StatsChat

Code state

Warning

Please be aware that for development purposes, these experiments use experimental Large Language Models (LLM's) not intended for production. They can present inaccurate information, hallucinated statements and offensive text by random chance or through malevolent prompts.

• Under development / Experimental
• Tested on macOS only
• Peer-reviewed
• Depends on external API's

Introduction

This is an experimental application for semantic search of ONS statistical publications. It uses LangChain to implement a fairly simple Retriaval Augmented Generation (RAG) using embedding search and QA information retrieval process.

Upon receiving a query, documents are returned as search results using embedding similarity to score relevance. Next, the relevant text is passed to a Large Language Model (LLM), which is prompted to write an answer to the original question, if it can, using only the information contained within the documents.

For this prototype, relevant web pages are scraped and the data stored in data/bulletins, the docstore / embedding store that is created is likewise in local folders and files, and the LLM is either run in memory or accessed through VertexAI.

Installation

The project requires specific versions of some packages so it is recommended to set up a virtual environment. Using venv and pip:

python3.10 -m venv env
source env/bin/activate

python -m pip install --upgrade pip
python -m pip install .

Note

If you are doing development work on statschat, you should install the package locally as editable with our optional dev dependencies:

python -m pip install -e ".[dev]"

Pre-commit actions

This repository contains a configuration of pre-commit hooks. These are language agnostic and focussed on repository security (such as detection of passwords and API keys).

If approaching this project as a developer, you are encouraged to install and enable pre-commits by running the following in your shell:

1. Install pre-commit:

   pip install pre-commit

2. Enable pre-commit:

   pre-commit install

Once pre-commits are activated, whenever you commit to this repository a series of checks will be executed. The use of active pre-commits are highly encouraged.

Note

Pre-commit hooks execute Python, so it expects a working Python build.

Usage

This main module statschat can be either called directly or deployed as an API (using fastapi). A lightweight flask front end is implemented separately in a subfolder and relies on the API running.

The first time you instantiate the Inquirer class, any ML models specified in the code will be downloaded to your machine. This will use a few GB of data and take a few minutes. App and search pipeline parameter are stored and can be updated by editing statschat/_config/main.toml.

We have included few EXAMPLE scraped data files in data/bulletins so that the preprocessing and app can be run as a small example system without waiting on webscraping.

With Vertex AI

If you wish to use Google's model API update the model variables in statschat/_config/main.toml:

• to use the question-answering system with Google's PaLM2 API set the generative_model_name parameter to text-unicorn or gemini-pro (their name for the model).
• for PaLM2 (Gecko) to create embeddings, set the embedding_model_name parameter to textembedding-gecko@001. You may also wish to disable the removal of near-identical documents in the preprocessing pipeline (line 59, statschat/embedding/preprocess.py), to reduce calls to the embedding API.

In addition to changing this parameter, you will need a Google Cloud Platform (GCP) project set up, with the Vertex AI API enabled. You will need to have the GCP Command Line Interface installed in the machine running this code, logged in to an account with sufficient permissions to access the API (you may need to set up application default credentials). Usually this can be achieved by running:

gcloud config set project "<PROJECT_ID>"
gcloud auth application-default login

Example endpoint commands

1. Webscraping the source documents (not included in the public repository, only examples in data/bulletins)

   python statschat/webscraping/main.py

2. Creating a local document store

   python statschat/embedding/preprocess.py

3. Updating an existing local document store with new articles

   python statschat/embedding/preprocess_update_db.py

4. Run the interactive Statschat API

   uvicorn fast-api.main_api:app

   The fastapi is set to respond to http requests on port 8000. When running, you can see docs at http://localhost:8000/docs.

5. Run the flask web interface

   python flask-app/app.py

   To use the user UI navigate in your browser to http://localhost:5000. Note that it requires the API to be running and the endpoind specified in the app.

6. Run the search evaluation pipeline

   python statschat/model_evaluation/evaluation.py

   The StatsChat pipeline is currently evaluated based on small number of test question. The main 'app_config.toml' determines pipeline setting used in evaluation and results are written to data/model_evaluation folder.

7. Testing

   python -m pytest

   Preferred unittesting framework is PyTest.

Search engine parameters

There are some key parameters in statschat/_config/main.toml that we're experimenting with to improve the search results, and the generated text answer. The current values are initial guesses:

Parameter	Current Value	Function
k_docs	10	Maximum number of search results to return
similarity_threshold	2.0	Cosine distance, a searched document is only returned if it is at least this similar (EQUAL or LOWER)
k_contexts	3	Number of top documents to pass to generative QA LLM

Data Science Campus

At the Data Science Campus we apply data science, and build skills, for public good across the UK and internationally. Get in touch with the Campus at [email protected].

License

The code, unless otherwise stated, is released under the MIT License.

The documentation for this work is subject to © Crown copyright and is available under the terms of the Open Government 3.0 licence.