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244 lines
8.0 KiB
Python
244 lines
8.0 KiB
Python
"""Wrapper around Qdrant vector database."""
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import uuid
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from operator import itemgetter
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from typing import Any, Callable, Iterable, List, Optional, Tuple
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.utils import get_from_dict_or_env
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from langchain.vectorstores import VectorStore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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class Qdrant(VectorStore):
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"""Wrapper around Qdrant vector database.
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To use you should have the ``qdrant-client`` package installed.
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Example:
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.. code-block:: python
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from langchain import Qdrant
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client = QdrantClient()
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collection_name = "MyCollection"
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qdrant = Qdrant(client, collection_name, embedding_function)
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"""
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CONTENT_KEY = "page_content"
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METADATA_KEY = "metadata"
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def __init__(self, client: Any, collection_name: str, embedding_function: Callable):
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"""Initialize with necessary components."""
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try:
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import qdrant_client
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except ImportError:
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raise ValueError(
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"Could not import qdrant-client python package. "
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"Please it install it with `pip install qdrant-client`."
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)
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if not isinstance(client, qdrant_client.QdrantClient):
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raise ValueError(
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f"client should be an instance of qdrant_client.QdrantClient, "
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f"got {type(client)}"
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)
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self.client: qdrant_client.QdrantClient = client
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self.collection_name = collection_name
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self.embedding_function = embedding_function
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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Returns:
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List of ids from adding the texts into the vectorstore.
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"""
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from qdrant_client.http import models as rest
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ids = [uuid.uuid4().hex for _ in texts]
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self.client.upsert(
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collection_name=self.collection_name,
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points=rest.Batch(
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ids=ids,
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vectors=[self.embedding_function(text) for text in texts],
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payloads=self._build_payloads(texts, metadatas),
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),
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)
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return ids
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def similarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query.
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"""
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results = self.similarity_search_with_score(query, k)
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return list(map(itemgetter(0), results))
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def similarity_search_with_score(
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self, query: str, k: int = 4
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query and score for each
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"""
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embedding = self.embedding_function(query)
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results = self.client.search(
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collection_name=self.collection_name,
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query_vector=embedding,
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with_payload=True,
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limit=k,
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)
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return [
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(
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self._document_from_scored_point(result),
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result.score,
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)
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for result in results
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]
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def max_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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embedding = self.embedding_function(query)
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results = self.client.search(
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collection_name=self.collection_name,
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query_vector=embedding,
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with_payload=True,
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with_vectors=True,
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limit=k,
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)
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embeddings = [result.vector for result in results]
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mmr_selected = maximal_marginal_relevance(embedding, embeddings, k=k)
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return [self._document_from_scored_point(results[i]) for i in mmr_selected]
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> "Qdrant":
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"""Construct Qdrant wrapper from raw documents.
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This is a user friendly interface that:
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1. Embeds documents.
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2. Creates an in memory docstore
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3. Initializes the Qdrant database
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This is intended to be a quick way to get started.
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Example:
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.. code-block:: python
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from langchain import Qdrant
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from langchain.embeddings import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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qdrant = Qdrant.from_texts(texts, embeddings)
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"""
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try:
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import qdrant_client
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except ImportError:
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raise ValueError(
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"Could not import qdrant-client python package. "
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"Please it install it with `pip install qdrant-client`."
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)
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from qdrant_client.http import models as rest
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# Just do a single quick embedding to get vector size
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partial_embeddings = embedding.embed_documents(texts[:1])
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vector_size = len(partial_embeddings[0])
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qdrant_host = get_from_dict_or_env(kwargs, "host", "QDRANT_HOST")
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kwargs.pop("host")
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collection_name = kwargs.pop("collection_name", uuid.uuid4().hex)
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distance_func = kwargs.pop("distance_func", "Cosine").upper()
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client = qdrant_client.QdrantClient(host=qdrant_host, **kwargs)
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client.recreate_collection(
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collection_name=collection_name,
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vectors_config=rest.VectorParams(
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size=vector_size,
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distance=rest.Distance[distance_func],
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),
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)
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# Now generate the embeddings for all the texts
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embeddings = embedding.embed_documents(texts)
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client.upsert(
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collection_name=collection_name,
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points=rest.Batch(
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ids=[uuid.uuid4().hex for _ in texts],
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vectors=embeddings,
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payloads=cls._build_payloads(texts, metadatas),
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),
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)
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return cls(client, collection_name, embedding.embed_query)
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@classmethod
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def _build_payloads(
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cls, texts: Iterable[str], metadatas: Optional[List[dict]]
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) -> List[dict]:
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payloads = []
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for i, text in enumerate(texts):
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if text is None:
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raise ValueError(
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"At least one of the texts is None. Please remove it before "
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"calling .from_texts or .add_texts on Qdrant instance."
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)
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payloads.append(
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{
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cls.CONTENT_KEY: text,
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cls.METADATA_KEY: metadatas[i] if metadatas is not None else None,
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}
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)
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return payloads
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@classmethod
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def _document_from_scored_point(cls, scored_point: Any) -> Document:
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return Document(
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page_content=scored_point.payload.get(cls.CONTENT_KEY),
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metadata=scored_point.payload.get(cls.METADATA_KEY) or {},
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)
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