id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
13ba630d8980-36 | cls,
scored_point: Any,
content_payload_key: str,
metadata_payload_key: str,
) -> Document:
return Document(
page_content=scored_point.payload.get(content_payload_key),
metadata=scored_point.payload.get(metadata_payload_key) or {},
)
@classmethod
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-37 | self, filter: Optional[DictFilter]
) -> Optional[rest.Filter]:
from qdrant_client.http import models as rest
if not filter:
return None
return rest.Filter(
must=[
condition
for key, value in filter.items()
for condition ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-38 | if hasattr(embeddings, "tolist"):
embedding = embedding.tolist()
embeddings.append(embedding)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embeddings
def _generate_rest_batches(
self,
texts: Iterab... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
f5e7d92f5acf-0 | Source code for langchain.vectorstores.mongodb_atlas
from __future__ import annotations
import logging
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Iterable,
List,
Optional,
Tuple,
TypeVar,
Union,
)
import numpy as np
from langchain.docstore.document import Document
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
f5e7d92f5acf-1 | embedding_key: str = "embedding",
):
"""
Args:
collection: MongoDB collection to add the texts to.
embedding: Text embedding model to use.
text_key: MongoDB field that will contain the text for each
document.
embedding_key: MongoDB fiel... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
f5e7d92f5acf-2 | metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
_metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
f5e7d92f5acf-3 | pre_filter: Optional[dict] = None,
post_filter_pipeline: Optional[List[Dict]] = None,
) -> List[Tuple[Document, float]]:
knn_beta = {
"vector": embedding,
"path": self._embedding_key,
"k": k,
}
if pre_filter:
knn_beta["filter"] = pre_fi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
f5e7d92f5acf-4 | may introduce breaking changes.
For more: https://www.mongodb.com/docs/atlas/atlas-search/knn-beta
Args:
query: Text to look up documents similar to.
k: Optional Number of Documents to return. Defaults to 4.
pre_filter: Optional Dictionary of argument(s) to prefilter ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
f5e7d92f5acf-5 | pre_filter: Optional Dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline: Optional Pipeline of MongoDB aggregation stages
following the knnBeta search.
Returns:
List of Documents most similar to the query and score for each
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
f5e7d92f5acf-6 | following the knnBeta search.
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self._embedding.embed_query(query)
docs = self._similarity_search_with_score(
query_embedding,
k=fetch_k,
pre_filter=pre_... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
f5e7d92f5acf-7 | vectorstore = MongoDBAtlasVectorSearch.from_texts(
texts,
embeddings,
metadatas=metadatas,
collection=collection
)
"""
if collection is None:
raise ValueError("Must provide 'collection' named para... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
4d4a7cedbb49-0 | Source code for langchain.vectorstores.hologres
"""VectorStore wrapper around a Hologres database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.b... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-1 | + """, 'proxima_vectors',
'{"embedding":{"algorithm":"Graph",
"distance_method":"SquaredEuclidean",
"build_params":{"min_flush_proxima_row_count" : 1,
"min_compaction_proxima_row_count" : 1,
"max_total_size_to_merge_mb" : 2000}}}');"""
)
self.conn.commit()
[docs] def get_by_id(self, id: str) -> Lis... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-2 | conjuncts.append("metadata->>%s=%s")
params.append(key)
params.append(val)
filter_clause = "where " + " and ".join(conjuncts)
sql = (
f"select document, metadata::text, "
f"pm_approx_squared_euclidean_distance(array{json.dumps(embedding)}"
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-3 | logger: Optional[logging.Logger] = None,
) -> None:
self.connection_string = connection_string
self.ndims = ndims
self.table_name = table_name
self.embedding_function = embedding_function
self.pre_delete_table = pre_delete_table
self.logger = logger or logging.getLogg... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-4 | metadatas = [{} for _ in texts]
connection_string = cls.get_connection_string(kwargs)
store = cls(
connection_string=connection_string,
embedding_function=embedding_function,
ndims=ndims,
table_name=table_name,
pre_delete_table=pre_delete_table... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-5 | Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
if ids is None... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-6 | """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.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Document... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-7 | embedding, k, filter
)
docs = [
(
Document(
page_content=result[0],
metadata=json.loads(result[1]),
),
result[2],
)
for result in results
]
return docs
[docs] @c... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-8 | pre_delete_table: bool = False,
**kwargs: Any,
) -> Hologres:
"""Construct Hologres wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a para... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-9 | embeddings
"""
connection_string = cls.get_connection_string(kwargs)
store = cls(
connection_string=connection_string,
ndims=ndims,
table_name=table_name,
embedding_function=embedding,
pre_delete_table=pre_delete_table,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
4d4a7cedbb49-10 | return cls.from_texts(
texts=texts,
pre_delete_collection=pre_delete_collection,
embedding=embedding,
metadatas=metadatas,
ids=ids,
ndims=ndims,
table_name=table_name,
**kwargs,
)
[docs] @classmethod
def conne... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
3039b2c69ed8-0 | Source code for langchain.vectorstores.milvus
"""Wrapper around the Milvus vector database."""
from __future__ import annotations
import logging
from typing import Any, Iterable, List, Optional, Tuple, Union
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from langchain.embedd... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-1 | Defaults to "Session".
index_params (Optional[dict]): Which index params to use. Defaults to
HNSW/AUTOINDEX depending on service.
search_params (Optional[dict]): Which search params to use. Defaults to
default of index.
drop_old (Optional[bool]): Whether to drop the curre... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-2 | write the client.pem path.
ca_pem_path (str): If use tls two-way authentication, need to write
the ca.pem path.
server_pem_path (str): If use tls one-way authentication, need to
write the server.pem path.
server_name (str): If use tls, need to write the common name.
E... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-3 | # Default search params when one is not provided.
self.default_search_params = {
"IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}},
"HN... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-4 | self._text_field = "text"
# In order for compatibility, the vector field needs to be called "vector"
self._vector_field = "vector"
self.fields: list[str] = []
# Create the connection to the server
if connection_args is None:
connection_args = DEFAULT_MILVUS_CONNECTION... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-5 | elif uri is not None:
given_address = uri.split("https://")[1]
elif address is not None:
given_address = address
else:
given_address = None
logger.debug("Missing standard address type for reuse atttempt")
# User defaults to empty string when gettin... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-6 | self._load()
def _create_collection(
self, embeddings: list, metadatas: Optional[list[dict]] = None
) -> None:
from pymilvus import (
Collection,
CollectionSchema,
DataType,
FieldSchema,
MilvusException,
)
from pymilvus.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-7 | # Create the vector field, supports binary or float vectors
fields.append(
FieldSchema(self._vector_field, infer_dtype_bydata(embeddings[0]), dim=dim)
)
# Create the schema for the collection
schema = CollectionSchema(fields)
# Create the collection
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-8 | if self.index_params is None:
self.index_params = {
"metric_type": "L2",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
}
try:
self.col.create_index(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-9 | def _load(self) -> None:
"""Load the collection if available."""
from pymilvus import Collection
if isinstance(self.col, Collection) and self._get_index() is not None:
self.col.load()
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[Lis... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-10 | embeddings = self.embedding_func.embed_documents(texts)
except NotImplementedError:
embeddings = [self.embedding_func.embed_query(x) for x in texts]
if len(embeddings) == 0:
logger.debug("Nothing to insert, skipping.")
return []
# If the collection hasn't been... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-11 | )
raise e
return pks
[docs] def similarity_search(
self,
query: str,
k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a simila... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-12 | Args:
embedding (List[float]): The embedding vector to search.
k (int, optional): How many results to return. Defaults to 4.
param (dict, optional): The search params for the index type.
Defaults to None.
expr (str, optional): Filtering expression. Default... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-13 | param (dict): The search params for the specified index.
Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keywo... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-14 | expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Tuple[Document, float]]: Result doc and score.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-15 | """Perform a search and return results that are reordered by MMR.
Args:
query (str): The text being searched.
k (int, optional): How many results to give. Defaults to 4.
fetch_k (int, optional): Total results to select k from.
Defaults to 20.
lambd... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-16 | expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a search and return results that are reordered by MMR.
Args:
embedding (str): The embedding vector being searched.
k (int, optional): How many results to ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-17 | ids = []
documents = []
scores = []
for result in res[0]:
meta = {x: result.entity.get(x) for x in output_fields}
doc = Document(page_content=meta.pop(self._text_field), metadata=meta)
documents.append(doc)
scores.append(result.score)
i... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3039b2c69ed8-18 | search_params: Optional[dict] = None,
drop_old: bool = False,
**kwargs: Any,
) -> Milvus:
"""Create a Milvus collection, indexes it with HNSW, and insert data.
Args:
texts (List[str]): Text data.
embedding (Embeddings): Embedding function.
metadata... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
3ebb4a9075e0-0 | Source code for langchain.vectorstores.chroma
"""Wrapper around ChromaDB embeddings platform."""
from __future__ import annotations
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
from lan... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-1 | .. code-block:: python
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma("langchain_store", embeddings)
"""
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-2 | major, minor, _ = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) < 4:
client_settings.chroma_db_impl = "duckdb+parquet"
_client_settings = client_settings
elif persist_directory:
# Maintain backwards compatibility... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-3 | n_results: int = 4,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Query the chroma collection."""
try:
import chromadb # noqa: F401
except ImportError:
raise ValueError(
"Could not import chromadb pytho... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-4 | if metadatas:
# fill metadatas with empty dicts if somebody
# did not specify metadata for all texts
length_diff = len(texts) - len(metadatas)
if length_diff:
metadatas = metadatas + [{}] * length_diff
empty_ids = []
non_empty_ids =... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-5 | embeddings=embeddings,
documents=texts,
ids=ids,
)
return ids
[docs] def similarity_search(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Run... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-6 | [docs] def similarity_search_by_vector_with_relevance_scores(
self,
embedding: List[float],
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Return docs most similar to embedding vector and s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-7 | Lower score represents more similarity.
"""
if self._embedding_function is None:
results = self.__query_collection(
query_texts=[query], n_results=k, where=filter
)
else:
query_embedding = self._embedding_function.embed_query(query)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-8 | self,
embedding: List[float],
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal r... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-9 | [docs] def max_marginal_relevance_search(
self,
query: str,
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal margi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-10 | self,
ids: Optional[OneOrMany[ID]] = None,
where: Optional[Where] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
where_document: Optional[WhereDocument] = None,
include: Optional[List[str]] = None,
) -> Dict[str, Any]:
"""Gets the collectio... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-11 | It will also be called automatically when the object is destroyed.
"""
if self._persist_directory is None:
raise ValueError(
"You must specify a persist_directory on"
"creation to persist the collection."
)
import chromadb
# Maintai... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-12 | client: Optional[chromadb.Client] = None,
collection_metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a raw documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephe... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-13 | collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.Client] = None, # Add this line
collection_metadata: Optional[Dict] = None,
**kwargs: Any,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3ebb4a9075e0-14 | """Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
self._collection.delete(ids=ids) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbcbe9768f05-0 | Source code for langchain.vectorstores.rocksetdb
"""Wrapper around Rockset vector database."""
from __future__ import annotations
import logging
from enum import Enum
from typing import Any, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
cbcbe9768f05-1 | collection_name: str,
text_key: str,
embedding_key: str,
workspace: str = "commons",
):
"""Initialize with Rockset client.
Args:
client: Rockset client object
collection: Rockset collection to insert docs / query
embeddings: Langchain Embed... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
cbcbe9768f05-2 | batch_size: int = 32,
**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.
id... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
cbcbe9768f05-3 | batch_size: int = 32,
**kwargs: Any,
) -> Rockset:
"""Create Rockset wrapper with existing texts.
This is intended as a quicker way to get started.
"""
# Sanitize imputs
assert client is not None, "Rockset Client cannot be None"
assert collection_name, "Collec... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
cbcbe9768f05-4 | vectors in Rockset.
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): Metadata filters supplied as a
SQL `where` condition string. Defaults to None.
eg. "price<=70.0 AND brand='Nintendo'"
NOTE: Please d... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
cbcbe9768f05-5 | """Accepts a query_embedding (vector), and returns documents with
similar embeddings."""
docs_and_scores = self.similarity_search_by_vector_with_relevance_scores(
embedding, k, distance_func, where_str, **kwargs
)
return [doc for doc, _ in docs_and_scores]
[docs] def simil... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
cbcbe9768f05-6 | self._text_key, type(v)
)
page_content = v
elif k == "dist":
assert isinstance(
v, float
), "Computed distance between vectors must of type `float`. \
But found {}".format(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
cbcbe9768f05-7 | collection=self._collection_name, data=batch, workspace=self._workspace
)
return [doc_status._id for doc_status in add_doc_res.data]
[docs] def delete_texts(self, ids: List[str]) -> None:
"""Delete a list of docs from the Rockset collection"""
try:
from rockset.models impo... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
21efb6091a59-0 | Source code for langchain.vectorstores.supabase
from __future__ import annotations
import uuid
from itertools import repeat
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
import numpy as np
from langchain.docstore.document import Docume... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
21efb6091a59-1 | ]
embeddings = OpenAIEmbeddings()
supabase_client = create_client("my_supabase_url", "my_supabase_key")
vector_store = SupabaseVectorStore.from_documents(
docs,
embeddings,
client=supabase_client,
table_name="documents",
query_name="mat... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
21efb6091a59-2 | @property
def embeddings(self) -> Embeddings:
return self._embedding
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
ids = ids or [str(uu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
21efb6091a59-3 | client=client,
embedding=embedding,
table_name=table_name,
query_name=query_name,
)
[docs] def add_vectors(
self,
vectors: List[List[float]],
documents: List[Document],
ids: List[str],
) -> List[str]:
return self._add_vectors(sel... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
21efb6091a59-4 | return self.similarity_search_by_vector_with_relevance_scores(
vectors[0], k=k, filter=filter
)
[docs] def match_args(
self, query: List[float], k: int, filter: Optional[Dict[str, Any]]
) -> Dict[str, Any]:
ret = dict(query_embedding=query, match_count=k)
if filter:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
21efb6091a59-5 | page_content=search.get("content", ""),
),
search.get("similarity", 0.0),
# Supabase returns a vector type as its string represation (!).
# This is a hack to convert the string to numpy array.
np.fromstring(
search.get("... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
21efb6091a59-6 | # is 500
chunk_size = 500
id_list: List[str] = []
for i in range(0, len(rows), chunk_size):
chunk = rows[i : i + chunk_size]
result = client.from_(table_name).upsert(chunk).execute() # type: ignore
if len(result.data) == 0:
raise Exception("Er... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
21efb6091a59-7 | )
matched_documents = [doc_tuple[0] for doc_tuple in result]
matched_embeddings = [doc_tuple[2] for doc_tuple in result]
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
matched_embeddings,
k=k,
lambda_mult=lambda... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
21efb6091a59-8 | id uuid,
content text,
metadata jsonb,
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGIN
RETURN query
SELECT
id,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
2784022ddcff-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-1 | column_map (Dict) : Column type map to project column name onto langchain
semantics. Must have keys: `text`, `id`, `vector`,
must be same size to number of columns. For example:
.. code-block:: python
{
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-2 | constraints and even sub-queries.
For more information, please visit
[myscale official site](https://docs.myscale.com/en/overview/)
"""
[docs] def __init__(
self,
embedding: Embeddings,
config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-3 | dim = len(embedding.embed_query("try this out"))
index_params = (
", " + ",".join([f"'{k}={v}'" for k, v in self.config.index_param.items()])
if self.config.index_param
else ""
)
schema_ = f"""
CREATE TABLE IF NOT EXISTS {self.config.database}.{sel... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-4 | [docs] def escape_str(self, value: str) -> str:
return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
def _build_istr(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
_data = []
for n in transac:
n = ","... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-5 | """
# Embed and create the documents
ids = ids or [sha1(t.encode("utf-8")).hexdigest() for t in texts]
colmap_ = self.config.column_map
transac = []
column_names = {
colmap_["id"]: ids,
colmap_["text"]: texts,
colmap_["vector"]: map(self._embed... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-6 | cls,
texts: Iterable[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
config: Optional[MyScaleSettings] = None,
text_ids: Optional[Iterable[str]] = None,
batch_size: int = 32,
**kwargs: Any,
) -> MyScale:
"""Create Myscale... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-7 | _repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n"
_repr += "-" * 51 + "\n"
for r in self.client.query(
f"DESC {self.config.database}.{self.config.table}"
).named_results():
_repr += (
f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-8 | Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-9 | Document(
page_content=r[self.config.column_map["text"]],
metadata=r[self.config.column_map["metadata"]],
)
for r in self.client.query(q_str).named_results()
]
except Exception as e:
logger.error(f"\033[91m\033[1m{ty... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
2784022ddcff-10 | ),
r["dist"],
)
for r in self.client.query(q_str).named_results()
]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] def drop(self) -> None:
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
b0239527e9ed-0 | Source code for langchain.vectorstores.tigris
from __future__ import annotations
import itertools
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple
from langchain.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.vectorstores import VectorStore
if TYPE_CHECKING:... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
b0239527e9ed-1 | """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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
b0239527e9ed-2 | 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(
(... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
b0239527e9ed-3 | 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"] = _... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
113b00c10c02-0 | Source code for langchain.vectorstores.clickhouse
"""Wrapper around open source ClickHouse VectorSearch capability."""
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, Union
from pydantic im... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-1 | Defaults to 'vector_table'.
metric (str) : Metric to compute distance,
supported are ('angular', 'euclidean', 'manhattan', 'hamming',
'dot'). Defaults to 'angular'.
https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-2 | return getattr(self, item)
class Config:
env_file = ".env"
env_prefix = "clickhouse_"
env_file_encoding = "utf-8"
[docs]class Clickhouse(VectorStore):
"""Wrapper around ClickHouse vector database
You need a `clickhouse-connect` python package, and a valid account
to connect to Cl... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-3 | assert self.config
assert self.config.host and self.config.port
assert (
self.config.column_map
and self.config.database
and self.config.table
and self.config.metric
)
for k in ["id", "embedding", "document", "metadata", "uuid"]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-4 | """
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self.embedding_function = embedding
self.dist_order = "ASC" # Only support ConsingDistance and L2Distance
# Create a connection to clickhouse
self.client = get_client(
host=self.config.h... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-5 | self.client.command(_insert_query)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
batch_size: int = 32,
ids: Optional[Iterable[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Insert more texts through the embedding... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-6 | )
transac.append(v)
if len(transac) == batch_size:
if t:
t.join()
t = Thread(target=self._insert, args=[transac, keys])
t.start()
transac = []
if len(transac) > 0:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-7 | Other keyword arguments will pass into
[clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)
Returns:
ClickHouse Index
"""
ctx = cls(embedding, config, **kwargs)
ctx.add_texts(texts, ids=text_ids, batch_size=bat... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-8 | if where_str:
where_str = f"PREWHERE {where_str}"
else:
where_str = ""
settings_strs = []
if self.config.index_query_params:
for k in self.config.index_query_params:
settings_strs.append(f"SETTING {k}={self.config.index_query_params[k]}")
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-9 | self.embedding_function.embed_query(query), k, where_str, **kwargs
)
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search with C... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
113b00c10c02-10 | ) -> List[Tuple[Document, float]]:
"""Perform a similarity search with ClickHouse
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
a228a16b6200-0 | Source code for langchain.vectorstores.analyticdb
"""VectorStore wrapper around a Postgres/PGVector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Type
from sqlalchemy import REAL, Column, String, Table, creat... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
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