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*   [Introduction](/v0.2/docs/introduction)
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    *   [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)
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    *   [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)
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    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
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    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
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    *   [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)
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    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
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    *   [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)
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    *   [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)
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*   [How-to guides](/v0.2/docs/how_to/)
*   How to map values to a database

On this page

How to map values to a database
===============================

In this guide we’ll go over strategies to improve graph database query generation by mapping values from user inputs to database. When using the built-in graph chains, the LLM is aware of the graph schema, but has no information about the values of properties stored in the database. Therefore, we can introduce a new step in graph database QA system to accurately map values.

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 zod

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

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

#### 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.

    []

Detecting entities in the user input[​](#detecting-entities-in-the-user-input "Direct link to Detecting entities in the user input")
------------------------------------------------------------------------------------------------------------------------------------

We have to extract the types of entities/values we want to map to a graph database. In this example, we are dealing with a movie graph, so we can map movies and people to the database.

    import { ChatPromptTemplate } from "@langchain/core/prompts";import { ChatOpenAI } from "@langchain/openai";import { z } from "zod";const llm = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 });const entities = z  .object({    names: z      .array(z.string())      .describe("All the person or movies appearing in the text"),  })  .describe("Identifying information about entities.");const prompt = ChatPromptTemplate.fromMessages([  ["system", "You are extracting person and movies from the text."],  [    "human",    "Use the given format to extract information from the following\ninput: {question}",  ],]);const entityChain = prompt.pipe(llm.withStructuredOutput(entities));

We can test the entity extraction chain.

    const entities = await entityChain.invoke({  question: "Who played in Casino movie?",});entities;

    { names: [ "Casino" ] }

We will utilize a simple `CONTAINS` clause to match entities to database. In practice, you might want to use a fuzzy search or a fulltext index to allow for minor misspellings.

    const matchQuery = `MATCH (p:Person|Movie)WHERE p.name CONTAINS $value OR p.title CONTAINS $valueRETURN coalesce(p.name, p.title) AS result, labels(p)[0] AS typeLIMIT 1`;const matchToDatabase = async (values) => {  let result = "";  for (const entity of values.names) {    const response = await graph.query(matchQuery, {      value: entity,    });    if (response.length > 0) {      result += `${entity} maps to ${response[0]["result"]} ${response[0]["type"]} in database\n`;    }  }  return result;};await matchToDatabase(entities);

    "Casino maps to Casino Movie in database\n"

Custom Cypher generating chain[​](#custom-cypher-generating-chain "Direct link to Custom Cypher generating chain")
------------------------------------------------------------------------------------------------------------------

We need to define a custom Cypher prompt that takes the entity mapping information along with the schema and the user question to construct a Cypher statement. We will be using the LangChain expression language to accomplish that.

    import { StringOutputParser } from "@langchain/core/output_parsers";import {  RunnablePassthrough,  RunnableSequence,} from "@langchain/core/runnables";// Generate Cypher statement based on natural language inputconst cypherTemplate = `Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:{schema}Entities in the question map to the following database values:{entities_list}Question: {question}Cypher query:`;const cypherPrompt = ChatPromptTemplate.fromMessages([  [    "system",    "Given an input question, convert it to a Cypher query. No pre-amble.",  ],  ["human", cypherTemplate],]);const llmWithStop = llm.bind({ stop: ["\nCypherResult:"] });const cypherResponse = RunnableSequence.from([  RunnablePassthrough.assign({ names: entityChain }),  RunnablePassthrough.assign({    entities_list: async (x) => matchToDatabase(x.names),    schema: async (_) => graph.getSchema(),  }),  cypherPrompt,  llmWithStop,  new StringOutputParser(),]);

    const cypher = await cypherResponse.invoke({  question: "Who played in Casino movie?",});cypher;

    'MATCH (:Movie {title: "Casino"})<-[:ACTED_IN]-(actor)\nRETURN actor.name'

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*   [Setup](#setup)
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