viruPVS/langchain-docs huggingface dataset:
'localhost'. Default: None
            path:
                Path in which the vectors will be stored while using local mode.
                Default: None
            collection_name:
                Name of the Qdrant collection to be used. If not provided,
                it will be created randomly. Default: None
            distance_func:
                Distance function. One of: "Cosine" / "Euclid" / "Dot".
                Default: "Cosine"
            content_payload_key:
                A payload key used to store the content of the document.
                Default: "page_content"
            metadata_payload_key:
                A payload key used to store the metadata of the document.
                Default: "metadata"
            vector_name:
                Name of the vector to be used internally in Qdrant.
                Default: None
            batch_size:
                How many vectors upload per-request.
                Default: 64
            shard_number: Number of shards in collection. Default is 1, minimum is 1.
            replication_factor:
                Replication factor for collection. Default is 1, minimum is 1.
                Defines how many copies of each shard will be created.
                Have effect only in distributed mode.
            write_consistency_factor:
                Write consistency factor for collection. Default is 1, minimum is 1.
                Defines how many replicas should apply the operation for us to consider
                it successful. Increasing this number will make the collection more
                resilient to inconsistencies, but will also make it fail if not enough
                replicas are available.
                Does not have any performance impact.
                Have effect only in distributed mode.
            on_disk_payload:
                If true - point`s payload will not be stored in memory.
                It will be read from the disk every time it is requested.
It will be read from the disk every time it is requested.
                This setting saves RAM by (slightly) increasing the response time.
                Note: those payload values that are involved in filtering and are
                indexed - remain in RAM.
            hnsw_config: Params for HNSW index
            optimizers_config: Params for optimizer
            wal_config: Params for Write-Ahead-Log
            quantization_config:
                Params for quantization, if None - quantization will be disabled
            init_from:
                Use data stored in another collection to initialize this collection
            force_recreate:
                Force recreating the collection
            **kwargs:
                Additional arguments passed directly into REST client initialization
        This is a user-friendly interface that:
        1. Creates embeddings, one for each text
        2. Initializes the Qdrant database as an in-memory docstore by default
           (and overridable to a remote docstore)
        3. Adds the text embeddings to the Qdrant database
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain_community.vectorstores import Qdrant
                from langchain_community.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")
        """
        qdrant = await cls.aconstruct_instance(
            texts,
            embedding,
            location,
            url,
            port,
            grpc_port,
            prefer_grpc,
            https,
            api_key,
            prefix,
            timeout,
            host,
            path,
            collection_name,
            distance_func,
            content_payload_key,
            metadata_payload_key,
            vector_name,
content_payload_key,
            metadata_payload_key,
            vector_name,
            shard_number,
            replication_factor,
            write_consistency_factor,
            on_disk_payload,
            hnsw_config,
            optimizers_config,
            wal_config,
            quantization_config,
            init_from,
            on_disk,
            force_recreate,
            **kwargs,
        )
        await qdrant.aadd_texts(texts, metadatas, ids, batch_size)
        return qdrant
[docs]    @classmethod
    def construct_instance(
        cls: Type[Qdrant],
        texts: List[str],
        embedding: Embeddings,
        location: Optional[str] = None,
        url: Optional[str] = None,
        port: Optional[int] = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,
        https: Optional[bool] = None,
        api_key: Optional[str] = None,
        prefix: Optional[str] = None,
        timeout: Optional[float] = None,
        host: Optional[str] = None,
        path: Optional[str] = None,
        collection_name: Optional[str] = None,
        distance_func: str = "Cosine",
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: Optional[str] = VECTOR_NAME,
        shard_number: Optional[int] = None,
        replication_factor: Optional[int] = None,
        write_consistency_factor: Optional[int] = None,
        on_disk_payload: Optional[bool] = None,
        hnsw_config: Optional[common_types.HnswConfigDiff] = None,
hnsw_config: Optional[common_types.HnswConfigDiff] = None,
        optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
        wal_config: Optional[common_types.WalConfigDiff] = None,
        quantization_config: Optional[common_types.QuantizationConfig] = None,
        init_from: Optional[common_types.InitFrom] = None,
        on_disk: Optional[bool] = None,
        force_recreate: bool = False,
        **kwargs: Any,
    ) -> Qdrant:
        try:
            import qdrant_client
        except ImportError:
            raise ValueError(
                "Could not import qdrant-client python package. "
                "Please install it with `pip install qdrant-client`."
            )
        from grpc import RpcError
        from qdrant_client.http import models as rest
        from qdrant_client.http.exceptions import UnexpectedResponse
        # Just do a single quick embedding to get vector size
        partial_embeddings = embedding.embed_documents(texts[:1])
        vector_size = len(partial_embeddings[0])
        collection_name = collection_name or uuid.uuid4().hex
        distance_func = distance_func.upper()
        client = qdrant_client.QdrantClient(
            location=location,
            url=url,
            port=port,
            grpc_port=grpc_port,
            prefer_grpc=prefer_grpc,
            https=https,
            api_key=api_key,
            prefix=prefix,
            timeout=timeout,
            host=host,
            path=path,
            **kwargs,
        )
        try:
            # Skip any validation in case of forced collection recreate.
            if force_recreate:
                raise ValueError
if force_recreate:
                raise ValueError
            # Get the vector configuration of the existing collection and vector, if it
            # was specified. If the old configuration does not match the current one,
            # an exception is being thrown.
            collection_info = client.get_collection(collection_name=collection_name)
            current_vector_config = collection_info.config.params.vectors
            if isinstance(current_vector_config, dict) and vector_name is not None:
                if vector_name not in current_vector_config:
                    raise QdrantException(
                        f"Existing Qdrant collection {collection_name} does not "
                        f"contain vector named {vector_name}. Did you mean one of the "
                        f"existing vectors: {', '.join(current_vector_config.keys())}? "
                        f"If you want to recreate the collection, set `force_recreate` "
                        f"parameter to `True`."
                    )
                current_vector_config = current_vector_config.get(vector_name)  # type: ignore[assignment]
            elif isinstance(current_vector_config, dict) and vector_name is None:
                raise QdrantException(
                    f"Existing Qdrant collection {collection_name} uses named vectors. "
                    f"If you want to reuse it, please set `vector_name` to any of the "
                    f"existing named vectors: "
                    f"{', '.join(current_vector_config.keys())}."  # noqa
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
            elif (
                not isinstance(current_vector_config, dict) and vector_name is not None
            ):
                raise QdrantException(
                    f"Existing Qdrant collection {collection_name} doesn't use named "
f"Existing Qdrant collection {collection_name} doesn't use named "
                    f"vectors. If you want to reuse it, please set `vector_name` to "
                    f"`None`. If you want to recreate the collection, set "
                    f"`force_recreate` parameter to `True`."
                )
            # Check if the vector configuration has the same dimensionality.
            if current_vector_config.size != vector_size:  # type: ignore[union-attr]
                raise QdrantException(
                    f"Existing Qdrant collection is configured for vectors with "
                    f"{current_vector_config.size} "  # type: ignore[union-attr]
                    f"dimensions. Selected embeddings are {vector_size}-dimensional. "
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
            current_distance_func = (
                current_vector_config.distance.name.upper()  # type: ignore[union-attr]
            )
            if current_distance_func != distance_func:
                raise QdrantException(
                    f"Existing Qdrant collection is configured for "
                    f"{current_distance_func} similarity, but requested "
                    f"{distance_func}. Please set `distance_func` parameter to "
                    f"`{current_distance_func}` if you want to reuse it. "
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
        except (UnexpectedResponse, RpcError, ValueError):
            vectors_config = rest.VectorParams(
                size=vector_size,
                distance=rest.Distance[distance_func],
                on_disk=on_disk,
            )
on_disk=on_disk,
            )
            # If vector name was provided, we're going to use the named vectors feature
            # with just a single vector.
            if vector_name is not None:
                vectors_config = {  # type: ignore[assignment]
                    vector_name: vectors_config,
                }
            client.recreate_collection(
                collection_name=collection_name,
                vectors_config=vectors_config,
                shard_number=shard_number,
                replication_factor=replication_factor,
                write_consistency_factor=write_consistency_factor,
                on_disk_payload=on_disk_payload,
                hnsw_config=hnsw_config,
                optimizers_config=optimizers_config,
                wal_config=wal_config,
                quantization_config=quantization_config,
                init_from=init_from,
                timeout=timeout,  # type: ignore[arg-type]
            )
        qdrant = cls(
            client=client,
            collection_name=collection_name,
            embeddings=embedding,
            content_payload_key=content_payload_key,
            metadata_payload_key=metadata_payload_key,
            distance_strategy=distance_func,
            vector_name=vector_name,
        )
        return qdrant
[docs]    @classmethod
    async def aconstruct_instance(
        cls: Type[Qdrant],
        texts: List[str],
        embedding: Embeddings,
        location: Optional[str] = None,
        url: Optional[str] = None,
        port: Optional[int] = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,
        https: Optional[bool] = None,
        api_key: Optional[str] = None,
        prefix: Optional[str] = None,
prefix: Optional[str] = None,
        timeout: Optional[float] = None,
        host: Optional[str] = None,
        path: Optional[str] = None,
        collection_name: Optional[str] = None,
        distance_func: str = "Cosine",
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: Optional[str] = VECTOR_NAME,
        shard_number: Optional[int] = None,
        replication_factor: Optional[int] = None,
        write_consistency_factor: Optional[int] = None,
        on_disk_payload: Optional[bool] = None,
        hnsw_config: Optional[common_types.HnswConfigDiff] = None,
        optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
        wal_config: Optional[common_types.WalConfigDiff] = None,
        quantization_config: Optional[common_types.QuantizationConfig] = None,
        init_from: Optional[common_types.InitFrom] = None,
        on_disk: Optional[bool] = None,
        force_recreate: bool = False,
        **kwargs: Any,
    ) -> Qdrant:
        try:
            import qdrant_client
        except ImportError:
            raise ValueError(
                "Could not import qdrant-client python package. "
                "Please install it with `pip install qdrant-client`."
            )
        from grpc import RpcError
        from qdrant_client.http import models as rest
        from qdrant_client.http.exceptions import UnexpectedResponse
        # Just do a single quick embedding to get vector size
        partial_embeddings = await embedding.aembed_documents(texts[:1])
        vector_size = len(partial_embeddings[0])
vector_size = len(partial_embeddings[0])
        collection_name = collection_name or uuid.uuid4().hex
        distance_func = distance_func.upper()
        client = qdrant_client.QdrantClient(
            location=location,
            url=url,
            port=port,
            grpc_port=grpc_port,
            prefer_grpc=prefer_grpc,
            https=https,
            api_key=api_key,
            prefix=prefix,
            timeout=timeout,
            host=host,
            path=path,
            **kwargs,
        )
        try:
            # Skip any validation in case of forced collection recreate.
            if force_recreate:
                raise ValueError
            # Get the vector configuration of the existing collection and vector, if it
            # was specified. If the old configuration does not match the current one,
            # an exception is being thrown.
            collection_info = client.get_collection(collection_name=collection_name)
            current_vector_config = collection_info.config.params.vectors
            if isinstance(current_vector_config, dict) and vector_name is not None:
                if vector_name not in current_vector_config:
                    raise QdrantException(
                        f"Existing Qdrant collection {collection_name} does not "
                        f"contain vector named {vector_name}. Did you mean one of the "
                        f"existing vectors: {', '.join(current_vector_config.keys())}? "
                        f"If you want to recreate the collection, set `force_recreate` "
                        f"parameter to `True`."
                    )
                current_vector_config = current_vector_config.get(vector_name)  # type: ignore[assignment]
            elif isinstance(current_vector_config, dict) and vector_name is None:
                raise QdrantException(
raise QdrantException(
                    f"Existing Qdrant collection {collection_name} uses named vectors. "
                    f"If you want to reuse it, please set `vector_name` to any of the "
                    f"existing named vectors: "
                    f"{', '.join(current_vector_config.keys())}."  # noqa
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
            elif (
                not isinstance(current_vector_config, dict) and vector_name is not None
            ):
                raise QdrantException(
                    f"Existing Qdrant collection {collection_name} doesn't use named "
                    f"vectors. If you want to reuse it, please set `vector_name` to "
                    f"`None`. If you want to recreate the collection, set "
                    f"`force_recreate` parameter to `True`."
                )
            # Check if the vector configuration has the same dimensionality.
            if current_vector_config.size != vector_size:  # type: ignore[union-attr]
                raise QdrantException(
                    f"Existing Qdrant collection is configured for vectors with "
                    f"{current_vector_config.size} "  # type: ignore[union-attr]
                    f"dimensions. Selected embeddings are {vector_size}-dimensional. "
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
            current_distance_func = (
                current_vector_config.distance.name.upper()  # type: ignore[union-attr]
            )
            if current_distance_func != distance_func:
                raise QdrantException(
                    f"Existing Qdrant collection is configured for "
f"Existing Qdrant collection is configured for "
                    f"{current_vector_config.distance} "  # type: ignore[union-attr]
                    f"similarity. Please set `distance_func` parameter to "
                    f"`{distance_func}` if you want to reuse it. If you want to "
                    f"recreate the collection, set `force_recreate` parameter to "
                    f"`True`."
                )
        except (UnexpectedResponse, RpcError, ValueError):
            vectors_config = rest.VectorParams(
                size=vector_size,
                distance=rest.Distance[distance_func],
                on_disk=on_disk,
            )
            # If vector name was provided, we're going to use the named vectors feature
            # with just a single vector.
            if vector_name is not None:
                vectors_config = {  # type: ignore[assignment]
                    vector_name: vectors_config,
                }
            client.recreate_collection(
                collection_name=collection_name,
                vectors_config=vectors_config,
                shard_number=shard_number,
                replication_factor=replication_factor,
                write_consistency_factor=write_consistency_factor,
                on_disk_payload=on_disk_payload,
                hnsw_config=hnsw_config,
                optimizers_config=optimizers_config,
                wal_config=wal_config,
                quantization_config=quantization_config,
                init_from=init_from,
                timeout=timeout,  # type: ignore[arg-type]
            )
        qdrant = cls(
            client=client,
            collection_name=collection_name,
            embeddings=embedding,
            content_payload_key=content_payload_key,
            metadata_payload_key=metadata_payload_key,
            distance_strategy=distance_func,
            vector_name=vector_name,
        )
distance_strategy=distance_func,
            vector_name=vector_name,
        )
        return qdrant
    @staticmethod
    def _cosine_relevance_score_fn(distance: float) -> float:
        """Normalize the distance to a score on a scale [0, 1]."""
        return (distance + 1.0) / 2.0
    def _select_relevance_score_fn(self) -> Callable[[float], float]:
        """
        The 'correct' relevance function
        may differ depending on a few things, including:
        - the distance / similarity metric used by the VectorStore
        - the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
        - embedding dimensionality
        - etc.
        """
        if self.distance_strategy == "COSINE":
            return self._cosine_relevance_score_fn
        elif self.distance_strategy == "DOT":
            return self._max_inner_product_relevance_score_fn
        elif self.distance_strategy == "EUCLID":
            return self._euclidean_relevance_score_fn
        else:
            raise ValueError(
                "Unknown distance strategy, must be cosine, "
                "max_inner_product, or euclidean"
            )
    def _similarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs and relevance scores in the range [0, 1].
        0 is dissimilar, 1 is most similar.
        Args:
            query: input text
            k: Number of Documents to return. Defaults to 4.
            **kwargs: kwargs to be passed to similarity search. Should include:
**kwargs: kwargs to be passed to similarity search. Should include:
                score_threshold: Optional, a floating point value between 0 to 1 to
                    filter the resulting set of retrieved docs
        Returns:
            List of Tuples of (doc, similarity_score)
        """
        return self.similarity_search_with_score(query, k, **kwargs)
    @classmethod
    def _build_payloads(
        cls,
        texts: Iterable[str],
        metadatas: Optional[List[dict]],
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> List[dict]:
        payloads = []
        for i, text in enumerate(texts):
            if text is None:
                raise ValueError(
                    "At least one of the texts is None. Please remove it before "
                    "calling .from_texts or .add_texts on Qdrant instance."
                )
            metadata = metadatas[i] if metadatas is not None else None
            payloads.append(
                {
                    content_payload_key: text,
                    metadata_payload_key: metadata,
                }
            )
        return payloads
    @classmethod
    def _document_from_scored_point(
        cls,
        scored_point: Any,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> Document:
        return Document(
            page_content=scored_point.payload.get(content_payload_key),
            metadata=scored_point.payload.get(metadata_payload_key) or {},
        )
    @classmethod
    def _document_from_scored_point_grpc(
        cls,
        scored_point: Any,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> Document: