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honcho
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feat: Adds Qdrant Vector Search

This commit is contained in:
Anush008 2026-05-14 18:37:35 +05:30
parent a420264152
commit 256ed8c9ad
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11 changed files with 326 additions and 9 deletions
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11
.env.template
View File

@ -266,7 +266,7 @@ 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 "qdrant"
VECTOR_STORE_TYPE=pgvector
# Migration flag: set to true when migration from pgvector is complete
@ -289,5 +289,14 @@ VECTOR_STORE_MIGRATED=false
# LanceDB-specific settings (local embedded mode)
# VECTOR_STORE_LANCEDB_PATH=./lancedb_data
# Qdrant-specific settings
# VECTOR_STORE_QDRANT_URL=http://localhost:6333
# VECTOR_STORE_QDRANT_API_KEY=your-qdrant-api-key
# VECTOR_STORE_QDRANT_PREFER_GRPC=false
# VECTOR_STORE_QDRANT_GRPC_PORT=6334
# VECTOR_STORE_QDRANT_HTTPS=false
# VECTOR_STORE_QDRANT_PREFIX=
# VECTOR_STORE_QDRANT_TIMEOUT=30
# Reconciliation interval for background sync (default: 5 minutes)
# VECTOR_STORE_RECONCILIATION_INTERVAL_SECONDS=300

3
CHANGELOG.md
View File

@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
### Added
- Support for using [Qdrant](https://qdrant.tech/) as the vector search provider.
- New `src/llm/` package as the single owner of provider runtime: clients, backends, history adapters, tool loop, request builder, credentials, and caching policy
- `AttemptPlan` dataclass captures per-retry provider selection (client, model, reasoning_effort, thinking_budget_tokens, selected_config) and pins it across stream-final retries so streaming doesn't bounce back to primary after the tool loop has settled on fallback
- Gemini JSON-schema sanitizer for `function_declarations` — strips keywords Gemini's validator rejects (`additionalProperties`, `allOf`, etc.) while preserving semantics for all other backends
@ -169,7 +170,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
- Prometheus token tracking for deriver and dialectic operations
- n8n integration
- Cloud Events for auditable telemetry
- External Vector Store support for turbopuffer and lancedb with reconciliation flow
- External Vector Store support for turbopuffer, lancedb, and qdrant with reconciliation flow
### Changed

2
README.md
View File

@ -412,7 +412,7 @@ Then modify the values as needed. The TOML file is organized into sections:
- `[webhook]` - Webhook configuration
- `[metrics]` - Prometheus pull-based metrics
- `[telemetry]` - CloudEvents telemetry for analytics
- `[vector_store]` - Vector store configuration (pgvector, turbopuffer, or lancedb)
- `[vector_store]` - Vector store configuration (pgvector, turbopuffer, lancedb, or qdrant)
- `[sentry]` - Error tracking and monitoring settings
### Using Environment Variables

9
config.toml.example
View File

@ -250,7 +250,7 @@ DEFAULT_LOCK_TTL_SECONDS = 5
# Vector store settings
[vector_store]
# Vector store type: "pgvector", "turbopuffer", or "lancedb"
# Vector store type: "pgvector", "turbopuffer", "lancedb", or "qdrant"
TYPE = "pgvector"
# Migration flag: set to true when migration from pgvector is complete
MIGRATED = false
@ -261,4 +261,11 @@ DIMENSIONS = 1536
# TURBOPUFFER_API_KEY = "your-turbopuffer-api-key"
# TURBOPUFFER_REGION = "us-east-1"
LANCEDB_PATH = "./lancedb_data"
# QDRANT_URL = "http://localhost:6333"
# QDRANT_API_KEY = "your-qdrant-api-key"
# QDRANT_PREFER_GRPC = false
# QDRANT_GRPC_PORT = 6334
# QDRANT_HTTPS = false
# QDRANT_PREFIX = ""
# QDRANT_TIMEOUT = 30
RECONCILIATION_INTERVAL_SECONDS = 300

2
docs/changelog/introduction.mdx
View File

@ -156,7 +156,7 @@ Welcome to the Honcho changelog! This section documents all notable changes to t
- Prometheus token tracking for deriver and dialectic operations
- n8n integration
- Cloud Events for auditable telemetry
- External Vector Store support for turbopuffer and lancedb with reconciliation flow
- External Vector Store support for turbopuffer, lancedb, and qdrant with reconciliation flow
### Changed

11
docs/v3/contributing/configuration.mdx
View File

@ -456,7 +456,7 @@ WEBHOOK_MAX_WORKSPACE_LIMIT=10
### Vector Store
```bash
VECTOR_STORE_TYPE=pgvector # Options: pgvector, turbopuffer, lancedb
VECTOR_STORE_TYPE=pgvector # Options: pgvector, turbopuffer, lancedb, qdrant
VECTOR_STORE_MIGRATED=false
VECTOR_STORE_NAMESPACE=honcho
VECTOR_STORE_DIMENSIONS=1536
@ -467,6 +467,15 @@ VECTOR_STORE_TURBOPUFFER_REGION=us-east-1
# LanceDB-specific
VECTOR_STORE_LANCEDB_PATH=./lancedb_data
# Qdrant-specific
VECTOR_STORE_QDRANT_URL=http://localhost:6333
VECTOR_STORE_QDRANT_API_KEY=your-qdrant-api-key # optional
VECTOR_STORE_QDRANT_PREFER_GRPC=false
VECTOR_STORE_QDRANT_GRPC_PORT=6334
VECTOR_STORE_QDRANT_HTTPS=false # optional, inferred from URL scheme
VECTOR_STORE_QDRANT_PREFIX= # optional, for reverse-proxy path prefix
VECTOR_STORE_QDRANT_TIMEOUT= # optional, request timeout in seconds
```
## Monitoring

1
pyproject.toml
View File

@ -33,6 +33,7 @@ dependencies = [
"json-repair>=0.49.0",
"turbopuffer>=1.8.1",
"lancedb>=0.25.3",
"qdrant-client>=1.18.0",
"pyarrow>=19.0.0",
"redis>=7.0.0,<8.0.0",
"cashews[redis]==7.4.4",

13
src/config.py
View File

@ -1166,12 +1166,12 @@ class DreamSettings(HonchoSettings):
class VectorStoreSettings(HonchoSettings):
"""Settings for vector store (pgvector, Turbopuffer, or LanceDB)."""
"""Settings for vector store (pgvector, Turbopuffer, LanceDB or Qdrant)."""
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", "qdrant"] = "pgvector"
MIGRATED: bool = False
@ -1197,6 +1197,15 @@ class VectorStoreSettings(HonchoSettings):
# LanceDB-specific settings (local embedded mode)
LANCEDB_PATH: str = "./lancedb_data"
# Qdrant-specific settings
QDRANT_URL: str = "http://localhost:6333"
QDRANT_API_KEY: str | None = None
QDRANT_PREFER_GRPC: bool = False
QDRANT_GRPC_PORT: int = 6334
QDRANT_HTTPS: bool | None = None
QDRANT_PREFIX: str | None = None
QDRANT_TIMEOUT: int | None = None
RECONCILIATION_INTERVAL_SECONDS: Annotated[int, Field(default=300, gt=0)] = (
300 # 5 minutes
)

4
src/vector_store/__init__.py
View File

@ -191,6 +191,10 @@ def _create_store_by_type(store_type: str) -> VectorStore:
from src.vector_store.lancedb import LanceDBVectorStore
return LanceDBVectorStore()
elif store_type == "qdrant":
from src.vector_store.qdrant import QdrantVectorStore
return QdrantVectorStore()
else:
raise ValueError(f"Unknown vector store type: {store_type}")

138
src/vector_store/qdrant.py Normal file
View File

@ -0,0 +1,138 @@
"""Qdrant vector store implementation."""
import logging
import uuid
from typing import Any
from qdrant_client import AsyncQdrantClient, models
from src.config import settings
from src.exceptions import VectorStoreError
from . import VectorQueryResult, VectorRecord, VectorStore
logger = logging.getLogger(__name__)
# Qdrant only allows UUIDs and +ve integers as point IDs.
# Ref: https://qdrant.tech/documentation/manage-data/points/#point-ids
# So we convert arbitrary strings to deterministic UUIDs.
def _point_id(string_id: str) -> str:
return str(uuid.uuid5(uuid.NAMESPACE_DNS, string_id))
class QdrantVectorStore(VectorStore):
"""Qdrant implementation of VectorStore. Each namespace maps to a collection."""
_client: AsyncQdrantClient
_vector_size: int
def __init__(self) -> None:
super().__init__()
self._client = AsyncQdrantClient(
url=settings.VECTOR_STORE.QDRANT_URL,
api_key=settings.VECTOR_STORE.QDRANT_API_KEY,
prefer_grpc=settings.VECTOR_STORE.QDRANT_PREFER_GRPC,
grpc_port=settings.VECTOR_STORE.QDRANT_GRPC_PORT,
https=settings.VECTOR_STORE.QDRANT_HTTPS,
prefix=settings.VECTOR_STORE.QDRANT_PREFIX,
timeout=settings.VECTOR_STORE.QDRANT_TIMEOUT,
)
self._vector_size = settings.VECTOR_STORE.DIMENSIONS
async def _ensure_collection(self, name: str) -> None:
if not await self._client.collection_exists(name):
await self._client.create_collection(
collection_name=name,
vectors_config=models.VectorParams(
size=self._vector_size,
distance=models.Distance.COSINE,
),
)
def _build_filter(self, filters: dict[str, Any]) -> models.Filter | None:
conditions: list[models.Condition] = []
for k, v in filters.items():
if isinstance(v, dict) and "in" in v:
conditions.append(
models.FieldCondition(key=k, match=models.MatchAny(any=v["in"])) # pyright: ignore[reportUnknownArgumentType]
)
else:
conditions.append(
models.FieldCondition(key=k, match=models.MatchValue(value=v)) # pyright: ignore[reportArgumentType]
)
return models.Filter(must=conditions) if conditions else None
async def upsert_many(self, namespace: str, vectors: list[VectorRecord]) -> None:
if not vectors:
return
await self._ensure_collection(namespace)
points = [
models.PointStruct(
id=_point_id(v.id),
vector=v.embedding,
payload={"_id": v.id, **v.metadata},
)
for v in vectors
]
try:
await self._client.upsert(collection_name=namespace, points=points)
except Exception as e:
logger.exception(
f"Failed to upsert {len(vectors)} vectors to namespace {namespace}"
)
raise VectorStoreError(
f"Qdrant upsert failed for namespace {namespace}"
) from e
async def query(
self,
namespace: str,
embedding: list[float],
*,
top_k: int = 10,
filters: dict[str, Any] | None = None,
max_distance: float | None = None,
) -> list[VectorQueryResult]:
if not await self._client.collection_exists(namespace):
return []
response = await self._client.query_points(
collection_name=namespace,
query=embedding,
limit=top_k,
query_filter=self._build_filter(filters) if filters else None,
with_payload=True,
)
results: list[VectorQueryResult] = []
for hit in response.points:
dist = 1.0 - float(hit.score)
if max_distance is not None and dist > max_distance:
continue
payload = dict(hit.payload or {})
results.append(
VectorQueryResult(
id=payload.pop("_id", str(hit.id)),
score=dist,
metadata=payload,
)
)
return results
async def delete_many(self, namespace: str, ids: list[str]) -> None:
if not ids:
return
if not await self._client.collection_exists(namespace):
return
await self._client.delete(
collection_name=namespace,
points_selector=[_point_id(i) for i in ids],
)
async def delete_namespace(self, namespace: str) -> None:
if await self._client.collection_exists(namespace):
await self._client.delete_collection(namespace)
async def close(self) -> None:
await self._client.close()

141
uv.lock
View File

@ -4,7 +4,8 @@ requires-python = ">=3.11"
resolution-markers = [
"python_full_version >= '3.14'",
"python_full_version == '3.13.*'",
"python_full_version < '3.13'",
"python_full_version == '3.12.*'",
"python_full_version < '3.12'",
]
[manifest]
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]
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