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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

12
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -84,7 +84,7 @@ MEMORY_QUERY_TOOL = "knowledge_search"
WEB_SEARCH_TOOL = "web_search"
RAG_TOOL_GROUP = "builtin::rag"
logger = get_logger(name=__name__, category="agents")
log = get_logger(name=__name__, category="agents")
class ChatAgent(ShieldRunnerMixin):
@ -612,7 +612,7 @@ class ChatAgent(ShieldRunnerMixin):
)
if n_iter >= self.agent_config.max_infer_iters:
logger.info(f"done with MAX iterations ({n_iter}), exiting.")
log.info(f"done with MAX iterations ({n_iter}), exiting.")
# NOTE: mark end_of_turn to indicate to client that we are done with the turn
# Do not continue the tool call loop after this point
message.stop_reason = StopReason.end_of_turn
@ -620,7 +620,7 @@ class ChatAgent(ShieldRunnerMixin):
break
if stop_reason == StopReason.out_of_tokens:
logger.info("out of token budget, exiting.")
log.info("out of token budget, exiting.")
yield message
break
@ -634,7 +634,7 @@ class ChatAgent(ShieldRunnerMixin):
message.content = [message.content] + output_attachments
yield message
else:
logger.debug(f"completion message with EOM (iter: {n_iter}): {str(message)}")
log.debug(f"completion message with EOM (iter: {n_iter}): {str(message)}")
input_messages = input_messages + [message]
else:
input_messages = input_messages + [message]
@ -889,7 +889,7 @@ class ChatAgent(ShieldRunnerMixin):
else:
tool_name_str = tool_name
logger.info(f"executing tool call: {tool_name_str} with args: {tool_call.arguments}")
log.info(f"executing tool call: {tool_name_str} with args: {tool_call.arguments}")
result = await self.tool_runtime_api.invoke_tool(
tool_name=tool_name_str,
kwargs={
@ -899,7 +899,7 @@ class ChatAgent(ShieldRunnerMixin):
**self.tool_name_to_args.get(tool_name_str, {}),
},
)
logger.debug(f"tool call {tool_name_str} completed with result: {result}")
log.debug(f"tool call {tool_name_str} completed with result: {result}")
return result

8
llama_stack/providers/inline/agents/meta_reference/agents.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 uuid
from collections.abc import AsyncGenerator
from datetime import UTC, datetime
@ -42,6 +41,7 @@ from llama_stack.apis.safety import Safety
from llama_stack.apis.tools import ToolGroups, ToolRuntime
from llama_stack.apis.vector_io import VectorIO
from llama_stack.core.datatypes import AccessRule
from llama_stack.log import get_logger
from llama_stack.providers.utils.kvstore import InmemoryKVStoreImpl, kvstore_impl
from llama_stack.providers.utils.pagination import paginate_records
from llama_stack.providers.utils.responses.responses_store import ResponsesStore
@ -51,7 +51,7 @@ from .config import MetaReferenceAgentsImplConfig
from .openai_responses import OpenAIResponsesImpl
from .persistence import AgentInfo
logger = logging.getLogger()
log = get_logger(name=__name__, category="agents")
class MetaReferenceAgentsImpl(Agents):
@ -268,7 +268,7 @@ class MetaReferenceAgentsImpl(Agents):
# Get the agent info using the key
agent_info_json = await self.persistence_store.get(agent_key)
if not agent_info_json:
logger.error(f"Could not find agent info for key {agent_key}")
log.error(f"Could not find agent info for key {agent_key}")
continue
try:
@ -281,7 +281,7 @@ class MetaReferenceAgentsImpl(Agents):
)
)
except Exception as e:
logger.error(f"Error parsing agent info for {agent_id}: {e}")
log.error(f"Error parsing agent info for {agent_id}: {e}")
continue
# Convert Agent objects to dictionaries

8
llama_stack/providers/inline/agents/meta_reference/openai_responses.py
View file

@ -75,7 +75,7 @@ from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefiniti
from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool
from llama_stack.providers.utils.responses.responses_store import ResponsesStore
logger = get_logger(name=__name__, category="openai_responses")
log = get_logger(name=__name__, category="openai_responses")
OPENAI_RESPONSES_PREFIX = "openai_responses:"
@ -544,12 +544,12 @@ class OpenAIResponsesImpl:
break
if function_tool_calls:
logger.info("Exiting inference loop since there is a function (client-side) tool call")
log.info("Exiting inference loop since there is a function (client-side) tool call")
break
n_iter += 1
if n_iter >= max_infer_iters:
logger.info(f"Exiting inference loop since iteration count({n_iter}) exceeds {max_infer_iters=}")
log.info(f"Exiting inference loop since iteration count({n_iter}) exceeds {max_infer_iters=}")
break
messages = next_turn_messages
@ -698,7 +698,7 @@ class OpenAIResponsesImpl:
)
return search_response.data
except Exception as e:
logger.warning(f"Failed to search vector store {vector_store_id}: {e}")
log.warning(f"Failed to search vector store {vector_store_id}: {e}")
return []
# Run all searches in parallel using gather

4
llama_stack/providers/inline/agents/meta_reference/persistence.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import json
import logging
import uuid
from datetime import UTC, datetime
@ -15,9 +14,10 @@ from llama_stack.core.access_control.access_control import AccessDeniedError, is
from llama_stack.core.access_control.datatypes import AccessRule
from llama_stack.core.datatypes import User
from llama_stack.core.request_headers import get_authenticated_user
from llama_stack.log import get_logger
from llama_stack.providers.utils.kvstore import KVStore
log = logging.getLogger(__name__)
log = get_logger(name=__name__, category="agents")
class AgentSessionInfo(Session):

4
llama_stack/providers/inline/agents/meta_reference/safety.py
View file

@ -5,13 +5,13 @@
# the root directory of this source tree.
import asyncio
import logging
from llama_stack.apis.inference import Message
from llama_stack.apis.safety import Safety, SafetyViolation, ViolationLevel
from llama_stack.log import get_logger
from llama_stack.providers.utils.telemetry import tracing
log = logging.getLogger(__name__)
log = get_logger(name=__name__, category="agents")
class SafetyException(Exception): # noqa: N818

9
llama_stack/providers/inline/inference/meta_reference/inference.py
View file

@ -73,11 +73,12 @@ from .config import MetaReferenceInferenceConfig
from .generators import LlamaGenerator
from .model_parallel import LlamaModelParallelGenerator
log = get_logger(__name__, category="inference")
# there's a single model parallel process running serving the model. for now,
# we don't support multiple concurrent requests to this process.
SEMAPHORE = asyncio.Semaphore(1)
logger = get_logger(__name__, category="inference")
def llama_builder_fn(config: MetaReferenceInferenceConfig, model_id: str, llama_model: Model) -> LlamaGenerator:
return LlamaGenerator(config, model_id, llama_model)
@ -144,7 +145,7 @@ class MetaReferenceInferenceImpl(
return model
async def load_model(self, model_id, llama_model) -> None:
log.info(f"Loading model `{model_id}`")
logger.info(f"Loading model `{model_id}`")
builder_params = [self.config, model_id, llama_model]
@ -166,7 +167,7 @@ class MetaReferenceInferenceImpl(
self.model_id = model_id
self.llama_model = llama_model
log.info("Warming up...")
logger.info("Warming up...")
await self.completion(
model_id=model_id,
content="Hello, world!",
@ -177,7 +178,7 @@ class MetaReferenceInferenceImpl(
messages=[UserMessage(content="Hi how are you?")],
sampling_params=SamplingParams(max_tokens=20),
)
log.info("Warmed up!")
logger.info("Warmed up!")
def check_model(self, request) -> None:
if self.model_id is None or self.llama_model is None:

6
llama_stack/providers/inline/inference/meta_reference/parallel_utils.py
View file

@ -12,7 +12,6 @@
import copy
import json
import logging
import multiprocessing
import os
import tempfile
@ -32,13 +31,14 @@ from fairscale.nn.model_parallel.initialize import (
from pydantic import BaseModel, Field
from torch.distributed.launcher.api import LaunchConfig, elastic_launch
from llama_stack.log import get_logger
from llama_stack.models.llama.datatypes import GenerationResult
from llama_stack.providers.utils.inference.prompt_adapter import (
ChatCompletionRequestWithRawContent,
CompletionRequestWithRawContent,
)
log = logging.getLogger(__name__)
log = get_logger(name=__name__, category="inference")
class ProcessingMessageName(str, Enum):
@ -236,7 +236,7 @@ def worker_process_entrypoint(
except StopIteration:
break
log.info("[debug] worker process done")
log.info("[debug] worker process done")
def launch_dist_group(

3
llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.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 collections.abc import AsyncGenerator
from llama_stack.apis.inference import (
@ -32,8 +31,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
from .config import SentenceTransformersInferenceConfig
log = logging.getLogger(__name__)
class SentenceTransformersInferenceImpl(
OpenAIChatCompletionToLlamaStackMixin,

62
llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py
View file

@ -6,7 +6,6 @@
import gc
import json
import logging
import multiprocessing
from pathlib import Path
from typing import Any
@ -28,6 +27,7 @@ from llama_stack.apis.post_training import (
LoraFinetuningConfig,
TrainingConfig,
)
from llama_stack.log import get_logger
from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device
from ..config import HuggingFacePostTrainingConfig
@ -44,7 +44,7 @@ from ..utils import (
split_dataset,
)
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="core")
class HFFinetuningSingleDevice:
@ -69,14 +69,14 @@ class HFFinetuningSingleDevice:
try:
messages = json.loads(row["chat_completion_input"])
if not isinstance(messages, list) or len(messages) != 1:
logger.warning(f"Invalid chat_completion_input format: {row['chat_completion_input']}")
log.warning(f"Invalid chat_completion_input format: {row['chat_completion_input']}")
return None, None
if "content" not in messages[0]:
logger.warning(f"Message missing content: {messages[0]}")
log.warning(f"Message missing content: {messages[0]}")
return None, None
return messages[0]["content"], row["expected_answer"]
except json.JSONDecodeError:
logger.warning(f"Failed to parse chat_completion_input: {row['chat_completion_input']}")
log.warning(f"Failed to parse chat_completion_input: {row['chat_completion_input']}")
return None, None
return None, None
@ -86,13 +86,13 @@ class HFFinetuningSingleDevice:
try:
dialog = json.loads(row["dialog"])
if not isinstance(dialog, list) or len(dialog) < 2:
logger.warning(f"Dialog must have at least 2 messages: {row['dialog']}")
log.warning(f"Dialog must have at least 2 messages: {row['dialog']}")
return None, None
if dialog[0].get("role") != "user":
logger.warning(f"First message must be from user: {dialog[0]}")
log.warning(f"First message must be from user: {dialog[0]}")
return None, None
if not any(msg.get("role") == "assistant" for msg in dialog):
logger.warning("Dialog must have at least one assistant message")
log.warning("Dialog must have at least one assistant message")
return None, None
# Convert to human/gpt format
@ -100,14 +100,14 @@ class HFFinetuningSingleDevice:
conversations = []
for msg in dialog:
if "role" not in msg or "content" not in msg:
logger.warning(f"Message missing role or content: {msg}")
log.warning(f"Message missing role or content: {msg}")
continue
conversations.append({"from": role_map[msg["role"]], "value": msg["content"]})
# Format as a single conversation
return conversations[0]["value"], conversations[1]["value"]
except json.JSONDecodeError:
logger.warning(f"Failed to parse dialog: {row['dialog']}")
log.warning(f"Failed to parse dialog: {row['dialog']}")
return None, None
return None, None
@ -198,7 +198,7 @@ class HFFinetuningSingleDevice:
"""
import asyncio
logger.info("Starting training process with async wrapper")
log.info("Starting training process with async wrapper")
asyncio.run(
self._run_training(
model=model,
@ -228,14 +228,14 @@ class HFFinetuningSingleDevice:
raise ValueError("DataConfig is required for training")
# Load dataset
logger.info(f"Loading dataset: {config.data_config.dataset_id}")
log.info(f"Loading dataset: {config.data_config.dataset_id}")
rows = await load_rows_from_dataset(self.datasetio_api, config.data_config.dataset_id)
if not self.validate_dataset_format(rows):
raise ValueError("Dataset is missing required fields: input_query, expected_answer, chat_completion_input")
logger.info(f"Loaded {len(rows)} rows from dataset")
log.info(f"Loaded {len(rows)} rows from dataset")
# Initialize tokenizer
logger.info(f"Initializing tokenizer for model: {model}")
log.info(f"Initializing tokenizer for model: {model}")
try:
tokenizer = AutoTokenizer.from_pretrained(model, **provider_config.model_specific_config)
@ -257,16 +257,16 @@ class HFFinetuningSingleDevice:
# This ensures consistent sequence lengths across the training process
tokenizer.model_max_length = provider_config.max_seq_length
logger.info("Tokenizer initialized successfully")
log.info("Tokenizer initialized successfully")
except Exception as e:
raise RuntimeError(f"Failed to initialize tokenizer: {str(e)}") from e
# Create and preprocess dataset
logger.info("Creating and preprocessing dataset")
log.info("Creating and preprocessing dataset")
try:
ds = self._create_dataset(rows, config, provider_config)
ds = self._preprocess_dataset(ds, tokenizer, provider_config)
logger.info(f"Dataset created with {len(ds)} examples")
log.info(f"Dataset created with {len(ds)} examples")
except Exception as e:
raise ValueError(f"Failed to create dataset: {str(e)}") from e
@ -293,11 +293,11 @@ class HFFinetuningSingleDevice:
Returns:
Configured SFTConfig object
"""
logger.info("Configuring training arguments")
log.info("Configuring training arguments")
lr = 2e-5
if config.optimizer_config:
lr = config.optimizer_config.lr
logger.info(f"Using custom learning rate: {lr}")
log.info(f"Using custom learning rate: {lr}")
# Validate data config
if not config.data_config:
@ -350,17 +350,17 @@ class HFFinetuningSingleDevice:
peft_config: Optional LoRA configuration
output_dir_path: Path to save the model
"""
logger.info("Saving final model")
log.info("Saving final model")
model_obj.config.use_cache = True
if peft_config:
logger.info("Merging LoRA weights with base model")
log.info("Merging LoRA weights with base model")
model_obj = trainer.model.merge_and_unload()
else:
model_obj = trainer.model
save_path = output_dir_path / "merged_model"
logger.info(f"Saving model to {save_path}")
log.info(f"Saving model to {save_path}")
model_obj.save_pretrained(save_path)
async def _run_training(
@ -380,13 +380,13 @@ class HFFinetuningSingleDevice:
setup_signal_handlers()
# Convert config dicts back to objects
logger.info("Initializing configuration objects")
log.info("Initializing configuration objects")
provider_config_obj = HuggingFacePostTrainingConfig(**provider_config)
config_obj = TrainingConfig(**config)
# Initialize and validate device
device = setup_torch_device(provider_config_obj.device)
logger.info(f"Using device '{device}'")
log.info(f"Using device '{device}'")
# Load dataset and tokenizer
train_dataset, eval_dataset, tokenizer = await self.load_dataset(model, config_obj, provider_config_obj)
@ -409,7 +409,7 @@ class HFFinetuningSingleDevice:
model_obj = load_model(model, device, provider_config_obj)
# Initialize trainer
logger.info("Initializing SFTTrainer")
log.info("Initializing SFTTrainer")
trainer = SFTTrainer(
model=model_obj,
train_dataset=train_dataset,
@ -420,9 +420,9 @@ class HFFinetuningSingleDevice:
try:
# Train
logger.info("Starting training")
log.info("Starting training")
trainer.train()
logger.info("Training completed successfully")
log.info("Training completed successfully")
# Save final model if output directory is provided
if output_dir_path:
@ -430,12 +430,12 @@ class HFFinetuningSingleDevice:
finally:
# Clean up resources
logger.info("Cleaning up resources")
log.info("Cleaning up resources")
if hasattr(trainer, "model"):
evacuate_model_from_device(trainer.model, device.type)
del trainer
gc.collect()
logger.info("Cleanup completed")
log.info("Cleanup completed")
async def train(
self,
@ -449,7 +449,7 @@ class HFFinetuningSingleDevice:
"""Train a model using HuggingFace's SFTTrainer"""
# Initialize and validate device
device = setup_torch_device(provider_config.device)
logger.info(f"Using device '{device}'")
log.info(f"Using device '{device}'")
output_dir_path = None
if output_dir:
@ -479,7 +479,7 @@ class HFFinetuningSingleDevice:
raise ValueError("DataConfig is required for training")
# Train in a separate process
logger.info("Starting training in separate process")
log.info("Starting training in separate process")
try:
# Setup multiprocessing for device
if device.type in ["cuda", "mps"]:

4
llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device_dpo.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import gc
import logging
import multiprocessing
from pathlib import Path
from typing import Any
@ -24,6 +23,7 @@ from llama_stack.apis.post_training import (
DPOAlignmentConfig,
TrainingConfig,
)
from llama_stack.log import get_logger
from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device
from ..config import HuggingFacePostTrainingConfig
@ -40,7 +40,7 @@ from ..utils import (
split_dataset,
)
logger = logging.getLogger(__name__)
logger = get_logger(__name__, category="core")
class HFDPOAlignmentSingleDevice:

4
llama_stack/providers/inline/post_training/huggingface/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 os
import signal
import sys
@ -19,10 +18,11 @@ from transformers import AutoConfig, AutoModelForCausalLM
from llama_stack.apis.datasetio import DatasetIO
from llama_stack.apis.post_training import Checkpoint, TrainingConfig
from llama_stack.log import get_logger
from .config import HuggingFacePostTrainingConfig
logger = logging.getLogger(__name__)
logger = get_logger(__name__, category="core")
def setup_environment():

7
llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.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 os
import time
from datetime import UTC, datetime
@ -19,6 +18,7 @@ from torch.utils.data import DataLoader, DistributedSampler
from torchtune import modules, training
from torchtune import utils as torchtune_utils
from torchtune.data import padded_collate_sft
from torchtune.models.llama3._tokenizer import Llama3Tokenizer
from torchtune.modules.loss import CEWithChunkedOutputLoss
from torchtune.modules.peft import (
get_adapter_params,
@ -45,6 +45,7 @@ from llama_stack.apis.post_training import (
)
from llama_stack.core.utils.config_dirs import DEFAULT_CHECKPOINT_DIR
from llama_stack.core.utils.model_utils import model_local_dir
from llama_stack.log import get_logger
from llama_stack.models.llama.sku_list import resolve_model
from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device
from llama_stack.providers.inline.post_training.torchtune.common import utils
@ -56,9 +57,7 @@ from llama_stack.providers.inline.post_training.torchtune.config import (
)
from llama_stack.providers.inline.post_training.torchtune.datasets.sft import SFTDataset
log = logging.getLogger(__name__)
from torchtune.models.llama3._tokenizer import Llama3Tokenizer
log = get_logger(name=__name__, category="core")
class LoraFinetuningSingleDevice:

4
llama_stack/providers/inline/safety/code_scanner/code_scanner.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
from llama_stack.apis.inference import Message
@ -15,13 +14,14 @@ 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.utils.inference.prompt_adapter import (
interleaved_content_as_str,
)
from .config import CodeScannerConfig
log = logging.getLogger(__name__)
log = get_logger(name=__name__, category="safety")
ALLOWED_CODE_SCANNER_MODEL_IDS = [
"CodeScanner",

6
llama_stack/providers/inline/safety/prompt_guard/prompt_guard.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 torch
@ -19,6 +18,7 @@ from llama_stack.apis.safety import (
)
from llama_stack.apis.shields import Shield
from llama_stack.core.utils.model_utils import model_local_dir
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import ShieldsProtocolPrivate
from llama_stack.providers.utils.inference.prompt_adapter import (
interleaved_content_as_str,
@ -26,10 +26,10 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import PromptGuardConfig, PromptGuardType
log = logging.getLogger(__name__)
PROMPT_GUARD_MODEL = "Prompt-Guard-86M"
log = get_logger(name=__name__, category="safety")
class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
def __init__(self, config: PromptGuardConfig, _deps) -> None:

13
llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py
View file

@ -7,7 +7,6 @@
import collections
import functools
import json
import logging
import random
import re
import string
@ -20,7 +19,9 @@ import nltk
from pythainlp.tokenize import sent_tokenize as sent_tokenize_thai
from pythainlp.tokenize import word_tokenize as word_tokenize_thai
logger = logging.getLogger()
from llama_stack.log import get_logger
log = get_logger(name=__name__, category="core")
WORD_LIST = [
"western",
@ -1726,7 +1727,7 @@ def get_langid(text: str, lid_path: str | None = None) -> str:
try:
line_langs.append(langdetect.detect(line))
except langdetect.LangDetectException as e:
logger.info("Unable to detect language for text %s due to %s", line, e) # refex: disable=pytotw.037
log.info("Unable to detect language for text %s due to %s", line, e) # refex: disable=pytotw.037
if len(line_langs) == 0:
return "en"
@ -1885,7 +1886,7 @@ class ResponseLanguageChecker(Instruction):
return langdetect.detect(value) == self._language
except langdetect.LangDetectException as e:
# Count as instruction is followed.
logger.info("Unable to detect language for text %s due to %s", value, e) # refex: disable=pytotw.037
log.info("Unable to detect language for text %s due to %s", value, e) # refex: disable=pytotw.037
return True
@ -3110,7 +3111,7 @@ class CapitalLettersEnglishChecker(Instruction):
return value.isupper() and langdetect.detect(value) == "en"
except langdetect.LangDetectException as e:
# Count as instruction is followed.
logger.info("Unable to detect language for text %s due to %s", value, e) # refex: disable=pytotw.037
log.info("Unable to detect language for text %s due to %s", value, e) # refex: disable=pytotw.037
return True
@ -3139,7 +3140,7 @@ class LowercaseLettersEnglishChecker(Instruction):
return value.islower() and langdetect.detect(value) == "en"
except langdetect.LangDetectException as e:
# Count as instruction is followed.
logger.info("Unable to detect language for text %s due to %s", value, e) # refex: disable=pytotw.037
log.info("Unable to detect language for text %s due to %s", value, e) # refex: disable=pytotw.037
return True

4
llama_stack/providers/inline/tool_runtime/rag/memory.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import asyncio
import logging
import secrets
import string
from typing import Any
@ -32,6 +31,7 @@ from llama_stack.apis.tools import (
ToolRuntime,
)
from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
from llama_stack.providers.utils.memory.vector_store import (
@ -42,7 +42,7 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import RagToolRuntimeConfig
from .context_retriever import generate_rag_query
log = logging.getLogger(__name__)
log = get_logger(name=__name__, category="tools")
def make_random_string(length: int = 8):

8
llama_stack/providers/inline/vector_io/faiss/faiss.py
View file

@ -8,7 +8,6 @@ import asyncio
import base64
import io
import json
import logging
from typing import Any
import faiss
@ -24,6 +23,7 @@ from llama_stack.apis.vector_io import (
QueryChunksResponse,
VectorIO,
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import (
HealthResponse,
HealthStatus,
@ -39,7 +39,7 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import FaissVectorIOConfig
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="core")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"
@ -83,7 +83,7 @@ class FaissIndex(EmbeddingIndex):
self.index = faiss.deserialize_index(np.load(buffer, allow_pickle=False))
self.chunk_ids = [chunk.chunk_id for chunk in self.chunk_by_index.values()]
except Exception as e:
logger.debug(e, exc_info=True)
log.debug(e, exc_info=True)
raise ValueError(
"Error deserializing Faiss index from storage. If you recently upgraded your Llama Stack, Faiss, "
"or NumPy versions, you may need to delete the index and re-create it again or downgrade versions.\n"
@ -262,7 +262,7 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
assert self.kvstore is not None
if vector_db_id not in self.cache:
logger.warning(f"Vector DB {vector_db_id} not found")
log.warning(f"Vector DB {vector_db_id} not found")
return
await self.cache[vector_db_id].index.delete()

14
llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
View file

@ -5,7 +5,6 @@
# the root directory of this source tree.
import asyncio
import logging
import re
import sqlite3
import struct
@ -24,6 +23,7 @@ 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.utils.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.api import KVStore
@ -35,7 +35,7 @@ from llama_stack.providers.utils.memory.vector_store import (
VectorDBWithIndex,
)
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="core")
# Specifying search mode is dependent on the VectorIO provider.
VECTOR_SEARCH = "vector"
@ -257,7 +257,7 @@ class SQLiteVecIndex(EmbeddingIndex):
except sqlite3.Error as e:
connection.rollback()
logger.error(f"Error inserting into {self.vector_table}: {e}")
log.error(f"Error inserting into {self.vector_table}: {e}")
raise
finally:
@ -306,7 +306,7 @@ class SQLiteVecIndex(EmbeddingIndex):
try:
chunk = Chunk.model_validate_json(chunk_json)
except Exception as e:
logger.error(f"Error parsing chunk JSON for id {_id}: {e}")
log.error(f"Error parsing chunk JSON for id {_id}: {e}")
continue
chunks.append(chunk)
scores.append(score)
@ -352,7 +352,7 @@ class SQLiteVecIndex(EmbeddingIndex):
try:
chunk = Chunk.model_validate_json(chunk_json)
except Exception as e:
logger.error(f"Error parsing chunk JSON for id {_id}: {e}")
log.error(f"Error parsing chunk JSON for id {_id}: {e}")
continue
chunks.append(chunk)
scores.append(score)
@ -447,7 +447,7 @@ class SQLiteVecIndex(EmbeddingIndex):
connection.commit()
except Exception as e:
connection.rollback()
logger.error(f"Error deleting chunk {chunk_id}: {e}")
log.error(f"Error deleting chunk {chunk_id}: {e}")
raise
finally:
cur.close()
@ -530,7 +530,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
async def unregister_vector_db(self, vector_db_id: str) -> None:
if vector_db_id not in self.cache:
logger.warning(f"Vector DB {vector_db_id} not found")
log.warning(f"Vector DB {vector_db_id} not found")
return
await self.cache[vector_db_id].index.delete()
del self.cache[vector_db_id]