antonum / LLM-Document-Chat

Using LlamaIndex, Redis, and OpenAI to chat with PDF documents. Supplementary material for blog post (link to come)

Redis-LLM-Document-Chat

This repository provides the materials for the joint Redis/Microsoft blog post here. It contains a Jupyter notebook that demonstrates how to use Redis as a vector database to store and retrieve document vectors. The notebook also shows how to use LlamaIndex to perform semantic search for context within documents and provide a chatbot-like experience using OpenAI.

LlamaIndex-example.mov

Setting up the Environment

The tutorial can run in multiple ways. The first step is to configure the .env file in this repository. This file contains the following variables:

OPENAI_TEXT_MODEL=text-davinci-003
OPENAI_DEPLOYMENT_TYPE=Text
OPENAI_EMBEDDING_MODEL=text-embedding-ada-002
OPENAI_EMBEDDINGS_ENGINE_QUERY=text-embedding-ada-002
OPENAI_API_KEY=<your key here>
OPENAI_TEMPERATURE=0.7
OPENAI_MAX_TOKENS=50
CHUNK_SIZE=500
CHUNK_OVERLAP=100
AZURE_EMBED_MODEL_DEPLOYMENT_NAME=<your deployment name here>
AZURE_TEXT_MODEL_DEPLOYMENT_NAME=<your deployment name here>
AZURE_OPENAI_API_BASE=https://<your deployment name>.openai.azure.com/
OPENAI_API_BASE=https://api.openai.com/v1/
REDIS_ADDRESS=localhost
REDIS_PORT=6379
REDIS_PASSWORD=

To Use Azure OpenAI

To use Azure OpenAI, you will need to follow these instructions

1. Create an Azure OpenAI resource
2. Create model deployments for the text and embedding models
3. Configure the .env file adding the specific values for your deployments

AZURE_EMBED_MODEL_DEPLOYMENT_NAME=<your deployment name here>
AZURE_TEXT_MODEL_DEPLOYMENT_NAME=<your deployment name here>
AZURE_OPENAI_API_BASE=https://<your deployment name>.openai.azure.com/

To Use OpenAI

To use OpenAI, you will need to follow these instructions

1. Create an OpenAI account
2. Create an OpenAI API key
3. Configure the .env file adding the specific values for your deployments

OPENAI_API_KEY=<your key here>

Azure Redis Enterprise, Redis Enterprise Cloud, and Redis Stack

The tutorial will require the usage of the Redis-stack container because of the use of RediSearch and RedisJSON. There are three options for Redis

1. Redis Enterprise Cloud
2. Azure Redis Enterprise (ACRE)
3. Redis Stack (docker)
4. Redis Stack (local)

For each, the following options are required

REDIS_PASSWORD=<your password here>
REDIS_ADDRESS=<your redis address here>
REDIS_PORT=<your redis port here>

There are some docker-compose.yml files in the docker directory that will help spin up redis-stack locally and redisinsight in the case where a remote Redis is being used (like ACRE).

Run

To run the script, follow these steps:

1. Clone this repository to your local machine.
2. copy the .env.template to .env and configure the values as outlined above.
3. If using a local Python environment, just run the notebook
4. Otherwise, select a docker-compose file to run and execute docker-compose up to start the environment.