Source code for langchain_community.vectorstores.azuresearch

from __future__ import annotations

import base64
import itertools
import json
import logging
import uuid
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    ClassVar,
    Collection,
    Dict,
    Iterable,
    List,
    Literal,
    Optional,
    Tuple,
    Type,
    Union,
)

import numpy as np
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import root_validator
from langchain_core.retrievers import BaseRetriever
from langchain_core.utils import get_from_env
from langchain_core.vectorstores import VectorStore

from langchain_community.vectorstores.utils import maximal_marginal_relevance

logger = logging.getLogger()

if TYPE_CHECKING:
    from azure.search.documents import SearchClient, SearchItemPaged
    from azure.search.documents.indexes.models import (
        CorsOptions,
        ScoringProfile,
        SearchField,
        SemanticConfiguration,
        VectorSearch,
    )

# Allow overriding field names for Azure Search
FIELDS_ID = get_from_env(
    key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id"
)
FIELDS_CONTENT = get_from_env(
    key="AZURESEARCH_FIELDS_CONTENT",
    env_key="AZURESEARCH_FIELDS_CONTENT",
    default="content",
)
FIELDS_CONTENT_VECTOR = get_from_env(
    key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
    env_key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
    default="content_vector",
)
FIELDS_METADATA = get_from_env(
    key="AZURESEARCH_FIELDS_TAG", env_key="AZURESEARCH_FIELDS_TAG", default="metadata"
)

MAX_UPLOAD_BATCH_SIZE = 1000


def _get_search_client(
    endpoint: str,
    key: str,
    index_name: str,
    semantic_configuration_name: Optional[str] = None,
    fields: Optional[List[SearchField]] = None,
    vector_search: Optional[VectorSearch] = None,
    semantic_configurations: Optional[
        Union[SemanticConfiguration, List[SemanticConfiguration]]
    ] = None,
    scoring_profiles: Optional[List[ScoringProfile]] = None,
    default_scoring_profile: Optional[str] = None,
    default_fields: Optional[List[SearchField]] = None,
    user_agent: Optional[str] = "langchain",
    cors_options: Optional[CorsOptions] = None,
) -> SearchClient:
    from azure.core.credentials import AzureKeyCredential
    from azure.core.exceptions import ResourceNotFoundError
    from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential
    from azure.search.documents import SearchClient
    from azure.search.documents.indexes import SearchIndexClient
    from azure.search.documents.indexes.models import (
        ExhaustiveKnnAlgorithmConfiguration,
        ExhaustiveKnnParameters,
        HnswAlgorithmConfiguration,
        HnswParameters,
        SearchIndex,
        SemanticConfiguration,
        SemanticField,
        SemanticPrioritizedFields,
        SemanticSearch,
        VectorSearch,
        VectorSearchAlgorithmKind,
        VectorSearchAlgorithmMetric,
        VectorSearchProfile,
    )

    default_fields = default_fields or []
    if key is None:
        credential = DefaultAzureCredential()
    elif key.upper() == "INTERACTIVE":
        credential = InteractiveBrowserCredential()
        credential.get_token("https://search.azure.com/.default")
    else:
        credential = AzureKeyCredential(key)
    index_client: SearchIndexClient = SearchIndexClient(
        endpoint=endpoint, credential=credential, user_agent=user_agent
    )
    try:
        index_client.get_index(name=index_name)
    except ResourceNotFoundError:
        # Fields configuration
        if fields is not None:
            # Check mandatory fields
            fields_types = {f.name: f.type for f in fields}
            mandatory_fields = {df.name: df.type for df in default_fields}
            # Check for missing keys
            missing_fields = {
                key: mandatory_fields[key]
                for key, value in set(mandatory_fields.items())
                - set(fields_types.items())
            }
            if len(missing_fields) > 0:
                # Helper for formatting field information for each missing field.
                def fmt_err(x: str) -> str:
                    return (
                        f"{x} current type: '{fields_types.get(x, 'MISSING')}'. "
                        f"It has to be '{mandatory_fields.get(x)}' or you can point "
                        f"to a different '{mandatory_fields.get(x)}' field name by "
                        f"using the env variable 'AZURESEARCH_FIELDS_{x.upper()}'"
                    )

                error = "\n".join([fmt_err(x) for x in missing_fields])
                raise ValueError(
                    f"You need to specify at least the following fields "
                    f"{missing_fields} or provide alternative field names in the env "
                    f"variables.\n\n{error}"
                )
        else:
            fields = default_fields
        # Vector search configuration
        if vector_search is None:
            vector_search = VectorSearch(
                algorithms=[
                    HnswAlgorithmConfiguration(
                        name="default",
                        kind=VectorSearchAlgorithmKind.HNSW,
                        parameters=HnswParameters(
                            m=4,
                            ef_construction=400,
                            ef_search=500,
                            metric=VectorSearchAlgorithmMetric.COSINE,
                        ),
                    ),
                    ExhaustiveKnnAlgorithmConfiguration(
                        name="default_exhaustive_knn",
                        kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN,
                        parameters=ExhaustiveKnnParameters(
                            metric=VectorSearchAlgorithmMetric.COSINE
                        ),
                    ),
                ],
                profiles=[
                    VectorSearchProfile(
                        name="myHnswProfile",
                        algorithm_configuration_name="default",
                    ),
                    VectorSearchProfile(
                        name="myExhaustiveKnnProfile",
                        algorithm_configuration_name="default_exhaustive_knn",
                    ),
                ],
            )

        # Create the semantic settings with the configuration
        if semantic_configurations:
            if not isinstance(semantic_configurations, list):
                semantic_configurations = [semantic_configurations]
            semantic_search = SemanticSearch(
                configurations=semantic_configurations,
                default_configuration_name=semantic_configuration_name,
            )
        elif semantic_configuration_name:
            # use default semantic configuration
            semantic_configuration = SemanticConfiguration(
                name=semantic_configuration_name,
                prioritized_fields=SemanticPrioritizedFields(
                    content_fields=[SemanticField(field_name=FIELDS_CONTENT)],
                ),
            )
            semantic_search = SemanticSearch(configurations=[semantic_configuration])
        else:
            # don't use semantic search
            semantic_search = None

        # Create the search index with the semantic settings and vector search
        index = SearchIndex(
            name=index_name,
            fields=fields,
            vector_search=vector_search,
            semantic_search=semantic_search,
            scoring_profiles=scoring_profiles,
            default_scoring_profile=default_scoring_profile,
            cors_options=cors_options,
        )
        index_client.create_index(index)
    # Create the search client
    return SearchClient(
        endpoint=endpoint,
        index_name=index_name,
        credential=credential,
        user_agent=user_agent,
    )


[docs]class AzureSearch(VectorStore): """`Azure Cognitive Search` vector store."""
[docs] def __init__( self, azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Union[Callable, Embeddings], search_type: str = "hybrid", semantic_configuration_name: Optional[str] = None, fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, semantic_configurations: Optional[ Union[SemanticConfiguration, List[SemanticConfiguration]] ] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, cors_options: Optional[CorsOptions] = None, *, vector_search_dimensions: Optional[int] = None, **kwargs: Any, ): from azure.search.documents.indexes.models import ( SearchableField, SearchField, SearchFieldDataType, SimpleField, ) """Initialize with necessary components.""" # Initialize base class self.embedding_function = embedding_function if isinstance(self.embedding_function, Embeddings): self.embed_query = self.embedding_function.embed_query else: self.embed_query = self.embedding_function default_fields = [ SimpleField( name=FIELDS_ID, type=SearchFieldDataType.String, key=True, filterable=True, ), SearchableField( name=FIELDS_CONTENT, type=SearchFieldDataType.String, ), SearchField( name=FIELDS_CONTENT_VECTOR, type=SearchFieldDataType.Collection(SearchFieldDataType.Single), searchable=True, vector_search_dimensions=vector_search_dimensions or len(self.embed_query("Text")), vector_search_profile_name="myHnswProfile", ), SearchableField( name=FIELDS_METADATA, type=SearchFieldDataType.String, ), ] user_agent = "langchain" if "user_agent" in kwargs and kwargs["user_agent"]: user_agent += " " + kwargs["user_agent"] self.client = _get_search_client( azure_search_endpoint, azure_search_key, index_name, semantic_configuration_name=semantic_configuration_name, fields=fields, vector_search=vector_search, semantic_configurations=semantic_configurations, scoring_profiles=scoring_profiles, default_scoring_profile=default_scoring_profile, default_fields=default_fields, user_agent=user_agent, cors_options=cors_options, ) self.search_type = search_type self.semantic_configuration_name = semantic_configuration_name self.fields = fields if fields else default_fields
@property def embeddings(self) -> Optional[Embeddings]: # TODO: Support embedding object directly return None
[docs] def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> List[str]: """Add texts data to an existing index.""" keys = kwargs.get("keys") # batching support if embedding function is an Embeddings object if isinstance(self.embedding_function, Embeddings): try: embeddings = self.embedding_function.embed_documents(texts) # type: ignore[arg-type] except NotImplementedError: embeddings = [self.embedding_function.embed_query(x) for x in texts] else: embeddings = [self.embedding_function(x) for x in texts] if len(embeddings) == 0: logger.debug("Nothing to insert, skipping.") return [] return self.add_embeddings(zip(texts, embeddings), metadatas, keys=keys)
[docs] def add_embeddings( self, text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None, ) -> List[str]: """Add embeddings to an existing index.""" ids = [] # Write data to index data = [] for i, (text, embedding) in enumerate(text_embeddings): # Use provided key otherwise use default key key = keys[i] if keys else str(uuid.uuid4()) # Encoding key for Azure Search valid characters key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii") metadata = metadatas[i] if metadatas else {} # Add data to index # Additional metadata to fields mapping doc = { "@search.action": "upload", FIELDS_ID: key, FIELDS_CONTENT: text, FIELDS_CONTENT_VECTOR: np.array(embedding, dtype=np.float32).tolist(), FIELDS_METADATA: json.dumps(metadata), } if metadata: additional_fields = { k: v for k, v in metadata.items() if k in [x.name for x in self.fields] } doc.update(additional_fields) data.append(doc) ids.append(key) # Upload data in batches if len(data) == MAX_UPLOAD_BATCH_SIZE: response = self.client.upload_documents(documents=data) # Check if all documents were successfully uploaded if not all(r.succeeded for r in response): raise Exception(response) # Reset data data = [] # Considering case where data is an exact multiple of batch-size entries if len(data) == 0: return ids # Upload data to index response = self.client.upload_documents(documents=data) # Check if all documents were successfully uploaded if all(r.succeeded for r in response): return ids else: raise Exception(response)
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool: """Delete by vector ID. Args: ids: List of ids to delete. Returns: bool: True if deletion is successful, False otherwise. """ if ids: res = self.client.delete_documents([{"id": i} for i in ids]) return len(res) > 0 else: return False
[docs] def similarity_search_with_relevance_scores( self, query: str, k: int = 4, **kwargs: Any ) -> List[Tuple[Document, float]]: score_threshold = kwargs.pop("score_threshold", None) result = self.vector_search_with_score(query, k=k, **kwargs) return ( result if score_threshold is None else [r for r in result if r[1] >= score_threshold] )
[docs] def vector_search_with_score( self, query: str, k: int = 4, filters: Optional[str] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return docs most similar to query. Args: query (str): Text to look up documents similar to. k (int, optional): Number of Documents to return. Defaults to 4. filters (str, optional): Filtering expression. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents most similar to the query and score for each """ embedding = self.embed_query(query) results = self._simple_search(embedding, "", k, filters=filters, **kwargs) return _results_to_documents(results)
[docs] def max_marginal_relevance_search_with_score( self, query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Perform a search and return results that are reordered by MMR. Args: query (str): Text to look up documents similar to. k (int, optional): How many results to give. Defaults to 4. fetch_k (int, optional): Total results to select k from. Defaults to 20. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5 filters (str, optional): Filtering expression. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents most similar to the query and score for each """ embedding = self.embed_query(query) results = self._simple_search(embedding, "", fetch_k, filters=filters, **kwargs) return _reorder_results_with_maximal_marginal_relevance( results, query_embedding=np.array(embedding), lambda_mult=lambda_mult, k=k )
[docs] def hybrid_search_with_score( self, query: str, k: int = 4, filters: Optional[str] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return docs most similar to query with a hybrid query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of Documents most similar to the query and score for each """ embedding = self.embed_query(query) results = self._simple_search(embedding, query, k, filters=filters, **kwargs) return _results_to_documents(results)
[docs] def hybrid_search_with_relevance_scores( self, query: str, k: int = 4, **kwargs: Any ) -> List[Tuple[Document, float]]: score_threshold = kwargs.pop("score_threshold", None) result = self.hybrid_search_with_score(query, k=k, **kwargs) return ( result if score_threshold is None else [r for r in result if r[1] >= score_threshold] )
[docs] def hybrid_max_marginal_relevance_search_with_score( self, query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return docs most similar to query with a hybrid query and reorder results by MMR. Args: query (str): Text to look up documents similar to. k (int, optional): Number of Documents to return. Defaults to 4. fetch_k (int, optional): Total results to select k from. Defaults to 20. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5 filters (str, optional): Filtering expression. Defaults to None. Returns: List of Documents most similar to the query and score for each """ embedding = self.embed_query(query) results = self._simple_search( embedding, query, fetch_k, filters=filters, **kwargs ) return _reorder_results_with_maximal_marginal_relevance( results, query_embedding=np.array(embedding), lambda_mult=lambda_mult, k=k )
def _simple_search( self, embedding: List[float], text_query: str, k: int, *, filters: Optional[str] = None, **kwargs: Any, ) -> SearchItemPaged[dict]: """Perform vector or hybrid search in the Azure search index. Args: embedding: A vector embedding to search in the vector space. text_query: A full-text search query expression; Use "*" or omit this parameter to perform only vector search. k: Number of documents to return. filters: Filtering expression. Returns: Search items """ from azure.search.documents.models import VectorizedQuery return self.client.search( search_text=text_query, vector_queries=[ VectorizedQuery( vector=np.array(embedding, dtype=np.float32).tolist(), k_nearest_neighbors=k, fields=FIELDS_CONTENT_VECTOR, ) ], filter=filters, top=k, **kwargs, )
[docs] def semantic_hybrid_search_with_score( self, query: str, k: int = 4, score_type: Literal["score", "reranker_score"] = "score", **kwargs: Any, ) -> List[Tuple[Document, float]]: """ Returns the most similar indexed documents to the query text. Args: query (str): The query text for which to find similar documents. k (int): The number of documents to return. Default is 4. score_type: Must either be "score" or "reranker_score". Defaulted to "score". filters: Filtering expression. Returns: List[Tuple[Document, float]]: A list of documents and their corresponding scores. """ score_threshold = kwargs.pop("score_threshold", None) docs_and_scores = self.semantic_hybrid_search_with_score_and_rerank( query, k=k, **kwargs ) if score_type == "score": return [ (doc, score) for doc, score, _ in docs_and_scores if score_threshold is None or score >= score_threshold ] elif score_type == "reranker_score": return [ (doc, reranker_score) for doc, _, reranker_score in docs_and_scores if score_threshold is None or reranker_score >= score_threshold ]
[docs] def semantic_hybrid_search_with_score_and_rerank( self, query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any ) -> List[Tuple[Document, float, float]]: """Return docs most similar to query with a hybrid query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filters: Filtering expression. Returns: List of Documents most similar to the query and score for each """ from azure.search.documents.models import VectorizedQuery results = self.client.search( search_text=query, vector_queries=[ VectorizedQuery( vector=np.array(self.embed_query(query), dtype=np.float32).tolist(), k_nearest_neighbors=k, fields=FIELDS_CONTENT_VECTOR, ) ], filter=filters, query_type="semantic", semantic_configuration_name=self.semantic_configuration_name, query_caption="extractive", query_answer="extractive", top=k, **kwargs, ) # Get Semantic Answers semantic_answers = results.get_answers() or [] semantic_answers_dict: Dict = {} for semantic_answer in semantic_answers: semantic_answers_dict[semantic_answer.key] = { "text": semantic_answer.text, "highlights": semantic_answer.highlights, } # Convert results to Document objects docs = [ ( Document( page_content=result.pop(FIELDS_CONTENT), metadata={ **( json.loads(result[FIELDS_METADATA]) if FIELDS_METADATA in result else { k: v for k, v in result.items() if k != FIELDS_CONTENT_VECTOR } ), **{ "captions": { "text": result.get("@search.captions", [{}])[0].text, "highlights": result.get("@search.captions", [{}])[ 0 ].highlights, } if result.get("@search.captions") else {}, "answers": semantic_answers_dict.get( result.get(FIELDS_ID, ""), "", ), }, }, ), float(result["@search.score"]), float(result["@search.reranker_score"]), ) for result in results ] return docs
[docs] @classmethod def from_texts( cls: Type[AzureSearch], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, azure_search_endpoint: str = "", azure_search_key: str = "", index_name: str = "langchain-index", fields: Optional[List[SearchField]] = None, **kwargs: Any, ) -> AzureSearch: # Creating a new Azure Search instance azure_search = cls( azure_search_endpoint, azure_search_key, index_name, embedding, fields=fields, **kwargs, ) azure_search.add_texts(texts, metadatas, **kwargs) return azure_search
[docs] @classmethod async def afrom_embeddings( cls: Type[AzureSearch], text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, azure_search_endpoint: str = "", azure_search_key: str = "", index_name: str = "langchain-index", fields: Optional[List[SearchField]] = None, **kwargs: Any, ) -> AzureSearch: return cls.from_embeddings( text_embeddings, embedding, metadatas=metadatas, azure_search_endpoint=azure_search_endpoint, azure_search_key=azure_search_key, index_name=index_name, fields=fields, **kwargs, )
[docs] @classmethod def from_embeddings( cls: Type[AzureSearch], text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, azure_search_endpoint: str = "", azure_search_key: str = "", index_name: str = "langchain-index", fields: Optional[List[SearchField]] = None, **kwargs: Any, ) -> AzureSearch: # Creating a new Azure Search instance text_embeddings, first_text_embedding = _peek(text_embeddings) if first_text_embedding is None: raise ValueError("Cannot create AzureSearch from empty embeddings.") vector_search_dimensions = len(first_text_embedding[1]) azure_search = cls( azure_search_endpoint=azure_search_endpoint, azure_search_key=azure_search_key, index_name=index_name, embedding_function=embedding, fields=fields, vector_search_dimensions=vector_search_dimensions, **kwargs, ) azure_search.add_embeddings(text_embeddings, metadatas, **kwargs) return azure_search
[docs] def as_retriever(self, **kwargs: Any) -> AzureSearchVectorStoreRetriever: # type: ignore """Return AzureSearchVectorStoreRetriever initialized from this VectorStore. Args: search_type (Optional[str]): Defines the type of search that the Retriever should perform. Can be "similarity" (default), "hybrid", or "semantic_hybrid". search_kwargs (Optional[Dict]): Keyword arguments to pass to the search function. Can include things like: score_threshold: Minimum relevance threshold for similarity_score_threshold fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR; 1 for minimum diversity and 0 for maximum. (Default: 0.5) filter: Filter by document metadata Returns: AzureSearchVectorStoreRetriever: Retriever class for VectorStore. """ tags = kwargs.pop("tags", None) or [] tags.extend(self._get_retriever_tags()) return AzureSearchVectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
[docs]class AzureSearchVectorStoreRetriever(BaseRetriever): """Retriever that uses `Azure Cognitive Search`.""" vectorstore: AzureSearch """Azure Search instance used to find similar documents.""" search_type: str = "hybrid" """Type of search to perform. Options are "similarity", "hybrid", "semantic_hybrid", "similarity_score_threshold", "hybrid_score_threshold", or "semantic_hybrid_score_threshold".""" k: int = 4 """Number of documents to return.""" search_kwargs: dict = {} """Search params. score_threshold: Minimum relevance threshold for similarity_score_threshold fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR; 1 for minimum diversity and 0 for maximum. (Default: 0.5) filter: Filter by document metadata """ allowed_search_types: ClassVar[Collection[str]] = ( "similarity", "similarity_score_threshold", "hybrid", "hybrid_score_threshold", "semantic_hybrid", "semantic_hybrid_score_threshold", ) class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @root_validator() def validate_search_type(cls, values: Dict) -> Dict: """Validate search type.""" if "search_type" in values: search_type = values["search_type"] if search_type not in cls.allowed_search_types: raise ValueError( f"search_type of {search_type} not allowed. Valid values are: " f"{cls.allowed_search_types}" ) return values def _get_relevant_documents( self, query: str, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any, ) -> List[Document]: params = {**self.search_kwargs, **kwargs} if self.search_type == "similarity": docs = self.vectorstore.vector_search(query, k=self.k, **params) elif self.search_type == "similarity_score_threshold": docs = [ doc for doc, _ in self.vectorstore.similarity_search_with_relevance_scores( query, k=self.k, **params ) ] elif self.search_type == "hybrid": docs = self.vectorstore.hybrid_search(query, k=self.k, **params) elif self.search_type == "hybrid_score_threshold": docs = [ doc for doc, _ in self.vectorstore.hybrid_search_with_relevance_scores( query, k=self.k, **params ) ] elif self.search_type == "semantic_hybrid": docs = self.vectorstore.semantic_hybrid_search(query, k=self.k, **params) elif self.search_type == "semantic_hybrid_score_threshold": docs = [ doc for doc, _ in self.vectorstore.semantic_hybrid_search_with_score( query, k=self.k, **params ) ] else: raise ValueError(f"search_type of {self.search_type} not allowed.") return docs
def _results_to_documents( results: SearchItemPaged[Dict], ) -> List[Tuple[Document, float]]: docs = [ ( _result_to_document(result), float(result["@search.score"]), ) for result in results ] return docs def _reorder_results_with_maximal_marginal_relevance( results: SearchItemPaged[Dict], query_embedding: np.ndarray, lambda_mult: float = 0.5, k: int = 4, ) -> List[Tuple[Document, float]]: # Convert results to Document objects docs = [ ( _result_to_document(result), float(result["@search.score"]), result[FIELDS_CONTENT_VECTOR], ) for result in results ] documents, scores, vectors = map(list, zip(*docs)) # Get the new order of results. new_ordering = maximal_marginal_relevance( query_embedding, vectors, k=k, lambda_mult=lambda_mult ) # Reorder the values and return. ret: List[Tuple[Document, float]] = [] for x in new_ordering: # Function can return -1 index if x == -1: break ret.append((documents[x], scores[x])) # type: ignore return ret def _result_to_document(result: Dict) -> Document: return Document( page_content=result.pop(FIELDS_CONTENT), metadata=json.loads(result[FIELDS_METADATA]) if FIELDS_METADATA in result else { key: value for key, value in result.items() if key != FIELDS_CONTENT_VECTOR }, ) def _peek(iterable: Iterable, default: Optional[Any] = None) -> Tuple[Iterable, Any]: try: iterator = iter(iterable) value = next(iterator) iterable = itertools.chain([value], iterator) return iterable, value except StopIteration: return iterable, default