if not connections.has_connection("default"):
            connections.connect(**connection_args)
        self.embedding_func = embedding_function
        self.collection_name = collection_name
        self.text_field = text_field
        self.auto_id = False
        self.primary_field = None
        self.vector_field = None
        self.fields = []
        self.col = Collection(self.collection_name)
        schema = self.col.schema
        # Grabbing the fields for the existing collection.
        for x in schema.fields:
            self.fields.append(x.name)
            if x.auto_id:
                self.fields.remove(x.name)
            if x.is_primary:
                self.primary_field = x.name
            if x.dtype == DataType.FLOAT_VECTOR or x.dtype == DataType.BINARY_VECTOR:
                self.vector_field = x.name
        # Default search params when one is not provided.
        self.index_params = {
            "IVF_FLAT": {"params": {"nprobe": 10}},
            "IVF_SQ8": {"params": {"nprobe": 10}},
            "IVF_PQ": {"params": {"nprobe": 10}},
            "HNSW": {"params": {"ef": 10}},
            "RHNSW_FLAT": {"params": {"ef": 10}},
            "RHNSW_SQ": {"params": {"ef": 10}},
            "RHNSW_PQ": {"params": {"ef": 10}},
            "IVF_HNSW": {"params": {"nprobe": 10, "ef": 10}},
            "ANNOY": {"params": {"search_k": 10}},
        }
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,


texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        partition_name: Optional[str] = None,
        timeout: Optional[int] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Insert text data into Milvus.
        When using add_texts() it is assumed that a collecton has already
        been made and indexed. If metadata is included, it is assumed that
        it is ordered correctly to match the schema provided to the Collection
        and that the embedding vector is the first schema field.
        Args:
            texts (Iterable[str]): The text being embedded and inserted.
            metadatas (Optional[List[dict]], optional): The metadata that
                corresponds to each insert. Defaults to None.
            partition_name (str, optional): The partition of the collection
                to insert data into. Defaults to None.
            timeout: specified timeout.
        Returns:
            List[str]: The resulting keys for each inserted element.
        """
        insert_dict: Any = {self.text_field: list(texts)}
        try:
            insert_dict[self.vector_field] = self.embedding_func.embed_documents(
                list(texts)
            )
        except NotImplementedError:
            insert_dict[self.vector_field] = [
                self.embedding_func.embed_query(x) for x in texts
            ]
        # Collect the metadata into the insert dict.
        if len(self.fields) > 2 and metadatas is not None:
            for d in metadatas:
                for key, value in d.items():
                    if key in self.fields:
                        insert_dict.setdefault(key, []).append(value)
        # Convert dict to list of lists for insertion
        insert_list = [insert_dict[x] for x in self.fields]
        # Insert into the collection.


# Insert into the collection.
        res = self.col.insert(
            insert_list, partition_name=partition_name, timeout=timeout
        )
        # Flush to make sure newly inserted is immediately searchable.
        self.col.flush()
        return res.primary_keys
    def _worker_search(
        self,
        query: str,
        k: int = 4,
        param: Optional[dict] = None,
        expr: Optional[str] = None,
        partition_names: Optional[List[str]] = None,
        round_decimal: int = -1,
        timeout: Optional[int] = None,
        **kwargs: Any,
    ) -> Tuple[List[float], List[Tuple[Document, Any, Any]]]:
        # Load the collection into memory for searching.
        self.col.load()
        # Decide to use default params if not passed in.
        if param is None:
            index_type = self.col.indexes[0].params["index_type"]
            param = self.index_params[index_type]
        # Embed the query text.
        data = [self.embedding_func.embed_query(query)]
        # Determine result metadata fields.
        output_fields = self.fields[:]
        output_fields.remove(self.vector_field)
        # Perform the search.
        res = self.col.search(
            data,
            self.vector_field,
            param,
            k,
            expr=expr,
            output_fields=output_fields,
            partition_names=partition_names,
            round_decimal=round_decimal,
            timeout=timeout,
            **kwargs,
        )
        # Organize results.
        ret = []
        for result in res[0]:
            meta = {x: result.entity.get(x) for x in output_fields}
            ret.append(
                (


ret.append(
                (
                    Document(page_content=meta.pop(self.text_field), metadata=meta),
                    result.distance,
                    result.id,
                )
            )
        return data[0], ret
[docs]    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        param: Optional[dict] = None,
        expr: Optional[str] = None,
        partition_names: Optional[List[str]] = None,
        round_decimal: int = -1,
        timeout: Optional[int] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Perform a search on a query string and return results.
        Args:
            query (str): The text being searched.
            k (int, optional): The amount of results ot return. Defaults to 4.
            param (dict, optional): The search params for the specified index.
                Defaults to None.
            expr (str, optional): Filtering expression. Defaults to None.
            partition_names (List[str], optional): Partitions to search through.
                Defaults to None.
            round_decimal (int, optional): Round the resulting distance. Defaults
                to -1.
            timeout (int, optional): Amount to wait before timeout error. Defaults
                to None.
            kwargs: Collection.search() keyword arguments.
        Returns:
            List[float], List[Tuple[Document, any, any]]: search_embedding,
                (Document, distance, primary_field) results.
        """
        _, result = self._worker_search(
            query, k, param, expr, partition_names, round_decimal, timeout, **kwargs
        )
        return [(x, y) for x, y, _ in result]


)
        return [(x, y) for x, y, _ in result]
[docs]    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        param: Optional[dict] = None,
        expr: Optional[str] = None,
        partition_names: Optional[List[str]] = None,
        round_decimal: int = -1,
        timeout: Optional[int] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Perform a search and return results that are reordered by MMR.
        Args:
            query (str): The text being searched.
            k (int, optional): How many results to give. Defaults to 4.
            fetch_k (int, optional): Total results to select k from.
                Defaults to 20.
            param (dict, optional): The search params for the specified index.
                Defaults to None.
            expr (str, optional): Filtering expression. Defaults to None.
            partition_names (List[str], optional): What partitions to search.
                Defaults to None.
            round_decimal (int, optional): Round the resulting distance. Defaults
                to -1.
            timeout (int, optional): Amount to wait before timeout error. Defaults
                to None.
        Returns:
            List[Document]: Document results for search.
        """
        data, res = self._worker_search(
            query,
            fetch_k,
            param,
            expr,
            partition_names,
            round_decimal,
            timeout,
            **kwargs,
        )
        # Extract result IDs.
        ids = [x for _, _, x in res]


# Extract result IDs.
        ids = [x for _, _, x in res]
        # Get the raw vectors from Milvus.
        vectors = self.col.query(
            expr=f"{self.primary_field} in {ids}",
            output_fields=[self.primary_field, self.vector_field],
        )
        # Reorganize the results from query to match result order.
        vectors = {x[self.primary_field]: x[self.vector_field] for x in vectors}
        search_embedding = data
        ordered_result_embeddings = [vectors[x] for x in ids]
        # Get the new order of results.
        new_ordering = maximal_marginal_relevance(
            np.array(search_embedding), ordered_result_embeddings, k=k
        )
        # Reorder the values and return.
        ret = []
        for x in new_ordering:
            if x == -1:
                break
            else:
                ret.append(res[x][0])
        return ret
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        param: Optional[dict] = None,
        expr: Optional[str] = None,
        partition_names: Optional[List[str]] = None,
        round_decimal: int = -1,
        timeout: Optional[int] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Perform a similarity search against the query string.
        Args:
            query (str): The text to search.
            k (int, optional): How many results to return. Defaults to 4.
            param (dict, optional): The search params for the index type.
                Defaults to None.
            expr (str, optional): Filtering expression. Defaults to None.


Defaults to None.
            expr (str, optional): Filtering expression. Defaults to None.
            partition_names (List[str], optional): What partitions to search.
                Defaults to None.
            round_decimal (int, optional): What decimal point to round to.
                Defaults to -1.
            timeout (int, optional): How long to wait before timeout error.
                Defaults to None.
        Returns:
            List[Document]: Document results for search.
        """
        _, docs_and_scores = self._worker_search(
            query, k, param, expr, partition_names, round_decimal, timeout, **kwargs
        )
        return [doc for doc, _, _ in docs_and_scores]
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> Milvus:
        """Create a Milvus collection, indexes it with HNSW, and insert data.
        Args:
            texts (List[str]): Text to insert.
            embedding (Embeddings): Embedding function to use.
            metadatas (Optional[List[dict]], optional): Dict metatadata.
                Defaults to None.
        Returns:
            VectorStore: The Milvus vector store.
        """
        try:
            from pymilvus import (
                Collection,
                CollectionSchema,
                DataType,
                FieldSchema,
                connections,
            )
            from pymilvus.orm.types import infer_dtype_bydata
        except ImportError:
            raise ValueError(
                "Could not import pymilvus python package. "
                "Please install it with `pip install pymilvus`."


"Please install it with `pip install pymilvus`."
            )
        # Connect to Milvus instance
        if not connections.has_connection("default"):
            connections.connect(**kwargs.get("connection_args", {"port": 19530}))
        # Determine embedding dim
        embeddings = embedding.embed_query(texts[0])
        dim = len(embeddings)
        # Generate unique names
        primary_field = "c" + str(uuid.uuid4().hex)
        vector_field = "c" + str(uuid.uuid4().hex)
        text_field = "c" + str(uuid.uuid4().hex)
        collection_name = "c" + str(uuid.uuid4().hex)
        fields = []
        # Determine metadata schema
        if metadatas:
            # Check if all metadata keys line up
            key = metadatas[0].keys()
            for x in metadatas:
                if key != x.keys():
                    raise ValueError(
                        "Mismatched metadata. "
                        "Make sure all metadata has the same keys and datatype."
                    )
            # Create FieldSchema for each entry in singular metadata.
            for key, value in metadatas[0].items():
                # Infer the corresponding datatype of the metadata
                dtype = infer_dtype_bydata(value)
                if dtype == DataType.UNKNOWN:
                    raise ValueError(f"Unrecognized datatype for {key}.")
                elif dtype == DataType.VARCHAR:
                    # Find out max length text based metadata
                    max_length = 0
                    for subvalues in metadatas:
                        max_length = max(max_length, len(subvalues[key]))
                    fields.append(
                        FieldSchema(key, DataType.VARCHAR, max_length=max_length + 1)
                    )
                else:
                    fields.append(FieldSchema(key, dtype))


)
                else:
                    fields.append(FieldSchema(key, dtype))
        # Find out max length of texts
        max_length = 0
        for y in texts:
            max_length = max(max_length, len(y))
        # Create the text field
        fields.append(
            FieldSchema(text_field, DataType.VARCHAR, max_length=max_length + 1)
        )
        # Create the primary key field
        fields.append(
            FieldSchema(primary_field, DataType.INT64, is_primary=True, auto_id=True)
        )
        # Create the vector field
        fields.append(FieldSchema(vector_field, DataType.FLOAT_VECTOR, dim=dim))
        # Create the schema for the collection
        schema = CollectionSchema(fields)
        # Create the collection
        collection = Collection(collection_name, schema)
        # Index parameters for the collection
        index = {
            "index_type": "HNSW",
            "metric_type": "L2",
            "params": {"M": 8, "efConstruction": 64},
        }
        # Create the index
        collection.create_index(vector_field, index)
        # Create the VectorStore
        milvus = cls(
            embedding,
            kwargs.get("connection_args", {"port": 19530}),
            collection_name,
            text_field,
        )
        # Add the texts.
        milvus.add_texts(texts, metadatas)
        return milvus
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.vectorstores.weaviate
"""Wrapper around weaviate vector database."""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional
from uuid import uuid4
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
[docs]class Weaviate(VectorStore):
    """Wrapper around Weaviate vector database.
    To use, you should have the ``weaviate-client`` python package installed.
    Example:
        .. code-block:: python
            import weaviate
            from langchain.vectorstores import Weaviate
            client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)
            weaviate = Weaviate(client, index_name, text_key)
    """
    def __init__(
        self,
        client: Any,
        index_name: str,
        text_key: str,
        attributes: Optional[List[str]] = None,
    ):
        """Initialize with Weaviate client."""
        try:
            import weaviate
        except ImportError:
            raise ValueError(
                "Could not import weaviate python package. "
                "Please install it with `pip install weaviate-client`."
            )
        if not isinstance(client, weaviate.Client):
            raise ValueError(
                f"client should be an instance of weaviate.Client, got {type(client)}"
            )
        self._client = client
        self._index_name = index_name
        self._text_key = text_key
        self._query_attrs = [self._text_key]
        if attributes is not None:
            self._query_attrs.extend(attributes)
[docs]    def add_texts(
        self,


[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Upload texts with metadata (properties) to Weaviate."""
        from weaviate.util import get_valid_uuid
        with self._client.batch as batch:
            ids = []
            for i, doc in enumerate(texts):
                data_properties = {
                    self._text_key: doc,
                }
                if metadatas is not None:
                    for key in metadatas[i].keys():
                        data_properties[key] = metadatas[i][key]
                _id = get_valid_uuid(uuid4())
                batch.add_data_object(data_properties, self._index_name, _id)
                ids.append(_id)
        return ids
[docs]    def similarity_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Look up similar documents in weaviate."""
        content: Dict[str, Any] = {"concepts": [query]}
        if kwargs.get("search_distance"):
            content["certainty"] = kwargs.get("search_distance")
        query_obj = self._client.query.get(self._index_name, self._query_attrs)
        result = query_obj.with_near_text(content).with_limit(k).do()
        docs = []
        for res in result["data"]["Get"][self._index_name]:
            text = res.pop(self._text_key)
            docs.append(Document(page_content=text, metadata=res))
        return docs
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,


cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> VectorStore:
        """Not implemented for Weaviate yet."""
        raise NotImplementedError("weaviate does not currently support `from_texts`.")
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.vectorstores.qdrant
"""Wrapper around Qdrant vector database."""
import uuid
from operator import itemgetter
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
MetadataFilter = Dict[str, Union[str, int, bool]]
[docs]class Qdrant(VectorStore):
    """Wrapper around Qdrant vector database.
    To use you should have the ``qdrant-client`` package installed.
    Example:
        .. code-block:: python
            from langchain import Qdrant
            client = QdrantClient()
            collection_name = "MyCollection"
            qdrant = Qdrant(client, collection_name, embedding_function)
    """
    CONTENT_KEY = "page_content"
    METADATA_KEY = "metadata"
    def __init__(
        self,
        client: Any,
        collection_name: str,
        embedding_function: Callable,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
    ):
        """Initialize with necessary components."""
        try:
            import qdrant_client
        except ImportError:
            raise ValueError(
                "Could not import qdrant-client python package. "
                "Please install it with `pip install qdrant-client`."
            )
        if not isinstance(client, qdrant_client.QdrantClient):
            raise ValueError(
                f"client should be an instance of qdrant_client.QdrantClient, "
                f"got {type(client)}"
            )


f"got {type(client)}"
            )
        self.client: qdrant_client.QdrantClient = client
        self.collection_name = collection_name
        self.embedding_function = embedding_function
        self.content_payload_key = content_payload_key or self.CONTENT_KEY
        self.metadata_payload_key = metadata_payload_key or self.METADATA_KEY
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        from qdrant_client.http import models as rest
        ids = [uuid.uuid4().hex for _ in texts]
        self.client.upsert(
            collection_name=self.collection_name,
            points=rest.Batch(
                ids=ids,
                vectors=[self.embedding_function(text) for text in texts],
                payloads=self._build_payloads(
                    texts,
                    metadatas,
                    self.content_payload_key,
                    self.metadata_payload_key,
                ),
            ),
        )
        return ids
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.


Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
        Returns:
            List of Documents most similar to the query.
        """
        results = self.similarity_search_with_score(query, k, filter)
        return list(map(itemgetter(0), results))
[docs]    def similarity_search_with_score(
        self, query: str, k: int = 4, filter: Optional[MetadataFilter] = None
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
        Returns:
            List of Documents most similar to the query and score for each
        """
        embedding = self.embedding_function(query)
        results = self.client.search(
            collection_name=self.collection_name,
            query_vector=embedding,
            query_filter=self._qdrant_filter_from_dict(filter),
            with_payload=True,
            limit=k,
        )
        return [
            (
                self._document_from_scored_point(
                    result, self.content_payload_key, self.metadata_payload_key
                ),
                result.score,
            )
            for result in results
        ]
[docs]    def max_marginal_relevance_search(
        self, query: str, k: int = 4, fetch_k: int = 20
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.
        Args:


among selected documents.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        embedding = self.embedding_function(query)
        results = self.client.search(
            collection_name=self.collection_name,
            query_vector=embedding,
            with_payload=True,
            with_vectors=True,
            limit=fetch_k,
        )
        embeddings = [result.vector for result in results]
        mmr_selected = maximal_marginal_relevance(embedding, embeddings, k=k)
        return [
            self._document_from_scored_point(
                results[i], self.content_payload_key, self.metadata_payload_key
            )
            for i in mmr_selected
        ]
[docs]    @classmethod
    def from_documents(
        cls,
        documents: List[Document],
        embedding: Embeddings,
        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,
        collection_name: Optional[str] = None,
        distance_func: str = "Cosine",
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        **kwargs: Any,
    ) -> "Qdrant":
        return cast(
            Qdrant,


) -> "Qdrant":
        return cast(
            Qdrant,
            super().from_documents(
                documents,
                embedding,
                url=url,
                port=port,
                grpc_port=grpc_port,
                prefer_grpc=prefer_grpc,
                https=https,
                api_key=api_key,
                prefix=prefix,
                timeout=timeout,
                host=host,
                collection_name=collection_name,
                distance_func=distance_func,
                content_payload_key=content_payload_key,
                metadata_payload_key=metadata_payload_key,
                **kwargs,
            ),
        )
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = 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,
        collection_name: Optional[str] = None,
        distance_func: str = "Cosine",
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        **kwargs: Any,
    ) -> "Qdrant":
        """Construct Qdrant wrapper from raw documents.
        Args:
            texts: A list of texts to be indexed in Qdrant.
            embedding: A subclass of `Embeddings`, responsible for text vectorization.
            metadatas:


metadatas:
                An optional list of metadata. If provided it has to be of the same
                length as a list of texts.
            url: either host or str of "Optional[scheme], host, Optional[port],
                Optional[prefix]". Default: `None`
            port: Port of the REST API interface. Default: 6333
            grpc_port: Port of the gRPC interface. Default: 6334
            prefer_grpc:
                If `true` - use gPRC interface whenever possible in custom methods.
            https: If `true` - use HTTPS(SSL) protocol. Default: `None`
            api_key: API key for authentication in Qdrant Cloud. Default: `None`
            prefix:
                If not `None` - add `prefix` to the REST URL path.
                Example: `service/v1` will result in
                    `http://localhost:6333/service/v1/{qdrant-endpoint}` for REST API.
                Default: `None`
            timeout:
                Timeout for REST and gRPC API requests.
                Default: 5.0 seconds for REST and unlimited for gRPC
            host:
                Host name of Qdrant service. If url and host are None, set to
                'localhost'. Default: `None`
            collection_name:
                Name of the Qdrant collection to be used. If not provided,
                will be created randomly.
            distance_func:
                Distance function. One of the: "Cosine" / "Euclid" / "Dot".
            content_payload_key:
                A payload key used to store the content of the document.
            metadata_payload_key:
                A payload key used to store the metadata of the document.
            **kwargs:
                Additional arguments passed directly into REST client initialization


**kwargs:
                Additional arguments passed directly into REST client initialization
        This is a user friendly interface that:
            1. Embeds documents.
            2. Creates an in memory docstore
            3. Initializes the Qdrant database
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain import Qdrant
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
        """
        try:
            import qdrant_client
        except ImportError:
            raise ValueError(
                "Could not import qdrant-client python package. "
                "Please install it with `pip install qdrant-client`."
            )
        from qdrant_client.http import models as rest
        # 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(
            url=url,
            port=port,
            grpc_port=grpc_port,
            prefer_grpc=prefer_grpc,
            https=https,
            api_key=api_key,
            prefix=prefix,
            timeout=timeout,
            host=host,
            **kwargs,
        )
        client.recreate_collection(
            collection_name=collection_name,
            vectors_config=rest.VectorParams(
                size=vector_size,
                distance=rest.Distance[distance_func],
            ),
        )
        # Now generate the embeddings for all the texts


),
        )
        # Now generate the embeddings for all the texts
        embeddings = embedding.embed_documents(texts)
        client.upsert(
            collection_name=collection_name,
            points=rest.Batch(
                ids=[uuid.uuid4().hex for _ in texts],
                vectors=embeddings,
                payloads=cls._build_payloads(
                    texts, metadatas, content_payload_key, metadata_payload_key
                ),
            ),
        )
        return cls(
            client=client,
            collection_name=collection_name,
            embedding_function=embedding.embed_query,
            content_payload_key=content_payload_key,
            metadata_payload_key=metadata_payload_key,
        )
    @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),


return Document(
            page_content=scored_point.payload.get(content_payload_key),
            metadata=scored_point.payload.get(metadata_payload_key) or {},
        )
    def _qdrant_filter_from_dict(self, filter: Optional[MetadataFilter]) -> Any:
        if filter is None or 0 == len(filter):
            return None
        from qdrant_client.http import models as rest
        return rest.Filter(
            must=[
                rest.FieldCondition(
                    key=f"{self.metadata_payload_key}.{key}",
                    match=rest.MatchValue(value=value),
                )
                for key, value in filter.items()
            ]
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
def dependable_faiss_import() -> Any:
    """Import faiss if available, otherwise raise error."""
    try:
        import faiss
    except ImportError:
        raise ValueError(
            "Could not import faiss python package. "
            "Please install it with `pip install faiss` "
            "or `pip install faiss-cpu` (depending on Python version)."
        )
    return faiss
[docs]class FAISS(VectorStore):
    """Wrapper around FAISS vector database.
    To use, you should have the ``faiss`` python package installed.
    Example:
        .. code-block:: python
            from langchain import FAISS
            faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
    """
    def __init__(
        self,
        embedding_function: Callable,
        index: Any,
        docstore: Docstore,
        index_to_docstore_id: Dict[int, str],
    ):
        """Initialize with necessary components."""
        self.embedding_function = embedding_function
        self.index = index
        self.docstore = docstore
        self.index_to_docstore_id = index_to_docstore_id
    def __add(


self.index_to_docstore_id = index_to_docstore_id
    def __add(
        self,
        texts: Iterable[str],
        embeddings: Iterable[List[float]],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError(
                "If trying to add texts, the underlying docstore should support "
                f"adding items, which {self.docstore} does not"
            )
        documents = []
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            documents.append(Document(page_content=text, metadata=metadata))
        # Add to the index, the index_to_id mapping, and the docstore.
        starting_len = len(self.index_to_docstore_id)
        self.index.add(np.array(embeddings, dtype=np.float32))
        # Get list of index, id, and docs.
        full_info = [
            (starting_len + i, str(uuid.uuid4()), doc)
            for i, doc in enumerate(documents)
        ]
        # Add information to docstore and index.
        self.docstore.add({_id: doc for _, _id, doc in full_info})
        index_to_id = {index: _id for index, _id, _ in full_info}
        self.index_to_docstore_id.update(index_to_id)
        return [_id for _, _id, _ in full_info]
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:


**kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError(
                "If trying to add texts, the underlying docstore should support "
                f"adding items, which {self.docstore} does not"
            )
        # Embed and create the documents.
        embeddings = [self.embedding_function(text) for text in texts]
        return self.__add(texts, embeddings, metadatas, **kwargs)
[docs]    def add_embeddings(
        self,
        text_embeddings: Iterable[Tuple[str, List[float]]],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            text_embeddings: Iterable pairs of string and embedding to
                add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError(
                "If trying to add texts, the underlying docstore should support "
                f"adding items, which {self.docstore} does not"
            )
        # Embed and create the documents.
        texts = [te[0] for te in text_embeddings]


texts = [te[0] for te in text_embeddings]
        embeddings = [te[1] for te in text_embeddings]
        return self.__add(texts, embeddings, metadatas, **kwargs)
[docs]    def similarity_search_with_score_by_vector(
        self, embedding: List[float], k: int = 4
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to 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
        """
        scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
        docs = []
        for j, i in enumerate(indices[0]):
            if i == -1:
                # This happens when not enough docs are returned.
                continue
            _id = self.index_to_docstore_id[i]
            doc = self.docstore.search(_id)
            if not isinstance(doc, Document):
                raise ValueError(f"Could not find document for id {_id}, got {doc}")
            docs.append((doc, scores[0][j]))
        return docs
[docs]    def similarity_search_with_score(
        self, query: str, k: int = 4
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to 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.embedding_function(query)


"""
        embedding = self.embedding_function(query)
        docs = self.similarity_search_with_score_by_vector(embedding, k)
        return docs
[docs]    def similarity_search_by_vector(
        self, embedding: List[float], k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the embedding.
        """
        docs_and_scores = self.similarity_search_with_score_by_vector(embedding, k)
        return [doc for doc, _ in docs_and_scores]
[docs]    def similarity_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to 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.
        """
        docs_and_scores = self.similarity_search_with_score(query, k)
        return [doc for doc, _ in docs_and_scores]
[docs]    def max_marginal_relevance_search_by_vector(
        self, embedding: List[float], k: int = 4, fetch_k: int = 20
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.
        Args:
            embedding: Embedding to look up documents similar to.


Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        _, indices = self.index.search(np.array([embedding], dtype=np.float32), fetch_k)
        # -1 happens when not enough docs are returned.
        embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
        mmr_selected = maximal_marginal_relevance(
            np.array([embedding], dtype=np.float32), embeddings, k=k
        )
        selected_indices = [indices[0][i] for i in mmr_selected]
        docs = []
        for i in selected_indices:
            if i == -1:
                # This happens when not enough docs are returned.
                continue
            _id = self.index_to_docstore_id[i]
            doc = self.docstore.search(_id)
            if not isinstance(doc, Document):
                raise ValueError(f"Could not find document for id {_id}, got {doc}")
            docs.append(doc)
        return docs
[docs]    def max_marginal_relevance_search(
        self, query: str, k: int = 4, fetch_k: int = 20
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.


fetch_k: Number of Documents to fetch to pass to MMR algorithm.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        embedding = self.embedding_function(query)
        docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
        return docs
[docs]    def merge_from(self, target: FAISS) -> None:
        """Merge another FAISS object with the current one.
        Add the target FAISS to the current one.
        Args:
            target: FAISS object you wish to merge into the current one
        Returns:
            None.
        """
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError("Cannot merge with this type of docstore")
        # Numerical index for target docs are incremental on existing ones
        starting_len = len(self.index_to_docstore_id)
        # Merge two IndexFlatL2
        self.index.merge_from(target.index)
        # Create new id for docs from target FAISS object
        full_info = []
        for i in target.index_to_docstore_id:
            doc = target.docstore.search(target.index_to_docstore_id[i])
            if not isinstance(doc, Document):
                raise ValueError("Document should be returned")
            full_info.append((starting_len + i, str(uuid.uuid4()), doc))
        # Add information to docstore and index_to_docstore_id.
        self.docstore.add({_id: doc for _, _id, doc in full_info})
        index_to_id = {index: _id for index, _id, _ in full_info}
        self.index_to_docstore_id.update(index_to_id)
    @classmethod
    def __from(
        cls,
        texts: List[str],
        embeddings: List[List[float]],


cls,
        texts: List[str],
        embeddings: List[List[float]],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> FAISS:
        faiss = dependable_faiss_import()
        index = faiss.IndexFlatL2(len(embeddings[0]))
        index.add(np.array(embeddings, dtype=np.float32))
        documents = []
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            documents.append(Document(page_content=text, metadata=metadata))
        index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
        docstore = InMemoryDocstore(
            {index_to_id[i]: doc for i, doc in enumerate(documents)}
        )
        return cls(embedding.embed_query, index, docstore, index_to_id)
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> FAISS:
        """Construct FAISS wrapper from raw documents.
        This is a user friendly interface that:
            1. Embeds documents.
            2. Creates an in memory docstore
            3. Initializes the FAISS database
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain import FAISS
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                faiss = FAISS.from_texts(texts, embeddings)
        """
        embeddings = embedding.embed_documents(texts)


"""
        embeddings = embedding.embed_documents(texts)
        return cls.__from(texts, embeddings, embedding, metadatas, **kwargs)
[docs]    @classmethod
    def from_embeddings(
        cls,
        text_embeddings: List[Tuple[str, List[float]]],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> FAISS:
        """Construct FAISS wrapper from raw documents.
        This is a user friendly interface that:
            1. Embeds documents.
            2. Creates an in memory docstore
            3. Initializes the FAISS database
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain import FAISS
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                faiss = FAISS.from_texts(texts, embeddings)
        """
        texts = [t[0] for t in text_embeddings]
        embeddings = [t[1] for t in text_embeddings]
        return cls.__from(texts, embeddings, embedding, metadatas, **kwargs)
[docs]    def save_local(self, folder_path: str) -> None:
        """Save FAISS index, docstore, and index_to_docstore_id to disk.
        Args:
            folder_path: folder path to save index, docstore,
                and index_to_docstore_id to.
        """
        path = Path(folder_path)
        path.mkdir(exist_ok=True, parents=True)
        # save index separately since it is not picklable
        faiss = dependable_faiss_import()
        faiss.write_index(self.index, str(path / "index.faiss"))


faiss.write_index(self.index, str(path / "index.faiss"))
        # save docstore and index_to_docstore_id
        with open(path / "index.pkl", "wb") as f:
            pickle.dump((self.docstore, self.index_to_docstore_id), f)
[docs]    @classmethod
    def load_local(cls, folder_path: str, embeddings: Embeddings) -> FAISS:
        """Load FAISS index, docstore, and index_to_docstore_id to disk.
        Args:
            folder_path: folder path to load index, docstore,
                and index_to_docstore_id from.
            embeddings: Embeddings to use when generating queries
        """
        path = Path(folder_path)
        # load index separately since it is not picklable
        faiss = dependable_faiss_import()
        index = faiss.read_index(str(path / "index.faiss"))
        # load docstore and index_to_docstore_id
        with open(path / "index.pkl", "rb") as f:
            docstore, index_to_docstore_id = pickle.load(f)
        return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.vectorstores.elastic_vector_search
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Dict, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore
def _default_text_mapping(dim: int) -> Dict:
    return {
        "properties": {
            "text": {"type": "text"},
            "vector": {"type": "dense_vector", "dims": dim},
        }
    }
def _default_script_query(query_vector: List[float]) -> Dict:
    return {
        "script_score": {
            "query": {"match_all": {}},
            "script": {
                "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",
                "params": {"query_vector": query_vector},
            },
        }
    }
[docs]class ElasticVectorSearch(VectorStore):
    """Wrapper around Elasticsearch as a vector database.
    Example:
        .. code-block:: python
            from langchain import ElasticVectorSearch
            elastic_vector_search = ElasticVectorSearch(
                "http://localhost:9200",
                "embeddings",
                embedding
            )
    """
    def __init__(self, elasticsearch_url: str, index_name: str, embedding: Embeddings):
        """Initialize with necessary components."""
        try:
            import elasticsearch
        except ImportError:
            raise ValueError(
                "Could not import elasticsearch python package. "
                "Please install it with `pip install elasticsearch`."
            )
        self.embedding = embedding
        self.index_name = index_name


)
        self.embedding = embedding
        self.index_name = index_name
        try:
            es_client = elasticsearch.Elasticsearch(elasticsearch_url)  # noqa
        except ValueError as e:
            raise ValueError(
                f"Your elasticsearch client string is misformatted. Got error: {e} "
            )
        self.client = es_client
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        try:
            from elasticsearch.helpers import bulk
        except ImportError:
            raise ValueError(
                "Could not import elasticsearch python package. "
                "Please install it with `pip install elasticsearch`."
            )
        requests = []
        ids = []
        embeddings = self.embedding.embed_documents(list(texts))
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            _id = str(uuid.uuid4())
            request = {
                "_op_type": "index",
                "_index": self.index_name,
                "vector": embeddings[i],
                "text": text,
                "metadata": metadata,
                "_id": _id,
            }
            ids.append(_id)
            requests.append(request)
        bulk(self.client, requests)
        # TODO: add option not to refresh


bulk(self.client, requests)
        # TODO: add option not to refresh
        self.client.indices.refresh(index=self.index_name)
        return ids
[docs]    def similarity_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to 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.
        """
        embedding = self.embedding.embed_query(query)
        script_query = _default_script_query(embedding)
        response = self.client.search(index=self.index_name, query=script_query)
        hits = [hit["_source"] for hit in response["hits"]["hits"][:k]]
        documents = [
            Document(page_content=hit["text"], metadata=hit["metadata"]) for hit in hits
        ]
        return documents
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> ElasticVectorSearch:
        """Construct ElasticVectorSearch wrapper from raw documents.
        This is a user-friendly interface that:
            1. Embeds documents.
            2. Creates a new index for the embeddings in the Elasticsearch instance.
            3. Adds the documents to the newly created Elasticsearch index.
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain import ElasticVectorSearch
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()


from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                elastic_vector_search = ElasticVectorSearch.from_texts(
                    texts,
                    embeddings,
                    elasticsearch_url="http://localhost:9200"
                )
        """
        elasticsearch_url = get_from_dict_or_env(
            kwargs, "elasticsearch_url", "ELASTICSEARCH_URL"
        )
        try:
            import elasticsearch
            from elasticsearch.helpers import bulk
        except ImportError:
            raise ValueError(
                "Could not import elasticsearch python package. "
                "Please install it with `pip install elasticearch`."
            )
        try:
            client = elasticsearch.Elasticsearch(elasticsearch_url)
        except ValueError as e:
            raise ValueError(
                "Your elasticsearch client string is misformatted. " f"Got error: {e} "
            )
        index_name = uuid.uuid4().hex
        embeddings = embedding.embed_documents(texts)
        dim = len(embeddings[0])
        mapping = _default_text_mapping(dim)
        # TODO would be nice to create index before embedding,
        # just to save expensive steps for last
        client.indices.create(index=index_name, mappings=mapping)
        requests = []
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            request = {
                "_op_type": "index",
                "_index": index_name,
                "vector": embeddings[i],
                "text": text,
                "metadata": metadata,
            }
            requests.append(request)
        bulk(client, requests)
        client.indices.refresh(index=index_name)
        return cls(elasticsearch_url, index_name, embedding)
By Harrison Chase


return cls(elasticsearch_url, index_name, embedding)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.prompts.loading
"""Load prompts from disk."""
import importlib
import json
import logging
from pathlib import Path
from typing import Union
import yaml
from langchain.output_parsers.regex import RegexParser
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.utilities.loading import try_load_from_hub
URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/prompts/"
logger = logging.getLogger(__file__)
def load_prompt_from_config(config: dict) -> BasePromptTemplate:
    """Load prompt from Config Dict."""
    if "_type" not in config:
        logger.warning("No `_type` key found, defaulting to `prompt`.")
    config_type = config.pop("_type", "prompt")
    if config_type not in type_to_loader_dict:
        raise ValueError(f"Loading {config_type} prompt not supported")
    prompt_loader = type_to_loader_dict[config_type]
    return prompt_loader(config)
def _load_template(var_name: str, config: dict) -> dict:
    """Load template from disk if applicable."""
    # Check if template_path exists in config.
    if f"{var_name}_path" in config:
        # If it does, make sure template variable doesn't also exist.
        if var_name in config:
            raise ValueError(
                f"Both `{var_name}_path` and `{var_name}` cannot be provided."
            )
        # Pop the template path from the config.
        template_path = Path(config.pop(f"{var_name}_path"))
        # Load the template.
        if template_path.suffix == ".txt":
            with open(template_path) as f:


if template_path.suffix == ".txt":
            with open(template_path) as f:
                template = f.read()
        else:
            raise ValueError
        # Set the template variable to the extracted variable.
        config[var_name] = template
    return config
def _load_examples(config: dict) -> dict:
    """Load examples if necessary."""
    if isinstance(config["examples"], list):
        pass
    elif isinstance(config["examples"], str):
        with open(config["examples"]) as f:
            if config["examples"].endswith(".json"):
                examples = json.load(f)
            elif config["examples"].endswith((".yaml", ".yml")):
                examples = yaml.safe_load(f)
            else:
                raise ValueError(
                    "Invalid file format. Only json or yaml formats are supported."
                )
        config["examples"] = examples
    else:
        raise ValueError("Invalid examples format. Only list or string are supported.")
    return config
def _load_output_parser(config: dict) -> dict:
    """Load output parser."""
    if "output_parsers" in config:
        if config["output_parsers"] is not None:
            _config = config["output_parsers"]
            output_parser_type = _config["_type"]
            if output_parser_type == "regex_parser":
                output_parser = RegexParser(**_config)
            else:
                raise ValueError(f"Unsupported output parser {output_parser_type}")
            config["output_parsers"] = output_parser
    return config
def _load_few_shot_prompt(config: dict) -> FewShotPromptTemplate:
    """Load the few shot prompt from the config."""
    # Load the suffix and prefix templates.
    config = _load_template("suffix", config)


config = _load_template("suffix", config)
    config = _load_template("prefix", config)
    # Load the example prompt.
    if "example_prompt_path" in config:
        if "example_prompt" in config:
            raise ValueError(
                "Only one of example_prompt and example_prompt_path should "
                "be specified."
            )
        config["example_prompt"] = load_prompt(config.pop("example_prompt_path"))
    else:
        config["example_prompt"] = load_prompt_from_config(config["example_prompt"])
    # Load the examples.
    config = _load_examples(config)
    config = _load_output_parser(config)
    return FewShotPromptTemplate(**config)
def _load_prompt(config: dict) -> PromptTemplate:
    """Load the prompt template from config."""
    # Load the template from disk if necessary.
    config = _load_template("template", config)
    config = _load_output_parser(config)
    return PromptTemplate(**config)
[docs]def load_prompt(path: Union[str, Path]) -> BasePromptTemplate:
    """Unified method for loading a prompt from LangChainHub or local fs."""
    if hub_result := try_load_from_hub(
        path, _load_prompt_from_file, "prompts", {"py", "json", "yaml"}
    ):
        return hub_result
    else:
        return _load_prompt_from_file(path)
def _load_prompt_from_file(file: Union[str, Path]) -> BasePromptTemplate:
    """Load prompt from file."""
    # Convert file to Path object.
    if isinstance(file, str):
        file_path = Path(file)
    else:
        file_path = file
    # Load from either json or yaml.
    if file_path.suffix == ".json":


# Load from either json or yaml.
    if file_path.suffix == ".json":
        with open(file_path) as f:
            config = json.load(f)
    elif file_path.suffix == ".yaml":
        with open(file_path, "r") as f:
            config = yaml.safe_load(f)
    elif file_path.suffix == ".py":
        spec = importlib.util.spec_from_loader(
            "prompt", loader=None, origin=str(file_path)
        )
        if spec is None:
            raise ValueError("could not load spec")
        helper = importlib.util.module_from_spec(spec)
        with open(file_path, "rb") as f:
            exec(f.read(), helper.__dict__)
        if not isinstance(helper.PROMPT, BasePromptTemplate):
            raise ValueError("Did not get object of type BasePromptTemplate.")
        return helper.PROMPT
    else:
        raise ValueError(f"Got unsupported file type {file_path.suffix}")
    # Load the prompt from the config now.
    return load_prompt_from_config(config)
type_to_loader_dict = {
    "prompt": _load_prompt,
    "few_shot": _load_few_shot_prompt,
    # "few_shot_with_templates": _load_few_shot_with_templates_prompt,
}
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.prompts.chat
"""Chat prompt template."""
from __future__ import annotations
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, List, Sequence, Tuple, Type, Union
from pydantic import BaseModel, Field
from langchain.memory.buffer import get_buffer_string
from langchain.prompts.base import BasePromptTemplate, StringPromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
    AIMessage,
    BaseMessage,
    ChatMessage,
    HumanMessage,
    PromptValue,
    SystemMessage,
)
class BaseMessagePromptTemplate(BaseModel, ABC):
    @abstractmethod
    def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
        """To messages."""
    @property
    @abstractmethod
    def input_variables(self) -> List[str]:
        """Input variables for this prompt template."""
[docs]class MessagesPlaceholder(BaseMessagePromptTemplate):
    """Prompt template that assumes variable is already list of messages."""
    variable_name: str
[docs]    def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
        """To a BaseMessage."""
        value = kwargs[self.variable_name]
        if not isinstance(value, list):
            raise ValueError(
                f"variable {self.variable_name} should be a list of base messages, "
                f"got {value}"
            )
        for v in value:
            if not isinstance(v, BaseMessage):
                raise ValueError(
                    f"variable {self.variable_name} should be a list of base messages,"
                    f" got {value}"
                )
        return value
    @property
    def input_variables(self) -> List[str]:
        """Input variables for this prompt template."""


def input_variables(self) -> List[str]:
        """Input variables for this prompt template."""
        return [self.variable_name]
class BaseStringMessagePromptTemplate(BaseMessagePromptTemplate, ABC):
    prompt: StringPromptTemplate
    additional_kwargs: dict = Field(default_factory=dict)
    @classmethod
    def from_template(cls, template: str, **kwargs: Any) -> BaseMessagePromptTemplate:
        prompt = PromptTemplate.from_template(template)
        return cls(prompt=prompt, **kwargs)
    @abstractmethod
    def format(self, **kwargs: Any) -> BaseMessage:
        """To a BaseMessage."""
    def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
        return [self.format(**kwargs)]
    @property
    def input_variables(self) -> List[str]:
        return self.prompt.input_variables
class ChatMessagePromptTemplate(BaseStringMessagePromptTemplate):
    role: str
    def format(self, **kwargs: Any) -> BaseMessage:
        text = self.prompt.format(**kwargs)
        return ChatMessage(
            content=text, role=self.role, additional_kwargs=self.additional_kwargs
        )
class HumanMessagePromptTemplate(BaseStringMessagePromptTemplate):
    def format(self, **kwargs: Any) -> BaseMessage:
        text = self.prompt.format(**kwargs)
        return HumanMessage(content=text, additional_kwargs=self.additional_kwargs)
class AIMessagePromptTemplate(BaseStringMessagePromptTemplate):
    def format(self, **kwargs: Any) -> BaseMessage:
        text = self.prompt.format(**kwargs)
        return AIMessage(content=text, additional_kwargs=self.additional_kwargs)
class SystemMessagePromptTemplate(BaseStringMessagePromptTemplate):
    def format(self, **kwargs: Any) -> BaseMessage:
        text = self.prompt.format(**kwargs)


text = self.prompt.format(**kwargs)
        return SystemMessage(content=text, additional_kwargs=self.additional_kwargs)
class ChatPromptValue(PromptValue):
    messages: List[BaseMessage]
    def to_string(self) -> str:
        """Return prompt as string."""
        return get_buffer_string(self.messages)
    def to_messages(self) -> List[BaseMessage]:
        """Return prompt as messages."""
        return self.messages
[docs]class ChatPromptTemplate(BasePromptTemplate, ABC):
    input_variables: List[str]
    messages: List[Union[BaseMessagePromptTemplate, BaseMessage]]
    @classmethod
    def from_role_strings(
        cls, string_messages: List[Tuple[str, str]]
    ) -> ChatPromptTemplate:
        messages = [
            ChatMessagePromptTemplate(
                content=PromptTemplate.from_template(template), role=role
            )
            for role, template in string_messages
        ]
        return cls.from_messages(messages)
    @classmethod
    def from_strings(
        cls, string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]
    ) -> ChatPromptTemplate:
        messages = [
            role(content=PromptTemplate.from_template(template))
            for role, template in string_messages
        ]
        return cls.from_messages(messages)
    @classmethod
    def from_messages(
        cls, messages: Sequence[Union[BaseMessagePromptTemplate, BaseMessage]]
    ) -> ChatPromptTemplate:
        input_vars = set()
        for message in messages:
            if isinstance(message, BaseMessagePromptTemplate):
                input_vars.update(message.input_variables)
        return cls(input_variables=list(input_vars), messages=messages)
[docs]    def format(self, **kwargs: Any) -> str:
        return self.format_prompt(**kwargs).to_string()


return self.format_prompt(**kwargs).to_string()
[docs]    def format_prompt(self, **kwargs: Any) -> PromptValue:
        kwargs = self._merge_partial_and_user_variables(**kwargs)
        result = []
        for message_template in self.messages:
            if isinstance(message_template, BaseMessage):
                result.extend([message_template])
            elif isinstance(message_template, BaseMessagePromptTemplate):
                rel_params = {
                    k: v
                    for k, v in kwargs.items()
                    if k in message_template.input_variables
                }
                message = message_template.format_messages(**rel_params)
                result.extend(message)
            else:
                raise ValueError(f"Unexpected input: {message_template}")
        return ChatPromptValue(messages=result)
[docs]    def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate:
        raise NotImplementedError
    @property
    def _prompt_type(self) -> str:
        raise NotImplementedError
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.prompts.few_shot_with_templates
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.prompts.base import (
    DEFAULT_FORMATTER_MAPPING,
    StringPromptTemplate,
)
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.prompts.prompt import PromptTemplate
[docs]class FewShotPromptWithTemplates(StringPromptTemplate, BaseModel):
    """Prompt template that contains few shot examples."""
    examples: Optional[List[dict]] = None
    """Examples to format into the prompt.
    Either this or example_selector should be provided."""
    example_selector: Optional[BaseExampleSelector] = None
    """ExampleSelector to choose the examples to format into the prompt.
    Either this or examples should be provided."""
    example_prompt: PromptTemplate
    """PromptTemplate used to format an individual example."""
    suffix: StringPromptTemplate
    """A PromptTemplate to put after the examples."""
    input_variables: List[str]
    """A list of the names of the variables the prompt template expects."""
    example_separator: str = "\n\n"
    """String separator used to join the prefix, the examples, and suffix."""
    prefix: Optional[StringPromptTemplate] = None
    """A PromptTemplate to put before the examples."""
    template_format: str = "f-string"
    """The format of the prompt template. Options are: 'f-string', 'jinja2'."""
    validate_template: bool = True
    """Whether or not to try validating the template."""
    @root_validator(pre=True)
    def check_examples_and_selector(cls, values: Dict) -> Dict:
        """Check that one and only one of examples/example_selector are provided."""


"""Check that one and only one of examples/example_selector are provided."""
        examples = values.get("examples", None)
        example_selector = values.get("example_selector", None)
        if examples and example_selector:
            raise ValueError(
                "Only one of 'examples' and 'example_selector' should be provided"
            )
        if examples is None and example_selector is None:
            raise ValueError(
                "One of 'examples' and 'example_selector' should be provided"
            )
        return values
    @root_validator()
    def template_is_valid(cls, values: Dict) -> Dict:
        """Check that prefix, suffix and input variables are consistent."""
        if values["validate_template"]:
            input_variables = values["input_variables"]
            expected_input_variables = set(values["suffix"].input_variables)
            expected_input_variables |= set(values["partial_variables"])
            if values["prefix"] is not None:
                expected_input_variables |= set(values["prefix"].input_variables)
            missing_vars = expected_input_variables.difference(input_variables)
            if missing_vars:
                raise ValueError(
                    f"Got input_variables={input_variables}, but based on "
                    f"prefix/suffix expected {expected_input_variables}"
                )
        return values
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    def _get_examples(self, **kwargs: Any) -> List[dict]:
        if self.examples is not None:
            return self.examples
        elif self.example_selector is not None:
            return self.example_selector.select_examples(kwargs)
        else:
            raise ValueError
[docs]    def format(self, **kwargs: Any) -> str:
        """Format the prompt with the inputs.
        Args:


"""Format the prompt with the inputs.
        Args:
            kwargs: Any arguments to be passed to the prompt template.
        Returns:
            A formatted string.
        Example:
        .. code-block:: python
            prompt.format(variable1="foo")
        """
        kwargs = self._merge_partial_and_user_variables(**kwargs)
        # Get the examples to use.
        examples = self._get_examples(**kwargs)
        # Format the examples.
        example_strings = [
            self.example_prompt.format(**example) for example in examples
        ]
        # Create the overall prefix.
        if self.prefix is None:
            prefix = ""
        else:
            prefix_kwargs = {
                k: v for k, v in kwargs.items() if k in self.prefix.input_variables
            }
            for k in prefix_kwargs.keys():
                kwargs.pop(k)
            prefix = self.prefix.format(**prefix_kwargs)
        # Create the overall suffix
        suffix_kwargs = {
            k: v for k, v in kwargs.items() if k in self.suffix.input_variables
        }
        for k in suffix_kwargs.keys():
            kwargs.pop(k)
        suffix = self.suffix.format(
            **suffix_kwargs,
        )
        pieces = [prefix, *example_strings, suffix]
        template = self.example_separator.join([piece for piece in pieces if piece])
        # Format the template with the input variables.
        return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs)
    @property
    def _prompt_type(self) -> str:
        """Return the prompt type key."""
        return "few_shot_with_templates"
[docs]    def dict(self, **kwargs: Any) -> Dict:
        """Return a dictionary of the prompt."""
        if self.example_selector:


"""Return a dictionary of the prompt."""
        if self.example_selector:
            raise ValueError("Saving an example selector is not currently supported")
        return super().dict(**kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.prompts.base
"""BasePrompt schema definition."""
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Union
import yaml
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.formatting import formatter
from langchain.schema import BaseMessage, BaseOutputParser, HumanMessage, PromptValue
def jinja2_formatter(template: str, **kwargs: Any) -> str:
    """Format a template using jinja2."""
    try:
        from jinja2 import Template
    except ImportError:
        raise ValueError(
            "jinja2 not installed, which is needed to use the jinja2_formatter. "
            "Please install it with `pip install jinja2`."
        )
    return Template(template).render(**kwargs)
DEFAULT_FORMATTER_MAPPING: Dict[str, Callable] = {
    "f-string": formatter.format,
    "jinja2": jinja2_formatter,
}
def check_valid_template(
    template: str, template_format: str, input_variables: List[str]
) -> None:
    """Check that template string is valid."""
    if template_format not in DEFAULT_FORMATTER_MAPPING:
        valid_formats = list(DEFAULT_FORMATTER_MAPPING)
        raise ValueError(
            f"Invalid template format. Got `{template_format}`;"
            f" should be one of {valid_formats}"
        )
    dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
    try:
        formatter_func = DEFAULT_FORMATTER_MAPPING[template_format]
        formatter_func(template, **dummy_inputs)
    except KeyError as e:
        raise ValueError(


except KeyError as e:
        raise ValueError(
            "Invalid prompt schema; check for mismatched or missing input parameters. "
            + str(e)
        )
class StringPromptValue(PromptValue):
    text: str
    def to_string(self) -> str:
        """Return prompt as string."""
        return self.text
    def to_messages(self) -> List[BaseMessage]:
        """Return prompt as messages."""
        return [HumanMessage(content=self.text)]
[docs]class BasePromptTemplate(BaseModel, ABC):
    """Base class for all prompt templates, returning a prompt."""
    input_variables: List[str]
    """A list of the names of the variables the prompt template expects."""
    output_parser: Optional[BaseOutputParser] = None
    """How to parse the output of calling an LLM on this formatted prompt."""
    partial_variables: Mapping[str, Union[str, Callable[[], str]]] = Field(
        default_factory=dict
    )
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    @abstractmethod
    def format_prompt(self, **kwargs: Any) -> PromptValue:
        """Create Chat Messages."""
    @root_validator()
    def validate_variable_names(cls, values: Dict) -> Dict:
        """Validate variable names do not include restricted names."""
        if "stop" in values["input_variables"]:
            raise ValueError(
                "Cannot have an input variable named 'stop', as it is used internally,"
                " please rename."
            )
        if "stop" in values["partial_variables"]:
            raise ValueError(
                "Cannot have an partial variable named 'stop', as it is used "
                "internally, please rename."
            )


"internally, please rename."
            )
        overall = set(values["input_variables"]).intersection(
            values["partial_variables"]
        )
        if overall:
            raise ValueError(
                f"Found overlapping input and partial variables: {overall}"
            )
        return values
[docs]    def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate:
        """Return a partial of the prompt template."""
        prompt_dict = self.__dict__.copy()
        prompt_dict["input_variables"] = list(
            set(self.input_variables).difference(kwargs)
        )
        prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs}
        return type(self)(**prompt_dict)
    def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]:
        # Get partial params:
        partial_kwargs = {
            k: v if isinstance(v, str) else v()
            for k, v in self.partial_variables.items()
        }
        return {**partial_kwargs, **kwargs}
[docs]    @abstractmethod
    def format(self, **kwargs: Any) -> str:
        """Format the prompt with the inputs.
        Args:
            kwargs: Any arguments to be passed to the prompt template.
        Returns:
            A formatted string.
        Example:
        .. code-block:: python
            prompt.format(variable1="foo")
        """
    @property
    @abstractmethod
    def _prompt_type(self) -> str:
        """Return the prompt type key."""
[docs]    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of prompt."""
        prompt_dict = super().dict(**kwargs)
        prompt_dict["_type"] = self._prompt_type


prompt_dict["_type"] = self._prompt_type
        return prompt_dict
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        """Save the prompt.
        Args:
            file_path: Path to directory to save prompt to.
        Example:
        .. code-block:: python
            prompt.save(file_path="path/prompt.yaml")
        """
        if self.partial_variables:
            raise ValueError("Cannot save prompt with partial variables.")
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path
        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)
        # Fetch dictionary to save
        prompt_dict = self.dict()
        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(prompt_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(prompt_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")
[docs]class StringPromptTemplate(BasePromptTemplate, ABC):
    """String prompt should expose the format method, returning a prompt."""
[docs]    def format_prompt(self, **kwargs: Any) -> PromptValue:
        """Create Chat Messages."""
        return StringPromptValue(text=self.format(**kwargs))
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.prompts.few_shot
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.prompts.base import (
    DEFAULT_FORMATTER_MAPPING,
    StringPromptTemplate,
    check_valid_template,
)
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.prompts.prompt import PromptTemplate
[docs]class FewShotPromptTemplate(StringPromptTemplate, BaseModel):
    """Prompt template that contains few shot examples."""
    examples: Optional[List[dict]] = None
    """Examples to format into the prompt.
    Either this or example_selector should be provided."""
    example_selector: Optional[BaseExampleSelector] = None
    """ExampleSelector to choose the examples to format into the prompt.
    Either this or examples should be provided."""
    example_prompt: PromptTemplate
    """PromptTemplate used to format an individual example."""
    suffix: str
    """A prompt template string to put after the examples."""
    input_variables: List[str]
    """A list of the names of the variables the prompt template expects."""
    example_separator: str = "\n\n"
    """String separator used to join the prefix, the examples, and suffix."""
    prefix: str = ""
    """A prompt template string to put before the examples."""
    template_format: str = "f-string"
    """The format of the prompt template. Options are: 'f-string', 'jinja2'."""
    validate_template: bool = True
    """Whether or not to try validating the template."""
    @root_validator(pre=True)
    def check_examples_and_selector(cls, values: Dict) -> Dict:
        """Check that one and only one of examples/example_selector are provided."""


"""Check that one and only one of examples/example_selector are provided."""
        examples = values.get("examples", None)
        example_selector = values.get("example_selector", None)
        if examples and example_selector:
            raise ValueError(
                "Only one of 'examples' and 'example_selector' should be provided"
            )
        if examples is None and example_selector is None:
            raise ValueError(
                "One of 'examples' and 'example_selector' should be provided"
            )
        return values
    @root_validator()
    def template_is_valid(cls, values: Dict) -> Dict:
        """Check that prefix, suffix and input variables are consistent."""
        if values["validate_template"]:
            check_valid_template(
                values["prefix"] + values["suffix"],
                values["template_format"],
                values["input_variables"] + list(values["partial_variables"]),
            )
        return values
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    def _get_examples(self, **kwargs: Any) -> List[dict]:
        if self.examples is not None:
            return self.examples
        elif self.example_selector is not None:
            return self.example_selector.select_examples(kwargs)
        else:
            raise ValueError
[docs]    def format(self, **kwargs: Any) -> str:
        """Format the prompt with the inputs.
        Args:
            kwargs: Any arguments to be passed to the prompt template.
        Returns:
            A formatted string.
        Example:
        .. code-block:: python
            prompt.format(variable1="foo")
        """
        kwargs = self._merge_partial_and_user_variables(**kwargs)
        # Get the examples to use.


# Get the examples to use.
        examples = self._get_examples(**kwargs)
        # Format the examples.
        example_strings = [
            self.example_prompt.format(**example) for example in examples
        ]
        # Create the overall template.
        pieces = [self.prefix, *example_strings, self.suffix]
        template = self.example_separator.join([piece for piece in pieces if piece])
        # Format the template with the input variables.
        return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs)
    @property
    def _prompt_type(self) -> str:
        """Return the prompt type key."""
        return "few_shot"
[docs]    def dict(self, **kwargs: Any) -> Dict:
        """Return a dictionary of the prompt."""
        if self.example_selector:
            raise ValueError("Saving an example selector is not currently supported")
        return super().dict(**kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.prompts.prompt
"""Prompt schema definition."""
from __future__ import annotations
from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Union
from pydantic import BaseModel, Extra, root_validator
from langchain.prompts.base import (
    DEFAULT_FORMATTER_MAPPING,
    StringPromptTemplate,
    check_valid_template,
)
[docs]class PromptTemplate(StringPromptTemplate, BaseModel):
    """Schema to represent a prompt for an LLM.
    Example:
        .. code-block:: python
            from langchain import PromptTemplate
            prompt = PromptTemplate(input_variables=["foo"], template="Say {foo}")
    """
    input_variables: List[str]
    """A list of the names of the variables the prompt template expects."""
    template: str
    """The prompt template."""
    template_format: str = "f-string"
    """The format of the prompt template. Options are: 'f-string', 'jinja2'."""
    validate_template: bool = True
    """Whether or not to try validating the template."""
    @property
    def _prompt_type(self) -> str:
        """Return the prompt type key."""
        return "prompt"
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
[docs]    def format(self, **kwargs: Any) -> str:
        """Format the prompt with the inputs.
        Args:
            kwargs: Any arguments to be passed to the prompt template.
        Returns:
            A formatted string.
        Example:
        .. code-block:: python
            prompt.format(variable1="foo")
        """
        kwargs = self._merge_partial_and_user_variables(**kwargs)


"""
        kwargs = self._merge_partial_and_user_variables(**kwargs)
        return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
    @root_validator()
    def template_is_valid(cls, values: Dict) -> Dict:
        """Check that template and input variables are consistent."""
        if values["validate_template"]:
            all_inputs = values["input_variables"] + list(values["partial_variables"])
            check_valid_template(
                values["template"], values["template_format"], all_inputs
            )
        return values
[docs]    @classmethod
    def from_examples(
        cls,
        examples: List[str],
        suffix: str,
        input_variables: List[str],
        example_separator: str = "\n\n",
        prefix: str = "",
    ) -> PromptTemplate:
        """Take examples in list format with prefix and suffix to create a prompt.
        Intended be used as a way to dynamically create a prompt from examples.
        Args:
            examples: List of examples to use in the prompt.
            suffix: String to go after the list of examples. Should generally
                set up the user's input.
            input_variables: A list of variable names the final prompt template
                will expect.
            example_separator: The separator to use in between examples. Defaults
                to two new line characters.
            prefix: String that should go before any examples. Generally includes
                examples. Default to an empty string.
        Returns:
            The final prompt generated.
        """
        template = example_separator.join([prefix, *examples, suffix])
        return cls(input_variables=input_variables, template=template)
[docs]    @classmethod
    def from_file(
        cls, template_file: Union[str, Path], input_variables: List[str]
    ) -> PromptTemplate:


) -> PromptTemplate:
        """Load a prompt from a file.
        Args:
            template_file: The path to the file containing the prompt template.
            input_variables: A list of variable names the final prompt template
                will expect.
        Returns:
            The prompt loaded from the file.
        """
        with open(str(template_file), "r") as f:
            template = f.read()
        return cls(input_variables=input_variables, template=template)
[docs]    @classmethod
    def from_template(cls, template: str) -> PromptTemplate:
        """Load a prompt template from a template."""
        input_variables = {
            v for _, v, _, _ in Formatter().parse(template) if v is not None
        }
        return cls(input_variables=list(sorted(input_variables)), template=template)
# For backwards compatibility.
Prompt = PromptTemplate
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.


Source code for langchain.prompts.example_selector.semantic_similarity
"""Example selector that selects examples based on SemanticSimilarity."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.vectorstores.base import VectorStore
def sorted_values(values: Dict[str, str]) -> List[Any]:
    """Return a list of values in dict sorted by key."""
    return [values[val] for val in sorted(values)]
[docs]class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
    """Example selector that selects examples based on SemanticSimilarity."""
    vectorstore: VectorStore
    """VectorStore than contains information about examples."""
    k: int = 4
    """Number of examples to select."""
    example_keys: Optional[List[str]] = None
    """Optional keys to filter examples to."""
    input_keys: Optional[List[str]] = None
    """Optional keys to filter input to. If provided, the search is based on
    the input variables instead of all variables."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    def add_example(self, example: Dict[str, str]) -> str:
        """Add new example to vectorstore."""
        if self.input_keys:
            string_example = " ".join(
                sorted_values({key: example[key] for key in self.input_keys})
            )
        else:
            string_example = " ".join(sorted_values(example))
        ids = self.vectorstore.add_texts([string_example], metadatas=[example])
        return ids[0]


return ids[0]
[docs]    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
        """Select which examples to use based on semantic similarity."""
        # Get the docs with the highest similarity.
        if self.input_keys:
            input_variables = {key: input_variables[key] for key in self.input_keys}
        query = " ".join(sorted_values(input_variables))
        example_docs = self.vectorstore.similarity_search(query, k=self.k)
        # Get the examples from the metadata.
        # This assumes that examples are stored in metadata.
        examples = [dict(e.metadata) for e in example_docs]
        # If example keys are provided, filter examples to those keys.
        if self.example_keys:
            examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
        return examples
[docs]    @classmethod
    def from_examples(
        cls,
        examples: List[dict],
        embeddings: Embeddings,
        vectorstore_cls: Type[VectorStore],
        k: int = 4,
        input_keys: Optional[List[str]] = None,
        **vectorstore_cls_kwargs: Any,
    ) -> SemanticSimilarityExampleSelector:
        """Create k-shot example selector using example list and embeddings.
        Reshuffles examples dynamically based on query similarity.
        Args:
            examples: List of examples to use in the prompt.
            embeddings: An iniialized embedding API interface, e.g. OpenAIEmbeddings().
            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
            k: Number of examples to select
            input_keys: If provided, the search is based on the input variables
                instead of all variables.


instead of all variables.
            vectorstore_cls_kwargs: optional kwargs containing url for vector store
        Returns:
            The ExampleSelector instantiated, backed by a vector store.
        """
        if input_keys:
            string_examples = [
                " ".join(sorted_values({k: eg[k] for k in input_keys}))
                for eg in examples
            ]
        else:
            string_examples = [" ".join(sorted_values(eg)) for eg in examples]
        vectorstore = vectorstore_cls.from_texts(
            string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
        )
        return cls(vectorstore=vectorstore, k=k, input_keys=input_keys)
[docs]class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector, BaseModel):
    """ExampleSelector that selects examples based on Max Marginal Relevance.
    This was shown to improve performance in this paper:
    https://arxiv.org/pdf/2211.13892.pdf
    """
    fetch_k: int = 20
    """Number of examples to fetch to rerank."""
[docs]    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
        """Select which examples to use based on semantic similarity."""
        # Get the docs with the highest similarity.
        if self.input_keys:
            input_variables = {key: input_variables[key] for key in self.input_keys}
        query = " ".join(sorted_values(input_variables))
        example_docs = self.vectorstore.max_marginal_relevance_search(
            query, k=self.k, fetch_k=self.fetch_k
        )
        # Get the examples from the metadata.
        # This assumes that examples are stored in metadata.
        examples = [dict(e.metadata) for e in example_docs]


examples = [dict(e.metadata) for e in example_docs]
        # If example keys are provided, filter examples to those keys.
        if self.example_keys:
            examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
        return examples
[docs]    @classmethod
    def from_examples(
        cls,
        examples: List[dict],
        embeddings: Embeddings,
        vectorstore_cls: Type[VectorStore],
        k: int = 4,
        input_keys: Optional[List[str]] = None,
        fetch_k: int = 20,
        **vectorstore_cls_kwargs: Any,
    ) -> MaxMarginalRelevanceExampleSelector:
        """Create k-shot example selector using example list and embeddings.
        Reshuffles examples dynamically based on query similarity.
        Args:
            examples: List of examples to use in the prompt.
            embeddings: An iniialized embedding API interface, e.g. OpenAIEmbeddings().
            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
            k: Number of examples to select
            input_keys: If provided, the search is based on the input variables
                instead of all variables.
            vectorstore_cls_kwargs: optional kwargs containing url for vector store
        Returns:
            The ExampleSelector instantiated, backed by a vector store.
        """
        if input_keys:
            string_examples = [
                " ".join(sorted_values({k: eg[k] for k in input_keys}))
                for eg in examples
            ]
        else:
            string_examples = [" ".join(sorted_values(eg)) for eg in examples]
        vectorstore = vectorstore_cls.from_texts(
            string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
        )


string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
        )
        return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 28, 2023.