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feat(search): add Grounding with Bing Search (bing_grounding) as a search provider
This commit is contained in:
mateo-berri
parent
28b433a007
commit
03a676995a
13 changed files with 996 additions and 0 deletions
3
litellm/llms/azure/search/__init__.py
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3
litellm/llms/azure/search/__init__.py
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from litellm.llms.azure.search.transformation import BingGroundingSearchConfig
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__all__ = ("BingGroundingSearchConfig",)
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353
litellm/llms/azure/search/transformation.py
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353
litellm/llms/azure/search/transformation.py
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"""
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Calls the Microsoft Foundry Responses API with the `bing_grounding` or `web_search`
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tool to search the web (Grounding with Bing Search).
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Microsoft docs: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/bing-grounding
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Setup:
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1. Set BING_GROUNDING_PROJECT_ENDPOINT to the Foundry project endpoint, e.g.
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https://<account>.services.ai.azure.com/api/projects/<project>
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2. Set BING_GROUNDING_MODEL to a model deployment in that project (e.g. gpt-4.1);
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it runs the grounded search and its tokens are billed on that deployment
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3. Optional: set BING_GROUNDING_CONNECTION_ID to a Grounding with Bing Search
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project connection id to use the `bing_grounding` tool; without it the
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project's built-in `web_search` tool is used
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4. Auth: pass api_key, or set BING_GROUNDING_TOKEN to an Entra bearer token for
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scope https://ai.azure.com/.default, or configure azure-identity
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(AZURE_CLIENT_ID / AZURE_CLIENT_SECRET / AZURE_TENANT_ID, managed identity,
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or any DefaultAzureCredential source) and the token is minted automatically
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Usage:
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response = litellm.search(
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query="latest AI developments",
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search_provider="bing_grounding",
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max_results=5,
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)
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"""
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from __future__ import annotations
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from collections.abc import Callable, Mapping
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from types import MappingProxyType
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from typing import TYPE_CHECKING, Final, Literal
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import httpx
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from pydantic import BaseModel, ConfigDict, ValidationError
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.llms.base_llm.search.transformation import (
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BaseSearchConfig,
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SearchResponse,
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SearchResult,
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)
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from litellm.secret_managers.main import get_secret_str
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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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_DOCS_URL: Final = "https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/bing-grounding"
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PROJECT_ENDPOINT_ENV: Final = "BING_GROUNDING_PROJECT_ENDPOINT"
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MODEL_ENV: Final = "BING_GROUNDING_MODEL"
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CONNECTION_ID_ENV: Final = "BING_GROUNDING_CONNECTION_ID"
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TOKEN_ENV: Final = "BING_GROUNDING_TOKEN"
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ENTRA_SCOPE: Final = "https://ai.azure.com/.default"
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_RESPONSES_PATH: Final = "/openai/v1/responses"
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_SNIPPET_FALLBACK_LENGTH: Final = 300
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class _Annotation(BaseModel):
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model_config = ConfigDict(extra="ignore", frozen=True)
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type: str = ""
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url: str | None = None
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title: str | None = None
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start_index: int | None = None
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end_index: int | None = None
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class _ContentPart(BaseModel):
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model_config = ConfigDict(extra="ignore", frozen=True)
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type: str = ""
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text: str = ""
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annotations: tuple[_Annotation, ...] = ()
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class _OutputItem(BaseModel):
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model_config = ConfigDict(extra="ignore", frozen=True)
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type: str = ""
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content: tuple[_ContentPart, ...] = ()
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class _ResponsesEnvelope(BaseModel):
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"""A Foundry Responses API body. `output` is required: a body without it is not a
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Responses API response and must not be reported as a successful empty search."""
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model_config = ConfigDict(extra="ignore", frozen=True)
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output: tuple[_OutputItem, ...]
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class _ErrorBody(BaseModel):
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model_config = ConfigDict(extra="ignore", frozen=True)
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message: str | None = None
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class _ErrorEnvelope(BaseModel):
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model_config = ConfigDict(extra="ignore", frozen=True)
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error: _ErrorBody | None = None
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def _unwrap_error_detail(error_message: str) -> str:
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"""
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Surface the human-readable message inside Foundry's error envelope.
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Tool failures nest a second JSON document as a string inside `error.message`
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(observed live for `bing_grounding` connection errors), so the unwrap runs twice.
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Falls back to the raw body for anything else.
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"""
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try:
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envelope: Final = _ErrorEnvelope.model_validate_json(error_message)
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except ValidationError:
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return error_message
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message: Final = envelope.error.message if envelope.error else None
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if message is None:
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return error_message
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try:
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nested: Final = _ErrorBody.model_validate_json(message)
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except ValidationError:
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return message
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return nested.message or message
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def _snippet(text: str, annotation: _Annotation) -> str:
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"""
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The text a citation supports, not the citation marker itself.
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A url_citation's start/end indices span the inline marker ("([host](url))"),
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which follows the claim it backs, so the snippet is the marker's own line up
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to where the marker starts.
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"""
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start: Final = annotation.start_index
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marker_start: Final = start if start is not None and 0 <= start <= len(text) else len(text)
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claim: Final = text[:marker_start].rsplit("\n", 1)[-1].strip()
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if claim:
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return claim[-_SNIPPET_FALLBACK_LENGTH:]
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return text[:_SNIPPET_FALLBACK_LENGTH]
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def _citation_results(envelope: _ResponsesEnvelope) -> tuple[SearchResult, ...]:
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"""One result per cited URL: first occurrence wins, order preserved as answered."""
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cited: Final = tuple(
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SearchResult(
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title=annotation.title or "",
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url=annotation.url or "",
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snippet=_snippet(part.text, annotation),
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date=None,
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last_updated=None,
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)
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for item in envelope.output
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if item.type == "message"
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for part in item.content
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if part.type == "output_text"
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for annotation in part.annotations
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if annotation.type == "url_citation" and annotation.url
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)
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first_by_url: Final = MappingProxyType({result.url: result for result in reversed(cited)})
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return tuple(first_by_url[url] for url in dict.fromkeys(result.url for result in cited))
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class _SearchConfiguration(BaseModel):
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model_config = ConfigDict(frozen=True)
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project_connection_id: str
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count: int | None = None
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class _BingGroundingParams(BaseModel):
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model_config = ConfigDict(frozen=True)
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search_configurations: tuple[_SearchConfiguration, ...]
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class _BingGroundingTool(BaseModel):
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model_config = ConfigDict(frozen=True)
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type: Literal["bing_grounding"] = "bing_grounding"
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bing_grounding: _BingGroundingParams
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class _UserLocation(BaseModel):
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model_config = ConfigDict(frozen=True)
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type: Literal["approximate"] = "approximate"
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country: str
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class _WebSearchTool(BaseModel):
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model_config = ConfigDict(frozen=True)
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type: Literal["web_search"] = "web_search"
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user_location: _UserLocation | None = None
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class _ResponsesRequest(BaseModel):
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model_config = ConfigDict(frozen=True)
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model: str
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input: str
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tools: tuple[_BingGroundingTool | _WebSearchTool, ...]
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def _search_tool(optional_params: Mapping[str, object]) -> _BingGroundingTool | _WebSearchTool:
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connection_id: Final = get_secret_str(CONNECTION_ID_ENV)
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max_results: Final = optional_params.get("max_results")
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country: Final = optional_params.get("country")
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if connection_id:
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configuration: Final = _SearchConfiguration(
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project_connection_id=connection_id,
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count=max_results if isinstance(max_results, int) else None,
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)
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return _BingGroundingTool(bing_grounding=_BingGroundingParams(search_configurations=(configuration,)))
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location: Final = _UserLocation(country=country.upper()) if isinstance(country, str) else None
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return _WebSearchTool(user_location=location)
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def _default_entra_token_minter() -> str:
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from litellm.secret_managers.get_azure_ad_token_provider import get_azure_ad_token_provider
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return get_azure_ad_token_provider(azure_scope=ENTRA_SCOPE)()
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class BingGroundingSearchConfig(BaseSearchConfig):
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def __init__(self, entra_token_minter: Callable[[], str] | None = None) -> None:
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super().__init__()
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self._entra_token_minter = entra_token_minter
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@staticmethod
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def ui_friendly_name() -> str:
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return "Grounding with Bing Search"
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def validate_environment(
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self,
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headers: dict[str, str], # mutable-ok: BaseSearchConfig.validate_environment signature
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api_key: str | None = None,
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api_base: str | None = None,
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**kwargs: object, # kwargs-ok: BaseSearchConfig.validate_environment signature
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) -> dict[str, str]: # mutable-ok: the http handler passes this straight to httpx as headers
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"""
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Validate environment and return headers.
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Returns a new dict rather than mutating ``headers``: the http handler calls this
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a second time after ``litellm/search/main.py`` already did, so it has to be idempotent.
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"""
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resolved_token: Final = self.resolve_server_api_key(
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caller_api_key=api_key,
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caller_api_base=api_base,
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key_env_vars=(TOKEN_ENV,),
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base_env_var=PROJECT_ENDPOINT_ENV,
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default_api_base=None,
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) or self._mint_entra_token(api_base)
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return { # mutable-ok: httpx requires a plain dict of headers
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**headers,
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"Authorization": f"Bearer {resolved_token}",
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"Content-Type": "application/json",
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}
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def _mint_entra_token(self, caller_api_base: str | None) -> str:
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self._assert_trusted_api_base_for_server_credential(
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caller_api_base, None, PROJECT_ENDPOINT_ENV, "Azure AD token"
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)
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minter: Final = self._entra_token_minter or _default_entra_token_minter
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try:
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return minter()
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except Exception as e:
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raise ValueError(
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f"Grounding with Bing Search: no credential available. Pass api_key, set {TOKEN_ENV} "
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f"to an Entra bearer token, or configure azure-identity (AZURE_CLIENT_ID / "
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f"AZURE_CLIENT_SECRET / AZURE_TENANT_ID or any DefaultAzureCredential source) "
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f"for scope {ENTRA_SCOPE}. Underlying error: {e}"
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) from e
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def get_complete_url(
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self,
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api_base: str | None,
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optional_params: dict[str, object], # mutable-ok: BaseSearchConfig.get_complete_url signature
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data: dict[str, object] | list[dict[str, object]] | None = None, # mutable-ok: base signature
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**kwargs: object, # kwargs-ok: BaseSearchConfig.get_complete_url signature
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) -> str:
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resolved_base: Final = api_base or get_secret_str(PROJECT_ENDPOINT_ENV)
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if not resolved_base:
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raise ValueError(
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f"{PROJECT_ENDPOINT_ENV} is not set. Set it to your Microsoft Foundry project "
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f"endpoint, e.g. https://<account>.services.ai.azure.com/api/projects/<project>."
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)
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trimmed: Final = resolved_base.rstrip("/")
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if trimmed.endswith(_RESPONSES_PATH):
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return trimmed
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return f"{trimmed}{_RESPONSES_PATH}"
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def transform_search_request(
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self,
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query: str | list[str], # mutable-ok: BaseSearchConfig.transform_search_request signature
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optional_params: dict[str, object], # mutable-ok: base signature
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**kwargs: object, # kwargs-ok: BaseSearchConfig.transform_search_request signature
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) -> dict[str, object]: # mutable-ok: the http handler passes this straight to httpx as the JSON body
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"""
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Transform Search request to the Foundry Responses API format.
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The unified params map as far as the API allows:
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- max_results -> the bing_grounding search configuration's `count` (the built-in
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web_search tool has no result-count knob, so it is dropped in that mode)
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- country -> web_search's approximate `user_location` (bing_grounding's `market`
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wants a full locale like en-US, which a bare country code cannot fill)
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- search_domain_filter, max_tokens_per_page -> no API equivalent, dropped
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"""
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model: Final = get_secret_str(MODEL_ENV)
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if not model:
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raise ValueError(
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f"{MODEL_ENV} is not set. Set it to a model deployment in the Foundry project "
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f"that runs the grounded search, e.g. gpt-4.1."
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)
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request: Final = _ResponsesRequest(
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model=model,
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input=" ".join(query) if isinstance(query, list) else query,
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tools=(_search_tool(optional_params),),
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)
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return request.model_dump(mode="json", exclude_none=True)
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def transform_search_response(
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self,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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**kwargs: object, # kwargs-ok: BaseSearchConfig.transform_search_response signature
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) -> SearchResponse:
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try:
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parsed: Final = _ResponsesEnvelope.model_validate_json(raw_response.content)
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except ValidationError as e:
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raise self.get_error_class(
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error_message=f"response does not match the Foundry Responses API schema: {e}",
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status_code=raw_response.status_code,
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headers=dict(raw_response.headers), # mutable-ok: BaseSearchConfig.get_error_class signature
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)
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results: Final = list(_citation_results(parsed)) # mutable-ok: SearchResponse.results is list[SearchResult]
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return SearchResponse(results=results, object="search")
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def get_error_class(
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self,
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error_message: str,
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status_code: int,
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headers: dict[str, str], # mutable-ok: BaseSearchConfig.get_error_class signature
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) -> Exception:
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detail: Final = _unwrap_error_detail(error_message).rstrip(". ")
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return BaseLLMException(
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status_code=status_code,
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message=f"Grounding with Bing Search: {detail}. See {_DOCS_URL} for details.",
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headers=headers,
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)
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@ -16890,6 +16890,14 @@
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"notes": "Web Search on Amazon Bedrock AgentCore, billed by AWS on the gateway"
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}
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},
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"bing_grounding/search": {
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"input_cost_per_query": 0.035,
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"litellm_provider": "bing_grounding",
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"mode": "search",
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"metadata": {
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"notes": "Grounding with Bing Search (G1 SKU): $35 per 1,000 transactions. Tokens for the Foundry model deployment that runs the grounded search are billed separately on that deployment."
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}
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},
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"tinyfish/search": {
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"input_cost_per_query": 0.0,
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"litellm_provider": "tinyfish",
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|
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@ -0,0 +1,40 @@
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# Web search via Microsoft Foundry: Grounding with Bing Search / the built-in
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# web_search tool, called through the Foundry Responses API.
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# See litellm/llms/azure/search/transformation.py for details.
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#
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# Required environment variables (the search router forwards only
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# search_provider / api_key / api_base from the litellm_params block, so
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# provider configuration rides env vars):
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# BING_GROUNDING_PROJECT_ENDPOINT: the Foundry project endpoint, e.g.
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# https://<account>.services.ai.azure.com/api/projects/<project>
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# BING_GROUNDING_MODEL: a model deployment in that project (e.g. gpt-4.1);
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# it runs the grounded search, its tokens are billed on that deployment
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# Optional:
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# BING_GROUNDING_CONNECTION_ID: a Grounding with Bing Search project
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# connection id; set it to use the bing_grounding tool ($35 per 1,000
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# transactions on the G1 SKU). Without it the project's built-in
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# web_search tool is used
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# BING_GROUNDING_TOKEN: an Entra bearer token for scope
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# https://ai.azure.com/.default. Without it (and without api_key below)
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# the token is minted via azure-identity (AZURE_CLIENT_ID /
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# AZURE_CLIENT_SECRET / AZURE_TENANT_ID, managed identity, or any other
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# DefaultAzureCredential source)
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model_list:
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- model_name: claude-sonnet
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litellm_params:
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model: bedrock/us.anthropic.claude-sonnet-5
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aws_region_name: us-east-1
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search_tools:
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- search_tool_name: bing-grounding-search
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litellm_params:
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search_provider: bing_grounding
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# Alternative to BING_GROUNDING_TOKEN / azure-identity:
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# api_key: os.environ/BING_GROUNDING_TOKEN
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litellm_settings:
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callbacks: ["websearch_interception"]
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websearch_interception_params:
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enabled_providers: ["bedrock"]
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search_tool_name: bing-grounding-search
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|
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@ -3844,6 +3844,7 @@ class SearchProviders(str, Enum):
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TINYFISH = "tinyfish"
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AGENTCORE = "agentcore"
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NIMBLE = "nimble"
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BING_GROUNDING = "bing_grounding"
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# Create a set of all search provider values for quick lookup
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|
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@ -9111,6 +9111,7 @@ class ProviderConfigManager:
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from litellm.llms.apiserpent.search.transformation import (
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APISerpentSearchConfig,
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)
|
||||
from litellm.llms.azure.search.transformation import BingGroundingSearchConfig
|
||||
from litellm.llms.bedrock.search.transformation import AgentCoreSearchConfig
|
||||
from litellm.llms.brave.search.transformation import BraveSearchConfig
|
||||
from litellm.llms.dataforseo.search.transformation import DataForSEOSearchConfig
|
||||
|
|
@ -9152,6 +9153,7 @@ class ProviderConfigManager:
|
|||
SearchProviders.TINYFISH: TinyfishSearchConfig,
|
||||
SearchProviders.AGENTCORE: AgentCoreSearchConfig,
|
||||
SearchProviders.NIMBLE: NimbleSearchConfig,
|
||||
SearchProviders.BING_GROUNDING: BingGroundingSearchConfig,
|
||||
}
|
||||
config_class: Final = PROVIDER_TO_CONFIG_MAP.get(provider, None)
|
||||
if config_class is None:
|
||||
|
|
|
|||
|
|
@ -16890,6 +16890,14 @@
|
|||
"notes": "Web Search on Amazon Bedrock AgentCore, billed by AWS on the gateway"
|
||||
}
|
||||
},
|
||||
"bing_grounding/search": {
|
||||
"input_cost_per_query": 0.035,
|
||||
"litellm_provider": "bing_grounding",
|
||||
"mode": "search",
|
||||
"metadata": {
|
||||
"notes": "Grounding with Bing Search (G1 SKU): $35 per 1,000 transactions. Tokens for the Foundry model deployment that runs the grounded search are billed separately on that deployment."
|
||||
}
|
||||
},
|
||||
"tinyfish/search": {
|
||||
"input_cost_per_query": 0.0,
|
||||
"litellm_provider": "tinyfish",
|
||||
|
|
|
|||
187
tests/search_tests/test_bing_grounding_search.py
Normal file
187
tests/search_tests/test_bing_grounding_search.py
Normal file
|
|
@ -0,0 +1,187 @@
|
|||
"""
|
||||
Tests for the Grounding with Bing Search (Microsoft Foundry) integration.
|
||||
"""
|
||||
|
||||
import json
|
||||
from unittest.mock import AsyncMock, Mock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
import litellm
|
||||
from tests.search_tests.base_search_unit_tests import BaseSearchTest
|
||||
|
||||
PROJECT_ENDPOINT = "https://acct.services.ai.azure.com/api/projects/proj"
|
||||
|
||||
_ANSWER_TEXT = (
|
||||
"LiteLLM is an open source LLM gateway ([github.com](https://github.com/BerriAI/litellm))\n"
|
||||
"The docs live on docs.litellm.ai ([docs.litellm.ai](https://docs.litellm.ai/))"
|
||||
)
|
||||
|
||||
|
||||
def _annotation(marker: str, url: str, title: str) -> dict:
|
||||
start = _ANSWER_TEXT.index(marker)
|
||||
return {
|
||||
"type": "url_citation",
|
||||
"url": url,
|
||||
"title": title,
|
||||
"start_index": start,
|
||||
"end_index": start + len(marker),
|
||||
}
|
||||
|
||||
|
||||
MOCK_BING_GROUNDING_RESPONSE = {
|
||||
"id": "resp_mock",
|
||||
"object": "response",
|
||||
"status": "completed",
|
||||
"model": "gpt-4.1",
|
||||
"output": [
|
||||
{"type": "web_search_call", "status": "completed"},
|
||||
{
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"type": "output_text",
|
||||
"text": _ANSWER_TEXT,
|
||||
"annotations": [
|
||||
_annotation(
|
||||
"([github.com](https://github.com/BerriAI/litellm))",
|
||||
"https://github.com/BerriAI/litellm",
|
||||
"BerriAI/litellm - GitHub",
|
||||
),
|
||||
_annotation(
|
||||
"([docs.litellm.ai](https://docs.litellm.ai/))",
|
||||
"https://docs.litellm.ai/",
|
||||
"LiteLLM Docs",
|
||||
),
|
||||
],
|
||||
}
|
||||
],
|
||||
},
|
||||
],
|
||||
"usage": {"input_tokens": 100, "output_tokens": 50},
|
||||
}
|
||||
|
||||
|
||||
def _mock_response():
|
||||
response = Mock()
|
||||
response.status_code = 200
|
||||
response.headers = {}
|
||||
response.content = json.dumps(MOCK_BING_GROUNDING_RESPONSE).encode()
|
||||
return response
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Local only tested search providers")
|
||||
class TestBingGroundingSearch(BaseSearchTest):
|
||||
"""
|
||||
E2E tests for Grounding with Bing Search that make real API calls.
|
||||
Inherits from BaseSearchTest to run standard search tests.
|
||||
"""
|
||||
|
||||
def get_search_provider(self) -> str:
|
||||
return "bing_grounding"
|
||||
|
||||
|
||||
class TestBingGroundingSearchTransformation:
|
||||
"""
|
||||
Full-stack tests through `litellm.search` / `litellm.asearch` with the HTTP layer mocked.
|
||||
Transformation details are unit-tested in tests/test_litellm/llms/azure/search/.
|
||||
"""
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _server_env(self, monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_PROJECT_ENDPOINT", PROJECT_ENDPOINT)
|
||||
monkeypatch.setenv("BING_GROUNDING_MODEL", "gpt-4.1")
|
||||
monkeypatch.setenv("BING_GROUNDING_TOKEN", "test-entra-token")
|
||||
monkeypatch.delenv("BING_GROUNDING_CONNECTION_ID", raising=False)
|
||||
|
||||
def test_bing_grounding_search_request_and_response(self):
|
||||
with patch( # test-quality-ok: litellm.search has no client injection seam
|
||||
"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
|
||||
return_value=_mock_response(),
|
||||
) as mock_post:
|
||||
response = litellm.search(
|
||||
query="what is litellm",
|
||||
search_provider="bing_grounding",
|
||||
max_results=5,
|
||||
country="us",
|
||||
)
|
||||
|
||||
assert mock_post.called
|
||||
call_kwargs = mock_post.call_args.kwargs
|
||||
assert call_kwargs["url"] == f"{PROJECT_ENDPOINT}/openai/v1/responses"
|
||||
assert call_kwargs["headers"]["Authorization"] == "Bearer test-entra-token"
|
||||
|
||||
request_body = call_kwargs["json"]
|
||||
assert request_body["model"] == "gpt-4.1"
|
||||
assert request_body["input"] == "what is litellm"
|
||||
assert request_body["tools"] == [
|
||||
{"type": "web_search", "user_location": {"type": "approximate", "country": "US"}}
|
||||
]
|
||||
|
||||
assert response.object == "search"
|
||||
assert len(response.results) == 2
|
||||
assert response.results[0].url == "https://github.com/BerriAI/litellm"
|
||||
assert response.results[0].title == "BerriAI/litellm - GitHub"
|
||||
assert response.results[0].snippet == "LiteLLM is an open source LLM gateway"
|
||||
assert response.results[1].url == "https://docs.litellm.ai/"
|
||||
assert response.results[1].snippet == "The docs live on docs.litellm.ai"
|
||||
|
||||
def test_connection_mode_sends_the_bing_grounding_tool(self, monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv(
|
||||
"BING_GROUNDING_CONNECTION_ID",
|
||||
"/subscriptions/sub/resourceGroups/rg/providers/Microsoft.CognitiveServices"
|
||||
"/accounts/acct/projects/proj/connections/bing-conn",
|
||||
)
|
||||
with patch( # test-quality-ok: litellm.search has no client injection seam
|
||||
"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
|
||||
return_value=_mock_response(),
|
||||
) as mock_post:
|
||||
litellm.search(
|
||||
query="what is litellm",
|
||||
search_provider="bing_grounding",
|
||||
max_results=3,
|
||||
)
|
||||
|
||||
request_body = mock_post.call_args.kwargs["json"]
|
||||
assert request_body["tools"] == [
|
||||
{
|
||||
"type": "bing_grounding",
|
||||
"bing_grounding": {
|
||||
"search_configurations": [
|
||||
{
|
||||
"project_connection_id": (
|
||||
"/subscriptions/sub/resourceGroups/rg/providers/Microsoft.CognitiveServices"
|
||||
"/accounts/acct/projects/proj/connections/bing-conn"
|
||||
),
|
||||
"count": 3,
|
||||
}
|
||||
]
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_bing_grounding_asearch(self):
|
||||
with patch( # test-quality-ok: litellm.asearch has no client injection seam
|
||||
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
|
||||
new=AsyncMock(return_value=_mock_response()),
|
||||
) as mock_post:
|
||||
response = await litellm.asearch(
|
||||
query="what is litellm",
|
||||
search_provider="bing_grounding",
|
||||
)
|
||||
|
||||
assert mock_post.call_args.kwargs["json"]["tools"] == [{"type": "web_search"}]
|
||||
assert len(response.results) == 2
|
||||
|
||||
def test_bing_grounding_search_tracks_cost(self, monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
|
||||
monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
|
||||
with patch( # test-quality-ok: litellm.search has no client injection seam
|
||||
"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
|
||||
return_value=_mock_response(),
|
||||
):
|
||||
response = litellm.search(query="pricing check", search_provider="bing_grounding")
|
||||
|
||||
assert response._hidden_params["response_cost"] == pytest.approx(0.035)
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
{
|
||||
"id": "resp_04bcf916b629f9be006a8c7c053e9881959a4efe7118cb3a34",
|
||||
"object": "response",
|
||||
"status": "completed",
|
||||
"model": "gpt-4.1",
|
||||
"output": [
|
||||
{
|
||||
"type": "web_search_call",
|
||||
"id": "ws_04bcf916b629f9be006a8c7c080e108195b3366ab048231129",
|
||||
"response_id": "resp_04bcf916b629f9be006a8c7c053e9881959a4efe7118cb3a34",
|
||||
"status": "completed",
|
||||
"action": {
|
||||
"type": "search",
|
||||
"queries": [
|
||||
"latest LiteLLM release GitHub LiteLLM",
|
||||
"LiteLLM github releases",
|
||||
"LiteLLM repo LiteLLM"
|
||||
],
|
||||
"query": "latest LiteLLM release GitHub"
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "message",
|
||||
"id": "msg_04bcf916b629f9be006a8c7c0c7b448195979bfafb3b28f1ba",
|
||||
"response_id": "resp_04bcf916b629f9be006a8c7c053e9881959a4efe7118cb3a34",
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"type": "output_text",
|
||||
"text": "You asked: “What is the latest LiteLLM release on GitHub? Answer with the version number.”\n\nI verified the most recent release information from the official GitHub repository for LiteLLM (BerriAI/litellm) using multiple sources, including GitHub itself and secondary trackers:\n\n- On the GitHub **Releases** page for BerriAI/litellm, the topmost entry is **v1.99.0‑rc.1**, marked as a pre‑release, published “yesterday” (relative to today, August 24, 2026) ([github.com](https://github.com/BerriAI/litellm/releases)). This indicates that version **v1.99.0‑rc.1** is the most recent tag available.\n- An external release‑tracking site (ReleaseAlert) confirms: **Latest version: v1.99.0‑rc.1**, last published August 22, 2026 ([releasealert.dev](https://releasealert.dev/github/BerriAI/litellm)).\n- The GitHub API (via `releases/latest`) currently points to **v1.98.0** as the latest **stable** release, with published date August 23, 2026 ([api.github.com](https://api.github.com/repos/BerriAI/litellm/releases/latest)).\n\nTo summarize:\n\n- The absolute **latest** release tag on GitHub is **v1.99.0‑rc.1** (release candidate), published recently (August 22, 2026) ([github.com](https://github.com/BerriAI/litellm/releases)).\n- The most recent **stable** release is **v1.98.0**, published August 23, 2026 ([api.github.com](https://api.github.com/repos/BerriAI/litellm/releases/latest)).\n\nSince you asked for the “latest LiteLLM release on GitHub,” without specifying stable vs. pre‑release, the correct answer is:\n\n**v1.99.0‑rc.1**\n\nLet me know if you'd like details on what's new in that release, or if you'd prefer the latest stable version.",
|
||||
"annotations": [
|
||||
{
|
||||
"type": "url_citation",
|
||||
"url": "https://github.com/BerriAI/litellm/releases",
|
||||
"start_index": 456,
|
||||
"end_index": 515,
|
||||
"title": "Releases · BerriAI/litellm - GitHub"
|
||||
},
|
||||
{
|
||||
"type": "url_citation",
|
||||
"url": "https://releasealert.dev/github/BerriAI/litellm",
|
||||
"start_index": 722,
|
||||
"end_index": 791,
|
||||
"title": "BerriAI/litellm on GitHub | Release Alert"
|
||||
},
|
||||
{
|
||||
"type": "url_citation",
|
||||
"url": "https://api.github.com/repos/BerriAI/litellm/releases/latest",
|
||||
"start_index": 936,
|
||||
"end_index": 1016,
|
||||
"title": "api.github.com"
|
||||
},
|
||||
{
|
||||
"type": "url_citation",
|
||||
"url": "https://github.com/BerriAI/litellm/releases",
|
||||
"start_index": 1160,
|
||||
"end_index": 1219,
|
||||
"title": "Releases · BerriAI/litellm - GitHub"
|
||||
},
|
||||
{
|
||||
"type": "url_citation",
|
||||
"url": "https://api.github.com/repos/BerriAI/litellm/releases/latest",
|
||||
"start_index": 1300,
|
||||
"end_index": 1380,
|
||||
"title": "api.github.com"
|
||||
}
|
||||
],
|
||||
"logprobs": []
|
||||
}
|
||||
],
|
||||
"status": "completed"
|
||||
}
|
||||
],
|
||||
"usage": {
|
||||
"input_tokens": 15195,
|
||||
"output_tokens": 467
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,311 @@
|
|||
import json
|
||||
from pathlib import Path
|
||||
from unittest.mock import Mock
|
||||
|
||||
import pytest
|
||||
|
||||
from litellm.llms.azure.search.transformation import BingGroundingSearchConfig
|
||||
|
||||
REAL_FIXTURE = json.loads((Path(__file__).parent / "foundry_responses_web_search_fixture.json").read_text())
|
||||
|
||||
RESPONSES_URL = "https://acct.services.ai.azure.com/api/projects/proj/openai/v1/responses"
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _clean_env(monkeypatch: pytest.MonkeyPatch):
|
||||
for var in (
|
||||
"BING_GROUNDING_PROJECT_ENDPOINT",
|
||||
"BING_GROUNDING_MODEL",
|
||||
"BING_GROUNDING_CONNECTION_ID",
|
||||
"BING_GROUNDING_TOKEN",
|
||||
):
|
||||
monkeypatch.delenv(var, raising=False)
|
||||
|
||||
|
||||
def _config(entra_token_minter=None) -> BingGroundingSearchConfig:
|
||||
return BingGroundingSearchConfig(entra_token_minter=entra_token_minter)
|
||||
|
||||
|
||||
def _resp(payload, status_code: int = 200):
|
||||
r = Mock()
|
||||
r.status_code = status_code
|
||||
r.headers = {}
|
||||
r.content = (payload if isinstance(payload, str) else json.dumps(payload)).encode()
|
||||
return r
|
||||
|
||||
|
||||
def _message_response(text: str, annotations: list) -> dict:
|
||||
return {
|
||||
"output": [
|
||||
{"type": "web_search_call", "status": "completed"},
|
||||
{
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"content": [{"type": "output_text", "text": text, "annotations": annotations}],
|
||||
},
|
||||
]
|
||||
}
|
||||
|
||||
|
||||
def _citation(url: str, title: str, start: int, end: int) -> dict:
|
||||
return {"type": "url_citation", "url": url, "title": title, "start_index": start, "end_index": end}
|
||||
|
||||
|
||||
def test_ui_friendly_name():
|
||||
assert _config().ui_friendly_name() == "Grounding with Bing Search"
|
||||
|
||||
|
||||
def test_validate_environment_with_explicit_key():
|
||||
headers = _config().validate_environment({}, api_key="explicit-token")
|
||||
assert headers["Authorization"] == "Bearer explicit-token"
|
||||
assert headers["Content-Type"] == "application/json"
|
||||
|
||||
|
||||
def test_validate_environment_reads_env_token(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_TOKEN", "env-token")
|
||||
assert _config().validate_environment({})["Authorization"] == "Bearer env-token"
|
||||
|
||||
|
||||
def test_validate_environment_falls_back_to_entra_minter():
|
||||
headers = _config(entra_token_minter=lambda: "entra-token").validate_environment({})
|
||||
assert headers["Authorization"] == "Bearer entra-token"
|
||||
|
||||
|
||||
def test_validate_environment_api_key_beats_env_token(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_TOKEN", "env-token")
|
||||
minter = Mock(return_value="entra-token")
|
||||
headers = _config(entra_token_minter=minter).validate_environment({}, api_key="explicit-token")
|
||||
assert headers["Authorization"] == "Bearer explicit-token"
|
||||
minter.assert_not_called()
|
||||
|
||||
|
||||
def test_validate_environment_env_token_beats_entra_minter(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_TOKEN", "env-token")
|
||||
minter = Mock(return_value="entra-token")
|
||||
assert _config(entra_token_minter=minter).validate_environment({})["Authorization"] == "Bearer env-token"
|
||||
minter.assert_not_called()
|
||||
|
||||
|
||||
def test_validate_environment_refuses_entra_token_for_caller_api_base():
|
||||
minter = Mock(return_value="entra-token")
|
||||
with pytest.raises(ValueError, match="Refusing to send the server-configured"):
|
||||
_config(entra_token_minter=minter).validate_environment({}, api_base="https://attacker.example.com")
|
||||
minter.assert_not_called()
|
||||
|
||||
|
||||
def test_validate_environment_entra_minter_failure_names_the_options():
|
||||
def failing_minter() -> str:
|
||||
raise RuntimeError("no az login")
|
||||
|
||||
with pytest.raises(ValueError, match="no credential available") as excinfo:
|
||||
_config(entra_token_minter=failing_minter).validate_environment({})
|
||||
message = str(excinfo.value)
|
||||
assert "BING_GROUNDING_TOKEN" in message
|
||||
assert "https://ai.azure.com/.default" in message
|
||||
assert "no az login" in message
|
||||
|
||||
|
||||
def test_validate_environment_does_not_mutate_and_is_idempotent():
|
||||
config = _config()
|
||||
caller_headers = {"X-Custom": "keep-me"}
|
||||
|
||||
once = config.validate_environment(caller_headers, api_key="k")
|
||||
twice = config.validate_environment(once, api_key="k")
|
||||
|
||||
assert caller_headers == {"X-Custom": "keep-me"}
|
||||
assert once == twice
|
||||
assert once["X-Custom"] == "keep-me"
|
||||
|
||||
|
||||
def test_get_complete_url_from_api_base():
|
||||
url = _config().get_complete_url("https://acct.services.ai.azure.com/api/projects/proj", {})
|
||||
assert url == RESPONSES_URL
|
||||
|
||||
|
||||
def test_get_complete_url_reads_env_endpoint(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_PROJECT_ENDPOINT", "https://acct.services.ai.azure.com/api/projects/proj/")
|
||||
assert _config().get_complete_url(None, {}) == RESPONSES_URL
|
||||
|
||||
|
||||
def test_get_complete_url_missing_endpoint_raises():
|
||||
with pytest.raises(ValueError, match="BING_GROUNDING_PROJECT_ENDPOINT"):
|
||||
_config().get_complete_url(None, {})
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"api_base",
|
||||
[
|
||||
"https://acct.services.ai.azure.com/api/projects/proj",
|
||||
"https://acct.services.ai.azure.com/api/projects/proj/",
|
||||
"https://acct.services.ai.azure.com/api/projects/proj/openai/v1/responses",
|
||||
"https://acct.services.ai.azure.com/api/projects/proj/openai/v1/responses/",
|
||||
],
|
||||
)
|
||||
def test_get_complete_url_appends_responses_path_exactly_once(api_base: str):
|
||||
assert _config().get_complete_url(api_base, {}) == RESPONSES_URL
|
||||
|
||||
|
||||
def test_transform_search_request_missing_model_raises():
|
||||
with pytest.raises(ValueError, match="BING_GROUNDING_MODEL"):
|
||||
_config().transform_search_request("q", {})
|
||||
|
||||
|
||||
def test_transform_search_request_web_search_mode_exact_body(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_MODEL", "gpt-4.1")
|
||||
body = _config().transform_search_request("latest AI developments", {"max_results": 5})
|
||||
assert body == {
|
||||
"model": "gpt-4.1",
|
||||
"input": "latest AI developments",
|
||||
"tools": [{"type": "web_search"}],
|
||||
}
|
||||
|
||||
|
||||
def test_transform_search_request_web_search_mode_maps_country(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_MODEL", "gpt-4.1")
|
||||
body = _config().transform_search_request("q", {"country": "us"})
|
||||
assert body["tools"] == [{"type": "web_search", "user_location": {"type": "approximate", "country": "US"}}]
|
||||
|
||||
|
||||
def test_transform_search_request_connection_mode_exact_body(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_MODEL", "gpt-4.1")
|
||||
monkeypatch.setenv("BING_GROUNDING_CONNECTION_ID", "conn-id")
|
||||
body = _config().transform_search_request("q", {"max_results": 5})
|
||||
assert body == {
|
||||
"model": "gpt-4.1",
|
||||
"input": "q",
|
||||
"tools": [
|
||||
{
|
||||
"type": "bing_grounding",
|
||||
"bing_grounding": {"search_configurations": [{"project_connection_id": "conn-id", "count": 5}]},
|
||||
}
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def test_transform_search_request_connection_mode_omits_count_without_max_results(
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
):
|
||||
monkeypatch.setenv("BING_GROUNDING_MODEL", "gpt-4.1")
|
||||
monkeypatch.setenv("BING_GROUNDING_CONNECTION_ID", "conn-id")
|
||||
body = _config().transform_search_request("q", {})
|
||||
assert body["tools"][0]["bing_grounding"]["search_configurations"] == [{"project_connection_id": "conn-id"}]
|
||||
|
||||
|
||||
def test_transform_search_request_joins_list_query(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("BING_GROUNDING_MODEL", "gpt-4.1")
|
||||
assert _config().transform_search_request(["foo", "bar"], {})["input"] == "foo bar"
|
||||
|
||||
|
||||
def test_transform_search_response_real_fixture_dedupes_and_preserves_order():
|
||||
resp = _config().transform_search_response(_resp(REAL_FIXTURE), logging_obj=Mock())
|
||||
|
||||
assert resp.object == "search"
|
||||
assert [r.url for r in resp.results] == [
|
||||
"https://github.com/BerriAI/litellm/releases",
|
||||
"https://releasealert.dev/github/BerriAI/litellm",
|
||||
"https://api.github.com/repos/BerriAI/litellm/releases/latest",
|
||||
]
|
||||
assert resp.results[0].title == "Releases · BerriAI/litellm - GitHub"
|
||||
assert resp.results[1].title == "BerriAI/litellm on GitHub | Release Alert"
|
||||
|
||||
|
||||
def test_transform_search_response_real_fixture_snippets_are_the_cited_claims():
|
||||
resp = _config().transform_search_response(_resp(REAL_FIXTURE), logging_obj=Mock())
|
||||
|
||||
assert resp.results[0].snippet.startswith("- On the GitHub **Releases** page for BerriAI/litellm")
|
||||
assert resp.results[1].snippet.startswith("- An external release")
|
||||
assert resp.results[2].snippet.startswith("- The GitHub API (via `releases/latest`)")
|
||||
for result in resp.results:
|
||||
assert "url_citation" not in result.snippet
|
||||
assert not result.snippet.startswith("([")
|
||||
|
||||
|
||||
def test_transform_search_response_snippet_falls_back_to_text_head_for_leading_citation():
|
||||
text = "([example.com](https://example.com)) trailing prose"
|
||||
payload = _message_response(text, [_citation("https://example.com", "Example", 0, 36)])
|
||||
resp = _config().transform_search_response(_resp(payload), logging_obj=Mock())
|
||||
assert resp.results[0].snippet == text
|
||||
|
||||
|
||||
def test_transform_search_response_snippet_without_indices_uses_last_line():
|
||||
payload = _message_response(
|
||||
"first line\nthe claim on the last line",
|
||||
[{"type": "url_citation", "url": "https://example.com", "title": "Example"}],
|
||||
)
|
||||
resp = _config().transform_search_response(_resp(payload), logging_obj=Mock())
|
||||
assert resp.results[0].snippet == "the claim on the last line"
|
||||
|
||||
|
||||
def test_transform_search_response_ignores_non_citation_annotations():
|
||||
payload = _message_response("text", [{"type": "file_citation", "url": "https://example.com"}])
|
||||
assert _config().transform_search_response(_resp(payload), logging_obj=Mock()).results == []
|
||||
|
||||
|
||||
def test_transform_search_response_ignores_citation_without_url():
|
||||
payload = _message_response("text", [{"type": "url_citation", "title": "no url"}])
|
||||
assert _config().transform_search_response(_resp(payload), logging_obj=Mock()).results == []
|
||||
|
||||
|
||||
def test_transform_search_response_no_message_output():
|
||||
payload = {"output": [{"type": "web_search_call", "status": "completed"}]}
|
||||
assert _config().transform_search_response(_resp(payload), logging_obj=Mock()).results == []
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"body",
|
||||
[
|
||||
"<html>502 Bad Gateway</html>",
|
||||
'{"output": "garbage"}',
|
||||
'{"output": null}',
|
||||
"{}",
|
||||
],
|
||||
)
|
||||
def test_transform_search_response_malformed_body_raises_instead_of_reporting_empty(body: str):
|
||||
with pytest.raises(Exception, match="Grounding with Bing Search"):
|
||||
_config().transform_search_response(_resp(body, status_code=502), logging_obj=Mock())
|
||||
|
||||
|
||||
def test_get_error_class_attributes_the_provider():
|
||||
error = _config().get_error_class(error_message="quota exceeded", status_code=429, headers={})
|
||||
assert error.status_code == 429
|
||||
assert "Grounding with Bing Search: quota exceeded" in str(error)
|
||||
assert "learn.microsoft.com" in str(error)
|
||||
|
||||
|
||||
def test_get_error_class_unwraps_the_nested_tool_error():
|
||||
nested_tool_error = json.dumps(
|
||||
{
|
||||
"error": "Tool_User_Error",
|
||||
"message": (
|
||||
"The specified connection ID 'conn-id' in tool config input was not found "
|
||||
"in the project or account connections."
|
||||
),
|
||||
"code": "invalid_tool_input",
|
||||
"tool": "bing_grounding",
|
||||
}
|
||||
)
|
||||
live_400_shape = json.dumps(
|
||||
{
|
||||
"error": {
|
||||
"message": nested_tool_error,
|
||||
"type": "invalid_request_error",
|
||||
"param": None,
|
||||
"code": "tool_user_error",
|
||||
}
|
||||
}
|
||||
)
|
||||
error = _config().get_error_class(error_message=live_400_shape, status_code=400, headers={})
|
||||
assert (
|
||||
"Grounding with Bing Search: The specified connection ID 'conn-id' in tool config input "
|
||||
"was not found in the project or account connections" in str(error)
|
||||
)
|
||||
assert "Tool_User_Error" not in str(error)
|
||||
|
||||
|
||||
def test_get_error_class_unwraps_a_plain_error_envelope():
|
||||
error = _config().get_error_class(
|
||||
error_message='{"error":{"message":"The api key is invalid.","code":"401"}}',
|
||||
status_code=401,
|
||||
headers={},
|
||||
)
|
||||
assert "Grounding with Bing Search: The api key is invalid" in str(error)
|
||||
|
|
@ -16,6 +16,7 @@ import pytest
|
|||
|
||||
import litellm
|
||||
from litellm.llms.apiserpent.search.transformation import APISerpentSearchConfig
|
||||
from litellm.llms.azure.search.transformation import BingGroundingSearchConfig
|
||||
from litellm.llms.base_llm.search.transformation import (
|
||||
BaseSearchConfig,
|
||||
_is_trusted_search_api_base,
|
||||
|
|
@ -59,6 +60,7 @@ _BASE_ENV_VARS = (
|
|||
"TINYFISH_API_BASE",
|
||||
"CRW_API_BASE",
|
||||
"NIMBLE_API_BASE",
|
||||
"BING_GROUNDING_PROJECT_ENDPOINT",
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -99,6 +101,7 @@ PROVIDERS: Tuple[ProviderSpec, ...] = (
|
|||
(TinyfishSearchConfig, {"TINYFISH_API_KEY": "srv"}, "caller-key", {}),
|
||||
(FastCRWSearchConfig, {"CRW_API_KEY": "srv"}, "caller-key", {}),
|
||||
(NimbleSearchConfig, {"NIMBLE_API_KEY": "srv"}, "caller-key", {}),
|
||||
(BingGroundingSearchConfig, {"BING_GROUNDING_TOKEN": "srv"}, "caller-key", {}),
|
||||
)
|
||||
|
||||
_IDS = tuple(spec[0].__name__ for spec in PROVIDERS)
|
||||
|
|
|
|||
BIN
ui/litellm-dashboard/public/assets/logos/bing.png
Normal file
BIN
ui/litellm-dashboard/public/assets/logos/bing.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 31 KiB |
|
|
@ -27,6 +27,7 @@ import { useZodForm } from "@/lib/forms/useZodForm";
|
|||
import SearchConnectionTest from "./SearchConnectionTest";
|
||||
import { buildSearchToolPayload } from "./searchToolPayload";
|
||||
import { AvailableSearchProvider, SearchTool } from "./types";
|
||||
import bingLogo from "../../../../../public/assets/logos/bing.png";
|
||||
import dataforseoLogo from "../../../../../public/assets/logos/dataforseo.png";
|
||||
import exaAiLogo from "../../../../../public/assets/logos/exa_ai.png";
|
||||
import googlePseLogo from "../../../../../public/assets/logos/google_pse.png";
|
||||
|
|
@ -44,6 +45,7 @@ const searchProviderLogoMap: Record<string, string> = {
|
|||
google_pse: googlePseLogo.src,
|
||||
dataforseo: dataforseoLogo.src,
|
||||
nimble: nimbleLogo.src,
|
||||
bing_grounding: bingLogo.src,
|
||||
};
|
||||
|
||||
interface SearchProviderLabelProps {
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue