mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-09 22:31:41 +00:00
feat: move MongoDB vector search to an optional sidecar
This commit is contained in:
Yuneng Jiang
parent
9dbfb060bd
commit
5c037299f4
16 changed files with 412 additions and 2196 deletions
2
.github/workflows/_test-unit-base.yml
vendored
2
.github/workflows/_test-unit-base.yml
vendored
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@ -113,7 +113,7 @@ jobs:
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if: steps.changes.outputs.decision != 'skip'
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timeout-minutes: 8
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run: |
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.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra mongodb
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.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml
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uv run --no-sync python -c 'import os, sys; print(sys.version); assert f"{sys.version_info.major}.{sys.version_info.minor}" == os.environ["UV_PYTHON"]'
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- name: Cache Prisma binaries
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@ -67,7 +67,6 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr
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--extra semantic-router \
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--extra saml \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13
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# Copy full source tree
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@ -90,7 +89,6 @@ RUN uv sync --frozen --no-default-groups --no-editable \
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--extra semantic-router \
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--extra saml \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13
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RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \
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@ -65,7 +65,6 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr
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--extra semantic-router \
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--extra saml \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13
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# Copy full source tree
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@ -88,7 +87,6 @@ RUN uv sync --frozen --no-default-groups --no-editable \
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--extra semantic-router \
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--extra saml \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13
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RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \
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@ -71,7 +71,6 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \
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--extra semantic-router \
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--extra saml \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13
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# Copy full source tree
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@ -100,7 +99,6 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \
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--extra semantic-router \
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--extra saml \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13 \
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--no-sources-package litellm-proxy-extras; \
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else \
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@ -111,7 +109,6 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \
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--extra semantic-router \
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--extra saml \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13; \
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fi
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@ -47,7 +47,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
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--extra extra_proxy \
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--extra semantic-router \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13
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# Stage 2 — copy source and install the project + workspace members.
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@ -60,7 +59,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
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--extra extra_proxy \
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--extra semantic-router \
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--extra bedrock-realtime \
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--extra mongodb \
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--python python3.13
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RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \
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@ -121,6 +121,9 @@ class RouterVectorStoreEmbeddingExecutor:
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class BaseVectorStoreConfig:
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def validate_create_vector_store(self) -> None:
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return None
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def get_supported_openai_params(self, model: str) -> list[VECTOR_STORE_OPENAI_PARAMS]:
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return []
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@ -9814,7 +9814,7 @@ class BaseLLMHTTPHandler:
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vector_store_search_optional_params=vector_store_search_optional_params,
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api_base=api_base,
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litellm_logging_obj=logging_obj,
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litellm_params=dict(litellm_params),
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litellm_params={**dict(litellm_params), "timeout": timeout},
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extra_body=extra_body,
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embedding_executor=embedding_executor,
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)
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@ -9859,6 +9859,10 @@ class BaseLLMHTTPHandler:
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data=request_data,
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timeout=timeout,
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)
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except httpx.TimeoutException:
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raise vector_store_provider_config.get_error_class(
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error_message="Vector store search exceeded the caller timeout.", status_code=408, headers={}
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) from None
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except Exception as e:
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raise self._handle_error(e=e, provider_config=vector_store_provider_config)
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@ -9943,7 +9947,7 @@ class BaseLLMHTTPHandler:
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vector_store_search_optional_params=vector_store_search_optional_params,
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api_base=api_base,
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litellm_logging_obj=logging_obj,
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litellm_params=dict(litellm_params),
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litellm_params={**dict(litellm_params), "timeout": timeout},
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extra_body=extra_body,
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embedding_executor=embedding_executor,
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)
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@ -9988,7 +9992,12 @@ class BaseLLMHTTPHandler:
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url=url,
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headers=headers,
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data=request_data,
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timeout=timeout,
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)
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except httpx.TimeoutException:
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raise vector_store_provider_config.get_error_class(
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error_message="Vector store search exceeded the caller timeout.", status_code=408, headers={}
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) from None
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except Exception as e:
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raise self._handle_error(e=e, provider_config=vector_store_provider_config)
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@ -10018,6 +10027,8 @@ class BaseLLMHTTPHandler:
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else:
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async_httpx_client = client
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vector_store_provider_config.validate_create_vector_store()
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headers: Final = vector_store_provider_config.validate_environment(
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headers=extra_headers or {}, litellm_params=litellm_params
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)
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@ -10088,6 +10099,8 @@ class BaseLLMHTTPHandler:
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else:
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sync_httpx_client = client
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vector_store_provider_config.validate_create_vector_store()
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headers: Final = vector_store_provider_config.validate_environment(
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headers=extra_headers or {}, litellm_params=litellm_params
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)
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@ -1,303 +0,0 @@
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"""Shared helpers for the MongoDB integrations. pymongo lives in the optional ``mongodb`` extra,
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so every import of it is deferred to call time."""
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import asyncio
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import threading
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import weakref
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from asyncio import AbstractEventLoop
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from collections import OrderedDict
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from collections.abc import Callable, Mapping
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from dataclasses import dataclass
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from types import MappingProxyType
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from typing import TYPE_CHECKING, Final, TypeAlias, TypeVar
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from litellm.exceptions import BadRequestError, ServiceUnavailableError, Timeout
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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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MONGODB_PROVIDER: Final = "mongodb"
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def config_error(message: str) -> BadRequestError:
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"""400 rather than the 500 a bare ValueError becomes once litellm.exception_type wraps it."""
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return BadRequestError(message=message, model=None, llm_provider=MONGODB_PROVIDER)
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def timeout_error(message: str) -> Timeout:
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return Timeout(message=message, model=None, llm_provider=MONGODB_PROVIDER)
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def unavailable_error(message: str) -> ServiceUnavailableError:
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"""litellm only retries 408, 409, 429 and 5xx, so a 400 here would make a failover permanent."""
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return ServiceUnavailableError(message=message, model=None, llm_provider=MONGODB_PROVIDER)
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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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SyncClientFactory: TypeAlias = Callable[..., "MongoClient"]
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AsyncClientFactory: TypeAlias = Callable[..., "AsyncMongoClient"]
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_K = TypeVar("_K")
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_V = TypeVar("_V")
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_AsyncClientCacheKey: TypeAlias = tuple[MongoClientKey, int]
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# CPython recycles id() aggressively, so the id alone would hand a new loop a closed loop's client
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_AsyncClientEntry: TypeAlias = tuple["weakref.ref[AbstractEventLoop]", "AsyncMongoClient"]
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_SyncClientCache: TypeAlias = "OrderedDict[MongoClientKey, MongoClient]"
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_AsyncClientCache: TypeAlias = "OrderedDict[_AsyncClientCacheKey, _AsyncClientEntry]"
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_sync_clients: Final[_SyncClientCache] = OrderedDict() # mutable-ok: process-level client cache
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_async_clients: Final[_AsyncClientCache] = OrderedDict() # mutable-ok: same cache, per loop
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# async searches reach the sync client through executor threads, so both caches are shared state
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_cache_lock: Final = threading.Lock()
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def _store_bounded(cache: "OrderedDict[_K, _V]", cache_key: "_K", value: "_V") -> None:
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"""Eviction only drops this cache's reference; an in-flight search keeps its client alive."""
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with _cache_lock:
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cache[cache_key] = value # mutable-ok: an LRU cache is mutable state by definition
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cache.move_to_end(cache_key)
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while len(cache) > _MAX_CACHED_CLIENTS:
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cache.popitem(last=False)
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def _mark_used(cache: "OrderedDict[_K, _V]", cache_key: "_K") -> None:
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with _cache_lock:
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if cache_key in cache:
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cache.move_to_end(cache_key)
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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 config_error(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 config_error(PYMONGO_INSTALL_HINT) from e
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return AsyncMongoClientClass
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def _client_kwargs(key: MongoClientKey) -> Mapping[str, object]:
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return MappingProxyType(
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{
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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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)
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def get_sync_client(key: MongoClientKey, client_class: SyncClientFactory | None = None) -> "MongoClient":
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cached: Final = _sync_clients.get(key)
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if cached is not None:
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_mark_used(_sync_clients, key)
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return cached
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build: Final = client_class if client_class is not None else import_sync_mongo_client()
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client: Final = build(key.connection_string, **_client_kwargs(key))
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_store_bounded(_sync_clients, key, client)
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return client
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def _purge_dead_loops() -> None:
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"""A cached client holds its loop alive, so a closed loop's entry would pin that client and its
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sockets for the life of the process."""
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with _cache_lock:
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for stale in tuple(
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cache_key
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for cache_key, (loop_ref, _) in _async_clients.items()
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if (cached_loop := loop_ref()) is None or cached_loop.is_closed()
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):
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del _async_clients[stale]
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def get_async_client(key: MongoClientKey, client_class: AsyncClientFactory | None = None) -> "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: Final = asyncio.get_running_loop()
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loop_key: Final = (key, id(loop))
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cached: Final = _async_clients.get(loop_key)
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if cached is not None and cached[0]() is loop:
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_mark_used(_async_clients, loop_key)
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return cached[1]
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_purge_dead_loops()
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build: Final = client_class if client_class is not None else import_async_mongo_client()
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client: Final = build(key.connection_string, **_client_kwargs(key))
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_store_bounded(_async_clients, loop_key, (weakref.ref(loop), client))
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return client
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def reset_client_cache() -> None:
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with _cache_lock:
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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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# Atlas reports a rejected user as code 8000 "AtlasError" where a self-managed mongod reports 18
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_AUTHENTICATION_MESSAGE_MARKERS: Final = ("bad auth", "authentication failed", "not authorized")
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_RESOLUTION_TIMEOUT_MARKERS: Final = ("resolution lifetime expired", "dns operation timed out")
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_UNKNOWN_HOSTNAME_MARKERS: Final = ("dns query name does not exist", "name or service not known")
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_CREDENTIAL_ESCAPING_MARKERS: Final = ("must be escaped according to rfc 3986", "bad database name")
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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 MongoDB 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 missing_index_error(index_name: str, database: str, collection: str) -> BadRequestError:
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"""$vectorSearch against a missing index, database or collection returns zero documents rather
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than failing, so an empty result set is checked against the catalogue and reported as this."""
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return config_error(
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f"{_index_hint(index_name, database, collection)} A vector search against a database, "
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"collection or index that does not exist returns no results rather than an error, so this "
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"was reported as an empty result set by MongoDB."
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)
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def index_not_ready_error(index_name: str, database: str, collection: str, status: str) -> BadRequestError:
|
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return config_error(
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f"The MongoDB Vector Search index '{index_name}' on '{database}.{collection}' is not queryable "
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f"yet; its status is {status}. Searches against it return no results until the build finishes."
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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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"""Returns the exception to raise, so callers keep the driver error as ``__cause__``."""
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try:
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from pymongo.errors import (
|
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ConfigurationError,
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ConnectionFailure,
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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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|
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if isinstance(error, ServerSelectionTimeoutError):
|
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return timeout_error(
|
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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. On a self-managed "
|
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"deployment it is usually the host or port in the URI, or a firewall between this process "
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f"and mongod. Either way it can also be an unresolvable hostname. Driver detail: {error}"
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)
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# ExecutionTimeout subclasses OperationFailure, so it has to be matched before it
|
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if isinstance(error, (NetworkTimeout, ExecutionTimeout)):
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return timeout_error(
|
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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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# ServerSelectionTimeoutError and NetworkTimeout also subclass ConnectionFailure, so this only
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# sees what those branches left
|
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if isinstance(error, ConnectionFailure):
|
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return unavailable_error(
|
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f"The connection to '{database}.{collection}' was dropped or refused. That is usually a "
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"replica set failover or a restarted node, so the search is worth retrying. If it keeps "
|
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"happening: on Atlas the usual cause is a connection string with no username and password, "
|
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"or a TLS failure, so confirm the URI is the one Atlas shows under Connect, Drivers; on a "
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"self-managed deployment, check that mongod is listening on the host and port in the URI. "
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f"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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detail: Final = str(error).lower()
|
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if code in (_AUTHENTICATION_FAILED_CODE, _UNAUTHORIZED_CODE) or any(
|
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marker in detail for marker in _AUTHENTICATION_MESSAGE_MARKERS
|
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):
|
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return config_error(
|
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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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if "dimension" in detail:
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return config_error(
|
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"The query embedding does not match the vector dimensions the 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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if "is not indexed as vector" in detail:
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return config_error(
|
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"mongodb_embedding_field names a field the MongoDB Vector Search index does not cover. "
|
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f"It must match the 'path' the index '{index_name}' was created on. Driver detail: {error}"
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)
|
||||
if "index" in detail and ("not found" in detail or "does not exist" in detail or "unknown" in detail):
|
||||
return config_error(f"{_index_hint(index_name, database, collection)} Driver detail: {error}")
|
||||
return config_error(
|
||||
f"MongoDB rejected the vector search against '{database}.{collection}' using index "
|
||||
f"'{index_name}'. Driver detail: {error}"
|
||||
)
|
||||
if isinstance(error, ConfigurationError):
|
||||
configuration_detail: Final = str(error).lower()
|
||||
if any(marker in configuration_detail for marker in _RESOLUTION_TIMEOUT_MARKERS):
|
||||
return timeout_error(
|
||||
"The DNS lookup for the cluster in mongodb_connection_string did not finish in time. "
|
||||
"A mongodb+srv:// URI needs an SRV lookup before any connection is attempted, so this "
|
||||
f"is DNS or the configured timeout, not MongoDB. Driver detail: {error}"
|
||||
)
|
||||
if any(marker in configuration_detail for marker in _UNKNOWN_HOSTNAME_MARKERS):
|
||||
return config_error(
|
||||
"The hostname in mongodb_connection_string does not exist in DNS. On Atlas, check the "
|
||||
"cluster name against the URI shown under Connect, Drivers. On a self-managed deployment, "
|
||||
f"check that the hostname resolves from this process. Driver detail: {error}"
|
||||
)
|
||||
if any(marker in configuration_detail for marker in _CREDENTIAL_ESCAPING_MARKERS):
|
||||
return config_error(
|
||||
"mongodb_connection_string could not be parsed. A username or password containing "
|
||||
"'@', '/', ':' or '%' has to be percent-encoded per RFC 3986, so 'p@ss/word' becomes "
|
||||
"'p%40ss%2Fword'. If the credentials are already encoded, check the database name in "
|
||||
f"the URI path instead. Driver detail: {error}"
|
||||
)
|
||||
return config_error(
|
||||
f"mongodb_connection_string is not a usable MongoDB connection string. Driver detail: {error}"
|
||||
)
|
||||
if isinstance(error, InvalidOperation):
|
||||
return config_error(f"The MongoDB client was already closed or is unusable. Driver detail: {error}")
|
||||
# An unreadable tlsCAFile or tlsCertificateKeyFile raises OSError, not a PyMongoError
|
||||
if isinstance(error, OSError) and error.filename:
|
||||
return config_error(
|
||||
f"'{error.filename}', named by a TLS option in mongodb_connection_string, could not be read. "
|
||||
"Check that tlsCAFile and tlsCertificateKeyFile point at files this process can open; inside "
|
||||
f"a container that is the path in the container, not on the host. Driver detail: {error}"
|
||||
)
|
||||
# pymongo raises a plain ValueError, not a PyMongoError, for an unusable port
|
||||
if isinstance(error, ValueError):
|
||||
return config_error(
|
||||
"The host and port in mongodb_connection_string could not be parsed. If the port is a "
|
||||
"number between 0 and 65535, the cause is usually an unescaped ':' in the password, which "
|
||||
f"has to be percent-encoded per RFC 3986 as '%3A'. Driver detail: {error}"
|
||||
)
|
||||
return error
|
||||
|
|
@ -1,37 +1,28 @@
|
|||
"""MongoDB Vector Search has no HTTP query API, so this is a direct provider that runs the
|
||||
``$vectorSearch`` aggregation through pymongo. ``vector_store_id`` is the search index name."""
|
||||
|
||||
from collections.abc import Callable, Mapping, Sequence
|
||||
from collections.abc import Mapping, Sequence
|
||||
from math import isfinite
|
||||
from types import MappingProxyType
|
||||
from typing import TYPE_CHECKING, Final, NoReturn
|
||||
from typing import TYPE_CHECKING, Final, Literal, NoReturn
|
||||
from urllib.parse import quote, urlsplit
|
||||
|
||||
import httpx
|
||||
from pydantic import BaseModel, ConfigDict
|
||||
from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError
|
||||
|
||||
from litellm.exceptions import AuthenticationError, BadRequestError, ServiceUnavailableError, Timeout
|
||||
from litellm.llms.base_llm.chat.transformation import BaseLLMException
|
||||
from litellm.llms.base_llm.vector_store.transformation import (
|
||||
BaseDirectVectorStoreConfig,
|
||||
BaseQueryEmbeddingVectorStoreConfig,
|
||||
LiteLLMVectorStoreEmbeddingExecutor,
|
||||
VectorStoreEmbeddingExecutor,
|
||||
)
|
||||
from litellm.llms.mongodb.common_utils import (
|
||||
DEFAULT_CONNECT_TIMEOUT_MS,
|
||||
DEFAULT_SERVER_SELECTION_TIMEOUT_MS,
|
||||
DEFAULT_SOCKET_TIMEOUT_MS,
|
||||
MongoClientKey,
|
||||
config_error,
|
||||
get_async_client,
|
||||
get_sync_client,
|
||||
index_not_ready_error,
|
||||
missing_index_error,
|
||||
translate_mongo_error,
|
||||
)
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
from litellm.types.utils import EmbeddingResponse
|
||||
from litellm.types.vector_stores import (
|
||||
BaseVectorStoreAuthCredentials,
|
||||
VectorStoreCreateOptionalRequestParams,
|
||||
VectorStoreResultContent,
|
||||
VectorStoreIndexEndpoints,
|
||||
VectorStoreSearchOptionalRequestParams,
|
||||
VectorStoreSearchResponse,
|
||||
VectorStoreSearchResult,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
|
|
@ -39,26 +30,45 @@ if TYPE_CHECKING:
|
|||
|
||||
DEFAULT_EMBEDDING_FIELD_NAME: Final = "embedding"
|
||||
DEFAULT_TEXT_FIELD_NAME: Final = "text"
|
||||
SCORE_FIELD_NAME: Final = "score"
|
||||
|
||||
DEFAULT_MAX_NUM_RESULTS: Final = 10
|
||||
MIN_MAX_NUM_RESULTS: Final = 1
|
||||
MAX_MAX_NUM_RESULTS: Final = 50
|
||||
|
||||
NUM_CANDIDATES_MULTIPLIER: Final = 10
|
||||
MIN_NUM_CANDIDATES: Final = 100
|
||||
MAX_NUM_CANDIDATES: Final = 10_000
|
||||
|
||||
MAX_QUERY_CHARACTERS: Final = 32_000
|
||||
|
||||
_EMPTY_EMBEDDING_CONFIG: Final = MappingProxyType({})
|
||||
|
||||
_SEARCH_ONLY_MESSAGE: Final = (
|
||||
"MongoDB vector store is search-only. Create the collection and its MongoDB Vector Search "
|
||||
"index in MongoDB directly, then register it here by index name."
|
||||
)
|
||||
|
||||
|
||||
def config_error(message: str) -> BadRequestError:
|
||||
return BadRequestError(message=message, model=None, llm_provider="mongodb")
|
||||
|
||||
|
||||
class _Content(BaseModel):
|
||||
model_config = ConfigDict(frozen=True, strict=True)
|
||||
type: Literal["text"]
|
||||
text: str
|
||||
|
||||
|
||||
class _Result(BaseModel):
|
||||
model_config = ConfigDict(frozen=True, strict=True, allow_inf_nan=False)
|
||||
score: float | None
|
||||
content: list[_Content]
|
||||
file_id: str | None
|
||||
filename: str | None
|
||||
|
||||
|
||||
class _SearchResponse(BaseModel):
|
||||
model_config = ConfigDict(frozen=True, strict=True)
|
||||
object: Literal["vector_store.search_results.page"]
|
||||
search_query: str
|
||||
data: list[_Result]
|
||||
|
||||
|
||||
class _MongoDBSearchParams(BaseModel):
|
||||
"""Typed view over the vector store's litellm_params; unrelated keys are ignored."""
|
||||
|
||||
|
|
@ -66,7 +76,6 @@ class _MongoDBSearchParams(BaseModel):
|
|||
|
||||
litellm_embedding_model: str | None = None
|
||||
litellm_embedding_config: Mapping[str, object] | None = None
|
||||
mongodb_connection_string: str | None = None
|
||||
mongodb_database: str | None = None
|
||||
mongodb_collection: str | None = None
|
||||
mongodb_text_field: str | None = None
|
||||
|
|
@ -91,21 +100,6 @@ class _MongoDBSearchParams(BaseModel):
|
|||
)
|
||||
return self.litellm_embedding_model
|
||||
|
||||
def require_connection_string(self) -> str:
|
||||
if not self.mongodb_connection_string:
|
||||
raise config_error(
|
||||
"mongodb_connection_string is required in litellm_params for the MongoDB vector store. "
|
||||
"Example: mongodb+srv://<user>:<password>@<cluster>.mongodb.net for Atlas, or "
|
||||
"mongodb://<user>:<password>@<host>:27017 for a self-managed deployment"
|
||||
)
|
||||
scheme: Final = self.mongodb_connection_string.split("://", 1)[0].lower()
|
||||
if scheme not in ("mongodb", "mongodb+srv"):
|
||||
raise config_error(
|
||||
"mongodb_connection_string must start with 'mongodb://' or 'mongodb+srv://', "
|
||||
f"got '{self.mongodb_connection_string.split('://', 1)[0]}://'"
|
||||
)
|
||||
return self.mongodb_connection_string
|
||||
|
||||
def require_database(self) -> str:
|
||||
if not self.mongodb_database:
|
||||
raise config_error(
|
||||
|
|
@ -127,30 +121,28 @@ _MONGODB_PARAM_PREFIX: Final = "mongodb_"
|
|||
_KNOWN_MONGODB_PARAMS: Final = frozenset(
|
||||
name for name in _MongoDBSearchParams.model_fields if name.startswith(_MONGODB_PARAM_PREFIX)
|
||||
)
|
||||
_RESPONSE_ADAPTER: Final = TypeAdapter(VectorStoreSearchResponse)
|
||||
|
||||
|
||||
class MongoDBVectorStoreConfig(BaseDirectVectorStoreConfig):
|
||||
def __init__(
|
||||
self,
|
||||
embedding_executor: VectorStoreEmbeddingExecutor | None = None,
|
||||
sync_client_factory: Callable[[MongoClientKey], object] | None = None,
|
||||
async_client_factory: Callable[[MongoClientKey], object] | None = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.embedding_executor: Final[VectorStoreEmbeddingExecutor] = (
|
||||
embedding_executor if embedding_executor is not None else LiteLLMVectorStoreEmbeddingExecutor()
|
||||
)
|
||||
self.sync_client_factory: Final[Callable[[MongoClientKey], object]] = (
|
||||
sync_client_factory if sync_client_factory is not None else get_sync_client
|
||||
)
|
||||
self.async_client_factory: Final[Callable[[MongoClientKey], object]] = (
|
||||
async_client_factory if async_client_factory is not None else get_async_client
|
||||
)
|
||||
class MongoDBVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig):
|
||||
def __init__(self, embedding_executor: VectorStoreEmbeddingExecutor | None = None) -> None:
|
||||
self.embedding_executor: Final = embedding_executor or LiteLLMVectorStoreEmbeddingExecutor()
|
||||
|
||||
def get_auth_credentials(self, litellm_params: Mapping[str, object]) -> BaseVectorStoreAuthCredentials:
|
||||
return BaseVectorStoreAuthCredentials()
|
||||
|
||||
def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
|
||||
return VectorStoreIndexEndpoints(read=[], write=[]) # mutable-ok: the TypedDict declares list fields
|
||||
|
||||
@staticmethod
|
||||
def _reject_unknown_params(litellm_params: Mapping[str, object]) -> None:
|
||||
"""Without this a mistyped mongodb_collection reads as 'mongodb_collection is required',
|
||||
naming a key the reader can see they have set."""
|
||||
if litellm_params.get("mongodb_connection_string") is not None:
|
||||
raise config_error(
|
||||
"MongoDB vector stores now use the BETA sidecar. Move mongodb_connection_string to "
|
||||
"MONGODB_CONNECTION_STRING in the sidecar, remove it from LiteLLM, and configure api_base and api_key."
|
||||
)
|
||||
unknown: Final = sorted(
|
||||
key for key in litellm_params if key.startswith(_MONGODB_PARAM_PREFIX) and key not in _KNOWN_MONGODB_PARAMS
|
||||
)
|
||||
|
|
@ -191,239 +183,182 @@ class MongoDBVectorStoreConfig(BaseDirectVectorStoreConfig):
|
|||
return configured
|
||||
return min(max(limit * NUM_CANDIDATES_MULTIPLIER, MIN_NUM_CANDIDATES), MAX_NUM_CANDIDATES)
|
||||
|
||||
@staticmethod
|
||||
def _timeout_ms(timeout: float | httpx.Timeout | None) -> tuple[int, int]:
|
||||
"""The connect and socket budgets pymongo is built with, in that order."""
|
||||
if isinstance(timeout, httpx.Timeout):
|
||||
return (
|
||||
int((timeout.connect or DEFAULT_CONNECT_TIMEOUT_MS / 1000) * 1000),
|
||||
int((timeout.read or DEFAULT_SOCKET_TIMEOUT_MS / 1000) * 1000),
|
||||
def validate_environment(
|
||||
self, headers: dict[str, object], litellm_params: GenericLiteLLMParams | None
|
||||
) -> dict[str, object]:
|
||||
if litellm_params is None:
|
||||
raise config_error("Configure api_base and api_key for the MongoDB BETA sidecar.")
|
||||
self._reject_unknown_params(dict(litellm_params))
|
||||
api_key: Final = litellm_params.api_key or get_secret_str("MONGODB_SIDECAR_API_KEY")
|
||||
if not api_key:
|
||||
raise config_error("MongoDB sidecar api_key is required. Set api_key or MONGODB_SIDECAR_API_KEY.")
|
||||
return {**headers, "Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
|
||||
def get_complete_url(self, api_base: str | None, litellm_params: dict[str, object]) -> str:
|
||||
if not api_base:
|
||||
raise config_error("MongoDB sidecar api_base is required, for example http://mongodb-sidecar:8080.")
|
||||
try:
|
||||
parsed: Final = urlsplit(api_base)
|
||||
valid: Final = parsed.scheme in ("http", "https") and bool(parsed.hostname) and parsed.port != 0
|
||||
except ValueError:
|
||||
raise config_error("MongoDB sidecar api_base must be a valid HTTP or HTTPS URL.") from None
|
||||
if not valid or parsed.username or parsed.password or parsed.query or parsed.fragment:
|
||||
raise config_error(
|
||||
"MongoDB sidecar api_base must be an HTTP or HTTPS URL without credentials, query, or fragment."
|
||||
)
|
||||
if timeout is None:
|
||||
return DEFAULT_CONNECT_TIMEOUT_MS, DEFAULT_SOCKET_TIMEOUT_MS
|
||||
return min(int(float(timeout) * 1000), DEFAULT_CONNECT_TIMEOUT_MS), int(float(timeout) * 1000)
|
||||
return api_base.rstrip("/")
|
||||
|
||||
@staticmethod
|
||||
def _timeout_ms(value: object) -> int:
|
||||
seconds: Final = value.read if isinstance(value, httpx.Timeout) else value
|
||||
if seconds is None:
|
||||
return 30_000
|
||||
if not isinstance(seconds, (int, float)) or not isfinite(seconds) or seconds <= 0:
|
||||
raise config_error("MongoDB search timeout must be a positive finite number.")
|
||||
return max(1, min(int(seconds * 1000), 30_000))
|
||||
|
||||
@classmethod
|
||||
def _client_key(cls, params: _MongoDBSearchParams, timeout: float | httpx.Timeout | None) -> MongoClientKey:
|
||||
connect_ms, socket_ms = cls._timeout_ms(timeout)
|
||||
return MongoClientKey(
|
||||
connection_string=params.require_connection_string(),
|
||||
connect_timeout_ms=connect_ms,
|
||||
socket_timeout_ms=socket_ms,
|
||||
server_selection_timeout_ms=min(connect_ms, DEFAULT_SERVER_SELECTION_TIMEOUT_MS),
|
||||
)
|
||||
def _params(
|
||||
cls,
|
||||
litellm_params: Mapping[str, object],
|
||||
optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
extra_body: Mapping[str, object] | None,
|
||||
) -> _MongoDBSearchParams:
|
||||
cls._reject_unknown_params(litellm_params)
|
||||
if extra_body:
|
||||
raise config_error("MongoDB vector store does not support extra_body overrides.")
|
||||
for unsupported in ("filters", "ranking_options", "rewrite_query"):
|
||||
if optional_params.get(unsupported) is not None:
|
||||
raise config_error(f"MongoDB vector store does not support the {unsupported} parameter.")
|
||||
try:
|
||||
params: Final = _MongoDBSearchParams.model_validate(litellm_params)
|
||||
except ValidationError:
|
||||
raise config_error(
|
||||
"Invalid MongoDB vector-store configuration. Check the database, collection, fields, and candidate count."
|
||||
) from None
|
||||
params.require_database()
|
||||
params.require_collection()
|
||||
params.require_embedding_model()
|
||||
cls._num_candidates(cls._limit(optional_params), params.mongodb_num_candidates)
|
||||
cls._timeout_ms(litellm_params.get("timeout"))
|
||||
return params
|
||||
|
||||
@classmethod
|
||||
def _pipeline(
|
||||
def _request(
|
||||
cls,
|
||||
vector_store_id: str,
|
||||
query_vector: Sequence[float],
|
||||
query_text: str,
|
||||
params: _MongoDBSearchParams,
|
||||
vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
) -> Sequence[Mapping[str, object]]:
|
||||
if vector_store_search_optional_params.get("filters") is not None:
|
||||
optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
api_base: str,
|
||||
embedding_response: EmbeddingResponse,
|
||||
timeout: object,
|
||||
) -> tuple[str, dict[str, object]]:
|
||||
if not embedding_response.data:
|
||||
raise config_error(
|
||||
"MongoDB vector store does not support the filters parameter yet. "
|
||||
"Restrict the collection or the MongoDB Vector Search index definition instead."
|
||||
"The embedding model returned no embedding for the search query. Check litellm_embedding_model."
|
||||
)
|
||||
if vector_store_search_optional_params.get("ranking_options") is not None:
|
||||
raise config_error(
|
||||
"MongoDB vector store does not support the ranking_options parameter yet. "
|
||||
"Every result already carries the vectorSearchScore, so filter or re-rank "
|
||||
"on that rather than having the threshold silently ignored."
|
||||
)
|
||||
if vector_store_search_optional_params.get("rewrite_query") is not None:
|
||||
raise config_error(
|
||||
"MongoDB vector store does not support the rewrite_query parameter. The query is "
|
||||
"embedded exactly as sent; rewrite it before calling if you need that."
|
||||
)
|
||||
limit: Final = cls._limit(vector_store_search_optional_params)
|
||||
search: Final = MappingProxyType(
|
||||
{
|
||||
"index": vector_store_id,
|
||||
"path": params.embedding_field,
|
||||
"queryVector": tuple(query_vector),
|
||||
"numCandidates": cls._num_candidates(limit, params.mongodb_num_candidates),
|
||||
"limit": limit,
|
||||
}
|
||||
)
|
||||
projection: Final = MappingProxyType(
|
||||
{params.text_field: 1, SCORE_FIELD_NAME: MappingProxyType({"$meta": "vectorSearchScore"})}
|
||||
)
|
||||
return [ # mutable-ok: pymongo rejects any non-list pipeline in common.validate_list
|
||||
MappingProxyType({"$vectorSearch": search}),
|
||||
MappingProxyType({"$project": projection}),
|
||||
]
|
||||
vector: Final = embedding_response.data[0]["embedding"]
|
||||
if not vector or any(not isinstance(value, (float, int)) or not isfinite(value) for value in vector):
|
||||
raise config_error("The embedding model must return a non-empty, finite query vector.")
|
||||
limit: Final = cls._limit(optional_params)
|
||||
return f"{api_base}/v1/vector_stores/{quote(vector_store_id, safe='')}/search", {
|
||||
"query": query_text,
|
||||
"query_vector": tuple(vector),
|
||||
"mongodb_database": params.require_database(),
|
||||
"mongodb_collection": params.require_collection(),
|
||||
"mongodb_embedding_field": params.embedding_field,
|
||||
"mongodb_text_field": params.text_field,
|
||||
"mongodb_num_candidates": cls._num_candidates(limit, params.mongodb_num_candidates),
|
||||
"max_num_results": limit,
|
||||
"timeout_ms": cls._timeout_ms(timeout),
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def _field_value(cls, document: Mapping[str, object], dotted_path: str) -> str | None:
|
||||
"""None means absent, which is what separates a mistyped field from genuinely empty text."""
|
||||
head, _, rest = dotted_path.partition(".")
|
||||
if head not in document:
|
||||
return None
|
||||
value: Final = document[head]
|
||||
if not rest:
|
||||
return None if value is None else str(value)
|
||||
return cls._field_value(value, rest) if isinstance(value, Mapping) else None
|
||||
|
||||
@classmethod
|
||||
def _to_result(cls, document: Mapping[str, object], text_field: str) -> VectorStoreSearchResult:
|
||||
document_id: Final = document.get("_id")
|
||||
identifier: Final = None if document_id is None else str(document_id)
|
||||
content: Final = [ # mutable-ok: VectorStoreSearchResult declares a list of content parts
|
||||
VectorStoreResultContent(text=cls._field_value(document, text_field) or "", type="text")
|
||||
]
|
||||
raw_score: Final = document.get(SCORE_FIELD_NAME)
|
||||
return VectorStoreSearchResult(
|
||||
score=float(raw_score) if isinstance(raw_score, (int, float)) else None,
|
||||
content=content,
|
||||
file_id=identifier,
|
||||
filename=identifier,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _raise_for_missing_text_field(
|
||||
cls, documents: Sequence[Mapping[str, object]], text_field: str, database: str, collection: str
|
||||
) -> None:
|
||||
"""$vectorSearch matches documents carrying no text, so a mistyped mongodb_text_field
|
||||
returns well-scored results with empty content instead of failing."""
|
||||
if documents and all(cls._field_value(document, text_field) is None for document in documents):
|
||||
raise config_error(
|
||||
f"None of the {len(documents)} matched documents in '{database}.{collection}' has a "
|
||||
f"'{text_field}' field, so every result would carry empty text. Set mongodb_text_field "
|
||||
"to the field holding the readable text; it accepts a dotted path such as metadata.body."
|
||||
)
|
||||
|
||||
@classmethod
|
||||
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=[ # mutable-ok: VectorStoreSearchResponse declares data as a list
|
||||
cls._to_result(document, text_field) for document in documents
|
||||
],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _raise_for_unusable_index(
|
||||
catalogue: Sequence[Mapping[str, object]], index_name: str, database: str, collection: str
|
||||
) -> None:
|
||||
"""mongod returns zero documents both for a query that matched nothing and for a missing
|
||||
database, collection or index, so the catalogue decides which one happened."""
|
||||
if not catalogue:
|
||||
raise missing_index_error(index_name, database, collection)
|
||||
entry: Final = catalogue[0]
|
||||
if not entry.get("queryable"):
|
||||
raise index_not_ready_error(index_name, database, collection, str(entry.get("status") or "unknown"))
|
||||
|
||||
@staticmethod
|
||||
def _embedding_vector(embedding_response: EmbeddingResponse) -> Sequence[float]:
|
||||
data: Final = embedding_response.data
|
||||
if not data:
|
||||
raise config_error(
|
||||
"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(
|
||||
def transform_search_vector_store_request(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
query: str | Sequence[str],
|
||||
vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
api_base: str,
|
||||
litellm_logging_obj: "LiteLLMLoggingObj",
|
||||
litellm_params: Mapping[str, object],
|
||||
extra_body: Mapping[str, object] | None = None,
|
||||
embedding_executor: VectorStoreEmbeddingExecutor | None = None,
|
||||
timeout: float | httpx.Timeout | None = None,
|
||||
) -> VectorStoreSearchResponse:
|
||||
self._reject_unknown_params(litellm_params)
|
||||
params: Final = _MongoDBSearchParams.model_validate(litellm_params)
|
||||
) -> tuple[str, dict[str, object]]:
|
||||
params: Final = self._params(litellm_params, vector_store_search_optional_params, extra_body)
|
||||
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 = (embedding_executor or self.embedding_executor).embed(
|
||||
params.require_embedding_model(),
|
||||
response: Final = (embedding_executor or self.embedding_executor).embed(
|
||||
params.require_embedding_model(), query_text, params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG
|
||||
)
|
||||
return self._request(
|
||||
vector_store_id,
|
||||
query_text,
|
||||
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
|
||||
params,
|
||||
vector_store_search_optional_params,
|
||||
api_base,
|
||||
response,
|
||||
litellm_params.get("timeout"),
|
||||
)
|
||||
|
||||
try:
|
||||
client: Final = self.sync_client_factory(key)
|
||||
target: Final = client[database][collection] # pyright: ignore[reportIndexIssue] # factory is typed as returning object so injected doubles are accepted
|
||||
documents: Final = tuple(target.aggregate(pipeline))
|
||||
except Exception as e:
|
||||
raise translate_mongo_error(e, index_name=vector_store_id, database=database, collection=collection) from e
|
||||
if not documents:
|
||||
try:
|
||||
catalogue: Final = tuple(target.list_search_indexes(vector_store_id))
|
||||
except Exception as e:
|
||||
raise translate_mongo_error(
|
||||
e, index_name=vector_store_id, database=database, collection=collection
|
||||
) from e
|
||||
self._raise_for_unusable_index(catalogue, vector_store_id, database, collection)
|
||||
self._raise_for_missing_text_field(documents, params.text_field, database, collection)
|
||||
return self._to_response(documents, query_text, params.text_field)
|
||||
|
||||
async def aexecute_search_vector_store_request(
|
||||
async def atransform_search_vector_store_request(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
query: str | Sequence[str],
|
||||
vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
|
||||
api_base: str,
|
||||
litellm_logging_obj: "LiteLLMLoggingObj",
|
||||
litellm_params: Mapping[str, object],
|
||||
extra_body: Mapping[str, object] | None = None,
|
||||
embedding_executor: VectorStoreEmbeddingExecutor | None = None,
|
||||
timeout: float | httpx.Timeout | None = None,
|
||||
) -> VectorStoreSearchResponse:
|
||||
self._reject_unknown_params(litellm_params)
|
||||
params: Final = _MongoDBSearchParams.model_validate(litellm_params)
|
||||
) -> tuple[str, dict[str, object]]:
|
||||
params: Final = self._params(litellm_params, vector_store_search_optional_params, extra_body)
|
||||
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 (embedding_executor or self.embedding_executor).aembed(
|
||||
params.require_embedding_model(),
|
||||
response: Final = await (embedding_executor or self.embedding_executor).aembed(
|
||||
params.require_embedding_model(), query_text, params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG
|
||||
)
|
||||
return self._request(
|
||||
vector_store_id,
|
||||
query_text,
|
||||
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
|
||||
params,
|
||||
vector_store_search_optional_params,
|
||||
api_base,
|
||||
response,
|
||||
litellm_params.get("timeout"),
|
||||
)
|
||||
|
||||
def transform_search_vector_store_response(
|
||||
self, response: httpx.Response, litellm_logging_obj: "LiteLLMLoggingObj"
|
||||
) -> VectorStoreSearchResponse:
|
||||
try:
|
||||
client: Final = self.async_client_factory(key)
|
||||
target: Final = client[database][collection] # pyright: ignore[reportIndexIssue] # factory is typed as returning object so injected doubles are accepted
|
||||
cursor: Final = await target.aggregate(pipeline)
|
||||
documents: Final = [ # mutable-ok: an async comprehension cannot build a tuple directly
|
||||
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
|
||||
if not documents:
|
||||
try:
|
||||
index_cursor: Final = await target.list_search_indexes(vector_store_id)
|
||||
catalogue: Final = [ # mutable-ok: an async comprehension cannot build a tuple directly
|
||||
entry async for entry in index_cursor
|
||||
]
|
||||
except Exception as e:
|
||||
raise translate_mongo_error(
|
||||
e, index_name=vector_store_id, database=database, collection=collection
|
||||
) from e
|
||||
self._raise_for_unusable_index(catalogue, vector_store_id, database, collection)
|
||||
self._raise_for_missing_text_field(documents, params.text_field, database, collection)
|
||||
return self._to_response(documents, query_text, params.text_field)
|
||||
validated: Final = _SearchResponse.model_validate_json(response.content)
|
||||
return _RESPONSE_ADAPTER.validate_python(validated.model_dump())
|
||||
except ValidationError:
|
||||
raise ServiceUnavailableError(
|
||||
message="MongoDB sidecar returned an invalid search response. Check the sidecar version and deployment.",
|
||||
model=None,
|
||||
llm_provider="mongodb",
|
||||
) from None
|
||||
|
||||
def get_error_class(
|
||||
self, error_message: str, status_code: int, headers: dict[str, object] | httpx.Headers
|
||||
) -> BaseLLMException:
|
||||
if status_code == 400:
|
||||
raise config_error(error_message)
|
||||
if status_code == 401:
|
||||
raise AuthenticationError(message="MongoDB sidecar rejected api_key.", model=None, llm_provider="mongodb")
|
||||
if status_code == 408:
|
||||
raise Timeout(message=error_message, model=None, llm_provider="mongodb")
|
||||
raise ServiceUnavailableError(
|
||||
message="MongoDB sidecar is unavailable. Check its address, health, and logs.",
|
||||
model=None,
|
||||
llm_provider="mongodb",
|
||||
)
|
||||
|
||||
def validate_create_vector_store(self) -> NoReturn:
|
||||
raise config_error(_SEARCH_ONLY_MESSAGE)
|
||||
|
||||
def transform_create_vector_store_request(
|
||||
self,
|
||||
vector_store_create_optional_params: VectorStoreCreateOptionalRequestParams,
|
||||
api_base: str,
|
||||
self, vector_store_create_optional_params: VectorStoreCreateOptionalRequestParams, api_base: str
|
||||
) -> NoReturn:
|
||||
raise config_error(_SEARCH_ONLY_MESSAGE)
|
||||
|
||||
|
|
|
|||
|
|
@ -114,7 +114,6 @@ 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.
|
||||
# The floor is 4.9 because that is the release AsyncMongoClient landed in.
|
||||
mongodb = ["pymongo>=4.9,<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.
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
|
|
@ -69,10 +69,11 @@ describe("VectorStoreForm", () => {
|
|||
});
|
||||
});
|
||||
|
||||
const MONGODB_URI = "mongodb+srv://user:pass@cluster0.mongodb.net";
|
||||
const MONGODB_SIDECAR_URL = "http://mongodb-sidecar:8080";
|
||||
|
||||
const MONGODB_REQUIRED_FORM_VALUES = {
|
||||
mongodb_connection_string: MONGODB_URI,
|
||||
api_base: MONGODB_SIDECAR_URL,
|
||||
api_key: "sidecar-test-key",
|
||||
mongodb_database: "sample_mflix",
|
||||
mongodb_collection: "embedded_movies",
|
||||
embedding_model: "text-embedding-ada-002",
|
||||
|
|
@ -127,7 +128,8 @@ describe("buildVectorStoreLitellmParams", () => {
|
|||
mongodb_num_candidates: "200",
|
||||
};
|
||||
const expected = {
|
||||
mongodb_connection_string: MONGODB_URI,
|
||||
api_base: MONGODB_SIDECAR_URL,
|
||||
api_key: "sidecar-test-key",
|
||||
mongodb_database: "sample_mflix",
|
||||
mongodb_collection: "embedded_movies",
|
||||
mongodb_embedding_field: "plot_embedding",
|
||||
|
|
@ -142,6 +144,7 @@ describe("buildVectorStoreLitellmParams", () => {
|
|||
it("sends only mongodb fields when an earlier provider left values in the form", () => {
|
||||
const formValues = {
|
||||
...MONGODB_REQUIRED_FORM_VALUES,
|
||||
mongodb_connection_string: "mongodb://obsolete-credentials",
|
||||
valkey_host: "left-over-from-valkey.example.com",
|
||||
valkey_port: "6379",
|
||||
aws_region_name: "us-west-2",
|
||||
|
|
@ -152,7 +155,8 @@ describe("buildVectorStoreLitellmParams", () => {
|
|||
expect(params).not.toHaveProperty("valkey_host");
|
||||
expect(params).not.toHaveProperty("valkey_port");
|
||||
expect(params).not.toHaveProperty("aws_region_name");
|
||||
expect(params.mongodb_connection_string).toBe(MONGODB_URI);
|
||||
expect(params.api_base).toBe(MONGODB_SIDECAR_URL);
|
||||
expect(params).not.toHaveProperty("mongodb_connection_string");
|
||||
});
|
||||
|
||||
it("omits a blank mongodb_num_candidates so litellm picks its own candidate count", () => {
|
||||
|
|
|
|||
|
|
@ -70,7 +70,6 @@ const PROVIDER_FIELD_NAMES = [
|
|||
"vector_bucket_name",
|
||||
"index_name",
|
||||
"aws_region_name",
|
||||
"mongodb_connection_string",
|
||||
"mongodb_database",
|
||||
"mongodb_collection",
|
||||
"mongodb_embedding_field",
|
||||
|
|
@ -107,7 +106,6 @@ const vectorStoreShape = {
|
|||
vector_bucket_name: optionalText,
|
||||
index_name: optionalText,
|
||||
aws_region_name: optionalText,
|
||||
mongodb_connection_string: optionalText,
|
||||
mongodb_database: optionalText,
|
||||
mongodb_collection: optionalText,
|
||||
mongodb_embedding_field: optionalText,
|
||||
|
|
@ -142,7 +140,7 @@ const VECTOR_STORE_ID_PLACEHOLDERS: Record<string, string> = {
|
|||
vertex_rag_engine: '6917529027641081856 (corpus ID from Vertex AI / "RAG Engine" console)',
|
||||
"vertex_ai/search_api": 'my-datastore_1234567890 (data store ID from Vertex AI / "Agent Search" console)',
|
||||
valkey: "my-search-index (FT index name in Valkey)",
|
||||
mongodb: "my-vector-index (Atlas Vector Search index name)",
|
||||
mongodb: "my-vector-index (MongoDB Vector Search index name)",
|
||||
};
|
||||
|
||||
const VERTEX_SEARCH_API_WITH_ENGINE_PLACEHOLDER = "Any identifier you'll use to reference this in LiteLLM";
|
||||
|
|
|
|||
|
|
@ -35,7 +35,8 @@ describe("getVectorStoreProviderLogoAndName", () => {
|
|||
});
|
||||
expect(vectorStoreProviderMap.MongoDB).toBe("mongodb");
|
||||
expect(getProviderSpecificFields("mongodb").map((field) => field.name)).toEqual([
|
||||
"mongodb_connection_string",
|
||||
"api_base",
|
||||
"api_key",
|
||||
"mongodb_database",
|
||||
"mongodb_collection",
|
||||
"embedding_model",
|
||||
|
|
@ -45,12 +46,10 @@ describe("getVectorStoreProviderLogoAndName", () => {
|
|||
]);
|
||||
});
|
||||
|
||||
it("hides the mongodb connection string, which carries the database password", () => {
|
||||
const connectionString = getProviderSpecificFields("mongodb").find(
|
||||
(field) => field.name === "mongodb_connection_string",
|
||||
);
|
||||
it("hides the mongodb sidecar API key", () => {
|
||||
const apiKey = getProviderSpecificFields("mongodb").find((field) => field.name === "api_key");
|
||||
|
||||
expect(connectionString).toMatchObject({ type: "password", required: true });
|
||||
expect(apiKey).toMatchObject({ type: "password", required: true });
|
||||
});
|
||||
|
||||
it("picks the mongodb embedding model from the proxy's models rather than a fixed list", () => {
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ export enum VectorStoreProviders {
|
|||
OpenAI = "OpenAI",
|
||||
Azure = "Azure OpenAI",
|
||||
Milvus = "Milvus",
|
||||
MongoDB = "MongoDB Atlas",
|
||||
MongoDB = "MongoDB (BETA)",
|
||||
Valkey = "Valkey",
|
||||
}
|
||||
|
||||
|
|
@ -175,18 +175,25 @@ export const vectorStoreProviderFields: Record<string, VectorStoreFieldConfig[]>
|
|||
],
|
||||
mongodb: [
|
||||
{
|
||||
name: "mongodb_connection_string",
|
||||
label: "Connection String",
|
||||
tooltip:
|
||||
"The full MongoDB connection string for your Atlas cluster, including the database user and password. Copy it from Atlas under Connect, Drivers (e.g. mongodb+srv://user:password@cluster.mongodb.net)",
|
||||
placeholder: "mongodb+srv://user:password@cluster.mongodb.net",
|
||||
name: "api_base",
|
||||
label: "Sidecar URL",
|
||||
tooltip: "The URL of your separately deployed MongoDB sidecar. Configure MongoDB credentials in the sidecar",
|
||||
placeholder: "http://mongodb-sidecar:8080",
|
||||
required: true,
|
||||
type: "text",
|
||||
},
|
||||
{
|
||||
name: "api_key",
|
||||
label: "Sidecar API Key",
|
||||
tooltip: "The MONGODB_SIDECAR_API_KEY configured in your MongoDB sidecar",
|
||||
placeholder: "Enter sidecar API key",
|
||||
required: true,
|
||||
type: "password",
|
||||
},
|
||||
{
|
||||
name: "mongodb_database",
|
||||
label: "Database",
|
||||
tooltip: "The Atlas database holding the collection you want to search",
|
||||
tooltip: "The MongoDB database holding the collection you want to search",
|
||||
placeholder: "sample_mflix",
|
||||
required: true,
|
||||
type: "text",
|
||||
|
|
@ -194,7 +201,7 @@ export const vectorStoreProviderFields: Record<string, VectorStoreFieldConfig[]>
|
|||
{
|
||||
name: "mongodb_collection",
|
||||
label: "Collection",
|
||||
tooltip: "The collection your Atlas Vector Search index was built on",
|
||||
tooltip: "The collection your MongoDB Vector Search index was built on",
|
||||
placeholder: "embedded_movies",
|
||||
required: true,
|
||||
type: "text",
|
||||
|
|
@ -212,7 +219,7 @@ export const vectorStoreProviderFields: Record<string, VectorStoreFieldConfig[]>
|
|||
name: "mongodb_embedding_field",
|
||||
label: "Vector Field Name",
|
||||
tooltip:
|
||||
"The field in each document that holds its embedding. It must match the path your Atlas Vector Search index was created on (default: embedding)",
|
||||
"The field in each document that holds its embedding. It must match the path your MongoDB Vector Search index was created on (default: embedding)",
|
||||
placeholder: "embedding",
|
||||
required: false,
|
||||
type: "text",
|
||||
|
|
@ -232,7 +239,7 @@ export const vectorStoreProviderFields: Record<string, VectorStoreFieldConfig[]>
|
|||
name: "mongodb_num_candidates",
|
||||
label: "Candidates Considered",
|
||||
tooltip:
|
||||
"How many nearest neighbours Atlas examines before returning the top results. Higher is more accurate and slower. Leave blank to let LiteLLM scale it with the requested result count",
|
||||
"How many nearest neighbours MongoDB examines before returning the top results. Higher is more accurate and slower. Leave blank to let LiteLLM scale it with the requested result count",
|
||||
placeholder: "100",
|
||||
required: false,
|
||||
type: "text",
|
||||
|
|
|
|||
79
uv.lock
generated
79
uv.lock
generated
|
|
@ -10,7 +10,7 @@ resolution-markers = [
|
|||
]
|
||||
|
||||
[options]
|
||||
exclude-newer = "2026-09-02T16:58:34.594994Z"
|
||||
exclude-newer = "2026-09-05T05:15:35.833796Z"
|
||||
exclude-newer-span = "P3D"
|
||||
|
||||
[manifest]
|
||||
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@ -4415,9 +4415,6 @@ mcp = [
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mlflow = [
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||||
{ name = "mlflow" },
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]
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mongodb = [
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proxy = [
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{ name = "apscheduler" },
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{ name = "azure-identity" },
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@ -4649,7 +4646,6 @@ requires-dist = [
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{ name = "pyroscope-io", marker = "sys_platform != 'win32' and extra == 'proxy'", specifier = ">=0.8.16,<1.0" },
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@ -4676,7 +4672,7 @@ requires-dist = [
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[[package]]
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||||
name = "pynacl"
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||||
version = "1.6.2"
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||||
|
|
|
|||
Loading…
Add table
Reference in a new issue