feat: add ChromaDB vector store backend

Adds chromadb as a VECTOR_STORE_TYPE with three client modes selected by
VECTOR_STORE_CHROMA_CLIENT_MODE: persistent (local embedded), http
(self-hosted server), and cloud (Chroma Cloud).

- Single sync-client code path offloaded via asyncio.to_thread (Chroma's
  persistent and cloud clients have no async variants)
- Namespaces map to collections as ns-{sha256hex}; original namespace kept
  in collection metadata
- Cosine space, precomputed embeddings only, lazy collection creation
- Write batches split at min(get_max_batch_size(), 300) for the Chroma
  Cloud per-request cap; None-valued metadata stripped for portability
- Transport failures raise VectorStoreError on writes (reconciler retries)
  and return empty results on queries, matching the Turbopuffer backend
- Ships as an optional extra (uv sync --extra chromadb) plus dev group;
  factory raises an actionable error when the SDK is missing

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

.env.template

@@ -295,7 +295,7 @@ LLM_OPENAI_API_KEY=your-api-key-here
 # =============================================================================
 # Vector Store Settings
 # =============================================================================
-# Vector store type: "pgvector", "turbopuffer", or "lancedb"
+# Vector store type: "pgvector", "turbopuffer", "lancedb", or "chromadb"
 VECTOR_STORE_TYPE=pgvector
 
 # Migration flag: set to true when migration from pgvector is complete
@@ -318,5 +318,17 @@ VECTOR_STORE_MIGRATED=false
 # LanceDB-specific settings (local embedded mode)
 # VECTOR_STORE_LANCEDB_PATH=./lancedb_data
 
+# ChromaDB-specific settings (requires the chromadb extra: uv sync --extra chromadb)
+# Client mode: "persistent" (local embedded), "http" (self-hosted server),
+# or "cloud" (Chroma Cloud; requires API key)
+# VECTOR_STORE_CHROMA_CLIENT_MODE=persistent
+# VECTOR_STORE_CHROMA_PATH=./chroma_data
+# VECTOR_STORE_CHROMA_HOST=localhost
+# VECTOR_STORE_CHROMA_PORT=8000
+# VECTOR_STORE_CHROMA_SSL=false
+# VECTOR_STORE_CHROMA_API_KEY=your-chroma-api-key
+# VECTOR_STORE_CHROMA_TENANT=your-tenant-id
+# VECTOR_STORE_CHROMA_DATABASE=your-database
+
 # Reconciliation interval for background sync (default: 5 minutes)
 # VECTOR_STORE_RECONCILIATION_INTERVAL_SECONDS=300

config.toml.example

@@ -272,7 +272,7 @@ DEFAULT_LOCK_TTL_SECONDS = 5
 
 # Vector store settings
 [vector_store]
-# Vector store type: "pgvector", "turbopuffer", or "lancedb"
+# Vector store type: "pgvector", "turbopuffer", "lancedb", or "chromadb"
 TYPE = "pgvector"
 # Migration flag: set to true when migration from pgvector is complete
 MIGRATED = false
@@ -281,4 +281,15 @@ NAMESPACE = "honcho"
 # TURBOPUFFER_API_KEY = "your-turbopuffer-api-key"
 # TURBOPUFFER_REGION = "us-east-1"
 LANCEDB_PATH = "./lancedb_data"
+# ChromaDB settings (requires the chromadb extra: uv sync --extra chromadb)
+# CHROMA_CLIENT_MODE: "persistent" (local embedded), "http" (self-hosted server),
+# or "cloud" (Chroma Cloud; requires API key)
+CHROMA_CLIENT_MODE = "persistent"
+CHROMA_PATH = "./chroma_data"
+# CHROMA_HOST = "localhost"
+# CHROMA_PORT = 8000
+# CHROMA_SSL = false
+# CHROMA_API_KEY = "your-chroma-api-key"
+# CHROMA_TENANT = "your-tenant-id"
+# CHROMA_DATABASE = "your-database"
 RECONCILIATION_INTERVAL_SECONDS = 300

docs/v3/contributing/changing-embeddings.mdx

@@ -57,6 +57,8 @@ If you are using `text-embedding-3-large` but truncating to 1536 (the default),
 
 For this case, either set `EMBEDDING_VECTOR_DIMENSIONS=1536` explicitly (so `auto` knows the operator opted in), or set `EMBEDDING_MODEL_CONFIG__DIMENSIONS_MODE=always`.
 
-## Backend swap (turbopuffer ↔ lancedb ↔ pgvector) is a different operation
+## Backend swap (turbopuffer ↔ lancedb ↔ chromadb ↔ pgvector) is a different operation
 
 Switching the *storage backend* at constant dim/model — for example moving from pgvector to Turbopuffer — is supported via `src/reconciler/sync_vectors.py` and `VECTOR_STORE_MIGRATED`. That flow is unchanged by the embedding-pipeline work and is documented separately. It is **not** the destroy + rebuild path described above.
+
+Note for external backends that lock dimensionality lazily: Turbopuffer namespaces and ChromaDB collections fix their vector dimension on first write, not at creation. The startup validator samples existing namespaces best-effort; a fresh deployment with no namespaces yet passes validation and locks the dim on the first ingested message.

docs/v3/contributing/configuration.mdx

@@ -545,7 +545,7 @@ WEBHOOK_MAX_WORKSPACE_LIMIT=10
 ### Vector Store
 
 ```bash
-VECTOR_STORE_TYPE=pgvector  # Options: pgvector, turbopuffer, lancedb
+VECTOR_STORE_TYPE=pgvector  # Options: pgvector, turbopuffer, lancedb, chromadb
 VECTOR_STORE_MIGRATED=false
 VECTOR_STORE_NAMESPACE=honcho
 # Embedding dim is configured via EMBEDDING_VECTOR_DIMENSIONS — see the
@@ -557,8 +557,26 @@ VECTOR_STORE_TURBOPUFFER_REGION=us-east-1
 
 # LanceDB-specific
 VECTOR_STORE_LANCEDB_PATH=./lancedb_data
+
+# ChromaDB-specific
+VECTOR_STORE_CHROMA_CLIENT_MODE=persistent  # Options: persistent, http, cloud
+VECTOR_STORE_CHROMA_PATH=./chroma_data      # persistent mode
+VECTOR_STORE_CHROMA_HOST=localhost          # http mode (self-hosted server)
+VECTOR_STORE_CHROMA_PORT=8000               # http mode
+VECTOR_STORE_CHROMA_SSL=false               # http mode
+VECTOR_STORE_CHROMA_API_KEY=your-chroma-api-key  # cloud mode (required)
+VECTOR_STORE_CHROMA_TENANT=your-tenant-id        # cloud mode (optional)
+VECTOR_STORE_CHROMA_DATABASE=your-database       # cloud mode (optional)
 ```
 
+The ChromaDB backend requires the `chromadb` extra, which is not installed by default:
+
+```bash
+uv sync --extra chromadb
+```
+
+ChromaDB's `persistent` mode stores data on local disk (embedded, like LanceDB) and is intended for development and small deployments: Honcho creates one Chroma collection per `(observer, observed)` peer pair, and embedded Chroma serializes writes across collections through a single SQLite file and keeps a bounded number of collection indices warm in memory. For production volume, use `cloud` (Chroma Cloud) or `http` against a self-hosted distributed Chroma deployment — both are designed for many collections.
+
 ## Monitoring
 
 ### Prometheus Metrics

pyproject.toml

@@ -40,6 +40,12 @@ dependencies = [
     "prometheus_client>=0.21.0",
     "cloudevents>=1.12.0,<2.0",
 ]
+
+[project.optional-dependencies]
+chromadb = [
+    "chromadb>=1.5.2",
+]
+
 [dependency-groups]
 dev = [
     "pytest>=8.2.2",
@@ -57,6 +63,7 @@ dev = [
     "scipy>=1.15.3",
     "boto3>=1.42.5",
     "pytest-xdist>=3.8.0",
+    "chromadb>=1.5.2",
 ]
 
 [tool.uv.workspace]

src/config.py

@@ -1301,12 +1301,12 @@ class DreamSettings(HonchoSettings):
 
 
 class VectorStoreSettings(HonchoSettings):
-    """Settings for vector store (pgvector, Turbopuffer, or LanceDB)."""
+    """Settings for vector store (pgvector, Turbopuffer, LanceDB, or ChromaDB)."""
 
     model_config = SettingsConfigDict(env_prefix="VECTOR_STORE_", extra="ignore")  # pyright: ignore
 
     # Vector store type to use
-    TYPE: Literal["pgvector", "turbopuffer", "lancedb"] = "pgvector"
+    TYPE: Literal["pgvector", "turbopuffer", "lancedb", "chromadb"] = "pgvector"
 
     MIGRATED: bool = False
 
@@ -1332,6 +1332,22 @@ class VectorStoreSettings(HonchoSettings):
     # LanceDB-specific settings (local embedded mode)
     LANCEDB_PATH: str = "./lancedb_data"
 
+    # ChromaDB-specific settings
+    # CHROMA_CLIENT_MODE selects the deployment shape:
+    # - "persistent": local embedded storage at CHROMA_PATH
+    # - "http": self-hosted Chroma server at CHROMA_HOST:CHROMA_PORT
+    # - "cloud": Chroma Cloud (requires CHROMA_API_KEY)
+    CHROMA_CLIENT_MODE: Literal["persistent", "http", "cloud"] = "persistent"
+    CHROMA_PATH: str = "./chroma_data"
+    CHROMA_HOST: str = "localhost"
+    CHROMA_PORT: Annotated[int, Field(default=8000, gt=0)] = 8000
+    CHROMA_SSL: bool = False
+    CHROMA_API_KEY: str | None = None
+    # Tenant/database are optional for cloud mode (resolved from the API key
+    # when omitted); ignored in persistent and http modes.
+    CHROMA_TENANT: str | None = None
+    CHROMA_DATABASE: str | None = None
+
     RECONCILIATION_INTERVAL_SECONDS: Annotated[int, Field(default=300, gt=0)] = (
         300  # 5 minutes
     )
@@ -1344,6 +1360,19 @@ class VectorStoreSettings(HonchoSettings):
             )
         return self
 
+    @model_validator(mode="after")
+    def _require_api_key_for_chroma_cloud(self) -> "VectorStoreSettings":
+        if (
+            self.TYPE == "chromadb"
+            and self.CHROMA_CLIENT_MODE == "cloud"
+            and not self.CHROMA_API_KEY
+        ):
+            raise ValueError(
+                "VECTOR_STORE_CHROMA_API_KEY must be set when TYPE is 'chromadb'"
+                + " and CHROMA_CLIENT_MODE is 'cloud'"
+            )
+        return self
+
 
 class AppSettings(HonchoSettings):
     # No env_prefix for app-level settings

src/startup/embedding_validator.py

@@ -6,7 +6,7 @@ that forbade non-1536 dims unless the operator asserted a VECTOR_STORE.MIGRATED
 flag — the schema introspection here is more accurate because it inspects
 actual state instead of operator-asserted state.
 
-For external stores (turbopuffer, lancedb) the check is best-effort: namespaces
+For external stores (turbopuffer, lancedb, chromadb) the check is best-effort: namespaces
 are per-workspace and lazy-created, so this validator can only sample existing
 ones. Full enumeration is available via `uv run python scripts/configure_embeddings.py --report`.
 """
@@ -76,7 +76,7 @@ async def validate_embedding_schema(
     dims = await _introspect_pgvector_dims_with_retry(engine, schema)
     _assert_pgvector_dims_match(dims, schema=schema, target_dim=target_dim)
 
-    if s.VECTOR_STORE.TYPE in ("turbopuffer", "lancedb"):
+    if s.VECTOR_STORE.TYPE in ("turbopuffer", "lancedb", "chromadb"):
         await _sample_external_namespaces(engine, target_dim=target_dim)
 
 

src/vector_store/__init__.py

@@ -205,6 +205,16 @@ def _create_store_by_type(store_type: str) -> VectorStore:
         from src.vector_store.lancedb import LanceDBVectorStore
 
         return LanceDBVectorStore()
+    elif store_type == "chromadb":
+        try:
+            from src.vector_store.chroma import ChromaVectorStore
+        except ImportError as e:
+            raise ValueError(
+                "VECTOR_STORE_TYPE=chromadb requires the chromadb extra."
+                + " Install it with: uv sync --extra chromadb"
+            ) from e
+
+        return ChromaVectorStore()
     else:
         raise ValueError(f"Unknown vector store type: {store_type}")
 

src/vector_store/chroma.py

@@ -0,0 +1,478 @@
+"""
+ChromaDB vector store implementation.
+
+This module provides a ChromaDB-based implementation of the VectorStore
+interface, supporting three deployment modes selected by
+VECTOR_STORE_CHROMA_CLIENT_MODE:
+
+- "persistent": local embedded storage (chromadb.PersistentClient)
+- "http": self-hosted Chroma server (chromadb.HttpClient)
+- "cloud": Chroma Cloud (chromadb.CloudClient)
+
+Chroma's persistent and cloud clients are synchronous, so every client call
+is offloaded through asyncio.to_thread to keep the event loop free. One code
+path covers all three modes.
+
+Each Honcho namespace maps to one Chroma collection. Honcho namespace strings
+({prefix}.{type}.{base64url-hash}) are not valid Chroma collection names
+(uppercase characters, arbitrary leading/trailing chars), so the collection
+name is a deterministic re-hash: "ns-" + sha256(namespace).hexdigest(). The
+original namespace is stored in collection metadata for debuggability.
+"""
+
+import asyncio
+import hashlib
+import logging
+from typing import TYPE_CHECKING, Any, cast
+
+import httpx
+
+from src.config import settings
+from src.exceptions import VectorStoreError
+
+from . import VectorQueryResult, VectorRecord, VectorStore
+
+if TYPE_CHECKING:
+    from chromadb.api import ClientAPI
+    from chromadb.api.models.Collection import Collection
+
+logger = logging.getLogger(__name__)
+
+# Chroma Cloud caps write batches at 300 records; local SQLite-backed builds
+# expose their own limit via client.get_max_batch_size(). Batches are split
+# to min of both so one upsert_many call never exceeds either.
+CLOUD_MAX_BATCH_SIZE = 300
+
+# Errors that indicate the store is unreachable/unavailable rather than a
+# logic error. Chroma's http/cloud clients surface transport failures as
+# httpx errors; persistent mode has no transport layer.
+_TRANSIENT_ERRORS: tuple[type[BaseException], ...] = (
+    httpx.TransportError,
+    ConnectionError,
+    TimeoutError,
+)
+
+
+class ChromaVectorStore(VectorStore):
+    """
+    ChromaDB implementation of the VectorStore interface.
+
+    Each namespace corresponds to a Chroma collection created with cosine
+    distance and no embedding function (Honcho always supplies precomputed
+    vectors). Collections are lazy-created on first write; reads and deletes
+    against a missing collection are empty results / no-ops.
+    """
+
+    _client: "ClientAPI | None"
+    _client_lock: asyncio.Lock
+    _max_batch_size: int | None
+
+    def __init__(self) -> None:
+        """Initialize the ChromaDB vector store."""
+        super().__init__()
+        self._client = None
+        self._client_lock = asyncio.Lock()
+        self._max_batch_size = None
+
+    def _create_client(self) -> "ClientAPI":
+        """Construct the sync Chroma client for the configured mode.
+
+        Runs inside a worker thread: PersistentClient does disk I/O and the
+        http/cloud clients issue a version heartbeat on construction.
+        """
+        import chromadb
+
+        mode = settings.VECTOR_STORE.CHROMA_CLIENT_MODE
+        if mode == "persistent":
+            return chromadb.PersistentClient(path=settings.VECTOR_STORE.CHROMA_PATH)
+        elif mode == "http":
+            return chromadb.HttpClient(
+                host=settings.VECTOR_STORE.CHROMA_HOST,
+                port=settings.VECTOR_STORE.CHROMA_PORT,
+                ssl=settings.VECTOR_STORE.CHROMA_SSL,
+            )
+        else:
+            api_key = settings.VECTOR_STORE.CHROMA_API_KEY
+            if not api_key:
+                raise ValueError(
+                    "VECTOR_STORE_CHROMA_API_KEY must be set for ChromaDB cloud mode"
+                )
+            return chromadb.CloudClient(
+                api_key=api_key,
+                tenant=settings.VECTOR_STORE.CHROMA_TENANT,
+                database=settings.VECTOR_STORE.CHROMA_DATABASE,
+            )
+
+    async def _get_client(self) -> "ClientAPI":
+        """Get or create the Chroma client (asyncio-safe)."""
+        if self._client is not None:
+            return self._client
+
+        async with self._client_lock:
+            if self._client is None:
+                self._client = await asyncio.to_thread(self._create_client)
+        return self._client
+
+    async def _get_batch_limit(self, client: "ClientAPI") -> int:
+        """Resolve the per-request write batch limit for this client."""
+        if self._max_batch_size is None:
+            try:
+                local_limit = await asyncio.to_thread(client.get_max_batch_size)
+            except Exception:
+                # Some server versions don't expose the pre-flight limit;
+                # fall back to the strictest known cap.
+                local_limit = CLOUD_MAX_BATCH_SIZE
+            self._max_batch_size = max(1, min(local_limit, CLOUD_MAX_BATCH_SIZE))
+        return self._max_batch_size
+
+    @staticmethod
+    def _collection_name(namespace: str) -> str:
+        """
+        Map a Honcho namespace to a valid Chroma collection name.
+
+        Honcho namespaces contain base64url hashes (mixed case, may end in
+        '-'/'_') which violate Chroma's naming rules (start/end with a
+        lowercase alphanumeric). A sha256 hex digest is deterministic,
+        injective, and valid under both OSS and Cloud naming rules.
+        """
+        digest = hashlib.sha256(namespace.encode("utf-8")).hexdigest()
+        return f"ns-{digest}"
+
+    @staticmethod
+    def _sanitize_metadata(metadata: dict[str, Any]) -> dict[str, Any] | None:
+        """
+        Drop metadata entries Chroma cannot store.
+
+        None values are rejected by Chroma Cloud and older servers (the key
+        is simply omitted instead — an absent key behaves the same as NULL
+        under equality filters). Reserved keys used by the record itself are
+        also excluded.
+        """
+        sanitized = {
+            k: v
+            for k, v in metadata.items()
+            if v is not None and k not in ("id", "embedding")
+        }
+        return sanitized or None
+
+    def _build_where(self, filters: dict[str, Any]) -> dict[str, Any] | None:
+        """
+        Convert a filter dict to Chroma's `where` format.
+
+        Supports filter formats:
+        - {"key": "value"} -> {"key": {"$eq": "value"}}
+        - {"key": {"in": [...]}} -> {"key": {"$in": [...]}}
+        Multiple filters combine with {"$and": [...]} (Chroma requires at
+        least two operands for $and, so a single clause is passed bare).
+
+        Raises:
+            ValueError: If a filter value is None (Chroma cannot match null;
+                write-side sanitization omits null keys entirely).
+        """
+        clauses: list[dict[str, Any]] = []
+        for key, value in filters.items():
+            if isinstance(value, dict) and "in" in value:
+                in_values = cast(list[Any], value["in"])
+                clauses.append({key: {"$in": list(in_values)}})
+            elif value is None:
+                raise ValueError(
+                    f"ChromaDB backend does not support filtering on null values (key: {key!r})"
+                )
+            else:
+                clauses.append({key: {"$eq": value}})
+
+        if not clauses:
+            return None
+        if len(clauses) == 1:
+            return clauses[0]
+        return {"$and": clauses}
+
+    async def _get_collection(self, namespace: str) -> "Collection | None":
+        """Get a collection if it exists, otherwise return None."""
+        from chromadb.errors import NotFoundError
+
+        client = await self._get_client()
+        try:
+            return await asyncio.to_thread(
+                client.get_collection, self._collection_name(namespace)
+            )
+        except NotFoundError:
+            return None
+
+    async def _get_or_create_collection(self, namespace: str) -> "Collection":
+        """Get or create the collection backing a namespace.
+
+        Created with cosine distance and no embedding function — Honcho
+        always supplies precomputed vectors. `space` is the only index knob
+        that carries across single-node (HNSW) and Cloud (SPANN).
+        """
+        client = await self._get_client()
+        return await asyncio.to_thread(
+            client.get_or_create_collection,
+            name=self._collection_name(namespace),
+            configuration=cast(Any, {"hnsw": {"space": "cosine"}}),
+            embedding_function=None,
+            metadata={"honcho_namespace": namespace},
+        )
+
+    async def upsert_many(
+        self,
+        namespace: str,
+        vectors: list[VectorRecord],
+    ) -> None:
+        """
+        Upsert multiple vectors into ChromaDB.
+
+        Args:
+            namespace: The namespace to store the vectors in
+            vectors: List of VectorRecord objects to upsert
+        """
+        if not vectors:
+            return
+
+        try:
+            collection = await self._get_or_create_collection(namespace)
+            client = await self._get_client()
+            batch_limit = await self._get_batch_limit(client)
+
+            for start in range(0, len(vectors), batch_limit):
+                batch = vectors[start : start + batch_limit]
+                await asyncio.to_thread(
+                    collection.upsert,
+                    ids=[v.id for v in batch],
+                    embeddings=cast(Any, [v.embedding for v in batch]),
+                    metadatas=cast(
+                        Any, [self._sanitize_metadata(v.metadata) for v in batch]
+                    ),
+                )
+
+            logger.debug(f"Upserted {len(vectors)} vectors to namespace {namespace}")
+        except _TRANSIENT_ERRORS as exc:
+            logger.warning(
+                "ChromaDB unavailable for upsert to namespace %s: %s",
+                namespace,
+                exc,
+            )
+            raise VectorStoreError(
+                f"ChromaDB unavailable for upsert to namespace {namespace}"
+            ) from exc
+        except Exception:
+            logger.exception(
+                f"Failed to upsert {len(vectors)} vectors to namespace {namespace}"
+            )
+            raise
+
+    async def query(
+        self,
+        namespace: str,
+        embedding: list[float],
+        *,
+        top_k: int = 10,
+        filters: dict[str, Any] | None = None,
+        max_distance: float | None = None,
+        include_attributes: bool | list[str] = True,
+    ) -> list[VectorQueryResult]:
+        """
+        Query for similar vectors in ChromaDB.
+
+        Args:
+            namespace: The namespace to query
+            embedding: The query embedding vector
+            top_k: Maximum number of results to return
+            filters: Optional metadata filters
+            max_distance: Optional maximum distance threshold (cosine distance)
+            include_attributes: Attributes to return with each result. False
+                skips metadata retrieval entirely; a list projects to those
+                fields (Chroma has no per-key projection, so the trim happens
+                client-side).
+
+        Returns:
+            List of VectorQueryResult objects, ordered by similarity (most similar first)
+        """
+        try:
+            collection = await self._get_collection(namespace)
+            if collection is None:
+                logger.debug(
+                    f"Collection for namespace {namespace} does not exist, returning empty results"
+                )
+                return []
+
+            where = self._build_where(filters) if filters else None
+            include: list[str] = ["distances"]
+            if include_attributes is not False:
+                include.append("metadatas")
+
+            query_kwargs: dict[str, Any] = {
+                "query_embeddings": [embedding],
+                "n_results": top_k,
+                "include": include,
+            }
+            if where is not None:
+                query_kwargs["where"] = where
+
+            response = cast(
+                dict[str, Any],
+                cast(
+                    object,
+                    await asyncio.to_thread(collection.query, **query_kwargs),
+                ),
+            )
+
+            # Chroma returns column-major, per-query-embedding lists; we send
+            # exactly one query embedding, so index 0 everywhere.
+            ids: list[str] = cast(list[str], (response.get("ids") or [[]])[0])
+            distances: list[float] = cast(
+                list[float], (response.get("distances") or [[]])[0]
+            )
+            metadatas_col = response.get("metadatas")
+            metadatas: list[dict[str, Any] | None] = (
+                metadatas_col[0] if metadatas_col else [None] * len(ids)
+            )
+
+            query_results: list[VectorQueryResult] = []
+            for row_id, dist, metadata in zip(ids, distances, metadatas, strict=True):
+                dist = float(dist)
+                if max_distance is not None and dist > max_distance:
+                    continue
+
+                row_metadata: dict[str, Any] = dict(metadata) if metadata else {}
+                if isinstance(include_attributes, list):
+                    row_metadata = {
+                        k: v for k, v in row_metadata.items() if k in include_attributes
+                    }
+
+                query_results.append(
+                    VectorQueryResult(
+                        id=str(row_id),
+                        score=dist,
+                        metadata=row_metadata,
+                    )
+                )
+
+            logger.debug(
+                f"Query returned {len(query_results)} results from namespace {namespace}"
+            )
+            return query_results
+
+        except _TRANSIENT_ERRORS as exc:
+            # Store unavailable — degrade to empty results, matching the
+            # Turbopuffer backend's behavior on 5xx.
+            logger.warning(
+                "ChromaDB unavailable for query on namespace %s, returning empty results: %s",
+                namespace,
+                exc,
+            )
+            return []
+        except Exception:
+            logger.exception(f"Failed to query namespace {namespace}")
+            raise
+
+    async def delete_many(self, namespace: str, ids: list[str]) -> None:
+        """
+        Delete multiple vectors from ChromaDB.
+
+        Args:
+            namespace: The namespace containing the vectors
+            ids: List of vector identifiers to delete
+        """
+        if not ids:
+            return
+
+        try:
+            collection = await self._get_collection(namespace)
+            if collection is None:
+                logger.debug(
+                    f"Collection for namespace {namespace} does not exist, nothing to delete"
+                )
+                return
+
+            client = await self._get_client()
+            batch_limit = await self._get_batch_limit(client)
+            for start in range(0, len(ids), batch_limit):
+                batch = ids[start : start + batch_limit]
+                await asyncio.to_thread(collection.delete, ids=batch)
+            logger.debug(f"Deleted {len(ids)} vectors from namespace {namespace}")
+        except _TRANSIENT_ERRORS as exc:
+            logger.warning(
+                "ChromaDB unavailable for delete from namespace %s: %s",
+                namespace,
+                exc,
+            )
+            raise VectorStoreError(
+                f"ChromaDB unavailable while deleting vectors in namespace {namespace}"
+            ) from exc
+        except Exception:
+            logger.exception(
+                f"Failed to delete {len(ids)} vectors from namespace {namespace}"
+            )
+            raise
+
+    async def delete_namespace(self, namespace: str) -> None:
+        """
+        Delete an entire namespace (collection) and all its vectors from ChromaDB.
+
+        Args:
+            namespace: The namespace to delete
+        """
+        from chromadb.errors import NotFoundError
+
+        try:
+            client = await self._get_client()
+            await asyncio.to_thread(
+                client.delete_collection, self._collection_name(namespace)
+            )
+            logger.debug(f"Deleted namespace {namespace}")
+        except NotFoundError:
+            logger.debug(f"Namespace {namespace} does not exist, nothing to delete")
+        except Exception:
+            logger.exception(f"Failed to delete namespace {namespace}")
+            raise
+
+    async def close(self) -> None:
+        """Close the ChromaDB client and release resources."""
+        if self._client is not None:
+            client = self._client
+            self._client = None
+            self._max_batch_size = None
+            # Refcounted close() landed in chromadb 1.5.2; guard for safety
+            # against client classes that don't expose it.
+            close_method = getattr(client, "close", None)
+            if callable(close_method):
+                await asyncio.to_thread(close_method)
+            logger.debug("ChromaDB client closed")
+
+    async def probe_namespace_dim(self, namespace: str) -> int | None:
+        """Recover the vector dimension of an existing Chroma collection.
+
+        Chroma locks a collection's dimensionality on first write but does
+        not expose it reliably through the collection model, so this peeks
+        one stored embedding. Returns ``None`` when the collection does not
+        exist (lazy-create model) or exists but has no records yet — in
+        both cases no dimension has been locked, which the startup
+        validator treats as "nothing to check".
+        """
+        collection = await self._get_collection(namespace)
+        if collection is None:
+            return None
+
+        peek = cast(
+            dict[str, Any],
+            cast(
+                object,
+                await asyncio.to_thread(
+                    collection.get, limit=1, include=cast(Any, ["embeddings"])
+                ),
+            ),
+        )
+        embeddings = peek.get("embeddings")
+        if embeddings is None or len(embeddings) == 0:
+            return None
+
+        first = embeddings[0]
+        try:
+            return int(len(first))
+        except TypeError as exc:
+            raise VectorStoreError(
+                f"ChromaDB collection for namespace {namespace!r} exists but"
+                + " returned an unreadable embedding; cannot probe dim."
+            ) from exc

tests/vector_store/test_chroma.py

@@ -0,0 +1,384 @@
+"""Tests for ChromaVectorStore filter translation, batching, and error handling."""
+
+from __future__ import annotations
+
+import re
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock
+
+import httpx
+import pytest
+
+from src.exceptions import VectorStoreError
+from src.vector_store import VectorRecord
+from src.vector_store.chroma import ChromaVectorStore
+
+chromadb = pytest.importorskip("chromadb")
+from chromadb.errors import NotFoundError  # noqa: E402
+
+# Chroma collection naming rules: 3-512 chars, starts/ends with a lowercase
+# alphanumeric, dots/dashes/underscores allowed in between.
+_CHROMA_NAME_PATTERN = re.compile(r"^[a-z0-9][a-z0-9._-]{1,510}[a-z0-9]$")
+
+
+def _query_response(
+    rows: list[tuple[str, float, dict[str, Any] | None]],
+) -> dict[str, Any]:
+    """Build a column-major Chroma query response for one query embedding."""
+    return {
+        "ids": [[r[0] for r in rows]],
+        "distances": [[r[1] for r in rows]],
+        "metadatas": [[r[2] for r in rows]],
+    }
+
+
+def _patch_collection(
+    store: ChromaVectorStore, collection: MagicMock, batch_limit: int = 300
+) -> None:
+    """Patch collection/client access so no real Chroma client is created."""
+    store._get_collection = AsyncMock(return_value=collection)  # pyright: ignore[reportPrivateUsage]
+    store._get_or_create_collection = AsyncMock(return_value=collection)  # pyright: ignore[reportPrivateUsage]
+    store._get_client = AsyncMock(return_value=MagicMock())  # pyright: ignore[reportPrivateUsage]
+    store._max_batch_size = batch_limit  # pyright: ignore[reportPrivateUsage]
+
+
+@pytest.fixture
+def store() -> ChromaVectorStore:
+    return ChromaVectorStore()
+
+
+@pytest.fixture
+def record() -> VectorRecord:
+    return VectorRecord(
+        id="doc_1", embedding=[0.1, 0.2, 0.3, 0.4], metadata={"foo": "bar"}
+    )
+
+
+# === Collection name mapping ===
+
+
+def test_collection_name_is_valid_and_deterministic(store: ChromaVectorStore) -> None:
+    ns = store.get_vector_namespace("document", "ws1", observer="a", observed="b")
+    name = store._collection_name(ns)  # pyright: ignore[reportPrivateUsage]
+
+    assert _CHROMA_NAME_PATTERN.match(name), name
+    assert name == store._collection_name(ns)  # pyright: ignore[reportPrivateUsage]
+
+
+def test_collection_name_distinct_for_distinct_namespaces(
+    store: ChromaVectorStore,
+) -> None:
+    ns_a = store.get_vector_namespace("document", "ws1", observer="a", observed="b")
+    ns_b = store.get_vector_namespace("document", "ws1", observer="b", observed="a")
+
+    assert store._collection_name(ns_a) != store._collection_name(ns_b)  # pyright: ignore[reportPrivateUsage]
+
+
+# === Filter translation ===
+
+
+def test_build_where_single_equality_is_bare_clause(store: ChromaVectorStore) -> None:
+    where = store._build_where({"level": "explicit"})  # pyright: ignore[reportPrivateUsage]
+    assert where == {"level": {"$eq": "explicit"}}
+
+
+def test_build_where_in_operator(store: ChromaVectorStore) -> None:
+    where = store._build_where({"session_name": {"in": ["s1", "s2"]}})  # pyright: ignore[reportPrivateUsage]
+    assert where == {"session_name": {"$in": ["s1", "s2"]}}
+
+
+def test_build_where_multiple_clauses_combine_with_and(
+    store: ChromaVectorStore,
+) -> None:
+    where = store._build_where(  # pyright: ignore[reportPrivateUsage]
+        {"level": "explicit", "session_name": {"in": ["s1"]}}
+    )
+    assert where == {
+        "$and": [
+            {"level": {"$eq": "explicit"}},
+            {"session_name": {"$in": ["s1"]}},
+        ]
+    }
+
+
+def test_build_where_empty_returns_none(store: ChromaVectorStore) -> None:
+    assert store._build_where({}) is None  # pyright: ignore[reportPrivateUsage]
+
+
+def test_build_where_null_value_raises(store: ChromaVectorStore) -> None:
+    with pytest.raises(ValueError, match="null"):
+        store._build_where({"session_name": None})  # pyright: ignore[reportPrivateUsage]
+
+
+# === Metadata sanitization ===
+
+
+def test_sanitize_metadata_drops_none_and_reserved_keys() -> None:
+    sanitized = ChromaVectorStore._sanitize_metadata(  # pyright: ignore[reportPrivateUsage]
+        {"level": "explicit", "session_name": None, "id": "x", "embedding": [0.1]}
+    )
+    assert sanitized == {"level": "explicit"}
+
+
+def test_sanitize_metadata_empty_returns_none() -> None:
+    assert ChromaVectorStore._sanitize_metadata({"session_name": None}) is None  # pyright: ignore[reportPrivateUsage]
+
+
+# === Upsert ===
+
+
+@pytest.mark.asyncio
+async def test_upsert_many_short_circuits_on_empty(store: ChromaVectorStore) -> None:
+    collection = MagicMock()
+    _patch_collection(store, collection)
+
+    await store.upsert_many("honcho.doc.test", [])
+
+    collection.upsert.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_upsert_many_splits_batches(store: ChromaVectorStore) -> None:
+    collection = MagicMock()
+    _patch_collection(store, collection, batch_limit=2)
+    vectors = [
+        VectorRecord(id=f"doc_{i}", embedding=[0.1, 0.2], metadata={"level": "e"})
+        for i in range(5)
+    ]
+
+    await store.upsert_many("honcho.doc.test", vectors)
+
+    assert collection.upsert.call_count == 3
+    first_call = collection.upsert.call_args_list[0]
+    assert first_call.kwargs["ids"] == ["doc_0", "doc_1"]
+    last_call = collection.upsert.call_args_list[-1]
+    assert last_call.kwargs["ids"] == ["doc_4"]
+
+
+@pytest.mark.asyncio
+async def test_upsert_many_raises_vector_store_error_on_transport_failure(
+    store: ChromaVectorStore, record: VectorRecord
+) -> None:
+    collection = MagicMock()
+    collection.upsert.side_effect = httpx.ConnectError("connection refused")
+    _patch_collection(store, collection)
+
+    with pytest.raises(VectorStoreError) as excinfo:
+        await store.upsert_many("honcho.doc.test", [record])
+
+    assert "honcho.doc.test" in str(excinfo.value)
+    assert isinstance(excinfo.value.__cause__, httpx.ConnectError)
+
+
+# === Query ===
+
+
+@pytest.mark.asyncio
+async def test_query_returns_empty_when_collection_missing(
+    store: ChromaVectorStore,
+) -> None:
+    store._get_collection = AsyncMock(return_value=None)  # pyright: ignore[reportPrivateUsage]
+
+    results = await store.query("honcho.msg.missing", [0.1, 0.2, 0.3, 0.4])
+
+    assert results == []
+
+
+@pytest.mark.asyncio
+async def test_query_returns_results_with_metadata(store: ChromaVectorStore) -> None:
+    collection = MagicMock()
+    collection.query.return_value = _query_response(
+        [("doc_1", 0.1, {"level": "explicit"}), ("doc_2", 0.5, None)]
+    )
+    _patch_collection(store, collection)
+
+    results = await store.query("honcho.doc.test", [0.1, 0.2, 0.3, 0.4])
+
+    assert [(r.id, r.score, r.metadata) for r in results] == [
+        ("doc_1", 0.1, {"level": "explicit"}),
+        ("doc_2", 0.5, {}),
+    ]
+
+
+@pytest.mark.asyncio
+async def test_query_applies_max_distance_client_side(
+    store: ChromaVectorStore,
+) -> None:
+    collection = MagicMock()
+    collection.query.return_value = _query_response(
+        [("near", 0.1, None), ("far", 0.9, None)]
+    )
+    _patch_collection(store, collection)
+
+    results = await store.query(
+        "honcho.doc.test", [0.1, 0.2, 0.3, 0.4], max_distance=0.5
+    )
+
+    assert [r.id for r in results] == ["near"]
+
+
+@pytest.mark.asyncio
+async def test_query_include_attributes_false_skips_metadata(
+    store: ChromaVectorStore,
+) -> None:
+    collection = MagicMock()
+    collection.query.return_value = {
+        "ids": [["doc_1"]],
+        "distances": [[0.1]],
+        "metadatas": None,
+    }
+    _patch_collection(store, collection)
+
+    results = await store.query(
+        "honcho.doc.test", [0.1, 0.2, 0.3, 0.4], include_attributes=False
+    )
+
+    assert collection.query.call_args.kwargs["include"] == ["distances"]
+    assert results[0].metadata == {}
+
+
+@pytest.mark.asyncio
+async def test_query_attribute_list_projects_client_side(
+    store: ChromaVectorStore,
+) -> None:
+    collection = MagicMock()
+    collection.query.return_value = _query_response(
+        [("doc_1", 0.1, {"level": "explicit", "session_name": "s1"})]
+    )
+    _patch_collection(store, collection)
+
+    results = await store.query(
+        "honcho.doc.test", [0.1, 0.2, 0.3, 0.4], include_attributes=["level"]
+    )
+
+    assert results[0].metadata == {"level": "explicit"}
+
+
+@pytest.mark.asyncio
+async def test_query_passes_translated_filters(store: ChromaVectorStore) -> None:
+    collection = MagicMock()
+    collection.query.return_value = _query_response([])
+    _patch_collection(store, collection)
+
+    await store.query(
+        "honcho.doc.test",
+        [0.1, 0.2, 0.3, 0.4],
+        filters={"session_name": {"in": ["s1", "s2"]}},
+    )
+
+    assert collection.query.call_args.kwargs["where"] == {
+        "session_name": {"$in": ["s1", "s2"]}
+    }
+
+
+@pytest.mark.asyncio
+async def test_query_returns_empty_on_transport_failure(
+    store: ChromaVectorStore,
+) -> None:
+    collection = MagicMock()
+    collection.query.side_effect = httpx.ConnectError("connection refused")
+    _patch_collection(store, collection)
+
+    results = await store.query("honcho.doc.test", [0.1, 0.2, 0.3, 0.4])
+
+    assert results == []
+
+
+# === Delete ===
+
+
+@pytest.mark.asyncio
+async def test_delete_many_short_circuits_on_empty(store: ChromaVectorStore) -> None:
+    collection = MagicMock()
+    _patch_collection(store, collection)
+
+    await store.delete_many("honcho.doc.test", [])
+
+    collection.delete.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_delete_many_noop_when_collection_missing(
+    store: ChromaVectorStore,
+) -> None:
+    store._get_collection = AsyncMock(return_value=None)  # pyright: ignore[reportPrivateUsage]
+
+    await store.delete_many("honcho.doc.missing", ["doc_1"])
+
+
+@pytest.mark.asyncio
+async def test_delete_many_raises_vector_store_error_on_transport_failure(
+    store: ChromaVectorStore,
+) -> None:
+    collection = MagicMock()
+    collection.delete.side_effect = httpx.ConnectError("connection refused")
+    _patch_collection(store, collection)
+
+    with pytest.raises(VectorStoreError):
+        await store.delete_many("honcho.doc.test", ["doc_1"])
+
+
+@pytest.mark.asyncio
+async def test_delete_namespace_noop_when_collection_missing(
+    store: ChromaVectorStore,
+) -> None:
+    client = MagicMock()
+    client.delete_collection.side_effect = NotFoundError("does not exist")
+    store._get_client = AsyncMock(return_value=client)  # pyright: ignore[reportPrivateUsage]
+
+    await store.delete_namespace("honcho.doc.missing")
+
+
+# === Real embedded round-trip ===
+
+
+@pytest.mark.asyncio
+async def test_persistent_round_trip(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: object
+) -> None:
+    """Exercise the real embedded client end-to-end in a tmp dir."""
+    monkeypatch.setattr(
+        "src.config.settings.VECTOR_STORE.CHROMA_CLIENT_MODE", "persistent"
+    )
+    monkeypatch.setattr("src.config.settings.VECTOR_STORE.CHROMA_PATH", str(tmp_path))
+
+    store = ChromaVectorStore()
+    ns = store.get_vector_namespace("document", "ws1", observer="a", observed="b")
+    try:
+        await store.upsert_many(
+            ns,
+            [
+                VectorRecord(
+                    id="doc_1",
+                    embedding=[0.1, 0.2, 0.3, 0.4],
+                    metadata={"level": "explicit", "session_name": "s1"},
+                ),
+                VectorRecord(
+                    id="doc_2",
+                    embedding=[0.9, 0.8, 0.7, 0.6],
+                    metadata={"level": "deductive", "session_name": None},
+                ),
+            ],
+        )
+
+        results = await store.query(ns, [0.1, 0.2, 0.3, 0.4], top_k=5)
+        assert [r.id for r in results] == ["doc_1", "doc_2"]
+        assert results[0].score < results[1].score
+        # None-valued metadata keys are stripped on write
+        assert "session_name" not in results[1].metadata
+
+        filtered = await store.query(
+            ns, [0.1, 0.2, 0.3, 0.4], top_k=5, filters={"level": "deductive"}
+        )
+        assert [r.id for r in filtered] == ["doc_2"]
+
+        assert await store.probe_namespace_dim(ns) == 4
+
+        await store.delete_many(ns, ["doc_1"])
+        remaining = await store.query(ns, [0.1, 0.2, 0.3, 0.4], top_k=5)
+        assert [r.id for r in remaining] == ["doc_2"]
+
+        await store.delete_namespace(ns)
+        assert await store.probe_namespace_dim(ns) is None
+    finally:
+        await store.close()

tests/vector_store/test_namespace_dim_probes.py

@@ -1,8 +1,8 @@
 """Per-store namespace dim probe tests.
 
-LanceDB has an embedded driver we can spin up in a tmp dir, so we exercise
-the real probe end-to-end. Turbopuffer needs a network + API key, so it is
-covered only by static analysis + the parsing test below.
+LanceDB and ChromaDB have embedded drivers we can spin up in a tmp dir, so we
+exercise their real probes end-to-end. Turbopuffer needs a network + API key,
+so it is covered only by static analysis + the parsing test below.
 """
 
 from __future__ import annotations
@@ -54,6 +54,53 @@ async def test_lancedb_probe_returns_none_for_missing_namespace(
         await store.close()
 
 
+@pytest.mark.asyncio
+async def test_chroma_probe_returns_declared_dim(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: object
+) -> None:
+    """Write real vectors at dim 8, confirm the probe recovers the dim."""
+    pytest.importorskip("chromadb")
+    from src.vector_store import VectorRecord
+    from src.vector_store.chroma import ChromaVectorStore
+
+    monkeypatch.setattr(
+        "src.config.settings.VECTOR_STORE.CHROMA_CLIENT_MODE", "persistent"
+    )
+    monkeypatch.setattr("src.config.settings.VECTOR_STORE.CHROMA_PATH", str(tmp_path))
+
+    store = ChromaVectorStore()
+    try:
+        await store.upsert_many(
+            "honcho.msg.probe", [VectorRecord(id="m1", embedding=[0.1] * 8)]
+        )
+
+        dim = await store.probe_namespace_dim("honcho.msg.probe")
+        assert dim == 8
+    finally:
+        await store.close()
+
+
+@pytest.mark.asyncio
+async def test_chroma_probe_returns_none_for_missing_namespace(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: object
+) -> None:
+    """Lazy-create model: probing a nonexistent collection is not an error."""
+    pytest.importorskip("chromadb")
+    from src.vector_store.chroma import ChromaVectorStore
+
+    monkeypatch.setattr(
+        "src.config.settings.VECTOR_STORE.CHROMA_CLIENT_MODE", "persistent"
+    )
+    monkeypatch.setattr("src.config.settings.VECTOR_STORE.CHROMA_PATH", str(tmp_path))
+
+    store = ChromaVectorStore()
+    try:
+        dim = await store.probe_namespace_dim("honcho.msg.does_not_exist")
+        assert dim is None
+    finally:
+        await store.close()
+
+
 def test_turbopuffer_vector_dim_regex_extracts_dim_from_type_string() -> None:
     """Turbopuffer's attribute type for a vector column is a bracket-prefixed
     dim with a width suffix: ``[768]f32``, ``[1536]f16``, ``[256]i8``. The

uv.lock

@@ -8,7 +8,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "2026-06-19T14:44:24.535479Z"
+exclude-newer = "2026-06-27T17:02:38.075926Z"
 exclude-newer-span = "P5D"
 
 [manifest]
@@ -245,6 +245,76 @@ wheels = [
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 ]
 
+[[package]]
+name = "bcrypt"
+version = "5.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d4/36/3329e2518d70ad8e2e5817d5a4cac6bba05a47767ec416c7d020a965f408/bcrypt-5.0.0.tar.gz", hash = "sha256:f748f7c2d6fd375cc93d3fba7ef4a9e3a092421b8dbf34d8d4dc06be9492dfdd", size = 25386, upload-time = "2025-09-25T19:50:47.829Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/13/85/3e65e01985fddf25b64ca67275bb5bdb4040bd1a53b66d355c6c37c8a680/bcrypt-5.0.0-cp313-cp313t-macosx_10_12_universal2.whl", hash = "sha256:f3c08197f3039bec79cee59a606d62b96b16669cff3949f21e74796b6e3cd2be", size = 481806, upload-time = "2025-09-25T19:49:05.102Z" },
+    { url = "https://files.pythonhosted.org/packages/44/dc/01eb79f12b177017a726cbf78330eb0eb442fae0e7b3dfd84ea2849552f3/bcrypt-5.0.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:200af71bc25f22006f4069060c88ed36f8aa4ff7f53e67ff04d2ab3f1e79a5b2", size = 268626, upload-time = "2025-09-25T19:49:06.723Z" },
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 [[package]]
 name = "websockets"
 version = "16.0"