Merge pull request #54 from mlverse/updates

Updates

Author: edgararuiz

python/README.md

@@ -4,60 +4,74 @@
 
 <!-- badges: start -->
 
+[![PyPi](https://img.shields.io/pypi/v/mlverse-mall.png)](https://pypi.org/project/mlverse-mall/)
 [![Python
 tests](https://github.com/mlverse/mall/actions/workflows/python-tests.yaml/badge.svg)](https://github.com/mlverse/mall/actions/workflows/python-tests.yaml)
-[![Code
+\| [![Package
 coverage](https://codecov.io/gh/mlverse/mall/branch/main/graph/badge.svg)](https://app.codecov.io/gh/mlverse/mall?branch=main)
-[![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
+
 <!-- badges: end -->
 
-Run multiple LLM predictions against a data frame. The predictions are
-processed row-wise over a specified column. It works using a
-pre-determined one-shot prompt, along with the current row’s content.
-`mall` has been implemented for both R and Python. The prompt that is
-use will depend of the type of analysis needed.
+Use Large Language Models (LLM) to run Natural Language Processing (NLP)
+operations against your data. It takes advantage of the LLMs general
+language training in order to get the predictions, thus removing the
+need to train a new NLP model. `mall` is available for R and Python.
 
-Currently, the included prompts perform the following:
+It works by running multiple LLM predictions against your data. The
+predictions are processed row-wise over a specified column. It relies on
+the “one-shot” prompt technique to instruct the LLM on a particular NLP
+operation to perform. The package includes prompts to perform the
+following specific NLP operations:
 
 - [Sentiment analysis](#sentiment)
 - [Text summarizing](#summarize)
 - [Classify text](#classify)
 - [Extract one, or several](#extract), specific pieces information from
   the text
 - [Translate text](#translate)
-- [Verify that something it true](#verify) about the text (binary)
-- [Custom prompt](#custom-prompt)
+- [Verify that something is true](#verify) about the text (binary)
 
-This package is inspired by the SQL AI functions now offered by vendors
-such as
-[Databricks](https://docs.databricks.com/en/large-language-models/ai-functions.html)
-and Snowflake. `mall` uses [Ollama](https://ollama.com/) to interact
-with LLMs installed locally.
+For other NLP operations, `mall` offers the ability for you to [write
+your own prompt](#custom-prompt).
 
-For **Python**, `mall` is a library extension to
-[Polars](https://pola.rs/). To interact with Ollama, it uses the
-official [Python library](https://github.com/ollama/ollama-python).
+`mall` is a library extension to [Polars](https://pola.rs/). To interact
+with Ollama, it uses the official [Python
+library](https://github.com/ollama/ollama-python).
 
 ``` python
 reviews.llm.sentiment("review")
 ```
 
 ## Motivation
 
-We want to new find ways to help data scientists use LLMs in their daily
-work. Unlike the familiar interfaces, such as chatting and code
+We want to new find new ways to help data scientists use LLMs in their
+daily work. Unlike the familiar interfaces, such as chatting and code
 completion, this interface runs your text data directly against the LLM.
+This package is inspired by the SQL AI functions now offered by vendors
+such as
+[Databricks](https://docs.databricks.com/en/large-language-models/ai-functions.html)
+and Snowflake.
 
 The LLM’s flexibility, allows for it to adapt to the subject of your
 data, and provide surprisingly accurate predictions. This saves the data
 scientist the need to write and tune an NLP model.
 
 In recent times, the capabilities of LLMs that can run locally in your
 computer have increased dramatically. This means that these sort of
-analysis can run in your machine with good accuracy. Additionally, it
-makes it possible to take advantage of LLM’s at your institution, since
-the data will not leave the corporate network.
+analysis can run in your machine with good accuracy. It also makes it
+possible to take advantage of LLMs at your institution, since the data
+will not leave the corporate network. Additionally, LLM management and
+integration platforms, such as [Ollama](https://ollama.com/), are now
+very easy to setup and use. `mall` uses Ollama as to interact with local
+LLMs.
+
+The development version of `mall` lets you **use external LLMs such as
+[OpenAI](https://openai.com/), [Gemini](https://gemini.google.com/) and
+[Anthropic](https://www.anthropic.com/)**. In R, `mall` uses the
+[`ellmer`](https://ellmer.tidyverse.org/index.html) package to integrate
+with the external LLM, and the
+[`chatlas`](https://posit-dev.github.io/chatlas/) package to integrate
+in Python.
 
 ## Get started
 
@@ -99,6 +113,11 @@ reviews = data.reviews
 reviews 
 ```
 
+    /Users/edgar/Projects/mall/python/.venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_format" in ContentToolResult has conflict with protected namespace "model_".
+
+    You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
+      warnings.warn(
+
 | review |
 |----|
 | "This has been the best TV I've ever used. Great screen, and sound." |

python/README.qmd

@@ -7,56 +7,64 @@ execute:
 <img src="https://mlverse.github.io/mall/site/images/favicon/apple-touch-icon-180x180.png" style="float:right" />
 
 <!-- badges: start -->
-[![Python tests](https://github.com/mlverse/mall/actions/workflows/python-tests.yaml/badge.svg)](https://github.com/mlverse/mall/actions/workflows/python-tests.yaml)
-[![Code coverage](https://codecov.io/gh/mlverse/mall/branch/main/graph/badge.svg)](https://app.codecov.io/gh/mlverse/mall?branch=main)
-[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
-<!-- badges: end -->
 
+[![PyPi](https://img.shields.io/pypi/v/mlverse-mall)](https://pypi.org/project/mlverse-mall/) [![Python tests](https://github.com/mlverse/mall/actions/workflows/python-tests.yaml/badge.svg)](https://github.com/mlverse/mall/actions/workflows/python-tests.yaml) \| [![Package coverage](https://codecov.io/gh/mlverse/mall/branch/main/graph/badge.svg)](https://app.codecov.io/gh/mlverse/mall?branch=main)
+
+<!-- badges: end -->
 
 
-Run multiple LLM predictions against a data frame. The predictions are processed 
-row-wise over a specified column. It works using a pre-determined one-shot prompt,
-along with the current row's content. `mall` has been implemented for both R
-and Python. The prompt that is use will depend of the type of analysis needed. 
+Use Large Language Models (LLM) to run Natural Language Processing (NLP) 
+operations against your data. It takes advantage of the LLMs general language
+training in order to get the predictions, thus removing the need to train a new
+NLP model. `mall` is available for R and Python.
 
-Currently, the included prompts perform the following: 
+It works by running multiple LLM predictions against your data. The predictions
+are processed row-wise over a specified column. It relies on the "one-shot" 
+prompt technique to instruct the LLM on a particular NLP operation to perform. 
+The package includes prompts to perform the following specific NLP operations:
 
-- [Sentiment analysis](#sentiment)
-- [Text summarizing](#summarize)
-- [Classify text](#classify)
-- [Extract one, or several](#extract), specific pieces information from the text
-- [Translate text](#translate)
-- [Verify that something it true](#verify) about the text (binary)
-- [Custom prompt](#custom-prompt)
+-   [Sentiment analysis](#sentiment)
+-   [Text summarizing](#summarize)
+-   [Classify text](#classify)
+-   [Extract one, or several](#extract), specific pieces information from the text
+-   [Translate text](#translate)
+-   [Verify that something is true](#verify) about the text (binary)
 
-This package is inspired by the SQL AI functions now offered by vendors such as
-[Databricks](https://docs.databricks.com/en/large-language-models/ai-functions.html) 
-and Snowflake.  `mall` uses [Ollama](https://ollama.com/) to  interact with LLMs 
-installed  locally. 
+For other NLP operations, `mall` offers the ability for you to [write your own prompt](#custom-prompt).
 
-For **Python**, `mall` is a library extension to [Polars](https://pola.rs/). To
+`mall` is a library extension to [Polars](https://pola.rs/). To
 interact with Ollama, it uses the official
 [Python library](https://github.com/ollama/ollama-python).
 
 ```python
 reviews.llm.sentiment("review")
 ```
-
 ## Motivation
 
-We want to new find ways to help data scientists use LLMs in their daily work. 
-Unlike the familiar interfaces, such as chatting and code completion, this interface
-runs your text data directly against the LLM.  
+We want to new find new ways to help data scientists use LLMs in their daily work.
+Unlike the familiar interfaces, such as chatting and code completion, this 
+interface runs your text data directly against the LLM. This package is inspired
+by the SQL AI functions now offered by vendors such as [Databricks](https://docs.databricks.com/en/large-language-models/ai-functions.html) 
+and Snowflake. 
 
-The LLM's flexibility, allows for it to adapt to the subject of your data, and 
-provide surprisingly accurate predictions. This saves the data scientist the
-need to write and tune an NLP model. 
+The LLM's flexibility, allows for it to adapt to the subject of your data, and
+provide surprisingly accurate predictions. This saves the data scientist the 
+need to write and tune an NLP model.
 
 In recent times, the capabilities of LLMs that can run locally in your computer 
-have increased dramatically. This means that these sort of analysis can run
-in your machine with good accuracy. Additionally, it makes it possible to take
-advantage of LLM's at your institution, since the data will not leave the
-corporate network. 
+have increased dramatically.  This means that these sort of analysis can run in 
+your machine with good accuracy. It also makes it possible to take 
+advantage of LLMs at your institution, since the data will not leave the 
+corporate network. Additionally, LLM management and integration platforms, such
+as [Ollama](https://ollama.com/), are now very easy to setup and use. `mall`
+uses Ollama as to interact with local LLMs.
+
+The development version of `mall` lets you **use external LLMs such as
+[OpenAI](https://openai.com/), [Gemini](https://gemini.google.com/) and
+[Anthropic](https://www.anthropic.com/)**. In R, `mall` uses the
+[`ellmer`](https://ellmer.tidyverse.org/index.html)
+package to integrate with the external LLM, and the 
+[`chatlas`](https://posit-dev.github.io/chatlas/) package to integrate in Python.
 
 ## Get started
 

python/mall/llm.py

@@ -54,15 +54,19 @@ def llm_call(x, msg, use, valid_resps="", convert=None, data_type=None):
 
         hash_call = build_hash(call)
         cache = cache_check(hash_call, use)
-
     if cache == "":
         if backend == "chatlas":
             chat = use.get("chat")
             ch = chat.chat(msg[0].get("content") + x, echo="none")
             out = ch.get_content()
             chat.set_turns(list())
-        if backend == "ollama":
-            resp = ollama.chat(
+        if backend == "ollama" or backend == "ollama-client":
+            if backend == "ollama":
+                chat_fun = ollama.chat
+            else:
+                client = use.get("client")
+                chat_fun = client.chat
+            resp = chat_fun(
                 model=use.get("model"),
                 messages=build_msg(x, msg),
                 options=use.get("options"),

python/mall/polars.py

@@ -1,3 +1,4 @@
+from ollama import Client
 from chatlas import Chat
 import polars as pl
 
@@ -45,9 +46,10 @@ def use(self, backend="", model="", _cache="_mall_cache", **kwargs):
 
         Parameters
         ------
-        backend : str | Chat
-            The name of the backend to use, or a `chatlas` chat object. 
-            At the beginning of the session it defaults to "ollama". 
+        backend : str | Chat | Client
+            The name of the backend to use, or an Ollama Client object,
+            or a `chatlas` Chat object.
+            At the beginning of the session it defaults to "ollama".
             If passing `""`, it will remain unchanged
         model : str
             The name of the model tha the backend should use. At the beginning
@@ -87,7 +89,7 @@ def use(self, backend="", model="", _cache="_mall_cache", **kwargs):
         ```
 
         ```{python}
-        # Use a `chatlas` object 
+        # Use a `chatlas` object
         from chatlas import ChatOpenAI
         chat = ChatOpenAI()
         reviews.llm.use(chat)
@@ -98,6 +100,10 @@ def use(self, backend="", model="", _cache="_mall_cache", **kwargs):
             self._use.update(dict(chat=backend))
             backend = ""
             model = ""
+        if isinstance(backend, Client):
+            self._use.update(dict(backend="ollama-client"))
+            self._use.update(dict(client=backend))
+            backend = ""
         if backend != "":
             self._use.update(dict(backend=backend))
         if model != "":

python/pyproject.toml

@@ -3,7 +3,7 @@ packages = ["mall"]
 
 [project]
 name = "mlverse-mall"
-version = "0.1.0.9000"
+version = "0.1.0.9001"
 description = "Run multiple 'Large Language Model' predictions against a table. The predictions run row-wise over a specified column."
 readme = "README.md"
 authors = [

r/man/llm_use.Rd

@@ -26,7 +26,7 @@
       
       -- mall session object 
       Backend: ellmer
-      LLM session: model:gpt-4o
+      LLM session: model:gpt-4.1
 
 # Ensures empty llm_use works with Chat
 
@@ -36,5 +36,5 @@
       
       -- mall session object 
       Backend: ellmer
-      LLM session: model:gpt-4o
+      LLM session: model:gpt-4.1