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129cd69 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | 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.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 "langchain[docarray]"`.'
)
class DocArrayIndex(VectorStore, ABC):
"""Base class for `DocArray` based vector stores."""
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]
embedding: Optional[NdArray] = Field(**embeddings_params)
metadata: Optional[dict]
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
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
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
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()
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
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
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