mirror of
https://github.com/BerriAI/litellm.git
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fix(vector-stores): keep image hits to image searches and reject a caller retrievalQuery
Port of the main branch change. Text searches return the same text hits as before, image hits come back only for query_image searches, a caller retrievalQuery answers 400 or drops under drop_params, a file_search tool carrying query_image searches again, and a non-string query next to an image answers 400
On this line the typed views use as_str_mapping and a local list helper, since decoded_json_object and as_object_list are not on lts/1.108.x
(cherry picked from commit 65a7206199)
This commit is contained in:
mateo-berri
parent
2930e510ff
commit
97d7ed4b89
15 changed files with 152 additions and 290 deletions
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@ -10,6 +10,8 @@ import httpx
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from typing_extensions import ReadOnly, TypedDict
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from litellm._logging import verbose_logger
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from litellm.exceptions import BadRequestError
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from litellm.litellm_core_utils.core_helpers import as_str_mapping, drops_unsupported_params
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from litellm.litellm_core_utils.url_utils import encode_url_path_segment
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from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
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from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
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@ -47,6 +49,11 @@ _BEDROCK_IMAGE_FORMATS: Final[Mapping[VectorStoreSearchImageMediaType, BedrockKB
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_BASE64_DATA_URI: Final = re.compile(r"data:(?P<media_type>[^;,]*)[^,]*;base64,(?P<data>.*)", re.DOTALL)
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_SOURCE_FILE_MIME_TYPE_KEY: Final = "x-amz-bedrock-kb-source-file-mime-type"
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_UNKNOWN_MEDIA_TYPE: Final = "application/octet-stream"
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_RETRIEVAL_QUERY_KEYS: Final = ("retrievalQuery", "retrieval_query")
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_RETRIEVAL_QUERY_UNSUPPORTED: Final = (
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"bedrock vector stores do not take retrievalQuery. Send query, or query_image to search by image. To drop it, "
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"set `litellm.drop_params=True` or for proxy:\n\n`litellm_settings:\n drop_params: true`\n"
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)
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class _QueryImageParams(TypedDict, total=False):
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@ -82,7 +89,7 @@ def _image_content(byte_content: str, metadata: Mapping[str, object]) -> VectorS
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def _result_content(
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content: BedrockKBContent | None, metadata: Mapping[str, object]
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content: BedrockKBContent | None, metadata: Mapping[str, object], include_images: bool
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) -> VectorStoreResultContent | None:
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if content is None:
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return None
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@ -90,7 +97,7 @@ def _result_content(
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if text is not None:
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return VectorStoreResultContent(text=text, type="text")
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byte_content: Final = content.get("byteContent")
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if content.get("type") != "IMAGE" or not byte_content:
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if not include_images or content.get("type") != "IMAGE" or not byte_content:
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return None
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return _image_content(byte_content, metadata)
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@ -275,14 +282,14 @@ class BedrockVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM):
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encoded_vector_store_id: Final = encode_url_path_segment(vector_store_id, field_name="vector_store_id")
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url: Final = f"{api_base}/{encoded_vector_store_id}/retrieve"
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caller_sources: Final = self._caller_sources(extra_body=extra_body, litellm_params=litellm_params)
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caller_retrieval_query: Final = self._caller_value(caller_sources, ("retrievalQuery", "retrieval_query"))
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caller_params: Final = as_str_mapping(litellm_params) or {}
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caller_sources: Final = self._caller_sources(extra_body=extra_body, litellm_params=caller_params)
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if self._caller_value(caller_sources, _RETRIEVAL_QUERY_KEYS) is not None and not drops_unsupported_params(
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caller_params.get("drop_params")
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):
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raise BadRequestError(message=_RETRIEVAL_QUERY_UNSUPPORTED, model="", llm_provider="bedrock")
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request_body: Final[dict[str, object]] = {
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"retrievalQuery": (
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_retrieval_query(query, vector_store_search_optional_params)
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if caller_retrieval_query is None
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else caller_retrieval_query
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),
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"retrievalQuery": _retrieval_query(query, vector_store_search_optional_params),
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}
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retrieval_config: dict[str, Any] = {}
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@ -398,10 +405,11 @@ class BedrockVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM):
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) -> VectorStoreSearchResponse:
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try:
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response_data: Final = BedrockKBResponse(**response.json())
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include_images: Final = litellm_logging_obj.model_call_details.get("query_image") is not None
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results: Final[list[VectorStoreSearchResult]] = []
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for item in response_data.get("retrievalResults", []) or []:
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metadata = item.get("metadata", {}) or {}
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result_content = _result_content(item.get("content"), metadata)
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result_content = _result_content(item.get("content"), metadata, include_images)
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if result_content is None:
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continue
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@ -80,7 +80,6 @@ from litellm.types.utils import (
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StandardLoggingVectorStoreRequest,
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VectorStoreSearchResponse,
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)
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from litellm.types.vector_stores import VectorStoreResultContent
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from litellm.utils import get_end_user_id_for_cost_tracking
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if TYPE_CHECKING:
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@ -1844,20 +1843,12 @@ def _get_vector_store_request_for_spend_logs_payload(
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)
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response_data = vector_store_search_response.get("data", []) or []
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for response_item in response_data:
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response_item["content"] = [
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_redacted_vector_store_content(content_item) for content_item in response_item.get("content", []) or []
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]
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for content_item in response_item.get("content", []) or []:
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if "text" in content_item:
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content_item["text"] = REDACTED_BY_LITELM_STRING
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return vector_store_request_metadata
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def _redacted_vector_store_content(content: VectorStoreResultContent) -> VectorStoreResultContent:
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without_text: Final[VectorStoreResultContent] = (
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{**content, "text": REDACTED_BY_LITELM_STRING} if "text" in content else content
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)
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source: Final = content.get("source")
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return without_text if source is None else {**without_text, "source": {**source, "data": REDACTED_BY_LITELM_STRING}}
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def _get_response_for_spend_logs_payload(
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payload: StandardLoggingPayload | None,
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kwargs: dict | None = None,
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@ -1,4 +1,4 @@
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from collections.abc import Iterator, Sequence
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from collections.abc import Sequence
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from typing import Any, Final
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from typing_extensions import NotRequired, ReadOnly, TypedDict
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@ -42,13 +42,6 @@ class _MessageView(TypedDict):
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message: ReadOnly[object]
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def _text_documents(search_data: _SearchDataView) -> Iterator[tuple[_SearchResultView, str]]:
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for result in search_data["results"]:
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for content in result.get("content", []):
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if content.get("type") == "text" and (content_text := content.get("text")):
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yield result, content_text
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class RAGQuery:
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CONTENT_PREFIX_STRING = "Context:\n\n"
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@ -133,17 +126,24 @@ class RAGQuery:
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search_response: Any,
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) -> list[str | dict[str, object]]:
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"""Extract text documents from vector store search response."""
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documents: Final[list[str | dict[str, object]]] = []
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search_data: Final[_SearchDataView] = {"results": search_response.get("data", [])}
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return [text for _, text in _text_documents(search_data)]
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for result in search_data["results"]:
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content_list = result.get("content", [])
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for content in content_list:
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if content.get("type") == "text" and content.get("text"):
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documents.append(content["text"])
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return documents
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@staticmethod
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def get_top_chunks_from_rerank(search_response: Any, rerank_response: Any) -> list[_SearchResultView]:
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"""Get the original search results corresponding to the top reranked results."""
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top_chunks: Final[list[_SearchResultView]] = []
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search_data: Final[_SearchDataView] = {"results": search_response.get("data", [])}
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document_results: Final = tuple(result for result, _ in _text_documents(search_data))
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original_results: Final = search_data["results"]
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reranked: Final[_RerankResultsView] = {"results": rerank_response.get("results", [])}
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return [
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document_results[index]
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for result in reranked["results"]
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if (index := result.get("index")) is not None and index < len(document_results)
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]
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for result in reranked["results"]:
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index = result.get("index")
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if index is not None and index < len(original_results):
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top_chunks.append(original_results[index])
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return top_chunks
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@ -166,7 +166,7 @@ async def _run_vector_searches(
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)
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results_data = response.get("data") if isinstance(response, dict) else getattr(response, "data", None)
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if results_data:
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all_results.extend(result for result in results_data if not _is_image_only_hit(result))
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all_results.extend(results_data)
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except Exception as exc:
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verbose_logger.warning(
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"file_search emulated: search failed for query='%s', vector_store_id='%s': %s",
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@ -190,24 +190,13 @@ def _get_field(result: object, key: str, default: object = None) -> object:
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return getattr(result, key, default)
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def _content_items(result: object) -> Iterable[object]:
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return cast(Iterable[object], _get_field(result, "content") or []) # cast-ok: iterated as today
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def _joined_content_text(result: object) -> str:
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"""Concatenate the text of every content chunk on a search result."""
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text_chunks: Final = [
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c.get("text", "") if isinstance(c, dict) else getattr(c, "text", "") for c in _content_items(result)
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]
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content_items: Final = cast(Iterable[object], _get_field(result, "content") or []) # cast-ok: iterated as today
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text_chunks: Final = [c.get("text", "") if isinstance(c, dict) else getattr(c, "text", "") for c in content_items]
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return " ".join(t for t in text_chunks if t)
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def _is_image_only_hit(result: object) -> bool:
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return not _joined_content_text(result) and any(
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_get_field(item, "type") == "image" for item in _content_items(result)
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)
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def _format_search_results_as_tool_output(
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results: list[VectorStoreSearchResult],
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) -> str:
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@ -293,7 +293,6 @@ async def asearch(
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max_num_results: int | None = None,
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ranking_options: dict | None = None,
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rewrite_query: bool | None = None,
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query_image: VectorStoreSearchQueryImage | None = None,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: dict[str, object] | None = None,
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@ -303,6 +302,8 @@ async def asearch(
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# LiteLLM specific params,
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custom_llm_provider: str | None = None,
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router: "Router | None" = None,
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*,
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query_image: VectorStoreSearchQueryImage | None = None,
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**kwargs,
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) -> VectorStoreSearchResponse:
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"""
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@ -368,7 +369,6 @@ def search(
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max_num_results: int | None = None,
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ranking_options: dict | None = None,
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rewrite_query: bool | None = None,
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query_image: VectorStoreSearchQueryImage | None = None,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: dict[str, object] | None = None,
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@ -378,6 +378,8 @@ def search(
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# LiteLLM specific params,
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custom_llm_provider: str | None = None,
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router: "Router | None" = None,
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*,
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query_image: VectorStoreSearchQueryImage | None = None,
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**kwargs,
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) -> VectorStoreSearchResponse | Coroutine[object, object, VectorStoreSearchResponse]:
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"""
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@ -49,10 +49,17 @@ SearchQuery: TypeAlias = TextSearchQuery[_Query] | ImageSearchQuery
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SearchQueryError: TypeAlias = QueryAndImageBothSent | NoQuerySent | InvalidQueryImage | QueryImageUnsupported
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def _has_text(query: str | Sequence[str]) -> bool:
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def _as_object_list(value: object) -> Sequence[object] | None:
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return value if isinstance(value, list) else None
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def _has_text(query: object) -> bool:
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if isinstance(query, str):
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return bool(query.strip())
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return any(item.strip() for item in query)
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items: Final = _as_object_list(query)
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if items is None:
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return True
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return any(not isinstance(item, str) or item.strip() for item in items)
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def _problem(detail: ErrorDetails) -> str:
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@ -1,6 +1,7 @@
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# litellm/proxy/vector_stores/vector_store_registry.py
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import json
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from collections.abc import Mapping, Sequence
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from dataclasses import fields, replace
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from datetime import datetime, timezone
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from itertools import chain
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from typing import (
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@ -8,19 +9,17 @@ from typing import (
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Any, # noqa: TID251 # untyped non_default_params dict is the only source of the unknown key type
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Final,
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cast, # noqa: TID251 # untyped non_default_params dict is the only source of the unknown key type
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get_args,
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)
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from pydantic import ConfigDict, TypeAdapter, ValidationError
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.core_helpers import remove_items_at_indices
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from litellm.litellm_core_utils.core_helpers import as_str_mapping, remove_items_at_indices
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from litellm.repositories.table_repositories import (
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ManagedVectorStoreIndexRepository,
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ManagedVectorStoresRepository,
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)
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from litellm.types.vector_stores import (
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VECTOR_STORE_OPENAI_PARAMS,
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LiteLLM_ManagedVectorStore,
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LiteLLM_ManagedVectorStoreIndex,
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LiteLLM_ManagedVectorStoreListResponse,
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@ -137,19 +136,15 @@ class VectorStoreRegistry:
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self.vector_store_ids_to_vector_store_map: dict[str, LiteLLM_ManagedVectorStore] = {}
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def _extract_tool_params(self, tool: dict) -> VectorStoreToolParams:
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"""
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Extract supported parameters from a tool definition.
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Dynamically extracts all parameters defined in VECTOR_STORE_OPENAI_PARAMS.
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"""
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# Get the list of supported param names from the Literal type
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declared_params: Final[tuple[object, ...]] = get_args(VECTOR_STORE_OPENAI_PARAMS)
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supported_params: Final = tuple(param for param in declared_params if isinstance(param, str))
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# Extract only the params that exist in the tool
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kwargs: Final = {param: tool.get(param) for param in supported_params if param in tool}
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return VectorStoreToolParams(**kwargs)
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tool_fields: Final = as_str_mapping(tool) or {}
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return replace(
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VectorStoreToolParams(),
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**{
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field.name: tool_fields[field.name]
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for field in fields(VectorStoreToolParams)
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if field.name in tool_fields
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},
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)
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def get_vector_store_ids_to_run(self, non_default_params: dict, tools: object = None) -> list[str]:
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"""
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@ -6,6 +6,7 @@ import httpx
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import pytest
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import respx
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import litellm
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from litellm.llms.bedrock.vector_stores.transformation import BedrockVectorStoreConfig
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@ -412,61 +413,73 @@ def test_transform_search_request_sends_query_image_as_an_image_retrieval_query(
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assert body["retrievalConfiguration"] == {"vectorSearchConfiguration": {"numberOfResults": 3}}
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CALLER_RETRIEVAL_QUERY: Final = {"type": "IMAGE", "image": {"format": "png", "inlineContent": "Y2FsbGVy"}}
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@pytest.mark.parametrize("key", ["retrievalQuery", "retrieval_query"])
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@pytest.mark.parametrize("source", ["extra_body", "litellm_params"])
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def test_transform_search_request_sends_the_callers_retrieval_query_in_place_of_the_built_one(
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key: str, source: str
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) -> None:
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caller_query: Final = {"type": "IMAGE", "image": {"format": "png", "inlineContent": "Y2FsbGVy"}}
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caller_fields: Final = {key: caller_query}
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def test_transform_search_request_rejects_a_callers_retrieval_query(key: str, source: str) -> None:
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caller_fields: Final = {key: CALLER_RETRIEVAL_QUERY}
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with pytest.raises(litellm.BadRequestError) as exc_info:
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_search_body(
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extra_body=caller_fields if source == "extra_body" else None,
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litellm_params=caller_fields if source == "litellm_params" else {},
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)
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assert exc_info.value.status_code == 400
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assert "query_image" in str(exc_info.value)
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def test_transform_search_request_drops_a_callers_retrieval_query_when_drop_params_is_on() -> None:
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body: Final = _search_body(
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extra_body=caller_fields if source == "extra_body" else None,
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litellm_params=caller_fields if source == "litellm_params" else {},
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extra_body={"retrievalQuery": CALLER_RETRIEVAL_QUERY}, litellm_params={"drop_params": True}
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)
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assert body["retrievalQuery"] == caller_query
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assert body["retrievalQuery"] == {"text": "hello"}
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def test_transform_search_request_prefers_the_callers_retrieval_query_over_query_image() -> None:
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caller_query: Final = {"text": "from extra_body"}
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body: Final = _image_search_body("image/jpeg", extra_body={"retrievalQuery": caller_query})
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assert body["retrievalQuery"] == caller_query
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QUERY_IMAGE: Final = {"type": "base64", "media_type": "image/jpeg", "data": "/9j/4AAQSkZJRg=="}
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IMAGE_HIT_METADATA: Final = {
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"x-amz-bedrock-kb-source-uri": "s3://kb-images/shapes/red-circle.jpg",
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"x-amz-bedrock-kb-source-file-mime-type": "image/jpeg",
|
||||
"x-amz-bedrock-kb-source-file-modality": "IMAGE",
|
||||
}
|
||||
TEXT_HIT_METADATA: Final = {"x-amz-bedrock-kb-source-uri": "s3://kb-docs/shapes.txt"}
|
||||
MIXED_RETRIEVAL_RESULTS: Final = [
|
||||
{
|
||||
"content": {"type": "IMAGE", "byteContent": "data:image/png;base64,/9j/4AAQSkZJRg=="},
|
||||
"metadata": IMAGE_HIT_METADATA,
|
||||
"location": {"type": "S3", "s3Location": {"uri": "s3://kb-images/shapes/red-circle.jpg"}},
|
||||
"score": 0.998,
|
||||
},
|
||||
{"content": {"type": "TEXT", "text": "a red circle"}, "metadata": TEXT_HIT_METADATA, "score": 0.5},
|
||||
{"content": {"type": "ROW", "row": []}, "metadata": {}, "score": 0.4},
|
||||
{"content": {"type": "AUDIO", "byteContent": "data:audio/mpeg;base64,SUQz"}, "metadata": {}, "score": 0.3},
|
||||
]
|
||||
TEXT_HIT: Final = {
|
||||
"score": 0.5,
|
||||
"content": [{"text": "a red circle", "type": "text"}],
|
||||
"file_id": "s3://kb-docs/shapes.txt",
|
||||
"filename": "shapes.txt",
|
||||
"attributes": TEXT_HIT_METADATA,
|
||||
}
|
||||
|
||||
|
||||
def _search_response(retrieval_results: list[dict[str, object]], query: str = "") -> dict[str, object]:
|
||||
def _search_response(
|
||||
retrieval_results: list[dict[str, object]], query: str = "", query_image: dict[str, str] | None = QUERY_IMAGE
|
||||
) -> dict[str, object]:
|
||||
config: Final = BedrockVectorStoreConfig()
|
||||
mock_log: Final = MagicMock()
|
||||
mock_log.model_call_details = {"query": query}
|
||||
mock_log.model_call_details = {"query": query, "query_image": query_image}
|
||||
return config.transform_search_vector_store_response(
|
||||
response=httpx.Response(200, json={"retrievalResults": retrieval_results}),
|
||||
litellm_logging_obj=mock_log,
|
||||
)
|
||||
|
||||
|
||||
def test_transform_search_response_returns_image_hits_next_to_text_hits() -> None:
|
||||
image_metadata: Final = {
|
||||
"x-amz-bedrock-kb-source-uri": "s3://kb-images/shapes/red-circle.jpg",
|
||||
"x-amz-bedrock-kb-source-file-mime-type": "image/jpeg",
|
||||
"x-amz-bedrock-kb-source-file-modality": "IMAGE",
|
||||
}
|
||||
text_metadata: Final = {"x-amz-bedrock-kb-source-uri": "s3://kb-docs/shapes.txt"}
|
||||
|
||||
response: Final = _search_response(
|
||||
[
|
||||
{
|
||||
"content": {"type": "IMAGE", "byteContent": "data:image/png;base64,/9j/4AAQSkZJRg=="},
|
||||
"metadata": image_metadata,
|
||||
"location": {"type": "S3", "s3Location": {"uri": "s3://kb-images/shapes/red-circle.jpg"}},
|
||||
"score": 0.998,
|
||||
},
|
||||
{"content": {"type": "TEXT", "text": "a red circle"}, "metadata": text_metadata, "score": 0.5},
|
||||
{"content": {"type": "ROW", "row": []}, "metadata": {}, "score": 0.4},
|
||||
{"content": {"type": "AUDIO", "byteContent": "data:audio/mpeg;base64,SUQz"}, "metadata": {}, "score": 0.3},
|
||||
]
|
||||
)
|
||||
def test_transform_search_response_to_an_image_search_returns_image_hits_next_to_text_hits() -> None:
|
||||
response: Final = _search_response(MIXED_RETRIEVAL_RESULTS)
|
||||
|
||||
assert response["search_query"] == ""
|
||||
assert response["data"] == [
|
||||
|
|
@ -480,18 +493,19 @@ def test_transform_search_response_returns_image_hits_next_to_text_hits() -> Non
|
|||
],
|
||||
"file_id": "s3://kb-images/shapes/red-circle.jpg",
|
||||
"filename": "red-circle.jpg",
|
||||
"attributes": image_metadata,
|
||||
},
|
||||
{
|
||||
"score": 0.5,
|
||||
"content": [{"text": "a red circle", "type": "text"}],
|
||||
"file_id": "s3://kb-docs/shapes.txt",
|
||||
"filename": "shapes.txt",
|
||||
"attributes": text_metadata,
|
||||
"attributes": IMAGE_HIT_METADATA,
|
||||
},
|
||||
TEXT_HIT,
|
||||
]
|
||||
|
||||
|
||||
def test_transform_search_response_to_a_text_search_returns_only_text_hits() -> None:
|
||||
response: Final = _search_response(MIXED_RETRIEVAL_RESULTS, query="red circle", query_image=None)
|
||||
|
||||
assert response["search_query"] == "red circle"
|
||||
assert response["data"] == [TEXT_HIT]
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("byte_content", "metadata", "media_type", "data"),
|
||||
[
|
||||
|
|
|
|||
|
|
@ -1046,53 +1046,3 @@ class TestEmulatedFileSearchHandler:
|
|||
assert set(searched_ids) == {"vs_two"}, (
|
||||
"A request-listed id the model picks should narrow the search to that store only"
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_H18_image_only_hits_are_left_out_of_the_answer_and_citations(self):
|
||||
from litellm.responses.file_search.emulated_handler import (
|
||||
aresponses_with_emulated_file_search,
|
||||
)
|
||||
|
||||
first_resp = self._make_mock_responses_api_response(include_function_call=True)
|
||||
final_resp = self._make_mock_responses_api_response(text="A red circle.")
|
||||
image_hit = {
|
||||
"file_id": "file-image",
|
||||
"filename": "circle.jpg",
|
||||
"score": 0.99,
|
||||
"content": [{"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": "/9j/"}}],
|
||||
}
|
||||
text_hit = {
|
||||
"file_id": "file-text",
|
||||
"filename": "shapes.txt",
|
||||
"score": 0.8,
|
||||
"content": [{"type": "text", "text": "The logo is a red circle."}],
|
||||
}
|
||||
mock_call_aresponses = AsyncMock(side_effect=[first_resp, final_resp])
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
import_module("litellm.responses.file_search.emulated_handler"),
|
||||
"_call_aresponses",
|
||||
new=mock_call_aresponses,
|
||||
),
|
||||
patch( # test-quality-ok: the vector store returns the hits under test
|
||||
"litellm.vector_stores.main.asearch",
|
||||
new=AsyncMock(return_value={"data": [image_hit, text_hit]}),
|
||||
),
|
||||
):
|
||||
result = await aresponses_with_emulated_file_search(
|
||||
input="What is the logo?",
|
||||
model="anthropic/claude-3-5-sonnet",
|
||||
tools=[{"type": "file_search", "vector_store_ids": ["vs_001"]}],
|
||||
include=["file_search_call.results"],
|
||||
)
|
||||
|
||||
def _get(item, key):
|
||||
return item[key] if isinstance(item, dict) else getattr(item, key, None)
|
||||
|
||||
follow_up_input = str(mock_call_aresponses.call_args_list[1].kwargs["input"])
|
||||
assert "file-image" not in follow_up_input
|
||||
assert "file-text" in follow_up_input
|
||||
assert [_get(hit, "file_id") for hit in _get(result.output[0], "search_results")] == ["file-text"]
|
||||
annotations = _get(_get(result.output[1], "content")[0], "annotations")
|
||||
assert [_get(annotation, "file_id") for annotation in annotations] == ["file-text"]
|
||||
|
|
|
|||
|
|
@ -863,22 +863,6 @@ def test_get_vector_store_request_for_spend_logs_payload_store_prompts_false(
|
|||
assert result[0]["vector_store_search_response"]["data"][0]["content"][0]["type"] == "text"
|
||||
|
||||
|
||||
def test_get_vector_store_request_for_spend_logs_payload_redacts_image_hit_data(monkeypatch: pytest.MonkeyPatch):
|
||||
from litellm.proxy import proxy_server
|
||||
|
||||
monkeypatch.setattr(proxy_server, "general_settings", {"store_prompts_in_spend_logs": False})
|
||||
image_hit: Final = {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": "/9j/4AAQ"}}
|
||||
|
||||
stored: Final = _get_vector_store_request_for_spend_logs_payload(
|
||||
[{"vector_store_search_response": {"data": [{"content": [image_hit]}]}}]
|
||||
)
|
||||
|
||||
assert stored is not None
|
||||
assert stored[0]["vector_store_search_response"]["data"][0]["content"] == [
|
||||
{"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": REDACTED_BY_LITELM_STRING}}
|
||||
]
|
||||
|
||||
|
||||
@patch("litellm.proxy.spend_tracking.spend_tracking_utils.should_store_prompts_and_responses_in_spend_logs")
|
||||
def test_get_vector_store_request_for_spend_logs_payload_null_input(mock_should_store):
|
||||
# When input is None
|
||||
|
|
|
|||
|
|
@ -441,60 +441,6 @@ async def test_aquery_forwards_filters_to_vector_store_search(
|
|||
assert request_body["max_num_results"] == 50
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_aquery_rerank_picks_the_text_hit_the_reranker_ranked_when_image_hits_come_first(monkeypatch):
|
||||
from litellm.types.rerank import RerankResponse
|
||||
|
||||
monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
|
||||
image_hit: Final = {
|
||||
"file_id": "file-image",
|
||||
"filename": "circle.jpg",
|
||||
"score": 0.99,
|
||||
"content": [{"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": "/9j/"}}],
|
||||
}
|
||||
text_hit: Final = {
|
||||
"file_id": "file-text",
|
||||
"filename": "shapes.txt",
|
||||
"score": 0.8,
|
||||
"content": [{"type": "text", "text": "The logo is a red circle."}],
|
||||
}
|
||||
reranked_documents: Final[list[object]] = []
|
||||
|
||||
async def fake_arerank(**kwargs: object) -> RerankResponse:
|
||||
reranked_documents.append(kwargs["documents"])
|
||||
return RerankResponse(id="rr_1", results=[{"index": 0, "relevance_score": 0.9}], meta={})
|
||||
|
||||
with respx.mock(assert_all_called=True) as respx_mock, patch("litellm.arerank", side_effect=fake_arerank):
|
||||
respx_mock.post("https://example.com/v1/vector_stores/vs_test_123/search").mock(
|
||||
return_value=httpx.Response(
|
||||
200,
|
||||
json={"object": "vector_store.search_results.page", "search_query": "q", "data": [image_hit, text_hit]},
|
||||
)
|
||||
)
|
||||
completion_route: Final = respx_mock.post("https://example.com/v1/chat/completions").mock(
|
||||
return_value=httpx.Response(
|
||||
200,
|
||||
content=(
|
||||
'{"id":"chatcmpl-test","object":"chat.completion","created":1,"model":"gpt-4o-mini",'
|
||||
'"choices":[{"index":0,"message":{"role":"assistant","content":"answer"},"finish_reason":"stop"}],'
|
||||
'"usage":{"prompt_tokens":1,"completion_tokens":1,"total_tokens":2}}'
|
||||
),
|
||||
)
|
||||
)
|
||||
await litellm.aquery(
|
||||
model="openai/gpt-4o-mini",
|
||||
messages=[{"role": "user", "content": "What is the logo?"}],
|
||||
retrieval_config={"vector_store_id": "vs_test_123", "custom_llm_provider": "openai"},
|
||||
rerank={"enabled": True, "model": "cohere/rerank-english-v3.0", "top_n": 1},
|
||||
api_key="sk-test",
|
||||
api_base="https://example.com/v1",
|
||||
)
|
||||
completion_body: Final = completion_route.calls.last.request.content.decode()
|
||||
|
||||
assert reranked_documents == [["The logo is a red circle."]]
|
||||
assert "The logo is a red circle." in completion_body
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_aquery_forwards_provider_retrieval_config_and_router_to_search():
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -165,6 +165,14 @@ def test_search_by_query_image_sends_a_bedrock_image_retrieval_query(query: str
|
|||
"Send either query or query_image, not both.",
|
||||
),
|
||||
({"custom_llm_provider": "bedrock"}, "query or query_image is required."),
|
||||
(
|
||||
{"custom_llm_provider": "bedrock", "query": 5, "query_image": QUERY_IMAGE},
|
||||
"Send either query or query_image, not both.",
|
||||
),
|
||||
(
|
||||
{"custom_llm_provider": "bedrock", "query": [1, 2], "query_image": QUERY_IMAGE},
|
||||
"Send either query or query_image, not both.",
|
||||
),
|
||||
(
|
||||
{"custom_llm_provider": "openai", "query_image": QUERY_IMAGE},
|
||||
"openai vector stores do not support query_image",
|
||||
|
|
|
|||
|
|
@ -377,6 +377,25 @@ def test_vector_store_ids_to_run_reads_every_store_id_a_malformed_vector_store_i
|
|||
assert VectorStoreRegistry().get_vector_store_ids_to_run(non_default_params={}, tools=tools) == ["vs_requested"]
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("tool_field", "tool_value"),
|
||||
[("query_image", {"type": "base64", "media_type": "image/jpeg", "data": "/9j/"}), ("rewrite_query", True)],
|
||||
)
|
||||
def test_file_search_tool_with_a_field_the_store_search_does_not_take_still_runs_its_store(
|
||||
tool_field: str, tool_value: object
|
||||
) -> None:
|
||||
now: Final = datetime.now(timezone.utc)
|
||||
registry: Final = VectorStoreRegistry(
|
||||
[LiteLLM_ManagedVectorStore(vector_store_id="vs_kb", created_at=now, updated_at=now)]
|
||||
)
|
||||
tools: Final = [{"type": "file_search", "vector_store_ids": ["vs_kb"], "max_num_results": 3, tool_field: tool_value}]
|
||||
|
||||
to_run: Final = registry.pop_vector_stores_to_run(non_default_params={}, tools=tools)
|
||||
|
||||
assert [store["vector_store_id"] for store in to_run] == ["vs_kb"]
|
||||
assert to_run[0]["litellm_params"] == {"max_num_results": 3}
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"vector_store_ids",
|
||||
["vs_allowed", ["vs_allowed", 12345], [12345], ["vs_allowed"]],
|
||||
|
|
|
|||
|
|
@ -93,33 +93,6 @@ describe("VectorStoreTester", () => {
|
|||
expect(screen.getByText("Filename:").parentElement).toHaveTextContent("notes.txt");
|
||||
});
|
||||
|
||||
it("shows an image hit as an image when the result is expanded", async () => {
|
||||
const user = userEvent.setup();
|
||||
mockSearch.mockResolvedValue({
|
||||
object: "vector_store.search_results.page",
|
||||
search_query: "",
|
||||
data: [
|
||||
{
|
||||
score: 0.88,
|
||||
content: [{ type: "image", source: { type: "base64", media_type: "image/jpeg", data: "/9j/4AAQ" } }],
|
||||
file_id: "file-2",
|
||||
filename: "red-circle.jpg",
|
||||
attributes: {},
|
||||
},
|
||||
],
|
||||
});
|
||||
renderTester();
|
||||
|
||||
await user.type(queryInput(), "red circle");
|
||||
await user.click(searchButton());
|
||||
await user.click(await screen.findByText("Result 1"));
|
||||
|
||||
expect(screen.getByRole("img", { name: "red-circle.jpg" })).toHaveAttribute(
|
||||
"src",
|
||||
"data:image/jpeg;base64,/9j/4AAQ",
|
||||
);
|
||||
});
|
||||
|
||||
it("warns instead of searching when the query is only whitespace", async () => {
|
||||
const user = userEvent.setup();
|
||||
renderTester();
|
||||
|
|
|
|||
|
|
@ -8,27 +8,11 @@ import { Separator } from "@/components/ui/separator";
|
|||
import { Textarea } from "@/components/ui/textarea";
|
||||
import { UiLoadingSpinner } from "@/components/ui/ui-loading-spinner";
|
||||
|
||||
interface VectorStoreTextContent {
|
||||
interface VectorStoreContent {
|
||||
text: string;
|
||||
type: string;
|
||||
}
|
||||
|
||||
interface VectorStoreImageContent {
|
||||
type: "image";
|
||||
source: {
|
||||
type: "base64";
|
||||
media_type: string;
|
||||
data: string;
|
||||
};
|
||||
}
|
||||
|
||||
type VectorStoreContent = VectorStoreTextContent | VectorStoreImageContent;
|
||||
|
||||
const isImageContent = (content: VectorStoreContent): content is VectorStoreImageContent => "source" in content;
|
||||
|
||||
const contentPreview = (content: VectorStoreContent): string =>
|
||||
isImageContent(content) ? content.source.media_type : content.text.substring(0, 100);
|
||||
|
||||
interface VectorStoreResult {
|
||||
score: number;
|
||||
content: VectorStoreContent[];
|
||||
|
|
@ -190,7 +174,7 @@ export const VectorStoreTester: React.FC<VectorStoreTesterProps> = ({ vectorStor
|
|||
{/* Show preview of content when collapsed */}
|
||||
{!isExpanded && result.content && result.content[0] && (
|
||||
<span className="ml-2 max-w-md truncate text-xs text-muted-foreground">
|
||||
- {contentPreview(result.content[0])}...
|
||||
- {result.content[0].text.substring(0, 100)}...
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
|
|
@ -210,15 +194,7 @@ export const VectorStoreTester: React.FC<VectorStoreTesterProps> = ({ vectorStor
|
|||
Content ({content.type})
|
||||
</div>
|
||||
<div className="max-h-40 overflow-y-auto rounded-sm border bg-muted/50 p-3 text-sm text-foreground">
|
||||
{isImageContent(content) ? (
|
||||
<img
|
||||
src={`data:${content.source.media_type};base64,${content.source.data}`}
|
||||
alt={result.filename || content.source.media_type}
|
||||
className="max-h-36 rounded-sm"
|
||||
/>
|
||||
) : (
|
||||
content.text
|
||||
)}
|
||||
{content.text}
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue