PDF-Chat

Chat with your PDF - RAG Tool

Are your PDF documents too long to read?

This RAG-powered AI tool will help you scan through your PDF documents and get direct answers.

Ask anything about your files and get answers instantly.

No more reading through 1000+ page PDF documents!

How to use

1. Get your Cohere API Key. > Get yours here
2. Upload your PDF document
3. Chat w/ your PDF!

How it Works

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The AI tool implements Cohere's Command R+ model which is RAG-optimized to tackle huge workloads of unstructured data.

It is then utilised together with another Cohere product: embed-english-v3.0 as the embedding model

Custom Knowledge Base

RAG implementation requires a knowledge base (or even a database/dataset) to be the 'source of truth' from which it answers to a user's queries.

from llama_index.core import SimpleDirectoryReader

input_path = input_dir_path

loader = SimpleDirectoryReader(
            input_dir = input_dir_path,
            required_exts=[".pdf"],
            recursive=True
        )
docs = loader.load_data()

Chunking

Involves breaking down large texts into smaller chunks or pieces. This function ofocurse includes a chunk size limit and chunk overlap, which prevents the output from being incomprehensible when concatenated.

This usually depends on the embedding model to be used

Chunking imporves retrieval speed and efficiency, as the problem is broken down into sub-problems for processing (Think of recursive algos)

from llama_index.core import SimpleDirectoryReader

loader = SimpleDirectoryReader(
            input_dir = input_dir_path2,
            required_exts=[".py", ".ipynb", ".js", ".ts", ".md"],
            recursive=True
        )
docs = loader.load_data()

Embeddings model

Refers to conversion of user input into numerical vectors that re better understood by computers, ML models.

Here, we use Cohere's embed-english-v3.0 as the embedding model.

from llama_index.embeddings.cohere import CohereEmbedding
from transformers import AutoModel, AutoTokenizer

llm = Cohere(api_key=API_KEY, model="command-r-plus")

embed_model = CohereEmbedding(
    cohere_api_key=API_KEY,
    model_name="embed-english-v3.0",
    input_type="search_query",
)

def load_embedding_model(model_name="facebook/bart-base"):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    embedding_model = AutoModel.from_pretrained(model_name)
    return tokenizer, embedding_model

Vector databases

Storage for vectors that are a numerical representation of the user-generated input

from llama_index.core import VectorStoreIndex
from llama_index.core.storage.storage_context import StorageContext

Conversational UI

Streamlit offers a simplistic interface for the RAG tool for uploading files and asking questions in a messaging-like UI

import streamlit as st

with col1:
    st.header(f"PDF-Chat")
    st.subheader(f"Chat with your PDF documents")

with col2:
    st.button("Clear ↺", on_click=reset_chat)

# Initialize chat history
if "messages" not in st.session_state:
    reset_chat()

PDF-Chat's Conversational UI

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Conclusion

MORE INFO TO BE ADDED