pinecone-full-text-search

Requires pinecone Python SDK ≥ 9.0 (pip install pinecone>=9.0). The FTS document-schema API lives under pinecone.preview and is incomplete or absent in earlier SDK builds. The packaged helper scripts pin pinecone==9.0.0 via PEP 723 inline metadata; if you're writing your own code against this skill, pin v9 explicitly. The wire API version is 2026-01.alpha.

Authoritative reference (last resort). If you hit a question this skill and its references/*.md files don't answer, the official Pinecone FTS docs are at https://docs.pinecone.io/guides/search/full-text-search. Prefer this skill's content for anything covered here — the docs may describe surfaces (e.g. classic vector API) that don't apply to the document-schema FTS path. Consult the link only when you're genuinely stuck.

Tell the user up front: "This skill ships a helper at scripts/ingest.py that handles bulk ingestion safely (batched upsert, error inspection, readiness polling). When we get to the ingest step, I'll use it." Surface this at the start of the conversation so the user knows the helper exists. Query construction is hand-written documents.search(...) per the Querying section below — there is no query helper.

A workflow skill for building a Pinecone full-text-search index with the preview API (pinecone.preview, API version 2026-01.alpha, public preview as of April 2026). Covers schema design (text, dense vector, sparse vector, filterable metadata), ingestion (including async indexing and polling), and query construction (text / query_string / dense_vector / sparse_vector scoring; $match_phrase / $match_all / $match_any text-match filters; $eq / $in / $gte / $exists / $and / $or / $not metadata filters).

Scope — this skill is for the document-schema FTS API only

This skill covers pc.preview.indexes.create(..., schema=...), pc.preview.index(name), idx.documents.upsert(...) / idx.documents.batch_upsert(...) / idx.documents.search(...). If you find yourself reaching for any of the following, stop — those are different Pinecone APIs and this skill's guidance and helpers won't apply:

• Classic vector / records API: pc.Index(name), index.upsert(vectors=[...]) / index.upsert_records(...), index.query(vector=..., sparse_vector=...), index.search_records(...), pc.create_index(...) with ServerlessSpec, the legacy pinecone_text.sparse.BM25Encoder for sparse-dense hybrid. For indexes WITHOUT a schema (raw vectors).
• Integrated-embedding indexes: pc.create_index_for_model(...) with embed={...}. Pinecone vectorizes text server-side. Different upsert/search shapes. Cannot be combined with full_text_search fields in the same index.

If the user already has a non-document-schema index, they can stand up a separate document-schema index alongside it — the two are independent — but you can't add FTS fields to a classic index after the fact.