If you've ever needed both keyword and semantic search on the same data, you've probably run a dense index and a sparse index side by side, queried both, and merged the two result sets by hand. Or you flattened your text into metadata, which gets you filtering but no ranking.

We just shipped the Documents API to fix that. It's a new way to define an index: instead of `vector_type` \+ `dimension` \+ `metric`, you pass a **schema** that can hold multiple text fields (each independently full-text searchable), a dense vector field, a sparse vector field, and metadata, all on the same document.

Here's what setup looks like:

    schema = (
        SchemaBuilder()
          .add_string_field("narrative", full_text_search={"language": "en"})
          .add_string_field("summary", full_text_search={"language": "en"})
          .add_dense_vector_field("embedding", dimension=1536, metric="cosine")
          .build()
    )
    
    pc.indexes.create(name="incident-postmortems", schema=schema)

Query it with `index.documents.search(...)` to score by text (real BM25, not sparse vectors), dense similarity, with phrase-match filters applied before scoring:

    index.documents.search(
        top_k=10,
        score_by=[{"type": "text", "field": "narrative", "query": "checkout latency"}],
        filter={"severity": {"$match_phrase":"sev2"}},
        include_fields=["narrative", "severity"],
    )

These schema-based indexes are also what makes full-text search possible, which is now generally available.

Your existing vectors-API indexes keep working as is, same `query`/`upsert`/`fetch`/`update`/`delete`/`list` methods. Today, there's no in-place conversion to a schema-based index, but if you want to take advantage of full-text search, you can adopt this approach with a new schema-based index + a reindex.

Here's the full write-up, a picture of before and after code examples, and the migration guide: [https://www.pinecone.io/learn/pinecone-documents-api/](https://www.pinecone.io/learn/pinecone-documents-api/)

Happy to answer questions in the comments.

Introducing schema-based indexes with text, dense, and sparse fields on the same document