from abc import ABC
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import Field
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
from docarray import BaseDoc
from docarray.index.abstract import BaseDocIndex
def _check_docarray_import() -> None:
try:
import docarray
da_version = docarray.__version__.split(".")
if int(da_version[0]) == 0 and int(da_version[1]) <= 31:
raise ImportError(
f"To use the DocArrayHnswSearch VectorStore the docarray "
f"version >=0.32.0 is expected, received: {docarray.__version__}."
f"To upgrade, please run: `pip install -U docarray`."
)
except ImportError:
raise ImportError(
"Could not import docarray python package. "
"Please install it with `pip install docarray`."
)
[docs]class DocArrayIndex(VectorStore, ABC):
"""Base class for `DocArray` based vector stores."""
[docs] def __init__(
self,
doc_index: "BaseDocIndex",
embedding: Embeddings,
):
"""Initialize a vector store from DocArray's DocIndex."""
self.doc_index = doc_index
self.embedding = embedding
@staticmethod
def _get_doc_cls(**embeddings_params: Any) -> Type["BaseDoc"]:
"""Get docarray Document class describing the schema of DocIndex."""
from docarray import BaseDoc
from docarray.typing import NdArray
class DocArrayDoc(BaseDoc):
text: Optional[str] = Field(default=None, required=False)
embedding: Optional[NdArray] = Field(**embeddings_params)
metadata: Optional[dict] = Field(default=None, required=False)
return DocArrayDoc
@property
def doc_cls(self) -> Type["BaseDoc"]:
if self.doc_index._schema is None:
raise ValueError("doc_index expected to have non-null _schema attribute.")
return self.doc_index._schema
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Embed texts and add to the vector store.
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.
"""
ids: List[str] = []
embeddings = self.embedding.embed_documents(list(texts))
for i, (t, e) in enumerate(zip(texts, embeddings)):
m = metadatas[i] if metadatas else {}
doc = self.doc_cls(text=t, embedding=e, metadata=m)
self.doc_index.index([doc])
ids.append(str(doc.id))
return ids
[docs] def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> 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 text and
cosine distance in float for each.
Lower score represents more similarity.
"""
query_embedding = self.embedding.embed_query(query)
query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
docs, scores = self.doc_index.find(query_doc, search_field="embedding", limit=k)
result = [
(Document(page_content=doc.text, metadata=doc.metadata), score)
for doc, score in zip(docs, scores)
]
return result
[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.
"""
results = self.similarity_search_with_score(query, k=k, **kwargs)
return [doc for doc, _ in results]
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
raise NotImplementedError()
[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 query vector.
"""
query_doc = self.doc_cls(embedding=embedding) # type: ignore
docs = self.doc_index.find(
query_doc, search_field="embedding", limit=k
).documents
result = [
Document(page_content=doc.text, metadata=doc.metadata) for doc in docs
]
return result
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> 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.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self.embedding.embed_query(query)
query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
docs = self.doc_index.find(
query_doc, search_field="embedding", limit=fetch_k
).documents
mmr_selected = maximal_marginal_relevance(
np.array(query_embedding), docs.embedding, k=k
)
results = [
Document(page_content=docs[idx].text, metadata=docs[idx].metadata)
for idx in mmr_selected
]
return results
