Pinecone向量存储

🌐 Pinecone Vector Store

PineconeVector 类提供了一个接口，用于访问 Pinecone 的向量数据库。它提供实时向量搜索，具有混合搜索、元数据过滤和命名空间管理等功能。

🌐 The PineconeVector class provides an interface to Pinecone's vector database. It provides real-time vector search, with features like hybrid search, metadata filtering, and namespace management.

构造函数选项

🌐 Constructor Options

构造函数接受所有 Pinecone 配置选项 以及 Mastra 特定字段。

🌐 The constructor accepts all Pinecone configuration options plus Mastra-specific fields.

id:

string

Unique identifier for this vector store instance

apiKey:

string

Pinecone API key

controllerHostUrl?:

string

Custom Pinecone controller host URL

additionalHeaders?:

Record<string, string>

Additional HTTP headers to include in requests

sourceTag?:

string

Source tag for request tracking

cloud?:

'aws' | 'gcp' | 'azure'

= aws

Cloud provider for new index creation

region?:

string

= us-east-1

Region for new index creation

方法

🌐 Methods

createIndex()

indexName:

string

Name of the index to create

dimension:

number

Vector dimension (must match your embedding model)

metric?:

'cosine' | 'euclidean' | 'dotproduct'

= cosine

Distance metric for similarity search. Use 'dotproduct' if you plan to use hybrid search.

upsert()

indexName:

string

Name of your Pinecone index

vectors:

number[][]

Array of dense embedding vectors

sparseVectors?:

{ indices: number[], values: number[] }[]

Array of sparse vectors for hybrid search. Each vector must have matching indices and values arrays.

metadata?:

Record<string, any>[]

Metadata for each vector

ids?:

string[]

Optional vector IDs (auto-generated if not provided)

namespace?:

string

Optional namespace to store vectors in. Vectors in different namespaces are isolated from each other.

query()

indexName:

string

Name of the index to query

vector:

number[]

Dense query vector to find similar vectors

sparseVector?:

{ indices: number[], values: number[] }

Optional sparse vector for hybrid search. Must have matching indices and values arrays.

topK?:

number

= 10

Number of results to return

filter?:

Record<string, any>

Metadata filters for the query

includeVector?:

boolean

= false

Whether to include the vector in the result

namespace?:

string

Optional namespace to query vectors from. Only returns results from the specified namespace.

listIndexes()

返回一个由索引名称组成的字符串数组。

🌐 Returns an array of index names as strings.

describeIndex()

indexName:

string

Name of the index to describe

返回：

🌐 Returns:

interface IndexStats {
  dimension: number;
  count: number;
  metric: "cosine" | "euclidean" | "dotproduct";
}

deleteIndex()

indexName:

string

Name of the index to delete

updateVector()

通过 ID 或元数据过滤器更新单个向量。必须提供 id 或 filter 中的一个，但不能同时提供两者。

🌐 Update a single vector by ID or by metadata filter. Either id or filter must be provided, but not both.

indexName:

string

Name of the index containing the vector

id?:

string

ID of the vector to update (mutually exclusive with filter)

filter?:

Record<string, any>

Metadata filter to identify vector(s) to update (mutually exclusive with id)

namespace?:

string

Optional namespace for the update operation

update:

object

Update parameters

update.vector?:

number[]

New vector values to update

update.metadata?:

Record<string, any>

New metadata to update

deleteVector()

indexName:

string

Name of the index containing the vector

id:

string

ID of the vector to delete

deleteVectors()

通过 ID 或元数据过滤器删除多个向量。必须提供 ids 或 filter 中的一个，但不能同时提供两者。

🌐 Delete multiple vectors by IDs or by metadata filter. Either ids or filter must be provided, but not both.

indexName:

string

Name of the index containing the vectors to delete

ids?:

string[]

Array of vector IDs to delete (mutually exclusive with filter)

filter?:

Record<string, any>

Metadata filter to identify vectors to delete (mutually exclusive with ids)

namespace?:

string

Optional namespace for the deletion operation

响应类型

🌐 Response Types

查询结果以此格式返回：

🌐 Query results are returned in this format:

interface QueryResult {
  id: string;
  score: number;
  metadata: Record<string, any>;
  vector?: number[]; // Only included if includeVector is true
}

错误处理

🌐 Error Handling

该存储会抛出可以被捕获的类型化错误：

🌐 The store throws typed errors that can be caught:

try {
  await store.query({
    indexName: "index_name",
    queryVector: queryVector,
  });
} catch (error) {
  if (error instanceof VectorStoreError) {
    console.log(error.code); // 'connection_failed' | 'invalid_dimension' | etc
    console.log(error.details); // Additional error context
  }
}

环境变量

🌐 Environment Variables

所需的环境变量：

🌐 Required environment variables:

• PINECONE_API_KEY：你的 Pinecone API 密钥

混合搜索

🌐 Hybrid Search

Pinecone 通过结合密集向量和稀疏向量来支持混合搜索。使用混合搜索的方法如下：

🌐 Pinecone supports hybrid search by combining dense and sparse vectors. To use hybrid search:

1. 使用 metric: 'dotproduct' 创建索引
2. 在执行 upsert 时，使用 sparseVectors 参数提供稀疏向量
3. 在查询时，使用 sparseVector 参数提供一个稀疏向量

相关

🌐 Related

• 元数据过滤器