Chat Toy

Summary

A toy application that implements basic RAG queries for documents, based on OpenAI ChatGPT API and Redit Vector Search.

Currently under construction. Please stay tuned!

Demo

For a local demo run

1. Prepare for environment

   • Install dependencies from pyproject with poetry install. This will install:

     • chattoy_server: the server application that manages running the backend service.
     • chattoy: the example client.

   • Creates .env environment file for your openAI token:

   OPENAI_API_KEY=sk-xxxx
   

2. Starts redis server for vector indexing

   Start redis stack server manually, or use docker:

   docker run -d --restart=always -p 6379:6379 -p 8000:8000 redis/redis-stack-server
   

   See:

   • https://redis.io/docs/stack/
   • https://redis.io/docs/stack/get-started/install/docker/

3. Init database and start Flask app server

   Init database

   chattoy_server init
   chattoy_server run
   

4. Queries API interface

   See definition in app.py. (Documentation to come soon...)

   e.g.:

   curl -XPOST -H 'content-type=application/json' -d @data.json localhost:5000/documents
   

   data.json:

   {"document": "example text here.", "type": "text"}