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feat: add auto-generated CI documentation pre-commit hook (#2890)

Browse source 
Our CI is entirely undocumented, this commit adds a README.md file with
a table of the current CI and what is does

---------

Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
This commit is contained in:
Nathan Weinberg 2025-07-25 11:57:01 -04:00 • committed by Mustafa Elbehery
parent 7f834339ba
commit b381ed6d64
93 changed files with 495 additions and 477 deletions
Download patch file Download diff file Expand all files Collapse all files

4
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -65,7 +65,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import FireworksImplConfig
from .models import MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference")
log = get_logger(name=__name__, category="inference")
class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):
@ -256,7 +256,7 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
"stream": bool(request.stream),
**self._build_options(request.sampling_params, request.response_format, request.logprobs),
}
logger.debug(f"params to fireworks: {params}")
log.debug(f"params to fireworks: {params}")
return params

3
llama_stack/providers/remote/inference/llama_openai_compat/llama.py
View file

@ -3,7 +3,6 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
@ -11,8 +10,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from .models import MODEL_ENTRIES
logger = logging.getLogger(__name__)
class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
"""

6
llama_stack/providers/remote/inference/nvidia/nvidia.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import warnings
from collections.abc import AsyncIterator
@ -33,6 +32,7 @@ from llama_stack.apis.inference import (
ToolChoice,
ToolConfig,
)
from llama_stack.log import get_logger
from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat
from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
@ -54,7 +54,7 @@ from .openai_utils import (
)
from .utils import _is_nvidia_hosted
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="inference")
class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
@ -75,7 +75,7 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
# TODO(mf): filter by available models
ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
logger.info(f"Initializing NVIDIAInferenceAdapter({config.url})...")
log.info(f"Initializing NVIDIAInferenceAdapter({config.url})...")
if _is_nvidia_hosted(config):
if not config.api_key:

7
llama_stack/providers/remote/inference/nvidia/utils.py
View file

@ -4,13 +4,14 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import httpx
from llama_stack.log import get_logger
from . import NVIDIAConfig
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="inference")
def _is_nvidia_hosted(config: NVIDIAConfig) -> bool:
@ -44,7 +45,7 @@ async def check_health(config: NVIDIAConfig) -> None:
RuntimeError: If the server is not running or ready
"""
if not _is_nvidia_hosted(config):
logger.info("Checking NVIDIA NIM health...")
log.info("Checking NVIDIA NIM health...")
try:
is_live, is_ready = await _get_health(config.url)
if not is_live:

10
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -85,7 +85,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .models import MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference")
log = get_logger(name=__name__, category="inference")
class OllamaInferenceAdapter(
@ -117,10 +117,10 @@ class OllamaInferenceAdapter(
return self._openai_client
async def initialize(self) -> None:
logger.info(f"checking connectivity to Ollama at `{self.config.url}`...")
log.info(f"checking connectivity to Ollama at `{self.config.url}`...")
health_response = await self.health()
if health_response["status"] == HealthStatus.ERROR:
logger.warning(
log.warning(
"Ollama Server is not running, make sure to start it using `ollama serve` in a separate terminal"
)
@ -339,7 +339,7 @@ class OllamaInferenceAdapter(
"options": sampling_options,
"stream": request.stream,
}
logger.debug(f"params to ollama: {params}")
log.debug(f"params to ollama: {params}")
return params
@ -437,7 +437,7 @@ class OllamaInferenceAdapter(
if provider_resource_id not in available_models:
available_models_latest = [m.model.split(":latest")[0] for m in response.models]
if provider_resource_id in available_models_latest:
logger.warning(
log.warning(
f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_resource_id}:latest'"
)
return model

3
llama_stack/providers/remote/inference/openai/openai.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
@ -12,8 +11,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from .config import OpenAIConfig
from .models import MODEL_ENTRIES
logger = logging.getLogger(__name__)
#
# This OpenAI adapter implements Inference methods using two mixins -

6
llama_stack/providers/remote/inference/tgi/tgi.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import logging
from collections.abc import AsyncGenerator
from huggingface_hub import AsyncInferenceClient, HfApi
@ -34,6 +33,7 @@ from llama_stack.apis.inference import (
ToolPromptFormat,
)
from llama_stack.apis.models import Model
from llama_stack.log import get_logger
from llama_stack.models.llama.sku_list import all_registered_models
from llama_stack.providers.datatypes import ModelsProtocolPrivate
from llama_stack.providers.utils.inference.model_registry import (
@ -58,7 +58,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
log = logging.getLogger(__name__)
logger = get_logger(__name__, category="core")
def build_hf_repo_model_entries():
@ -307,7 +307,7 @@ class TGIAdapter(_HfAdapter):
async def initialize(self, config: TGIImplConfig) -> None:
if not config.url:
raise ValueError("You must provide a URL in run.yaml (or via the TGI_URL environment variable) to use TGI.")
log.info(f"Initializing TGI client with url={config.url}")
logger.info(f"Initializing TGI client with url={config.url}")
self.client = AsyncInferenceClient(
model=config.url,
)

4
llama_stack/providers/remote/inference/together/together.py
View file

@ -61,7 +61,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import TogetherImplConfig
from .models import MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference")
log = get_logger(name=__name__, category="inference")
class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):
@ -232,7 +232,7 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
"stream": request.stream,
**self._build_options(request.sampling_params, request.logprobs, request.response_format),
}
logger.debug(f"params to together: {params}")
log.debug(f"params to together: {params}")
return params
async def embeddings(

3
llama_stack/providers/remote/post_training/nvidia/utils.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import warnings
from typing import Any
@ -15,8 +14,6 @@ from llama_stack.providers.remote.post_training.nvidia.config import SFTLoRADefa
from .config import NvidiaPostTrainingConfig
logger = logging.getLogger(__name__)
def warn_unsupported_params(config_dict: Any, supported_keys: set[str], config_name: str) -> None:
keys = set(config_dict.__annotations__.keys()) if isinstance(config_dict, BaseModel) else config_dict.keys()

8
llama_stack/providers/remote/safety/bedrock/bedrock.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import json
import logging
from typing import Any
from llama_stack.apis.inference import Message
@ -16,12 +15,13 @@ from llama_stack.apis.safety import (
ViolationLevel,
)
from llama_stack.apis.shields import Shield
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import ShieldsProtocolPrivate
from llama_stack.providers.utils.bedrock.client import create_bedrock_client
from .config import BedrockSafetyConfig
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="safety")
class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):
@ -76,13 +76,13 @@ class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):
"""
shield_params = shield.params
logger.debug(f"run_shield::{shield_params}::messages={messages}")
log.debug(f"run_shield::{shield_params}::messages={messages}")
# - convert the messages into format Bedrock expects
content_messages = []
for message in messages:
content_messages.append({"text": {"text": message.content}})
logger.debug(f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:")
log.debug(f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:")
response = self.bedrock_runtime_client.apply_guardrail(
guardrailIdentifier=shield.provider_resource_id,

3
llama_stack/providers/remote/safety/nvidia/nvidia.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
from typing import Any
import requests
@ -17,8 +16,6 @@ from llama_stack.providers.utils.inference.openai_compat import convert_message_
from .config import NVIDIASafetyConfig
logger = logging.getLogger(__name__)
class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
def __init__(self, config: NVIDIASafetyConfig) -> None:

10
llama_stack/providers/remote/safety/sambanova/sambanova.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import json
import logging
from typing import Any
import litellm
@ -20,12 +19,13 @@ from llama_stack.apis.safety import (
)
from llama_stack.apis.shields import Shield
from llama_stack.core.request_headers import NeedsRequestProviderData
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import ShieldsProtocolPrivate
from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict_new
from .config import SambaNovaSafetyConfig
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="safety")
CANNED_RESPONSE_TEXT = "I can't answer that. Can I help with something else?"
@ -66,7 +66,7 @@ class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProvide
"guard" not in shield.provider_resource_id.lower()
or shield.provider_resource_id.split("sambanova/")[-1] not in self.environment_available_models
):
logger.warning(f"Shield {shield.provider_resource_id} not available in {list_models_url}")
log.warning(f"Shield {shield.provider_resource_id} not available in {list_models_url}")
async def unregister_shield(self, identifier: str) -> None:
pass
@ -79,9 +79,9 @@ class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProvide
raise ValueError(f"Shield {shield_id} not found")
shield_params = shield.params
logger.debug(f"run_shield::{shield_params}::messages={messages}")
log.debug(f"run_shield::{shield_params}::messages={messages}")
content_messages = [await convert_message_to_openai_dict_new(m) for m in messages]
logger.debug(f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:")
log.debug(f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:")
response = litellm.completion(
model=shield.provider_resource_id, messages=content_messages, api_key=self._get_api_key()

16
llama_stack/providers/remote/vector_io/chroma/chroma.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import asyncio
import json
import logging
from typing import Any
from urllib.parse import urlparse
@ -20,6 +19,7 @@ from llama_stack.apis.vector_io import (
QueryChunksResponse,
VectorIO,
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
from llama_stack.providers.utils.kvstore import kvstore_impl
@ -32,8 +32,6 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig
log = logging.getLogger(__name__)
ChromaClientType = chromadb.api.AsyncClientAPI | chromadb.api.ClientAPI
VERSION = "v3"
@ -43,6 +41,8 @@ OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:chroma:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:chroma:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:chroma:{VERSION}::"
logger = get_logger(__name__, category="core")
# this is a helper to allow us to use async and non-async chroma clients interchangeably
async def maybe_await(result):
@ -92,7 +92,7 @@ class ChromaIndex(EmbeddingIndex):
doc = json.loads(doc)
chunk = Chunk(**doc)
except Exception:
log.exception(f"Failed to parse document: {doc}")
logger.exception(f"Failed to parse document: {doc}")
continue
score = 1.0 / float(dist) if dist != 0 else float("inf")
@ -137,7 +137,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
inference_api: Api.inference,
files_api: Files | None,
) -> None:
log.info(f"Initializing ChromaVectorIOAdapter with url: {config}")
logger.info(f"Initializing ChromaVectorIOAdapter with url: {config}")
self.config = config
self.inference_api = inference_api
self.client = None
@ -150,7 +150,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
self.vector_db_store = self.kvstore
if isinstance(self.config, RemoteChromaVectorIOConfig):
log.info(f"Connecting to Chroma server at: {self.config.url}")
logger.info(f"Connecting to Chroma server at: {self.config.url}")
url = self.config.url.rstrip("/")
parsed = urlparse(url)
@ -159,7 +159,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
self.client = await chromadb.AsyncHttpClient(host=parsed.hostname, port=parsed.port)
else:
log.info(f"Connecting to Chroma local db at: {self.config.db_path}")
logger.info(f"Connecting to Chroma local db at: {self.config.db_path}")
self.client = chromadb.PersistentClient(path=self.config.db_path)
self.openai_vector_stores = await self._load_openai_vector_stores()
@ -182,7 +182,7 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
async def unregister_vector_db(self, vector_db_id: str) -> None:
if vector_db_id not in self.cache:
log.warning(f"Vector DB {vector_db_id} not found")
logger.warning(f"Vector DB {vector_db_id} not found")
return
await self.cache[vector_db_id].index.delete()

16
llama_stack/providers/remote/vector_io/milvus/milvus.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import asyncio
import logging
import os
from typing import Any
@ -21,6 +20,7 @@ from llama_stack.apis.vector_io import (
QueryChunksResponse,
VectorIO,
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
from llama_stack.providers.utils.kvstore import kvstore_impl
@ -34,7 +34,7 @@ from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collecti
from .config import MilvusVectorIOConfig as RemoteMilvusVectorIOConfig
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="core")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:milvus:{VERSION}::"
@ -68,7 +68,7 @@ class MilvusIndex(EmbeddingIndex):
)
if not await asyncio.to_thread(self.client.has_collection, self.collection_name):
logger.info(f"Creating new collection {self.collection_name} with nullable sparse field")
log.info(f"Creating new collection {self.collection_name} with nullable sparse field")
# Create schema for vector search
schema = self.client.create_schema()
schema.add_field(
@ -147,7 +147,7 @@ class MilvusIndex(EmbeddingIndex):
data=data,
)
except Exception as e:
logger.error(f"Error inserting chunks into Milvus collection {self.collection_name}: {e}")
log.error(f"Error inserting chunks into Milvus collection {self.collection_name}: {e}")
raise e
async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
@ -203,7 +203,7 @@ class MilvusIndex(EmbeddingIndex):
return QueryChunksResponse(chunks=filtered_chunks, scores=filtered_scores)
except Exception as e:
logger.error(f"Error performing BM25 search: {e}")
log.error(f"Error performing BM25 search: {e}")
# Fallback to simple text search
return await self._fallback_keyword_search(query_string, k, score_threshold)
@ -247,7 +247,7 @@ class MilvusIndex(EmbeddingIndex):
self.client.delete, collection_name=self.collection_name, filter=f'chunk_id == "{chunk_id}"'
)
except Exception as e:
logger.error(f"Error deleting chunk {chunk_id} from Milvus collection {self.collection_name}: {e}")
log.error(f"Error deleting chunk {chunk_id} from Milvus collection {self.collection_name}: {e}")
raise
@ -288,10 +288,10 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
)
self.cache[vector_db.identifier] = index
if isinstance(self.config, RemoteMilvusVectorIOConfig):
logger.info(f"Connecting to Milvus server at {self.config.uri}")
log.info(f"Connecting to Milvus server at {self.config.uri}")
self.client = MilvusClient(**self.config.model_dump(exclude_none=True))
else:
logger.info(f"Connecting to Milvus Lite at: {self.config.db_path}")
log.info(f"Connecting to Milvus Lite at: {self.config.db_path}")
uri = os.path.expanduser(self.config.db_path)
self.client = MilvusClient(uri=uri)

14
llama_stack/providers/remote/vector_io/pgvector/pgvector.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
from typing import Any
import psycopg2
@ -22,6 +21,7 @@ from llama_stack.apis.vector_io import (
QueryChunksResponse,
VectorIO,
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
from llama_stack.providers.utils.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.api import KVStore
@ -33,8 +33,6 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import PGVectorVectorIOConfig
log = logging.getLogger(__name__)
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:pgvector:{VERSION}::"
VECTOR_INDEX_PREFIX = f"vector_index:pgvector:{VERSION}::"
@ -42,6 +40,8 @@ OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:pgvector:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:pgvector:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:pgvector:{VERSION}::"
logger = get_logger(__name__, category="core")
def check_extension_version(cur):
cur.execute("SELECT extversion FROM pg_extension WHERE extname = 'vector'")
@ -187,7 +187,7 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
self.metadatadata_collection_name = "openai_vector_stores_metadata"
async def initialize(self) -> None:
log.info(f"Initializing PGVector memory adapter with config: {self.config}")
logger.info(f"Initializing PGVector memory adapter with config: {self.config}")
self.kvstore = await kvstore_impl(self.config.kvstore)
await self.initialize_openai_vector_stores()
@ -203,7 +203,7 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
version = check_extension_version(cur)
if version:
log.info(f"Vector extension version: {version}")
logger.info(f"Vector extension version: {version}")
else:
raise RuntimeError("Vector extension is not installed.")
@ -216,13 +216,13 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
"""
)
except Exception as e:
log.exception("Could not connect to PGVector database server")
logger.exception("Could not connect to PGVector database server")
raise RuntimeError("Could not connect to PGVector database server") from e
async def shutdown(self) -> None:
if self.conn is not None:
self.conn.close()
log.info("Connection to PGVector database server closed")
logger.info("Connection to PGVector database server closed")
async def register_vector_db(self, vector_db: VectorDB) -> None:
# Persist vector DB metadata in the KV store

9
llama_stack/providers/remote/vector_io/qdrant/qdrant.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import asyncio
import logging
import uuid
from typing import Any
@ -24,6 +23,7 @@ from llama_stack.apis.vector_io import (
VectorStoreChunkingStrategy,
VectorStoreFileObject,
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
@ -35,13 +35,14 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
log = logging.getLogger(__name__)
CHUNK_ID_KEY = "_chunk_id"
# KV store prefixes for vector databases
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:qdrant:{VERSION}::"
logger = get_logger(__name__, category="core")
def convert_id(_id: str) -> str:
"""
@ -96,7 +97,7 @@ class QdrantIndex(EmbeddingIndex):
points_selector=models.PointIdsList(points=[convert_id(chunk_id)]),
)
except Exception as e:
log.error(f"Error deleting chunk {chunk_id} from Qdrant collection {self.collection_name}: {e}")
logger.error(f"Error deleting chunk {chunk_id} from Qdrant collection {self.collection_name}: {e}")
raise
async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
@ -118,7 +119,7 @@ class QdrantIndex(EmbeddingIndex):
try:
chunk = Chunk(**point.payload["chunk_content"])
except Exception:
log.exception("Failed to parse chunk")
logger.exception("Failed to parse chunk")
continue
chunks.append(chunk)

16
llama_stack/providers/remote/vector_io/weaviate/weaviate.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
import logging
from typing import Any
import weaviate
@ -19,6 +18,7 @@ from llama_stack.apis.files.files import Files
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
from llama_stack.core.request_headers import NeedsRequestProviderData
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
from llama_stack.providers.utils.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.api import KVStore
@ -33,8 +33,6 @@ from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collecti
from .config import WeaviateVectorIOConfig
log = logging.getLogger(__name__)
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:weaviate:{VERSION}::"
VECTOR_INDEX_PREFIX = f"vector_index:weaviate:{VERSION}::"
@ -42,6 +40,8 @@ OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:weaviate:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:weaviate:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:weaviate:{VERSION}::"
logger = get_logger(__name__, category="core")
class WeaviateIndex(EmbeddingIndex):
def __init__(
@ -102,7 +102,7 @@ class WeaviateIndex(EmbeddingIndex):
chunk_dict = json.loads(chunk_json)
chunk = Chunk(**chunk_dict)
except Exception:
log.exception(f"Failed to parse document: {chunk_json}")
logger.exception(f"Failed to parse document: {chunk_json}")
continue
score = 1.0 / doc.metadata.distance if doc.metadata.distance != 0 else float("inf")
@ -171,7 +171,7 @@ class WeaviateVectorIOAdapter(
def _get_client(self) -> weaviate.Client:
if "localhost" in self.config.weaviate_cluster_url:
log.info("using Weaviate locally in container")
logger.info("using Weaviate locally in container")
host, port = self.config.weaviate_cluster_url.split(":")
key = "local_test"
client = weaviate.connect_to_local(
@ -179,7 +179,7 @@ class WeaviateVectorIOAdapter(
port=port,
)
else:
log.info("Using Weaviate remote cluster with URL")
logger.info("Using Weaviate remote cluster with URL")
key = f"{self.config.weaviate_cluster_url}::{self.config.weaviate_api_key}"
if key in self.client_cache:
return self.client_cache[key]
@ -197,7 +197,7 @@ class WeaviateVectorIOAdapter(
self.kvstore = await kvstore_impl(self.config.kvstore)
else:
self.kvstore = None
log.info("No kvstore configured, registry will not persist across restarts")
logger.info("No kvstore configured, registry will not persist across restarts")
# Load existing vector DB definitions
if self.kvstore is not None:
@ -254,7 +254,7 @@ class WeaviateVectorIOAdapter(
client = self._get_client()
sanitized_collection_name = sanitize_collection_name(vector_db_id, weaviate_format=True)
if sanitized_collection_name not in self.cache or client.collections.exists(sanitized_collection_name) is False:
log.warning(f"Vector DB {sanitized_collection_name} not found")
logger.warning(f"Vector DB {sanitized_collection_name} not found")
return
client.collections.delete(sanitized_collection_name)
await self.cache[sanitized_collection_name].index.delete()