id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
144943ee2fe3-2 | f"{response.text}"
)
return False
return True
def _index_doc(self, doc_id: str, text: str, metadata: dict) -> bool:
request: dict[str, Any] = {}
request["customer_id"] = self._vectara_customer_id
request["corpus_id"] = self._vectara_corpus_id
request["... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
144943ee2fe3-3 | ids = [md5(text.encode("utf-8")).hexdigest() for text in texts]
for i, doc in enumerate(texts):
doc_id = ids[i]
metadata = metadatas[i] if metadatas else {}
succeeded = self._index_doc(doc_id, doc, metadata)
if not succeeded:
self._delete_doc(doc_i... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
144943ee2fe3-4 | "start": 0,
"num_results": k,
"context_config": {
"sentences_before": 3,
"sentences_after": 3,
},
"corpus_key": [
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
144943ee2fe3-5 | """Return Vectara documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 5.
filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be "doc.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
144943ee2fe3-6 | vectara.add_texts(texts, metadatas)
return vectara
[docs] def as_retriever(self, **kwargs: Any) -> VectaraRetriever:
return VectaraRetriever(vectorstore=self, **kwargs)
class VectaraRetriever(VectorStoreRetriever):
vectorstore: Vectara
search_kwargs: dict = Field(default_factory=lambda: {"alp... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c19a1d3d6450-0 | Source code for langchain.vectorstores.opensearch_vector_search
"""Wrapper around OpenSearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-1 | try:
opensearch = _import_opensearch()
client = opensearch(opensearch_url, **kwargs)
except ValueError as e:
raise ValueError(
f"OpenSearch client string provided is not in proper format. "
f"Got error: {e} "
)
return client
def _validate_embeddings_and_bu... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-2 | request = {
"_op_type": "index",
"_index": index_name,
vector_field: embeddings[i],
text_field: text,
"metadata": metadata,
"_id": _id,
}
requests.append(request)
ids.append(_id)
bulk(client, requests)
client.indices... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-3 | "parameters": {"ef_construction": ef_construction, "m": m},
},
}
}
},
}
def _default_approximate_search_query(
query_vector: List[float],
k: int = 4,
vector_field: str = "vector_field",
) -> Dict:
"""For Approximate k-NN Search, this is the def... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-4 | return search_query
def _default_script_query(
query_vector: List[float],
space_type: str = "l2",
pre_filter: Dict = MATCH_ALL_QUERY,
vector_field: str = "vector_field",
) -> Dict:
"""For Script Scoring Search, this is the default query."""
return {
"query": {
"script_score":... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-5 | source = __get_painless_scripting_source(space_type, query_vector)
return {
"query": {
"script_score": {
"query": pre_filter,
"script": {
"source": source,
"params": {
"field": vector_field,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-6 | """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.
bulk_size: Bulk API request count; Default: 500
Returns:
List ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-7 | text_field,
mapping,
)
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
By default supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Args... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-8 | pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {"match_all": {}}
Optional Args for Painless Scripting Search:
search_type: "painless_scripting"; default: "approximate_search"
space_type: "l2Squared", "l1Norm", "cosineSimi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-9 | if search_type == "approximate_search":
boolean_filter = _get_kwargs_value(kwargs, "boolean_filter", {})
subquery_clause = _get_kwargs_value(kwargs, "subquery_clause", "must")
lucene_filter = _get_kwargs_value(kwargs, "lucene_filter", {})
if boolean_filter != {} and lucen... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-10 | embedding, space_type, pre_filter, vector_field
)
else:
raise ValueError("Invalid `search_type` provided as an argument")
response = self.client.search(index=self.index_name, body=search_query)
hits = [hit for hit in response["hits"]["hits"][:k]]
documents_with_sc... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-11 | vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
Optional Keyword Args for Approximate Search:
engine: "nmslib", "faiss", "lucene"; default: "nm... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-12 | _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
dim = len(embeddings[0])
# Get the index name from either from kwargs or ENV Variable
# before falling back to random generation
index_name = get_from_dict_or_env(
kwargs, "index_name", "OPENSEARCH_INDEX_NAME", de... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c19a1d3d6450-13 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
edfbc22374a7-0 | Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import os
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base imp... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-1 | return faiss
def _default_relevance_score_fn(score: float) -> float:
"""Return a similarity score on a scale [0, 1]."""
# The 'correct' relevance function
# may differ depending on a few things, including:
# - the distance / similarity metric used by the VectorStore
# - the scale of your embeddings ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-2 | self._normalize_L2 = normalize_L2
def __add(
self,
texts: Iterable[str],
embeddings: Iterable[List[float]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
if not isinstance(self.docstore, AddableMixi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-3 | return [_id for _, _id, _ in full_info]
[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.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-4 | ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"add... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-5 | raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append((doc, scores[0][j]))
return docs
[docs] def similarity_search_with_score(
self, query: str, k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-6 | Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k)
return [doc for doc, _ in docs_and_scores]
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-7 | docs = []
for i in selected_indices:
if i == -1:
# This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise V... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-8 | Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the current one
Returns:
None.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError("Cannot merge with this type of docstore")
# Numerica... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-9 | vector = np.array(embeddings, dtype=np.float32)
if normalize_L2:
faiss.normalize_L2(vector)
index.add(vector)
documents = []
if ids is None:
ids = [str(uuid.uuid4()) for _ in texts]
for i, text in enumerate(texts):
metadata = metadatas[i] if me... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-10 | return cls.__from(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
**kwargs,
)
[docs] @classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-11 | Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
# save index separately since it is not... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
edfbc22374a7-12 | docstore, index_to_docstore_id = pickle.load(f)
return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs a... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
2250ee167c5f-0 | Source code for langchain.vectorstores.typesense
"""Wrapper around Typesense vector search"""
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
fro... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
2250ee167c5f-1 | *,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ValueError(
"Could not import typesense python package. "... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
2250ee167c5f-2 | ]
def _create_collection(self, num_dim: int) -> None:
fields = [
{"name": "vec", "type": "float[]", "num_dim": num_dim},
{"name": f"{self._text_key}", "type": "string"},
{"name": ".*", "type": "auto"},
]
self._typesense_client.collections.create(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
2250ee167c5f-3 | self,
query: str,
k: int = 4,
filter: Optional[str] = "",
) -> List[Tuple[Document, float]]:
"""Return typesense documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. De... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
2250ee167c5f-4 | k: Number of Documents to return. Defaults to 4.
filter: typesense filter_by expression to filter documents on
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_score = self.similarity_search_with_score(query, k=k, filter=filter)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
2250ee167c5f-5 | }
typesense_api_key = typesense_api_key or get_from_env(
"typesense_api_key", "TYPESENSE_API_KEY"
)
client_config = {
"nodes": [node],
"api_key": typesense_api_key,
"connection_timeout_seconds": connection_timeout_seconds,
}
return ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
2b979b39aa64-0 | Source code for langchain.vectorstores.tair
"""Wrapper around Tair Vector."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
2b979b39aa64-1 | data_type: str,
**kwargs: Any,
) -> bool:
index = self.client.tvs_get_index(self.index_name)
if index is not None:
logger.info("Index already exists")
return False
self.client.tvs_create_index(
self.index_name,
dim,
distance... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
2b979b39aa64-2 | Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
# Creates embedding vector from user quer... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
2b979b39aa64-3 | if "tair_url" in kwargs:
kwargs.pop("tair_url")
distance_type = tairvector.DistanceMetric.InnerProduct
if "distance_type" in kwargs:
distance_type = kwargs.pop("distance_typ")
index_type = tairvector.IndexType.HNSW
if "index_type" in kwargs:
index_type... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
2b979b39aa64-4 | cls,
documents: List[Document],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadata",
**kwargs: Any,
) -> Tair:
texts = [d.page_content for d in docum... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
2b979b39aa64-5 | # index not exist
logger.info("Index does not exist")
return False
return True
[docs] @classmethod
def from_existing_index(
cls,
embedding: Embeddings,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadat... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
47ec3506a6cc-0 | Source code for langchain.vectorstores.weaviate
"""Wrapper around weaviate vector database."""
from __future__ import annotations
import datetime
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-1 | if weaviate_api_key is not None
else None
)
client = weaviate.Client(weaviate_url, auth_client_secret=auth)
return client
def _default_score_normalizer(val: float) -> float:
return 1 - 1 / (1 + np.exp(val))
def _json_serializable(value: Any) -> Any:
if isinstance(value, datetime.datetime):
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-2 | )
if not isinstance(client, weaviate.Client):
raise ValueError(
f"client should be an instance of weaviate.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._embedding = embedding
self._text_key = text_k... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-3 | class_name=self._index_name,
uuid=_id,
vector=vector,
)
ids.append(_id)
return ids
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-4 | if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("additional"):
query_obj = query_obj.with_additional(kwargs.get("additional"))
result = query_obj.with_near_text(content).with_limit(k).do()
if "errors" in result:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-5 | 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... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-6 | Args:
embedding: Embedding 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... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-7 | raise ValueError(
"_embedding cannot be None for similarity_search_with_score"
)
content: Dict[str, Any] = {"concepts": [query]}
if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-8 | """
if self._relevance_score_fn is None:
raise ValueError(
"relevance_score_fn must be provided to"
" Weaviate constructor to normalize scores"
)
docs_and_scores = self.similarity_search_with_score(query, k=k, **kwargs)
return [
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-9 | text_key = "text"
schema = _default_schema(index_name)
attributes = list(metadatas[0].keys()) if metadatas else None
# check whether the index already exists
if not client.schema.contains(schema):
client.schema.create_class(schema)
with client.batch as batch:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
47ec3506a6cc-10 | relevance_score_fn=relevance_score_fn,
by_text=by_text,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
34feb71b9247-0 | Source code for langchain.vectorstores.pinecone
"""Wrapper around Pinecone vector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Callable, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
34feb71b9247-1 | f"got {type(index)}"
)
self._index = index
self._embedding_function = embedding_function
self._text_key = text_key
self._namespace = namespace
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Opt... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
34feb71b9247-2 | k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to r... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
34feb71b9247-3 | Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documen... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
34feb71b9247-4 | pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
"""
try:
import pinecone
except ImportError:
raise ValueError(
"Could not import pinecone python pa... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
34feb71b9247-5 | for j, line in enumerate(lines_batch):
metadata[j][text_key] = line
to_upsert = zip(ids_batch, embeds, metadata)
# upsert to Pinecone
index.upsert(vectors=list(to_upsert), namespace=namespace)
return cls(index, embedding.embed_query, text_key, namespace)
[docs... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
d47fec865d70-0 | Source code for langchain.vectorstores.base
"""Interface for vector stores."""
from __future__ import annotations
import asyncio
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TypeVar
from pydantic import BaseModel, ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-1 | Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the Collection
texts = [doc.page_content for doc in documents]
metad... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-2 | self, query: str, search_type: str, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_type == "mmr":
return await se... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-3 | query, k=k, **kwargs
)
if any(
similarity < 0.0 or similarity > 1.0
for _, similarity in docs_and_similarities
):
warnings.warn(
"Relevance scores must be between"
f" 0 and 1, got {docs_and_similarities}"
)
s... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-4 | func = partial(self.similarity_search_with_relevance_scores, query, k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-5 | [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 optimiz... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-6 | )
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Ret... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-7 | ) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs] @classmethod
async def afrom_document... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-8 | class VectorStoreRetriever(BaseRetriever, BaseModel):
vectorstore: VectorStore
search_type: str = "similarity"
search_kwargs: dict = Field(default_factory=dict)
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
def valida... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
d47fec865d70-9 | query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def aget_relevant_documents(self, query: str) -> List[Document]:
if self.search_type == "similarity":
docs = await self.vectorstore.as... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
e80b11d40c49-0 | Source code for langchain.vectorstores.myscale
"""Wrapper around MyScale vector database."""
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from pydantic import BaseSettings
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-1 | .. code-block:: python
{
'id': 'text_id',
'vector': 'text_embedding',
'text': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
}... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-2 | config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScale Wrapper to LangChain
embedding_function (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-3 | CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map['id']} String,
{self.config.column_map['text']} String,
{self.config.column_map['vector']} Array(Float32),
{self.config.column_map['metadata']} JSON,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-4 | _data.append(f"({n})")
i_str = f"""
INSERT INTO TABLE
{self.config.database}.{self.config.table}({ks})
VALUES
{','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> N... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-5 | column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) - set(column_names)) >= 0
keys, values = zip(*column_names.items())
try:
t = None
for v in self.pgbar(
zip(*values), desc="Inserting data...", total=len(metadatas)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-6 | texts (Iterable[str]): List or tuple of strings to be added
config (MyScaleSettings, Optional): Myscale configuration
text_ids (Optional[Iterable], optional): IDs for the texts.
Defaults to None.
batch_size (int, optional): Batchsi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-7 | ).named_results():
_repr += (
f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n"
)
_repr += "-" * 51 + "\n"
return _repr
def _build_qstr(
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
) -> str:
q_emb... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-8 | of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of Documents
"""
return self.similarity_search_by_v... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-9 | ]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] def similarity_search_with_relevance_scores(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e80b11d40c49-10 | return []
[docs] def drop(self) -> None:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}"
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
e09cf5ad6674-0 | Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
import uuid
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple
import numpy as np
from langchain.docstore.document imp... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-1 | returns:
nearest_indices: List, indices of nearest neighbors
"""
if data_vectors.shape[0] == 0:
return [], []
# Calculate the distance between the query_vector and all data_vectors
distances = distance_metric_map[distance_metric](query_embedding, data_vectors)
nearest_indices = np.ar... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-2 | embeddings = OpenAIEmbeddings()
vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedd... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-3 | if self.verbose:
print(
f"Deep Lake Dataset in {dataset_path} already exists, "
f"loading from the storage"
)
self.ds.summary()
else:
if "overwrite" in kwargs:
del kwargs["overwrite"]
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-4 | **kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], opti... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-5 | if batch_size == 0:
return []
batched = [
elements[i : i + batch_size] for i in range(0, len(elements), batch_size)
]
ingest().eval(
batched,
self.ds,
num_workers=min(self.num_workers, len(batched) // max(self.num_workers, 1)),
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-6 | take [Deep Lake filter]
(https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)
Defaults to None.
maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-7 | distance_metric=distance_metric.lower(),
)
view = view[indices]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance(
query_emb,
embeddings[indices]... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-8 | maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
Lis... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-9 | filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
return self._search_helper(
query=query,
k=k,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-10 | 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
amon... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-11 | ) -> DeepLake:
"""Create a Deep Lake dataset from a raw documents.
If a dataset_path is specified, the dataset will be persisted in that location,
otherwise by default at `./deeplake`
Args:
path (str, pathlib.Path): - The full path to the dataset. Can be:
- De... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-12 | dataset_path=dataset_path, embedding_function=embedding, **kwargs
)
deeplake_dataset.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return deeplake_dataset
[docs] def delete(
self,
ids: Any[List[str], None] = None,
filter: Any[Dict[str, str], None] = None,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
e09cf5ad6674-13 | try:
import deeplake
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake`."
)
deeplake.delete(path, large_ok=True, force=True)
[docs] def delete_dataset(sel... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
c87cb1386257-0 | Source code for langchain.vectorstores.atlas
"""Wrapper around Atlas by Nomic."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
c87cb1386257-1 | is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool): Whether to reset this project if it
already exists. Default False.
Generally userful during development and testing.
"""
try:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
c87cb1386257-2 | metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]]): An optional list of ids.
refresh(bool): Whether or not to refresh indices with the updated data.
Default True.
Returns:
List[str]: List of IDs of the added texts... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
c87cb1386257-3 | else:
if metadatas is None:
data = [
{"text": text, AtlasDB._ATLAS_DEFAULT_ID_FIELD: ids[i]}
for i, text in enumerate(texts)
]
else:
for i, text in enumerate(texts):
metadatas[i]["text"] =... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
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