🧠 GraphRAG: eBay User Agreement Chatbot

A Knowledge Graph-Powered Conversational AI built to answer legal and policy-related queries from the eBay User Agreement using Neo4j, LLMs (Meta LLaMA 3B), and Memory via FAISS.

🌐 Website - DEMO

💡 Project Motivation

Reading long user agreements is painful. This project creates an intelligent chatbot that:

• Understands natural language queries
• Retrieves facts from a Neo4j-based Knowledge Graph
• Enhances responses using memory of past conversations
• Uses open-source LLMs to generate grounded, concise, and transparent answers

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🔁 End-to-End Architecture

1. 🧱 Steps:

1. User submits a query via Streamlit UI.
2. Named Entities are extracted using SpaCy + RE.
3. Matching triples are fetched from Neo4j KG.
4. Memory module (FAISS) adds past Q&A context.
5. Prompt is dynamically injected and sent to LLaMA-3B.
6. Response is streamed and displayed to the user.

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2. 🧠 Knowledge Graph Construction

• Text source: eBay User Agreement PDF
• Preprocessing: cleaned and tokenized using SpaCy
• NER & RE: Custom rules + pre-trained SpaCy models
• Triplets: Extracted using pattern matching and OpenIE-style RE
• Storage: JSON + CSV → Loaded into Neo4j (local or Aura Free)
• Tools: graph_builder.py, KG_creation.ipynb

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3. 🔍 Query-to-KG Translation

• Input query is processed for Named Entities.
• Synonyms are expanded using Sentence Transformers.
• KG is queried using Cypher to retrieve matching triplets.
• Top-k results ranked based on entity similarity & relevance.
• Implemented in retriever.py using match (s)-[r]->(o) pattern.

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4. 💬 Prompting Strategy

• Format: [Triples] + [Memory] → Context Window
• Model: Meta LLaMA-3B (Instruct-tuned)
• Sent via HF endpoint with streaming

📌 Example

System: You are a legal assistant for eBay User Agreement.
Context:

* \[User] may terminate the agreement with 30 days notice.
* \[eBay] may restrict access for violation.
  Memory:
* Q: What if I break the policy? A: Your access may be restricted.
  Question: Can I end the agreement anytime?

Answer:

eBay allows termination with 30 days’ notice. However, immediate termination may depend on specific conditions outlined in Section X.

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5. ▶️ Running the Chatbot

• Install dependencies: pip install -r requirements.txt

• Add your Hugging Face token in the UI sidebar.

• Add Neo4j credentials in .streamlit/secrets.toml

• Run: streamlit run app.py

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6. 🧠 Model Details & Streaming

• Model: Meta LLaMA-3B-Instruct (via HuggingFace)
• Endpoint: HuggingFace Inference Endpoint (stream=True)
• Temperature: 0 - 0.2 for factual output
• Streaming: Enabled to simulate real-time response using requests with stream

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🚀 Features

✅ Knowledge Graph-based reasoning

✅ Memory-augmented retrieval (FAISS)

✅ Legal/Policy Q&A grounded in real documents

✅ Streamlit-powered UI with chat history and controls

✅ Chat save/load functionality

✅ Real-time LLM responses using HuggingFace inference endpoint

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🧱 Project Structure

.
├── app.py                          # Main Streamlit app
├── requirements.txt               # Dependencies
├── create_code.py                 # Code generation helper
├── chat_history.json              # Sample chat history
│
├── Src/                           # Core logic modules
│   ├── memory.py                  # Persistent memory using Chroma
│   ├── retriever.py               # Entity extractor & KG triple retriever
│   ├── prompt_injector.py         # Prompt builder & LLM streaming query
│   └── graph_builder.py           # For KG construction
│
├── Triples/                       # Triplets extracted from the source doc
│   ├── graphrag_triplets.csv/json
│   ├── triples_raw.json
│   ├── triples_structured.json
│   └── knowledge_graph_triplets.json
│
├── KG/                            # Visuals & summaries
│   ├── knowledge_graph_image.png
│   └── summary.json
│
├── NER/                           # Extracted named entities
│   └── ner_entities.json
│
├── Data/
│   ├── Ebay_user_agreement.pdf
│   └── cleaned_ebay_user_agreement.txt
│
├── Notebooks/                     # Jupyter notebooks for exploration
│   ├── KG_creation.ipynb
│   ├── preprocessing.ipynb
│   └── graphrag-quering-kg-and-llm-prompting.ipynb
│
├── .streamlit/
│   └── secrets.toml               # API keys & credentials
├── .gitignore
└── README.md

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⚙️ Setup & Installation

1. Clone the repository

git clone https://github.com/MohitGupta0123/GraphRAG-Ebay-User-Aggrement-Chatbot.git
cd GraphRAG-Ebay-User-Aggrement-Chatbot

2. Install dependencies

pip install -r requirements.txt

3. Token Configuration

This app prompts for your Hugging Face token (HF_TOKEN) securely at runtime in the sidebar.
You no longer need to store the token in secrets.toml.

However, Neo4j credentials are still required in .streamlit/secrets.toml:

NEO4J_URI = "bolt://your_neo4j_uri"
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "your_password"
NEO4J_DATABASE = "neo4j"

4. Launch the app

streamlit run app.py

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🧠 How It Works

1. User Input

You ask a question like:

"Can I terminate the agreement anytime?"

2. Entity Extraction

Entities like terminate, agreement are extracted.

3. Knowledge Graph Retrieval

Relevant triples from Neo4j are retrieved.

4. Memory Recall

Past similar Q&A are pulled from persistent memory (Faiss).

5. Prompt Generation

Triples + memory form a context which is sent to LLaMA-3B via Hugging Face API.

6. Answer Generation

The LLM answers based only on retrieved facts — no hallucination.

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💾 Save & Load Chat

You can download your chat as a .json file and re-upload it to continue your session. All retrieved triples and memory are retained across sessions!

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📸 Demo Snapshots

Knowledge Graph Visualization

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📌 Tech Stack

• Frontend: Streamlit
• LLM: Meta LLaMA-3B-Instruct via HuggingFace
• Graph: Neo4j (Aura Free or Local)
• Embeddings: SentenceTransformers
• Memory Store: FAISS
• Triplet Extraction: SpaCy / RE Pipelines
• NER: Custom + pre-trained models

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🛡 Limitations

• Currently optimized for the eBay User Agreement
• Requires manual graph building from text
• Needs HuggingFace token (streaming)

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📬 Contact

For suggestions or collaboration:

• 📧 mgmohit1111@gmail.com
• 💼 LinkedIn

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🧠 Acknowledgement

• GraphRAG research from Meta AI
• Neo4j Knowledge Graphs
• LangChain Memory Chains