以编程方式使用文档

Neo4j 是一个开源图形数据库，内置支持向量相似性搜索。它支持近似最近邻搜索、欧几里得和余弦相似度，以及结合向量和关键词检索的混合搜索。

本指南帮助您开始使用 Neo4j 向量存储。有关所有 Neo4jVectorStore 功能和配置的详细文档，请参阅 API 参考.

概述

集成详情

类包PY 支持下载版本
Neo4jVectorStore@langchain/neo4j✅!NPM - 下载!NPM - 版本

设置

安装 @langchain/neo4j、与 MongoDB 兼容的 neo4j-driver, @langchain/core，以及一个嵌入提供者（本指南使用 OpenAI 嵌入):

npm install @langchain/neo4j @langchain/core neo4j-driver @langchain/openai
yarn add @langchain/neo4j @langchain/core neo4j-driver @langchain/openai
pnpm add @langchain/neo4j @langchain/core neo4j-driver @langchain/openai

设置一个 Neo4j 使用 docker-compose

Neo4j 提供了一个预构建的 Docker 镜像，可用于快速设置自托管 Neo4j 数据库实例。 在下方创建一个名为 docker-compose.yml:

services:
  database:
    image: neo4j
    ports:
      - 7687:7687
      - 7474:7474
    environment:
      - NEO4J_AUTH=neo4j/pleaseletmein

然后在同一目录中运行 docker compose up 启动容器。

您可以找到更多关于如何设置 Neo4j 的更多信息，请访问其 网站.

用法

// Configuration object for Neo4j connection and other related settings
const config = {
  url: "bolt://localhost:7687", // URL for the Neo4j instance
  username: "neo4j", // Username for Neo4j authentication
  password: "pleaseletmein", // Password for Neo4j authentication
  indexName: "vector", // Name of the vector index
  keywordIndexName: "keyword", // Name of the keyword index if using hybrid search
  searchType: "vector" as const, // Type of search (e.g., vector, hybrid)
  nodeLabel: "Chunk", // Label for the nodes in the graph
  textNodeProperty: "text", // Property of the node containing text
  embeddingNodeProperty: "embedding", // Property of the node containing embedding
};

const documents = [
  { pageContent: "what's this", metadata: { a: 2 } },
  { pageContent: "Cat drinks milk", metadata: { a: 1 } },
];

const neo4jVectorIndex = await Neo4jVectorStore.fromDocuments(
  documents,
  new OpenAIEmbeddings(),
  config
);

const results = await neo4jVectorIndex.similaritySearch("water", 1);

console.log(results);

/*
  [ Document { pageContent: 'Cat drinks milk', metadata: { a: 1 } } ]
*/

await neo4jVectorIndex.close();

使用 retrievalQuery 参数自定义响应

/*
 * The retrievalQuery is a customizable Cypher query fragment used in the Neo4jVectorStore class to define how
 * search results should be retrieved and presented from the Neo4j database. It allows developers to specify
 * the format and structure of the data returned after a similarity search.
 * Mandatory columns for `retrievalQuery`:
 *
 * 1. text:
 *    - Description: Represents the textual content of the node.
 *    - Type: String
 *
 * 2. score:
 *    - Description: Represents the similarity score of the node in relation to the search query. A
 *      higher score indicates a closer match.
 *    - Type: Float (ranging between 0 and 1, where 1 is a perfect match)
 *
 * 3. metadata:
 *    - Description: Contains additional properties and information about the node. This can include
 *      any other attributes of the node that might be relevant to the application.
 *    - Type: Object (key-value pairs)
 *    - Example: { "id": "12345", "category": "Books", "author": "John Doe" }
 *
 * Note: While you can customize the `retrievalQuery` to fetch additional columns or perform
 * transformations, never omit the mandatory columns. The names of these columns (`text`, `score`,
 * and `metadata`) should remain consistent. Renaming them might lead to errors or unexpected behavior.
 */

// Configuration object for Neo4j connection and other related settings
const config = {
  url: "bolt://localhost:7687", // URL for the Neo4j instance
  username: "neo4j", // Username for Neo4j authentication
  password: "pleaseletmein", // Password for Neo4j authentication
  retrievalQuery: `
    RETURN node.text AS text, score, {a: node.a * 2} AS metadata
  `,
};

const documents = [
  { pageContent: "what's this", metadata: { a: 2 } },
  { pageContent: "Cat drinks milk", metadata: { a: 1 } },
];

const neo4jVectorIndex = await Neo4jVectorStore.fromDocuments(
  documents,
  new OpenAIEmbeddings(),
  config
);

const results = await neo4jVectorIndex.similaritySearch("water", 1);

console.log(results);

/*
  [ Document { pageContent: 'Cat drinks milk', metadata: { a: 2 } } ]
*/

await neo4jVectorIndex.close();

从现有图形实例化 Neo4jVectorStore

/**
 * `fromExistingGraph` Method:
 *
 * Description:
 * This method initializes a `Neo4jVectorStore` instance using an existing graph in the Neo4j database.
 * It's designed to work with nodes that already have textual properties but might not have embeddings.
 * The method will compute and store embeddings for nodes that lack them.
 *
 * Note:
 * This method is particularly useful when you have a pre-existing graph with textual data and you want
 * to enhance it with vector embeddings for similarity searches without altering the original data structure.
 */

// Configuration object for Neo4j connection and other related settings
const config = {
  url: "bolt://localhost:7687", // URL for the Neo4j instance
  username: "neo4j", // Username for Neo4j authentication
  password: "pleaseletmein", // Password for Neo4j authentication
  indexName: "wikipedia",
  nodeLabel: "Wikipedia",
  textNodeProperties: ["title", "description"],
  embeddingNodeProperty: "embedding",
  searchType: "hybrid" as const,
};

// You should have a populated Neo4j database to use this method
const neo4jVectorIndex = await Neo4jVectorStore.fromExistingGraph(
  new OpenAIEmbeddings(),
  config
);

await neo4jVectorIndex.close();

元数据过滤

/**
 * `similaritySearch` Method with Metadata Filtering:
 *
 * Description:
 * This method facilitates advanced similarity searches within a Neo4j vector index, leveraging both text embeddings and metadata attributes.
 * The third parameter, `filter`, allows for the specification of metadata-based conditions that pre-filter the nodes before performing the similarity search.
 * This approach enhances the search precision by allowing users to query based on complex metadata criteria alongside textual similarity.
 * Metadata filtering also support the following operators:
 *
 *  $eq: Equal
 *  $ne: Not Equal
 *  $lt: Less than
 *  $lte: Less than or equal
 *  $gt: Greater than
 *  $gte: Greater than or equal
 *  $in: In a list of values
 *  $nin: Not in a list of values
 *  $between: Between two values
 *  $like: Text contains value
 *  $ilike: lowered text contains value
 *
 * The filter supports a range of query operations such as equality checks, range queries, and compound conditions (using logical operators like $and, $or).
 * This makes it highly adaptable to varied use cases requiring detailed and specific retrieval of documents based on both content and contextual information.
 *
 * Note:
 * Effective use of this method requires a well-structured Neo4j database where nodes are enriched with both text and metadata properties.
 * The method is particularly useful in scenarios where the integration of text analysis with detailed metadata querying is crucial, such as in content recommendation systems, detailed archival searches, or any application where contextual relevance is key.
 */

// Configuration object for Neo4j connection and other related settings
const config = {
  url: "bolt://localhost:7687", // URL for the Neo4j instance
  username: "neo4j", // Username for Neo4j authentication
  password: "pleaseletmein", // Password for Neo4j authentication
  indexName: "vector", // Name of the vector index
  keywordIndexName: "keyword", // Name of the keyword index if using hybrid search
  searchType: "vector" as const, // Type of search (e.g., vector, hybrid)
  nodeLabel: "Chunk", // Label for the nodes in the graph
  textNodeProperty: "text", // Property of the node containing text
  embeddingNodeProperty: "embedding", // Property of the node containing embedding
};

const documents = [
  { pageContent: "what's this", metadata: { a: 2 } },
  { pageContent: "Cat drinks milk", metadata: { a: 1 } },
];

const neo4jVectorIndex = await Neo4jVectorStore.fromDocuments(
  documents,
  new OpenAIEmbeddings(),
  config
);

const filter = { a: { $eq: 1 } };
const results = await neo4jVectorIndex.similaritySearch("water", 1, { filter });

console.log(results);

/*
  [ Document { pageContent: 'Cat drinks milk', metadata: { a: 1 } } ]
*/

await neo4jVectorIndex.close();

安全

确保数据库连接使用凭证 范围狭窄，仅包含必要的权限。 否则可能会导致数据损坏或丢失，因为调用 代码可能会尝试导致删除、修改 的命令（在适当提示的情况下）或读取敏感数据（如果数据库中存在 此类数据）。 防止此类负面结果的最佳方法是（如适用） 限制授予此工具所用凭证的权限。 例如，为数据库创建只读用户是一个很好的方法 确保调用代码无法修改或删除数据。

API 参考

有关所有 Neo4jVectorStore 功能和配置的详细文档，请参阅 API 参考.

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