Pinecone Vector Store

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Node Details

• Name: Pinecone_VectorStores

• Type: Pinecone

• Version: 5.0

• Category: Vector Stores

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Base Classes

• Pinecone

• VectorStoreRetriever

• BaseRetriever

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Credentials

• Type: pineconeApi

• Required Fields:

  • pineconeApiKey

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Input Parameters

1. Document (optional, list)

   • Type: Document

   • Description: List of documents to be embedded and stored

2. Embeddings (required)

   • Type: Embeddings

   • Description: Embedding model to use for vectorizing documents

3. Record Manager (optional)

   • Type: RecordManager

   • Description: Keeps track of records to prevent duplication

4. Pinecone Index (required)

   • Type: string

   • Description: Name of the Pinecone index to use

5. Pinecone Namespace (optional)

   • Type: string

   • Description: Namespace within the Pinecone index

6. File Upload (optional)

   • Type: boolean

   • Description: Enables file upload functionality in the chat

7. Pinecone Text Key (optional)

   • Type: string

   • Default: “text”

   • Description: Key in the metadata for storing text

8. Pinecone Metadata Filter (optional)

   • Type: json

   • Description: Filter to apply on metadata during queries

9. Top K (optional)

   • Type: number

   • Default: 4

   • Description: Number of top results to fetch

10. MMR Parameters (optional)

    • Various parameters for Maximal Marginal Relevance search

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Outputs

1. Pinecone Retriever

   • Type: Retriever

   • Description: A retriever object for querying the Pinecone vector store

2. Pinecone Vector Store

   • Type: VectorStore

   • Description: The Pinecone vector store object

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Functionality

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Upsert Method

• Adds or updates documents in the Pinecone index

• Handles file upload and chat ID association if enabled

• Supports record management to prevent duplication

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Delete Method

• Removes documents from the Pinecone index based on provided IDs

• Supports deletion through record manager or direct ID-based deletion

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Init Method

• Initializes the Pinecone vector store with the provided configuration

• Sets up metadata filters, including chat ID filtering if file upload is enabled

• Returns either a vector store or a retriever based on the node configuration

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Use Cases

• Semantic search applications

• Question-answering systems

• Document retrieval systems

• Recommendation engines

• Any application requiring efficient similarity search on vector data

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Notes

• This node integrates closely with LangChain’s implementation of Pinecone

• It supports advanced features like MMR search and metadata filtering

• The node is designed to work within a larger workflow, potentially connecting with other nodes for complex AI and ML tasks