RetrievalMind 0.1.1

A custom Retrieval-Augmented Generation (RAG) framework for AI Agent applications.

RetrievalMind

RetrievalMind is an end-to-end Retrieval-Augmented Generation (RAG) pipeline framework designed to efficiently ingest, embed, store, and retrieve information from documents. This project serves as a personal learning and educational framework for implementing RAG functionalities in AI agents.

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Table of Contents

• Project Overview
• Features
• Project Structure
• Installation
• Usage
• Example Output
• Future Enhancements
• License

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Project Overview

RetrievalMind allows you to:

1. Ingest Documents – Load PDFs or text files into a structured format for processing.
2. Generate Embeddings – Convert textual content into vector representations using pre-trained models.
3. Vector Store Management – Store and manage embeddings in ChromaDB for efficient retrieval.
4. Information Retrieval – Retrieve top relevant content for user queries using a similarity-based search.

This framework can be extended and reused for AI projects requiring knowledge retrieval or RAG-based reasoning, making it ideal for personal learning and experimentation.

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Features

• Supports PDF and text file ingestion with customizable chunking.
• Generates semantic embeddings using all-miniLM-L6-v2 model.
• Persistent vector storage with ChromaDB.
• Flexible retrieval pipeline for natural language queries.
• Modular structure for easy integration into AI agents and projects.

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Project Structure

RetrievalMind/
├── data
│   ├── pdf
│   │   └── Company_Policy_Document.pdf
│   ├── text_files
│   │   ├── large_language_model.txt
│   │   ├── machine_learning.txt
│   │   └── python.txt
│   └── vector_store
│       ├── <UUID>/
│       └── chroma.sqlite3
├── main.py
├── src
│   ├── data_ingestion
│   │   ├── pdf_ingestor.py
│   │   └── text_ingestor.py
│   ├── embeddings_manager
│   │   └── embedding_manager.py
│   ├── rag_retriver
│   │   └── retriver.py
│   └── vector_store_manager
│       └── vector_store.py

• data/pdf/ – Stores PDF documents for ingestion.
• data/text_files/ – Stores plain text documents.
• data/vector_store/ – ChromaDB storage for embeddings.
• src/ – Core modular code for ingestion, embeddings, retrieval, and vector store management.
• main.py – Demonstrates the full RAG pipeline workflow.

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Installation

1. Clone the repository:

git clone https://github.com/Himanshu7921/RetrievalMind

2. Install dependencies:

pip install -r requirements.txt

Dependencies include: langchain, chromadb, sentence-transformers, PyMuPDF, etc.

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Usage

from src.data_ingestion import PDFDocumentIngestor
from src.embeddings_manager import EmbeddingManager
from src.vector_store_manager import VectorStore
from src.rag_retriver import Retrieval

# Step 1: Load and chunk PDF
pdf_ingestor = PDFDocumentIngestor(file_path="data/pdf/Company_Policy_Document.pdf", loader_type='mu')
pdf_loader = pdf_ingestor.load_document()
document_chunks = pdf_loader.load()
chunk_texts = [chunk.page_content for chunk in document_chunks]

# Step 2: Generate embeddings
embedding_manager = EmbeddingManager()
chunk_embeddings = embedding_manager.generate_embeddings(chunk_texts)

# Step 3: Store chunks in vector store
vector_store = VectorStore(collection_name="company_policy_collection", persist_directory="data/vector_store", document_type="PDF")
vector_store.add_document(documents=document_chunks, embeddings=chunk_embeddings)

# Step 4: Initialize retrieval pipeline
retrieval_pipeline = Retrieval(vector_store=vector_store, embedding_manager=embedding_manager)
query_text = "What is the termination policy of the company?"
retrieved_results = retrieval_pipeline.retrieve(query=query_text, top_k=1, score_threshold=0)

if retrieved_results:
    print("Retrieved Content:\n", retrieved_results[0]['content'])
else:
    print("No relevant documents found.")

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Example Output

[INFO] Vector store ready: 'company_policy_collection'
[INFO] Current document count: 4
[INFO] Successfully added 4 documents to 'company_policy_collection'.
[INFO] Total documents in collection: 8
Retrieved Document Content:
7. Customer Support Policy
7.1 Customer queries will be acknowledged within 24 hours of receipt.
...
8. Termination Policy
8.1 Employment may be terminated due to misconduct, non-performance, or policy violation.
...

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Future Enhancements

• Support for multi-format document ingestion (Word, CSV).
• Advanced query ranking with hybrid embeddings + keyword search.
• Integration with LLMs for generative responses using retrieved knowledge.
• Dashboard UI for visualizing vector store and retrieval results.

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License

This project is for personal learning and educational purposes. No commercial use permitted.