pinecone-query

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Pinecone Query Skill

Search for records in Pinecone integrated indexes using natural language text queries via the Pinecone MCP server.

通过Pinecone MCP服务器，使用自然语言文本查询在Pinecone集成索引中搜索记录。

What is this skill for?

此技能的用途是什么？

This skill provides a simple way to query integrated indexes (indexes with built-in Pinecone embedding models) using text queries. The MCP server automatically converts your text into embeddings and searches the index.

此技能提供了一种简单的方式，使用文本查询集成索引（内置Pinecone嵌入模型的索引）。MCP服务器会自动将你的文本转换为嵌入向量并搜索索引。

Prerequisites

前提条件

Required:

✅ Pinecone MCP server must be configured - Check if MCP tools are available
✅ PINECONE_API_KEY environment variable must be set - Get a free API key at https://app.pinecone.io/?sessionType=signup
✅ Index must be an integrated index - Uses Pinecone embedding models (e.g., multilingual-e5-large, llama-text-embed-v2, pinecone-sparse-english-v0)

必需项：

✅ 必须配置Pinecone MCP服务器 - 检查MCP工具是否可用
✅ 必须设置PINECONE_API_KEY环境变量 - 可在https://app.pinecone.io/?sessionType=signup获取免费API密钥
✅ 索引必须为集成索引 - 使用Pinecone嵌入模型（例如multilingual-e5-large、llama-text-embed-v2、pinecone-sparse-english-v0）

When NOT to use this skill

不适合使用此技能的场景

Use the CLI skill instead if:

❌ Your index is a standard index (no integrated embedding model)
❌ You need to query with custom vector values (not text)
❌ You need advanced vector operations (fetch by ID, list vectors, bulk operations)
❌ Your index uses third-party embedding models (OpenAI, HuggingFace, Cohere)

MCP Limitation: The Pinecone MCP currently only supports integrated indexes. For all other use cases, use the Pinecone CLI skill.

请改用CLI技能的情况：

❌ 你的索引是标准索引（无集成嵌入模型）
❌ 你需要使用自定义向量值查询（而非文本）
❌ 你需要高级向量操作（按ID获取、列出向量、批量操作）
❌ 你的索引使用第三方嵌入模型（OpenAI、HuggingFace、Cohere）

MCP限制：Pinecone MCP目前仅支持集成索引。所有其他使用场景，请使用Pinecone CLI技能。

How it works

工作原理

Utilize Pinecone MCP's

search-records

tool to search for records within a specified Pinecone integrated index using a text query.

利用Pinecone MCP的

search-records

工具，通过文本查询在指定的Pinecone集成索引中搜索记录。

Workflow

工作流程

IMPORTANT: Before proceeding, verify the Pinecone MCP tools are available. If MCP tools are not accessible:

Inform the user that the Pinecone MCP server needs to be configured
Check if
```
PINECONE_API_KEY
```
environment variable is set
Direct them to the MCP setup documentation or the
```
pinecone-help
```
skill

Parse the user's input for:
- ```
query
```
  (required): The text to search for.
- ```
index
```
  (required): The name of the Pinecone index to search.
- ```
namespace
```
  (optional): The namespace within the index.
- ```
reranker
```
  (optional): The reranking model to use for improved relevance.
If the user omits required arguments:
- If only the index name is provided, use the
```
describe-index
```
  tool to retrieve available namespaces and ask the user to choose.
- If only a query is provided, use
```
list-indexes
```
  to get available indexes, ask the user to pick one, then use
```
describe-index
```
  for namespaces if needed.
Call the
```
search-records
```
tool with the gathered arguments to perform the search.
Format and display the returned results in a clear, readable table including field highlights (such as ID, score, and relevant metadata).

重要提示：在继续之前，请确认Pinecone MCP工具可用。 如果无法访问MCP工具：

告知用户需要配置Pinecone MCP服务器
检查是否已设置
```
PINECONE_API_KEY
```
环境变量
引导他们查看MCP设置文档或
```
pinecone-help
```
技能

解析用户输入以获取：
- ```
query
```
  （必填）：要搜索的文本。
- ```
index
```
  （必填）：要搜索的Pinecone索引名称。
- ```
namespace
```
  （可选）：索引内的命名空间。
- ```
reranker
```
  （可选）：用于提升相关性的重排序模型。
如果用户遗漏必填参数：
- 如果仅提供了索引名称，使用
```
describe-index
```
  工具获取可用命名空间，然后请用户选择。
- 如果仅提供了查询内容，使用
```
list-indexes
```
  获取可用索引，请用户选择一个，然后根据需要使用
```
describe-index
```
  获取命名空间。
使用收集到的参数调用
```
search-records
```
工具执行搜索。
将返回的结果格式化为清晰易读的表格，包括字段高亮（如ID、分数和相关元数据）。

Troubleshooting

故障排除

PINECONE_API_KEY
is required. Get a free key at https://app.pinecone.io/?sessionType=signup

If you get an access error, the key is likely missing. Ask the user to set it and restart their IDE or agent session:

Terminal:
```
export PINECONE_API_KEY="your-key"
```
IDE without shell inheritance: add
```
PINECONE_API_KEY=your-key
```
to a
```
.env
```
file

IMPORTANT At the moment, the /query command can only be used with integrated indexes, which use hosted Pinecone embedding models to embed and search for data. If a user attempts to query an index that uses a third party API model such as OpenAI, or HuggingFace embedding models, remind them that this capability is not available yet with the Pinecone MCP server.

If required arguments are missing, prompt the user to supply them, using Pinecone MCP tools as needed (e.g.,
```
list-indexes
```
,
```
describe-index
```
).
Guide the user interactively through argument selection until the search can be completed.
If an invalid value is provided for any argument (e.g., nonexistent index or namespace), surface the error and suggest valid options.

需要PINECONE_API_KEY。可在https://app.pinecone.io/?sessionType=signup获取免费密钥

如果遇到访问错误，很可能是密钥缺失。请用户设置密钥并重启IDE或Agent会话：

终端：
```
export PINECONE_API_KEY="your-key"
```
无shell继承的IDE：将
```
PINECONE_API_KEY=your-key
```
添加到
```
.env
```
文件中

重要提示 目前，/query命令仅可用于集成索引，这类索引使用托管的Pinecone嵌入模型来嵌入和搜索数据。如果用户尝试查询使用第三方API模型（如OpenAI或HuggingFace嵌入模型）的索引，请提醒他们Pinecone MCP服务器目前尚不支持此功能。

如果缺少必填参数，提示用户提供必要信息，必要时使用Pinecone MCP工具（例如
```
list-indexes
```
、
```
describe-index
```
）。
交互式引导用户选择参数，直到可以完成搜索。
如果任何参数提供了无效值（例如不存在的索引或命名空间），显示错误并建议有效选项。

Tools Reference

工具参考

```
search-records
```
: Search records in a given index with optional metadata filtering and reranking.
```
list-indexes
```
: List all available Pinecone indexes.
```
describe-index
```
: Get index configuration and namespaces.
```
describe-index-stats
```
: Get stats including record counts and namespaces.
```
rerank-documents
```
: Rerank returned documents using a specified reranking model.
Ask the user interactively to clarify missing information when needed.

```
search-records
```
：在指定索引中搜索记录，支持可选的元数据过滤和重排序。
```
list-indexes
```
：列出所有可用的Pinecone索引。
```
describe-index
```
：获取索引配置和命名空间信息。
```
describe-index-stats
```
：获取统计信息，包括记录数量和命名空间。
```
rerank-documents
```
：使用指定的重排序模型对返回的文档进行重排序。
必要时交互式询问用户以明确缺失的信息。