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*   Build a Question Answering application over a Graph Database

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Build a Question Answering application over a Graph Database
============================================================

In this guide we’ll go over the basic ways to create a Q&A chain over a graph database. These systems will allow us to ask a question about the data in a graph database and get back a natural language answer.

⚠️ Security note ⚠️[​](#security-note "Direct link to ⚠️ Security note ⚠️")
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Building Q&A systems of graph databases requires executing model-generated graph queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your chain/agent’s needs. This will mitigate though not eliminate the risks of building a model-driven system. For more on general security best practices, [see here](/v0.2/docs/security).

Architecture[​](#architecture "Direct link to Architecture")
------------------------------------------------------------

At a high-level, the steps of most graph chains are:

1.  **Convert question to a graph database query**: Model converts user input to a graph database query (e.g. Cypher).
2.  **Execute graph database query**: Execute the graph database query.
3.  **Answer the question**: Model responds to user input using the query results.

![sql_usecase.png](/v0.2/assets/images/graph_usecase-34d891523e6284bb6230b38c5f8392e5.png)

Setup[​](#setup "Direct link to Setup")
---------------------------------------

#### Install dependencies[​](#install-dependencies "Direct link to Install dependencies")

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i langchain @langchain/community @langchain/openai neo4j-driver

    yarn add langchain @langchain/community @langchain/openai neo4j-driver

    pnpm add langchain @langchain/community @langchain/openai neo4j-driver

#### Set environment variables[​](#set-environment-variables "Direct link to Set environment variables")

We’ll use OpenAI in this example:

    OPENAI_API_KEY=your-api-key# Optional, use LangSmith for best-in-class observabilityLANGSMITH_API_KEY=your-api-keyLANGCHAIN_TRACING_V2=true

Next, we need to define Neo4j credentials. Follow [these installation steps](https://neo4j.com/docs/operations-manual/current/installation/) to set up a Neo4j database.

    NEO4J_URI="bolt://localhost:7687"NEO4J_USERNAME="neo4j"NEO4J_PASSWORD="password"

The below example will create a connection with a Neo4j database and will populate it with example data about movies and their actors.

    import "neo4j-driver";import { Neo4jGraph } from "@langchain/community/graphs/neo4j_graph";const url = Deno.env.get("NEO4J_URI");const username = Deno.env.get("NEO4J_USER");const password = Deno.env.get("NEO4J_PASSWORD");const graph = await Neo4jGraph.initialize({ url, username, password });// Import movie informationconst moviesQuery = `LOAD CSV WITH HEADERS FROM 'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies_small.csv'AS rowMERGE (m:Movie {id:row.movieId})SET m.released = date(row.released),    m.title = row.title,    m.imdbRating = toFloat(row.imdbRating)FOREACH (director in split(row.director, '|') |     MERGE (p:Person {name:trim(director)})    MERGE (p)-[:DIRECTED]->(m))FOREACH (actor in split(row.actors, '|') |     MERGE (p:Person {name:trim(actor)})    MERGE (p)-[:ACTED_IN]->(m))FOREACH (genre in split(row.genres, '|') |     MERGE (g:Genre {name:trim(genre)})    MERGE (m)-[:IN_GENRE]->(g))`;await graph.query(moviesQuery);

    Schema refreshed successfully.

    []

Graph schema[​](#graph-schema "Direct link to Graph schema")
------------------------------------------------------------

In order for an LLM to be able to generate a Cypher statement, it needs information about the graph schema. When you instantiate a graph object, it retrieves the information about the graph schema. If you later make any changes to the graph, you can run the `refreshSchema` method to refresh the schema information.

    await graph.refreshSchema();console.log(graph.getSchema());

    Node properties are the following:Movie {imdbRating: FLOAT, id: STRING, released: DATE, title: STRING}, Person {name: STRING}, Genre {name: STRING}Relationship properties are the following:The relationships are the following:(:Movie)-[:IN_GENRE]->(:Genre), (:Person)-[:DIRECTED]->(:Movie), (:Person)-[:ACTED_IN]->(:Movie)

Great! We’ve got a graph database that we can query. Now let’s try hooking it up to an LLM.

Chain[​](#chain "Direct link to Chain")
---------------------------------------

Let’s use a simple chain that takes a question, turns it into a Cypher query, executes the query, and uses the result to answer the original question.

![graph_chain.webp](/v0.2/assets/images/graph_chain-6379941793e0fa985e51e4bda0329403.webp)

LangChain comes with a built-in chain for this workflow that is designed to work with Neo4j: [GraphCypherQAChain](https://python.langchain.com/docs/use_cases/graph/graph_cypher_qa)

    import { GraphCypherQAChain } from "langchain/chains/graph_qa/cypher";import { ChatOpenAI } from "@langchain/openai";const llm = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 });const chain = GraphCypherQAChain.fromLLM({  llm,  graph,});const response = await chain.invoke({  query: "What was the cast of the Casino?",});console.log(response);

    { result: "James Woods, Joe Pesci, Robert De Niro, Sharon Stone" }

### Next steps[​](#next-steps "Direct link to Next steps")

For more complex query-generation, we may want to create few-shot prompts or add query-checking steps. For advanced techniques like this and more check out:

*   [Prompting strategies](/v0.2/docs/how_to/graph_prompting): Advanced prompt engineering techniques.
*   [Mapping values](/v0.2/docs/how_to/graph_mapping/): Techniques for mapping values from questions to database.
*   [Semantic layer](/v0.2/docs/how_to/graph_semantic): Techniques for working implementing semantic layers.
*   [Constructing graphs](/v0.2/docs/how_to/graph_constructing): Techniques for constructing knowledge graphs.

* * *

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*   [⚠️ Security note ⚠️](#security-note)
*   [Architecture](#architecture)
*   [Setup](#setup)
*   [Graph schema](#graph-schema)
*   [Chain](#chain)
    *   [Next steps](#next-steps)