Research Assistant Agent

This repo contains a hybrid Research Assistant that combines an exact Knowledge Graph (Neo4j) with a Retrieval‑Augmented Generation pipeline (FAISS + Cross‑Encoder + FLAN‑T5) behind a sleek Streamlit interface.

Overview

This project demonstrates a two‑pronged approach to question answering over a corpus of research papers:

• Structured Queries via Neo4j
  Exact, lightning‑fast answers for graph‑able questions (e.g. “Which approaches used ResNet backbone?”).

• Free‑Form QA via RAG Pipeline
  Semantic search (FAISS) + keyword filtering + cross‑encoder reranking + an LLM (FLAN‑T5) to handle open‑ended queries.

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Features

• Graph‑Based Retrieval with Cypher queries for high‑precision lookups.
• Vector‑Based Retrieval using FAISS for deep semantic similarity.
• Cross‑Encoder Reranking to boost relevance of top FAISS hits.
• LLM‑Based Synthesis with HuggingFace’s FLAN‑T5.
• Interactive UI powered by Streamlit, complete with real‑time metrics and example prompts.
• Containerized Demo via Docker Compose for one‑command deployment.

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Prerequisites

• Docker & Docker Compose (for containerized demo)
• Python 3.10+ and pip (for local development)
• (Optional) Conda for environment management

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Installation

Local Setup

1. Clone the repository

   git clone https://github.com/abdulvahapmutlu/research-agent.git
   cd research-agent
   

2. Create and activate your environment
   Conda:

   conda env create -f environment.yml
   conda activate agent
   

   venv:

   python -m venv venv
   source venv/bin/activate   # macOS/Linux
   .\venv\Scripts\activate    # Windows
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Set environment variables

   export NEO4J_URI="bolt://localhost:7687"
   export NEO4J_USER="neo4j"
   export NEO4J_PASS="password"
   

   Windows (PowerShell):

   $env:NEO4J_URI="bolt://localhost:7687"
   $env:NEO4J_USER="neo4j"
   $env:NEO4J_PASS="password"
   

5. Populate the Neo4j graph
   Make sure Neo4j is running (e.g. via docker-compose up neo4j), then:

   python graph/import_to_neo4j.py
   

6. Launch the Streamlit app

   streamlit run app.py
   

   Open your browser at http://localhost:8501.

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Docker Compose (One‑Command Demo)

1. Add your Neo4j credentials into the neo4j.environment and app.environment sections of docker-compose.yml.
2. Run:

   docker-compose up --build
   

3. Browse to http://localhost:8501 for the live demo.

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Usage

• Structured Questions (graph route):

  • “Which approaches use ResNet backbone?”

• Free‑Form Questions (RAG fallback):

  • “Summarize the Transformer training setup.”

Type your query into the text box and hit Ask. The UI will display either a graph‑backed result or a generated answer with source snippets.

⚠️ Important Note ⚠️

This is a demo project; therefore, it is only for demonstration and it may not answer each query. To specialize, you need to recreate it with your own data or use an LLM API

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Configuration

• Reader Model: Override google/flan-t5-large by setting READER_MODEL in your environment.
• Streamlit Port: Change via streamlit run app.py --server.port <port>.
• Neo4j: Adjust bolt URI, username, and password via environment variables.

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Troubleshooting

• AuthError: Ensure your Neo4j password is ≥ 8 characters and matches the env vars.
• Slow RAG: Reduce SEMANTIC_K or use a smaller reader model.
• Missing Index: Rebuild with python retriever_embeddings.py.

License

MIT License. See LICENSE for details.