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479 lines
17 KiB
Python
479 lines
17 KiB
Python
"""Wrapper around Qdrant vector database."""
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from __future__ import annotations
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import uuid
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import warnings
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from hashlib import md5
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from operator import itemgetter
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from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
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import numpy as np
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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.vectorstores import VectorStore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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MetadataFilter = Dict[str, Union[str, int, bool]]
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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 qdrant_client import QdrantClient
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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__(
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self,
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client: Any,
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collection_name: str,
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embeddings: Optional[Embeddings] = None,
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content_payload_key: str = CONTENT_KEY,
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metadata_payload_key: str = METADATA_KEY,
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embedding_function: Optional[Callable] = None, # deprecated
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):
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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 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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if embeddings is None and embedding_function is None:
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raise ValueError(
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"`embeddings` value can't be None. Pass `Embeddings` instance."
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)
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if embeddings is not None and embedding_function is not None:
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raise ValueError(
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"Both `embeddings` and `embedding_function` are passed. "
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"Use `embeddings` only."
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)
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self.embeddings = embeddings
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self._embeddings_function = embedding_function
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self.client: qdrant_client.QdrantClient = client
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self.collection_name = collection_name
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self.content_payload_key = content_payload_key or self.CONTENT_KEY
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self.metadata_payload_key = metadata_payload_key or self.METADATA_KEY
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if embedding_function is not None:
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warnings.warn(
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"Using `embedding_function` is deprecated. "
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"Pass `Embeddings` instance to `embeddings` instead."
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)
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if not isinstance(embeddings, Embeddings):
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warnings.warn(
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"`embeddings` should be an instance of `Embeddings`."
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"Using `embeddings` as `embedding_function` which is deprecated"
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)
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self._embeddings_function = embeddings
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self.embeddings = None
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def _embed_query(self, query: str) -> List[float]:
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"""Embed query text.
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Used to provide backward compatibility with `embedding_function` argument.
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Args:
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query: Query text.
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Returns:
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List of floats representing the query embedding.
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"""
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if self.embeddings is not None:
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embedding = self.embeddings.embed_query(query)
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else:
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if self._embeddings_function is not None:
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embedding = self._embeddings_function(query)
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else:
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raise ValueError("Neither of embeddings or embedding_function is set")
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return embedding.tolist() if hasattr(embedding, "tolist") else embedding
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def _embed_texts(self, texts: Iterable[str]) -> List[List[float]]:
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"""Embed search texts.
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Used to provide backward compatibility with `embedding_function` argument.
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Args:
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texts: Iterable of texts to embed.
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Returns:
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List of floats representing the texts embedding.
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"""
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if self.embeddings is not None:
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embeddings = self.embeddings.embed_documents(list(texts))
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if hasattr(embeddings, "tolist"):
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embeddings = embeddings.tolist()
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elif self._embeddings_function is not None:
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embeddings = []
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for text in texts:
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embedding = self._embeddings_function(text)
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if hasattr(embeddings, "tolist"):
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embedding = embedding.tolist()
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embeddings.append(embedding)
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else:
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raise ValueError("Neither of embeddings or embedding_function is set")
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return embeddings
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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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texts = list(
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texts
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) # otherwise iterable might be exhausted after id calculation
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ids = [md5(text.encode("utf-8")).hexdigest() for text 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.construct(
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ids=ids,
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vectors=self._embed_texts(texts),
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payloads=self._build_payloads(
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texts,
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metadatas,
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self.content_payload_key,
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self.metadata_payload_key,
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),
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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,
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query: str,
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k: int = 4,
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filter: Optional[MetadataFilter] = None,
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**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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filter: Filter by metadata. Defaults to None.
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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, filter)
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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, filter: Optional[MetadataFilter] = None
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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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filter: Filter by metadata. Defaults to None.
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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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results = self.client.search(
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collection_name=self.collection_name,
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query_vector=self._embed_query(query),
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query_filter=self._qdrant_filter_from_dict(filter),
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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(
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result, self.content_payload_key, self.metadata_payload_key
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),
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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,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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**kwargs: Any,
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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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Defaults to 20.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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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._embed_query(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=fetch_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(
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np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
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)
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return [
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self._document_from_scored_point(
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results[i], self.content_payload_key, self.metadata_payload_key
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)
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for i in mmr_selected
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]
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@classmethod
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def from_texts(
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cls: Type[Qdrant],
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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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location: Optional[str] = None,
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url: Optional[str] = None,
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port: Optional[int] = 6333,
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grpc_port: int = 6334,
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prefer_grpc: bool = False,
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https: Optional[bool] = None,
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api_key: Optional[str] = None,
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prefix: Optional[str] = None,
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timeout: Optional[float] = None,
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host: Optional[str] = None,
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path: Optional[str] = None,
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collection_name: Optional[str] = None,
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distance_func: str = "Cosine",
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content_payload_key: str = CONTENT_KEY,
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metadata_payload_key: str = METADATA_KEY,
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**kwargs: Any,
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) -> Qdrant:
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"""Construct Qdrant wrapper from a list of texts.
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Args:
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texts: A list of texts to be indexed in Qdrant.
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embedding: A subclass of `Embeddings`, responsible for text vectorization.
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metadatas:
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An optional list of metadata. If provided it has to be of the same
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length as a list of texts.
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location:
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If `:memory:` - use in-memory Qdrant instance.
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If `str` - use it as a `url` parameter.
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If `None` - fallback to relying on `host` and `port` parameters.
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url: either host or str of "Optional[scheme], host, Optional[port],
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Optional[prefix]". Default: `None`
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port: Port of the REST API interface. Default: 6333
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grpc_port: Port of the gRPC interface. Default: 6334
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prefer_grpc:
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If true - use gPRC interface whenever possible in custom methods.
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Default: False
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https: If true - use HTTPS(SSL) protocol. Default: None
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api_key: API key for authentication in Qdrant Cloud. Default: None
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prefix:
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If not None - add prefix to the REST URL path.
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Example: service/v1 will result in
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http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
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Default: None
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timeout:
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Timeout for REST and gRPC API requests.
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Default: 5.0 seconds for REST and unlimited for gRPC
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host:
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Host name of Qdrant service. If url and host are None, set to
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'localhost'. Default: None
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path:
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Path in which the vectors will be stored while using local mode.
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Default: None
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collection_name:
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Name of the Qdrant collection to be used. If not provided,
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it will be created randomly. Default: None
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distance_func:
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Distance function. One of: "Cosine" / "Euclid" / "Dot".
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Default: "Cosine"
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content_payload_key:
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A payload key used to store the content of the document.
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Default: "page_content"
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metadata_payload_key:
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A payload key used to store the metadata of the document.
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Default: "metadata"
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**kwargs:
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Additional arguments passed directly into REST client initialization
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This is a user friendly interface that:
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1. Creates embeddings, one for each text
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2. Initializes the Qdrant database as an in-memory docstore by default
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(and overridable to a remote docstore)
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3. Adds the text embeddings to 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, "localhost")
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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 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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collection_name = collection_name or uuid.uuid4().hex
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distance_func = distance_func.upper()
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client = qdrant_client.QdrantClient(
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location=location,
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url=url,
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port=port,
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grpc_port=grpc_port,
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prefer_grpc=prefer_grpc,
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https=https,
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api_key=api_key,
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prefix=prefix,
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timeout=timeout,
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host=host,
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path=path,
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**kwargs,
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)
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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.construct(
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ids=[md5(text.encode("utf-8")).hexdigest() for text in texts],
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vectors=embeddings,
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payloads=cls._build_payloads(
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texts, metadatas, content_payload_key, metadata_payload_key
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),
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),
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)
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return cls(
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client=client,
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collection_name=collection_name,
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embeddings=embedding,
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content_payload_key=content_payload_key,
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metadata_payload_key=metadata_payload_key,
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)
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@classmethod
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def _build_payloads(
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cls,
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texts: Iterable[str],
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metadatas: Optional[List[dict]],
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content_payload_key: str,
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metadata_payload_key: str,
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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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metadata = metadatas[i] if metadatas is not None else None
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payloads.append(
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{
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content_payload_key: text,
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metadata_payload_key: metadata,
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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(
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cls,
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scored_point: Any,
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content_payload_key: str,
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metadata_payload_key: str,
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) -> Document:
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return Document(
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page_content=scored_point.payload.get(content_payload_key),
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metadata=scored_point.payload.get(metadata_payload_key) or {},
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)
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def _qdrant_filter_from_dict(self, filter: Optional[MetadataFilter]) -> Any:
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if filter is None or 0 == len(filter):
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return None
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from qdrant_client.http import models as rest
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return rest.Filter(
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must=[
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rest.FieldCondition(
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key=f"{self.metadata_payload_key}.{key}",
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match=rest.MatchValue(value=value),
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)
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for key, value in filter.items()
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]
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)
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