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README.md

📃 Chat Your PDF!

This example Streamlit app demonstrates how to build a simple chatbot powered by Redis, LangChain, and Google's Vertex AI. It contains the following elements:

  • ⚙️ LangChain for app orchestration, agent construction, and tools
  • 🖥️ Streamlit for the front end and conversational interface
  • ☁️ GCP Vertex AI Palm 2 models for embedding creation and chat completion
  • 💥 Redis and RedisVL client for Retrieval-Augmented Generation (RAG), LLM Semantic Caching, and chat history persistence

Chat Your PDF screenshot

About

Redis is well-versed to power chatbots thanks to its flexible data models, query engine, and high performance. This enables users to leverage redis for a variety of gen AI needs:

  • RAG -- ensures that relevant context is retrieved from Redis as a Vector Database, given a users question
  • Semantic Caching -- ensures that duplicate requests for identical or very similar information are not exhuastive. Ex: 
    streamlit    | Full Response Time (secs) 1.6435627937316895
streamlit    | Cache Response Time (secs) 0.11130380630493164
  • Chat History -- ensures distributed & low latency access to conversation history in Redis Lists

Setting up the app environment

Obtain google cloud credentials

You need a valid GCP project, service account, an an application JSON key file in order to auth with GCP. The credentials file will be mounted to the docker container of the chatbot app and exposed through the GOOGLE_APPLICATION_CREDENTIALS environment variable. For more information check out this link.

  1. Download a GCP credentials JSON file:
    • Go to "IAM & Admin" panel in the GCP console.
    • On the left navbar select "Service Accounts".
    • Select the name of the service account.
    • On the top bar, select the "Keys" tab.
    • Above the list of active keys, select "ADD KEY" to create a new JSON key file.
  2. Move the credentials file into the root level of this folder here as gcp_credentials.json 
    mv ~/Downloads/<your-gcp-secret-key-file>.json  app/gcp_credentials.json

Update environment configuration

The project comes with a template .env.template file with the following values. Make a coy of this as .env. Update the values below accordingly.

CHUNK_SIZE=500
CHUNK_OVERLAP=100
DOCS_FOLDER="pdfs/"
REDIS_URL="redis://localhost:6379"
GCP_PROJECT_ID="YOUR_GCP_PROJECT_NAME" #nifty-456098
GCP_LOCATION="YOUR_VERTEXAI_REGION"    #us-central1
  • Update the GCP_PROJECT_ID and GCP_LOCATION variables based on your GCP project and vertex AI configuration.
  • Update the REDIS_URL based on your Redis Enterprise database deployed in GCP.

Run

To run the app, follow these steps:

  1. Clone this repository to your local machine.
  2. Set up your GCP credentials as outlined above.
  3. Copy the .env.template to .env and configure the values as outlined above.
  4. Run the app with Docker compose: `docker-compose up.
  5. Hit your brower at http://localhost:8080/.