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feat(vector_stores): add MongoDB Atlas vector store provider
Atlas Vector Search has no HTTP query API, since the Data API and HTTPS Endpoints are end-of-life, so this provider extends BaseDirectVectorStoreConfig and runs the $vectorSearch aggregation through pymongo rather than shaping an httpx request. That is the same seam Valkey uses for RESP. vector_store_id names the Atlas Search index, matching Valkey, with the database and collection supplied through litellm_params. pymongo lives in a new optional `mongodb` extra and is imported lazily, so the base install still pulls no MongoDB driver. The floor is 4.17 because that is where dnspython became a core dependency instead of the `srv` extra, and Atlas issues mongodb+srv:// URIs that will not resolve without it. Clients are cached per connection rather than opened per search. Measured against Atlas, a fresh client costs ~890ms versus ~80ms warm, so copying the Valkey open-and-close-per-call pattern would have added ~810ms to every query.
This commit is contained in:
Yuneng Jiang
parent
31ca4ddf32
commit
a616b8aaed
8 changed files with 590 additions and 2 deletions
0
litellm/llms/mongodb/__init__.py
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0
litellm/llms/mongodb/__init__.py
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163
litellm/llms/mongodb/common_utils.py
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163
litellm/llms/mongodb/common_utils.py
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@ -0,0 +1,163 @@
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"""Shared helpers for MongoDB Atlas integrations.
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pymongo ships in the optional ``mongodb`` extra, so every import of it is
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deferred to call time and raises an actionable error when it is absent.
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Clients are cached per connection because building one costs an SRV lookup, a
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TLS handshake and topology discovery: measured at ~890ms against Atlas versus
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~80ms on a warm client, so a client per search would dominate query latency.
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"""
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import asyncio
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Final
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if TYPE_CHECKING:
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from pymongo import AsyncMongoClient, MongoClient
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PYMONGO_INSTALL_HINT: Final = (
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"The MongoDB vector store requires the 'pymongo' package. "
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"Run 'pip install litellm[mongodb]' (or 'pip install pymongo') to install it."
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)
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DEFAULT_CONNECT_TIMEOUT_MS: Final = 10_000
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DEFAULT_SOCKET_TIMEOUT_MS: Final = 30_000
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DEFAULT_SERVER_SELECTION_TIMEOUT_MS: Final = 10_000
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_MAX_CACHED_CLIENTS: Final = 32
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_APP_NAME: Final = "litellm"
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@dataclass(frozen=True, slots=True)
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class MongoClientKey:
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connection_string: str
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connect_timeout_ms: int
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socket_timeout_ms: int
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server_selection_timeout_ms: int
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_sync_clients: dict[MongoClientKey, "MongoClient"] = {} # mutable-ok: process-level connection cache, see module docstring
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_async_clients: dict[tuple[MongoClientKey, int], "AsyncMongoClient"] = {} # mutable-ok: same cache, keyed per event loop
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def import_sync_mongo_client() -> "type[MongoClient]":
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try:
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from pymongo import MongoClient as SyncMongoClient
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except ImportError as e:
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raise ValueError(PYMONGO_INSTALL_HINT) from e
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return SyncMongoClient
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def import_async_mongo_client() -> "type[AsyncMongoClient]":
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try:
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from pymongo import AsyncMongoClient as AsyncMongoClientClass
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except ImportError as e:
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raise ValueError(PYMONGO_INSTALL_HINT) from e
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return AsyncMongoClientClass
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def _client_kwargs(key: MongoClientKey) -> dict[str, object]:
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return { # mutable-ok: pymongo's client constructor takes keyword arguments
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"connectTimeoutMS": key.connect_timeout_ms,
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"socketTimeoutMS": key.socket_timeout_ms,
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"serverSelectionTimeoutMS": key.server_selection_timeout_ms,
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"appname": _APP_NAME,
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}
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def get_sync_client(key: MongoClientKey) -> "MongoClient":
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cached: Final = _sync_clients.get(key)
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if cached is not None:
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return cached
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client: Final = import_sync_mongo_client()(key.connection_string, **_client_kwargs(key))
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if len(_sync_clients) < _MAX_CACHED_CLIENTS:
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_sync_clients[key] = client
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return client
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def get_async_client(key: MongoClientKey) -> "AsyncMongoClient":
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"""Async clients bind to the loop that created them, so the cache is keyed per loop."""
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loop_key: Final = (key, id(asyncio.get_running_loop()))
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cached: Final = _async_clients.get(loop_key)
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if cached is not None:
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return cached
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client: Final = import_async_mongo_client()(key.connection_string, **_client_kwargs(key))
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if len(_async_clients) < _MAX_CACHED_CLIENTS:
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_async_clients[loop_key] = client
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return client
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def reset_client_cache() -> None:
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_sync_clients.clear()
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_async_clients.clear()
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_AUTHENTICATION_FAILED_CODE: Final = 18
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_UNAUTHORIZED_CODE: Final = 13
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def _index_hint(index_name: str, database: str, collection: str) -> str:
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return (
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f"No queryable Atlas Vector Search index named '{index_name}' was found on "
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f"'{database}.{collection}'. Confirm the index exists on that exact collection, that its "
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"status is READY rather than still building, and that the vector store id matches the index name."
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)
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def translate_mongo_error(error: Exception, index_name: str, database: str, collection: str) -> Exception:
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"""Turn a driver failure into a message that names the misconfiguration, never a silent empty result.
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Returns the exception to raise so callers keep the original as ``__cause__``.
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"""
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try:
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from pymongo.errors import (
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ConfigurationError,
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ExecutionTimeout,
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InvalidOperation,
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NetworkTimeout,
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OperationFailure,
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ServerSelectionTimeoutError,
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)
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except ImportError:
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return error
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if isinstance(error, ServerSelectionTimeoutError):
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return ValueError(
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"Could not reach the MongoDB deployment before the timeout. On Atlas this is usually the "
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"project's IP access list not containing this host, or a paused cluster; it can also be an "
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f"unresolvable hostname. Driver detail: {error}"
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)
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if isinstance(error, OperationFailure):
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code: Final = error.code
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if code in (_AUTHENTICATION_FAILED_CODE, _UNAUTHORIZED_CODE):
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return ValueError(
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"MongoDB rejected the credentials in mongodb_connection_string, or the database user "
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f"lacks read access to '{database}.{collection}'. Driver detail: {error.details}"
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)
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detail: Final = str(error).lower()
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if "index" in detail and ("not found" in detail or "does not exist" in detail or "unknown" in detail):
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return ValueError(f"{_index_hint(index_name, database, collection)} Driver detail: {error}")
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if "dimension" in detail or "numdimensions" in detail or "queryvector" in detail:
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return ValueError(
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"The query embedding does not match the vector dimensions the Atlas index was built for. "
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"litellm_embedding_model must be the same model that produced the stored vectors. "
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f"Driver detail: {error}"
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)
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return ValueError(
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f"MongoDB rejected the vector search against '{database}.{collection}' using index "
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f"'{index_name}'. Driver detail: {error}"
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)
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if isinstance(error, (NetworkTimeout, ExecutionTimeout)):
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return ValueError(
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f"The MongoDB vector search against '{database}.{collection}' timed out before returning. "
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f"Driver detail: {error}"
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)
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if isinstance(error, ConfigurationError):
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return ValueError(
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"mongodb_connection_string is not a usable MongoDB connection string. "
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f"Driver detail: {error}"
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)
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if isinstance(error, InvalidOperation):
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return ValueError(f"The MongoDB client was already closed or is unusable. Driver detail: {error}")
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return error
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0
litellm/llms/mongodb/vector_stores/__init__.py
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litellm/llms/mongodb/vector_stores/__init__.py
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337
litellm/llms/mongodb/vector_stores/transformation.py
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litellm/llms/mongodb/vector_stores/transformation.py
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"""MongoDB Atlas vector store provider.
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Atlas Vector Search has no HTTP query API (the Data API and HTTPS Endpoints are
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end-of-life), so this config extends BaseDirectVectorStoreConfig and runs the
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``$vectorSearch`` aggregation itself through pymongo instead of shaping an httpx
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request.
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``vector_store_id`` is the Atlas Search index name, matching the Valkey provider
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where the id names the index; the database and collection it covers come from
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litellm_params.
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"""
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from collections.abc import Awaitable, Callable, Mapping, Sequence
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from types import MappingProxyType
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from typing import TYPE_CHECKING, Final, NoReturn
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import httpx
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from pydantic import BaseModel, ConfigDict
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import litellm
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from litellm.llms.base_llm.vector_store.transformation import BaseDirectVectorStoreConfig
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from litellm.llms.mongodb.common_utils import (
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DEFAULT_CONNECT_TIMEOUT_MS,
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DEFAULT_SERVER_SELECTION_TIMEOUT_MS,
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DEFAULT_SOCKET_TIMEOUT_MS,
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MongoClientKey,
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get_async_client,
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get_sync_client,
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translate_mongo_error,
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)
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from litellm.types.utils import EmbeddingResponse
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from litellm.types.vector_stores import (
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VectorStoreCreateOptionalRequestParams,
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VectorStoreResultContent,
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VectorStoreSearchOptionalRequestParams,
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VectorStoreSearchResponse,
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VectorStoreSearchResult,
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)
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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DEFAULT_EMBEDDING_FIELD_NAME: Final = "embedding"
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DEFAULT_TEXT_FIELD_NAME: Final = "text"
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SCORE_FIELD_NAME: Final = "score"
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DEFAULT_MAX_NUM_RESULTS: Final = 10
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MIN_MAX_NUM_RESULTS: Final = 1
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MAX_MAX_NUM_RESULTS: Final = 50
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NUM_CANDIDATES_MULTIPLIER: Final = 10
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MIN_NUM_CANDIDATES: Final = 100
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MAX_NUM_CANDIDATES: Final = 10_000
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MAX_QUERY_CHARACTERS: Final = 32_000
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_EMPTY_EMBEDDING_CONFIG: Final = MappingProxyType({})
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_SEARCH_ONLY_MESSAGE: Final = (
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"MongoDB vector store is search-only. Create the collection and its Atlas Vector Search "
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"index in MongoDB directly, then register it here by index name."
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)
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class _MongoDBSearchParams(BaseModel):
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"""Typed view over the vector store's litellm_params; unrelated keys are ignored."""
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model_config = ConfigDict(frozen=True, extra="ignore")
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litellm_embedding_model: str | None = None
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litellm_embedding_config: Mapping[str, object] | None = None
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mongodb_connection_string: str | None = None
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mongodb_database: str | None = None
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mongodb_collection: str | None = None
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mongodb_text_field: str | None = None
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mongodb_embedding_field: str | None = None
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mongodb_num_candidates: int | None = None
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@property
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def text_field(self) -> str:
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return self.mongodb_text_field or DEFAULT_TEXT_FIELD_NAME
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@property
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def embedding_field(self) -> str:
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return self.mongodb_embedding_field or DEFAULT_EMBEDDING_FIELD_NAME
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def require_embedding_model(self) -> str:
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if not self.litellm_embedding_model:
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raise ValueError(
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"litellm_embedding_model is required in litellm_params for the MongoDB vector store. "
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"It must be the same model that produced the vectors stored in "
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f"'{self.mongodb_collection or '<collection>'}.{self.embedding_field}', or search results "
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"will be meaningless. Example: litellm_embedding_model: openai/text-embedding-3-small"
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)
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return self.litellm_embedding_model
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def require_connection_string(self) -> str:
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if not self.mongodb_connection_string:
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raise ValueError(
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"mongodb_connection_string is required in litellm_params for the MongoDB vector store. "
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"Example: mongodb+srv://<user>:<password>@<cluster>.mongodb.net"
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)
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scheme: Final = self.mongodb_connection_string.split("://", 1)[0].lower()
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if scheme not in ("mongodb", "mongodb+srv"):
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raise ValueError(
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"mongodb_connection_string must start with 'mongodb://' or 'mongodb+srv://', "
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f"got '{self.mongodb_connection_string.split('://', 1)[0]}://'"
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)
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return self.mongodb_connection_string
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def require_database(self) -> str:
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if not self.mongodb_database:
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raise ValueError(
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"mongodb_database is required in litellm_params for the MongoDB vector store. "
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"Example: mongodb_database: sample_mflix"
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)
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return self.mongodb_database
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def require_collection(self) -> str:
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if not self.mongodb_collection:
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raise ValueError(
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"mongodb_collection is required in litellm_params for the MongoDB vector store. "
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"Example: mongodb_collection: embedded_movies"
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)
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return self.mongodb_collection
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class MongoDBVectorStoreConfig(BaseDirectVectorStoreConfig):
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def __init__(
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self,
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embedding_fn: Callable[..., EmbeddingResponse] | None = None,
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aembedding_fn: Callable[..., Awaitable[EmbeddingResponse]] | None = None,
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sync_client_factory: Callable[[MongoClientKey], object] | None = None,
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async_client_factory: Callable[[MongoClientKey], object] | None = None,
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) -> None:
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super().__init__()
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self.embedding_fn = embedding_fn if embedding_fn is not None else litellm.embedding
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self.aembedding_fn = aembedding_fn if aembedding_fn is not None else litellm.aembedding
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self.sync_client_factory = sync_client_factory if sync_client_factory is not None else get_sync_client
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self.async_client_factory = async_client_factory if async_client_factory is not None else get_async_client
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@staticmethod
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def _query_text(query: str | Sequence[str]) -> str:
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text: Final = query if isinstance(query, str) else " ".join(query)
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if not text.strip():
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raise ValueError("query must not be empty")
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if len(text) > MAX_QUERY_CHARACTERS:
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raise ValueError(f"query must be at most {MAX_QUERY_CHARACTERS} characters, got {len(text)}")
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return text
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@staticmethod
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def _limit(vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams) -> int:
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requested: Final = vector_store_search_optional_params.get("max_num_results")
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if requested is None:
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return DEFAULT_MAX_NUM_RESULTS
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if not MIN_MAX_NUM_RESULTS <= requested <= MAX_MAX_NUM_RESULTS:
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raise ValueError(
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f"max_num_results must be between {MIN_MAX_NUM_RESULTS} and {MAX_MAX_NUM_RESULTS}, got {requested}"
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)
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return requested
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@staticmethod
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def _num_candidates(limit: int, configured: int | None) -> int:
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if configured is not None:
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if not limit <= configured <= MAX_NUM_CANDIDATES:
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raise ValueError(
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f"mongodb_num_candidates must be between max_num_results ({limit}) and "
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f"{MAX_NUM_CANDIDATES}, got {configured}"
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)
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return configured
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return min(max(limit * NUM_CANDIDATES_MULTIPLIER, MIN_NUM_CANDIDATES), MAX_NUM_CANDIDATES)
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@staticmethod
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def _client_key(params: _MongoDBSearchParams, timeout: float | httpx.Timeout | None) -> MongoClientKey:
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if isinstance(timeout, httpx.Timeout):
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connect_ms: Final = int((timeout.connect or DEFAULT_CONNECT_TIMEOUT_MS / 1000) * 1000)
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socket_ms: Final = int((timeout.read or DEFAULT_SOCKET_TIMEOUT_MS / 1000) * 1000)
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elif timeout is not None:
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connect_ms = min(int(float(timeout) * 1000), DEFAULT_CONNECT_TIMEOUT_MS)
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socket_ms = int(float(timeout) * 1000)
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else:
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connect_ms = DEFAULT_CONNECT_TIMEOUT_MS
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socket_ms = DEFAULT_SOCKET_TIMEOUT_MS
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return MongoClientKey(
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connection_string=params.require_connection_string(),
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connect_timeout_ms=connect_ms,
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socket_timeout_ms=socket_ms,
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server_selection_timeout_ms=min(connect_ms, DEFAULT_SERVER_SELECTION_TIMEOUT_MS),
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)
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@classmethod
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def _pipeline(
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cls,
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vector_store_id: str,
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query_vector: Sequence[float],
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params: _MongoDBSearchParams,
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vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
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) -> list[dict[str, object]]:
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if vector_store_search_optional_params.get("filters") is not None:
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raise ValueError(
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"MongoDB vector store does not support the filters parameter yet. "
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"Restrict the collection or the Atlas Vector Search index definition instead."
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)
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limit: Final = cls._limit(vector_store_search_optional_params)
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return [ # mutable-ok: pymongo's aggregate contract is a list of stage dicts
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{
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"$vectorSearch": {
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"index": vector_store_id,
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"path": params.embedding_field,
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"queryVector": list(query_vector),
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"numCandidates": cls._num_candidates(limit, params.mongodb_num_candidates),
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"limit": limit,
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}
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},
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{"$project": {params.text_field: 1, SCORE_FIELD_NAME: {"$meta": "vectorSearchScore"}}},
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]
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@staticmethod
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def _field_value(document: Mapping[str, object], dotted_path: str) -> str:
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current: object = document
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for segment in dotted_path.split("."):
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if not isinstance(current, Mapping):
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return ""
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current = current.get(segment)
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return "" if current is None else str(current)
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@classmethod
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def _to_result(cls, document: Mapping[str, object], text_field: str) -> VectorStoreSearchResult:
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document_id: Final = document.get("_id")
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identifier: Final = None if document_id is None else str(document_id)
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content: Final = [ # mutable-ok: VectorStoreSearchResult declares a list of content parts
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VectorStoreResultContent(text=cls._field_value(document, text_field), type="text")
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]
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raw_score: Final = document.get(SCORE_FIELD_NAME)
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return VectorStoreSearchResult(
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score=float(raw_score) if isinstance(raw_score, (int, float)) else None,
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content=content,
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file_id=identifier,
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filename=identifier,
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)
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@classmethod
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def _to_response(
|
||||
cls, documents: Sequence[Mapping[str, object]], query_text: str, text_field: str
|
||||
) -> VectorStoreSearchResponse:
|
||||
return VectorStoreSearchResponse(
|
||||
object="vector_store.search_results.page",
|
||||
search_query=query_text,
|
||||
data=[cls._to_result(document, text_field) for document in documents],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _embedding_vector(embedding_response: EmbeddingResponse) -> Sequence[float]:
|
||||
data: Final = embedding_response.data
|
||||
if not data:
|
||||
raise ValueError(
|
||||
"The embedding model returned no embedding for the search query, so there is nothing "
|
||||
"to search MongoDB with. Check the embedding deployment named by litellm_embedding_model."
|
||||
)
|
||||
return data[0]["embedding"]
|
||||
|
||||
def execute_search_vector_store_request(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
query: str | Sequence[str],
|
||||
vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
litellm_logging_obj: "LiteLLMLoggingObj",
|
||||
litellm_params: Mapping[str, object],
|
||||
timeout: float | httpx.Timeout | None = None,
|
||||
) -> VectorStoreSearchResponse:
|
||||
params: Final = _MongoDBSearchParams.model_validate(litellm_params)
|
||||
query_text: Final = self._query_text(query)
|
||||
key: Final = self._client_key(params, timeout)
|
||||
database: Final = params.require_database()
|
||||
collection: Final = params.require_collection()
|
||||
|
||||
embedding_response: Final = self.embedding_fn(
|
||||
model=params.require_embedding_model(),
|
||||
input=[query_text], # mutable-ok: litellm.embedding's input contract is a list
|
||||
**(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
|
||||
)
|
||||
pipeline: Final = self._pipeline(
|
||||
vector_store_id, self._embedding_vector(embedding_response), params, vector_store_search_optional_params
|
||||
)
|
||||
|
||||
client: Final = self.sync_client_factory(key)
|
||||
try:
|
||||
documents: Final = list(client[database][collection].aggregate(pipeline)) # pyright: ignore[reportIndexIssue] # factory is typed as returning object so injected doubles are accepted
|
||||
except Exception as e:
|
||||
raise translate_mongo_error(
|
||||
e, index_name=vector_store_id, database=database, collection=collection
|
||||
) from e
|
||||
return self._to_response(documents, query_text, params.text_field)
|
||||
|
||||
async def aexecute_search_vector_store_request(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
query: str | Sequence[str],
|
||||
vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
litellm_logging_obj: "LiteLLMLoggingObj",
|
||||
litellm_params: Mapping[str, object],
|
||||
timeout: float | httpx.Timeout | None = None,
|
||||
) -> VectorStoreSearchResponse:
|
||||
params: Final = _MongoDBSearchParams.model_validate(litellm_params)
|
||||
query_text: Final = self._query_text(query)
|
||||
key: Final = self._client_key(params, timeout)
|
||||
database: Final = params.require_database()
|
||||
collection: Final = params.require_collection()
|
||||
|
||||
embedding_response: Final = await self.aembedding_fn(
|
||||
model=params.require_embedding_model(),
|
||||
input=[query_text], # mutable-ok: litellm.embedding's input contract is a list
|
||||
**(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
|
||||
)
|
||||
pipeline: Final = self._pipeline(
|
||||
vector_store_id, self._embedding_vector(embedding_response), params, vector_store_search_optional_params
|
||||
)
|
||||
|
||||
client: Final = self.async_client_factory(key)
|
||||
try:
|
||||
cursor: Final = await client[database][collection].aggregate(pipeline) # pyright: ignore[reportIndexIssue] # factory is typed as returning object so injected doubles are accepted
|
||||
documents: Final = [document async for document in cursor]
|
||||
except Exception as e:
|
||||
raise translate_mongo_error(
|
||||
e, index_name=vector_store_id, database=database, collection=collection
|
||||
) from e
|
||||
return self._to_response(documents, query_text, params.text_field)
|
||||
|
||||
def transform_create_vector_store_request(
|
||||
self,
|
||||
vector_store_create_optional_params: VectorStoreCreateOptionalRequestParams,
|
||||
api_base: str,
|
||||
) -> NoReturn:
|
||||
raise NotImplementedError(_SEARCH_ONLY_MESSAGE)
|
||||
|
||||
def transform_create_vector_store_response(self, response: httpx.Response) -> NoReturn:
|
||||
raise NotImplementedError(_SEARCH_ONLY_MESSAGE)
|
||||
|
|
@ -3867,6 +3867,7 @@ class LlmProviders(str, Enum):
|
|||
PG_VECTOR = "pg_vector"
|
||||
S3_VECTORS = "s3_vectors"
|
||||
VALKEY = "valkey"
|
||||
MONGODB = "mongodb"
|
||||
HELICONE = "helicone"
|
||||
HYPERBOLIC = "hyperbolic"
|
||||
RECRAFT = "recraft"
|
||||
|
|
|
|||
|
|
@ -9022,6 +9022,12 @@ class ProviderConfigManager:
|
|||
)
|
||||
|
||||
return ValkeyVectorStoreConfig()
|
||||
elif litellm.LlmProviders.MONGODB == provider:
|
||||
from litellm.llms.mongodb.vector_stores.transformation import (
|
||||
MongoDBVectorStoreConfig,
|
||||
)
|
||||
|
||||
return MongoDBVectorStoreConfig()
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
|
|
|
|||
|
|
@ -112,6 +112,12 @@ utils = [
|
|||
]
|
||||
caching = ["diskcache>=5.6.3,<6.0"]
|
||||
mcp = ["mcp>=1.28.1,<2.0"]
|
||||
# Driver for the MongoDB Atlas vector store. Atlas Vector Search has no HTTP query
|
||||
# API, so that provider talks to the cluster over the wire protocol. Imported lazily
|
||||
# and kept out of the base install, which never needs a MongoDB driver. The floor is
|
||||
# 4.17 because that is where dnspython became a core dependency rather than the `srv`
|
||||
# extra, and Atlas hands out mongodb+srv:// URIs that do not resolve without it.
|
||||
mongodb = ["pymongo>=4.17,<5.0"]
|
||||
# SAML SSO for the admin UI. python3-saml pulls in xmlsec/lxml, whose wheels
|
||||
# bundle the native libxmlsec1/libxml2 libraries, so no system packages are
|
||||
# required. Kept out of the base `proxy` extra so it stays optional.
|
||||
|
|
|
|||
79
uv.lock
generated
79
uv.lock
generated
|
|
@ -10,7 +10,7 @@ resolution-markers = [
|
|||
]
|
||||
|
||||
[options]
|
||||
exclude-newer = "2026-08-29T17:58:57.633306Z"
|
||||
exclude-newer = "2026-08-30T07:50:56.793842Z"
|
||||
exclude-newer-span = "P3D"
|
||||
|
||||
[manifest]
|
||||
|
|
@ -4323,6 +4323,9 @@ mcp = [
|
|||
mlflow = [
|
||||
{ name = "mlflow" },
|
||||
]
|
||||
mongodb = [
|
||||
{ name = "pymongo" },
|
||||
]
|
||||
proxy = [
|
||||
{ name = "apscheduler" },
|
||||
{ name = "azure-identity" },
|
||||
|
|
@ -4551,6 +4554,7 @@ requires-dist = [
|
|||
{ name = "pydantic", specifier = ">=2.10.0,<3.0.0" },
|
||||
{ name = "pydantic-settings", specifier = ">=2.14.1,<3.0" },
|
||||
{ name = "pyjwt", marker = "extra == 'proxy'", specifier = ">=2.13.0,<3.0" },
|
||||
{ name = "pymongo", marker = "extra == 'mongodb'", specifier = ">=4.17,<5.0" },
|
||||
{ name = "pynacl", marker = "extra == 'proxy'", specifier = ">=1.6.2,<2.0" },
|
||||
{ name = "pypdf", marker = "extra == 'proxy-runtime'", specifier = ">=6.12.0,<7.0" },
|
||||
{ name = "pyroscope-io", marker = "sys_platform != 'win32' and extra == 'proxy'", specifier = ">=0.8.16,<1.0" },
|
||||
|
|
@ -4577,7 +4581,7 @@ requires-dist = [
|
|||
{ name = "uvloop", marker = "sys_platform != 'win32' and extra == 'proxy'", specifier = ">=0.21.0,<1.0" },
|
||||
{ name = "websockets", marker = "extra == 'proxy'", specifier = ">=15.0.1,<16.0" },
|
||||
]
|
||||
provides-extras = ["proxy", "cli", "extra-proxy", "utils", "caching", "mcp", "saml", "semantic-router", "mlflow", "grpc", "stt-nvidia-riva", "google", "bedrock-realtime", "proxy-runtime"]
|
||||
provides-extras = ["proxy", "cli", "extra-proxy", "utils", "caching", "mcp", "mongodb", "saml", "semantic-router", "mlflow", "grpc", "stt-nvidia-riva", "google", "bedrock-realtime", "proxy-runtime"]
|
||||
|
||||
[package.metadata.requires-dev]
|
||||
ci = [
|
||||
|
|
@ -7518,6 +7522,77 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/d5/6f/9ac2548e290764781f9e7e2aaf0685b086379dabfb29ca38536985471eaf/pylint-4.0.5-py3-none-any.whl", hash = "sha256:00f51c9b14a3b3ae08cff6b2cdd43f28165c78b165b628692e428fb1f8dc2cf2", size = 536694, upload-time = "2026-02-20T09:07:31.028Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pymongo"
|
||||
version = "4.17.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "dnspython" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ca/64/50be6fbac9c79fe2e4c17401a467da2d8764d82833d83cec325afe5cab32/pymongo-4.17.0.tar.gz", hash = "sha256:70ffa08ba641468cc068cf46c06b34f01a8ce3489f6411309fcb5ceabe6b2fc0", size = 2523370, upload-time = "2026-04-20T16:39:53.524Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/c9/77/28ebbf69772a4341d530831c7a006cdb06877ac23075cb53b0a227df4fe1/pymongo-4.17.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:47b021363cd923ace5edc7a1d63c0ff8a6d9d43859b8a1ba23645f5afae63221", size = 819234, upload-time = "2026-04-20T16:37:20.888Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/88/cf/5a70cee503ff9a2fea20607607f14d189f4d975960ac0945ec306ee7b695/pymongo-4.17.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:422fa50d7d7f5c22ea0953554396c9ef95684a2d775f860bd75a7b510538dfca", size = 819969, upload-time = "2026-04-20T16:37:24.187Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/23/d5/07b7e27e662c58d872efd104a0e8055eb6569aa1b6d4da436f3fdee7f897/pymongo-4.17.0-cp310-cp310-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:addd0498ebbdc6354227f6ed457ed9fce442d48a3bb30d5b5bad33e104996561", size = 1244510, upload-time = "2026-04-20T16:37:26.069Z" },
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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[[package]]
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||||
name = "pynacl"
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||||
version = "1.6.2"
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|
|
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|||
Loading…
Add table
Reference in a new issue