Query PDFs using GPT - Open AI + Pinecone (vector store) + LlamaIndex
Easiest way would be to run the app locally by cloning the repo:
git clone https://github.com/pranav-kural/pdfgpt.git
Add a .env file with your Open AI and Pinecone API keys (or add these values to .env_sample and rename it to .env)
OPENAI_API_KEY={Open AI API Key}
PINECONE_KEY={Pinecone API Key}Update Pinecone information (index name and region) and any other parameters in the params.py file.
Then, run the server using:
uvicorn main:app --reload
Using below endpoint, provide URL to the PDF file you want the API to create a vector store for. This vector store will then be stored in Pinecone and will be queried when queries are made to the chatbot.
/create?document_url={document_url}
If you've already created an index, and want to load it from Pinecone, use the load endpoint
/load
To make a query and get response:
/query?q={query text}
Below is a brief overview of what happens when user makes a query:
- The query embeddings are generated
- Vector store is searched with the query embeddings to extract closest neighbors
- Closest neighbor embedding values are used to retrieve most relevant portion of content that relate to given query
- Chat completion request is made to Chat model (ex, OpenAI GPT 3.5 turbo) by providing it the content related to the query as context and the query itself
- Response returned
To learn more on how the API works on underneath check my blog post on PDF-GPT: LlamaIndex + Open AI + Pinecone + FastAPI
Example screenshot for a query based on the sample document:
- Add authentication, API Key and user management to the API
- Add authentication for endpoints (including verification of API key through session id)
- Ability for users to create, save, delete and interact with multiple chatbots