Query PDF and CSV with LLM

This repo host fastAPI app for querying PDF and CSV file to look for information using natural language.

The query flow for pdf file was implemented as follow:

1. Read text from pdf file
2. Split the text into chunks
3. Encode the chunks into embedding vectors using huggingface GTR-T5 or OpenAI Ada text embedding
4. Upload text chunks and embedding vectors to vector database Qdrant
5. Get user query text and encode into embedding vector
6. Search vector database for text chunk whose embedding are closest to query embedding based on cosine similarity
7. Get answer from FLAN-T5 or OpenAI GPT-3 based user query text and text chunk

The query flow for pdf file was implemented as follow:

1. Read csv file as pandas df
2. convert each row to a text chunks
3. The rest of the steps are similar to query PDF file

Setup

1. Clone this repo

   git clone https://github.com/haizadtarik/queryfile.git
   

2. Install dependencies

   cd queryPDF
   python -m pip install -r requirements.txt
   

3. Pull Qdrant image from docker hub

   docker pull qdrant/qdrant
   

4. Run Qdrant base

   docker run -p 6333:6333 -v $(pwd)/qdrant_storage:/qdrant/storage qdrant/qdrant
   

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   NOTE

   To use OpenAI embeddings or GPT create .env and put your API key there

   OPENAI_KEY=<OPEN_API_KEY>
   

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5. Bring up fastAPI server

   python server.py