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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
    
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    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
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    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

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*   [How-to guides](/v0.2/docs/how_to/)
*   How to construct knowledge graphs

On this page

How to construct knowledge graphs
=================================

In this guide we’ll go over the basic ways of constructing a knowledge graph based on unstructured text. The constructed graph can then be used as knowledge base in a RAG application. At a high-level, the steps of constructing a knowledge are from text are:

1.  Extracting structured information from text: Model is used to extract structured graph information from text.
2.  Storing into graph database: Storing the extracted structured graph information into a graph database enables downstream RAG applications

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.

    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 });

LLM Graph Transformer[​](#llm-graph-transformer "Direct link to LLM Graph Transformer")
---------------------------------------------------------------------------------------

Extracting graph data from text enables the transformation of unstructured information into structured formats, facilitating deeper insights and more efficient navigation through complex relationships and patterns. The LLMGraphTransformer converts text documents into structured graph documents by leveraging a LLM to parse and categorize entities and their relationships. The selection of the LLM model significantly influences the output by determining the accuracy and nuance of the extracted graph data.

    import { ChatOpenAI } from "@langchain/openai";import { LLMGraphTransformer } from "@langchain/community/experimental/graph_transformers/llm";const model = new ChatOpenAI({  temperature: 0,  model: "gpt-4-turbo-preview",});const llmGraphTransformer = new LLMGraphTransformer({  llm: model,});

Now we can pass in example text and examine the results.

    import { Document } from "@langchain/core/documents";let text = `Marie Curie, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.She was, in 1906, the first woman to become a professor at the University of Paris.`;const result = await llmGraphTransformer.convertToGraphDocuments([  new Document({ pageContent: text }),]);console.log(`Nodes: ${result[0].nodes.length}`);console.log(`Relationships:${result[0].relationships.length}`);

    Nodes: 8Relationships:7

Note that the graph construction process is non-deterministic since we are using LLM. Therefore, you might get slightly different results on each execution. Examine the following image to better grasp the structure of the generated knowledge graph.

![graph_construction1.png](/v0.2/assets/images/graph_construction1-2b4d31978d58696d5a6a52ad92ae088f.png)

Additionally, you have the flexibility to define specific types of nodes and relationships for extraction according to your requirements.

    const llmGraphTransformerFiltered = new LLMGraphTransformer({  llm: model,  allowedNodes: ["PERSON", "COUNTRY", "ORGANIZATION"],  allowedRelationships: ["NATIONALITY", "LOCATED_IN", "WORKED_AT", "SPOUSE"],  strictMode: false,});const result_filtered =  await llmGraphTransformerFiltered.convertToGraphDocuments([    new Document({ pageContent: text }),  ]);console.log(`Nodes: ${result_filtered[0].nodes.length}`);console.log(`Relationships:${result_filtered[0].relationships.length}`);

    Nodes: 6Relationships:4

For a better understanding of the generated graph, we can again visualize it.

![graph_construction1.png](/v0.2/assets/images/graph_construction2-8b43506ae0fb3a006eaa4ba83fea8af5.png)

Storing to graph database[​](#storing-to-graph-database "Direct link to Storing to graph database")
---------------------------------------------------------------------------------------------------

The generated graph documents can be stored to a graph database using the `addGraphDocuments` method.

    await graph.addGraphDocuments(result_filtered);

* * *

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*   [Setup](#setup)
*   [LLM Graph Transformer](#llm-graph-transformer)
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