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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
    *   [🦜🛠️ LangSmith](/v0.2/docs/langsmith/)
        
    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
    *   [v0.2](/v0.2/docs/versions/v0_2)
    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   [Tutorials](/v0.2/docs/tutorials/)
*   Build a Question Answering application over a Graph Database

On this page

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 ⚠️")
---------------------------------------------------------------------------

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.schema);

    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?",});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)
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