🧠 Multi-Agent RAG Intelligence Platform

A collaborative AI engine for research intelligence, trend forecasting, narrative generation,
and technical insight discovery.

This project is a modular, end-to-end intelligence system powered by a network of specialized agents.
Together, they ingest research papers, extract insights, analyze signals, build narratives, generate forecasts,
and present everything through a clean and intuitive Streamlit interface.

A playground for experimentation.
A foundation for real-world analytical tools.
A step toward autonomous reasoning engines.

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✨ Key Features

🔹 Multi-Agent Cognitive Architecture

A coordinated ensemble of autonomous agents, each performing a distinct thinking role:

• Retrieval Agent – extracts relevant information from the vector store
• Trend Agent – uncovers emerging signals & important shifts
• Velocity Agent – measures acceleration & rate-of-change of trends
• Forecast Agent – predicts future directions
• Narrative Agent – converts analysis into human-like summaries
• Timeline Agent – generates chronological intelligence flows
• Evaluator Agent – scores quality, consistency & coherence

Each agent works independently yet communicates through an orchestrated LangGraph-style pipeline.

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📥 Data Pipeline

1. Ingestion

Fetches research papers, PDFs, and structured documents.

2. Preprocessing

Cleans, chunks, embeds metadata, normalizes content.

3. Vectorization

Documents are embedded and stored in ChromaDB (chroma_db/).

4. Multi-Agent Processing

Agents collaborate to generate:

• insights
• narratives
• trend convergence signals
• velocity indicators
• forecasts
• timeline flows
• complete research intelligence reports

5. Interactive Streamlit Interface

Query the system, explore trends, or export full reports with a click.

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🗂 Project Structure

multi-agent-rag-platform/
│
├── agents/               # Individual agent logic
├── app/                  # Streamlit UI
├── chroma_db/            # Local vector store
├── data_ingestion/       # Fetching & preprocessing scripts
├── graph/                # Multi-agent orchestration pipeline
├── prompts/              # Engineered prompts for each agent
├── utils/                # PDF export, citations, helpers
├── visuals/              # Trend and timeline visualizations
└── requirements.txt      # Dependencies

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🚀 Run Locally

1. Create a virtual environment

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

2. Install dependencies

pip install -r requirements.txt

3. Start the Streamlit App

streamlit run app/streamlit_app.py

Visit the app at:
http://localhost:8501

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📊 Visual Intelligence

The platform dynamically generates:

• trend charts
• TRL (Technology Readiness Level) indicators
• convergence graphs
• velocity badges
• timeline visualizations

All are accessible directly in the Streamlit UI
and can be exported as PDF reports.

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🧰 Technologies Used

• Python
• Streamlit
• ChromaDB
• LangChain / LangGraph
• Matplotlib
• PDF generation utilities
• Custom multi-agent orchestration

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🎯 Purpose of This Project

Built as a foundation for:

• research intelligence
• technology monitoring
• trend forecasting
• narrative generation
• automated analysis pipelines
• multi-agent LLM experimentation

A system that thinks, reasons, and tells the evolving story of technology.

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📜 License

This project is open-source under the MIT License.
See the LICENSE file for details.