Research Agent with LangGraph and RAG

A powerful research agent built with LangChain, LangGraph, and RAG (Retrieval-Augmented Generation) that can search through local documents and web content to answer research questions.

Features

• RAG-based Document Search: Search through local PDF, TXT, and Markdown files
• Web Scraping: Extract information from web pages
• LangGraph Workflow: Intelligent multi-step research process
• Vector Database: Chroma for efficient document storage and retrieval
• Google Gemini Integration: Powered by Google's Gemini LLM
• CLI Interface: Easy-to-use command-line interface
• Modular Architecture: Clean, maintainable code structure

Architecture

START
  ↓
Query Analysis (determine rag/web/both)
  ↓
Research Planning (break into sub-questions)
  ↓
  ├─→ RAG Retrieval ──┐
  ├─→ Web Scraping ───┤
  └─→ Both ───────────┘
        ↓
  Relevance Check
        ↓
  ┌─────┴─────┐
  │           │
Sufficient  Insufficient
  │           │
  │     Re-query/Web Search
  │           │
  └─────┬─────┘
        ↓
   Synthesis
        ↓
      END

Installation

1. Clone or download this repository

cd Agent

2. Create a virtual environment

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Set up environment variables

Copy .env.example to .env and add your Google Gemini API key:

cp .env.example .env

Edit .env and add your API key:

GEMINI_API_KEY=your_api_key_here

Get your Gemini API key from: https://makersuite.google.com/app/apikey

Quick Start

1. Download Large Documents (Optional)

Download documents directly from URLs and automatically ingest them:

# Download a single document
python main.py download --url https://arxiv.org/pdf/1706.03762.pdf --auto-ingest

# Download multiple documents
python main.py download --url https://arxiv.org/pdf/1706.03762.pdf https://arxiv.org/pdf/2005.14165.pdf --auto-ingest

# Download with custom filename
python main.py download --url https://example.com/paper.pdf --filename research_paper.pdf

Popular sources for large documents:

• Research Papers: ArXiv - https://arxiv.org/pdf/[paper-id].pdf
• Books: Project Gutenberg - Public domain books
• Documentation: Python, Django, TensorFlow docs (PDF format)
• Wikipedia: Use the Wikipedia PDF API

2. Ingest Local Documents

Add your documents to the data/documents/ folder, then run:

python main.py ingest --path data/documents

Supported file types: PDF, TXT, MD (Markdown)

3. Query the Agent

python main.py query "What is machine learning?"

4. Interactive Mode

python main.py interactive

Usage Examples

Command Line

Ingest multiple directories

python main.py ingest --path data/documents data/papers data/notes

Research query

python main.py query "Explain neural networks in simple terms"

Programmatic Usage

from main import ResearchAgent

# Initialize agent
agent = ResearchAgent()

# Ingest documents
agent.ingest_documents(["data/documents"])

# Research a query
result = agent.research("What is deep learning?")

print(result["answer"])
print(result["sources"])

Run Examples

# Basic research example
python examples/basic_research.py

# Web scraping example
python examples/web_research.py

Project Structure

Agent/
├── src/
│   ├── config.py              # Configuration management
│   ├── graph/
│   │   ├── state.py           # LangGraph state
│   │   ├── nodes.py           # Workflow nodes
│   │   └── workflow.py        # Workflow construction
│   ├── rag/
│   │   ├── vector_store.py    # Chroma vector store
│   │   ├── embeddings.py      # Embedding generation
│   │   ├── retriever.py       # Document retrieval
│   │   └── chunking.py        # Text chunking
│   ├── loaders/
│   │   ├── file_loader.py     # Document loaders
│   │   ├── web_scraper.py     # Web scraping
│   │   └── document_processor.py  # Processing
│   ├── llm/
│   │   ├── gemini_client.py   # Gemini API client
│   │   └── prompts.py         # Prompt templates
│   └── utils/
│       └── logger.py          # Logging
├── data/
│   ├── documents/             # Local documents
│   └── chroma_db/             # Vector database
├── examples/
│   ├── basic_research.py      # Basic example
│   └── web_research.py        # Web example
├── main.py                    # CLI entry point
├── requirements.txt
└── README.md

Configuration

All configuration is managed through environment variables in .env:

# Google Gemini API
GEMINI_API_KEY=your_api_key_here

# Vector Store
CHROMA_PERSIST_DIRECTORY=./data/chroma_db

# Document Processing
CHUNK_SIZE=1000
CHUNK_OVERLAP=200
MAX_FILE_SIZE_MB=50

# Agent Configuration
MAX_ITERATIONS=3
RETRIEVAL_TOP_K=5
RELEVANCE_THRESHOLD=7.0

# LLM Configuration
GEMINI_MODEL=gemini-1.5-pro
TEMPERATURE=0.7
MAX_TOKENS=8192

# Logging
LOG_LEVEL=INFO
LOG_FILE=./logs/agent.log

How It Works

1. Document Ingestion

• Documents are loaded from local files (PDF, TXT, MD)
• Text is extracted and preprocessed
• Documents are split into chunks (1000 chars with 200 char overlap)
• Embeddings are generated using Google's text-embedding-004
• Chunks are stored in Chroma vector database

2. Query Processing

• User submits a research query
• Query Analysis: Determines if local search, web search, or both are needed
• Research Planning: Breaks complex queries into sub-questions
• Retrieval: Searches vector store for relevant documents
• Relevance Check: LLM evaluates if retrieved documents are sufficient
• Synthesis: LLM generates comprehensive answer with citations

3. LangGraph Workflow

The agent uses LangGraph to orchestrate a multi-step research process:

• Conditional routing based on query analysis
• Iterative refinement if initial results are insufficient
• Automatic fallback to web search if local knowledge is incomplete

Advanced Usage

Custom Vector Store

from src.rag.vector_store import create_vector_store

# Create a custom collection
vector_store = create_vector_store(
    collection_name="my_research",
    persist_directory="./custom_db"
)

Web Scraping

from src.loaders.web_scraper import WebScraper

scraper = WebScraper()
documents = scraper.scrape_urls([
    "https://example.com/article1",
    "https://example.com/article2"
])

Custom Workflow

from src.graph.workflow import ResearchWorkflow
from src.rag.vector_store import create_vector_store

vector_store = create_vector_store()
workflow = ResearchWorkflow(vector_store)

result = workflow.run("Your research question")

Troubleshooting

API Key Error

If you see "GEMINI_API_KEY is required":

• Make sure you've created a .env file
• Add your API key: GEMINI_API_KEY=your_key_here
• Restart the agent

No Documents Found

If the agent can't find documents:

• Check that files are in data/documents/ folder
• Ensure file formats are supported (PDF, TXT, MD)
• Try running ingest command again

Import Errors

If you see import errors:

• Make sure virtual environment is activated
• Run pip install -r requirements.txt again
• Check Python version (3.8+ required)

Limitations

• Web Search: Currently requires manual URL input. Integrate with Google Custom Search API or similar for automatic web search
• File Types: Limited to PDF, TXT, and MD. Can be extended to support DOCX, HTML, etc.
• Multimodal: Text-only. Can be enhanced to support images, tables, etc.

Future Enhancements

• Integration with Google Custom Search API
• Support for more file types (DOCX, HTML, CSV)
• Multi-modal support (images, tables)
• Conversation history and context
• Streaming responses
• API server mode
• Web UI

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

License

MIT License

Acknowledgments

• Built with LangChain and LangGraph
• Powered by Google Gemini
• Vector database by Chroma