Full-text search overview

You can also use the Pinecone console to create indexes with document schemas, upsert documents, search documents, and fetch or delete documents by ID.

Full-text search ranks documents by keyword and phrase relevance using BM25 scoring, with optional Lucene query syntax. Because an index with a document schema can also declare dense and sparse vector fields, the same index can rank by semantic or sparse-vector similarity, so one index can cover keyword and semantic search.

When to use it

Reach for full-text search when exact words, phrases, names, codes, or identifiers matter, not just semantic similarity. For semantic-only or vector-only workloads, an index with dense vectors (the Vectors API) is simpler. See Search overview to choose the right approach.

Full-text search requires API version 2026-07: send X-Pinecone-Api-Version: 2026-07 on REST requests, or use the 2026-07 Python SDK (v10 or later).

Capabilities

What full-text search does and doesn’t match:

Capability	Status
Fuzzy matching (typo tolerance)	Supported in `query_string` queries only (query syntax)
Stemming	Supported, opt-in per field at index creation (stemming)
Stop-word removal	Supported, opt-in per field at index creation (tokens & analyzers)
Substring / n-gram matching	Supported, opt-in per field at index creation. Can’t be combined with stemming or stop words on the same field (n-grams)
Synonym expansion	Not supported. A search for `car` won’t match a document containing only `automobile`. Use semantic search or hybrid search for synonyms or paraphrases.

How it works

Pinecone’s Documents API stores typed fields you declare in a schema. End to end, the flow is short: create an index with a schema that declares your ranking fields, upsert your data as JSON documents, then search by choosing one ranking signal per search request with score_by. The end-to-end example below stitches all three steps into one runnable script.

You upsert data as JSON documents.
You declare how each field should be indexed via a schema, as a string field with full_text_search enabled (BM25 scoring), a dense_vector field, or a sparse_vector field. The schema is for ranking fields only; metadata fields are not declared.
Pinecone indexes each field’s content according to the type of the field declared in the schema. Any other fields on the upserted documents are automatically stored and indexed for filtering, no schema declaration required.

For the field types you can declare, see Schema field types. Filterable metadata is not part of the schema. Any field you upsert that is not declared in the schema is stored on the document, returned via include_fields, and automatically indexed for filtering, see Metadata fields. Every search ranks by one scoring type. The score_by clause selects the scoring method for the request:

text, BM25 token matching over one or more FTS-enabled string fields.
query_string, Lucene query syntax across one or more FTS-enabled string fields, including cross-field boolean queries.
dense_vector, vector similarity against a dense_vector field.
sparse_vector, sparse-vector similarity against a sparse_vector field.

The same index can support all four when the schema declares the corresponding fields, but a given request commits to one scoring type. To narrow the candidates a vector ranking sees, combine the score_by with a metadata filter, including the text-match operators $match_phrase, $match_all, and $match_any on FTS-enabled string fields, plus the standard logical and comparison operators ($and, $or, $not, $exists, etc.). The filter narrows what’s eligible; the score_by ranks what remains. This is the most common hybrid pattern. For example, on an index whose schema declares both a dense_vector field (review_embedding) and an FTS-enabled string field (review_text), this single request runs semantic search across the corpus but only over documents whose review_text contains the exact phrase “beautifully written”:

Python

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"}},
)

The dense ranking still controls the order of results; the text-match filter just narrows what’s eligible to be ranked.

End-to-end example

A complete run from index creation through search. Copy this into a single file, set PINECONE_API_KEY, and run.

For a runnable version, see this Google Colab notebook, which upserts and searches a sample Wikipedia dataset.

Python

import os
import time
from pinecone import Pinecone, SchemaBuilder

pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])

# 1. Create an index. Setting `full_text_search` on a `string` field enables BM25.
schema = (
    SchemaBuilder()
      .add_string_field(name="title", full_text_search={"language": "en"})
      .add_string_field(name="body", full_text_search={"language": "en"})
      .build()
)
pc.indexes.create(name="articles-quickstart", schema=schema)

# Wait for the index to be ready before upserting (no timeout — add a loop cap in production).
while not pc.indexes.describe(name="articles-quickstart").status.ready:
    time.sleep(2)

index = pc.Index(name="articles-quickstart")

# 2. Upsert documents. Fields declared in the schema (`title`, `body`) are
#    BM25-indexed; any extra fields (`category`, `year`) are stored on the
#    document and auto-indexed for filtering as metadata.
index.documents.upsert(
    namespace="example-namespace",
    documents=[
        {
            "_id": "doc1",
            "title": "Machine learning in 2024",
            "body": "Machine learning models are revolutionizing natural language processing",
            "category": "technology",
            "year": 2024,
        },
        {
            "_id": "doc2",
            "title": "Vector databases",
            "body": "Vector databases enable fast similarity search across embeddings",
            "category": "technology",
            "year": 2023,
        },
        {
            "_id": "doc3",
            "title": "Quantum computing",
            "body": "Quantum computers leverage superposition for faster computation",
            "category": "science",
            "year": 2024,
        },
    ],
)

# 3. Search with BM25 ranking on `body`, narrowed by a metadata filter and a
#    phrase-match text filter on the FTS field. The relevance score comes back
#    as `_score` (not `score`).
response = index.documents.search(
    namespace="example-namespace",
    top_k=5,
    score_by=[
        {
            "type": "text",
            "fields": ["body"],
            "query": "machine learning",
        }
    ],
    filter={
        "year": {"$gte": 2024},
        "body": {"$match_phrase": "natural language"},
    },
    include_fields=["title", "body", "category", "year"],
)

for match in response.matches:
    print(match._id, match._score, getattr(match, "title", ""))

What each piece does:

SchemaBuilder().add_string_field(..., full_text_search={"language": "en"}) declares a BM25-indexed text field. Without full_text_search, the string field would be rejected at index creation — schemas only declare ranking fields.
index.documents.upsert(...) writes plain JSON documents. Schema fields are validated; non-schema fields (category, year here) are stored and auto-indexed for filtering. For large datasets, use Import instead.
score_by=[{"type": "text", ...}] picks BM25 as the scoring type. One scoring type per request; combine scoring with text matching via filter rather than mixing scoring methods.
filter narrows candidates before ranking. Standard operators ($eq, $gte, etc.) apply to any metadata field; the text-match operators ($match_phrase, $match_all, $match_any) only apply to FTS-enabled string fields.
_score is the system-owned relevance score. A user metadata field named score would be returned alongside, untouched.

Filters vs. scoring

Filters are deterministic — each document either matches or it doesn’t — and they apply before scoring. Scoring methods (text/BM25, query_string/Lucene, dense_vector, sparse_vector) order whatever remains after filtering, and only the top top_k hits are returned (max 10,000). When you’re combining text matching with vector ranking, start with the hard yes/no constraints as filters (including the text-match operators $match_phrase, $match_all, $match_any on FTS-enabled string fields), then pick a score_by method to rank whatever remains. Use BM25 (score_by text or query_string) when keyword and phrase ranking order matters, not just inclusion.

Schema definition

The schema is required at index creation and declares the fields that drive ranking or vector search. Filterable metadata is not declared in the schema: any field you upsert that isn’t in the schema is automatically stored and indexed for filtering.

Schema field types

Type	Purpose	Key options
`dense_vector`	ANN similarity search	`dimension` (required), `metric` (`cosine`, `dotproduct`, `euclidean`)
`sparse_vector`	Sparse-vector similarity search with values from a custom sparse encoder	None
`string` (text)	Full-text search. Set `full_text_search` to enable BM25, for example, `{ "language": "en" }`, or `{}` for defaults.	`language`, `stemming`, `stop_words`, `ngram` (all optional, under `full_text_search`)

Schemas can only declare ranking fields. Declaring a metadata-only field (a string field without full_text_search, or a string_list, float, or boolean field) is rejected at index creation with a 400 error. Metadata fields are auto-indexed at upsert time. See Metadata fields.

Field names must be unique, non-empty strings, and must not start with _ or $. The _ prefix is reserved for system-managed fields (for example, _id, _score); $ is reserved for filter operators. Field names are also limited to 64 bytes. Every document has a required _id field, which carries its unique identifier. A user metadata field named score is allowed, and match scores are returned as _score to avoid collisions.

Indexes with document schemas do not support integrated inference fields such as semantic_text. To use dense or sparse vector ranking in an index with a document schema, declare a dense_vector or sparse_vector field and provide vector values at upsert time.

A string field with full_text_search isn’t metadata and doesn’t count toward the 40 KB metadata limit for documents. Use these FTS-enabled string fields for searchable chunk text. Indexes with document schemas do not support combining integrated inference fields, such as semantic_text fields, with full-text-search fields. To combine semantic ranking with full-text search, declare a dense_vector field alongside one or more FTS-enabled string fields and provide dense vector values when you upsert documents.

Example schemas

A text-only schema. The minimal {} config enables FTS with all defaults; sub-fields like language, stemming, and stop_words are optional overrides:

{
  "name": "articles",
  "deployment": {
    "deployment_type": "managed",
    "cloud": "aws",
    "region": "us-east-1"
  },
  "schema": {
    "fields": {
      "title": {
        "type": "string",
        "full_text_search": { "language": "en" }
      },
      "body": {
        "type": "string",
        "description": "The main body text of the article",
        "full_text_search": {
          "language": "en",
          "stemming": true,
          "stop_words": true
        }
      }
    }
  }
}

Including full_text_search, even an empty object {}, is what turns full-text search on for a string field. Without it, the field is rejected at index creation, because schemas only declare ranking fields.

A multi-field schema with text, dense, and sparse vectors:

{
  "name": "articles-hybrid",
  "deployment": {
    "deployment_type": "managed",
    "cloud": "aws",
    "region": "us-east-1"
  },
  "schema": {
    "fields": {
      "title": {
        "type": "string",
        "full_text_search": { "language": "en" }
      },
      "body": {
        "type": "string",
        "full_text_search": { "language": "en" }
      },
      "embedding": {
        "type": "dense_vector",
        "dimension": 1536,
        "metric": "cosine"
      },
      "sparse_embedding": {
        "type": "sparse_vector"
      }
    }
  }
}

Documents upserted into either schema can carry additional fields, for example, category (string), tags (array of strings), year (number), or in_stock (boolean). These fields are stored on the document, returned via include_fields, and automatically indexed for filtering. They do not need to be declared in the schema.

Metadata fields

Metadata fields are not declared in the schema. Any field you include on an upserted document that is not declared in the schema is treated as metadata: it is stored on the document, returned via include_fields, and automatically indexed for filtering with the standard operators ($eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $exists, $and, $or, $not). Metadata field types are inferred from the values you upsert: strings, numbers (stored as floating point), booleans, and arrays of strings are all supported. You can mix metadata field types across documents in the same index.

Schema migration is not yet supported. Once an index is created, you cannot add, remove, or modify fields. Plan your schema carefully.

Schema validation

Documents are validated against the index schema on upsert. If any document is invalid, the entire upsert fails and nothing is written. For the validation rules, see Schema validation.

Filter operators

Filters are applied before the search runs, so the search only considers documents that match. On document indexes, a filter can use the comparison and set operators ($eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $exists), the logical operators $and, $or, and $not, and the text-match operators ($match_phrase, $match_all, $match_any) on FTS-enabled string fields. Multiple fields at the top level of a filter object combine with implicit AND. For operator details and examples, see Filter by metadata.

Search examples

These examples combine score_by scoring with filter narrowing on a document index.

Token matching with a filter

curl -X POST "https://articles-abc123.svc.us-east-1.pinecone.io/namespaces/__default__/documents/search" \
  -H "Api-Key: {{YOUR_API_KEY}}" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["title", "body", "category", "year"],
    "filter": {
      "category": { "$eq": "technology" },
      "year": { "$gte": 2024 }
    },
    "score_by": [{
      "type": "text",
      "fields": ["body"],
      "query": "machine learning"
    }],
    "top_k": 10
  }'

Cross-field boolean query (query_string)

curl -X POST "https://articles-abc123.svc.us-east-1.pinecone.io/namespaces/__default__/documents/search" \
  -H "Api-Key: {{YOUR_API_KEY}}" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["title", "body"],
    "score_by": [{
      "type": "query_string",
      "query": "title:(quantum) OR body:(machine learning)"
    }],
    "top_k": 10
  }'

Dense ranking with a phrase-match filter

curl -X POST "https://articles-abc123.svc.us-east-1.pinecone.io/namespaces/__default__/documents/search" \
  -H "Api-Key: {{YOUR_API_KEY}}" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["title", "body"],
    "filter": { "body": { "$match_phrase": "machine learning" } },
    "score_by": [{
      "type": "dense_vector",
      "fields": ["embedding"],
      "values": [0.12, 0.34, 0.56]
    }],
    "top_k": 10
  }'

BM25 ranking with a text-match filter

curl -X POST "https://articles-abc123.svc.us-east-1.pinecone.io/namespaces/__default__/documents/search" \
  -H "Api-Key: {{YOUR_API_KEY}}" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["body", "category", "year"],
    "filter": {
      "$and": [
        { "body": { "$match_all": "federal reserve" } },
        { "category": { "$eq": "finance" } }
      ]
    },
    "score_by": [{
      "type": "text",
      "fields": ["body"],
      "query": "monetary policy impact"
    }],
    "top_k": 10
  }'

This restricts the candidate set to finance articles whose body contains both “federal” and “reserve”, then ranks those candidates by BM25 score against “monetary policy impact”.

Phrase filter with negation

curl -X POST "https://articles-abc123.svc.us-east-1.pinecone.io/namespaces/__default__/documents/search" \
  -H "Api-Key: {{YOUR_API_KEY}}" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["body", "category"],
    "filter": {
      "$and": [
        { "body": { "$match_phrase": "large language model" } },
        { "body": { "$not": { "$match_any": "spam advertisement" } } }
      ]
    },
    "score_by": [{
      "type": "text",
      "fields": ["body"],
      "query": "recent advances in generative AI"
    }],
    "top_k": 10
  }'

This requires the exact phrase “large language model” and excludes documents containing “spam” or “advertisement”. For the full request and response schema, see Search documents.

Troubleshooting

Document not appearing in search results

Check indexing latency: new documents may take up to 1 minute to become searchable; schemas with multiple indexed fields may take slightly longer.
Verify the upsert response shows the expected upserted_count.
Confirm you’re searching the same namespace where you upserted.
With type: "text", multi-word queries use token OR matching — documents need not contain the full phrase. Try a single-term query first to confirm the document is searchable.
If using filters, ensure the document’s field values match your filter conditions. Metadata fields are auto-indexed at upsert time, so any field present on a document can be filtered on; filtering on a field that no document contains returns no results.

Unexpected search results

type: "text" uses OR across terms. machine learning matches documents that contain “machine”, “learning”, or both (BM25 ranking). For an exact phrase, use type: "query_string" with body:("machine learning") or a $match_phrase filter.
type: "query_string" defaults to OR for unquoted terms. body:(machine learning) matches documents containing either term. Use AND or + for required terms.
Operators like AND, OR, NOT, *, ~, and ^ only work with type: "query_string". With type: "text", they are treated as literal words.

Query syntax errors

Query syntax errors only apply to type: "query_string". With type: "text", any input is valid as a literal string to be tokenized.

Unmatched quotes ("machine learning): Close all quotes.
Empty query: Provide at least one search term.
Invalid boolean syntax (AND machine): Operators need terms on both sides.
Unbalanced parentheses: Match all opening and closing parens.
Unknown field name: Field names in the query must match text-searchable fields in the schema.

API errors

401 Unauthorized: Check the Api-Key header.
400 Bad Request: Check JSON syntax and required fields. Examples: fields array with more than one element for dense_vector/sparse_vector; missing mutually-exclusive field for Fetch/Delete.
404 Not Found: Verify the index name and host URL.
Missing API version: Add X-Pinecone-Api-Version: 2026-07.

Upsert errors

Type mismatch: Ensure values match declared schema types.
Invalid _id: Every document must have a non-empty _id string.
Reserved names: Field names cannot start with _ (reserved for system-managed fields like _id and _score) or $ (reserved for filter operators), and must be at most 64 bytes.

Slow search performance

Reduce query complexity: Boolean operators and large phrase slop are more expensive than simple term queries.
Simplify filters: Filters are applied before scoring, so broad filters increase the search space.
For cost-sensitive workloads, use read_capacity.mode: "Dedicated" to get predictable latency.

Common request-shape pitfalls

When a request is rejected with a 4xx that doesn’t seem to match your intent, the cause is usually one of these:

Sparse-vector score_by clauses use sparse_values, not values. The values key is for dense_vector. A sparse clause needs the full object: "sparse_values": { "indices": [...], "values": [...] }.
Every score_by clause must include type. It’s the discriminator that selects the scoring method (text, query_string, dense_vector, sparse_vector). Omitting it returns a 400.
Every document must have a non-empty _id string. There is no default; the upsert request fails if any document in the batch is missing _id or has an empty value.
Wait for status.ready: true before searching. A newly created index can briefly return empty results. For Dedicated read capacity, also wait for read_capacity.status.state: "Ready".
The match-score response field is _score, not score. A user metadata field named score is allowed and is returned alongside the system-owned _score.
Namespace is part of the URL path. Use __default__ (the literal string) if you don’t need partitioning. An empty path segment is rejected.
dense_vector queries use values, not query. Only text and query_string clauses use query (a string). dense_vector and sparse_vector use values (a float array) and sparse_values (an {indices, values} object) respectively.

Requirements and limitations

All requests require X-Pinecone-Api-Version: 2026-07.
The REST API, Python SDK (pinecone), and Pinecone console are the supported entry points.
Endpoint compatibility: indexes with document schemas use the /namespaces/{namespace}/documents/* endpoints; dense, sparse, and integrated-inference indexes continue to use /vectors/* (and /records/* for integrated inference). The two endpoint families are index-type-specific and don’t cross over.
Supported deployment modes: managed (serverless) with read_capacity.mode of OnDemand or Dedicated.
Changing an index from dedicated read capacity back to on-demand read capacity is not supported. To move from dedicated read capacity to on-demand, create a new on-demand index and reingest your data.
Schemas declare ranking fields only: text fields (string with full_text_search), dense_vector, and sparse_vector. Text-only, text + dense vector, and combined dense + sparse + text schemas are all supported in a single index. Metadata-only field declarations (string without full_text_search, string_list, float, boolean) are rejected at index creation; metadata is auto-indexed at upsert time.
Schema and document limits: a schema can contain up to 100 full_text_search string fields; each full_text_search string field can be up to 100 KB and 10,000 tokens; tokens can be up to 256 bytes before analyzer truncation; each document can be up to 2 MB; each upsert request can contain up to 1000 documents and 2 MB.
Metadata size: metadata fields on a document (everything outside FTS-enabled string fields) are limited to 40 KB per document in total. This limit does not apply to full_text_search text fields.
Vector-field cardinality: 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.
Field-name policy: schema and metadata field names must not start with _ (reserved for system-managed fields like _id and _score) or $ (reserved for filter operators), and are limited to 64 bytes.
The match-score response field is _score (renamed from score so that user metadata named score can coexist with the system-owned match score in the flat response payload).
A single search request ranks by one scoring type. Multi-field BM25 is supported: name several fields in one text clause, or pass multiple text clauses, which the server combines into one ranking; a query_string clause can also target several fields. Every contributing field weighs equally in 2026-07; there is no per-field weight parameter. To combine BM25 ranking with dense_vector or sparse_vector ranking, restrict the dense (or sparse) search with a text-match filter ($match_phrase, $match_all, $match_any) on the full-text field, or run separate searches and merge the results client-side.
Newly upserted documents are indexed asynchronously and may not be searchable immediately.
Partial updates: POST /namespaces/{namespace}/documents/upsert replaces the entire document for a given _id. For field-level changes, use POST /namespaces/{namespace}/documents/update, which patches only the fields you specify (removing others with _remove_fields) per ID, or applies the same patch in bulk to every document matching a metadata filter (with set_fields / remove_fields), leaving unmentioned fields unchanged.
Schemas are fixed at index creation. Adding, removing, or retyping fields after creation is not yet supported. Existing indexes created before 2026-07 cannot be backfilled with a schema. To use FTS, dense + FTS, or any Documents API query in 2026-07, create a new index with the desired schema and reindex documents.
Metadata is auto-indexed: any field on an upserted document that is not declared in the schema is automatically indexed for filtering. The schema declares only ranking fields (FTS-enabled string, dense_vector, sparse_vector); declaring metadata-only fields (string without full_text_search, string_list, float, boolean) is rejected at index creation. Track metadata field names and types in your application. Pinecone infers the type from the values you upsert.
Bulk import from object storage is supported for indexes with document schemas via JSONL files, see Prepare document-schema files (JSONL). Semantic-text (auto-embedded) fields are not yet supported in schemas.
Maximum results per query: top_k is capped at 10,000. Full-text search is optimized for ranked retrieval rather than aggregation- or count-style queries.
Indexes cannot be created in CMEK-enabled projects.
Backup and restore are not yet supported.
describe_index_stats is supported. In the REST response, totalVectorCount and namespaces are accurate for every schema. For the full response schema, see Get index stats.
describe_index_stats vector fields: in the same REST response, dimension and metric describe the schema’s dense_vector field, and vectorType is dense. When a schema declares no dense_vector field, metric falls back to dotproduct, and dimension is 0 on a text-only schema (vectorType is text) or is omitted when the schema declares a sparse_vector field (vectorType is sparse).
Namespace operations: you can create, list, describe, and delete namespaces on an index with a document schema, and describing a namespace returns its record_count and size_bytes.
Fuzzy matching (term~, term~N) is available only in query_string scoring, not in type: "text" or in $match_* filters.
Single-term prefix wildcards (auto*) are not supported; use phrase prefix ("word auto"*) instead, or configure a field for substring search.

Pricing

Reads and writes on indexes with document schemas are metered using the same read units (RUs) and write units (WUs) model as vector indexes.

Get started

Core concepts

Index data

Search

Optimize

Manage data

Manage cost

Move to production

Admin

Operations

Using pods

Full-text search overview

When to use it

Capabilities

How it works

End-to-end example

Filters vs. scoring

Schema definition

Schema field types

Example schemas

Metadata fields

Schema validation

Filter operators

Search examples

Token matching with a filter

Cross-field boolean query (query_string)

Dense ranking with a phrase-match filter

BM25 ranking with a text-match filter

Phrase filter with negation

Troubleshooting

Requirements and limitations

Pricing

​When to use it

​Capabilities

​How it works

​End-to-end example

​Filters vs. scoring

​Schema definition

​Schema field types

​Example schemas

​Metadata fields

​Schema validation

​Filter operators

​Search examples

​Token matching with a filter

​Cross-field boolean query (query_string)

​Dense ranking with a phrase-match filter

​BM25 ranking with a text-match filter

​Phrase filter with negation

​Troubleshooting

​Requirements and limitations

​Pricing

When to use it

Capabilities

How it works

End-to-end example

Filters vs. scoring

Schema definition

Schema field types

Example schemas

Metadata fields

Schema validation

Filter operators

Search examples

Token matching with a filter

Cross-field boolean query (query_string)

Dense ranking with a phrase-match filter

BM25 ranking with a text-match filter

Phrase filter with negation

Troubleshooting

Requirements and limitations

Pricing