• January 18, 2024
• 3 min read

Langroid: Knolwedge Graph RAG powered by Neo4j

"Chat" with various sources of information

LLMs are increasingly being used to let users converse in natural language with a variety of types of data sources:

• unstructured text documents: a user's query is augmented with "relevant" documents or chunks (retrieved from an embedding-vector store) and fed to the LLM to generate a response -- this is the idea behind Retrieval Augmented Generation (RAG).
• SQL Databases: An LLM translates a user's natural language question into an SQL query, which is then executed by another module, sending results to the LLM, so it can generate a natural language response based on the results.
• Tabular datasets: similar to the SQL case, except instead of an SQL Query, the LLM generates a Pandas dataframe expression.

Langroid has had specialized Agents for the above scenarios: DocChatAgent for RAG with unstructured text documents, SQLChatAgent for SQL databases, and TableChatAgent for tabular datasets.

Adding support for Neo4j Knowledge Graphs

Analogous to the SQLChatAgent, Langroid now has a Neo4jChatAgent to interact with a Neo4j knowledge graph using natural language. This Agent has access to two key tools that enable it to handle a user's queries:

• GraphSchemaTool to get the schema of a Neo4j knowledge graph.
• CypherRetrievalTool to generate Cypher queries from a user's query. Cypher is a specialized query language for Neo4j, and even though it is not as widely known as SQL, most LLMs today can generate Cypher Queries.

Setting up a basic Neo4j-based RAG chatbot is straightforward. First ensure you set these environment variables (or provide them in a .env file):

NEO4J_URI=<uri>
NEO4J_USERNAME=<username>
NEO4J_PASSWORD=<password>
NEO4J_DATABASE=<database>

Then you can configure and define a Neo4jChatAgent like this:

import langroid as lr
import langroid.language_models as lm

from langroid.agent.special.neo4j.neo4j_chat_agent import (
    Neo4jChatAgent,
    Neo4jChatAgentConfig,
    Neo4jSettings,
)

llm_config = lm.OpenAIGPTConfig()

load_dotenv()

neo4j_settings = Neo4jSettings()

kg_rag_agent_config = Neo4jChatAgentConfig(
    neo4j_settings=neo4j_settings,
    llm=llm_config, 
)
kg_rag_agent = Neo4jChatAgent(kg_rag_agent_config)
kg_rag_task = lr.Task(kg_rag_agent, name="kg_RAG")
kg_rag_task.run()

Example: PyPi Package Dependency Chatbot

In the Langroid-examples repository, there is an example python script showcasing tools/Function-calling + RAG using a DependencyGraphAgent derived from Neo4jChatAgent. This agent uses two tools, in addition to the tools available to Neo4jChatAgent:

• GoogleSearchTool to find package version and type information, as well as to answer other web-based questions after acquiring the required information from the dependency graph.
• DepGraphTool to construct a Neo4j knowledge-graph modeling the dependency structure for a specific package, using the API at DepsDev.

In response to a user's query about dependencies, the Agent decides whether to use a Cypher query or do a web search. Here is what it looks like in action:

Chatting with the `DependencyGraphAgent` (derived from Langroid's `Neo4jChatAgent`). When a user specifies a Python package name (in this case "chainlit"), the agent searches the web using `GoogleSearchTool` to find the version of the package, and then uses the `DepGraphTool` to construct the dependency graph as a neo4j knowledge graph. The agent then answers questions by generating Cypher queries to the knowledge graph, or by searching the web.