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):

type	Required keys	When to pick this
text	field (string FTS), query	Open-ended keyword search; BM25 ranking on one field
query_string	query (Lucene), fields optional	Lucene boost (^N), proximity (~N), cross-field boolean, phrase prefix
dense_vector	field (dense_vector), values (list of floats)	Semantic / mood / topic ranking
sparse_vector	field (sparse_vector), sparse_values ({indices, values})	Custom sparse-encoder ranking

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:

Field type	Legal operators
string with FTS	$match_phrase, $match_all, $match_any
string filterable	$eq, $ne, $in, $nin, $exists
string_list filterable	$in, $nin, $exists
float filterable	$eq, $ne, $gt, $gte, $lt, $lte, $exists
boolean filterable	$eq, $exists
logical wrappers	$and: [filters], $or: [filters], $not: filter

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, "100 docs ingested" silently becomes "73 docs ingested + 27 lost."
3. Polls until searchable. After upsert, Pinecone is still building the inverted index. A documents.search call during that window returns empty. Without the poll, the user debugs their query code for an hour without finding the indexing race.

You provide a prepared, schema-conformant JSONL file and the index name; the script does the rest. Schema validation is upstream concerns (your prep pipeline, or prepare_documents.py when it lands) — ingest.py trusts what you hand it.

Invocation:

uv run --script .claude/skills/pinecone-fts-index/scripts/ingest.py \
  --data processed.jsonl \
  --index <index_name> \
  --sentinel-field <fts_field>

Flags:

Flag	Short	Required	Purpose
--data	-d	yes	Path to JSONL file with prepared documents (one per line)
--index	-i	yes	Pinecone index name (must already exist)
--sentinel-field	-f	yes	An FTS-enabled field on the index, used for the readiness-poll query. Pick the longest free-text field on your schema.
--namespace	-n	no	Default __default__
--batch-size	-b	no	Default 100. Reduce for large dense vectors. A 50-doc batch with 3072-dim float vectors lands ~5-10 MB and can be rejected; drop to --batch-size 50 (or lower) at high dimensions.
--poll-deadline	—	no	Default 300 (seconds). Time to wait for documents to become searchable before giving up.
--sentinel	-s	no	Token used for the readiness-poll query. Default: first whitespace-separated token of doc[0][sentinel-field].

What the script prints:

Loading processed.jsonl ...
Loaded 5000 document(s).
Sentinel: body='The'

Upserting in batches of 100 ...
  batch @     0:  100 docs in  0.42s  (total: 100/5000)
  batch @   100:  100 docs in  0.39s  (total: 200/5000)
  ...

Upsert complete: 5000 doc(s) in 21.4s.

Polling for searchability (deadline 300s) ...
Searchable after 12.3s (3 probe(s)).

Done — total 33.7s.

If a batch fails, the script prints every error message and exits non-zero. If the poll deadline expires, the script prints a hint about why (sentinel field isn't FTS-enabled, deadline too tight, docs structurally upserted but rejected by the inverted-index builder) and exits non-zero. Don't suppress these errors — they're surfacing real problems with the data or the index.

When you should NOT use the script:

• The user is doing per-doc patch updates (single-doc documents.upsert calls with selective fields). The script is for bulk loads, not per-record operations.
• The user is ingesting from a non-JSONL source (CSV, Parquet, Postgres dump). Convert to JSONL first; the script doesn't parse other formats.
• The user explicitly asks you to write the ingestion code from scratch (teaching context). Honor the request and follow the canonical pattern: documents.batch_upsert + result.has_errors inspection + documents.search polling with sentinel and deadline.

The script lives at .claude/skills/pinecone-fts-index/scripts/ingest.py. PEP 723 inline-metadata script — uv run --script installs typer and pinecone automatically on first invocation. No setup needed.

Use cases

Three concrete shapes to model your task on. Match the user's request to the closest one and follow its steps; improvise if the task is genuinely a hybrid.

UC-1: Index a new corpus end-to-end

Trigger. "Index this CSV / JSONL / folder for search," "build a search backend over [my articles / products / tickets / transcripts]," "make my [dataset] searchable."

For unprocessed / messy data, load the onboarding walkthrough first. If the user is showing up with raw data (unclear field types, possibly long text fields exceeding FTS limits, comma-separated tag strings, dates as strings, possibly duplicate IDs, etc.) and they haven't given you an explicit schema, read references/onboarding-walkthrough.md and follow it stage-by-stage. It's a conversational guide — meet the data, surface the processing decisions to the user, propose a schema, confirm before creating, then process+ingest+verify together. The walkthrough exists because schemas are immutable and "onboarding a new corpus" is a high-stakes flow that benefits from explicit user buy-in at each decision point.

If the user already gave you a clean JSONL + a schema spec, follow the abbreviated steps below.

Steps (when data is already prepared and the schema is decided):

1. Inspect the corpus shape — text fields, structured metadata, do you also need a vector? Match it to one of the canonical shapes in references/schema-design.md (articles, products, tickets, image library, code).
2. Pick analyzer settings on each text field — language, stemming, stop_words. Stemming on for long prose, off for proper nouns / identifiers.
3. Assemble the schema with SchemaBuilder and confirm it with the user before calling indexes.create — schemas are immutable in 2026-01.alpha, so a wrong call costs a re-ingest.
4. Create the index, poll describe() until status.ready: true.
5. Run scripts/ingest.py --data <jsonl> --index <name> --sentinel-field <fts_field> — see the Ingesting — use the packaged helper section above. The script handles batch_upsert + per-batch error inspection + post-upsert readiness polling in one invocation. Don't hand-write the loop unless the user explicitly asks you to.
6. (The script polls automatically — by the time it exits cleanly, the index is searchable. If you skip the script and roll your own, you must poll documents.search with a sentinel query and a deadline; batch_upsert returning ≠ searchable.)
7. Validate with one or two probe queries against fields you know contain the sentinel content.

Result. A working documents.search call against the user's data, returning ranked matches.

UC-2: Add a dense (or sparse) signal to a text-only corpus

Trigger. "Add semantic search," "add embeddings," "make this hybrid," or any prompt that describes a query pattern text alone can't serve (visual similarity, mood, cross-modal "looks like").

Steps.

1. Confirm the new signal represents a modality or signal text can't express — image / audio / external score, or a different corpus than the existing FTS field. Re-encoding the same text into a dense field is an anti-pattern (references/schema-design.md → "When to add a dense field at all").
2. Because schemas are immutable, plan a new index, not a migration. Get user confirmation before recreating.
3. Pick an embedding provider and pin its output dimension at schema time. Beware payload-size pitfalls at native dimensions — Gemini-3072 etc. need truncation (references/ingestion.md → "Dense-vector payload size").
4. Schema → create → wait Ready → ingest with embeddings inline or pre-cached.
5. Validate with a hybrid query: dense_vector score_by + text-match filter ($match_phrase / $match_all). That's the supported single-call cross-modal shape.

Result. One index, two retrieval shapes — pure text and dense+filter hybrid — both runnable without further setup.

UC-3: Build a documents.search call from a natural-language user prompt (agent mode)

Trigger. Agent receives a user prompt like "find articles about machine learning that mention TensorFlow and were published after 2024" or "documents about climate policy ranked by similarity to this paragraph." The index already exists.

Steps.

1. (Optional) Discover the schema by calling pc.preview.indexes.describe(<NAME>) and reading schema.fields. Skip if you already know the field types from earlier in the conversation.
2. Decompose the user's prompt into score_by / filter shapes using the agent-mode decomposition table below. (Hard requirements → filter. Ranking signals → score_by. Always include include_fields explicitly.)
3. Construct the documents.search(...) call following the rules in the Querying section above — one scoring type per request, operator/field-type matching, include_fields always set.
4. Execute the call. The response carries resp.matches; iterate to get m._id, m._score, and field values via m.to_dict(). Use the matches in whatever shape the user asked for.
5. If results come back empty or wrong, walk the failure tree in Common gotchas.

Result. Live search results matching the user's intent.

The four common UC-3 mistakes to actively avoid:

• Mixing scoring types in score_by (server rejects). Put hard requirements in filter; rank by one signal in score_by.
• Putting hard requirements in score_by as BM25 terms instead of in filter as $match_all / $match_phrase (returns ranked results that don't guarantee the term is present).
• Operator/field-type mismatches (e.g. $match_all on a float field, $gt on a string field). Consult the operator table in the Querying section.
• Omitting include_fields (some SDK builds 400/422). Always pass it explicitly.

Agent-mode query decomposition

Map user prompt cues to API shapes. Read top-down — identify the cue, copy the corresponding shape.

User prompt cue	API shape
Open-ended keywords ("articles about machine learning", search-bar query)	score_by=[{"type": "text", "field": "<field>", "query": "<terms>"}] — BM25 token-OR
Exact phrase, drives ranking ("rank by 'beautifully written'")	score_by=[{"type": "query_string", "query": '<field>:("phrase here")'}]
Exact phrase, hard requirement ("must contain 'machine learning'")	filter={"<field>": {"$match_phrase": "machine learning"}}
Required tokens, any order ("must mention TensorFlow", "must be about Illinois")	filter={"<field>": {"$match_all": "tokens space-separated"}} — preferred over query_string +token because it's a true hard filter, doesn't contribute to score
At least one of these tokens ("contains AI or ML or robotics")	filter={"<field>": {"$match_any": "AI ML robotics"}}
Excluded tokens ("not about deprecated", "no opinion pieces")	filter={"$not": {"<field>": {"$match_any": "deprecated opinion"}}} — or -token inside query_string
Boolean / boost / slop / phrase-prefix ("weight 'eagle' 3x", "within N words")	score_by=[{"type": "query_string", "query": '<expr with ^N / ~N / "…"*>'}] — only Lucene supports these
Cross-field boolean ("title or body contains X")	score_by=[{"type": "query_string", "query": 'title:(X) OR body:(X)'}]
Numeric / date / range / boolean metadata ("after 2024", "rating > 4", "in stock")	filter={"<field>": {"$gt": ..., "$gte": ..., "$eq": ..., "$exists": true}}
Category / tag / list membership ("category = fiction", "tagged X")	filter={"<field>": {"$in": [...]}} (works on string and string_list filterable fields)
Semantic similarity / mood / topic ("articles about ML", "documents that feel sombre")	score_by=[{"type": "dense_vector", "field": "<embedding_field>", "values": embed(<text>)}] — requires a dense_vector field
Visual appearance / cross-modal text query against an image corpus	Same dense_vector shape, with the embedding model that produced the stored image vectors. Multimodal embedders (Gemini-2 etc.) map a text query into the image space.
Hybrid: lexical requirement + semantic ranking ("articles about ML that mention TensorFlow")	Lexical → filter ($match_all / $match_phrase); semantic → score_by (dense_vector). Single call.

Two structural rules the agent must enforce, no exceptions:

• One scoring type per request. score_by accepts text / query_string / dense_vector / sparse_vector, but a request ranks by one. Don't mix dense + text in score_by — the server rejects it. Multi-field BM25 is the only "list" pattern that's allowed (multiple text clauses, or one cross-field query_string).
• Hybrid = filter + score_by, not two score_by clauses. When a prompt has both a lexical requirement and a semantic ranking signal, lexical goes in filter (via $match_* operators) and semantic goes in score_by. If both signals genuinely need to drive ranking, run two searches and merge IDs client-side.

Workflow at a glance

Three phases. Each has its own reference file — consult it before writing code for that phase.

1. Design the schema. Decide which string fields are full-text-searchable, which are filterable metadata, whether you need a dense_vector field (and whether it earns its place), whether you also need a sparse_vector field, and which numeric / boolean / array filters to declare. Schemas are fixed at index creation in 2026-01.alpha — plan carefully. → references/schema-design.md
2. Ingest documents. For bulk loads from a prepared JSONL, run the bundled scripts/ingest.py helper (it does batch_upsert + error inspection + readiness polling correctly by construction — see the Ingesting — use the packaged helper section above). For per-doc patch updates, hand-call documents.upsert. Either way, documents are indexed asynchronously after the HTTP call returns; batch_upsert returning 202 ≠ searchable. → references/ingestion.md for the canonical pattern in detail.
3. Query the index. A single search request ranks by one scoring type — pass exactly one of text, query_string, dense_vector, or sparse_vector in score_by (multi-field BM25 is supported via multiple text clauses or a cross-field query_string). Layer filter={...} for text-match ($match_phrase / $match_all / $match_any) and metadata filters ($eq / $in / $gte / $exists / $and / $or / $not). Control the response payload with include_fields. → references/querying.md

Quick template

End-to-end skeleton for a minimal text + filterable-metadata index. Copy it and edit every spot marked # TODO:. The template deliberately omits external embedding calls so it stays generic; see references/ingestion.md for dense / sparse field patterns and embedding-provider integration, and references/querying.md for the four scoring shapes plus text-match and metadata filters.

import time
from pinecone import Pinecone
from pinecone.preview import SchemaBuilder

INDEX_NAME = "my-fts-index"        # TODO: name your index (lowercase alphanumeric + hyphens, ≤45 chars)
NAMESPACE = "__default__"          # TODO: pick a namespace; auto-created on first upsert

pc = Pinecone()                    # reads PINECONE_API_KEY
# TODO: preprod backends require an x-environment header on the client:
#   pc = Pinecone(additional_headers={"x-environment": "preprod-aws-0"})

# 1. Schema — one FTS string field, one filterable string, one filterable float.
#    Field names must NOT start with `_` (reserved for `_id` / `_score`) or `$`
#    (reserved for filter operators), and are limited to 64 bytes.
schema = (
    SchemaBuilder()
    .add_string_field("body", full_text_search={"language": "en"})  # TODO: rename for your content
    .add_string_field("category", filterable=True)                   # TODO: any exact-match metadata
    .add_integer_field("year", filterable=True)                      # TODO: any numeric filter — emits `"type": "float"` on the wire
    .build()
)

# 2. Create the index. read_capacity defaults to {"mode": "OnDemand"}; pass
#    {"mode": "Dedicated", ...} only if you specifically want provisioned reads.
if not pc.preview.indexes.exists(INDEX_NAME):
    pc.preview.indexes.create(name=INDEX_NAME, schema=schema)

# 3. Wait for the index itself to become Ready.
while not pc.preview.indexes.describe(INDEX_NAME).status.ready:
    time.sleep(5)

idx = pc.preview.index(name=INDEX_NAME)

# 4. Upsert a single document. `_id` is required, every other field is optional.
#    upsert REPLACES the document on conflict — there is no per-field merge in 2026-01.alpha.
idx.documents.upsert(
    namespace=NAMESPACE,
    documents=[{
        "_id": "doc-1",
        "body": "Full-text search is great for keyword queries.",
        "category": "intro",
        "year": 2025.0,
    }],
)

# 5. Poll until the FTS side is searchable (upsert returns BEFORE docs are indexed).
deadline = time.time() + 300
while time.time() < deadline:
    resp = idx.documents.search(
        namespace=NAMESPACE, top_k=1,
        score_by=[{"type": "text", "field": "body", "query": "search"}],  # TODO: sentinel query likely to hit
        include_fields=[],          # required on every search; [] = lightest payload (ids + _score only)
    )
    if resp.matches:
        break
    time.sleep(5)

# 6. Search — text scoring composed with metadata filter.
resp = idx.documents.search(
    namespace=NAMESPACE,
    top_k=5,
    score_by=[{"type": "text", "field": "body", "query": "keyword queries"}],
    filter={"year": {"$gte": 2024}},        # TODO: adjust filter or drop it
    include_fields=["*"],                    # "*" = all stored fields; [] = `_id` + `_score` only
)
for m in resp.matches:
    print(m._id, getattr(m, "_score", getattr(m, "score", None)), m.to_dict())

Common gotchas

• One scoring type per search request. score_by accepts text, query_string, dense_vector, or sparse_vector — but a request ranks by one type. Multi-field BM25 is fine (pass several text clauses, or a single cross-field query_string). To combine BM25 ranking with a dense_vector (or sparse_vector) signal, restrict the dense search with a text-match filter operator ($match_phrase / $match_all / $match_any) on the lexical field, not by mixing types in score_by. The "blend a dense vector and a text clause in score_by" pattern is rejected by the server.
• Text-match filter operators are the cross-modal hinge. $match_phrase (exact phrase), $match_all (every token, any order), $match_any (at least one token) are filter-side operators on full_text_search fields. Each takes a single string (max 128 tokens). They reuse the field's tokenizer / stemmer, compose under $and / $or / $not, and are the supported way to compose lexical pre-filtering with dense or sparse ranking. Phrase slop ("…"~N), term boost (^N), and phrase prefix ("… word"*) are scoring-only — they live in query_string, not in filter.
• Preprod backends need additional_headers={"x-environment": "..."} on the Pinecone() client. Missing the header lands you on prod and you'll see "index not found" / empty-result symptoms that look like code bugs but aren't.
• include_fields is required on every documents.search(...) call. When omitted, defaults to [] (_id + _score only). Pass ["*"] for all stored fields or a list of names to project. Omitting it on some SDK builds yields 400 / 422 instead of the documented default; always pass it explicitly to avoid surprises.
• Match score is _score; doc id is _id. Public-preview docs return the system match score on the _score field so a user metadata field literally named score can coexist. Always prefer _score on read; some older SDK builds may still surface plain score, so for defensive code use getattr(m, "_score", getattr(m, "score", None)).
• Reserved field names: leading _ and $, max 64 bytes. _ is for system fields (_id, _score); $ is for filter operators. Schema validation rejects names that violate either rule. Length cap is bytes, not characters — be careful with non-ASCII names.
• Vector-field cardinality: at most one dense_vector and at most one sparse_vector per index in 2026-01.alpha. Multiple text fields are fine.
• batch_upsert failures are silent by default. The return value carries has_errors, failed_batch_count, and a list of BatchError objects with error_message. If you don't inspect them, you'll see "Uploaded 0 / N" and an indefinite "not yet indexed" poll — with the real cause (payload-too-large, schema mismatch, reserved field name) hidden. Always print result.errors[*].error_message before downstream steps.
• Dense-vector payload size matters at batch time. A 50-doc batch with 3072-dim float vectors lands around 5–10 MB and can be rejected by the preview backend. If every batch fails, try reducing the embedding dimension via your provider's truncation knob (e.g. Gemini's output_dimensionality=768) before debugging schema.
• Async indexing: batch_upsert returning ≠ searchable. The server builds inverted indexes in the background after the HTTP call returns. If you query immediately you'll see empty result sets. Always poll documents.search with a sentinel query and a deadline (pattern in references/ingestion.md).
• String FTS field shape is full_text_search={...} (dict). Pass {} to enable with all server defaults. User-settable sub-fields: language, stemming, stop_words. Server-applied (visible in describe() responses but NOT settable at index creation): lowercase (default true) and max_token_length (default 40). Stemming is opt-in (default false); stop_words is opt-in (default false, opposite of pre-public-preview docs). The earlier SDK shape full_text_searchable=True, language="en" is legacy and should be avoided.
• Schemas are fixed at index creation in 2026-01.alpha. Adding, removing, or retyping fields after creation is not supported. Changing dimension or metric on an existing vector field requires a new index. Plan the schema once.
• No partial / per-field updates. documents.upsert always replaces the entire document for a given _id. To update one field, fetch the doc, modify in client code, and upsert the full doc back under the same _id.
• Document operations: search supports filter, fetch and delete do not. Fetch is ID-only (POST /documents/fetch with ids: [...]); delete accepts only ids or delete_all: true. To act on a metadata expression, search first to collect IDs, then fetch or delete those IDs.
• Namespaces auto-create on first upsert. Pass any namespace string to documents.upsert / batch_upsert and the namespace is created on the fly; documents from different namespaces are fully isolated. Use "__default__" if you don't need partitioning. Caveat: the namespace management endpoints (POST /namespaces, GET /namespaces, DELETE /namespaces/{namespace}) and describe_index_stats are NOT yet supported on indexes with document schemas — you can write to a namespace, you just can't list / delete them via the API yet.
• Document and request size limits (preview): per-document max 2 MB; per-request max 2 MB and 1000 documents; per FTS-enabled string field max 100 KB and 10,000 tokens (tokens > 256 bytes are truncated by the analyzer); per-document filterable metadata (everything not in an FTS field) max 40 KB. A schema can declare up to 100 FTS string fields. For long-prose corpora, chunk before ingest — see references/ingestion.md.
• score_by clause shape — singular field is canonical for text/dense_vector/sparse_vector; only query_string takes a fields array.
  • text: {"type":"text", "field":"<fts_field>", "query":"<terms>"}.
  • query_string: {"type":"query_string", "query":"<lucene>", "fields":["<a>","<b>"]} (the optional fields array; query_string also accepts a bare "fields":"body" string and the legacy "field":"body" as an alias).
  • dense_vector: {"type":"dense_vector", "field":"<dense_field>", "values":[/*floats*/]}.
  • sparse_vector: {"type":"sparse_vector", "field":"<sparse_field>", "sparse_values":{"indices":[...],"values":[...]}} — note sparse_values (NOT values) for sparse clauses.
• Single-term prefix wildcards aren't supported. auto* doesn't work in query_string; use phrase prefix ("machine lea"* — phrase must contain at least two terms, last term is matched as prefix).
• Indexes can't be created in CMEK-enabled projects, no backup/restore, no fuzzy or regex search, no S3 bulk import for document-shaped indexes in 2026-01.alpha. If any of these are hard requirements, the public-preview FTS surface isn't yet ready.

Extension points

Currently shipped under scripts/:

• scripts/ingest.py — bulk-ingest a prepared JSONL into an existing FTS index. Handles batch_upsert in safe-sized chunks, inspects every batch's result.errors and aborts loudly on failure, then polls documents.search with a sentinel + deadline until docs are searchable. Schema-agnostic: takes only --data, --index, --sentinel-field. Usage in Ingesting — use the packaged helper section above.

Query construction does NOT have a packaged helper — write documents.search(...) calls directly per the Querying section above.