This page shows you how to model your data for efficient ingestion, retrieval, and management in Pinecone.

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Record format

When you upsert raw text for Pinecone to convert to vectors automatically, each record consists of the following:
  • ID: A unique string identifier for the record.
  • Text: The raw text for Pinecone to convert to a dense vector for semantic search or a sparse vector for lexical search, depending on the embedding model integrated with the index. This field name must match the embed.field_map defined in the index.
  • Metadata (optional): All additional fields are stored as record metadata. You can filter by metadata when searching or deleting records.
Upserting raw text is supported only for indexes with integrated embedding.
Example:
{
  "_id": "document1#chunk1", 
  "chunk_text": "First chunk of the document content...", // Text to convert to a vector. 
  "document_id": "document1", // This and subsequent fields stored as metadata. 
  "document_title": "Introduction to Vector Databases",
  "chunk_number": 1,
  "document_url": "https://example.com/docs/document1", 
  "created_at": "2024-01-15",
  "document_type": "tutorial"
}

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Use structured IDs

Use a structured, human-readable format for record IDs, including ID prefixes that reflect the type of data you’re storing, for example:
  • Document chunks: document_id#chunk_number
  • User data: user_id#data_type#item_id
  • Multi-tenant data: tenant_id#document_id#chunk_id
Choose a delimiter for your ID prefixes that won’t appear elsewhere in your IDs. Common patterns include:
  • document1#chunk1 - Using hash delimiter
  • document1_chunk1 - Using underscore delimiter
  • document1:chunk1 - Using colon delimiter
Structuring IDs in this way provides several advantages:
  • Efficiency: Applications can quickly identify which record it should operate on.
  • Clarity: Developers can easily understand what they’re looking at when examining records.
  • Flexibility: ID prefixes enable list operations for fetching and updating records.

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Include metadata

Include metadata key-value pairs that support your application’s key operations, for example: Metadata keys must be strings, and metadata values must be one of the following data types:
  • String
  • Number (integer or floating point, gets converted to a 64-bit floating point)
  • Boolean (true, false)
  • List of strings
Pinecone supports 40 KB of metadata per record.

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Example

This example demonstrates how to manage document chunks in Pinecone using structured IDs and comprehensive metadata. It covers the complete lifecycle of chunked documents: upserting, searching, fetching, updating, and deleting chunks, and updating an entire document.

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Upsert chunks

When upserting documents that have been split into chunks, combine structured IDs with comprehensive metadata:
Upserting raw text is supported only for indexes with integrated embedding.
Python
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# To get the unique host for an index, 
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")

index.upsert_records(
  "example-namespace",
  [
    {
      "_id": "document1#chunk1", 
      "chunk_text": "First chunk of the document content...",
      "document_id": "document1",
      "document_title": "Introduction to Vector Databases",
      "chunk_number": 1,
      "document_url": "https://example.com/docs/document1",
      "created_at": "2024-01-15",
      "document_type": "tutorial"
    },
    {
      "_id": "document1#chunk2", 
      "chunk_text": "Second chunk of the document content...",
      "document_id": "document1",
      "document_title": "Introduction to Vector Databases", 
      "chunk_number": 2,
      "document_url": "https://example.com/docs/document1",
      "created_at": "2024-01-15",
      "document_type": "tutorial"
    },
    {
      "_id": "document1#chunk3", 
      "chunk_text": "Third chunk of the document content...",
      "document_id": "document1",
      "document_title": "Introduction to Vector Databases",
      "chunk_number": 3, 
      "document_url": "https://example.com/docs/document1",
      "created_at": "2024-01-15",
      "document_type": "tutorial"
    },
  ]
)

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Search chunks

To search the chunks of a document, use a metadata filter expression that limits the search appropriately:
Searching with text is supported only for indexes with integrated embedding.
Python
from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# To get the unique host for an index, 
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")

filtered_results = index.search(
    namespace="example-namespace", 
    query={
        "inputs": {"text": "What is a vector database?"}, 
        "top_k": 3,
        "filter": {"document_id": "document1"}
    },
    fields=["chunk_text"]
)

print(filtered_results)

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Fetch chunks

To retrieve all chunks for a specific document, first list the record IDs using the document prefix, and then fetch the complete records:
Python
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# To get the unique host for an index, 
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")

# List all chunks for document1 using ID prefix
chunk_ids = []
for record_id in index.list(prefix='document1#', namespace='example-namespace'):
    chunk_ids.append(record_id)

print(f"Found {len(chunk_ids)} chunks for document1")

# Fetch the complete records by ID
if chunk_ids:
    records = index.fetch(ids=chunk_ids, namespace='example-namespace')
    
    for record_id, record_data in records['vectors'].items():
        print(f"Chunk ID: {record_id}")
        print(f"Chunk text: {record_data['metadata']['chunk_text']}")
        # Process the vector values and metadata as needed
Pinecone is eventually consistent, so it’s possible that a write (upsert, update, or delete) followed immediately by a read (query, list, or fetch) may not return the latest version of the data. If your use case requires retrieving data immediately, consider implementing a small delay or retry logic after writes.

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Update chunks

To update specific chunks within a document, first list the chunk IDs, and then update individual records:
Python
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# To get the unique host for an index, 
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")

# List all chunks for document1
chunk_ids = []
for record_id in index.list(prefix='document1#', namespace='example-namespace'):
    chunk_ids.append(record_id)

# Update specific chunks (e.g., update chunk 2)
if 'document1#chunk2' in chunk_ids:
    index.update(
        id='document1#chunk2',
        values=[<new dense vector>],
        set_metadata={
            "document_id": "document1",
            "document_title": "Introduction to Vector Databases - Revised",
            "chunk_number": 2,
            "chunk_text": "Updated second chunk content...",
            "document_url": "https://example.com/docs/document1",
            "created_at": "2024-01-15",
            "updated_at": "2024-02-15",
            "document_type": "tutorial"
        },
        namespace='example-namespace'
    )
    print("Updated chunk 2 successfully")

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Delete chunks

To delete chunks of a document, use a metadata filter expression that limits the deletion appropriately:
Python
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# To get the unique host for an index, 
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")

# Delete chunks 1 and 3
index.delete(
    namespace="example-namespace",
    filter={
        "document_id": {"$eq": "document1"},
        "chunk_number": {"$in": [1, 3]}
    }
)

# Delete all chunks for a document
index.delete(
    namespace="example-namespace",
    filter={
        "document_id": {"$eq": "document1"}
    }
)

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Update an entire document

When the amount of chunks or ordering of chunks for a document changes, the recommended approach is to first delete all chunks using a metadata filter, and then upsert the new chunks:
Python
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# To get the unique host for an index, 
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")

# Step 1: Delete all existing chunks for the document
index.delete(
    namespace="example-namespace",
    filter={
        "document_id": {"$eq": "document1"}
    }
)

print("Deleted existing chunks for document1")

# Step 2: Upsert the updated document chunks
index.upsert(
  namespace="example-namespace", 
  vectors=[
    {
      "id": "document1#chunk1",
      "values": [<updated dense vector>],
      "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases - Updated Edition",
        "chunk_number": 1,
        "chunk_text": "Updated first chunk with new content...",
        "document_url": "https://example.com/docs/document1",
        "created_at": "2024-02-15",
        "document_type": "tutorial",
        "version": "2.0"
      }
    },
    {
      "id": "document1#chunk2",
      "values": [<updated dense vector>],
      "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases - Updated Edition",
        "chunk_number": 2,
        "chunk_text": "Updated second chunk with new content...",
        "document_url": "https://example.com/docs/document1",
        "created_at": "2024-02-15",
        "document_type": "tutorial",
        "version": "2.0"
      }
    }
    # Add more chunks as needed for the updated document
  ]
)

print("Successfully updated document1 with new chunks")

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Data freshness

Pinecone is eventually consistent, so it’s possible that a write (upsert, update, or delete) followed immediately by a read (query, list, or fetch) may not return the latest version of the data. If your use case requires retrieving data immediately, consider implementing a small delay or retry logic after writes.