from __future__ import annotations
import itertools
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
if TYPE_CHECKING:
from tigrisdb import TigrisClient
from tigrisdb import VectorStore as TigrisVectorStore
from tigrisdb.types.filters import Filter as TigrisFilter
from tigrisdb.types.vector import Document as TigrisDocument
[docs]
class Tigris(VectorStore):
"""`Tigris` vector store."""
[docs]
def __init__(self, client: TigrisClient, embeddings: Embeddings, index_name: str):
"""Initialize Tigris vector store."""
try:
import tigrisdb # noqa: F401
except ImportError:
raise ImportError(
"Could not import tigrisdb python package. "
"Please install it with `pip install tigrisdb`"
)
self._embed_fn = embeddings
self._vector_store = TigrisVectorStore(client.get_search(), index_name)
@property
def embeddings(self) -> Embeddings:
return self._embed_fn
@property
def search_index(self) -> TigrisVectorStore:
return self._vector_store
[docs]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = 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.
ids: Optional list of ids for documents.
Ids will be autogenerated if not provided.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
docs = self._prep_docs(texts, metadatas, ids)
result = self.search_index.add_documents(docs)
return [r.id for r in result]
[docs]
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[TigrisFilter] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
docs_with_scores = self.similarity_search_with_score(query, k, filter)
return [doc for doc, _ in docs_with_scores]
[docs]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[TigrisFilter] = None,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[TigrisFilter]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
vector = self._embed_fn.embed_query(query)
result = self.search_index.similarity_search(
vector=vector, k=k, filter_by=filter
)
docs: List[Tuple[Document, float]] = []
for r in result:
docs.append(
(
Document(
page_content=r.doc["text"], metadata=r.doc.get("metadata")
),
r.score,
)
)
return docs
[docs]
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
client: Optional[TigrisClient] = None,
index_name: Optional[str] = None,
**kwargs: Any,
) -> Tigris:
"""Return VectorStore initialized from texts and embeddings."""
if not index_name:
raise ValueError("`index_name` is required")
if not client:
client = TigrisClient()
store = cls(client, embedding, index_name)
store.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return store
def _prep_docs(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]],
ids: Optional[List[str]],
) -> List[TigrisDocument]:
embeddings: List[List[float]] = self._embed_fn.embed_documents(list(texts))
docs: List[TigrisDocument] = []
for t, m, e, _id in itertools.zip_longest(
texts, metadatas or [], embeddings or [], ids or []
):
doc: TigrisDocument = {
"text": t,
"embeddings": e or [],
"metadata": m or {},
}
if _id:
doc["id"] = _id
docs.append(doc)
return docs
