Implementing Graph-Based Retrieval Augmented Generation

This document outlines the implementation of a system that combines the power of Large Language Models (LLMs) with structured knowledge from a graph database to perform advanced question answering. This approach, known as Graph-Based Retrieval Augmented Generation (RAG), allows us to answer complex queries that require understanding relationships between entities.

We will use LangChain to orchestrate the process, OpenAI for language modeling, and Neo4j as our graph database.

Table of Contents

• Page 1: Introduction to Graph-Based RAG
• Page 2: Knowledge Graph Setup with Neo4j
• Page 3: Implementing Graph-Aware Retrieval with LangChain
• Page 4: Enhancing the RAG Chain with Context and Memory
• Page 5: Advanced Considerations and Future Directions

Page 1: Introduction to Graph-Based RAG

1.1 What is Graph-Based RAG?

Traditional RAG systems retrieve information from unstructured data sources (like text documents) using vector similarity search. Graph-Based RAG enhances this process by leveraging the structured knowledge stored in a graph database. A graph database represents data as nodes (entities) and edges (relationships), allowing us to model complex relationships.

In Graph-Based RAG:

1. We store our knowledge in a graph database (e.g., Neo4j).
2. When a user asks a question, we use the graph to find relevant entities and relationships.
3. We combine this structured information with the user’s query and feed it to an LLM.
4. The LLM generates a more accurate and contextually relevant answer, grounded in the graph data.

1.2 Benefits of Graph-Based RAG

• Enhanced Accuracy: By leveraging structured relationships, we can retrieve more precise and relevant information.
• Multi-Hop Reasoning: Graphs enable us to answer complex questions that require traversing multiple relationships (“find movies directed by the director of ‘Inception’”).
• Contextual Understanding: The graph structure provides rich context, helping the LLM understand the relationships between entities.
• Explainability: The graph structure provides a clear and interpretable way to understand how the answer was derived.

1.3 Use Case Example

Consider a movie database stored in a graph. Nodes represent movies, directors, actors, and genres, and edges represent relationships like “directed by,” “acted in,” and “has genre.”

A user asks: “Which actors starred in movies directed by Christopher Nolan?”

A Graph-Based RAG system would:

1. Find the “Christopher Nolan” node.
2. Traverse the “directed by” edges to find the movies he directed.
3. Traverse the “acted in” edges from those movies to find the actors.
4. Provide this structured information to the LLM, which then generates the answer.

Page 2: Knowledge Graph Setup with Neo4j

2.1 Choosing a Graph Database: Neo4j

We will use Neo4j, a popular open-source graph database, to store our knowledge graph. Neo4j uses Cypher, a powerful graph query language.

2.2 Installing and Setting Up Neo4j

Instructions for installing Neo4j can be found on the official Neo4j website. This document assumes you have a running Neo4j instance.

2.3 Defining the Graph Schema

A well-defined schema is crucial for effective retrieval. Here’s the schema we’ll use for our movie database:

• Nodes:
  • Movie: properties – title (string), year (integer), rating (float)
  • Director: properties – name (string)
  • Actor: properties – name (string)
  • Genre: properties – name (string)
• Relationships:
  • (Movie)-[:DIRECTED_BY]->(Director)
  • (Movie)-[:ACTED_IN]->(Actor)
  • (Movie)-[:HAS_GENRE]->(Genre)

2.4 Creating the Graph in Neo4j

You can use the Neo4j Browser or Cypher queries to create the nodes and relationships. Here’s an example Cypher script:

CREATE (m:Movie {title: 'The Matrix', year: 1999, rating: 8.7})
CREATE (d:Director {name: 'Lana Wachowski'})
CREATE (a1:Actor {name: 'Keanu Reeves'})
CREATE (a2:Actor {name: 'Laurence Fishburne'})
CREATE (g1:Genre {name: 'Science Fiction'})
CREATE (g2:Genre {name: 'Action'})
CREATE (m)-[:DIRECTED_BY]->(d)
CREATE (m)-[:ACTED_IN]->(a1)
CREATE (m)-[:ACTED_IN]->(a2)
CREATE (m)-[:HAS_GENRE]->(g1)
CREATE (m)-[:HAS_GENRE]->(g2);

CREATE (m2:Movie {title: 'Inception', year: 2010, rating: 8.8})
CREATE (d2:Director {name: 'Christopher Nolan'})
CREATE (a3:Actor {name: 'Leonardo DiCaprio'})
CREATE (m2)-[:DIRECTED_BY]->(d2)
CREATE (m2)-[:ACTED_IN]->(a3)
CREATE (m2)-[:HAS_GENRE]->(g1)
CREATE (m2)-[:HAS_GENRE]->(g3:Genre {name: 'Thriller'});
    

This script creates two movies, two directors, three actors, and three genres, and connects them with the appropriate relationships.

Page 3: Implementing Graph-Aware Retrieval with LangChain

3.1 LangChain Integration

LangChain simplifies the process of interacting with LLMs and graph databases. We’ll use it to:

• Connect to Neo4j.
• Generate Cypher queries from natural language questions.
• Execute the queries and retrieve results.
• Pass the results to an LLM to generate a natural language answer.

3.2 Connecting to Neo4j with LangChain

The following Python code demonstrates how to connect to Neo4j using LangChain:

from langchain_community.graphs import Neo4jGraph
from langchain_openai import OpenAI

# Initialize LLM and Graph Database Connection
llm = OpenAI(temperature=0.1, model_name="gpt-4-turbo-preview")
graph = Neo4jGraph(
    url="bolt://localhost:7687",  # Replace with your Neo4j URL
    username="neo4j",             # Replace with your Neo4j username
    password="your_password"      # Replace with your Neo4j password
)
    

This code establishes a connection to your Neo4j instance. Make sure to replace the placeholders with your actual Neo4j credentials.

3.3 Generating Cypher Queries with LangChain

LangChain’s `GraphCypherQAChain` can translate natural language questions into Cypher queries. Here’s how to use it:

from langchain.chains import GraphCypherQAChain

# Create a chain for question answering over the graph
graph_qa_chain = GraphCypherQAChain.from_llm(
    graph=graph,
    llm=llm,
    verbose=True,  # Set to True for debugging
)

# Example query
query = "Who directed the movie 'The Matrix'?"
response = graph_qa_chain.run(query)
print(response)
    

The `GraphCypherQAChain` takes the graph connection and the LLM as input. When you provide a query, it generates a Cypher query, executes it against Neo4j, and uses the LLM to generate a natural language answer. Setting `verbose=True` will print the generated Cypher query, which is helpful for debugging.

Page 4: Enhancing the RAG Chain with Context and Memory

4.1 Providing Graph Schema Context

To improve the accuracy of Cypher query generation, it’s crucial to provide the LLM with a clear description of the graph schema. We can do this using a prompt template.

4.2 Implementing a Robust RAG Chain

The following code demonstrates a more robust Graph-Based RAG chain with schema context and conversation memory:

from langchain_community.graphs import Neo4jGraph
from langchain_openai import OpenAI
from langchain.chains import GraphCypherQAChain
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory

# 1. Initialize LLM and Graph Database Connection
llm = OpenAI(temperature=0.1, model_name="gpt-4-turbo-preview")
graph = Neo4jGraph(
    url="bolt://localhost:7687",
    username="neo4j",
    password="your_password"
)

# 2. Define the Graph Schema
graph_schema = """
The graph contains nodes representing movies, directors, actors, and genres.
- Movie nodes have properties: title (string), year (integer), rating (float).
- Director nodes have properties: name (string).
- Actor nodes have properties: name (string).
- Genre nodes have properties: name (string).

There are relationships between these nodes:
- (Movie)-[:DIRECTED_BY]->(Director)
- (Movie)-[:ACTED_IN]->(Actor)
- (Movie)-[:HAS_GENRE]->(Genre)

When asking questions, refer to the node properties and relationship types as described above.
For example, to find the director of a movie, use the DIRECTED_BY relationship.
To find actors in a movie, use the ACTED_IN relationship.
To find the genre of a movie, use the HAS_GENRE relationship.
"""

# 3. Create a More Robust Graph-Aware QA Chain with Memory and Schema Context
memory = ConversationBufferMemory(memory_key="chat_history")

prompt = PromptTemplate(
    input_variables=["query", "graph_schema", "chat_history"],
    template="""You are a knowledgeable agent capable of answering questions about a movie database stored as a graph.
The graph schema is as follows:
{graph_schema}

Use the provided graph schema to generate accurate Cypher queries to retrieve information.
Only use the relationship types and node properties described in the schema.

Previous conversation history:
{chat_history}

Question: {query}
Cypher query:""",
)

cypher_generation_chain = LLMChain(llm=llm, prompt=prompt, output_key="query")

final_prompt = PromptTemplate(
    input_variables=["result", "query"],
    template="""Based on the graph data retrieved by the Cypher query:
{result}

Answer the following question: {query}""",
)

answer_generation_chain = LLMChain(llm=llm, prompt=final_prompt, output_key="answer")

from langchain.chains import SequentialChain

graph_based_rag_chain = SequentialChain(
    memory=memory,
    chains=[cypher_generation_chain, GraphCypherQAChain.from_llm(graph=graph, llm=llm, verbose=True, cypher_field="query"), answer_generation_chain],
    input_variables=["query"],
    output_variables=["answer"],
    verbose=True,
)

# 4. Example Usage
user_query1 = "Who directed the movie 'The Matrix'?"
response1 = graph_based_rag_chain({"query": user_query1, "graph_schema": graph_schema})
print(f"Question: {user_query1}")
print(f"Answer: {response1['answer']}")
print("-" * 40)

user_query2 = "Which actors starred in 'The Matrix'?"
response2 = graph_based_rag_chain({"query": user_query2, "graph_schema": graph_schema})
print(f"Question: {user_query2}")
print(f"Answer: {response2['answer']}")
print("-" * 40)

user_query3 = "Tell me about movies directed by the director of 'The Matrix'."
response3 = graph_based_rag_chain({"query": user_query3, "graph_schema": graph_schema})
print(f"Question: {user_query3}")
print(f"Answer: {response3['answer']}")
print("-" * 40)

user_query4 = "What genres are associated with 'The Matrix'?"
response4 = graph_based_rag_chain({"query": user_query4, "graph_schema": graph_schema})
print(f"Question: {user_query4}")
print(f"Answer: {response4['answer']}")
print("-" * 40)

user_query5 = "List all movies released in the same year as 'The Matrix'."
response5 = graph_based_rag_chain({"query": user_query5, "graph_schema": graph_schema})
print(f"Question: {user_query5}")
print(f"Answer: {response5['answer']}")
print("-" * 40)
    

Key improvements in this code:

• We define the `graph_schema` as a string, providing a clear description of the graph structure to the LLM.
• We use a `PromptTemplate` in the `cypher_generation_chain` to instruct the LLM to use the schema when generating Cypher queries.
• We use `ConversationBufferMemory` to maintain a chat history, allowing the model to answer follow-up questions.
• We use a `SequentialChain` to orchestrate the process of generating the Cypher query, executing it, and generating the final answer.

Page 5: Advanced Considerations and Future Directions

5.1 Advanced Techniques

While the previous code provides a solid foundation, here are some advanced techniques to further enhance Graph-Based RAG:

• More Sophisticated Schema Design: A well-designed graph schema is crucial. Consider using techniques like node labels, property constraints, and indexing to optimize performance.
• Advanced Cypher Query Generation: For very complex queries, you might need more sophisticated Cypher generation logic, potentially involving multiple queries or graph traversal algorithms.
• Hybrid Retrieval: Combine graph-based retrieval with traditional vector-based retrieval for improved accuracy and robustness. For example, use vector search to find relevant documents and then use the graph to refine the results.
• Graph Embeddings: Represent nodes and relationships as vectors to perform semantic similarity searches within the graph.
• **LLM Fine-tuning: ** Fine-tuning the LLM on a dataset of question-Cypher query pairs can significantly improve the accuracy of Cypher generation.

5.2 Future Directions

Graph-Based RAG is a rapidly evolving field. Future research directions include:

• Automated Graph Construction: Developing methods to automatically extract knowledge graphs from unstructured data.
• Dynamic Graph Updates: Efficiently updating the graph in response to changes in the underlying data.
• Multi-modal Graph RAG: Extending Graph-Based RAG to handle multi-modal data, such as images and videos, in the graph.
• Explainable AI: Leveraging the graph structure to provide more transparent and explainable answers.

5.3 Conclusion

Graph-Based RAG offers a powerful approach to building question answering systems that can reason over structured knowledge. By combining the strengths of graph databases and LLMs, we can achieve greater accuracy, contextuality, and explainability. The techniques and code outlined in this document provide a starting point for implementing your own Graph-Based RAG systems.