhost:11434") +@click.option("-b", "--embedding-gen-batch-size", default=32) +@click.option("-w", "--max-parallel-workers", default=16) +@click.option("-p", "--limit-num-papers", default=None, type=int) +@click.option("-f", "--input-json-file", default=f"{PROJECT_ROOT}/data/neurips-2025-orals-posters.json") +@click.option("-o", "--output-file", default=f"{PROJECT_ROOT}/graphs/knowledge_graph.pkl") +@click.option("-s", "--similarity-threshold", default=0.8) +def main( + embedding_model: str, + ollama_server_url: str, + embedding_gen_batch_size: int, + max_parallel_workers: int, + limit_num_papers: int, + input_json_file: str, + output_file: str, + similarity_threshold: float +): + + kg = PaperKnowledgeGraph( + embedding_model=f"ollama/{embedding_model}", + ollama_base_url=ollama_server_url, + embedding_gen_batch_size=embedding_gen_batch_size, + max_parallel_workers=max_parallel_workers, + limit_num_papers=limit_num_papers + ) + + # Load papers from JSON file + kg.load_papers_from_json(input_json_file) + + # Build the graph (parallel embedding generation) + kg.build_graph() + + # Optionally connect similar papers based on embeddings (parallel) + kg.connect_similar_papers(similarity_threshold=similarity_threshold) + + # Save the graph to disk + save_graph( + graph=kg.graph, + output_path=output_file + ) + + # Print statistics + stats = kg.get_graph_statistics() + LOGGER.info("\nGraph Statistics:") + for key, value in stats.items(): + LOGGER.info(f" {key}: {value}") + + # Test run: Find similar papers + if kg.papers_data: + first_paper_id = kg.papers_data[0].get('uid', kg.papers_data[0].get('id')) + LOGGER.debug(f"\nPapers similar to '{kg.graph.nodes[first_paper_id]['name']}':") + similar = kg.find_similar_papers(first_paper_id, top_k=3) + for pid, sim, name in similar: + LOGGER.debug(f" - {name} (similarity: {sim:.3f})") + + +# Run +if __name__ == "__main__": + main() diff --git a/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/__init__.py b/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/__init__.py new file mode 100644 index 0000000..96af716 --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/__init__.py @@ -0,0 +1,15 @@ +from enum import Enum + +from agentic_nav.tools.knowledge_graph.graph_traversal_strategies.breadth_first_random import _graph_traversal_bfs_random +from agentic_nav.tools.knowledge_graph.graph_traversal_strategies.depth_first_random import _graph_traversal_dfs_random +from agentic_nav.tools.knowledge_graph.graph_traversal_strategies.neo4j_builtin import _graph_traversal_cypher + + +class TraversalStrategy(Enum): + """Traversal strategy options""" + BFS = "breadth_first" + DFS = "depth_first" + BFS_RANDOM = "breadth_first_random" + DFS_RANDOM = "depth_first_random" + + diff --git a/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/breadth_first_random.py b/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/breadth_first_random.py new file mode 100644 index 0000000..fc83ea1 --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/breadth_first_random.py @@ -0,0 +1,80 @@ +import neo4j +from typing import List, Dict, Any, Optional, Set +from collections import deque +import random + + +def _graph_traversal_bfs_random( + db_driver: neo4j.Driver, + start_paper_id: str, + n_hops: int, + relationship_type: Optional[str], + max_results: Optional[int], + max_branches: int +) -> List[Dict[str, Any]]: + """ + BFS traversal with random neighbor sampling. + Explores level by level, randomly sampling neighbors at each level. + """ + with db_driver.session() as session: + visited: Set[str] = {start_paper_id} + queue = deque([(start_paper_id, 0)]) # (paper_id, distance) + papers = [] + + # Build relationship type filter + if relationship_type: + rel_filter = f":{':'.join([relationship_type])}" + else: + rel_filter = "" + + while queue: + if max_results and type(max_results) is int and len(papers) >= max_results: + break + + current_id, distance = queue.popleft() + + # Stop if we've reached max depth + if distance >= n_hops: + continue + + # Query to get all neighbors + query = f""" + MATCH (p:Paper {{id: $paper_id}})-[r{rel_filter}]->(neighbor:Paper) + RETURN neighbor.id as id, + neighbor.name as name, + neighbor.abstract as abstract, + neighbor.topic as topic + """ + + result = session.run(query, paper_id=current_id) + neighbors = list(result) + + # Randomly sample neighbors + if neighbors: + sampled_neighbors = random.sample( + neighbors, + min(max_branches, len(neighbors)) + ) + + for record in sampled_neighbors: + neighbor_id = record['id'] + + if neighbor_id not in visited: + visited.add(neighbor_id) + + paper = { + 'id': neighbor_id, + 'name': record['name'], + 'abstract': record['abstract'], + 'topic': record['topic'], + 'distance': distance + 1 + } + papers.append(paper) + + # Add to queue for next level + queue.append((neighbor_id, distance + 1)) + + if max_results and type(max_results) is int and len(papers) >= max_results: + break + + return papers \ No newline at end of file diff --git a/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/depth_first_random.py b/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/depth_first_random.py new file mode 100644 index 0000000..e11b8f6 --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/depth_first_random.py @@ -0,0 +1,78 @@ +from typing import List, Dict, Any, Optional, Set +import random + +import neo4j + + +def _graph_traversal_dfs_random( + db_driver: neo4j.Driver, + start_paper_id: str, + n_hops: int, + relationship_type: Optional[str], + max_results: Optional[int], + max_branches: int +) -> List[Dict[str, Any]]: + """ + DFS traversal with random neighbor sampling. + Explores deeply along random branches before backtracking. + """ + with db_driver.session() as session: + visited: Set[str] = {start_paper_id} + papers = [] + + # Build relationship type filter + if relationship_type: + rel_filter = f":{':'.join([relationship_type])}" + else: + rel_filter = "" + + def dfs_traverse(paper_id: str, distance: int): + """Recursive DFS helper""" + if max_results and len(papers) >= max_results: + return + + if distance >= n_hops: + return + + # Query to get all neighbors + query = f""" + MATCH (p:Paper {{id: $paper_id}})-[r{rel_filter}]->(neighbor:Paper) + RETURN neighbor.id as id, + neighbor.name as name, + neighbor.abstract as abstract, + neighbor.topic as topic + """ + + result = session.run(query, paper_id=paper_id) + neighbors = list(result) + + # Randomly sample neighbors + if neighbors: + sampled_neighbors = random.sample( + neighbors, + min(max_branches, len(neighbors)) + ) + + for record in sampled_neighbors: + neighbor_id = record['id'] + + if neighbor_id not in visited: + if max_results and len(papers) >= max_results: + return + + visited.add(neighbor_id) + + paper = { + 'id': neighbor_id, + 'name': record['name'], + 'abstract': record['abstract'], + 'topic': record['topic'], + 'distance': distance + 1 + } + papers.append(paper) + + # Recursively explore this branch + dfs_traverse(neighbor_id, distance + 1) + + dfs_traverse(start_paper_id, 0) + return papers \ No newline at end of file diff --git a/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/neo4j_builtin.py b/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/neo4j_builtin.py new file mode 100644 index 0000000..c90930f --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/graph_traversal_strategies/neo4j_builtin.py @@ -0,0 +1,50 @@ +from typing import List, Dict, Any, Optional + +import neo4j + + +_DB_GRAPH_TRAVERSAL_QUERY = lambda rel_filter, n_hops: f""" + MATCH path = (start:Paper)-[{rel_filter}*1..{n_hops}]-(related:Paper) + WHERE start.id IN $start_paper_ids + AND related.id <> start.id + WITH related, min(length(path)) as min_distance + RETURN DISTINCT related.id as id, + related.name as name, + related.abstract as abstract, + related.topic as topic, + min_distance as distance + ORDER BY min_distance, related.name + """ + + +def _graph_traversal_cypher( + db_driver: neo4j.Driver, + start_paper_id: str, + n_hops: int, + relationship_type: Optional[str], + max_results: Optional[int] +) -> List[Dict[str, Any]]: + """Original Cypher-based traversal (BFS/DFS handled by Neo4j)""" + with db_driver.session() as session: + if relationship_type: + rel_filter = f":{':'.join([relationship_type])}" + else: + rel_filter = "" + + query = _DB_GRAPH_TRAVERSAL_QUERY(rel_filter=rel_filter, n_hops=n_hops) + if max_results: + query += f" LIMIT {max_results}" + + result = session.run(query, start_paper_ids=[start_paper_id]) + papers = [] + for record in result: + paper = { + 'id': record['id'], + 'name': record['name'], + 'abstract': record['abstract'], + 'topic': record['topic'], + 'distance': record['distance'] + } + papers.append(paper) + + return papers \ No newline at end of file diff --git a/agentic_nav/tools/knowledge_graph/neo4j_db_importer.py b/agentic_nav/tools/knowledge_graph/neo4j_db_importer.py new file mode 100644 index 0000000..d23e366 --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/neo4j_db_importer.py @@ -0,0 +1,537 @@ +""" +Neo4j exporter for PaperKnowledgeGraph +Exports NetworkX graph to Neo4j database with proper handling of embeddings and relationships +""" +import logging +import os + +import click +import networkx as nx +from neo4j import GraphDatabase +from typing import Dict, Any +import numpy as np +from tqdm import tqdm +from pathlib import Path + +from agentic_nav.tools.knowledge_graph.file_handler import load_graph +from agentic_nav.utils.logger import setup_logging + + +# Setup logging +setup_logging( + log_dir="logs", + level=os.environ.get("AGENTIC_NAV_LOG_LEVEL", "INFO") +) +LOGGER = logging.getLogger(__name__) +PROJECT_ROOT = Path(__file__).parent.parent.parent.parent +NEO4J_USERNAME = os.environ.get("NEO4J_USERNAME", "neo4j") +NEO4J_PASSWORD = os.environ.get("NEO4J_PASSWORD") +NEO4J_DB_URI = os.environ.get("NEO4J_DB_URI", "bolt://neo4j_db:7687") + + +class Neo4jImporter: + """Import PaperKnowledgeGraph to Neo4j database.""" + + def __init__( + self, + uri: str = NEO4J_DB_URI, + username: str = NEO4J_USERNAME, + password: str = NEO4J_PASSWORD + ): + """Initialize Neo4j connection.""" + self.driver = GraphDatabase.driver(uri, auth=(username, password)) + self.driver.verify_connectivity() + LOGGER.info(f"Connected to Neo4j at {uri}") + + def close(self): + """Close the Neo4j driver connection.""" + self.driver.close() + + def clear_database(self, batch_size=500): + with self.driver.session() as session: + deleted_total = 0 + while True: + result = session.run(""" + MATCH (n) + WITH n LIMIT $batch_size + DETACH DELETE n + RETURN count(n) as deleted + """, + batch_size=batch_size + ) + + deleted = result.single()["deleted"] + deleted_total += deleted + LOGGER.info(f"Deleted {deleted} nodes (total: {deleted_total})") + + if deleted == 0: + break + + def create_indexes(self, embedding_dimension: int = 768): + """Create indexes for better query performance, including vector index.""" + with self.driver.session() as session: + # Create index on paper IDs + session.run("CREATE INDEX paper_id IF NOT EXISTS FOR (p:Paper) ON (p.id)") + + # Create index on topic names + session.run("CREATE INDEX topic_name IF NOT EXISTS FOR (t:Topic) ON (t.name)") + + # Create index on author IDs + session.run("CREATE INDEX author_id IF NOT EXISTS FOR (a:Author) ON (a.author_id)") + + # Create index on author names (useful for searching) + session.run("CREATE INDEX author_name IF NOT EXISTS FOR (a:Author) ON (a.fullname)") + + # Create vector index for embeddings (Neo4j 5.11+) + try: + session.run(""" + CREATE VECTOR INDEX paper_embeddings IF NOT EXISTS + FOR (p:Paper) + ON p.embedding + OPTIONS { + indexConfig: { + `vector.dimensions`: $dimension, + `vector.similarity_function`: 'cosine' + } + } + """, dimension=embedding_dimension) + LOGGER.info(f"Created vector index for {embedding_dimension}-dimensional embeddings") + except Exception as e: + LOGGER.warning(f"Warning: Could not create vector index: {e}") + LOGGER.warning("Vector indexes require Neo4j 5.11+ or Enterprise Edition") + + LOGGER.info("Created standard indexes") + + def _export_paper_nodes(self, kg: nx.Graph, batch_size: int): + """Export paper nodes to Neo4j with all attributes.""" + paper_nodes = [(n, d) for n, d in kg.nodes(data=True) + if d.get('node_type') == 'paper'] + + LOGGER.info(f"\nExporting {len(paper_nodes)} paper nodes...") + + with self.driver.session() as session: + for i in tqdm(range(0, len(paper_nodes), batch_size), desc="Paper nodes"): + batch = paper_nodes[i:i + batch_size] + papers_data = [] + + for node_id, data in batch: + # Convert embedding to list if it's numpy array + embedding = data.get('embedding', []) + if isinstance(embedding, np.ndarray): + embedding = embedding.tolist() + + paper_dict = { + "id": node_id, + "name": data.get('name', ''), + "abstract": data.get('abstract', ''), + "topic": data.get('topic', ''), + "keywords": data.get('keywords', []), + "decision": data.get('decision', ''), + "session": data.get('session', ''), + "session_start_time": data.get('session_start_time', ''), + "session_end_time": data.get('session_end_time', ''), + "presentation_type": data.get('presentation_type', ''), + "room_name": data.get('room_name', ''), + "project_url": data.get('project_url', ''), + "poster_position": data.get('poster_position', ''), + "paper_url": data.get("paper_url", ""), + "sourceid": data.get("sourceid", ""), + "virtualsite_url": data.get("virtualsite_url", ""), + 'embedding': embedding + } + papers_data.append(paper_dict) + + # Batch create paper nodes + session.run(""" + UNWIND $papers AS paper + CREATE (p:Paper { + id: paper.id, + name: paper.name, + abstract: paper.abstract, + topic: paper.topic, + keywords: paper.keywords, + decision: paper.decision, + session: paper.session, + session_start_time: paper.session_start_time, + session_end_time: paper.session_end_time, + presentation_type: paper.presentation_type, + room_name: paper.room_name, + project_url: paper.project_url, + poster_position: paper.poster_position, + paper_url: paper.paper_url, + sourceid: paper.sourceid, + virtualsite_url: paper.virtualsite_url, + embedding: paper.embedding + }) + """, papers=papers_data) + + LOGGER.info(f"Exported {len(paper_nodes)} paper nodes") + + def _export_topic_hierarchy(self, kg: nx.Graph): + """ + Export topic nodes with hierarchical structure to Neo4j. + Splits topics like "Deep Learning->Theory" into separate nodes with parent-child relationships. + """ + # Collect all unique topic paths from paper nodes + topic_paths = set() + for node_id, data in kg.nodes(data=True): + if data.get('node_type') == 'paper': + topic = data.get('topic', '') + if topic: + topic_paths.add(topic) + + LOGGER.info(f"Processing {len(topic_paths)} unique topic paths...") + + # Parse topic paths and create hierarchy + all_topics = set() + topic_relationships = [] + + for path in topic_paths: + parts = [p.strip() for p in path.split('->')] + + # Add all topic parts + for part in parts: + all_topics.add(part) + + # Create parent-child relationships + for i in range(len(parts) - 1): + topic_relationships.append({ + 'parent': parts[i], + 'child': parts[i + 1] + }) + + LOGGER.info( + f"Creating {len(all_topics)} topic nodes with {len(set(tuple(r.items()) for r in topic_relationships))} " + f"hierarchical relationships..." + ) + + with self.driver.session() as session: + # Create all topic nodes (using MERGE to avoid duplicates) + topics_data = [{'name': topic} for topic in all_topics] + session.run(""" + UNWIND $topics AS topic + MERGE (t:Topic {name: topic.name}) + """, topics=topics_data) + + # Create hierarchical relationships between topics (deduplicate first) + if topic_relationships: + # Remove duplicates + unique_rels = list({(r['parent'], r['child']): r for r in topic_relationships}.values()) + session.run(""" + UNWIND $rels AS rel + MATCH (parent:Topic {name: rel.parent}) + MATCH (child:Topic {name: rel.child}) + MERGE (child)-[:SUBTOPIC_OF]->(parent) + """, rels=unique_rels) + + LOGGER.info(f"Exported {len(all_topics)} topic nodes with hierarchy") + + def _connect_papers_to_topics(self, kg: nx.Graph, batch_size: int): + """ + Connect papers to their leaf topic nodes. + For "Deep Learning->Theory", connects paper to "Theory" node. + """ + paper_topic_connections = [] + + for node_id, data in kg.nodes(data=True): + if data.get('node_type') == 'paper': + topic = data.get('topic', '') + if topic: + # Get the leaf topic (last part after splitting) + parts = [p.strip() for p in topic.split('->')] + leaf_topic = parts[-1] + + paper_topic_connections.append({ + 'paper_id': node_id, + 'topic_name': leaf_topic, + 'full_path': topic # Store full path as property + }) + + LOGGER.info(f"Connecting {len(paper_topic_connections)} papers to topics...") + + with self.driver.session() as session: + for i in tqdm(range(0, len(paper_topic_connections), batch_size), + desc="Paper-Topic connections"): + batch = paper_topic_connections[i:i + batch_size] + + session.run(""" + UNWIND $connections AS conn + MATCH (p:Paper {id: conn.paper_id}) + MATCH (t:Topic {name: conn.topic_name}) + MERGE (p)-[r:BELONGS_TO_TOPIC]->(t) + SET r.full_path = conn.full_path + """, connections=batch) + + LOGGER.info(f"Connected papers to leaf topics") + + def _export_similarity_relationships(self, kg: nx.Graph, batch_size: int): + """Export similarity relationships between papers to Neo4j.""" + # Filter only similarity edges + similarity_edges = [ + (source, target, data) + for source, target, data in kg.edges(data=True) + if data.get('relationship') == 'similar_to' + ] + + LOGGER.info(f"Exporting {len(similarity_edges)} similarity relationships...") + + with self.driver.session() as session: + for i in tqdm(range(0, len(similarity_edges), batch_size), + desc="Similarity relationships"): + batch = similarity_edges[i:i + batch_size] + + edges_data = [{ + 'source': source, + 'target': target, + 'similarity': data.get('similarity', 0.0) + } for source, target, data in batch] + + session.run(""" + UNWIND $edges AS edge + MATCH (p1:Paper {id: edge.source}) + MATCH (p2:Paper {id: edge.target}) + MERGE (p1)-[:SIMILAR_TO {similarity: edge.similarity}]->(p2) + """, edges=edges_data) + + LOGGER.info(f"Exported {len(similarity_edges)} similarity relationships") + + def _export_authors_and_relationships(self, kg: nx.Graph, batch_size: int): + """ + Export author nodes from NetworkX graph (where they already exist as separate nodes) + and create IS_AUTHOR_OF relationships between authors and papers. + + Author nodes in NetworkX have composite IDs like "12345 - John Doe" + """ + # Collect author nodes from the graph + author_nodes = [ + (node_id, data) + for node_id, data in kg.nodes(data=True) + if data.get('node_type') != 'paper' and data.get('node_type') != 'topic' + ] + + LOGGER.info(f"Found {len(author_nodes)} author nodes in graph...") + + # Extract author data + all_authors = [] + for node_id, data in author_nodes: + # Parse composite ID "12345 - John Doe" + parts = node_id.split(' - ', 1) + author_id = parts[0].strip() if len(parts) > 0 else "" + + author_dict = { + 'composite_id': node_id, # Store the full composite ID + 'author_id': author_id, + 'fullname': data.get('fullname', ''), + 'institution': data.get('institution', ''), + 'url': data.get('url', '') + } + all_authors.append(author_dict) + + LOGGER.info(f"Exporting {len(all_authors)} unique authors...") + + with self.driver.session() as session: + # Create author nodes in batches + for i in tqdm(range(0, len(all_authors), batch_size), desc="Author nodes"): + batch = all_authors[i:i + batch_size] + + session.run(""" + UNWIND $authors AS author + MERGE (a:Author {composite_id: author.composite_id}) + ON CREATE SET + a.author_id = author.author_id, + a.fullname = author.fullname, + a.institution = author.institution, + a.url = author.url + ON MATCH SET + a.author_id = author.author_id, + a.fullname = author.fullname, + a.institution = author.institution, + a.url = author.url + """, authors=batch) + + LOGGER.info(f"Exported {len(all_authors)} author nodes") + + # Method 1: Try to collect author-paper relationships from graph edges + author_paper_edges = [ + (source, target, data) + for source, target, data in kg.edges(data=True) + if data.get('relationship') == 'is_author_of' + ] + + LOGGER.info(f"Found {len(author_paper_edges)} IS_AUTHOR_OF edges in graph") + + # Method 2: If no edges found, extract from paper node 'authors' attribute + if len(author_paper_edges) == 0: + LOGGER.warning("No IS_AUTHOR_OF edges found in graph. Extracting from paper 'authors' attribute...") + + paper_author_relationships = [] + for node_id, data in kg.nodes(data=True): + if data.get('node_type') == 'paper': + authors = data.get('authors', []) + + if authors and isinstance(authors, list) and len(authors) > 0: + # Check if authors are stored as dicts + if isinstance(authors[0], dict): + for author in authors: + author_id = str(author.get('id', '')) + fullname = author.get('fullname', '') + if author_id and fullname: + composite_id = f"{author_id} - {fullname}" + paper_author_relationships.append({ + 'author_id': composite_id, + 'paper_id': node_id + }) + + LOGGER.info(f"Extracted {len(paper_author_relationships)} relationships from paper attributes") + + # Create relationships from extracted data + with self.driver.session() as session: + for i in tqdm(range(0, len(paper_author_relationships), batch_size), + desc="Author-Paper relationships"): + batch = paper_author_relationships[i:i + batch_size] + + session.run(""" + UNWIND $edges AS edge + MATCH (a:Author {composite_id: edge.author_id}) + MATCH (p:Paper {id: edge.paper_id}) + MERGE (a)-[:IS_AUTHOR_OF]->(p) + """, edges=batch) + + LOGGER.info(f"Created {len(paper_author_relationships)} author-paper relationships") + else: + # Create relationships from graph edges + with self.driver.session() as session: + for i in tqdm(range(0, len(author_paper_edges), batch_size), + desc="Author-Paper relationships"): + batch = author_paper_edges[i:i + batch_size] + + edges_data = [{ + 'author_id': source, # composite ID like "12345 - John Doe" + 'paper_id': target + } for source, target, data in batch] + + session.run(""" + UNWIND $edges AS edge + MATCH (a:Author {composite_id: edge.author_id}) + MATCH (p:Paper {id: edge.paper_id}) + MERGE (a)-[:IS_AUTHOR_OF]->(p) + """, edges=edges_data) + + LOGGER.info(f"Created {len(author_paper_edges)} author-paper relationships") + + def import_graph(self, kg_path: str, batch_size: int = 100, embedding_dimension: int = 768): + """Import the entire knowledge graph to Neo4j.""" + LOGGER.info(f"Loading graph from path {kg_path}") + kg = load_graph(kg_path) + + LOGGER.info("Starting Neo4j export...") + + # Clear and prepare database + self.clear_database() + self.create_indexes(embedding_dimension) + + # Export paper nodes + self._export_paper_nodes(kg, batch_size) + + # Export authors and author-paper relationships + self._export_authors_and_relationships(kg, batch_size) + + # Export topic hierarchy + self._export_topic_hierarchy(kg) + + # Connect papers to topics + self._connect_papers_to_topics(kg, batch_size) + + # Export similarity relationships + self._export_similarity_relationships(kg, batch_size) + + LOGGER.info("Export completed successfully!") + + def verify_export(self) -> Dict[str, Any]: + """Verify the export by checking node and relationship counts.""" + with self.driver.session() as session: + # Count papers + result = session.run("MATCH (p:Paper) RETURN count(p) as count") + paper_count = result.single()['count'] + + # Count topics + result = session.run("MATCH (t:Topic) RETURN count(t) as count") + topic_count = result.single()['count'] + + # Count authors + result = session.run("MATCH (a:Author) RETURN count(a) as count") + author_count = result.single()['count'] + + # Count relationships + result = session.run("MATCH ()-[r]->() RETURN count(r) as count") + rel_count = result.single()['count'] + + # Count similarity relationships + result = session.run("MATCH ()-[r:SIMILAR_TO]->() RETURN count(r) as count") + similarity_count = result.single()['count'] + + # Count topic hierarchy relationships + result = session.run("MATCH ()-[r:SUBTOPIC_OF]->() RETURN count(r) as count") + subtopic_count = result.single()['count'] + + # Count author relationships (updated relationship name) + result = session.run("MATCH ()-[r:IS_AUTHOR_OF]->() RETURN count(r) as count") + is_author_of_count = result.single()['count'] + + stats = { + 'papers': paper_count, + 'topics': topic_count, + 'authors': author_count, + 'total_relationships': rel_count, + 'similarity_relationships': similarity_count, + 'subtopic_relationships': subtopic_count, + 'is_author_of_relationships': is_author_of_count + } + + LOGGER.info("Neo4j Database Statistics:") + for key, value in stats.items(): + LOGGER.info(f" {key}: {value}") + + return stats + + +@click.command() +@click.option("-g", "--graph-path", help="Path to the knowledge graph file (pickle).", default=f"{PROJECT_ROOT}/graphs/knowledge_graph.pkl") +@click.option("-l", "--neo4j-uri", help="Database URI", default="bolt://localhost:7687") +@click.option("-u", "--neo4j-username", help="Database user", default=NEO4J_USERNAME) +@click.option("-p", "--neo4j-password", help="Database password", default=NEO4J_PASSWORD) +@click.option("-b", "--batch-size", help="Batch size for node insertion", default=100) +@click.option("-e", "--embedding-dimension", help="Vector embedding dimensions", default=768) +def main( + graph_path: str, + neo4j_uri: str, + neo4j_username: str, + neo4j_password: str, + batch_size: int = 100, + embedding_dimension: int = 768 +): + """ + Convenience function to export a knowledge graph to Neo4j. + + Args: + graph_path: PaperKnowledgeGraph instance + neo4j_uri: Neo4j connection URI + neo4j_username: Neo4j username + neo4j_password: Neo4j password + batch_size: Batch size for processing + embedding_dimension: Dimension of embedding vectors (default: 768) + """ + importer = Neo4jImporter(neo4j_uri, neo4j_username, neo4j_password) + try: + importer.import_graph( + graph_path, + batch_size, + embedding_dimension + ) + importer.verify_export() + finally: + importer.close() + + +if __name__ == "__main__": + main() diff --git a/agentic_nav/tools/knowledge_graph/retriever.py b/agentic_nav/tools/knowledge_graph/retriever.py new file mode 100644 index 0000000..89b5e76 --- /dev/null +++ b/agentic_nav/tools/knowledge_graph/retriever.py @@ -0,0 +1,620 @@ +import logging +import numpy as np +import random +import os + +from neo4j import GraphDatabase +from pathlib import Path + +from typing import List, Dict, Any, Optional + +from agentic_nav.tools.knowledge_graph.graph_traversal_strategies import ( + TraversalStrategy, + _graph_traversal_dfs_random, + _graph_traversal_cypher, + _graph_traversal_bfs_random +) + +from agentic_nav.utils.embedding_generator import batch_embed_documents + + +PROJECT_ROOT = Path(__file__).parent.parent.parent.parent +LOGGER = logging.getLogger(__name__) +EMBEDDING_MODEL_NAME = os.environ.get("EMBEDDING_MODEL_NAME", "ollama/nomic-embed-text") +EMBEDDING_MODEL_API_BASE = os.environ.get("EMBEDDING_MODEL_API_BASE", "http://localhost:11435") +NEO4J_DB_URI = os.environ.get("NEO4J_DB_URI", "bolt://neo4j_db:7687") +NEO4J_DB_NODE_RETURN_LIMIT = int(os.environ.get("NEO4J_DB_NODE_RETURN_LIMIT", 200)) + + +class Neo4jGraphWorker: + """Search and traversal operations for Neo4j paper knowledge graph.""" + + _DB_SIMILARITY_SEARCH_QUERY = """ + MATCH (node:Paper) + WHERE ($day IS NULL OR node.session_start_time IS NOT NULL) + WITH node + WHERE ($day IS NULL OR date(datetime(node.session_start_time)).dayOfWeek = $day) + AND ($time_ranges IS NULL OR + any(range IN $time_ranges WHERE + time(datetime(node.session_start_time)) >= time(range.start) + AND time(datetime(node.session_start_time)) <= time(range.end))) + WITH collect(node) as filtered_nodes + CALL db.index.vector.queryNodes('paper_embeddings', $top_k, $query_embedding) + YIELD node, score + WHERE node IN filtered_nodes OR ($day IS NULL AND $time_ranges IS NULL) + RETURN node.id as id, + node.name as name, + node.abstract as abstract, + node.topic as topic, + node.paper_url as paper_url, + node.session as session, + node.session_start_time as session_start_time, + node.session_end_time as session_end_time, + node.presentation_type as presentation_type, + node.room_name as room_name, + node.project_url as project_url, + node.poster_position as poster_position, + node.sourceid as sourceid, + node.virtualsite_url as virtualsite_url, + node.decision as decision, + [(a:Author)-[:IS_AUTHOR_OF]->(node) | a] as authors, + score + ORDER BY score DESC + LIMIT $limit + """ + + _DB_NEIGHBORHOOD_SEARCH_QUERY = """ + MATCH (p:Paper)-[r]-(neighbor) + WHERE p.id IN $paper_ids + AND type(r) IN $allowed_rel_types + AND 'Paper' IN labels(neighbor) + AND (type(r) <> 'SIMILAR_TO' OR r.similarity >= $min_similarity) + RETURN neighbor.id as id, + neighbor.name as name, + neighbor.abstract as abstract, + neighbor.topic as topic, + neighbor.paper_url as paper_url, + neighbor.session as session, + neighbor.session_start_time as session_start_time, + neighbor.session_end_time as session_end_time, + neighbor.presentation_type as presentation_type, + neighbor.room_name as room_name, + neighbor.project_url as project_url, + neighbor.poster_position as poster_position, + neighbor.sourceid as sourceid, + neighbor.virtualsite_url as virtualsite_url, + neighbor.decision as decision, + [(a:Author)-[:IS_AUTHOR_OF]->(neighbor) | a] as authors, + p.id as source_paper_id, + type(r) as relationship_type, + CASE WHEN type(r) = 'SIMILAR_TO' THEN r.similarity ELSE null END as similarity + ORDER BY similarity DESC + LIMIT $limit + """ + + # Find the DB query for graph traversal in the graph_traversal sub-folder. + _DB_PAPERS_BY_AUTHOR = """ + MATCH (a:Author)-[:IS_AUTHOR_OF]->(p:Paper) + WHERE a.fullname = $author_name + WITH p, collect(DISTINCT a) as all_authors + RETURN p.id as id, + p.name as name, + p.abstract as abstract, + p.topic as topic, + p.paper_url as paper_url, + p.decision as decision, + p.session as session, + p.session_start_time as session_start_time, + p.session_end_time as session_end_time, + p.presentation_type as presentation_type, + p.room_name as room_name, + p.project_url as project_url, + p.poster_position as poster_position, + p.sourceid as sourceid, + p.virtualsite_url as virtualsite_url, + all_authors as authors + ORDER BY p.name + LIMIT $limit + """ + + _DB_PAPERS_BY_AUTHOR_FUZZY = """ + MATCH (a:Author)-[:IS_AUTHOR_OF]->(p:Paper) + WHERE toLower(a.fullname) CONTAINS toLower($author_name) + WITH p, collect(DISTINCT a) as all_authors + RETURN p.id as id, + p.name as name, + p.abstract as abstract, + p.topic as topic, + p.paper_url as paper_url, + p.decision as decision, + p.session as session, + p.session_start_time as session_start_time, + p.session_end_time as session_end_time, + p.presentation_type as presentation_type, + p.room_name as room_name, + p.project_url as project_url, + p.poster_position as poster_position, + p.sourceid as sourceid, + p.virtualsite_url as virtualsite_url, + all_authors as authors + ORDER BY p.name + LIMIT $limit + """ + + _DB_PAPERS_BY_TOPIC = """ + MATCH (p:Paper)-[:BELONGS_TO_TOPIC]->(t:Topic {name: $topic_name}) + RETURN p.id as id, + p.name as name, + p.abstract as abstract, + p.topic as topic, + p.paper_url as paper_url, + p.decision as decision, + p.session as session, + p.session_start_time as session_start_time, + p.session_end_time as session_end_time, + p.presentation_type as presentation_type, + p.room_name as room_name, + p.project_url as project_url, + p.poster_position as poster_position, + p.sourceid as sourceid, + p.virtualsite_url as virtualsite_url, + [(a:Author)-[:IS_AUTHOR_OF]->(p) | a] as authors + ORDER BY p.name + LIMIT $limit + """ + + _DB_PAPERS_BY_TOPIC_AND_SUBTOPIC = """ + MATCH (t:Topic {name: $topic_name}) + OPTIONAL MATCH (subtopic:Topic)-[:SUBTOPIC_OF*]->(t) + WITH t, collect(DISTINCT subtopic) + t as all_topics + UNWIND all_topics as topic + MATCH (p:Paper)-[:BELONGS_TO_TOPIC]->(topic) + WITH DISTINCT p + RETURN p.id as id, + p.name as name, + p.abstract as abstract, + p.topic as topic, + p.paper_url as paper_url, + p.decision as decision, + p.session as session, + p.session_start_time as session_start_time, + p.session_end_time as session_end_time, + p.presentation_type as presentation_type, + p.room_name as room_name, + p.project_url as project_url, + p.poster_position as poster_position, + p.sourceid as sourceid, + p.virtualsite_url as virtualsite_url, + [(a:Author)-[:IS_AUTHOR_OF]->(p) | a] as authors + ORDER BY p.name + LIMIT $limit + """ + + def __init__( + self, + uri: str = NEO4J_DB_URI, + username: str = "neo4j", + password: str = "password" + ): + """Initialize Neo4j connection.""" + self.driver = GraphDatabase.driver(uri, auth=(username, password)) + self.driver.verify_connectivity() + LOGGER.info(f"Connected to Neo4j at {uri}") + + def close(self): + """Close the Neo4j driver connection.""" + self.driver.close() + + @staticmethod + def embed_user_query( + text: str, + embedding_model: str = EMBEDDING_MODEL_NAME, + api_base: str = EMBEDDING_MODEL_API_BASE + ): + emb = batch_embed_documents( + texts=[text], + batch_size=1, + api_base=api_base, + embedding_model=embedding_model + ).tolist()[0] + + return emb + + def similarity_search( + self, + user_query: str, + day: Optional[str] = None, + timeslots: Optional[List[str]] = None, + top_k: int = 5, + min_similarity: Optional[float] = None + ) -> List[Dict[str, Any]]: + """ + Perform vector similarity search on paper embeddings. + + Args: + user_query: User query (str) + day: Conference day as date string (e.g., "2024-12-10") or None + timeslots: List of time ranges as strings (e.g., ["09:00:00-12:00:00"]) or None + top_k: Number of top results to return + min_similarity: Optional minimum similarity threshold (0-1) + + Returns: + List of dictionaries containing paper information and similarity scores + """ + + # Generate text embedding + query_embedding = self.embed_user_query( + text=user_query + ) + + # Convert numpy array to list if needed + if isinstance(query_embedding, np.ndarray): + query_embedding = query_embedding.tolist() + + # Parse day and timeslots for the query + day_filter = None + time_ranges = [] + + if day: + # Convert date string to day of week (1=Monday, 7=Sunday) + from datetime import datetime + date_obj = datetime.strptime(day, "%Y-%m-%d") + day_filter = date_obj.isoweekday() + + if timeslots: + # Parse timeslot ranges (e.g., "09:00:00-12:00:00") + for slot in timeslots: + if '-' in slot: + start, end = slot.split('-') + time_ranges.append({'start': start.strip(), 'end': end.strip()}) + else: + # If no range, assume it's a single time point with some buffer + time_ranges.append({'start': slot.strip(), 'end': slot.strip()}) + + with self.driver.session() as session: + result = session.run( + self._DB_SIMILARITY_SEARCH_QUERY, + query_embedding=query_embedding, + top_k=top_k, + limit=NEO4J_DB_NODE_RETURN_LIMIT, + day=day_filter, + time_ranges=time_ranges if time_ranges else None + ) + papers = [] + for record in result: + paper = { + 'id': record['id'], + 'name': record['name'], + 'abstract': record['abstract'], + 'topic': record['topic'], + 'similarity_score': record['score'], + 'paper_url': record['paper_url'], + 'decision': record['decision'], + 'session': record['session'], + 'session_start_time': record['session_start_time'], + 'session_end_time': record['session_end_time'], + 'presentation_type': record['presentation_type'], + 'room_name': record['room_name'], + 'github_url': record['project_url'], + 'poster_position': record['poster_position'], + 'sourceid': record['sourceid'], + 'virtualsite_url': record['virtualsite_url'], + 'authors': [a['fullname'] for a in record['authors']] + } + + # Apply minimum similarity filter if specified + if min_similarity is None or paper['similarity_score'] >= min_similarity: + # IMPORTANT: We don't return the similarity as the model has high affinity to scores like that... + del paper["similarity_score"] + papers.append(paper) + + return papers + + def neighborhood_search( + self, + paper_id: str, + relationship_types: List[str] = ["SIMILAR_TO"], + min_similarity: float = 0.7 + ) -> Dict[str, Any]: + """ + Find immediate neighbors of given paper nodes. + + Args: + paper_id: Paper ID to find neighbors for + relationship_types: Optional list of relationship types to filter + (e.g., ['SIMILAR_TO', 'IS_AUTHOR_OF', 'BELONGS_TO_TOPIC', 'SUBTOPIC_OF']) + min_similarity (float): A minimum similarity score in the range of 0 - 1. Often a good value is 0.75 or 0.8. + + + Returns: + Dictionary with neighbors grouped by relationship type + """ + allowed_rel_types = ['SIMILAR_TO', 'IS_AUTHOR_OF', 'BELONGS_TO_TOPIC', 'SUBTOPIC_OF'] + for rel_type in relationship_types: + if rel_type not in allowed_rel_types: + raise ValueError(f"Unsupported relationship type: {rel_type}. Supported relationship types: {allowed_rel_types}") + + with self.driver.session() as session: + result = session.run( + self._DB_NEIGHBORHOOD_SEARCH_QUERY, + paper_ids=[paper_id], + allowed_rel_types=relationship_types, + min_similarity=min_similarity, + limit=NEO4J_DB_NODE_RETURN_LIMIT + ) + + # Organize results by relationship type + neighbors = {} + + for record in result: + # Use the dict() object in Record to manipulate the data. Records are immutable. + record = record.data() + rel_type = record["relationship_type"] + if rel_type not in neighbors.keys(): + neighbors[rel_type] = [] + else: + if "similarity" in record.keys(): + # IMPORTANT: We don't return the similarity as the model has high affinity to scores like that... + del record["similarity"] + + neighbors[rel_type].append(record) + + return neighbors + + def graph_traversal( + self, + start_paper_id: str, + n_hops: int = 2, + relationship_type: Optional[str] = None, + max_results: Optional[int] = None, + strategy: str = "breadth_first_random", + max_branches: Optional[int] = None, + random_seed: Optional[int] = None + ) -> List[Dict[str, Any]]: + """ + Traverse the graph for n hops from starting paper nodes. + + Args: + start_paper_id: Paper ID to start traversal from + n_hops: Number of hops to traverse (1-5 recommended) + relationship_type: Optional list of relationship types to traverse + max_results: Optional maximum number of results to return + strategy: Traversal strategy (breadth_first, depth_first, breadth_first_random, depth_first_random) + max_branches: Maximum number of random neighbors to explore per node (only for random strategies) + random_seed: Optional seed for reproducible random sampling + + Returns: + List of papers found through traversal with distance information + """ + if random_seed is not None: + random.seed(random_seed) + + # Use original Cypher-based approach for non-random strategies + if strategy in ["breadth_first", "depth_first"]: + LOGGER.debug(f"Doing a graph traversal with neo4j's built-in strategy") + return _graph_traversal_cypher( + self.driver, + start_paper_id, + n_hops, + relationship_type, + max_results + ) + + # Use Python-based traversal for random strategies + elif strategy == "breadth_first_random": + LOGGER.debug(f"Doing a graph traversal with a random sampling breadth first strategy") + return _graph_traversal_bfs_random( + self.driver, + start_paper_id, + n_hops, + relationship_type, + max_results, + max_branches or 3 + ) + + elif strategy == "depth_first_random": + LOGGER.debug(f"Doing a graph traversal with a random sampling depth first strategy") + return _graph_traversal_dfs_random( + self.driver, + start_paper_id, + n_hops, + relationship_type, + max_results, + max_branches or 3 + ) + + else: + raise ValueError(f"Unsupported traversal strategy: {strategy}. " + f"Supported strategies: breadth_first, depth_first, breadth_first_random, depth_first_random") + + def search_papers_by_author( + self, + author_name: str, + fuzzy: bool = True + ) -> List[Dict[str, Any]]: + """ + Find all papers by a specific author. + + Args: + author_name: Author name or partial name + fuzzy: Whether to use fuzzy matching (CONTAINS vs exact match) + + Returns: + List of papers by the author + """ + with self.driver.session() as session: + if fuzzy: + query = self._DB_PAPERS_BY_AUTHOR_FUZZY + else: + query = self._DB_PAPERS_BY_AUTHOR + + result = session.run(query, author_name=author_name) + + papers = [] + for record in result: + paper = { + 'id': record['id'], + 'name': record['name'], + 'abstract': record['abstract'], + 'topic': record['topic'], + 'author_name': record['author_name'], + 'paper_url': record['paper_url'], + 'decision': record['decision'], + 'session': record['session'], + 'session_start_time': record['session_start_time'], + 'session_end_time': record['session_end_time'], + 'presentation_type': record['presentation_type'], + 'room_name': record['room_name'], + 'github_url': record['project_url'], + 'poster_position': record['poster_position'], + 'sourceid': record['sourceid'], + 'virtualsite_url': record['virtualsite_url'], + } + papers.append(paper) + + return papers + + def search_papers_by_topic( + self, + topic_name: str, + include_subtopics: bool = True + ) -> List[Dict[str, Any]]: + """ + Find all papers in a specific topic. + + Args: + topic_name: Topic name + include_subtopics: Whether to include papers from subtopics + + Returns: + List of papers in the topic + """ + with self.driver.session() as session: + if include_subtopics: + # Find topic and all its subtopics + query = self._DB_PAPERS_BY_TOPIC_AND_SUBTOPIC + else: + query = self._DB_PAPERS_BY_TOPIC + + result = session.run(query, topic_name=topic_name, limit=NEO4J_DB_NODE_RETURN_LIMIT) + + papers = [] + for record in result: + paper = { + 'id': record['id'], + 'name': record['name'], + 'abstract': record['abstract'], + 'topic': record['topic'], + 'paper_url': record['paper_url'], + 'decision': record['decision'], + 'session': record['session'], + 'session_start_time': record['session_start_time'], + 'session_end_time': record['session_end_time'], + 'presentation_type': record['presentation_type'], + 'room_name': record['room_name'], + 'github_url': record['project_url'], + 'poster_position': record['poster_position'], + 'sourceid': record['sourceid'], + 'virtualsite_url': record['virtualsite_url'], + } + papers.append(paper) + + return papers + + def get_collaboration_network( + self, + author_name: str, + n_hops: int = 2 + ) -> Dict[str, Any]: + """ + Find collaboration network: authors who co-authored papers. + + Args: + author_name: Starting author name + n_hops: Degrees of separation to explore + + Returns: + Dictionary with collaborators and shared papers + """ + with self.driver.session() as session: + query = f""" + MATCH (a1:Author) + WHERE toLower(a1.fullname) CONTAINS toLower($author_name) + MATCH path = (a1)<-[:AUTHORED_BY]-(p:Paper)-[:AUTHORED_BY]->(a2:Author) + WHERE a1 <> a2 + WITH a1, a2, collect(DISTINCT p) as shared_papers, length(path) as distance + RETURN a1.fullname as source_author, + a2.fullname as collaborator, + a2.institution as institution, + [p IN shared_papers | {{id: p.id, name: p.name}}] as papers, + size(shared_papers) as paper_count + ORDER BY paper_count DESC + """ + + result = session.run(query, author_name=author_name) + + collaborations = [] + for record in result: + collab = { + 'source_author': record['source_author'], + 'collaborator': record['collaborator'], + 'institution': record['institution'], + 'shared_papers': record['papers'], + 'paper_count': record['paper_count'] + } + collaborations.append(collab) + + return { + 'author': author_name, + 'collaborators': collaborations, + 'total_collaborators': len(collaborations) + } + + +# Test +if __name__ == "__main__": + # Initialize searcher + searcher = Neo4jGraphWorker( + uri=NEO4J_DB_URI, + username=os.environ.get("NEO4J_USERNAME", "neo4j"), + password=os.environ.get("NEO4J_PASSWORD") + ) + + try: + # Example 1: Similarity search + print("\n" + "=" * 60) + print("Example 1: Similarity Search") + print("=" * 60) + user_query = "Reinforcement learning" + similar_papers = searcher.similarity_search(user_query, top_k=30) + for i, paper in enumerate(similar_papers, 1): + print(f"\n{i}. {paper['name']}") + print(f" Topic: {paper['topic']}") + # print(f" Similarity: {paper['similarity_score']:.4f}") + + # Example 2: Neighborhood search + if similar_papers: + print("\n" + "=" * 60) + print("Example 2: Neighborhood Search") + print("=" * 60) + paper_id = similar_papers[0]['id'] + neighbors = searcher.neighborhood_search(paper_id, min_similarity=0.75) + print(f"\nNeighbors of: {similar_papers[0]['name']}") + for rel_type, neighbors in neighbors.items(): + print(f" \n{rel_type.upper()} RELATIONSHIPS:") + for neighbor in neighbors: + print(f" - {neighbor['name']}") # (similarity: {neighbor['similarity']:.4f}) + + # Example 3: Graph traversal + print("\n" + "=" * 60) + print("Example 3: Graph Traversal (2 hops)") + print("=" * 60) + if similar_papers: + paper_ids = similar_papers[0]['id'] + related = searcher.graph_traversal(paper_ids, n_hops=2) + print(f"\nFound {len(related)} related papers through traversal") + for paper in related[:5]: # Show first 5 + print(f" - {paper['name']} (distance: {paper['distance']})") + + finally: + searcher.close() diff --git a/agentic_nav/tools/session_routing/__init__.py b/agentic_nav/tools/session_routing/__init__.py new file mode 100644 index 0000000..be2f730 --- /dev/null +++ b/agentic_nav/tools/session_routing/__init__.py @@ -0,0 +1,210 @@ +""" +Session routing tool for building personalized conference visiting schedules. + +This tool helps NeurIPS 2025 conference attendees create optimized schedules +for visiting poster sessions based on their research interests, preferred dates, +and time slots. +""" + +import os +from typing import Union, List, Optional +from neo4j import GraphDatabase + +from agentic_nav.tools.knowledge_graph import search_similar_papers +from agentic_nav.tools.session_routing.scheduler import ScheduleBuilder +from agentic_nav.tools.session_routing.utils import parse_date_input, parse_time_preference + + +# Environment variables for Neo4j connection +NEO4J_DB_URI = os.getenv("NEO4J_DB_URI", "bolt://localhost:7687") +NEO4J_USERNAME = os.getenv("NEO4J_USERNAME", "neo4j") +NEO4J_PASSWORD = os.getenv("NEO4J_PASSWORD", "") + + +def build_visit_schedule( + topics: Union[str, List[str]], + dates: Union[str, List[str]] = None, + time_preferences: str = None, + max_papers: int = 20, + min_similarity: float = 0.6 +) -> str: + # TODO: Add filter for ["poster", "oral"]. Make sure to match orals with poster counterpart. + """ + Build a personalized visiting schedule for NeurIPS 2025 conference poster sessions. + + This tool helps you create an optimized schedule by: + 1. Finding papers relevant to your research interests (topics) + 2. Filtering by your preferred dates and time slots + 3. Scoring papers by relevance to your topics + 4. Clustering papers by room location to minimize walking + 5. Organizing chronologically for easy navigation + + The schedule includes paper titles, locations, poster positions, and relevance scores. + + Args: + topics: Research topic(s) of interest. Can be a single topic string or a list of topics. + Examples: "transformer architectures", ["reinforcement learning", "multi-agent systems"] + dates: Conference date(s) to include. Can be: + - ISO format: "2025-12-02" or ["2025-12-02", "2025-12-03"] + - Day names: "Tuesday", "Wednesday" + - None (default): include all conference days (Dec 2-7, 2025) + time_preferences: Preferred time slot(s). Can be: + - Preset: "morning" (8am-12pm), "afternoon" (12pm-5pm), "evening" (5pm-9pm) + - Range: "9:00-12:00" or "14-17" + - None (default): include all time slots + max_papers: Maximum number of papers to include in schedule (default: 20) + min_similarity: Minimum similarity score for paper relevance (0.0-1.0, default: 0.6) + + Returns: + Formatted markdown schedule organized by date, time slot, and room location. + All times are displayed in conference local time (PST/UTC-8). + + Restrictions: + - Requires Neo4j database connection (NEO4J_DB_URI, NEO4J_USERNAME, NEO4J_PASSWORD) + - Requires Paper nodes with session timing and location fields + - Conference dates: December 2-7, 2025 in San Diego/Mexico City (UTC-8) + + Notes: + - Papers are scored by similarity to your topics using embedding search + - Schedule optimizes for both relevance and room clustering + - Time zones are automatically converted from UTC to PST + - Poster positions help you quickly locate papers in exhibition halls + + Raises: + ValueError: If topics is empty or dates cannot be parsed + Exception: If Neo4j connection fails + + Example: + >>> build_visit_schedule( + ... topics=["machine learning", "computer vision"], + ... dates="2025-12-02", + ... time_preferences="morning", + ... max_papers=15 + ... ) + # Your NeurIPS 2025 Conference Schedule + + ## Tuesday, December 2, 2025 + + ### 9:00 AM - 11:00 AM PST + + **Hall A** + - **Poster #123** | Attention Mechanisms in Vision Transformers + - Authors: John Doe, Jane Doe, et al. + - Topic: Computer Vision + - Relevance: 0.92 + ... + """ + # Type coercion for parameters that may come as strings from LLM tool calls + if isinstance(topics, str): + # If topics is a single string, treat as one topic + topics = [topics] + elif topics is None: + raise ValueError("Topics parameter is required. Please provide at least one research topic.") + + if max_papers is not None and not isinstance(max_papers, int): + max_papers = int(max_papers) + + if min_similarity is not None and not isinstance(min_similarity, float): + min_similarity = float(min_similarity) + + # Parse dates + parsed_dates = None + if dates: + if isinstance(dates, str): + dates = [dates] + + parsed_dates = [] + for date_str in dates: + parsed = parse_date_input(date_str) + if parsed: + parsed_dates.append(parsed) + + if not parsed_dates: + parsed_dates = None # Fall back to all dates if parsing fails + + # Parse time preferences (convert to UTC for database query) + time_range = None + if time_preferences: + local_time_range = parse_time_preference(time_preferences) + if local_time_range: + # Convert PST to UTC (add 8 hours) + start_utc = (local_time_range[0] + 8) % 24 + end_utc = (local_time_range[1] + 8) % 24 + time_range = (start_utc, end_utc) + + # Step 1: Search for papers matching each topic using existing tool + all_paper_ids = set() + relevance_scores = {} + + for topic in topics: + try: + + from llm_agents.tools.knowledge_graph.retriever import Neo4jGraphWorker + + worker = Neo4jGraphWorker( + uri=NEO4J_DB_URI, + username=NEO4J_USERNAME, + password=NEO4J_PASSWORD + ) + + papers = worker.similarity_search( + user_query=topic, + top_k=max_papers * 2, + min_similarity=min_similarity + ) + + worker.close() + + # Extract paper IDs and scores + for paper in papers: + paper_id = paper.get('id') + score = paper.get('score', 0.0) + + if paper_id: + all_paper_ids.add(paper_id) + # Keep highest score if paper matches multiple topics + if paper_id not in relevance_scores or score > relevance_scores[paper_id]: + relevance_scores[paper_id] = score + + except Exception as e: + # If search fails for one topic, continue with others + continue + + if not all_paper_ids: + return "No papers found matching your topics. Try broadening your search criteria or adjusting the minimum similarity threshold." + + # Step 2: Initialize schedule builder + driver = GraphDatabase.driver(NEO4J_DB_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD)) + builder = ScheduleBuilder(driver) + + try: + # Step 3: Filter papers by date and time + filtered_papers = builder.filter_by_datetime( + paper_ids=list(all_paper_ids), + dates=parsed_dates, + time_range=time_range + ) + + if not filtered_papers: + return "No papers found matching your date and time preferences. Try expanding your time range or selecting different dates." + + # Step 4: Score papers by relevance + scored_papers = builder.score_papers(filtered_papers, relevance_scores) + + # Step 5: Optimize schedule (chronological + room clustering) + schedule = builder.optimize_schedule(scored_papers, max_papers=max_papers) + + # Step 6: Format as markdown + markdown_output = builder.format_as_markdown(schedule, include_abstracts=False) + + return markdown_output + + finally: + builder.close() + + +__all__ = ['build_visit_schedule'] + + +if __name__ == "__main__": + print(build_visit_schedule(topics=["federated learning"], max_papers=200, dates=["Wednesday"])) diff --git a/agentic_nav/tools/session_routing/scheduler.py b/agentic_nav/tools/session_routing/scheduler.py new file mode 100644 index 0000000..cee2b34 --- /dev/null +++ b/agentic_nav/tools/session_routing/scheduler.py @@ -0,0 +1,377 @@ +""" +Schedule builder for NeurIPS 2025 conference paper sessions. + +This module provides the ScheduleBuilder class that handles filtering, +scoring, and organizing papers into optimized visiting schedules. +""" + +from datetime import datetime +from typing import List, Dict, Any, Optional, Tuple +from collections import defaultdict +import neo4j + +from agentic_nav.tools.session_routing.utils import ( + convert_utc_to_local, + format_time_slot, + format_date_header, + cluster_papers_by_room, + parse_time_preference +) + + +class ScheduleBuilder: + """ + Build optimized conference visiting schedules. + + This class handles filtering papers by date/time, scoring by relevance, + clustering by room location, and formatting the final schedule. + """ + + def __init__(self, neo4j_driver: neo4j.Driver): + """ + Initialize the schedule builder. + + Args: + neo4j_driver: Neo4j database driver for querying papers + """ + self.driver = neo4j_driver + + def filter_by_datetime( + self, + paper_ids: List[str], + dates: Optional[List[datetime]] = None, + time_range: Optional[Tuple[int, int]] = None + ) -> List[Dict[str, Any]]: + """ + Filter papers by date and time preferences. + + Args: + paper_ids: List of paper IDs to filter + dates: List of conference dates to include (None = all dates) + time_range: Tuple of (start_hour, end_hour) in UTC (None = all times) + + Returns: + List of paper dictionaries with full details including session times + + Example: + >>> builder.filter_by_datetime(['paper1', 'paper2'], dates=[datetime(2025,12,2)]) + """ + if not paper_ids: + return [] + + # Deduplicate paper_ids to ensure we only query each paper once + unique_paper_ids = list(set(paper_ids)) + + # Build Cypher query to get full paper details including authors via relationship + # Relationship is IS_AUTHOR_OF (uppercase) and author property is 'fullname' + query = """ + MATCH (p:Paper) + WHERE p.id IN $paper_ids + OPTIONAL MATCH (a:Author)-[:IS_AUTHOR_OF]-(p) + WITH p, collect(a.fullname) as authors + RETURN DISTINCT p.id as id, + p.name as name, + p.abstract as abstract, + p.topic as topic, + p.session as session, + p.session_start_time as session_start_time, + p.session_end_time as session_end_time, + p.room_name as room_name, + p.poster_position as poster_position, + p.presentation_type as presentation_type, + p.url as url, + authors + """ + + with self.driver.session() as session: + result = session.run(query, paper_ids=unique_paper_ids) + papers = [dict(record) for record in result] + + # Deduplicate papers by ID (just in case) + seen_ids = set() + unique_papers = [] + for paper in papers: + paper_id = paper.get('id') + if paper_id and paper_id not in seen_ids: + seen_ids.add(paper_id) + unique_papers.append(paper) + + papers = unique_papers + + # Filter by date if specified + if dates: + date_strs = [d.strftime("%Y-%m-%d") for d in dates] + papers = [ + p for p in papers + if p.get('session_start_time') and + any(date_str in p['session_start_time'] for date_str in date_strs) + ] + + # Filter by time range if specified (convert UTC time range) + if time_range: + start_hour, end_hour = time_range + filtered_papers = [] + + for paper in papers: + try: + start_time_str = paper.get('session_start_time', '') + if not start_time_str: + continue + + # Parse UTC time + if 'T' in start_time_str: + dt = datetime.fromisoformat(start_time_str.replace('Z', '')) + else: + continue + + # Check if paper session falls within time range (UTC) + if start_hour <= dt.hour < end_hour: + filtered_papers.append(paper) + + except (ValueError, AttributeError): + # If we can't parse time, include the paper to be safe + filtered_papers.append(paper) + + papers = filtered_papers + + return papers + + def score_papers( + self, + papers: List[Dict[str, Any]], + relevance_scores: Dict[str, float] + ) -> List[Dict[str, Any]]: + """ + Add relevance scores to papers. + + Args: + papers: List of paper dictionaries + relevance_scores: Dict mapping paper_id to relevance score + + Returns: + Papers with added 'relevance_score' field, sorted by score descending + + Example: + >>> builder.score_papers(papers, {'paper1': 0.95, 'paper2': 0.87}) + """ + scored_papers = [] + + for paper in papers: + paper_id = paper.get('id') + score = relevance_scores.get(paper_id, 0.0) + + paper_with_score = paper.copy() + paper_with_score['relevance_score'] = score + scored_papers.append(paper_with_score) + + # Sort by relevance score (highest first) + scored_papers.sort(key=lambda p: p['relevance_score'], reverse=True) + + return scored_papers + + def optimize_schedule( + self, + papers: List[Dict[str, Any]], + max_papers: int = 20 + ) -> Dict[str, Dict[str, List[Dict[str, Any]]]]: + """ + Optimize schedule by grouping papers chronologically and by room. + + Args: + papers: List of scored paper dictionaries + max_papers: Maximum number of papers to include + + Returns: + Nested dict: {date: {time_slot: {room: [papers]}}} + + Example: + >>> schedule = builder.optimize_schedule(papers, max_papers=15) + """ + # Deduplicate papers by ID first + seen_ids = set() + unique_papers = [] + for paper in papers: + paper_id = paper.get('id') + if paper_id and paper_id not in seen_ids: + seen_ids.add(paper_id) + unique_papers.append(paper) + + # Limit to top papers by relevance + top_papers = unique_papers[:max_papers] + + # Group by date and time + schedule = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) + + for paper in top_papers: + try: + start_time = paper.get('session_start_time', '') + if not start_time: + continue + + # Extract date + date_str = start_time.split('T')[0] + + # Create time slot key + end_time = paper.get('session_end_time', '') + time_slot = format_time_slot(start_time, end_time) if end_time else start_time + + # Get room (handle None values, fallback to session for Mexico City papers) + room = paper.get('room_name') + if not room: + # Use session as fallback (e.g., for Mexico City papers) + room = paper.get('session') or 'N/A' + + # Add to schedule + schedule[date_str][time_slot][room].append(paper) + + except (ValueError, AttributeError, IndexError): + # Skip papers with invalid time data + continue + + return schedule + + def format_as_markdown( + self, + schedule: Dict[str, Dict[str, List[Dict[str, Any]]]], + include_abstracts: bool = False + ) -> str: + """ + Format schedule as structured markdown. + + Args: + schedule: Nested schedule dictionary + include_abstracts: Whether to include paper abstracts (default: False) + + Returns: + Formatted markdown string with format: + "Date (MM dd, yyyy) - Time Slot - Session Name - Location" + + Example: + >>> markdown = builder.format_as_markdown(schedule) + """ + if not schedule: + return "No papers found matching your criteria." + + output = ["# Your NeurIPS 2025 Conference Schedule\n"] + + # Flatten schedule into list of blocks for better formatting + schedule_blocks = [] + + for date_str in sorted(schedule.keys()): + time_slots = schedule[date_str] + + for time_slot in sorted(time_slots.keys()): + rooms = time_slots[time_slot] + + for room_or_session in sorted(rooms.keys()): + papers_in_block = rooms[room_or_session] + + # Sort papers by poster position ID (numerically) + def poster_sort_key(paper): + poster_pos = paper.get('poster_position') + if not poster_pos: + return float('inf') # Put papers without position at end + + # Remove '#' prefix if present + if isinstance(poster_pos, str) and poster_pos.startswith('#'): + poster_pos = poster_pos[1:] + + # Convert to integer for numerical sorting + try: + return int(poster_pos) + except (ValueError, TypeError): + return float('inf') # Put invalid positions at end + + papers_in_block.sort(key=poster_sort_key) + + schedule_blocks.append({ + 'date': date_str, + 'time_slot': time_slot, + 'room_or_session': room_or_session, + 'papers': papers_in_block + }) + + # Format each schedule block + total_papers = 0 + for block in schedule_blocks: + date_str = block['date'] + time_slot = block['time_slot'] + room_or_session = block['room_or_session'] + papers = block['papers'] + + total_papers += len(papers) + + # Get session and location from first paper (all papers in block share these) + if papers: + first_paper = papers[0] + session_name = first_paper.get('session', 'N/A') + actual_room = first_paper.get('room_name') + + # Determine location: use room if available, otherwise indicate session-based location + if actual_room: + location = actual_room + else: + location = "Mexico City" # Papers without room are from Mexico City + + else: + session_name = room_or_session + location = room_or_session + + # Format date as "Month DD, YYYY" + try: + from datetime import datetime + dt = datetime.fromisoformat(date_str) + formatted_date = dt.strftime("%B %d, %Y") + except: + formatted_date = date_str + + # Create a comprehensive header + header = f"## {formatted_date} - {time_slot} - {session_name} - {location}\n" + output.append(f"\n{header}") + + # List papers in this block + for paper in papers: + title = paper.get('name', 'Untitled') + poster_pos = paper.get('poster_position', 'N/A') + # TODO: This needs to be the distance between the user input query and the paper embedding, i.e., + # compare encoded user_input with "embedding" in database. + relevance = paper.get('relevance_score', 0) + topic = paper.get('topic', 'General') + pres_type = paper.get('presentation_type', 'Poster') + authors = paper.get('authors', 'N/A') + + # Format authors for display + if isinstance(authors, list): + authors_str = ', '.join(authors) if authors else 'N/A' + elif authors and authors != 'N/A': + authors_str = str(authors) + else: + authors_str = 'N/A' + + # Format paper entry + output.append(f"- **{pres_type} {poster_pos.replace('#', '') if poster_pos is not None else ''}** | {title}") + output.append(f" - Authors: {authors_str}") + output.append(f" - Topic: {topic}") + + # Add paper URL if available + paper_url = paper.get('url') + if paper_url: + output.append(f" - URL: {paper_url}") + + output.append(f" - Relevance: {relevance:.2f}") + + if include_abstracts and paper.get('abstract'): + abstract = paper['abstract'][:200] + "..." if len(paper['abstract']) > 200 else paper['abstract'] + output.append(f" - Abstract: {abstract}") + + output.append("") # Blank line between papers + + # Add summary footer + output.append(f"\n---\n**Total Papers in Schedule: {total_papers}**") + + return "\n".join(output) + + def close(self): + """Close the Neo4j driver connection.""" + if self.driver: + self.driver.close() diff --git a/agentic_nav/tools/session_routing/utils.py b/agentic_nav/tools/session_routing/utils.py new file mode 100644 index 0000000..264dab1 --- /dev/null +++ b/agentic_nav/tools/session_routing/utils.py @@ -0,0 +1,253 @@ +""" +Utility functions for session routing and schedule building. + +This module provides helper functions for time zone conversion, +date parsing, and formatting schedule outputs. +""" + +from datetime import datetime, timedelta +from typing import Optional, Tuple +import re + + +def convert_utc_to_local(utc_time_str: str, timezone_offset: int = -8) -> str: + """ + Convert UTC time string to local conference time. + + Args: + utc_time_str: ISO format UTC time string (e.g., "2025-12-02T17:00:00Z") + timezone_offset: Hours offset from UTC (default: -8 for PST/Mexico City) + + Returns: + Local time string in format "9:00 AM PST" + + Raises: + ValueError: If time string cannot be parsed + + Example: + >>> convert_utc_to_local("2025-12-02T17:00:00Z") + "9:00 AM PST" + """ + try: + # Handle various UTC time formats + utc_time_str = utc_time_str.strip() + if utc_time_str.endswith('Z'): + utc_time_str = utc_time_str[:-1] + elif '+' in utc_time_str or utc_time_str.count('-') > 2: + # Has timezone info, extract just the datetime part + utc_time_str = utc_time_str.split('+')[0].split('T')[0] + 'T' + utc_time_str.split('T')[1].split('+')[0].split('-')[0] + + # Parse the UTC time + if 'T' in utc_time_str: + utc_dt = datetime.fromisoformat(utc_time_str) + else: + # Try parsing without T separator + utc_dt = datetime.strptime(utc_time_str, "%Y-%m-%d %H:%M:%S") + + # Apply timezone offset + local_dt = utc_dt + timedelta(hours=timezone_offset) + + # Format as human-readable time + hour = local_dt.hour + minute = local_dt.minute + am_pm = "AM" if hour < 12 else "PM" + hour_12 = hour if hour <= 12 else hour - 12 + hour_12 = 12 if hour_12 == 0 else hour_12 + + if minute == 0: + time_str = f"{hour_12}:00 {am_pm} PST" + else: + time_str = f"{hour_12}:{minute:02d} {am_pm} PST" + + return time_str + except (ValueError, AttributeError) as e: + raise ValueError(f"Could not parse time string '{utc_time_str}': {e}") + + +def parse_date_input(date_str: str) -> Optional[datetime]: + """ + Parse flexible date input formats. + + Supports: + - ISO format: "2025-12-02" + - Day names: "Monday", "Tuesday", etc. + - Relative: "today", "tomorrow" + + Args: + date_str: Date string in various formats + + Returns: + Datetime object or None if parsing fails + + Example: + >>> parse_date_input("2025-12-02") + datetime.datetime(2025, 12, 2, 0, 0) + """ + if not date_str: + return None + + date_str = date_str.strip().lower() + + # Try ISO format first + try: + return datetime.fromisoformat(date_str) + except ValueError: + pass + + # Try common date formats + for fmt in ["%Y-%m-%d", "%m/%d/%Y", "%d/%m/%Y", "%B %d, %Y", "%b %d, %Y"]: + try: + return datetime.strptime(date_str, fmt) + except ValueError: + continue + + # Handle day names (for NeurIPS 2025: Dec 2-7, 2025) + conference_start = datetime(2025, 12, 2) # Tuesday + day_mapping = { + 'monday': conference_start - timedelta(days=1), + 'tuesday': conference_start, + 'wednesday': conference_start + timedelta(days=1), + 'thursday': conference_start + timedelta(days=2), + 'friday': conference_start + timedelta(days=3), + 'saturday': conference_start + timedelta(days=4), + 'sunday': conference_start + timedelta(days=5), + } + + if date_str in day_mapping: + return day_mapping[date_str] + + return None + + +def parse_time_preference(time_pref: str) -> Optional[Tuple[int, int]]: + """ + Parse time preference string into hour range. + + Args: + time_pref: Time preference like "morning", "afternoon", "9:00-12:00" + + Returns: + Tuple of (start_hour, end_hour) in 24-hour format, or None + + Example: + >>> parse_time_preference("morning") + (8, 12) + >>> parse_time_preference("9:00-15:00") + (9, 15) + """ + if not time_pref: + return None + + time_pref = time_pref.strip().lower() + + # Predefined time slots + presets = { + 'morning': (8, 12), + 'afternoon': (12, 17), + 'evening': (17, 21), + 'early': (8, 10), + 'late': (19, 21), + } + + if time_pref in presets: + return presets[time_pref] + + # Parse time range format: "9:00-12:00" or "09:00-12:00" or "9-12" + range_pattern = r'(\d{1,2})(?::(\d{2}))?[\s\-]+(\d{1,2})(?::(\d{2}))?' + match = re.match(range_pattern, time_pref) + + if match: + start_hour = int(match.group(1)) + end_hour = int(match.group(3)) + return (start_hour, end_hour) + + return None + + +def format_time_slot(start_time: str, end_time: str) -> str: + """ + Format time slot for display. + + Args: + start_time: Start time in UTC format + end_time: End time in UTC format + + Returns: + Formatted time range string + + Example: + >>> format_time_slot("2025-12-02T17:00:00Z", "2025-12-02T19:00:00Z") + "9:00 AM - 11:00 AM PST" + """ + try: + start_local = convert_utc_to_local(start_time) + end_local = convert_utc_to_local(end_time) + + # Remove PST from start time if both are same timezone + if start_local.endswith(' PST') and end_local.endswith(' PST'): + start_local = start_local[:-4] + + return f"{start_local} - {end_local}" + except ValueError: + return f"{start_time} - {end_time}" + + +def format_date_header(date_str: str) -> str: + """ + Format date for section headers. + + Args: + date_str: Date string (ISO format or datetime) + + Returns: + Formatted date like "Tuesday, December 2, 2025" + + Example: + >>> format_date_header("2025-12-02") + "Tuesday, December 2, 2025" + """ + try: + if isinstance(date_str, str): + dt = datetime.fromisoformat(date_str.split('T')[0]) + else: + dt = date_str + + return dt.strftime("%A, %B %d, %Y") + except (ValueError, AttributeError): + return str(date_str) + + +def cluster_papers_by_room(papers: list, time_slot_key: str = 'session') -> dict: + """ + Group papers by room within their time slots. + + Args: + papers: List of paper dictionaries with room_name and session info + time_slot_key: Key to group by time slots (default: 'session') + + Returns: + Nested dict: {time_slot: {room_name: [papers]}} + + Example: + >>> papers = [ + ... {'session': 'Morning', 'room_name': 'Hall A', 'name': 'Paper 1'}, + ... {'session': 'Morning', 'room_name': 'Hall A', 'name': 'Paper 2'}, + ... ] + >>> cluster_papers_by_room(papers) + {'Morning': {'Hall A': [...]}} + """ + clustered = {} + + for paper in papers: + time_slot = paper.get(time_slot_key, 'Unknown Session') + room = paper.get('room_name', 'Unknown Room') + + if time_slot not in clustered: + clustered[time_slot] = {} + + if room not in clustered[time_slot]: + clustered[time_slot][room] = [] + + clustered[time_slot][room].append(paper) + + return clustered diff --git a/agentic_nav/utils/__init__.py b/agentic_nav/utils/__init__.py new file mode 100644 index 0000000..f760def --- /dev/null +++ b/agentic_nav/utils/__init__.py @@ -0,0 +1,3 @@ +from agentic_nav.utils.tooling import infer_tool, _json_type +from agentic_nav.utils.logger import setup_logging +from agentic_nav.utils.embedding_generator import batch_embed_documents \ No newline at end of file diff --git a/agentic_nav/utils/cli/__init__.py b/agentic_nav/utils/cli/__init__.py new file mode 100644 index 0000000..514cc23 --- /dev/null +++ b/agentic_nav/utils/cli/__init__.py @@ -0,0 +1,3 @@ +from agentic_nav.utils.cli.editor import open_editor +from agentic_nav.utils.cli.help import print_help +from agentic_nav.utils.cli.history import show_history \ No newline at end of file diff --git a/agentic_nav/utils/cli/editor.py b/agentic_nav/utils/cli/editor.py new file mode 100644 index 0000000..ab74c88 --- /dev/null +++ b/agentic_nav/utils/cli/editor.py @@ -0,0 +1,29 @@ +import os +import tempfile + + +def open_editor(initial_text=""): + editor = os.environ.get("EDITOR") + if not editor: + # Minimal sensible defaults + if os.name == "nt": + editor = "notepad" + else: + editor = "nano" + with tempfile.NamedTemporaryFile(suffix=".md", delete=False, mode="w+", encoding="utf-8") as tf: + path = tf.name + tf.write(initial_text) + tf.flush() + try: + # Open editor and wait + rc = os.system(f'{editor} "{path}"') + if rc != 0: + print(f"(editor exit code {rc})") + with open(path, "r", encoding="utf-8") as f: + content = f.read() + finally: + try: + os.unlink(path) + except Exception: + pass + return content.strip() \ No newline at end of file diff --git a/agentic_nav/utils/cli/help.py b/agentic_nav/utils/cli/help.py new file mode 100644 index 0000000..bf45fb1 --- /dev/null +++ b/agentic_nav/utils/cli/help.py @@ -0,0 +1,14 @@ + + +def print_help(): + help_text = """ +Commands: + /help Show this help + /exit Exit the chat + /system Set or replace system prompt (multi-line via $EDITOR) + /edit Compose multi-line user message via $EDITOR + /history Show conversation history (JSON) + /save Save conversation history to a file (JSON) +Typing anything else will send it as a user message. +""" + print(help_text) \ No newline at end of file diff --git a/agentic_nav/utils/cli/history.py b/agentic_nav/utils/cli/history.py new file mode 100644 index 0000000..333898b --- /dev/null +++ b/agentic_nav/utils/cli/history.py @@ -0,0 +1,11 @@ + +def show_history(messages): + for i, m in enumerate(messages): + ts = m.get("_ts", "") + role = m.get("role", "") + content = m.get("content", "") + header = f"[{i}] {role} {ts}" + print(header) + print("-" * len(header)) + print(content) + print() \ No newline at end of file diff --git a/agentic_nav/utils/embedding_generator.py b/agentic_nav/utils/embedding_generator.py new file mode 100644 index 0000000..7f19750 --- /dev/null +++ b/agentic_nav/utils/embedding_generator.py @@ -0,0 +1,153 @@ +import logging + +import litellm +import numpy as np +import spaces + +from litellm import embedding +from sentence_transformers import SentenceTransformer +from tqdm import tqdm + +from typing import List + + +LOGGER = logging.getLogger(__name__) +local_embedding_model = None + +AVAILABLE_REMOTE_EMBEDDING_PROVIDERS_BASES = ["ollama_embed", "ollama.com", "localhost"] + + +class EmbeddingResponse: + def __init__(self, embeddings): + self.data = [{ + "embedding": emb.tolist(), + "index": idx + } for idx, emb in enumerate(embeddings)] + + +def _get_local_model(embedding_model_name: str = "nomic-ai/nomic-embed-text-v1.5"): + """Lazy load the embedding model only once""" + global local_embedding_model + if local_embedding_model is None: + LOGGER.info(f"Loading embedding model: {embedding_model_name}") + local_embedding_model = SentenceTransformer( + embedding_model_name, + trust_remote_code=True + ) + return local_embedding_model + + +@spaces.GPU +def embed_hf_spaces(input, embedding_model_name: str = "nomic-ai/nomic-embed-text-v1.5", api_base=None, **kwargs): + """ + Drop-in replacement for litellm.embedding() + + Args: + input: Single string or list of strings to embed + embedding_model_name: HuggingFace model name to use + api_base: Ignored for local embedding + **kwargs: Additional args like num_ctx (ignored for local) + + Returns: + Object with same structure as LiteLLM response + """ + # Get model (loads only on first call) + model_instance = _get_local_model(embedding_model_name) + + texts = [input] if isinstance(input, str) else input + embeddings = model_instance.encode( + texts, + convert_to_tensor=True, + show_progress_bar=False, + normalize_embeddings=True + ) + + embeddings_np = embeddings.cpu().numpy() + + return EmbeddingResponse(embeddings_np) + + +def embedding_fn(model, input, api_base, **kwargs): + if api_base == "hf_spaces_local": + return embed_hf_spaces(input=input, embedding_model_name=model, api_base=api_base, **kwargs) + elif any(provider in api_base for provider in AVAILABLE_REMOTE_EMBEDDING_PROVIDERS_BASES): + return embedding(input=input, model=model, api_base=api_base, **kwargs) + else: + raise NotImplementedError( + f"Unknown api_base for provider {api_base}. " + f"Supported providers: {AVAILABLE_REMOTE_EMBEDDING_PROVIDERS_BASES + ['hf_spaces_local']}" + ) + + +def batch_embed_documents( + texts: List[str], + batch_size: int = 1, + embedding_model: str = "nomic-ai/nomic-embed-text-v1.5", + api_base: str = "hf_spaces_local", + show_progress: bool = False, +) -> np.ndarray: + + if not texts: + return np.array([], dtype="float32").reshape(0, 0) + + if None in texts: + LOGGER.warning(f"WARNING: Detected documents with 'None' values. Replacing 'None' with an empty string...") + texts = ['' if doc is None else doc for doc in texts] + + vecs: List[List[float]] = [] + for i in tqdm(range(0, len(texts), batch_size), disable=not show_progress): + chunk = texts[i:i + batch_size] + try: + resp = embedding_fn( + model=embedding_model, + input=chunk, + api_base=api_base, + **{"num_ctx": 2048} + ) + except Exception as e: + LOGGER.error(f"Error during embedding batch {i}-{i + batch_size}: {e}. Falling back to single sample processing") + individual_responses = [] + ctr = i + for sample in chunk: + try: + individual_responses.append( + embedding_fn( + model=embedding_model, + input=sample, + api_base=api_base, + **{"num_ctx": 2048} + ) + ) + except litellm.BadRequestError: + LOGGER.error(f"Encountered error processing paper #{ctr}. Please inspect and retry afterwards.") + ctr += 1 + + LOGGER.debug(f"Single sample response from embedding model: {individual_responses}") + + # Extract embeddings from individual responses + for individual_resp in individual_responses: + vecs.extend([d["embedding"] for d in individual_resp.data]) + else: + # Normal batch processing + vecs.extend([d["embedding"] for d in resp.data]) + + arr = np.array(vecs, dtype="float32") + # cosine similarity: normalize to unit length and use IndexFlatIP + norms = np.linalg.norm(arr, axis=1, keepdims=True) + 1e-12 + return arr / norms + + +if __name__ == "__main__": + res = batch_embed_documents( + texts=[ + "test1", + "test2", + "test3", + "test4", + "test5" + ], + batch_size=2, + embedding_model="ollama/nomic-embed-text", + api_base="http://localhost:11435" + ) + print(f"Result shape: {res.shape}") \ No newline at end of file diff --git a/agentic_nav/utils/file_handlers.py b/agentic_nav/utils/file_handlers.py new file mode 100644 index 0000000..dc421ea --- /dev/null +++ b/agentic_nav/utils/file_handlers.py @@ -0,0 +1,10 @@ +import json + + +def save_chat_history(messages, path): + try: + with open(path, "w", encoding="utf-8") as f: + json.dump(messages, f, indent=2, ensure_ascii=False) + print(f"Saved to {path}") + except Exception as e: + print("Save failed:", e) \ No newline at end of file diff --git a/agentic_nav/utils/logger.py b/agentic_nav/utils/logger.py new file mode 100644 index 0000000..443bd26 --- /dev/null +++ b/agentic_nav/utils/logger.py @@ -0,0 +1,49 @@ +import logging +import logging.handlers + +from datetime import datetime +from pathlib import Path + + + +def setup_logging(log_dir: str = "logs", level: str = "INFO", console_level: str = "WARNING"): + """ + Configure logging for the entire application. + + Args: + log_dir: Directory for log files + level: Root logger level (DEBUG, INFO, WARNING, ERROR, CRITICAL) + console_level: Console handler level - defaults to WARNING to avoid + interfering with CLI display. Set to INFO for verbose output. + """ + Path(log_dir).mkdir(exist_ok=True) + + # Root logger configuration + root_logger = logging.getLogger() + root_logger.setLevel(getattr(logging, level.upper())) + + # Console handler - set to WARNING by default to not interfere with CLI display + console_handler = logging.StreamHandler() + console_handler.setLevel(getattr(logging, console_level.upper())) + console_format = logging.Formatter( + "%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S" + ) + console_handler.setFormatter(console_format) + + # File handler - for production + time_now = datetime.now().strftime("%Y-%m-%d_%H-%M") + + file_handler = logging.handlers.RotatingFileHandler( + f"{log_dir}/{time_now}_llm_agents.log", + maxBytes=10 * 1024 * 1024, # 10MB + backupCount=5 + ) + file_handler.setLevel(logging.DEBUG) + file_format = logging.Formatter( + "%(asctime)s - %(name)s - %(levelname)s - %(funcName)s:%(lineno)d - %(message)s" + ) + file_handler.setFormatter(file_format) + + root_logger.addHandler(console_handler) + root_logger.addHandler(file_handler) diff --git a/agentic_nav/utils/tooling.py b/agentic_nav/utils/tooling.py new file mode 100644 index 0000000..38068b5 --- /dev/null +++ b/agentic_nav/utils/tooling.py @@ -0,0 +1,44 @@ +from __future__ import annotations + +import inspect + +from typing import Any, Dict, List, Callable, get_args, get_origin, Literal + + +def _json_type(t: Any) -> Dict[str, Any]: + origin, args = get_origin(t), get_args(t) + if origin is Literal: + return {"type": "string", "enum": list(args)} + if origin in (list, List): + return {"type": "array", "items": {"type": "string"}} + if t in (str,): return {"type": "string"} + if t in (int,): return {"type": "integer"} + if t in (float,): return {"type": "number"} + if t in (bool,): return {"type": "boolean"} + return {"type": "string"} + + +def infer_tool(func: Callable[..., Any], tool_args: Dict[Any, Any]) -> Dict[str, Any]: + sig = inspect.signature(func) + hints = getattr(func, "__annotations__", {}) + props, required = {}, [] + for name, p in sig.parameters.items(): + if name in ("self", "cls"): continue + schema = _json_type(hints.get(name, str)) + if p.default is inspect._empty: required.append(name) + props[name] = schema + + parameter_values = {} + for arg_name, arg_val in tool_args.items(): + if arg_name in props.keys(): + parameter_values[arg_name] = arg_val + + return { + "type": "function", + "function": { + "name": func.__name__, + "description": (inspect.getdoc(func) or f"Call {func.__name__}"), + "parameters": {"type": "object", "properties": props, "required": required}, + }, + "parameter_properties_values": parameter_values + } diff --git a/build_json_rag.py b/build_json_rag.py deleted file mode 100644 index b54ce62..0000000 --- a/build_json_rag.py +++ /dev/null @@ -1,67 +0,0 @@ -# rag_build_json.py -from __future__ import annotations -from dataclasses import dataclass -import time -from typing import List, Dict, Any, Tuple -import json, os, math -from pathlib import Path - -import numpy as np -import faiss -from litellm import embedding -from tqdm import tqdm - -API_BASE = "http://localhost:11434" -EMBED_MODEL = "ollama/nomic-embed-text" # pull with: ollama pull nomic-embed-text -INDEX_DIR = "rag_index_json" - - -def _load_papers(json_path: str) -> List[Paper]: - data = json.loads(Path(json_path).read_text()) - papers = data["results"] - return papers - -def _embed(texts: List[str], batch: int = 1) -> np.ndarray: - vecs: List[List[float]] = [] - for i in tqdm(range(0, len(texts), batch)): - chunk = texts[i:i+batch] - retry = 0 - max_retries = 100 - while retry < max_retries: - try: - resp = embedding(model=EMBED_MODEL, input=chunk, api_base=API_BASE) - break - except Exception as e: - print(f"Error during embedding batch {i}-{i+batch}: {e}") - # print(chunk) - retry += 1 - if retry >= max_retries: - raise Exception("Max retries reached for embedding.") - print(f"Retrying {retry} ...") - time.sleep(0.1) - - vecs.extend([d["embedding"] for d in resp["data"]]) - # time.sleep(5) # avoid rate limit - arr = np.array(vecs, dtype="float32") - # cosine similarity: normalize to unit length and use IndexFlatIP - norms = np.linalg.norm(arr, axis=1, keepdims=True) + 1e-12 - return arr / norms - -def build_index(json_path: str, out_dir: str = INDEX_DIR) -> None: - out = Path(out_dir); out.mkdir(parents=True, exist_ok=True) - papers = _load_papers(json_path) - - texts = [f"{p['name']}\n\n{p['abstract']}" if "name" in p and "abstract" in p else "" for p in papers] - X = _embed(texts) - dim = X.shape[1] - index = faiss.IndexFlatIP(dim) - index.add(X) - - faiss.write_index(index, str(out / "faiss.index")) - # metadata alongside embeddings - (out / "meta.json").write_text(json.dumps(papers, ensure_ascii=False, indent=4, sort_keys=True)) - print(f"Indexed {len(papers)} papers → {out.resolve()}") - -if __name__ == "__main__": - # build_index("data/papers_test.json") - build_index("data/neurips-2025-orals-posters.json") diff --git a/docker-compose.yaml b/docker-compose.yaml new file mode 100644 index 0000000..0f9f424 --- /dev/null +++ b/docker-compose.yaml @@ -0,0 +1,137 @@ +services: + neo4j_db: + image: neo4j:5.26.0 + container_name: neo4j_db + expose: + - "7474" + - "7687" + ports: + - "7474:7474" # HTTP + - "7687:7687" # Bolt + environment: + # Authentication + - NEO4J_AUTH=${NEO4J_USERNAME}/${NEO4J_PASSWORD} + + # Memory settings + - NEO4J_server_memory_heap_initial__size=512m + - NEO4J_server_memory_heap_max__size=2G + - NEO4J_server_memory_pagecache_size=2G + - NEO4J_db_memory_transaction_total_max=3G + - NEO4J_dbms_memory_transaction_total_max=3G + + # APOC plugin (optional but recommended) + - NEO4J_PLUGINS=["apoc"] + + # Accept license (required for Enterprise features, remove if using Community) + # - NEO4J_ACCEPT_LICENSE_AGREEMENT=yes + volumes: + - neo4j_data:/data + - neo4j_logs:/logs + - neo4j_import:/var/lib/neo4j/import + - neo4j_plugins:/plugins + restart: unless-stopped + healthcheck: + test: [ "CMD-SHELL", "cypher-shell -u ${NEO4J_USERNAME:-neo4j} -p ${NEO4J_PASSWORD:-llm_agents} 'RETURN 1'" ] + interval: 10s + timeout: 5s + retries: 10 + start_period: 30s + networks: + - agentic_nav_net + + ollama_embed: + image: ollama/ollama:latest + container_name: ollama_embed + ports: + - "11435:11434" + volumes: + - ~/.ollama:/root/.ollama + environment: + - OLLAMA_HOST=0.0.0.0 + - NVIDIA_VISIBLE_DEVICES=all + - NVIDIA_DRIVER_CAPABILITIES=compute,utility + - EMBEDDING_MODEL_NAME=${EMBEDDING_MODEL_NAME} + restart: always + entrypoint: [ "/bin/bash", "-c", "\ + ollama serve & \ + sleep 5 && \ + ollama pull $${EMBEDDING_MODEL_NAME#ollama/} && \ + wait" ] + networks: + - agentic_nav_net + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [ gpu ] + + ollama_agent: + image: ollama/ollama:latest + container_name: ollama_agent + ports: + - "11436:11434" + volumes: + - ~/.ollama:/root/.ollama + environment: + - OLLAMA_HOST=0.0.0.0 + - NVIDIA_VISIBLE_DEVICES=all + - NVIDIA_DRIVER_CAPABILITIES=compute,utility + - AGENT_MODEL_NAME=${AGENT_MODEL_NAME} + restart: always + entrypoint: [ "/bin/bash", "-c", "\ + ollama serve & \ + sleep 5 && \ + ollama pull $${AGENT_MODEL_NAME#ollama_chat/} && \ + wait" ] + networks: + - agentic_nav_net + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [ gpu ] + + webinterface: + build: + context: . + dockerfile: Dockerfile + container_name: llm-agents-web + ports: + - "7860:7860" + environment: + - PYTHONUNBUFFERED=1 + - OLLAMA_API_KEY=${OLLAMA_API_KEY} + - NEO4J_USERNAME=${NEO4J_USERNAME:-neo4j} + - NEO4J_PASSWORD=${NEO4J_PASSWORD:-llm_agents} + - NEO4J_DB_URI=${NEO4J_DB_URI} + - POPULATE_DATABASE_NIPS2025=false + - EMBEDDING_MODEL_NAME=${EMBEDDING_MODEL_NAME} + - EMBEDDING_MODEL_API_BASE=http://ollama_embed:11434 + - AGENT_MODEL_NAME=${AGENT_MODEL_NAME} + - AGENT_MODEL_API_BASE=http://ollama_agent:11434 + - NEO4J_DB_NODE_RETURN_LIMIT=${NEO4J_DB_NODE_RETURN_LIMIT} + restart: unless-stopped + networks: + - agentic_nav_net + depends_on: + neo4j_db: + condition: service_healthy + ollama_embed: + condition: service_started + ollama_agent: + condition: service_started + + +networks: + agentic_nav_net: + + +volumes: + neo4j_data: + neo4j_logs: + neo4j_import: + neo4j_plugins: diff --git a/graphs/.gitkeep b/graphs/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/main_ui.py b/main_ui.py deleted file mode 100644 index bb8d3c9..0000000 --- a/main_ui.py +++ /dev/null @@ -1,217 +0,0 @@ -import os -import shutil -import sys -import tempfile -import json -import datetime - -import click -from rich.console import Console -from rich.markdown import Markdown - -""" -Lightweight terminal chat UI that interacts with an agent implementation. - -Contract with main.py: -- main.py should export a function with this signature: - def agent_respond(messages: list[dict]) -> dict: - # messages is a list of {"role": "system|user|assistant", "content": str} - # returns a single assistant message dict: {"role": "assistant", "content": str} -- If your main.py uses a different API, adapt the import below or provide a thin shim. - -Features: -- Renders assistant messages as Markdown (uses rich if available, falls back to plain text). -- Multi-line input via $EDITOR using the "/edit" command. -- Commands: /help, /exit, /system, /edit, /history, /save -""" - - -# Try to import agent_respond from main.py -try: - from agent_neurips_2025 import agent_respond, msgs # main.py should define this -except Exception as e: - raise RuntimeError( - "Could not import agent_respond from main.py. " - "Please implement def agent_respond(messages: list[dict]) -> dict in main.py" - ) - -console = Console(soft_wrap=True) -def render_markdown(text): - console.print(Markdown(text)) - -PROMPT = "You> " - -def open_editor(initial_text=""): - editor = os.environ.get("EDITOR") - if not editor: - # Minimal sensible defaults - if os.name == "nt": - editor = "notepad" - else: - editor = "nano" - with tempfile.NamedTemporaryFile(suffix=".md", delete=False, mode="w+", encoding="utf-8") as tf: - path = tf.name - tf.write(initial_text) - tf.flush() - try: - # Open editor and wait - rc = os.system(f'{editor} "{path}"') - if rc != 0: - print(f"(editor exit code {rc})") - with open(path, "r", encoding="utf-8") as f: - content = f.read() - finally: - try: - os.unlink(path) - except Exception: - pass - return content.strip() - -def print_help(): - help_text = """ -Commands: - /help Show this help - /exit Exit the chat - /system Set or replace system prompt (multi-line via $EDITOR) - /edit Compose multi-line user message via $EDITOR - /history Show conversation history (JSON) - /save Save conversation history to a file (JSON) -Typing anything else will send it as a user message. -""" - print(help_text) - -def show_history(messages): - for i, m in enumerate(messages): - ts = m.get("_ts", "") - role = m.get("role", "") - content = m.get("content", "") - header = f"[{i}] {role} {ts}" - print(header) - print("-" * len(header)) - print(content) - print() - -def save_history(messages, path): - try: - with open(path, "w", encoding="utf-8") as f: - json.dump(messages, f, indent=2, ensure_ascii=False) - print(f"Saved to {path}") - except Exception as e: - print("Save failed:", e) - -@click.command() -@click.option("-a", "--api-base", default="http://localhost:11434", type=str, help="Base URL to access the inference endpoint.") -@click.option("-k", "--api-key", default=None, type=str, help="API key for authentication.") -@click.option("-m", "--model", default="ollama_chat/gpt-oss:20b", type=str, help="Specify the model you want to use in LiteLLM format.") -@click.option("-t", "--temperature", default=0.2, type=float, help="Specify the model temperature.") -@click.option("--max-tokens", default=6000, type=int, help="Specify the max. number of model output tokens.") -@click.option("-c", "--num-ctx", default=131072, type=int, help="Specify the model context window.") -@click.option("-l", "--max-num-papers", default=50, type=int, help="Specify the maximum number of papers to retrieve.") -def main(api_base, api_key, model, temperature, max_tokens, num_ctx, max_num_papers): - messages = msgs.copy() - # Optionally seed with a system message; leave empty by default - # messages.append({"role": "system", "content": "You are a helpful assistant.", "_ts": str(datetime.utcnow())}) - print("Lightweight Chat UI. Type /help for commands.") - - # Config for the LLM messages - llm_config = { - "temperature": temperature, - "max_tokens": max_tokens, - "num_ctx": num_ctx - } - - # Parameters to limit the tool calling scope - tool_args = { - "num_records": max_num_papers - } - - while True: - try: - line = input(PROMPT).strip() - except (EOFError, KeyboardInterrupt): - print() - break - - if not line: - continue - - if line.startswith("/"): - parts = line.split(maxsplit=1) - cmd = parts[0].lower() - arg = parts[1] if len(parts) > 1 else "" - if cmd == "/help": - print_help() - continue - elif cmd == "/exit": - print("Goodbye.") - break - elif cmd == "/edit": - content = open_editor() - if content: - msg = {"role": "user", "content": content, "_ts": str(datetime.datetime.now(datetime.UTC))} - messages.append(msg) - else: - print("(no content)") - continue - elif cmd == "/system": - content = open_editor() - if content: - # Replace or append system message as first element - sys_msg = {"role": "system", "content": content, "_ts": str(datetime.datetime.now(datetime.UTC))} - # Remove existing system messages - messages = [m for m in messages if m.get("role") != "system"] - messages.insert(0, sys_msg) - print("System prompt set.") - else: - print("(no content)") - continue - elif cmd == "/history": - show_history(messages) - continue - elif cmd == "/save": - path = arg.strip() or "chat_history.json" - save_history(messages, path) - continue - else: - print("Unknown command. Type /help.") - continue - else: - # Regular single-line user message - msg = {"role": "user", "content": line, "_ts": str(datetime.datetime.now(datetime.UTC))} - messages.append(msg) - - # Call the agent - try: - # Provide agent_respond with a copy without timestamps - msgs_for_agent = [{"role": m["role"], "content": m["content"]} for m in messages] - updated_msgs = agent_respond( - msgs_for_agent, - model_name=model, - api_base=api_base, - api_key=api_key, - llm_args=llm_config, - tool_args=tool_args - ) - for msg in updated_msgs: - if "_ts" not in msg and "role" in msg and msg.get("role") == "assistant": - msg["_ts"] = str(datetime.datetime.now(datetime.UTC)) - # messages = updated_msgs # TODO: Currently not using this due to issue with tool call mismatch error - assistant_msg = updated_msgs[-1] - messages.append(assistant_msg) - - # Render assistant message as Markdown - if console: - render_markdown(assistant_msg["content"]) - else: - print("\nAssistant (Markdown):\n") - render_markdown(assistant_msg["content"]) - print() - except KeyboardInterrupt: - print("\n(interrupted)") - continue - except Exception as e: - print("Error from agent:", e) - # keep going - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index ab42def..bdf3f23 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,18 +1,34 @@ [project] -name = "llm-agents" +name = "agentic-nav" version = "0.1.0" -description = "Add your description here" +description = "Conference navigation agent leveraging graph databases and semantic search to provide paper recommendations, research network exploration, and automated schedule generation for NeurIPS 2025 attendees." readme = "README.md" -requires-python = ">=3.14" +authors = [ + {name = "Shiqiang Wang", email = "s.wang9@exeter.ac.uk"}, + {name = "Herbert Woisetschläger", email = "herbert.woisetschlaeger@tum.de"} +] + +requires-python = ">=3.10" dependencies = [ - "faiss-cpu>=1.12.0", - "httpx>=0.28.1", + "aiofiles", + "einops", + "flask", + "gradio[mcp,oauth]", + "hatchling", + "httpx", + "kaleido", "litellm", - "pydantic>=2.12.3", - "pydantic-settings>=2.11.0", - "rich>=14.2.0", - "sentence-transformers>=5.1.2", - "typer>=0.20.0", + "neo4j", + "prompt-toolkit", + "pydantic", + "pydantic-settings", + "pyvis>=0.3.2", + "rich>=13.0.0", + "sentence-transformers", + "spaces>=0.44b3", + "toon-format", + "torch==2.8.0", + "typer", ] [tool.uv.workspace] @@ -22,3 +38,23 @@ members = [ [tool.uv.sources] litellm = { git = "https://github.com/shiqiangw/litellm.git" } +toon-format = { git = "https://github.com/toon-format/toon-python.git" } + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[dependency-groups] +dev = [ + "pytest>=9.0.1", + "pytest-asyncio>=1.3.0", + "pytest-cov>=7.0.0", + "pytest-mock>=3.15.1", +] + +[tool.hatchling.build.targets.wheel] +packages = ["agentic_nav"] + +[project.scripts] +agentic-nav-cli = "agentic_nav.frontend.cli:main" +agentic-nav-web = "agentic_nav.frontend.browser_ui:main" diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..53083b7 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,26 @@ +[pytest] +minversion = 6.0 +testpaths = tests +python_files = test_*.py +python_classes = Test* +python_functions = test_* +addopts = + --strict-markers + --strict-config + --verbose +markers = + unit: Unit tests + integration: Integration tests (currently skipped, require full setup) + slow: Slow tests that require external services + neo4j: Tests requiring Neo4j database + ollama: Tests requiring Ollama service + no_auto_env: Tests that should not use automatic environment variable loading +asyncio_mode = auto +asyncio_default_fixture_loop_scope = function + +[coverage:run] +source = . +omit = + */tests/* + */test_* + setup.py \ No newline at end of file diff --git a/scripts/docker-entrypoint.sh b/scripts/docker-entrypoint.sh new file mode 100644 index 0000000..eced6cb --- /dev/null +++ b/scripts/docker-entrypoint.sh @@ -0,0 +1,14 @@ +#!/bin/bash +set -e + +echo "Neo4j is up - executing command" + +if [ "${POPULATE_DATABASE_NIPS2025}" = "true" ]; then + echo "Importing NeurIPS 2025 papers..." + bash scripts/import_neurips2025_kg.sh +else + echo "Skipping NeurIPS 2025 paper import (POPULATE_DATABASE_NIPS2025 is not set to 'true')" +fi + +echo "Starting main application..." +exec "$@" \ No newline at end of file diff --git a/scripts/import_neurips2025_kg.sh b/scripts/import_neurips2025_kg.sh new file mode 100644 index 0000000..edf6626 --- /dev/null +++ b/scripts/import_neurips2025_kg.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +# Download the pre-built knowledge graph +wget -O graphs/neurips2025_knowledge_graph.pkl https://syncandshare.lrz.de/dl/fiJPiUkKp1SZAqRX2m76S6/knowledge_graph_thresh_0.6_v3.pkl + +# Import the knowledge graph to the database +uv run llm_agents/tools/knowledge_graph/neo4j_db_importer.py \ + --graph-path graphs/neurips2025_knowledge_graph.pkl \ + --neo4j-uri bolt://neo4j_db:7687 \ + --neo4j-username $NEO4J_USERNAME \ + --neo4j-password $NEO4J_PASSWORD \ + --batch-size 100 \ + --embedding-dimension 768 diff --git a/scripts/prepare_gradio.sh b/scripts/prepare_gradio.sh new file mode 100644 index 0000000..cd3cfd6 --- /dev/null +++ b/scripts/prepare_gradio.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +set -e + +# Only initialize submodules if not in Docker (gradio folder not present) +if [ ! -d "gradio/.git" ]; then + echo "Initializing and updating git submodules..." + git submodule update --init --recursive + cd gradio + echo "Pinned gradio version to GIT revision 648169d85fbeeffc184115c4c92b12957f2a162f (Nov. 12, 2025)" + git checkout 648169d85fbeeffc184115c4c92b12957f2a162f + cd .. +fi + +echo "Building Gradio frontend..." +cd gradio +bash scripts/build_frontend.sh +cd .. \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..5f19b37 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +# Test package \ No newline at end of file diff --git a/tests/agents/__init__.py b/tests/agents/__init__.py new file mode 100644 index 0000000..50a8f04 --- /dev/null +++ b/tests/agents/__init__.py @@ -0,0 +1 @@ +# Agent tests \ No newline at end of file diff --git a/tests/agents/test_base.py b/tests/agents/test_base.py new file mode 100644 index 0000000..5541312 --- /dev/null +++ b/tests/agents/test_base.py @@ -0,0 +1,388 @@ +""" +Tests for the base LLMAgent class. +""" +import json +import pytest +from unittest.mock import Mock, patch, MagicMock +from dataclasses import asdict +from datetime import datetime, UTC + +from llm_agents.agents.base import LLMAgent + + +class TestLLMAgent: + """Test the base LLMAgent class.""" + + @pytest.fixture + def agent(self): + """Create a test agent instance.""" + agent = LLMAgent( + model="ollama_chat/gpt-oss:20b", + api_base="http://localhost:11436", + api_key="test-key", + llm_args={"temperature": 0.5}, + global_tool_args={"num_records": 5} + ) + + agent.setup_session() + return agent + + @pytest.fixture + def mock_tools(self): + """Create mock tools for testing.""" + def mock_tool_1(query: str, num_results: int = 10): + """Mock search tool.""" + return {"results": f"Found {num_results} results for '{query}'"} + + def mock_tool_2(paper_id: str): + """Mock retrieval tool.""" + return {"paper": f"Details for paper {paper_id}"} + + return [mock_tool_1, mock_tool_2] + + def test_agent_initialization(self, agent): + """Test agent initialization with default values.""" + assert agent.model == "ollama_chat/gpt-oss:20b" + assert agent.api_base == "http://localhost:11436" + assert agent.api_key == "test-key" + assert agent.llm_args == {"temperature": 0.5} + assert agent.global_tool_args == {"num_records": 5} + assert agent.max_interaction_rounds == 10 + assert agent.messages == [] + + def test_agent_default_initialization(self): + """Test agent initialization with default values.""" + agent = LLMAgent() + assert agent.model == "ollama_chat/gpt-oss:20b" + assert agent.api_base == "http://localhost:11434" + assert agent.api_key is None + assert "temperature" in agent.llm_args + assert "max_tokens" in agent.llm_args + + @patch('llm_agents.agents.base.litellm') + def test_test_llm_connection_success(self, mock_litellm, agent): + """Test successful LLM connection test.""" + mock_response = Mock() + mock_response.choices = [Mock()] + mock_response.choices[0].message.content = "Test response" + mock_litellm.completion.return_value = mock_response + + agent.test_llm_connection() + + mock_litellm.completion.assert_called_once() + call_args = mock_litellm.completion.call_args + assert call_args.kwargs['model'] == "ollama_chat/gpt-oss:20b" + assert call_args.kwargs['api_base'] == "http://localhost:11436" + assert call_args.kwargs['api_key'] == "test-key" + + @patch('llm_agents.agents.base.litellm') + def test_test_llm_connection_failure(self, mock_litellm, agent, caplog): + """Test failed LLM connection test.""" + mock_litellm.completion.side_effect = Exception("Connection failed") + + agent.test_llm_connection() + + assert "Model not available or connection failed" in caplog.text + + def test_setup_session(self, agent, mock_tools): + """Test session setup with tools.""" + agent.tools = mock_tools + agent.setup_session() + + assert agent.tool_registry is not None + assert len(agent.tool_registry) == 2 + assert "mock_tool_1" in agent.tool_registry + assert "mock_tool_2" in agent.tool_registry + assert agent.tool_descriptions is not None + + def test_setup_session_custom_tools(self, agent, mock_tools): + """Test session setup with custom tool functions.""" + agent.setup_session(tool_funcs=mock_tools[:1]) # Only first tool + + assert len(agent.tool_registry) == 1 + assert "mock_tool_1" in agent.tool_registry + assert "mock_tool_2" not in agent.tool_registry + + @patch('llm_agents.agents.base.litellm') + def test_send_to_llm_text_response(self, mock_litellm, agent): + """Test _send_to_llm with text-only response.""" + # Mock streaming response + mock_chunks = [ + {"choices": [{"delta": {"content": "Hello "}}]}, + {"choices": [{"delta": {"content": "world!"}}]}, + {"choices": [{"delta": {}}]} # End marker + ] + mock_litellm.completion.return_value = iter(mock_chunks) + + messages = [{"role": "user", "content": "test"}] + collected, calls = agent._send_to_llm(messages, "test-model", "http://test.com", "test-key") + + assert collected == "Hello world!" + assert calls == [] + + @patch('llm_agents.agents.base.litellm') + def test_send_to_llm_with_tool_calls(self, mock_litellm, agent): + """Test _send_to_llm with tool calls in response.""" + mock_tool_calls = [{ + "id": "call_1", + "function": {"name": "search_papers", "arguments": "{}"} + }] + + mock_chunks = [ + {"choices": [{"delta": {"content": "I'll search for papers."}}]}, + {"choices": [{"delta": {"tool_calls": mock_tool_calls}}]}, + {"choices": [{"delta": {}}]} + ] + mock_litellm.completion.return_value = iter(mock_chunks) + + messages = [{"role": "user", "content": "search papers"}] + collected, calls = agent._send_to_llm(messages, "test-model", "http://test.com", "test-key") + + assert collected == "I'll search for papers." + assert len(calls) == 1 + assert calls[0]["id"] == "call_1" + + def test_call_tool_success(self, agent, mock_tools): + """Test successful tool execution.""" + agent.tools = mock_tools + agent.setup_session() + + tool_call = { + "id": "call_1", + "function": { + "name": "mock_tool_1", + "arguments": '{"query": "test query", "num_results": 3}' + } + } + + result = agent.call_tool(tool_call) + + assert result["role"] == "tool" + assert result["tool_call_id"] == "call_1" + assert result["name"] == "mock_tool_1" + + content = json.loads(result["content"]) + assert "Found 3 results for 'test query'" in content["results"] + + def test_call_tool_invalid_json_args(self, agent, mock_tools): + """Test tool call with invalid JSON arguments.""" + agent.tools = mock_tools + agent.setup_session() + + tool_call = { + "id": "call_1", + "function": { + "name": "mock_tool_1", + "arguments": { + "query": "invalid json" + } + } + } + + result = agent.call_tool(tool_call) + + # Should still work with empty args + assert result["role"] == "tool" + assert result["name"] == "mock_tool_1" + + def test_set_get_history(self, agent, sample_message): + """Test setting and getting message history.""" + messages = [sample_message] + agent.set_history(messages) + + assert agent.get_history() == messages + + def test_set_system_prompt(self, agent): + """Test setting system prompt.""" + messages = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"} + ] + + new_system_prompt = "You are a helpful assistant." + updated_messages = agent.set_system_prompt(new_system_prompt, messages) + + assert len(updated_messages) == 3 + assert updated_messages[0]["role"] == "system" + assert updated_messages[0]["content"] == new_system_prompt + assert updated_messages[1]["role"] == "user" + assert updated_messages[2]["role"] == "assistant" + + def test_set_system_prompt_replaces_existing(self, agent): + """Test that setting system prompt replaces existing system message.""" + messages = [ + {"role": "system", "content": "Old system prompt"}, + {"role": "user", "content": "Hello"} + ] + + new_system_prompt = "New system prompt" + updated_messages = agent.set_system_prompt(new_system_prompt, messages) + + # Should still have 2 messages, with system prompt replaced + assert len(updated_messages) == 2 + assert updated_messages[0]["role"] == "system" + assert updated_messages[0]["content"] == new_system_prompt + assert updated_messages[1]["role"] == "user" + + def test_get_system_prompt(self, agent): + """Test getting system prompt from messages.""" + messages = [ + {"role": "system", "content": "System prompt"}, + {"role": "user", "content": "Hello"} + ] + agent.set_history(messages) + + system_msg = agent.get_system_prompt() + assert system_msg["role"] == "system" + assert system_msg["content"] == "System prompt" + + def test_get_system_prompt_none(self, agent): + """Test getting system prompt when none exists.""" + messages = [{"role": "user", "content": "Hello"}] + agent.set_history(messages) + + system_msg = agent.get_system_prompt() + assert system_msg is None + + def test_get_most_recent_assistant_message(self, agent): + """Test getting most recent assistant message.""" + messages = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi!"}, + {"role": "user", "content": "How are you?"}, + {"role": "assistant", "content": "I'm doing well!"} + ] + agent.set_history(messages) + + recent_msg = agent.get_most_recent_assistant_message() + assert recent_msg["content"] == "I'm doing well!" + + def test_get_most_recent_assistant_message_none(self, agent): + """Test getting most recent assistant message when none exists.""" + messages = [{"role": "user", "content": "Hello"}] + agent.set_history(messages) + + recent_msg = agent.get_most_recent_assistant_message() + assert recent_msg is None + + def test_interact_assertions(self, agent): + """Test that interact method validates inputs properly.""" + agent.remove_session() + with pytest.raises(AssertionError, match="Make sure to call 'setup_session\\(\\)' before the first interaction."): + agent.interact({"role": "user", "content": "test"}) + + agent.tool_registry = {} + agent.tool_descriptions = [] + + with pytest.raises(AssertionError, match="must contain a 'role' key"): + agent.interact({"content": "test"}) + + with pytest.raises(AssertionError, match="must contain a 'content' key"): + agent.interact({"role": "user"}) + + @patch('llm_agents.agents.base.litellm') + def test_interact_single_round(self, mock_litellm, agent, mock_tools, sample_message): + """Test single interaction round without tool calls.""" + agent.tools = mock_tools + agent.setup_session() + + mock_chunks = [ + {"choices": [{"delta": {"content": "Hello there!"}}]}, + {"choices": [{"delta": {}}]} + ] + mock_litellm.completion.return_value = iter(mock_chunks) + + result_messages = agent.interact(sample_message) + + assert len(result_messages) == 2 # user + assistant + assert result_messages[0] == sample_message + assert result_messages[1]["role"] == "assistant" + assert result_messages[1]["content"] == "Hello there!" + + @patch('llm_agents.agents.base.litellm') + def test_interact_with_tool_calls(self, mock_litellm, agent, mock_tools, sample_message): + """Test interaction with tool calls.""" + agent.tools = mock_tools + agent.setup_session() + + # First call - assistant makes tool call + mock_tool_calls = [{ + "id": "call_1", + "function": {"name": "mock_tool_1", "arguments": '{"query": "test"}'} + }] + + first_chunks = [ + {"choices": [{"delta": {"content": "I'll search for that."}}]}, + {"choices": [{"delta": {"tool_calls": mock_tool_calls}}]}, + {"choices": [{"delta": {}}]} + ] + + # Second call - assistant responds to tool result + second_chunks = [ + {"choices": [{"delta": {"content": "Here are the results!"}}]}, + {"choices": [{"delta": {}}]} + ] + + mock_litellm.completion.side_effect = [iter(first_chunks), iter(second_chunks)] + + result_messages = agent.interact(sample_message) + + # Should have: user message, assistant response with tool call, tool result, final assistant response + assert len(result_messages) == 4 + assert result_messages[0] == sample_message + assert result_messages[1]["role"] == "assistant" + assert "tool_calls" in result_messages[1] + assert result_messages[2]["role"] == "tool" + assert result_messages[3]["role"] == "assistant" + assert result_messages[3]["content"] == "Here are the results!" + + @patch('llm_agents.agents.base.litellm') + def test_interact_stateless(self, mock_litellm, agent, mock_tools): + """Test stateless interaction generator.""" + agent.tools = mock_tools + agent.setup_session() + + mock_chunks = [ + {"choices": [{"delta": {"content": "Hello "}}]}, + {"choices": [{"delta": {"content": "world!"}}]}, + {"choices": [{"delta": {}}]} + ] + mock_litellm.completion.return_value = iter(mock_chunks) + + messages = [{"role": "user", "content": "test"}] + + # Collect all yielded message states + states = list(agent.interact_stateless(messages, "test-model", "http://test.com", "test-key")) + + # Should have multiple states as content streams in + assert len(states) > 0 + final_state = states[-1] + assert len(final_state) == 2 # user + assistant + assert final_state[1]["role"] == "assistant" + assert final_state[1]["content"] == "Hello world!" + + def test_interact_stateless_assertions(self, agent): + """Test that interact_stateless validates setup properly.""" + messages = [{"role": "user", "content": "test"}] + agent.remove_session() + + with pytest.raises(AssertionError, match="Make sure to call 'setup_session\\(\\)' before the first interaction."): + list(agent.interact_stateless(messages, "ollama_chat/gpt-oss:20b", "http://localhost:11436", "api_key")) + + @patch('llm_agents.agents.base.datetime') + def test_message_timestamp_addition(self, mock_datetime, agent, mock_tools): + """Test that messages get timestamps added automatically.""" + mock_datetime.now.return_value = datetime(2024, 1, 1, 12, 0, 0, tzinfo=UTC) + mock_datetime.UTC = UTC + + agent.tools = mock_tools + agent.setup_session() + + message = {"role": "user", "content": "test"} # No timestamp + + with patch.object(agent, '_send_to_llm', return_value=("response", [])): + agent.interact(message) + + # Message should have timestamp added + assert "_ts" in agent.messages[0] + assert agent.messages[0]["_ts"] == "2024-01-01 12:00:00+00:00" diff --git a/tests/agents/test_neurips2025_conference.py b/tests/agents/test_neurips2025_conference.py new file mode 100644 index 0000000..1e1a898 --- /dev/null +++ b/tests/agents/test_neurips2025_conference.py @@ -0,0 +1,120 @@ +""" +Tests for the NeurIPS2025Agent class. +""" +import pytest +from unittest.mock import patch + +from llm_agents.agents.neurips2025_conference import NeurIPS2025Agent, DEFAULT_NEURIPS2025_AGENT_ARGS + + +class TestNeurIPS2025Agent: + """Test the NeurIPS2025Agent class.""" + + def test_default_args(self): + """Test default agent arguments configuration.""" + args = DEFAULT_NEURIPS2025_AGENT_ARGS + + assert "model" in args + assert "api_base" in args + assert "api_key" in args + assert "llm_args" in args + assert "global_tool_args" in args + + # Check LLM args structure + assert "temperature" in args["llm_args"] + assert "max_tokens" in args["llm_args"] + assert "num_ctx" in args["llm_args"] + + # Check tool args + assert "max_num_papers" in args["global_tool_args"] + + def test_agent_initialization_default(self): + """Test agent initialization with default configuration.""" + agent = NeurIPS2025Agent() + + # Should have system message pre-configured + assert len(agent.messages) == 1 + assert agent.messages[0]["role"] == "system" + assert "NeurIPS 2025 papers" in agent.messages[0]["content"] + assert "search tool" in agent.messages[0]["content"] + + # Should have the right tools + assert len(agent.tools) == 3 + tool_names = [tool.__name__ for tool in agent.tools] + assert "search_similar_papers" in tool_names + assert "find_neighboring_papers" in tool_names + assert "traverse_graph" in tool_names + + def test_agent_initialization_custom_args(self): + """Test agent initialization with custom arguments.""" + custom_args = { + "model": "custom-model", + "api_base": "http://custom.com", + "api_key": "custom-key", + "llm_args": {"temperature": 0.8}, + "global_tool_args": {"max_num_papers": 20} + } + + agent = NeurIPS2025Agent(**custom_args) + + assert agent.model == "custom-model" + assert agent.api_base == "http://custom.com" + assert agent.api_key == "custom-key" + assert agent.llm_args == {"temperature": 0.8} + assert agent.global_tool_args == {"max_num_papers": 20} + + def test_system_prompt_content(self): + """Test that system prompt contains expected guidance.""" + agent = NeurIPS2025Agent() + system_msg = agent.messages[0]["content"] + + # Check key instruction components + assert "NeurIPS 2025 papers" in system_msg + assert "search tool" in system_msg + assert "paper titles and abstracts as input keywords" in system_msg + assert "cite titles, abstracts, and OpenReview URLs" in system_msg + + def test_agent_inherits_base_functionality(self): + """Test that agent properly inherits from LLMAgent.""" + agent = NeurIPS2025Agent() + + # Should inherit base methods + assert hasattr(agent, 'setup_session') + assert hasattr(agent, 'interact') + assert hasattr(agent, 'interact_stateless') + assert hasattr(agent, 'test_llm_connection') + assert hasattr(agent, 'set_history') + assert hasattr(agent, 'get_history') + + @patch('llm_agents.tools.search_similar_papers') + @patch('llm_agents.tools.find_neighboring_papers') + @patch('llm_agents.tools.traverse_graph') + def test_tools_import(self, mock_traverse, mock_neighboring, mock_search): + """Test that tools are properly imported and available.""" + agent = NeurIPS2025Agent() + agent.setup_session() + + # Tools should be registered + assert "search_similar_papers" in agent.tool_registry + assert "find_neighboring_papers" in agent.tool_registry + assert "traverse_graph" in agent.tool_registry + + def test_environment_variable_integration(self): + """Test integration with environment variables.""" + import sys + import importlib + + with patch.dict('os.environ', { + 'AGENT_MODEL_NAME': 'env-model', + 'AGENT_MODEL_API_BASE': 'http://env-base.com', + 'OLLAMA_API_KEY': 'env-key' + }): + # Remove from cache and reimport + if 'llm_agents.agents.neurips2025_conference' in sys.modules: + del sys.modules['llm_agents.agents.neurips2025_conference'] + + from llm_agents.agents.neurips2025_conference import DEFAULT_NEURIPS2025_AGENT_ARGS + + assert DEFAULT_NEURIPS2025_AGENT_ARGS["model"] == "env-model" + assert DEFAULT_NEURIPS2025_AGENT_ARGS["api_base"] == "http://env-base.com" + assert DEFAULT_NEURIPS2025_AGENT_ARGS["api_key"] == "env-key" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..ec5b1e1 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,167 @@ +""" +Pytest configuration and shared fixtures for the agentic_nav test suite. +""" +import os +import pytest +import tempfile +from unittest.mock import Mock, patch +from datetime import datetime, timezone +from pathlib import Path + + +# Test environment setup +@pytest.fixture(autouse=True) +def setup_test_environment(request): + """Set up test environment variables.""" + + # Skip auto environment setup for tests marked with no_auto_env + if hasattr(request, 'node') and request.node.get_closest_marker('no_auto_env'): + yield {} + return + + test_env = { + "NEO4J_URI": "bolt://localhost:7687", + "NEO4J_USERNAME": "neo4j", + "NEO4J_PASSWORD": "llm_agents", + "EMBEDDING_MODEL_NAME": "nomic-embed-text", + "EMBEDDING_MODEL_API_BASE": "http://localhost:11435", + "AGENT_MODEL_NAME": "gpt-oss:20b", + "AGENT_MODEL_API_BASE": "http://localhost:11436", + "OLLAMA_API_KEY": "test-api-key", + "AGENTIC_NAV_LOG_LEVEL": "DEBUG", + "POPULATE_DATABASE_NIPS2025": "false" + } + + # Temporarily set test environment variables + old_env = {} + for key, value in test_env.items(): + old_env[key] = os.environ.get(key) + os.environ[key] = value + + yield test_env + + # Restore original environment + for key, value in old_env.items(): + if value is None: + os.environ.pop(key, None) + else: + os.environ[key] = value + + +@pytest.fixture +def temp_dir(): + """Create a temporary directory for test files.""" + with tempfile.TemporaryDirectory() as temp_path: + yield Path(temp_path) + + +@pytest.fixture +def mock_datetime(): + """Mock datetime to return a fixed timestamp.""" + fixed_time = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc) + with patch('agentic_nav.agents.base.datetime') as mock_dt: + mock_dt.now.return_value = fixed_time + mock_dt.UTC = timezone.utc + yield mock_dt + + +@pytest.fixture +def sample_message(): + """Sample message for testing agent interactions.""" + return { + "role": "user", + "content": "Test message", + "_ts": "2024-01-01T12:00:00+00:00" + } + + +@pytest.fixture +def sample_tool_call(): + """Sample tool call for testing.""" + return { + "id": "test_call_id", + "function": { + "name": "search_similar_papers", + "arguments": '{"query": "machine learning", "num_results": 5}' + } + } + + +@pytest.fixture +def mock_litellm_response(): + """Mock LiteLLM response for testing.""" + mock_response = Mock() + mock_response.choices = [Mock()] + mock_response.choices[0].message.content = "Test response" + mock_response.choices[0].message.tool_calls = None + return mock_response + + +@pytest.fixture +def mock_litellm_stream(): + """Mock LiteLLM streaming response.""" + chunks = [ + {"choices": [{"delta": {"content": "Test "}}]}, + {"choices": [{"delta": {"content": "response"}}]}, + {"choices": [{"delta": {}}]} # End of stream + ] + return iter(chunks) + + +@pytest.fixture +def mock_neo4j_driver(): + """Mock Neo4j driver for database tests.""" + with patch('neo4j.GraphDatabase.driver') as mock_driver: + mock_session = Mock() + mock_driver.return_value.session.return_value = mock_session + yield mock_driver, mock_session + + +@pytest.fixture +def mock_embedding_model(): + """Mock embedding model for vector tests.""" + with patch('sentence_transformers.SentenceTransformer') as mock_model: + mock_instance = Mock() + mock_instance.encode.return_value = [[0.1, 0.2, 0.3]] # Sample embedding + mock_model.return_value = mock_instance + yield mock_instance + + +@pytest.fixture +def sample_paper_data(): + """Sample paper data for knowledge graph tests.""" + return [ + { + "id": "paper1", + "title": "Test Paper 1", + "abstract": "This is a test abstract for paper 1", + "authors": ["Author 1", "Author 2"], + "keywords": ["machine learning", "AI"] + }, + { + "id": "paper2", + "title": "Test Paper 2", + "abstract": "This is a test abstract for paper 2", + "authors": ["Author 2", "Author 3"], + "keywords": ["deep learning", "neural networks"] + } + ] + + +@pytest.fixture +def mock_gradio_interface(): + """Mock Gradio interface for frontend tests.""" + with patch('gradio.Interface') as mock_interface: + mock_instance = Mock() + mock_interface.return_value = mock_instance + yield mock_instance + + +@pytest.fixture +def mock_prompt_session(): + """Mock prompt_toolkit session for CLI tests.""" + with patch('prompt_toolkit.PromptSession') as mock_session: + mock_instance = Mock() + mock_instance.prompt.return_value = "test input" + mock_session.return_value = mock_instance + yield mock_instance diff --git a/tests/frontend/__init__.py b/tests/frontend/__init__.py new file mode 100644 index 0000000..71dc1a7 --- /dev/null +++ b/tests/frontend/__init__.py @@ -0,0 +1 @@ +# Frontend tests \ No newline at end of file diff --git a/tests/frontend/test_browser_ui.py b/tests/frontend/test_browser_ui.py new file mode 100644 index 0000000..f73b954 --- /dev/null +++ b/tests/frontend/test_browser_ui.py @@ -0,0 +1,294 @@ +""" +Tests for the browser UI frontend. +""" +import pytest +from unittest.mock import patch, Mock + + +class TestBrowserUIFunctions: + """Test functions from browser_ui module that can be tested in isolation.""" + + def test_module_imports(self): + """Test that module imports work correctly.""" + try: + from llm_agents.frontend import browser_ui + assert hasattr(browser_ui, 'LOGGER') + except ImportError as e: + pytest.skip(f"Could not import browser_ui: {e}") + + @patch('llm_agents.frontend.browser_ui.NeurIPS2025Agent') + def test_agent_initialization(self, mock_agent_class): + """Test global agent initialization.""" + from llm_agents.frontend.browser_ui import initialize_agent + + mock_agent = Mock() + mock_agent_class.return_value = mock_agent + + # Call the function + result = initialize_agent() + + # Verify agent was created correctly + mock_agent_class.assert_called_once() + mock_agent.setup_session.assert_called_once() + assert result == mock_agent + + def test_configure_agent_function(self): + """Test the initialize_agent function.""" + from llm_agents.frontend.browser_ui import configure_agent + + current_config = {} + + result = configure_agent( + api_base="http://test.com", + api_key="test-key", + model="test-model", + temperature=0.8, + max_tokens=5000, + num_ctx=100000, + max_num_papers=15, + current_config=current_config + ) + + # Verify config was updated correctly + assert current_config["model"] == "test-model" + assert current_config["api_base"] == "http://test.com" + assert current_config["api_key"] == "test-key" + assert current_config["llm_args"]["temperature"] == 0.8 + assert current_config["llm_args"]["max_tokens"] == 5000 + assert current_config["llm_args"]["num_ctx"] == 100000 + assert current_config["global_tool_args"]["max_num_papers"] == 15 + + def test_initialize_agent_api_key_masking(self): + """Test that API key is masked in logged config.""" + from llm_agents.frontend.browser_ui import configure_agent + + current_config = {} + + with patch('llm_agents.frontend.browser_ui.LOGGER') as mock_logger: + configure_agent( + api_base="http://test.com", + api_key="secret-key-123", + model="test-model", + temperature=0.5, + max_tokens=1000, + num_ctx=50000, + max_num_papers=10, + current_config=current_config + ) + + # Verify logging occurred + mock_logger.info.assert_called() + + # The function should mask sensitive information in logs + # (though the current implementation may not do this yet) + + +class TestBrowserUIIntegration: + """ + Integration tests for browser UI (may require mocking Gradio). + TODO: Write integration tests. + """ + + @pytest.mark.integration + def test_gradio_interface_creation(self): + """Test that Gradio interface can be created without errors.""" + pytest.skip("Integration test - requires full Gradio setup") + + # This would test the actual Gradio interface creation + # but requires more complex setup and mocking + + @pytest.mark.integration + def test_chat_functionality(self): + """Test chat functionality through Gradio interface.""" + pytest.skip("Integration test - requires full Gradio setup") + + # This would test the chat functionality + # but requires Gradio interface to be running + + @pytest.mark.integration + def test_system_prompt_functionality(self): + """Test system prompt editing through Gradio interface.""" + pytest.skip("Integration test - requires full Gradio setup") + + @pytest.mark.integration + def test_history_save_functionality(self): + """Test conversation history saving through Gradio interface.""" + pytest.skip("Integration test - requires full Gradio setup") + + @pytest.mark.integration + def test_configuration_persistence(self): + """Test that configuration persists across interactions.""" + pytest.skip("Integration test - requires full Gradio setup") + + +class TestBrowserUIMain: + """Test the main function for browser UI.""" + + @patch('llm_agents.frontend.browser_ui.initialize_agent') + @patch('llm_agents.frontend.browser_ui.gr') + def test_main_function_exists(self, mock_gr, mock_initialize_agent): + """Test that main function exists and can be called.""" + from llm_agents.frontend.browser_ui import main + + # Mock the agent instance + mock_agent = Mock() + mock_agent.get_system_prompt.return_value = { + "role": "system", + "content": "Test system prompt" + } + mock_initialize_agent.return_value = mock_agent + + # Mock the Blocks context manager + mock_webapp = Mock() + mock_gr.Blocks.return_value.__enter__ = Mock(return_value=mock_webapp) + mock_gr.Blocks.return_value.__exit__ = Mock(return_value=False) + + # Mock the launch method to prevent actual server startup + mock_webapp.launch = Mock() + + # Mock all Gradio components used in main() + mock_gr.Markdown = Mock(return_value=Mock()) + mock_gr.State = Mock(return_value=Mock()) + mock_gr.Row = Mock(return_value=Mock(__enter__=Mock(), __exit__=Mock())) + mock_gr.Column = Mock(return_value=Mock(__enter__=Mock(), __exit__=Mock())) + mock_gr.Accordion = Mock(return_value=Mock(__enter__=Mock(), __exit__=Mock())) + mock_gr.Textbox = Mock(return_value=Mock()) + mock_gr.Slider = Mock(return_value=Mock()) + mock_gr.Number = Mock(return_value=Mock()) + mock_gr.Button = Mock(return_value=Mock(click=Mock(return_value=Mock(then=Mock())))) + mock_gr.Chatbot = Mock(return_value=Mock()) + mock_gr.Code = Mock(return_value=Mock()) + + # Mock themes + mock_gr.themes.Default = Mock(return_value=Mock()) + mock_gr.themes.sizes.spacing_sm = "sm" + mock_gr.themes.sizes.radius_none = "none" + + # Call main - should not raise an error + main() + + # Verify Blocks was created with expected parameters + mock_gr.Blocks.assert_called_once() + call_kwargs = mock_gr.Blocks.call_args[1] + assert call_kwargs['title'] == "SciAgent For NeurIPS 2025" + + # Verify launch was called with expected parameters + mock_webapp.launch.assert_called_once() + launch_kwargs = mock_webapp.launch.call_args[1] + assert launch_kwargs['server_name'] == "0.0.0.0" + assert launch_kwargs['server_port'] == 7860 + assert launch_kwargs['share'] is False + assert launch_kwargs['show_error'] is True + assert launch_kwargs['debug'] is True + + @patch('llm_agents.frontend.browser_ui.initialize_agent') + @patch('llm_agents.frontend.browser_ui.gr') + def test_main_creates_ui_components(self, mock_gr, mock_initialize_agent): + """Test that main function creates necessary UI components.""" + from llm_agents.frontend.browser_ui import main + + # Track all gr component calls + component_calls = [] + + def track_component(name): + def wrapper(*args, **kwargs): + component_calls.append(name) + mock_instance = Mock() + # Mock context manager for components that use 'with' statements + mock_instance.__enter__ = Mock(return_value=mock_instance) + mock_instance.__exit__ = Mock(return_value=False) + # Mock the click method for buttons (returns self for chaining) + if name == 'Button': + mock_click = Mock(return_value=mock_instance) + mock_click.then = Mock(return_value=mock_instance) + mock_instance.click = mock_click + # Mock submit method for textboxes + if name == 'Textbox': + mock_submit = Mock(return_value=mock_instance) + mock_submit.then = Mock(return_value=mock_instance) + mock_instance.submit = mock_submit + return mock_instance + + return wrapper + + # Mock the agent instance + mock_agent = Mock() + mock_agent.get_system_prompt.return_value = { + "role": "system", + "content": "Test system prompt" + } + mock_initialize_agent.return_value = mock_agent + + # Mock the Blocks context manager + mock_webapp = Mock() + mock_webapp.__enter__ = Mock(return_value=mock_webapp) + mock_webapp.__exit__ = Mock(return_value=False) + mock_webapp.launch = Mock() + mock_gr.Blocks.return_value = mock_webapp + + # Mock Gradio components to track creation + mock_gr.Markdown = track_component('Markdown') + mock_gr.State = track_component('State') + mock_gr.Chatbot = track_component('Chatbot') + mock_gr.Textbox = track_component('Textbox') + mock_gr.Button = track_component('Button') + mock_gr.Slider = track_component('Slider') + mock_gr.Number = track_component('Number') + mock_gr.Accordion = track_component('Accordion') + mock_gr.Row = track_component('Row') + mock_gr.Column = track_component('Column') + mock_gr.Code = track_component('Code') + + # Mock themes + mock_gr.themes.Default = Mock(return_value=Mock()) + mock_gr.themes.sizes.spacing_sm = "sm" + mock_gr.themes.sizes.radius_none = "none" + + main() + + # Verify key components were created + assert 'Markdown' in component_calls + assert 'State' in component_calls + assert 'Chatbot' in component_calls + assert 'Textbox' in component_calls + assert 'Button' in component_calls + assert 'Slider' in component_calls + assert 'Accordion' in component_calls + + # Verify multiple instances of common components + assert component_calls.count('Textbox') >= 5 + assert component_calls.count('Button') >= 5 + assert component_calls.count('Markdown') >= 3 + + def test_main_entry_point(self): + """Test that the module can be used as entry point.""" + try: + import llm_agents.frontend.browser_ui + # Verify the module has the expected main function + assert hasattr(llm_agents.frontend.browser_ui, 'main') + assert callable(llm_agents.frontend.browser_ui.main) + except ImportError: + pytest.fail("Could not import browser_ui module") + + @patch('llm_agents.frontend.browser_ui.initialize_agent') + def test_main_initializes_agent(self, mock_initialize_agent): + """Test that main function initializes the agent.""" + from llm_agents.frontend.browser_ui import main + + # Mock the agent instance + mock_agent = Mock() + mock_agent.get_system_prompt.return_value = { + "role": "system", + "content": "Test prompt" + } + mock_initialize_agent.return_value = mock_agent + + # Patch launch to prevent server startup + with patch('gradio.Blocks.launch'): + main() + + # Verify initialize_agent was called + mock_initialize_agent.assert_called_once() + + # Verify agent methods were accessed + assert mock_agent.get_system_prompt.called diff --git a/tests/frontend/test_browser_ui_env.py b/tests/frontend/test_browser_ui_env.py new file mode 100644 index 0000000..de7825c --- /dev/null +++ b/tests/frontend/test_browser_ui_env.py @@ -0,0 +1,36 @@ +""" +Isolated test for browser UI environment variable usage. +This test is in a separate file to avoid interference from global mocks in other test files. +""" +import pytest +import sys +from unittest.mock import MagicMock + + +@pytest.mark.no_auto_env +def test_environment_variable_usage_isolated(monkeypatch): + """Test that environment variables are used correctly in complete isolation.""" + + # Clear only the specific browser_ui module to force fresh import + # Don't clear parent packages to avoid breaking the import structure + if 'llm_agents.frontend.browser_ui' in sys.modules: + del sys.modules['llm_agents.frontend.browser_ui'] + + monkeypatch.setenv('EMBEDDING_MODEL_NAME', 'test-embed-model') + monkeypatch.setenv('EMBEDDING_MODEL_API_BASE', 'http://test-embed.com') + monkeypatch.setenv('AGENT_MODEL_API_BASE', 'http://test-agent.com') + monkeypatch.setenv('OLLAMA_API_KEY', 'test-key') + + import llm_agents.frontend.browser_ui as browser_ui + + # Verify these are NOT mocks + assert not isinstance(browser_ui.EMBEDDING_MODEL_NAME, MagicMock), f"EMBEDDING_MODEL_NAME is a mock: {type(browser_ui.EMBEDDING_MODEL_NAME)}" + assert not isinstance(browser_ui.EMBEDDING_MODEL_API_BASE, MagicMock), f"EMBEDDING_MODEL_API_BASE is a mock: {type(browser_ui.EMBEDDING_MODEL_API_BASE)}" + assert not isinstance(browser_ui.AGENT_MODEL_API_BASE, MagicMock), f"AGENT_MODEL_API_BASE is a mock: {type(browser_ui.AGENT_MODEL_API_BASE)}" + assert not isinstance(browser_ui.OLLAMA_API_KEY, MagicMock), f"OLLAMA_API_KEY is a mock: {type(browser_ui.OLLAMA_API_KEY)}" + + # Test that environment variables are correctly loaded + assert browser_ui.EMBEDDING_MODEL_NAME == 'test-embed-model', f"Expected 'test-embed-model', got {browser_ui.EMBEDDING_MODEL_NAME}" + assert browser_ui.EMBEDDING_MODEL_API_BASE == 'http://test-embed.com', f"Expected 'http://test-embed.com', got {browser_ui.EMBEDDING_MODEL_API_BASE}" + assert browser_ui.AGENT_MODEL_API_BASE == 'http://test-agent.com', f"Expected 'http://test-agent.com', got {browser_ui.AGENT_MODEL_API_BASE}" + assert browser_ui.OLLAMA_API_KEY == 'test-key', f"Expected 'test-key', got {browser_ui.OLLAMA_API_KEY}" diff --git a/tests/frontend/test_cli.py b/tests/frontend/test_cli.py new file mode 100644 index 0000000..94e62da --- /dev/null +++ b/tests/frontend/test_cli.py @@ -0,0 +1,364 @@ +""" +Tests for the CLI frontend. +""" +import pytest +import asyncio +from unittest.mock import patch, Mock, MagicMock, AsyncMock +from pathlib import Path + +# Import the module under test +from llm_agents.frontend.cli import ( + create_prompt_session, + render_markdown, + stream_agent_response_sync, + async_interact, + print_welcome, + main +) + + +class TestCreatePromptSession: + """Test the create_prompt_session function.""" + + @patch('llm_agents.frontend.cli.PromptSession') + @patch('llm_agents.frontend.cli.FileHistory') + def test_create_prompt_session_basic(self, mock_file_history, mock_prompt_session): + """Test basic prompt session creation.""" + mock_session_instance = Mock() + mock_prompt_session.return_value = mock_session_instance + + result = create_prompt_session() + + # Verify FileHistory was created with correct path + home_path = str(Path.home() / ".llm_agents_history") + mock_file_history.assert_called_once_with(home_path) + + # Verify PromptSession was configured correctly + mock_prompt_session.assert_called_once() + call_kwargs = mock_prompt_session.call_args.kwargs + + assert 'history' in call_kwargs + assert 'auto_suggest' in call_kwargs + assert 'completer' in call_kwargs + assert call_kwargs['complete_while_typing'] is True + assert call_kwargs['enable_open_in_editor'] is True + assert call_kwargs['multiline'] is False + + assert result == mock_session_instance + + +class TestRenderMarkdown: + """Test the render_markdown function.""" + + @patch('llm_agents.frontend.cli.console') + def test_render_markdown_without_title(self, mock_console): + """Test markdown rendering without title.""" + render_markdown("# Test Markdown") + + mock_console.print.assert_called_once() + # Should be called with Markdown object, not Panel + call_args = mock_console.print.call_args[0][0] + assert hasattr(call_args, 'markup') # Markdown object + + @patch('llm_agents.frontend.cli.console') + @patch('llm_agents.frontend.cli.Panel') + def test_render_markdown_with_title(self, mock_panel, mock_console): + """Test markdown rendering with title.""" + mock_panel_instance = Mock() + mock_panel.return_value = mock_panel_instance + + render_markdown("# Test Markdown", title="Test Title") + + # Should create Panel with title + mock_panel.assert_called_once() + call_args = mock_panel.call_args + assert call_args.kwargs['title'] == "Test Title" + assert call_args.kwargs['border_style'] == "blue" + + mock_console.print.assert_called_once_with(mock_panel_instance) + + +class TestStreamAgentResponse: + """Test the stream_agent_response_sync function.""" + + def test_stream_agent_response_basic(self): + """Test basic streaming response functionality.""" + # Create mock agent + mock_agent = Mock() + mock_agent.get_history.return_value = [ + {"role": "user", "content": "Hello"} + ] + + # Mock interact_stateless to yield message updates + message_updates = [ + [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi"} + ], + [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"} + ] + ] + mock_agent.interact_stateless.return_value = iter(message_updates) + + # Mock Live context manager + with patch('llm_agents.frontend.cli.Live') as mock_live: + mock_live_instance = Mock() + mock_live.return_value.__enter__.return_value = mock_live_instance + + message = {"role": "user", "content": "test"} + stream_agent_response_sync(mock_agent, message) + + # Verify agent's set_history was called with final messages + mock_agent.set_history.assert_called_once_with(message_updates[-1]) + + # Verify Live was used for streaming display + mock_live_instance.update.assert_called() + + def test_stream_agent_response_keyboard_interrupt(self): + """Test handling of keyboard interrupt during streaming.""" + mock_agent = Mock() + mock_agent.get_history.return_value = [] + + # Mock interact_stateless to raise KeyboardInterrupt + mock_agent.interact_stateless.side_effect = KeyboardInterrupt() + + with patch('llm_agents.frontend.cli.Live') as mock_live: + mock_live_instance = Mock() + mock_live.return_value.__enter__.return_value = mock_live_instance + + message = {"role": "user", "content": "test"} + + with pytest.raises(KeyboardInterrupt): + stream_agent_response_sync(mock_agent, message) + + # Verify Live was stopped + mock_live_instance.stop.assert_called_once() + + def test_stream_agent_response_with_tool_calls(self): + """Test streaming response with tool calls.""" + mock_agent = Mock() + mock_agent.get_history.return_value = [] + + # Mock response with tool calls + message_updates = [ + [ + {"role": "assistant", "content": "I'll help with that.", "tool_calls": [ + {"function": {"name": "search_papers"}} + ]} + ] + ] + mock_agent.interact_stateless.return_value = iter(message_updates) + + with patch('llm_agents.frontend.cli.Live') as mock_live: + mock_live_instance = Mock() + mock_live.return_value.__enter__.return_value = mock_live_instance + + message = {"role": "user", "content": "test"} + stream_agent_response_sync(mock_agent, message) + + # Should have updated with tool execution info + mock_live_instance.update.assert_called() + + +class TestAsyncInteract: + """Test the async_interact function.""" + + @pytest.mark.asyncio + async def test_async_interact_success(self): + """Test successful async interaction.""" + mock_agent = Mock() + message = {"role": "user", "content": "test"} + + with patch('llm_agents.frontend.cli.asyncio.to_thread') as mock_to_thread: + mock_to_thread.return_value = None # Successful completion + + await async_interact(mock_agent, message) + + # Verify stream_agent_response_sync was called in thread + mock_to_thread.assert_called_once() + + @pytest.mark.asyncio + async def test_async_interact_keyboard_interrupt(self): + """Test async interaction with keyboard interrupt.""" + mock_agent = Mock() + message = {"role": "user", "content": "test"} + + with patch('llm_agents.frontend.cli.asyncio.to_thread') as mock_to_thread: + mock_to_thread.side_effect = KeyboardInterrupt() + + # Should handle KeyboardInterrupt gracefully + await async_interact(mock_agent, message) + + @pytest.mark.asyncio + async def test_async_interact_exception(self): + """Test async interaction with general exception.""" + mock_agent = Mock() + message = {"role": "user", "content": "test"} + + with patch('llm_agents.frontend.cli.asyncio.to_thread') as mock_to_thread: + with patch('llm_agents.frontend.cli.console') as mock_console: + mock_to_thread.side_effect = Exception("Test error") + + await async_interact(mock_agent, message) + + # Should print error message + mock_console.print.assert_called() + + +class TestPrintWelcome: + """Test the print_welcome function.""" + + @patch('llm_agents.frontend.cli.console') + def test_print_welcome(self, mock_console): + """Test that welcome message is printed.""" + print_welcome() + + mock_console.print.assert_called_once() + # Verify welcome text contains expected elements + call_args = mock_console.print.call_args[0][0] + welcome_text = str(call_args) + + assert "LLM Agent Chat Interface" in welcome_text + assert "/help" in welcome_text + assert "/exit" in welcome_text + assert "Ctrl+C" in welcome_text + + +class TestMain: + """Test the main CLI function.""" + + @patch('llm_agents.frontend.cli.setup_logging') + @patch('llm_agents.frontend.cli.NeurIPS2025Agent') + @patch('llm_agents.frontend.cli.create_prompt_session') + @patch('llm_agents.frontend.cli.print_welcome') + def test_main_initialization(self, mock_welcome, mock_session, mock_agent_class, mock_setup_logging): + """Test main function initialization.""" + # Mock agent instance + mock_agent = Mock() + mock_agent_class.return_value = mock_agent + + # Mock prompt session to exit immediately + mock_session_instance = Mock() + mock_session_instance.prompt.side_effect = EOFError() + mock_session.return_value = mock_session_instance + + # Import click for testing + from click.testing import CliRunner + + runner = CliRunner() + result = runner.invoke(main, []) + + # Verify initialization steps + mock_setup_logging.assert_called_once() + mock_welcome.assert_called_once() + mock_agent_class.assert_called_once() + mock_agent.setup_session.assert_called_once() + + @patch('llm_agents.frontend.cli.setup_logging') + @patch('llm_agents.frontend.cli.NeurIPS2025Agent') + @patch('llm_agents.frontend.cli.create_prompt_session') + @patch('llm_agents.frontend.cli.print_welcome') + def test_main_with_custom_params(self, mock_welcome, mock_session, mock_agent_class, mock_setup_logging): + """Test main function with custom CLI parameters.""" + mock_agent = Mock() + mock_agent_class.return_value = mock_agent + + mock_session_instance = Mock() + mock_session_instance.prompt.side_effect = EOFError() + mock_session.return_value = mock_session_instance + + from click.testing import CliRunner + + runner = CliRunner() + result = runner.invoke(main, [ + '--temperature', '0.8', + '--max-tokens', '4000', + '--num-ctx', '65536', + '--max-num-papers', '20' + ]) + + # Verify agent was created with custom parameters + call_args = mock_agent_class.call_args + llm_config = call_args.kwargs['llm_args'] + tool_args = call_args.kwargs['global_tool_args'] + + assert llm_config['temperature'] == 0.8 + assert llm_config['max_tokens'] == 4000 + assert llm_config['num_ctx'] == 65536 + assert tool_args['num_records'] == 20 + + @patch('llm_agents.frontend.cli.setup_logging') + @patch('llm_agents.frontend.cli.NeurIPS2025Agent') + @patch('llm_agents.frontend.cli.create_prompt_session') + @patch('llm_agents.frontend.cli.print_welcome') + @patch('llm_agents.frontend.cli.asyncio') + def test_main_user_interaction(self, mock_asyncio, mock_welcome, mock_session, mock_agent_class, mock_setup_logging): + """Test main function user interaction loop.""" + mock_agent = Mock() + mock_agent_class.return_value = mock_agent + + # Mock session to return user input then exit + mock_session_instance = Mock() + mock_session_instance.prompt.side_effect = [ + "test question", # First input + EOFError() # Exit + ] + mock_session.return_value = mock_session_instance + + from click.testing import CliRunner + + runner = CliRunner() + result = runner.invoke(main, []) + + # Verify asyncio.run was called for user interaction + mock_asyncio.run.assert_called() + + @patch('llm_agents.frontend.cli.setup_logging') + @patch('llm_agents.frontend.cli.NeurIPS2025Agent') + @patch('llm_agents.frontend.cli.create_prompt_session') + @patch('llm_agents.frontend.cli.print_welcome') + def test_main_help_command(self, mock_welcome, mock_session, mock_agent_class, mock_setup_logging): + """Test main function with help command.""" + mock_agent = Mock() + mock_agent_class.return_value = mock_agent + + # Mock session to return help command then exit + mock_session_instance = Mock() + mock_session_instance.prompt.side_effect = [ + "/help", # Help command + EOFError() # Exit + ] + mock_session.return_value = mock_session_instance + + with patch('llm_agents.frontend.cli.print_help') as mock_print_help: + from click.testing import CliRunner + + runner = CliRunner() + result = runner.invoke(main, []) + + # Verify help was printed + mock_print_help.assert_called_once() + + @patch('llm_agents.frontend.cli.setup_logging') + @patch('llm_agents.frontend.cli.NeurIPS2025Agent') + @patch('llm_agents.frontend.cli.create_prompt_session') + @patch('llm_agents.frontend.cli.print_welcome') + def test_main_exit_command(self, mock_welcome, mock_session, mock_agent_class, mock_setup_logging): + """Test main function with exit command.""" + mock_agent = Mock() + mock_agent_class.return_value = mock_agent + + # Mock session to return exit command + mock_session_instance = Mock() + mock_session_instance.prompt.side_effect = ["/exit"] + mock_session.return_value = mock_session_instance + + from click.testing import CliRunner + + runner = CliRunner() + result = runner.invoke(main, []) + + # Should exit cleanly + assert result.exit_code == 0 \ No newline at end of file diff --git a/tests/tools/__init__.py b/tests/tools/__init__.py new file mode 100644 index 0000000..7c65534 --- /dev/null +++ b/tests/tools/__init__.py @@ -0,0 +1 @@ +# Tools tests \ No newline at end of file diff --git a/tests/tools/test_init.py b/tests/tools/test_init.py new file mode 100644 index 0000000..083bf6b --- /dev/null +++ b/tests/tools/test_init.py @@ -0,0 +1,82 @@ +from unittest.mock import patch, Mock +import pytest + +from llm_agents.tools import get_all_tools +from llm_agents.tools.knowledge_graph import search_similar_papers, find_neighboring_papers, traverse_graph + +class TestGetAllTools: + """Test the get_all_tools function.""" + + @patch('llm_agents.tools.knowledge_graph.search_similar_papers') + @patch('llm_agents.tools.knowledge_graph.find_neighboring_papers') + @patch('llm_agents.tools.knowledge_graph.traverse_graph') + def test_get_all_tools_returns_list(self, mock_traverse, mock_neighbors, mock_search): + """Test that get_all_tools returns a list of all tools.""" + # Mock the tool functions + mock_search_func = Mock() + mock_neighbors_func = Mock() + mock_traverse_func = Mock() + + mock_search.return_value = mock_search_func + mock_neighbors.return_value = mock_neighbors_func + mock_traverse.return_value = mock_traverse_func + + # Call the function + result = get_all_tools() + + # Verify return type is list + assert isinstance(result, list) + + @patch('llm_agents.tools.knowledge_graph.search_similar_papers') + @patch('llm_agents.tools.knowledge_graph.find_neighboring_papers') + @patch('llm_agents.tools.knowledge_graph.traverse_graph') + def test_get_all_tools_returns_correct_count(self, mock_traverse, mock_neighbors, mock_search): + """Test that get_all_tools returns the correct number of tools.""" + # Call the function + result = get_all_tools() + + # Verify we get exactly 3 tools + assert len(result) == 3 + + @patch('llm_agents.tools.knowledge_graph.search_similar_papers') + @patch('llm_agents.tools.knowledge_graph.find_neighboring_papers') + @patch('llm_agents.tools.knowledge_graph.traverse_graph') + def test_get_all_tools_contains_all_expected_tools(self, mock_traverse, mock_neighbors, mock_search): + """Test that get_all_tools contains all expected tool functions.""" + # Call the function + result = get_all_tools() + + # Verify all expected tools are in the result + assert search_similar_papers in result + assert find_neighboring_papers in result + assert traverse_graph in result + + @patch('llm_agents.tools.knowledge_graph.search_similar_papers') + @patch('llm_agents.tools.knowledge_graph.find_neighboring_papers') + @patch('llm_agents.tools.knowledge_graph.traverse_graph') + def test_get_all_tools_order_matches_all_declaration(self, mock_traverse, mock_neighbors, mock_search): + """Test that get_all_tools returns tools in the same order as __all__.""" + # Call the function + result = get_all_tools() + + # Verify order matches __all__ + assert result[0] == search_similar_papers + assert result[1] == find_neighboring_papers + assert result[2] == traverse_graph + + def test_get_all_tools_no_duplicates(self): + """Test that get_all_tools returns no duplicate tools.""" + # Call the function + result = get_all_tools() + + # Verify no duplicates + assert len(result) == len(set(result)) + + def test_get_all_tools_all_callable(self): + """Test that all returned tools are callable.""" + # Call the function + result = get_all_tools() + + # Verify all items are callable + for tool in result: + assert callable(tool) diff --git a/tests/tools/test_knowledge_graph_tools.py b/tests/tools/test_knowledge_graph_tools.py new file mode 100644 index 0000000..97f6c65 --- /dev/null +++ b/tests/tools/test_knowledge_graph_tools.py @@ -0,0 +1,402 @@ +""" +Tests for knowledge graph tool functions. +""" +import pytest +import sys +import importlib +from unittest.mock import patch, Mock, MagicMock + + +def reload_knowledge_graph_module(): + """Reload the knowledge graph module to pick up environment changes.""" + if 'llm_agents.tools.knowledge_graph' in sys.modules: + importlib.reload(sys.modules['llm_agents.tools.knowledge_graph']) + else: + import llm_agents.tools.knowledge_graph + + from llm_agents.tools.knowledge_graph import ( + search_similar_papers, + find_neighboring_papers, + traverse_graph + ) + return search_similar_papers, find_neighboring_papers, traverse_graph + + +class TestSearchSimilarPapers: + """Test the search_similar_papers function.""" + + # def test_search_similar_papers_basic(self): + # """Test basic similarity search functionality.""" + # with patch.dict('os.environ', { + # 'NEO4J_URI': 'bolt://localhost:7687', + # 'NEO4J_USERNAME': 'neo4j', + # 'NEO4J_PASSWORD': 'llm_agents' + # }): + # # Reload module to pick up patched environment variables + # search_similar_papers, _, _ = reload_knowledge_graph_module() + # + # with patch('llm_agents.tools.knowledge_graph.Neo4jGraphWorker') as mock_worker_class, \ + # patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode: + # + # # Mock the worker instance + # mock_worker = Mock() + # mock_worker_class.return_value = mock_worker + # + # # Mock search results + # mock_papers = [ + # {"id": "paper1", "title": "Test Paper 1", "score": 0.95}, + # {"id": "paper2", "title": "Test Paper 2", "score": 0.90} + # ] + # mock_worker.similarity_search.return_value = mock_papers + # + # # Mock encoding + # mock_encode.return_value = "encoded_papers" + # + # # Call the function + # result = search_similar_papers( + # user_query="machine learning", + # num_papers_to_return=5, + # min_similarity=0.8 + # ) + # + # # Verify worker was created with correct params (patched values) + # mock_worker_class.assert_called_once_with( + # uri='bolt://localhost:7687', + # username='neo4j', + # password='llm_agents' + # ) + # + # # Verify search was called correctly + # mock_worker.similarity_search.assert_called_once_with( + # user_query="machine learning", + # top_k=5, + # min_similarity=0.8 + # ) + # + # # Verify encoding was applied + # mock_encode.assert_called_once_with(mock_papers) + # + # # Verify return value + # assert result == "encoded_papers" + + def test_search_similar_papers_default_params(self): + """Test search with default parameters.""" + with patch.dict('os.environ', { + 'NEO4J_URI': 'bolt://localhost:7687', + 'NEO4J_USERNAME': 'neo4j', + 'NEO4J_PASSWORD': 'llm_agents' + }): + # Reload module to pick up patched environment variables + search_similar_papers, _, _ = reload_knowledge_graph_module() + + with patch('llm_agents.tools.knowledge_graph.Neo4jGraphWorker') as mock_worker_class, \ + patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode: + + mock_worker = Mock() + mock_worker_class.return_value = mock_worker + mock_worker.similarity_search.return_value = [] + mock_encode.return_value = "empty_results" + + result = search_similar_papers("test query") + + # Verify default parameters were used + mock_worker.similarity_search.assert_called_once_with( + user_query="test query", + top_k=10, # default + min_similarity=None # default + ) + + # @pytest.mark.no_auto_env + # def test_search_uses_environment_variables(self): + # """Test that function uses environment variables for Neo4j connection.""" + # with patch.dict('os.environ', { + # 'NEO4J_URI': 'bolt://custom:7687', + # 'NEO4J_USERNAME': 'custom_user', + # 'NEO4J_PASSWORD': 'custom_pass' + # }): + # # Reload module to pick up patched environment variables + # search_similar_papers, _, _ = reload_knowledge_graph_module() + # + # with patch('neo4j.GraphDatabase.driver') as mock_driver, \ + # patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode, \ + # patch('llm_agents.utils.embedding_generator.embedding') as mock_embedding: + # + # # Setup the Neo4j driver mock + # mock_driver_instance = Mock() + # mock_driver.return_value = mock_driver_instance + # mock_driver_instance.verify_connectivity.return_value = True + # + # # Setup session mock to support context manager protocol + # mock_session = Mock() + # mock_session.__enter__ = Mock(return_value=mock_session) + # mock_session.__exit__ = Mock(return_value=False) + # + # # Make session.run() return an iterable result (empty list) + # mock_result = Mock() + # mock_result.__iter__ = Mock(return_value=iter([])) # Empty iterator + # mock_session.run.return_value = mock_result + # mock_driver_instance.session.return_value = mock_session + # + # # Mock embedding responses + # mock_response = Mock() + # mock_response.data = [{"embedding": [0.1, 0.2, 0.3]}] + # mock_response.__getitem__ = lambda self, key: mock_response.data if key == "data" else None + # mock_embedding.return_value = mock_response + # + # # Mock the search results + # mock_encode.return_value = "result" + # + # # Call the function + # result = search_similar_papers("test") + # + # # Verify the driver was called with the correct environment variables + # mock_driver.assert_called_with( + # "bolt://custom:7687", + # auth=("custom_user", "custom_pass") + # ) + # + # # Verify the result + # assert result == "result" + + +class TestFindNeighboringPapers: + """Test the find_neighboring_papers function.""" + + # def test_find_neighboring_papers_basic(self): + # """Test basic neighborhood search functionality.""" + # with patch.dict('os.environ', { + # 'NEO4J_URI': 'bolt://localhost:7687', + # 'NEO4J_USERNAME': 'neo4j', + # 'NEO4J_PASSWORD': 'llm_agents' + # }): + # # Reload module to pick up patched environment variables + # _, find_neighboring_papers, _ = reload_knowledge_graph_module() + # + # with patch('llm_agents.tools.knowledge_graph.Neo4jGraphWorker') as mock_worker_class, \ + # patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode, \ + # patch('llm_agents.tools.knowledge_graph.random.shuffle') as mock_shuffle: + # + # mock_worker = Mock() + # mock_worker_class.return_value = mock_worker + # + # # Mock neighborhood search results + # mock_neighbors = { + # "similar_papers": [ + # {"neighbor": {"id": "paper1", "title": "Similar Paper 1"}}, + # {"neighbor": {"id": "paper2", "title": "Similar Paper 2"}} + # ] + # } + # mock_worker.neighborhood_search.return_value = mock_neighbors + # mock_encode.return_value = "encoded_neighbors" + # + # result = find_neighboring_papers( + # paper_id="test_paper_id", + # relationship_types=["SIMILAR_TO"], + # neighbor_entity="similar_papers", + # num_neighbors_to_return=5 + # ) + # + # # Verify worker creation + # mock_worker_class.assert_called_once_with( + # uri='bolt://localhost:7687', + # username='neo4j', + # password='llm_agents' + # ) + # + # # Verify neighborhood search + # mock_worker.neighborhood_search.assert_called_once_with( + # paper_id="test_paper_id", + # relationship_types=["SIMILAR_TO"] + # ) + # + # # Verify shuffle was called + # mock_shuffle.assert_called_once() + # + # # Verify encoding + # mock_encode.assert_called_once() + # + # assert result == "encoded_neighbors" + + def test_find_neighboring_papers_string_relationship_type(self): + """Test that string relationship_type is converted to list.""" + with patch.dict('os.environ', { + 'NEO4J_URI': 'bolt://localhost:7687', + 'NEO4J_USERNAME': 'neo4j', + 'NEO4J_PASSWORD': 'llm_agents' + }): + # Reload module to pick up patched environment variables + _, find_neighboring_papers, _ = reload_knowledge_graph_module() + + with patch('llm_agents.tools.knowledge_graph.Neo4jGraphWorker') as mock_worker_class, \ + patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode: + + mock_worker = Mock() + mock_worker_class.return_value = mock_worker + mock_worker.neighborhood_search.return_value = {"similar_papers": []} + mock_encode.return_value = "result" + + find_neighboring_papers( + paper_id="test_id", + relationship_types="SIMILAR_TO", # String instead of list + neighbor_entity="similar_papers" + ) + + # Should convert string to list + mock_worker.neighborhood_search.assert_called_once_with( + paper_id="test_id", + relationship_types=["SIMILAR_TO"] + ) + + def test_find_neighboring_papers_defaults(self): + """Test function with default parameters.""" + with patch.dict('os.environ', { + 'NEO4J_URI': 'bolt://localhost:7687', + 'NEO4J_USERNAME': 'neo4j', + 'NEO4J_PASSWORD': 'llm_agents' + }): + # Reload module to pick up patched environment variables + _, find_neighboring_papers, _ = reload_knowledge_graph_module() + + with patch('llm_agents.tools.knowledge_graph.Neo4jGraphWorker') as mock_worker_class, \ + patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode: + + mock_worker = Mock() + mock_worker_class.return_value = mock_worker + mock_worker.neighborhood_search.return_value = {"similar_papers": []} + mock_encode.return_value = "result" + + find_neighboring_papers("test_id") + + # Verify defaults are used + mock_worker.neighborhood_search.assert_called_once_with( + paper_id="test_id", + relationship_types=["SIMILAR_TO"] # default + ) + + +class TestTraverseGraph: + """Test the traverse_graph function.""" + + # def test_traverse_graph_basic(self): + # """Test basic graph traversal functionality.""" + # with patch.dict('os.environ', { + # 'NEO4J_URI': 'bolt://localhost:7687', + # 'NEO4J_USERNAME': 'neo4j', + # 'NEO4J_PASSWORD': 'llm_agents' + # }): + # # Reload module to pick up patched environment variables + # _, _, traverse_graph = reload_knowledge_graph_module() + # + # with patch('llm_agents.tools.knowledge_graph.Neo4jGraphWorker') as mock_worker_class, \ + # patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode: + # + # mock_worker = Mock() + # mock_worker_class.return_value = mock_worker + # + # mock_papers = [ + # {"id": "paper1", "title": "Traversed Paper 1"}, + # {"id": "paper2", "title": "Traversed Paper 2"} + # ] + # mock_worker.graph_traversal.return_value = mock_papers + # mock_encode.return_value = "encoded_traversal" + # + # result = traverse_graph( + # start_paper_id="start_id", + # n_hops=3, + # relationship_type="SIMILAR_TO", + # max_results=50, + # strategy="depth_first", + # max_branches=3, + # random_seed=123 + # ) + # + # # Verify worker creation + # mock_worker_class.assert_called_once_with( + # uri='bolt://localhost:7687', + # username='neo4j', + # password='llm_agents' + # ) + # + # # Verify traversal call + # mock_worker.graph_traversal.assert_called_once_with( + # start_paper_id="start_id", + # n_hops=3, + # relationship_type="SIMILAR_TO", + # max_results=50, + # strategy="depth_first", + # max_branches=3, + # random_seed=123 + # ) + # + # # Verify encoding + # mock_encode.assert_called_once_with(mock_papers) + # + # assert result == "encoded_traversal" + + def test_traverse_graph_defaults(self): + """Test graph traversal with default parameters.""" + with patch.dict('os.environ', { + 'NEO4J_URI': 'bolt://localhost:7687', + 'NEO4J_USERNAME': 'neo4j', + 'NEO4J_PASSWORD': 'llm_agents' + }): + # Reload module to pick up patched environment variables + _, _, traverse_graph = reload_knowledge_graph_module() + + with patch('llm_agents.tools.knowledge_graph.Neo4jGraphWorker') as mock_worker_class, \ + patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode: + + mock_worker = Mock() + mock_worker_class.return_value = mock_worker + mock_worker.graph_traversal.return_value = [] + mock_encode.return_value = "result" + + traverse_graph("start_id") + + # Verify default parameters + mock_worker.graph_traversal.assert_called_once_with( + start_paper_id="start_id", + n_hops=2, # default + relationship_type="BELONGS_TO_TOPIC", # default + max_results=30, # default + strategy="breadth_first_random", # default + max_branches=2, # default + random_seed=42 # default + ) + + def test_traverse_graph_optional_none_values(self): + """Test graph traversal with None values for optional parameters.""" + with patch.dict('os.environ', { + 'NEO4J_URI': 'bolt://localhost:7687', + 'NEO4J_USERNAME': 'neo4j', + 'NEO4J_PASSWORD': 'llm_agents' + }): + # Reload module to pick up patched environment variables + _, _, traverse_graph = reload_knowledge_graph_module() + + with patch('llm_agents.tools.knowledge_graph.Neo4jGraphWorker') as mock_worker_class, \ + patch('llm_agents.tools.knowledge_graph.toon_encode') as mock_encode: + + mock_worker = Mock() + mock_worker_class.return_value = mock_worker + mock_worker.graph_traversal.return_value = [] + mock_encode.return_value = "result" + + traverse_graph( + start_paper_id="start_id", + relationship_type=None, + max_results=None, + max_branches=None, + random_seed=None + ) + + # Verify None values are passed through + mock_worker.graph_traversal.assert_called_once_with( + start_paper_id="start_id", + n_hops=2, # default + relationship_type=None, + max_results=None, + strategy="breadth_first_random", # default + max_branches=None, + random_seed=None + ) diff --git a/tests/tools/test_neo4j_graph_worker.py b/tests/tools/test_neo4j_graph_worker.py new file mode 100644 index 0000000..bf64845 --- /dev/null +++ b/tests/tools/test_neo4j_graph_worker.py @@ -0,0 +1,349 @@ +""" +Tests for the Neo4jGraphWorker class. +""" +import pytest +from unittest.mock import Mock, patch, MagicMock +import numpy as np + +from llm_agents.tools.knowledge_graph.retriever import Neo4jGraphWorker + + +class TestNeo4jGraphWorker: + """Test the Neo4jGraphWorker class.""" + + @pytest.fixture + def mock_driver(self): + """Mock Neo4j driver.""" + driver = Mock() + session = Mock() + # Properly mock the context manager behavior + driver.session.return_value = MagicMock() + driver.session.return_value.__enter__ = Mock(return_value=session) + driver.session.return_value.__exit__ = Mock(return_value=None) + return driver, session + + @pytest.fixture + def worker(self, mock_driver): + """Create worker instance with mocked driver.""" + driver, session = mock_driver + with patch('llm_agents.tools.knowledge_graph.retriever.GraphDatabase.driver', return_value=driver): + worker = Neo4jGraphWorker( + uri="bolt://localhost:7687", + username="neo4j", + password="test_password" + ) + return worker, session + + def test_initialization(self): + """Test worker initialization.""" + with patch('llm_agents.tools.knowledge_graph.retriever.GraphDatabase.driver') as mock_driver: + worker = Neo4jGraphWorker( + uri="bolt://test:7687", + username="test_user", + password="test_pass" + ) + + mock_driver.assert_called_once_with( + "bolt://test:7687", + auth=("test_user", "test_pass") + ) + + @patch.object(Neo4jGraphWorker, 'embed_user_query') + def test_similarity_search(self, mock_embed, worker): + """Test similarity search functionality.""" + worker_instance, mock_session = worker + + # Mock embedding generation + mock_embed.return_value = [0.1, 0.2, 0.3] + + # Mock database query results - the code iterates over result directly + mock_records = [ + Mock(id='paper1', name='Test Paper 1', abstract='Test abstract 1', topic='ML', score=0.95), + Mock(id='paper2', name='Test Paper 2', abstract='Test abstract 2', topic='AI', score=0.90) + ] + # Configure record access as dict-like + for record in mock_records: + record.__getitem__ = lambda self, key: getattr(self, key) + mock_session.run.return_value = mock_records + + # Call similarity search + results = worker_instance.similarity_search( + user_query="machine learning", + top_k=5, + min_similarity=0.8 + ) + + # Verify embedding generation was called + mock_embed.assert_called_once_with(text="machine learning") + + # Verify database query was executed + mock_session.run.assert_called_once() + call_args = mock_session.run.call_args + assert "db.index.vector.queryNodes" in call_args[0][0] + assert call_args[1]['top_k'] == 5 + assert call_args[1]['query_embedding'] == [0.1, 0.2, 0.3] + + # Verify results filtering by min_similarity + assert len(results) == 2 + assert results[0]['id'] == 'paper1' + assert results[0]['similarity_score'] == 0.95 + + @patch.object(Neo4jGraphWorker, 'embed_user_query') + def test_similarity_search_no_min_similarity(self, mock_embed, worker): + """Test similarity search without minimum similarity filtering.""" + worker_instance, mock_session = worker + + mock_embed.return_value = [0.1, 0.2, 0.3] + + mock_records = [ + Mock(id='paper1', name='Test', abstract='Test', topic='ML', score=0.5), + Mock(id='paper2', name='Test', abstract='Test', topic='AI', score=0.3) + ] + # Configure record access as dict-like + for record in mock_records: + record.__getitem__ = lambda self, key: getattr(self, key) + mock_session.run.return_value = mock_records + + results = worker_instance.similarity_search( + user_query="test", + top_k=10, + min_similarity=None + ) + + # Should return all results when no min_similarity filter + assert len(results) == 2 + + def test_neighborhood_search(self, worker): + """Test neighborhood search functionality.""" + worker_instance, mock_session = worker + + mock_records = [ + Mock(), + Mock() + ] + # Configure records as dict-like objects + mock_records[0].__getitem__ = lambda self, key: { + 'source_paper_id': 'paper1', + 'neighbor': {'id': 'paper2', 'name': 'Neighbor Paper'}, + 'relationship_type': 'SIMILAR_TO', + 'relationship_properties': {'similarity': 0.85}, + 'neighbor_labels': ['Paper'] + }[key] + + mock_records[1].__getitem__ = lambda self, key: { + 'source_paper_id': 'paper1', + 'neighbor': {'fullname': 'Author Name'}, + 'relationship_type': 'AUTHORED_BY', + 'relationship_properties': {}, + 'neighbor_labels': ['Author'] + }[key] + + mock_session.run.return_value = mock_records + + results = worker_instance.neighborhood_search( + paper_id="paper1", + relationship_types=["SIMILAR_TO", "AUTHORED_BY"] + ) + + # Verify query was constructed and executed + mock_session.run.assert_called_once() + call_args = mock_session.run.call_args + query = call_args[0][0] + assert "MATCH (p:Paper)" in query + assert "WHERE p.id IN $paper_ids" in query + assert call_args[1]['paper_ids'] == ["paper1"] + + # Verify results structure + assert 'similar_papers' in results + assert 'authors' in results + assert len(results['similar_papers']) == 1 + assert len(results['authors']) == 1 + + def test_neighborhood_search_relationship_filter(self, worker): + """Test neighborhood search with relationship type filtering.""" + worker_instance, mock_session = worker + + mock_session.run.return_value = [] + + worker_instance.neighborhood_search( + paper_id="paper1", + relationship_types=["SIMILAR_TO"] + ) + + call_args = mock_session.run.call_args + query = call_args[0][0] + + # Should include relationship type filter + assert ":SIMILAR_TO" in query + + @patch('llm_agents.tools.knowledge_graph.retriever._graph_traversal_bfs_random') + def test_graph_traversal_bfs_random(self, mock_bfs, worker): + """Test graph traversal with breadth-first random strategy.""" + worker_instance, mock_session = worker + + mock_papers = [{'id': 'paper1', 'name': 'Traversed Paper'}] + mock_bfs.return_value = mock_papers + + results = worker_instance.graph_traversal( + start_paper_id="start_paper", + n_hops=2, + relationship_type="SIMILAR_TO", + max_results=30, + strategy="breadth_first_random", + max_branches=3, + random_seed=42 + ) + + # Verify strategy function was called (it uses driver, not session) + mock_bfs.assert_called_once_with( + worker_instance.driver, + "start_paper", + 2, + "SIMILAR_TO", + 30, + 3 + ) + + assert results == mock_papers + + @patch('llm_agents.tools.knowledge_graph.retriever._graph_traversal_cypher') + def test_graph_traversal_breadth_first(self, mock_cypher, worker): + """Test graph traversal with breadth-first strategy.""" + worker_instance, mock_session = worker + + mock_papers = [{'id': 'paper1'}] + mock_cypher.return_value = mock_papers + + results = worker_instance.graph_traversal( + start_paper_id="start", + strategy="breadth_first" + ) + + mock_cypher.assert_called_once() + assert results == mock_papers + + def test_graph_traversal_invalid_strategy(self, worker): + """Test graph traversal with invalid strategy raises error.""" + worker_instance, mock_session = worker + + with pytest.raises(ValueError, match="Unsupported traversal strategy"): + worker_instance.graph_traversal( + start_paper_id="start", + strategy="invalid_strategy" + ) + + def test_papers_by_author(self, worker): + """Test papers by author search.""" + worker_instance, mock_session = worker + + mock_records = [ + Mock(id='paper1', name='Paper by Author', abstract='Abstract', topic='ML', author_name='Test Author') + ] + # Configure record access as dict-like + mock_records[0].__getitem__ = lambda self, key: { + 'id': 'paper1', + 'name': 'Paper by Author', + 'abstract': 'Abstract', + 'topic': 'ML', + 'author_name': 'Test Author' + }[key] + mock_session.run.return_value = mock_records + + results = worker_instance.search_papers_by_author("Test Author", fuzzy=False) + + # Verify exact match query was used + call_args = mock_session.run.call_args + assert "a.fullname = $author_name" in call_args[0][0] + assert call_args[1]['author_name'] == "Test Author" + + assert len(results) == 1 + assert results[0]['author_name'] == 'Test Author' + + def test_papers_by_author_fuzzy(self, worker): + """Test fuzzy papers by author search.""" + worker_instance, mock_session = worker + + mock_session.run.return_value = [] + + worker_instance.search_papers_by_author("Test Author", fuzzy=True) + + # Verify fuzzy query was used + call_args = mock_session.run.call_args + assert "CONTAINS" in call_args[0][0] + assert "toLower" in call_args[0][0] + + def test_papers_by_topic(self, worker): + """Test papers by topic search.""" + worker_instance, mock_session = worker + + mock_records = [ + Mock(id='paper1', name='Topic Paper', abstract='Abstract', topic='Machine Learning') + ] + # Configure record access as dict-like + mock_records[0].__getitem__ = lambda self, key: { + 'id': 'paper1', + 'name': 'Topic Paper', + 'abstract': 'Abstract', + 'topic': 'Machine Learning' + }[key] + mock_session.run.return_value = mock_records + + results = worker_instance.search_papers_by_topic("Machine Learning") + + call_args = mock_session.run.call_args + assert "t:Topic {name: $topic_name}" in call_args[0][0] + assert call_args[1]['topic_name'] == "Machine Learning" + + assert len(results) == 1 + + def test_papers_by_topic_with_subtopics(self, worker): + """Test papers by topic including subtopics.""" + worker_instance, mock_session = worker + + mock_session.run.return_value = [] + + worker_instance.search_papers_by_topic("ML", include_subtopics=True) + + call_args = mock_session.run.call_args + query = call_args[0][0] + assert "SUBTOPIC_OF" in query + assert "collect(DISTINCT subtopic)" in query + + def test_get_similar_papers(self, worker): + """Test get similar papers functionality.""" + worker_instance, mock_session = worker + + mock_records = [ + Mock(id='similar1', name='Similar Paper', abstract='Abstract', topic='ML', similarity=0.92) + ] + # Configure record access as dict-like + mock_records[0].__getitem__ = lambda self, key: { + 'id': 'similar1', + 'name': 'Similar Paper', + 'abstract': 'Abstract', + 'topic': 'ML', + 'similarity': 0.92 + }[key] + mock_session.run.return_value = mock_records + + results = worker_instance.find_similar_papers_direct("paper1", min_similarity=0.8) + + call_args = mock_session.run.call_args + assert "SIMILAR_TO" in call_args[0][0] + assert call_args[1]['paper_id'] == "paper1" + assert call_args[1]['min_similarity'] == 0.8 + + assert len(results) == 1 + assert results[0]['similarity'] == 0.92 + + def test_close(self, worker): + """Test worker close method.""" + worker_instance, mock_session = worker + + # Mock the driver + mock_driver = Mock() + worker_instance.driver = mock_driver + + worker_instance.close() + + mock_driver.close.assert_called_once() \ No newline at end of file diff --git a/tests/utils/__init__.py b/tests/utils/__init__.py new file mode 100644 index 0000000..09a81f6 --- /dev/null +++ b/tests/utils/__init__.py @@ -0,0 +1 @@ +# Utility tests \ No newline at end of file diff --git a/tests/utils/test_embedding_generator.py b/tests/utils/test_embedding_generator.py new file mode 100644 index 0000000..957047b --- /dev/null +++ b/tests/utils/test_embedding_generator.py @@ -0,0 +1,205 @@ +""" +Tests for the embedding generator utility. +""" +import pytest +import numpy as np +from unittest.mock import patch, Mock + +from llm_agents.utils.embedding_generator import batch_embed_documents + + +def create_mock_response(embeddings_list): + """Helper function to create properly formatted mock response.""" + mock_response = Mock() + mock_data = [{"embedding": emb} for emb in embeddings_list] + mock_response.data = mock_data + mock_response.__getitem__ = lambda self, key: mock_data if key == "data" else None + return mock_response + + +class TestBatchEmbedDocuments: + """Test the batch_embed_documents function.""" + + @patch('llm_agents.utils.embedding_generator.embedding') + def test_batch_embed_documents_basic(self, mock_embedding): + """Test basic embedding generation functionality.""" + # Mock embedding response + mock_embedding.return_value = create_mock_response([ + [0.1, 0.2, 0.3], + [0.4, 0.5, 0.6] + ]) + + texts = ["first document", "second document"] + result = batch_embed_documents( + texts=texts, + batch_size=2, + embedding_model="test-model", + api_base="http://test.com" + ) + + # Verify embedding was called correctly + mock_embedding.assert_called_once_with( + model="test-model", + input=texts, + api_base="http://test.com", + num_ctx=2048 + ) + + # Verify result is numpy array with correct shape + assert isinstance(result, np.ndarray) + assert result.shape == (2, 3) + + # Verify the embeddings are normalized (unit vectors) + # The function normalizes embeddings, so we check the direction is correct + expected_0_normalized = np.array([0.1, 0.2, 0.3]) / np.linalg.norm([0.1, 0.2, 0.3]) + expected_1_normalized = np.array([0.4, 0.5, 0.6]) / np.linalg.norm([0.4, 0.5, 0.6]) + + np.testing.assert_allclose(result[0], expected_0_normalized, rtol=1e-5) + np.testing.assert_allclose(result[1], expected_1_normalized, rtol=1e-5) + + @patch('llm_agents.utils.embedding_generator.embedding') + def test_batch_embed_documents_with_batching(self, mock_embedding): + """Test embedding with multiple batches.""" + # Mock responses for each batch + mock_embedding.side_effect = [ + create_mock_response([[0.1, 0.2]]), + create_mock_response([[0.3, 0.4]]) + ] + + texts = ["doc1", "doc2"] + result = batch_embed_documents( + texts=texts, + batch_size=1, # Force multiple batches + embedding_model="test-model", + api_base="http://test.com" + ) + + # Verify embedding was called twice + assert mock_embedding.call_count == 2 + + # Check first call + first_call = mock_embedding.call_args_list[0] + assert first_call[1]['input'] == ["doc1"] + + # Check second call + second_call = mock_embedding.call_args_list[1] + assert second_call[1]['input'] == ["doc2"] + + # Verify combined result (embeddings are normalized) + assert result.shape == (2, 2) + expected_0_normalized = np.array([0.1, 0.2]) / np.linalg.norm([0.1, 0.2]) + expected_1_normalized = np.array([0.3, 0.4]) / np.linalg.norm([0.3, 0.4]) + np.testing.assert_allclose(result[0], expected_0_normalized, rtol=1e-5) + np.testing.assert_allclose(result[1], expected_1_normalized, rtol=1e-5) + + @patch('llm_agents.utils.embedding_generator.embedding') + def test_batch_embed_documents_with_none_values(self, mock_embedding, caplog): + """Test handling of None values in input texts.""" + mock_embedding.return_value = create_mock_response([ + [0.1, 0.2], + [0.3, 0.4] + ]) + + texts = ["valid document", None] + + batch_embed_documents(texts=texts, batch_size=2) # Process both items in single batch + + # Verify warning was logged + assert "WARNING: Detected documents with 'None' values" in caplog.text + + # Verify None was replaced with empty string + call_args = mock_embedding.call_args + assert call_args[1]['input'] == ["valid document", ""] + + @patch('llm_agents.utils.embedding_generator.embedding') + def test_batch_embed_documents_error_fallback(self, mock_embedding, caplog): + """Test fallback to single sample processing on batch error.""" + # First call (batch) raises exception + mock_embedding.side_effect = [ + Exception("Batch failed"), + # Individual calls succeed + create_mock_response([[0.1, 0.2]]), + create_mock_response([[0.3, 0.4]]) + ] + + texts = ["doc1", "doc2"] + result = batch_embed_documents(texts=texts, batch_size=2) + + # Verify error was logged + assert "Error during embedding batch" in caplog.text + assert "Falling back to single sample processing" in caplog.text + + # Verify fallback individual calls were made + assert mock_embedding.call_count == 3 # 1 failed batch + 2 individual + + # Verify result is still correct + assert result.shape == (2, 2) + + @patch('llm_agents.utils.embedding_generator.embedding') + def test_batch_embed_documents_bad_request_fallback(self, mock_embedding, caplog): + """Test handling of BadRequestError during individual processing.""" + from litellm import BadRequestError + + # Batch call fails, individual calls have mixed results + mock_embedding.side_effect = [ + Exception("Batch failed"), + BadRequestError("Bad request", model="test-model", llm_provider="test"), # First individual fails + create_mock_response([[0.3, 0.4]]) # Second succeeds + ] + + texts = ["problematic doc", "good doc"] + result = batch_embed_documents(texts=texts, batch_size=2) + + # Should handle BadRequestError gracefully + assert "Encountered error processing paper" in caplog.text + + # Result should only have the successful embedding (failed one is skipped) + assert result.shape == (1, 2) + expected_normalized = np.array([0.3, 0.4]) / np.linalg.norm([0.3, 0.4]) + np.testing.assert_allclose(result[0], expected_normalized, rtol=1e-5) + + @patch('llm_agents.utils.embedding_generator.embedding') + def test_batch_embed_documents_default_params(self, mock_embedding): + """Test function with default parameters.""" + mock_embedding.return_value = create_mock_response([[0.1, 0.2, 0.3]]) + + batch_embed_documents(texts=["test doc"]) + + # Verify default parameters were used + call_args = mock_embedding.call_args + assert call_args[1]['model'] == "ollama/nomic-embed-text" + assert call_args[1]['api_base'] == "http://localhost:11435" + assert call_args[1]['num_ctx'] == 2048 + + def test_batch_embed_documents_empty_input(self): + """Test function with empty input list.""" + result = batch_embed_documents(texts=[]) + + # Should return empty numpy array + assert isinstance(result, np.ndarray) + assert result.shape[0] == 0 + + @patch('llm_agents.utils.embedding_generator.embedding') + def test_batch_embed_documents_single_document(self, mock_embedding): + """Test embedding of single document.""" + mock_embedding.return_value = create_mock_response([[0.1, 0.2, 0.3, 0.4]]) + + result = batch_embed_documents(texts=["single document"]) + + assert result.shape == (1, 4) + expected_normalized = np.array([0.1, 0.2, 0.3, 0.4]) / np.linalg.norm([0.1, 0.2, 0.3, 0.4]) + np.testing.assert_allclose(result[0], expected_normalized, rtol=1e-5) + + @patch('llm_agents.utils.embedding_generator.tqdm') + @patch('llm_agents.utils.embedding_generator.embedding') + def test_batch_embed_documents_progress_bar(self, mock_embedding, mock_tqdm): + """Test that progress bar is used for batch processing.""" + mock_embedding.return_value = create_mock_response([[0.1, 0.2]]) + + # Mock tqdm to return the range as-is + mock_tqdm.return_value = range(0, 2, 1) + + batch_embed_documents(texts=["doc1", "doc2"], batch_size=1) + + # Verify tqdm was called with the range + mock_tqdm.assert_called_once_with(range(0, 2, 1)) \ No newline at end of file diff --git a/tests/utils/test_logging.py b/tests/utils/test_logging.py new file mode 100644 index 0000000..d6422bd --- /dev/null +++ b/tests/utils/test_logging.py @@ -0,0 +1,181 @@ +""" +Tests for the logging utility. +""" +import pytest +import logging +import tempfile +from pathlib import Path +from unittest.mock import patch, Mock + +from llm_agents.utils.logger import setup_logging + + +class TestSetupLogging: + """Test the setup_logging function.""" + + def test_setup_logging_creates_log_directory(self): + """Test that log directory is created if it doesn't exist.""" + with tempfile.TemporaryDirectory() as temp_dir: + log_dir = Path(temp_dir) / "test_logs" + assert not log_dir.exists() + + setup_logging(log_dir=str(log_dir), level="INFO") + + assert log_dir.exists() + assert log_dir.is_dir() + + def test_setup_logging_existing_directory(self): + """Test that function works with existing log directory.""" + with tempfile.TemporaryDirectory() as temp_dir: + log_dir = Path(temp_dir) / "existing_logs" + log_dir.mkdir() + + # Should not raise error + setup_logging(log_dir=str(log_dir), level="INFO") + + @patch('llm_agents.utils.logging.datetime') + def test_setup_logging_creates_handlers(self, mock_datetime): + """Test that console and file handlers are created.""" + mock_datetime.now.return_value.strftime.return_value = "2024-01-01_12-00" + + with tempfile.TemporaryDirectory() as temp_dir: + # Clear any existing handlers + root_logger = logging.getLogger() + for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) + + setup_logging(log_dir=temp_dir, level="DEBUG") + + # Check that handlers were added + assert len(root_logger.handlers) == 2 + + # Check handler types + handler_types = [type(h).__name__ for h in root_logger.handlers] + assert "StreamHandler" in handler_types + assert "RotatingFileHandler" in handler_types + + def test_setup_logging_sets_log_levels(self): + """Test that log levels are set correctly.""" + with tempfile.TemporaryDirectory() as temp_dir: + root_logger = logging.getLogger() + + # Test DEBUG level + setup_logging(log_dir=temp_dir, level="DEBUG") + assert root_logger.level == logging.DEBUG + + # Clear handlers and test INFO level + for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) + + setup_logging(log_dir=temp_dir, level="INFO") + assert root_logger.level == logging.INFO + + def test_setup_logging_invalid_level(self): + """Test behavior with invalid log level.""" + with tempfile.TemporaryDirectory() as temp_dir: + # Should raise AttributeError for invalid level + with pytest.raises(AttributeError): + setup_logging(log_dir=temp_dir, level="INVALID") + + @patch('llm_agents.utils.logging.datetime') + def test_setup_logging_file_naming(self, mock_datetime): + """Test that log files are named correctly.""" + mock_datetime.now.return_value.strftime.return_value = "2024-01-01_12-30" + + with tempfile.TemporaryDirectory() as temp_dir: + setup_logging(log_dir=temp_dir, level="INFO") + + # Check that log file was created with correct name + log_files = list(Path(temp_dir).glob("*.log")) + assert len(log_files) == 1 + assert log_files[0].name == "2024-01-01_12-30_llm_agents.log" + + def test_setup_logging_handler_levels(self): + """Test that handlers have correct log levels.""" + with tempfile.TemporaryDirectory() as temp_dir: + root_logger = logging.getLogger() + # Clear existing handlers + for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) + + setup_logging(log_dir=temp_dir, level="DEBUG") + + # Find console and file handlers + console_handler = None + file_handler = None + + for handler in root_logger.handlers: + if isinstance(handler, logging.StreamHandler) and not hasattr(handler, 'baseFilename'): + console_handler = handler + elif hasattr(handler, 'baseFilename'): + file_handler = handler + + # Verify handler levels + assert console_handler is not None + assert console_handler.level == logging.INFO + + assert file_handler is not None + assert file_handler.level == logging.DEBUG + + def test_setup_logging_formatters(self): + """Test that formatters are set correctly.""" + with tempfile.TemporaryDirectory() as temp_dir: + root_logger = logging.getLogger() + # Clear existing handlers + for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) + + setup_logging(log_dir=temp_dir, level="INFO") + + for handler in root_logger.handlers: + formatter = handler.formatter + assert formatter is not None + assert isinstance(formatter, logging.Formatter) + + # Check that format string contains expected elements + format_string = formatter._fmt + assert "%(asctime)s" in format_string + assert "%(levelname)s" in format_string + assert "%(name)s" in format_string + assert "%(message)s" in format_string + + @patch('llm_agents.utils.logging.logging.handlers.RotatingFileHandler') + def test_setup_logging_rotating_file_config(self, mock_rotating_handler): + """Test that rotating file handler is configured correctly.""" + mock_handler_instance = Mock() + mock_rotating_handler.return_value = mock_handler_instance + + with tempfile.TemporaryDirectory() as temp_dir: + setup_logging(log_dir=temp_dir, level="INFO") + + # Verify RotatingFileHandler was created with correct parameters + mock_rotating_handler.assert_called_once() + call_args = mock_rotating_handler.call_args + + # Check maxBytes and backupCount parameters + assert call_args.kwargs['maxBytes'] == 10 * 1024 * 1024 # 10MB + assert call_args.kwargs['backupCount'] == 5 + + def test_setup_logging_default_parameters(self): + """Test function with default parameters.""" + # Clean up any existing log directory first + default_log_dir = Path("logs") + + # Clear existing handlers to avoid interference + root_logger = logging.getLogger() + for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) + + try: + setup_logging() + + # Check default log directory was created + assert default_log_dir.exists() + + # Check default log level + assert root_logger.level == logging.INFO + + finally: + # Clean up handlers + for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) \ No newline at end of file diff --git a/tools/rag_search.py b/tools/rag_search.py deleted file mode 100644 index ed6c26c..0000000 --- a/tools/rag_search.py +++ /dev/null @@ -1,56 +0,0 @@ -from __future__ import annotations -from typing import List, Dict, Any -import json -from pathlib import Path -import numpy as np -import faiss -from litellm import embedding -import time - -API_BASE = "http://localhost:11434" -EMBED_MODEL = "ollama/nomic-embed-text" -INDEX_DIR = "rag_index_json" - -# TODO: Make this configurable - -# Load once at import (fast) -_FAISS = faiss.read_index(str(Path(INDEX_DIR) / "faiss.index")) -_META = json.loads(Path(INDEX_DIR, "meta.json").read_text()) - -def _embed_query(q: str) -> np.ndarray: - resp = embedding(model=EMBED_MODEL, input=[q], api_base=API_BASE) - v = np.array(resp["data"][0]["embedding"], dtype="float32") - v = v / (np.linalg.norm(v) + 1e-12) - return v.reshape(1, -1) - -def _snippet(text: str, max_chars: int = 240) -> str: - text = " ".join(text.split()) - return text if len(text) <= max_chars else text[:max_chars-1] + "…" - -def search_papers(keywords_to_search: str, num_records: int = 20) -> Dict[str, Any]: - """ - Search most relevant papers over title+abstract. The number of records returned can be controlled via `num_records`. - Returns JSON with 'keywords_to_search' and 'results' (the results include metadata of the paper). - """ - try: - num_records = int(num_records) - except Exception: - num_records = 20 - - if len(keywords_to_search) == 0 or num_records <= 0: - return {"query": keywords_to_search, "results": []} - - # start_time = time.time() - qv = _embed_query(keywords_to_search) - scores, idxs = _FAISS.search(qv, num_records) - scores, idxs = scores[0].tolist(), idxs[0].tolist() - # print(f"Search for '{keywords_to_search}' took {time.time() - start_time:.3f}s") - - results = [] - for score, i in zip(scores, idxs): - if i == -1: - continue - m = _META[i] - text = f"{m['name']}\n\n{m['abstract']}" - results.append(m) - return {"query": keywords_to_search, "results": results} diff --git a/utils/tool_chat.py b/utils/tool_chat.py deleted file mode 100644 index ca752fb..0000000 --- a/utils/tool_chat.py +++ /dev/null @@ -1,160 +0,0 @@ -from __future__ import annotations -from typing import Any, Dict, List, Callable, get_args, get_origin, Literal, Annotated, Union -from dataclasses import dataclass, field -import json, inspect -import litellm -import os -from rich.console import Console -from rich.live import Live -from rich.text import Text - - -# litellm._turn_on_debug() - - -# ---- Minimal auto-schema from function signature ---- -def _json_type(t: Any) -> Dict[str, Any]: - origin, args = get_origin(t), get_args(t) - if origin is Literal: - return {"type": "string", "enum": list(args)} - if origin in (list, List): - return {"type": "array", "items": {"type": "string"}} - if t in (str,): return {"type": "string"} - if t in (int,): return {"type": "integer"} - if t in (float,): return {"type": "number"} - if t in (bool,): return {"type": "boolean"} - return {"type": "string"} - -def infer_tool(func: Callable[..., Any], tool_args: Dict[Any, Any]) -> Dict[str, Any]: - sig = inspect.signature(func) - hints = getattr(func, "__annotations__", {}) - props, required = {}, [] - for name, p in sig.parameters.items(): - if name in ("self", "cls"): continue - schema = _json_type(hints.get(name, str)) - if p.default is inspect._empty: required.append(name) - props[name] = schema - - parameter_values = {} - for arg_name, arg_val in tool_args.items(): - if arg_name in props.keys(): - parameter_values[arg_name] = arg_val - - return { - "type": "function", - "function": { - "name": func.__name__, - "description": (inspect.getdoc(func) or f"Call {func.__name__}"), - "parameters": {"type": "object", "properties": props, "required": required}, - }, - "parameter_properties_values": parameter_values - } - -# ---- LiteLLM chat wrapper with tool loop ---- -@dataclass -class ToolChat: - model: str - api_base: str - api_key: str - default_params: Dict[str, Any] = field(default_factory=lambda: {"temperature": 0.2, "max_tokens": 6000, "num_ctx": 131072}) - - def tool_loop( - self, - messages: List[Dict[str, Any]], - tool_funcs: List[Callable[..., Any]], - tool_args: Dict[Any, Any], - max_rounds: int = 10 - ) -> Dict[str, Any]: - registry = {fn.__name__: fn for fn in tool_funcs} - tools = [infer_tool(fn, tool_args=tool_args) for fn in registry.values()] - msgs = list(messages) - - console = Console() - - print(self.api_key) - with console.screen(): - console.print("\n[bold green]Assistant is working...[/bold green]\n") - - for _ in range(max_rounds): - stream_iter = litellm.completion( - model=self.model, - messages=msgs, - tools=tools, - tool_choice="auto", - api_base=self.api_base, - api_key=self.api_key, - stream=True, - **self.default_params, - ) - - collected = "" - calls = [] - - for chunk in stream_iter: - choices = chunk.get("choices", []) or [] - if not choices: - continue - choice = choices[0] - - # try several places where partial content may appear - content = None - delta = choice.get("delta") - - if "content" in delta: - content = delta["content"] - elif "message" in delta and isinstance(delta["message"], dict): - content = delta["message"].get("content") - - if "tool_calls" in delta: - calls.extend(delta["tool_calls"] or []) - - if content is None: - msg = choice.get("message") - if isinstance(msg, dict): - content = msg.get("content") - - if content is None: - content = choice.get("text") - - if content: - if not isinstance(content, str): - try: - content = json.dumps(content, ensure_ascii=False) - except Exception: - content = str(content) - console.print(content, end="", style="cyan") - - collected += content - # append the assembled assistant message so tool execution sees the assistant's follow-up - msgs.append({"role": "assistant", "content": collected}) - - if not calls: - return msgs - else: - # calls_tmp = [] - # for c in calls: - # c_tmp = dict(c) - # if "function" in c_tmp: - # c_tmp["function"] = dict(c_tmp["function"]) - # calls_tmp.append(c_tmp) - # msgs[-1]["tool_calls"] = calls_tmp - msgs[-1]["tool_calls"] = calls - console.print(f"tool_calls: {calls}", style="cyan") - - # execute tools and append results - for call in calls: - name = call["function"]["name"] - args = call["function"].get("arguments", "{}") - try: - parsed = json.loads(args) if isinstance(args, str) else (args or {}) - except json.JSONDecodeError: - parsed = {} - out = registry[name](**parsed) - msgs.append({ - "role": "tool", - "tool_call_id": call.get("id"), - "name": name, - "content": json.dumps(out, ensure_ascii=False), - }) - - return msgs diff --git a/uv.lock b/uv.lock index c69d078..e9e2b15 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,86 @@ version = 1 revision = 3 -requires-python = ">=3.14" +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.13'", + "python_full_version == '3.12.*'", + "python_full_version == '3.11.*'", + "python_full_version < '3.11'", +] + +[[package]] +name = "agentic-nav" +version = "0.1.0" +source = { editable = "." } +dependencies = [ + { name = "aiofiles" }, + { name 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