README: Retrieval-Augmented Generation (RAG) Model for QA Bot

This project implements a Retrieval-Augmented Generation (RAG) model for a Question Answering (QA) bot, utilizing Pinecone as the vector database and Cohere's API for text generation. The QA bot retrieves relevant information from a dataset and generates coherent answers.

Table of Contents

• Introduction
• Setup
• Data Preparation
• Model Training
• Usage
• Examples
• Contributing
• License

Introduction

The goal of this project is to develop a QA bot that combines retrieval-based and generative approaches. By leveraging Pinecone's vector database, the bot efficiently retrieves relevant documents, and using Cohere's language models, it generates accurate and contextually relevant answers.

Setup

1. Install Required Libraries:

   pip install pinecone-client sentence-transformers cohere

2. Initialize Pinecone:

   from pinecone import Pinecone
   
   pc = Pinecone(api_key="your_pinecone_api_key")
   index = pc.Index("quickstart")

3. Load the Embedding Model:

   from sentence_transformers import SentenceTransformer
   
   model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

4. Initialize Cohere API:

   import cohere
   
   cohere_client = cohere.Client("your_cohere_api_key")

Data Preparation

Prepare your dataset by compiling documents that the QA bot will reference. For each document, generate embeddings using the loaded model:

documents = [
    "Pinecone is a vector database used for fast and scalable machine learning applications.",
    "Cohere is a platform that provides NLP models for various use cases like text generation.",
    "The capital of France is Paris."
]

embeddings = model.encode(documents)

Model Training

No additional training is required for the pre-built models used in this project. Ensure that the models are properly loaded and the embeddings are correctly generated.

Usage

Upsert the generated embeddings into the Pinecone index:

for i, embedding in enumerate(embeddings):
    index.upsert(vectors=[(f"doc_{i}", embedding.tolist())])

Define the QA function that retrieves relevant documents and generates answers:

def qa_bot(query):
    query_embedding = model.encode([query])[0]
    results = index.query(
        vector=query_embedding.tolist(),
        top_k=5,
        include_values=True,
        include_metadata=True
    )
    retrieved_docs = [result['id'] for result in results['matches']]
    answer = generate_answer(retrieved_docs, query)
    return answer

Implement the generate_answer function using Cohere's API:

def generate_answer(retrieved_docs, query):
    context = " ".join(retrieved_docs)
    response = cohere_client.generate(
        prompt=f"{context}\n\nQuestion: {query}\nAnswer:",
        max_tokens=50
    )
    return response.generations[0].text.strip()

Examples

query = "What is Pinecone?"
answer = qa_bot(query)
print(f"Question: {query}\nAnswer: {answer}")

Output:

Question: What is Pinecone?
Answer: Pinecone is a vector database used for fast and scalable machine learning applications.

Contributing

Contributions are welcome! Please open an issue or submit a pull request with your proposed changes.

License

This project is licensed under the MIT License.