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.

Querying — construct documents.search(...) calls

For any task that asks you to query an FTS index, you write a documents.search(...) call directly. The schema is authoritative — describe the index live before constructing the call so you know which fields are FTS-enabled, which are filterable, and which are vectors.

Workflow:

1. Discover the schema. Call pc.preview.indexes.describe(<index>) and read the schema.fields dict. Each field's class indicates its type (PreviewStringField, PreviewIntegerField, PreviewDenseVectorField, etc.); attributes tell you whether it's FTS-enabled (full_text_search), filterable, or carries a dimension. Skip this step only if you've already seen the schema in this conversation.
2. Construct the call matching the rules below — one scoring type per request, hard requirements in filter, ranking signals in score_by, include_fields explicit on every call.
3. Execute with idx = pc.preview.index(name=<index>); resp = idx.documents.search(...) and read resp.matches.

Canonical shapes:

# Pure BM25 keyword search
resp = idx.documents.search(
    namespace="__default__",
    top_k=10,
    score_by=[{"type": "text", "field": "body", "query": "machine learning"}],
    filter={"year": {"$gt": 2024}, "category": {"$eq": "ai"}},  # optional
    include_fields=["*"],   # always pass explicitly
)

# Hybrid: dense ranking with a lexical filter (one type in score_by + filter narrows)
resp = idx.documents.search(
    namespace="__default__",
    top_k=10,
    score_by=[{"type": "dense_vector", "field": "embedding", "values": query_embedding}],
    filter={"body": {"$match_all": "TensorFlow"}, "year": {"$gt": 2024}},
    include_fields=["*"],
)

Key rules (the server enforces these; following them locally keeps the agent loop tight):

• score_by is a list of clauses, but exactly one scoring type per request (server rejects mixed types). Multi-field BM25 is the one exception: multiple text clauses, or one query_string with fields: [...]. To combine BM25 + dense signals, restrict the dense search with a text-match filter ($match_all / $match_phrase / $match_any); do NOT mix scoring types in score_by.
• filter keys are field names (must exist in schema and be filterable) OR logical operators ($and, $or, $not). Field values are operator dicts ({"$gt": 5}, NOT bare values).
• include_fields is required on every call. Pass ["*"] for all stored fields, [] for ids+score only, or a list of names. Some SDK builds 400/422 if it's omitted.

Clause shapes (for score_by):

text / dense_vector / sparse_vector use singular field. Only query_string accepts a fields array (and also accepts singular field as an alias). sparse_vector uses sparse_values (NOT values) — distinct from dense.

Filter operators by field type:

Match shape on response:

for m in resp.matches:
    m._id        # document id
    m._score     # match score (NOT `score`); some older SDK builds may also surface `score`
    m.to_dict()  # full doc payload (when include_fields includes the field)

For deeper coverage — multi-field BM25, Lucene patterns, hybrid composition, RRF merges, common error symptoms — see references/querying.md. For schema field types and what they enable on the query side, see references/schema-design.md.

Ingesting — use the packaged helper

For any task that asks you to bulk-ingest a JSONL file into an existing FTS index, the canonical path is to invoke the bundled helper, NOT to hand-write a Python script. Do not read the script's source — everything you need is in this section.

The script does three things bare-LLM ingest code reliably skips, each of which corresponds to a silent production failure:

1. Bulk-upserts in batches. No per-doc upsert loops.
2. Inspects every batch result. batch_upsert returns 202 even when individual documents fail; the failures live in result.errors / result.has_errors. Without inspection,