concinno-skills-vector 0.2.0

Remote vector database agent skills (Pinecone + Chroma + Weaviate) for Concinno — thin adapters for agent upsert/query. Concinno core's ZIQRetrieval + FTRL remains the full retrieval stack; this is the outbound-to-vendor slot.

concinno-skills-vector

Remote vector database skills for Concinno. Pinecone + Chroma + Weaviate via their official Python SDKs — thin call(**kwargs) adapters for agents that need to upsert/query a vendor-hosted vector store.

Status

0.2.0 — six tools covering the canonical "talk to a vector DB" agent need. Concinno core already ships ZIQRetrieval — a full BM25 + dense + FTRL online-learning retrieval engine for local knowledge; this sub-package is the orthogonal "read/write external vendor index" slot.

New in 0.2.0: WeaviateQuery accepts MongoDB-style dict filters ($eq / $ne / $gt / $gte / $lt / $lte / $in / $and / $or) and translates them to Weaviate v4's typed Filter DSL server-side:

WeaviateQuery().call(
    action="query",
    collection="Articles",
    vector=[...],
    filter={"category": "ml", "year": {"$gte": 2024}},
    # → Filter.all_of([by("category").equal("ml"),
    #                  by("year").greater_or_equal(2024)])
    weaviate_url="https://...",
    weaviate_api_key="wk-...",
)

Tool	Library	License	Purpose
PineconeUpsert	pinecone (v5+)	Apache-2.0	Upsert vectors into a Pinecone index
PineconeQuery	pinecone (v5+)	Apache-2.0	Query a Pinecone index by vector
ChromaAdd	chromadb (v0.5+)	Apache-2.0	Add vectors to a Chroma collection (local or remote)
ChromaQuery	chromadb (v0.5+)	Apache-2.0	Query a Chroma collection by vector
WeaviateAdd	weaviate-client (v4.9+)	BSD-3	Insert vectors into a Weaviate v4 collection
WeaviateQuery	weaviate-client (v4.9+)	BSD-3	Query a Weaviate v4 collection by vector

Install

pip install concinno-skills-vector

All three vendor SDKs come in as hard dependencies. Consumers who only need one vendor path can install with --no-deps and pick the SDK they want.

Scope vs Concinno core

Concinno core's concinno.rag.ZIQRetrieval is a full retrieval stack — BM25 + dense embedding + FTRL online-learning routing between the two arms, with persistence, reranking, and per-namespace SPS (Structural Prior SOTA) tuning. It targets the "I want an intelligent retriever" slot.

This sub-package is an outbound adapter — the "my agent already uses Pinecone / Chroma / Weaviate as its vector store, let the agent upsert / query it directly" slot. No embedding model, no scoring opinion, no router.

The two can coexist in the same ToolRegistry; they do not overlap.

Why this sub-package vs LangChain retrievers

LangChain retrievers are readers only (no online learning, per-query routing is pluggable but not built-in adaptive). Concinno's own ZIQRetrieval in core has the FTRL online-learning router; this sub-package just provides clean agent-callable access to the three most common vendor stores without reinventing the router.

Safety

Every tool routes through a shared _safety module before touching the vendor:

1. top_k cap — query defaults to 10 and hard-caps at 1000. Larger pagination must be done caller-side.
2. Batch cap — upsert / add / insert caps at 10_000 rows per call. Split larger jobs into multiple calls.
3. Vector dimension consistency — the first row of a batch defines the dimension; all subsequent rows must match. Mixed-dim batches corrupt vendor indexes with no rollback.
4. Filter type — filters must be JSON-serialisable dict (or None). String / script / callable filter bodies are rejected at the tool layer. WeaviateQuery now translates MongoDB-style dict filters to Weaviate's typed Filter DSL ($eq / $ne / $gt / $gte / $lt / $lte / $in / $and / $or). Unsupported operators ($not / $nin / $regex, etc.) surface as {"error": "..."} rather than being silently dropped.

Credentials

No credentials are stored. Each call takes the vendor-specific credential kwargs (api_key, weaviate_url + weaviate_api_key, chroma_host + chroma_port or chroma_persist_dir). Callers who want indirection resolve upstream:

import os
from concinno_skills_vector import PineconeQuery

PineconeQuery().call(
    action="query",
    api_key=os.environ["PINECONE_API_KEY"],
    index="my-index",
    vector=[0.1] * 1536,
    top_k=10,
)

Or via Concinno's CredentialStore:

from concinno.core.credentials import CredentialStore
cs = CredentialStore()
key = cs.resolve({"$ref": "env:PINECONE_API_KEY"})

Usage via Concinno ToolRegistry

When the consumer sets CONCINNO_LOAD_PLUGINS=1, the default registry auto-mounts all six tools:

import os
os.environ["CONCINNO_LOAD_PLUGINS"] = "1"

from concinno.tools.registry import get_default_registry
reg = get_default_registry()
expected = {
    "PineconeUpsert", "PineconeQuery",
    "ChromaAdd", "ChromaQuery",
    "WeaviateAdd", "WeaviateQuery",
}
assert expected.issubset(set(reg.list_deferred()))

Direct Python usage

from concinno_skills_vector import (
    PineconeUpsert, PineconeQuery,
    ChromaAdd, ChromaQuery,
    WeaviateAdd, WeaviateQuery,
)

# ── Pinecone ──────────────────────────────────────────────────────
PineconeUpsert().call(
    action="upsert",
    api_key="pk-...",
    index="products",
    vectors=[[0.1, 0.2, ...], [0.3, 0.4, ...]],
    ids=["sku-1", "sku-2"],
    metadata=[{"category": "book"}, {"category": "book"}],
    namespace="prod",
)
# → {"ok": True, "upserted": 2}

PineconeQuery().call(
    action="query",
    api_key="pk-...",
    index="products",
    vector=[0.1, 0.2, ...],
    top_k=5,
    filter={"category": {"$eq": "book"}},
)
# → {"matches": [{"id": "sku-1", "score": 0.95, "metadata": {...}}, ...]}

# ── Chroma ────────────────────────────────────────────────────────
ChromaAdd().call(
    action="add",
    collection="docs",
    vectors=[[...]],
    ids=["doc-1"],
    metadata=[{"source": "manual"}],
    chroma_persist_dir="~/.myapp/chroma",
)
# → {"ok": True, "added": 1}

ChromaQuery().call(
    action="query",
    collection="docs",
    vector=[...],
    top_k=3,
    chroma_host="localhost",
    chroma_port=8000,
)
# → {"matches": [{"id": "doc-1", "score": 0.12, "metadata": {...}}]}

# ── Weaviate ──────────────────────────────────────────────────────
WeaviateAdd().call(
    action="add",
    collection="Article",
    vectors=[[...], [...]],
    ids=["550e8400-e29b-41d4-a716-446655440000", "..."],
    metadata=[{"title": "..."}, {"title": "..."}],
    weaviate_url="https://my-cluster.weaviate.network",
    weaviate_api_key="wk-...",
)
# → {"ok": True, "added": 2, "failed": 0}

WeaviateQuery().call(
    action="query",
    collection="Article",
    vector=[...],
    top_k=10,
    filter={"category": "ml", "year": {"$gte": 2024}},  # NEW in 0.2.0
    weaviate_url="https://my-cluster.weaviate.network",
    weaviate_api_key="wk-...",
)
# → {"matches": [{"id": "550e8400-...", "score": 0.08, "metadata": {...}}]}

All tools return either {"ok": True, ...} / {"matches": [...]} on success or {"error": "..."} on any validation or vendor-SDK failure — same shape as other Concinno built-in tools.

What this package is NOT

• Not a retrieval engine. Use concinno.rag.ZIQRetrieval for BM25 + dense + FTRL routing.
• Not a migration tool. Index creation / schema changes go through each vendor's admin API, not these Tool call methods.
• Not an embedding service. Callers supply pre-computed vectors. Embedding models live in their own tools (e.g. Concinno core's ZIQRetrieval owns sentence-transformers).
• Not a secrets store. Credentials are per-call.
• Not a full Weaviate Filter DSL translator. WeaviateQuery covers the MongoDB-style operator subset listed above ($eq/$ne/$gt/$gte/$lt/$lte/$in/$and/$or). Weaviate-specific operators without a MongoDB analogue (contains_all, like, within_geo_range, cross-reference filters) require calling the Weaviate SDK directly.

License

Apache-2.0. See LICENSE in the Concinno monorepo.