Pinecone has two ways to model your data, and the choice is made when you create the index: an index created with a document schema holds documents, while an index created with a dense or sparse vector type holds records. Both hold a unique `_id` and optional metadata and live in [namespaces](/guides/index-data-indexing-overview#namespaces); they differ in how many ranking signals a single item can carry and which API you call. Both are fully supported.

- **[Documents](#documents)** are the unit of data in an index with a **document schema**, the model behind [full-text search](/guides/index-data-search-full-text-search). A document is a JSON object that can carry text fields (ranked with BM25 and Lucene queries), a dense vector, a sparse vector, and metadata in a single index, and you pick the ranking signal per query with `score_by`. Use documents for text-first search or any workload that needs more than one ranking signal in one index.
- **[Records](#records)** are the unit of data for [indexes with dense vectors](/guides/core-concepts-key-terms#index-with-dense-vectors) and [indexes with sparse vectors](/guides/core-concepts-key-terms#index-with-sparse-vectors), the Vectors API. A record carries one vector (or raw text for Pinecone to embed) plus metadata. Use records for vector-only workloads.

:::callout{intent="tip"}
Deciding how tenants share indexes and namespaces? See [Design for multitenancy](/guides/index-data-design-for-multitenancy).
:::

## Documents

A document is the unit of data in an index with a document schema, a JSON object with a required `_id` field, the ranking fields declared in the index's schema, and any number of metadata fields. Documents support multiple field types in a single record: a `dense_vector` field (for [semantic search](/guides/index-data-search-semantic-search)), a `sparse_vector` field (for [sparse-vector search](/guides/index-data-search-lexical-search)), one or more `string` fields with `full_text_search` enabled (for [full-text search](/guides/index-data-search-full-text-search) with BM25 and Lucene queries), plus any metadata you upsert alongside them.

The schema, declared at index creation, tells Pinecone how to rank each ranking field. Schema field types:

- `dense_vector`, indexed for ANN similarity search.
- `sparse_vector`, indexed for sparse-vector search.
- `string` with a nested `full_text_search` config object (`{}` enables with all defaults; optional sub-fields: `language`, `stemming`, `stop_words`), indexed for **BM25** ranking and Lucene queries. Lowercasing and the token length cap are server-applied and can't be overridden.

Metadata fields aren't declared in the schema. Any field you upsert that's not declared in the schema is stored on the document, returned via `include_fields`, and automatically indexed for filtering. Pinecone infers the metadata field type from the values you upsert: strings, numbers (floating point), booleans, and arrays of strings are all supported.

Document fields can hold structured values: a metadata `string_list` field holds an array of strings; a `dense_vector` field holds an array of floats; a `sparse_vector` field is an object with two parallel arrays, `indices` (token positions) and `values` (token weights).

A schema can declare up to 100 `string` fields with `full_text_search` enabled, but at most one `dense_vector` field and at most one `sparse_vector` field per index.

Example document for an index with `title`, `body`, `embedding`, and `category` fields:

```json theme={null}
{
  "_id": "document1#chunk1",
  "title": "Introduction to Vector Databases",
  "body": "First chunk of the document content...",
  "embedding": [0.0236, -0.0329, ..., -0.0104, 0.0086],
  "category": "tutorial"
}
```

Field-name rules:

- Must be unique, non-empty strings.
- Must not start with `_` (reserved for system-managed fields like `_id` and `_score`) or `$` (reserved for filter operators).
- Limited to 64 bytes.

For the full schema reference (language and analyzer options, multi-field schemas, scoring methods), see [Full-text search](/guides/index-data-search-full-text-search).

:::callout{intent="note"}
A document is the unit of retrieval: `top_k` and `_score` are computed per document, not per sub-section. Pinecone doesn't split a single document into multiple in-document chunks at index time. If your source content is longer than what you want to retrieve as one hit (a long article, a PDF, a transcript), do the chunking in your application before upsert and store each chunk as its own document, with an ID like `document1#chunk1`, `document1#chunk2`, and a metadata field that ties chunks back to the parent document for grouping at query time.
:::

### Schema validation

Each document in an [upsert](/guides/database-2026-07-data-plane-upsert-documents) request's `documents` array is validated against the schema. If any document fails validation, **the entire upsert fails** and nothing is written.

| Scenario                                                             | Result                                                                       |
| -------------------------------------------------------------------- | ---------------------------------------------------------------------------- |
| Field value doesn't match declared type (for schema-declared fields) | **Error**, request fails                                                     |
| Document or request exceeds a size or count limit                    | **Error**, request fails                                                     |
| Field not in schema                                                  | Stored on the document and auto-indexed for filtering as metadata            |
| Field name starts with `_` or `$`                                    | **Error**, request fails                                                     |
| Schema field missing from a document                                 | OK, schema fields are optional, as long as the document carries at least one |
| Document with only `_id` and metadata (no schema fields)             | **Error**, request fails                                                     |
| Document missing `_id`                                               | **Error**, request fails                                                     |

### Schema patterns

The same document model supports several common schema shapes. Pick the one that matches the signal you want to rank by, and plan your fields up front: schema migration isn't supported after index creation. Filters are deterministic per document and apply before scoring; choose your hard yes/no constraints (including text-match operators on FTS-enabled `string` fields) first, then pick a `score_by` method to rank whatever remains. See [Filters vs. scoring](/guides/index-data-search-full-text-search#filters-vs-scoring).

:::callout{intent="note"}
The Python snippets in each accordion below assume an initialized client and the schema-builder import:

```python Python theme={null}
from pinecone import Pinecone, SchemaBuilder

pc = Pinecone(api_key="YOUR_API_KEY")
```

Each accordion shows the pattern-specific schema, an example document, and a search snippet. The control-plane (`pc.indexes.create(...)`) and data-plane (`index = pc.Index(name=...)`) calls in the snippets reuse this `pc`.
:::

:::::accordion-group
:::accordion{title="Single text field — keyword search only (FTS)"}
Use when you want BM25 keyword ranking on one piece of text per document (a review body, a support ticket, a product description) and you don't have embeddings to manage.

```python Python theme={null}
from pinecone import SchemaBuilder

schema = (
    SchemaBuilder()
    .add_string_field("review_text", full_text_search={"language": "en"})
    .build()
)

pc.indexes.create(name="book-reviews", schema=schema)
```

A document upserted into this index looks like:

```json theme={null}
{
  "_id": "review-1234",
  "review_text": "Beautifully written exploration of contact, communication, and civilization across cosmic distances. The pacing is uneven but the central premise carries you through."
}
```

Search with a single `text` clause (the score\_by `type`, not a field type — this clause runs BM25 ranking on the named string field):

```python Python theme={null}
index.documents.search(
    namespace="reviews",
    top_k=10,
    score_by=[{"type": "text", "fields": ["review_text"], "query": "civilization"}],
)
```

See [Full-text search](/guides/index-data-search-full-text-search).
:::

:::accordion{title="Multi-field FTS — score across two text fields (e.g. body + summary)"}
Use when a document has more than one piece of text that should both contribute to ranking, for example, a long `review_text` plus a short `review_summary`. Pinecone combines the per-field BM25 scores into one ranking per document.

```python Python theme={null}
schema = (
    SchemaBuilder()
    .add_string_field("review_text", full_text_search={"language": "en"})
    .add_string_field("review_summary", full_text_search={"language": "en"})
    .build()
)

pc.indexes.create(name="book-reviews-multi", schema=schema)
```

A document upserted into this index looks like:

```json theme={null}
{
  "_id": "review-1234",
  "review_text": "Beautifully written exploration of contact, communication, and civilization across cosmic distances. The pacing is uneven but the central premise carries you through.",
  "review_summary": "Monumental science fiction with uneven pacing",
  "category": "science-fiction",
  "rating": 4.5
}
```

`category` and `rating` aren't declared in the schema. They're upserted as metadata, automatically indexed for filtering, and usable in `filter` expressions.

Pass two `text` clauses in `score_by`; the server combines them into one ranking, with each contributing field weighted equally in `2026-07`.

```python Python theme={null}
index = pc.Index(name="book-reviews-multi")

index.documents.search(
    namespace="reviews",
    top_k=5,
    score_by=[
        {"type": "text", "fields": ["review_text"],    "query": "disappointing"},
        {"type": "text", "fields": ["review_summary"], "query": "Disappointing"},
    ],
    include_fields=["*"],
)
```
:::

::::accordion{title="Dense + FTS — semantic and keyword in one index"}
Most workloads that combine semantic ranking with keyword matching reach for this pattern: rank by dense (or sparse) similarity, restricted to documents that contain a specific term or phrase. Common examples include semantic search over patents, regulatory filings, internal knowledge bases, or other technical literature where the right answer must contain a specific term. A single schema can include one `dense_vector` field plus any number of FTS-enabled string fields:

```python Python theme={null}
schema = (
    SchemaBuilder()
    .add_string_field("book_title", full_text_search={"language": "en"})
    .add_string_field("review_text", full_text_search={"language": "en"})
    .add_dense_vector_field("review_embedding", dimension=1024, metric="cosine")
    .build()
)

pc.indexes.create(name="book-reviews-dense", schema=schema)
```

A document upserted into this index looks like:

```json theme={null}
{
  "_id": "review-1234",
  "book_title": "The Three-Body Problem",
  "review_text": "Beautifully written exploration of contact, communication, and civilization across cosmic distances.",
  "review_embedding": [0.012, -0.087, 0.153, ...]
}
```

`review_embedding` is a 1024-dim list of floats produced by your dense embedding model. Use the same model at query time so the query vector lives in the same space.

A single search request ranks by one scoring type. With this schema you have two query options:

Option A, dense ranking restricted by a text-match filter (the most common hybrid pattern):

```python Python theme={null}
index = pc.Index(name="book-reviews-dense")

# query_embedding is a 1024-dim list of floats from your embedding model.
query_embedding = embed("beautifully written, hard sci-fi")

index.documents.search(
    namespace="reviews",
    top_k=5,
    score_by=[
        {"type": "dense_vector", "fields": ["review_embedding"], "values": query_embedding},
    ],
    filter={"review_text": {"$match_phrase": "beautifully written"}},
)
```

Option B, run BM25 and dense searches separately and merge client-side (when you want both signals to contribute to ranking, for example via [reciprocal rank fusion](/guides/index-data-search-reciprocal-rank-fusion)):

```python Python theme={null}
dense_hits = index.documents.search(
    namespace="reviews", top_k=50,
    score_by=[{"type": "dense_vector", "fields": ["review_embedding"], "values": query_embedding}],
)
bm25_hits = index.documents.search(
    namespace="reviews", top_k=50,
    score_by=[{"type": "text", "fields": ["review_text"], "query": "beautifully written"}],
)
# Merge dense_hits + bm25_hits in your application (e.g. RRF) to produce final ranking.
```

See [Hybrid search](/guides/index-data-search-hybrid-search) for a fuller discussion.

:::callout{intent="tip"}
The `dense_vector` field's source content is independent of the FTS-enabled `string` fields it sits alongside. You can embed images (with a multimodal embedding model) and pair them with FTS-enabled `string` fields holding captions, geography, or taxonomy, then query the image vector with a text description and restrict matches with FTS filters on those `string` fields. The schema doesn't constrain what the dense vector represents; it just stores a vector of the declared dimension.
:::
::::

:::accordion{title="Multi-signal index — dense + sparse + FTS in one schema"}
Use when a single document is best described by more than one ranking signal, for example, a video catalog where each item has frame embeddings (dense), auto-generated captions you've encoded as sparse vectors (sparse), and a transcript text field (BM25/Lucene). One schema declares all three; you pick the ranking signal per query with `score_by`. You don't manage a second index or any cross-index linkage.

```python Python theme={null}
schema = (
    SchemaBuilder()
    .add_dense_vector_field("frame_embedding", dimension=1024, metric="cosine")
    .add_sparse_vector_field("caption_sparse")
    .add_string_field("transcript", full_text_search={"language": "en"})
    .build()
)

pc.indexes.create(name="video-catalog", schema=schema)
```

A `language` field upserted alongside these ranking fields is treated as metadata: stored on the document, returned via `include_fields`, and auto-indexed for filtering.

A document upserted into this index looks like:

```json theme={null}
{
  "_id": "video-7890#scene-3",
  "frame_embedding": [0.012, -0.087, 0.153, ...],
  "caption_sparse": {
    "indices": [42, 1077, 9821],
    "values":  [0.41, 0.33, 0.18]
  },
  "transcript": "I think we should go now before it gets dark.",
  "language": "en"
}
```

`frame_embedding` is a 1024-dim list of floats from your dense vision model. `caption_sparse` is the output of your sparse encoder, an object with parallel `indices` (token IDs) and `values` (token weights) arrays.

The same index supports three different query shapes. All three assume:

```python Python theme={null}
index = pc.Index(name="video-catalog")

# Replace with the outputs of your encoders.
query_embedding = embed_image(query_image)                 # 1024-dim list of floats
query_sparse    = sparse_encode("scene with a lighthouse") # {"indices": [...], "values": [...]}
```

Semantic frame search, ranking by visual similarity:

```python Python theme={null}
index.documents.search(
    namespace="videos",
    top_k=10,
    score_by=[{"type": "dense_vector", "fields": ["frame_embedding"], "values": query_embedding}],
)
```

Caption search, ranking by sparse-vector similarity over your encoded captions:

```python Python theme={null}
index.documents.search(
    namespace="videos",
    top_k=10,
    score_by=[{"type": "sparse_vector", "fields": ["caption_sparse"], "sparse_values": query_sparse}],
)
```

Semantic search restricted to a spoken phrase, narrowing semantic frame ranking to clips where the transcript contains a specific phrase:

```python Python theme={null}
index.documents.search(
    namespace="videos",
    top_k=10,
    score_by=[{"type": "dense_vector", "fields": ["frame_embedding"], "values": query_embedding}],
    filter={"transcript": {"$match_phrase": "I love you"}},
)
```

`score_by` selects one ranking signal per request, but every signal stays addressable on the same documents.
:::

:::accordion{title="Sparse + dense hybrid — single-vector index (Vectors API)"}
Use when you're modeling data with the [Vectors API](#records) (not the Documents API) and want to combine a sparse and dense vector in one record on a single index. For new document-centric projects with text data, prefer the document-index Dense + FTS pattern above.

```json theme={null}
{
  "id": "doc1#chunk1",
  "values": [0.0236, -0.0329, ..., -0.0104, 0.0086],
  "sparse_values": {
    "indices": [822745112, 1009084850, ...],
    "values":  [1.7958984, 0.41577148, ...]
  },
  "metadata": { "document_id": "doc1", "chunk_number": 1 }
}
```

See [Hybrid search](/guides/index-data-search-hybrid-search).
:::
:::::

## Records

Records are how you model data for [indexes with dense vectors](/guides/core-concepts-key-terms#index-with-dense-vectors) and [indexes with sparse vectors](/guides/core-concepts-key-terms#index-with-sparse-vectors). Each record carries one vector (dense, sparse, or both for single-index hybrid) plus optional metadata, and you can upsert raw text in place of a vector when the index is [integrated with an embedding model](/guides/index-data-create-an-index#embedding-models).

:::::tabs
::::tab{title="Vectors"}
When you upsert pre-generated vectors, each record consists of the following:

- **ID**: A unique string identifier for the record.
- **Vector**: A dense vector for [semantic search](/guides/index-data-search-semantic-search), a sparse vector for [sparse-vector search](/guides/index-data-search-lexical-search), or both for single-index [hybrid search](/guides/index-data-search-hybrid-search) (Vectors API).
- **Metadata** (optional): A flat JSON document containing key-value pairs with additional information (nested objects aren't supported). You can filter by metadata when searching or deleting records.

:::callout{intent="note"}
When importing data from object storage, records must be in Parquet format. For more details, see [Import data](/guides/index-data-import-data#3-prepare-your-data).
:::

Example:

:::code-group
```json Dense theme={null}
{
  "id": "document1#chunk1", 
  "values": [0.0236663818359375, -0.032989501953125, ..., -0.01041412353515625, 0.0086669921875], 
  "metadata": {
    "document_id": "document1",
    "document_title": "Introduction to Vector Databases",
    "chunk_number": 1,
    "chunk_text": "First chunk of the document content...",
    "document_url": "https://example.com/docs/document1",
    "created_at": "2024-01-15",
    "document_type": "tutorial"
  }
}
```

```json Sparse theme={null}
{
  "id": "document1#chunk1", 
  "sparse_values": {
    "values": [1.7958984, 0.41577148, ..., 4.4414062, 3.3554688],
    "indices": [822745112, 1009084850, ..., 3517203014, 3590924191]
  },
  "metadata": {
    "document_id": "document1",
    "document_title": "Introduction to Vector Databases",
    "chunk_number": 1,
    "chunk_text": "First chunk of the document content...",
    "document_url": "https://example.com/docs/document1",
    "created_at": "2024-01-15",
    "document_type": "tutorial"
  }
}
```

```json Hybrid theme={null}
{
  "id": "document1#chunk1", 
  "values": [0.0236663818359375, -0.032989501953125, ..., -0.01041412353515625, 0.0086669921875], 
  "sparse_values": {
    "values": [1.7958984, 0.41577148, ..., 4.4414062, 3.3554688],
    "indices": [822745112, 1009084850, ..., 3517203014, 3590924191]
  },
  "metadata": {
    "document_id": "document1",
    "document_title": "Introduction to Vector Databases",
    "chunk_number": 1,
    "chunk_text": "First chunk of the document content...",
    "document_url": "https://example.com/docs/document1",
    "created_at": "2024-01-15",
    "document_type": "tutorial"
  }
}
```
:::
::::

::::tab{title="Text"}
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](/guides/index-data-search-semantic-search) or a sparse vector for [sparse-vector search](/guides/index-data-search-lexical-search), depending on the [embedding model](/guides/index-data-create-an-index#embedding-models) 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.

:::callout{intent="note"}
Upserting raw text is supported only for [indexes with integrated embedding](/guides/index-data-indexing-overview#vector-embedding).
:::

Example:

```json theme={null}
{
  "_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"
}
```
::::
:::::

## 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.

## Include metadata

Include [metadata key-value pairs](/guides/index-data-indexing-overview#metadata) that support your application's key operations, for example:

- **Enable query-time filtering**: Add fields for time ranges, categories, or other criteria for [filtering searches for increased accuracy and relevance](/guides/index-data-search-filter-by-metadata).
- **Link related chunks**: Use fields like `document_id` and `chunk_number` to keep track of related records and enable efficient [chunk deletion](#delete-chunks) and [document updates](#update-an-entire-document).
- **Link back to original data**: Include `chunk_text` or `document_url` for traceability and user display.

Metadata keys must be strings, and metadata values must be one of the following data types:

- String
- Number (stored as a 64-bit floating point)
- Boolean (true, false)
- List of strings

:::callout{intent="note"}
Pinecone supports 40 KB of metadata per record or document. `full_text_search` string fields aren't metadata and don't count toward this limit. Each `full_text_search` string field is limited to 100 KB and 10,000 tokens.
:::

## 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.

### Upsert chunks

When [upserting](/guides/index-data-upsert-data) documents that have been split into chunks, combine structured IDs with comprehensive metadata:

:::::tabs
::::tab{title="Upsert text"}
:::callout{intent="note"}
Upserting raw text is supported only for [indexes with integrated embedding](/guides/index-data-create-an-index#integrated-embedding).
:::

```python Python theme={null}
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"
    },
  ]
)
```
::::

:::tab{title="Upsert vectors"}
```python Python theme={null}
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(
  namespace="example-namespace",
  vectors=[
    {
      "id": "document1#chunk1", 
      "values": [0.0236663818359375, -0.032989501953125, ..., -0.01041412353515625, 0.0086669921875], 
      "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases",
        "chunk_number": 1,
        "chunk_text": "First chunk of the document content...",
        "document_url": "https://example.com/docs/document1",
        "created_at": "2024-01-15",
        "document_type": "tutorial"
      }
    },
    {
      "id": "document1#chunk2", 
      "values": [-0.0412445068359375, 0.028839111328125, ..., 0.01953125, -0.0174560546875],
      "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases", 
        "chunk_number": 2,
        "chunk_text": "Second chunk of the document content...",
        "document_url": "https://example.com/docs/document1",
        "created_at": "2024-01-15",
        "document_type": "tutorial"
      }
    },
    {
      "id": "document1#chunk3", 
      "values": [0.0512237548828125, 0.041656494140625, ..., 0.02130126953125, -0.0394287109375],
      "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases",
        "chunk_number": 3, 
        "chunk_text": "Third chunk of the document content...",
        "document_url": "https://example.com/docs/document1",
        "created_at": "2024-01-15",
        "document_type": "tutorial"
      }
    }
  ]
)
```
:::
:::::

### Search chunks

To search the chunks of a document, use a [metadata filter expression](/guides/index-data-search-filter-by-metadata#metadata-filter-expressions) that limits the search appropriately:

:::::tabs
::::tab{title="Search with text"}
:::callout{intent="note"}
Searching with text is supported only for [indexes with integrated embedding](/guides/index-data-create-an-index#integrated-embedding).
:::

```python Python theme={null}
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)
```
::::

:::tab{title="Search with a vector"}
```python Python theme={null}
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")

filtered_results = index.query(
    namespace="example-namespace",
    vector=[0.0236663818359375,-0.032989501953125, ..., -0.01041412353515625,0.0086669921875], 
    top_k=3,
    filter={
        "document_id": {"$eq": "document1"}
    },
    include_metadata=True,
    include_values=False
)

print(filtered_results)
```
:::
:::::

### Fetch chunks

To retrieve all chunks for a specific document, first [list the record IDs](/guides/manage-data-list-record-ids) using the document prefix, and then [fetch](/guides/manage-data-fetch-data) the complete records:

```python Python theme={null}
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
```

:::callout{intent="note"}
Pinecone is [eventually consistent](/guides/index-data-check-data-freshness), 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](/guides/move-to-production-error-handling#implement-retry-logic) after writes.
:::

### Update chunks

To [update](/guides/manage-data-update-data) specific chunks within a document, first list the chunk IDs, and then update individual records:

```python Python theme={null}
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:
    new_vector = ...  # from your embedding model
    index.update(
        id='document1#chunk2',
        values=new_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")
```

### Delete chunks

To [delete](/guides/manage-data-delete-data#delete-records-by-metadata) chunks of a document, use a [metadata filter expression](/guides/index-data-search-filter-by-metadata#metadata-filter-expressions) that limits the deletion appropriately:

```python Python theme={null}
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"}
    }
)
```

### Update an entire document

:::callout{intent="tip"}
If you need to update most of the records in a large namespace, [contact Support](https://app.pinecone.io/organizations/-/settings/support/ticket) for help creating an export to enable a faster and more cost-effective approach.
:::

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](/guides/manage-data-delete-data#delete-records-by-metadata), and then [upsert](/guides/index-data-upsert-data) the new chunks:

```python Python theme={null}
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
chunk1_vector = ...  # from your embedding model
chunk2_vector = ...
index.upsert(
  namespace="example-namespace", 
  vectors=[
    {
      "id": "document1#chunk1",
      "values": chunk1_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": chunk2_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")
```

## Data freshness

Pinecone is [eventually consistent](/guides/index-data-check-data-freshness), 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](/guides/move-to-production-error-handling#implement-retry-logic) after writes.

## Related pages

- [Account management](./account-management-index.md)
- [Admin](./admin-2-index.md)
- [Admin](./admin-index.md)
- [APIs](./apis-index.md)
- [Architecture](./architecture-index.md)
- [Assistants](./assistants-index.md)
- [Bring Your Own Cloud](./bring-your-own-cloud-index.md)
- [Build an assistant](./build-an-assistant-index.md)
- [Build an integration](./build-an-integration-index.md)
- [Changelog](./changelog-index.md)

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