id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
581ba3862030-0 | Source code for langchain.vectorstores.hologres
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.schema.embeddings import Embeddings
from langchain.schema.vectorst... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-1 | array_length(embedding, 1) = {self.ndims}),
metadata json,
document text);"""
)
self.cursor.execute(
f"call set_table_property('{self.table_name}'"
+ """, 'proxima_vectors',
'{"embedding":{"algorithm":"Graph",
"distance_method":"SquaredEuclidean",
"build_params":{"min_flush_prox... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-2 | params = []
filter_clause = ""
if filter is not None:
conjuncts = []
for key, val in filter.items():
conjuncts.append("metadata->>%s=%s")
params.append(key)
params.append(val)
filter_clause = "where " + " and ".join(conj... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-3 | embedding_function: Embeddings,
ndims: int = ADA_TOKEN_COUNT,
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
pre_delete_table: bool = False,
logger: Optional[logging.Logger] = None,
) -> None:
self.connection_string = connection_string
self.ndims = ndims
sel... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-4 | ) -> Hologres:
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
if not metadatas:
metadatas = [{} for _ in texts]
connection_string = cls.get_connection_string(kwargs)
store = cls(
connection_string=connection_string,
embedding_func... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-5 | **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.
kwargs: vectorstore specific parameters... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-6 | k: int = 4,
filter: Optional[dict] = None,
**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.
filter (Opt... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-7 | ) -> List[Tuple[Document, float]]:
results: List[Tuple[str, str, float]] = self.storage.query_nearest_neighbours(
embedding, k, filter
)
docs = [
(
Document(
page_content=result[0],
metadata=json.loads(result[1]),
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-8 | ndims: int = ADA_TOKEN_COUNT,
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
ids: Optional[List[str]] = None,
pre_delete_table: bool = False,
**kwargs: Any,
) -> Hologres:
"""Construct Hologres wrapper from raw documents and pre-
generated embeddings.
Return... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-9 | **kwargs: Any,
) -> Hologres:
"""
Get intsance of an existing Hologres store.This method will
return the instance of the store without inserting any new
embeddings
"""
connection_string = cls.get_connection_string(kwargs)
store = cls(
connection_st... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
581ba3862030-10 | """
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
connection_string = cls.get_connection_string(kwargs)
kwargs["connection_string"] = connection_string
return cls.from_texts(
texts=texts,
pre_delete_collection=pre_... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
e42e95d96e59-0 | Source code for langchain.vectorstores.atlas
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.schema.embeddings import Embeddings
from langchain.schema.vectorstore impor... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e42e95d96e59-1 | description (str): A description for your project.
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 useful d... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e42e95d96e59-2 | """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]]): An optional list of ids.
refresh(b... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e42e95d96e59-3 | self.project.add_embeddings(embeddings=embeddings, data=data)
# Text upload case
else:
if metadatas is None:
data = [
{"text": text, AtlasDB._ATLAS_DEFAULT_ID_FIELD: ids[i]}
for i, text in enumerate(texts)
]
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e42e95d96e59-4 | Returns:
List[Document]: List of documents most similar to the query text.
"""
if self._embedding_function is None:
raise NotImplementedError(
"AtlasDB requires an embedding_function for text similarity search!"
)
_embedding = self._embedding_f... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e42e95d96e59-5 | embedding (Optional[Embeddings]): Embedding function. Defaults to None.
metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
ids (Optional[List[str]]): Optional list of document IDs. If None,
ids will be auto created
description (str): A description ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e42e95d96e59-6 | def from_documents(
cls: Type[AtlasDB],
documents: List[Document],
embedding: Optional[Embeddings] = None,
ids: Optional[List[str]] = None,
name: Optional[str] = None,
api_key: Optional[str] = None,
persist_directory: Optional[str] = None,
description: str... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e42e95d96e59-7 | texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(
name=name,
api_key=api_key,
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
descripti... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
c4e331783e11-0 | Source code for langchain.vectorstores.starrocks
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 langchain.docstore.document import Document
from langchain.pydantic_v1 import BaseSettin... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-1 | for idx, datum in enumerate(value):
k = columns[idx][0]
r[k] = datum
result.append(r)
debug_output(result)
cursor.close()
return result
[docs]class StarRocksSettings(BaseSettings):
"""StarRocks client configuration.
Attribute:
StarRocks_host (str) : An URL to ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-2 | "metadata": "metadata",
}
database: str = "default"
table: str = "langchain"
def __getitem__(self, item: str) -> Any:
return getattr(self, item)
class Config:
env_file = ".env"
env_prefix = "starrocks_"
env_file_encoding = "utf-8"
[docs]class StarRocks(VectorStore):
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-3 | except ImportError:
# Just in case if tqdm is not installed
self.pgbar = lambda x, **kwargs: x
super().__init__()
if config is not None:
self.config = config
else:
self.config = StarRocksSettings()
assert self.config
assert self.con... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-4 | [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)
@property
def embeddings(self) -> Embeddings:
return self.embedding_function
def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-5 | """Insert more texts through the embeddings and add to the VectorStore.
Args:
texts: Iterable of strings to add to the VectorStore.
ids: Optional list of ids to associate with the texts.
batch_size: Batch size of insertion
metadata: Optional column data to be inse... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-6 | if t:
t.join()
self._insert(transac, keys)
return [i for i in ids]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] @classmethod
def from_texts(
cls,
tex... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-7 | """Text representation for StarRocks Vector Store, prints backends, username
and schemas. Easy to use with `str(StarRocks())`
Returns:
repr: string to show connection info and data schema
"""
_repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ "
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-8 | return _repr
def _build_query_sql(
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
) -> str:
q_emb_str = ",".join(map(str, q_emb))
if where_str:
where_str = f"WHERE {where_str}"
else:
where_str = ""
q_str = f"""
SEL... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-9 | """
return self.similarity_search_by_vector(
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,
)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-10 | 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]]:
"""Perform a similarity search with StarRocks
Args:
query (str): query string
k (int, optio... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
c4e331783e11-11 | f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}",
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata"] | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
9c41bedce0e7-0 | Source code for langchain.vectorstores.typesense
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.schema.embeddings import Embeddings
from langchain.schema.vectorstore import Vecto... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
9c41bedce0e7-1 | *,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ImportError(
"Could not import typesense python package. ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
9c41bedce0e7-2 | for _id, vec, text, metadata in zip(_ids, embedded_texts, texts, _metadatas)
]
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": ".*", "t... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
9c41bedce0e7-3 | return [doc["id"] for doc in docs]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 10,
filter: Optional[str] = "",
) -> List[Tuple[Document, float]]:
"""Return typesense documents most similar to query, along with scores.
Args:
query... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
9c41bedce0e7-4 | ) -> List[Document]:
"""Return typesense documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter: typesense filter_by expression ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
9c41bedce0e7-5 | "Please install it with `pip install typesense`."
)
node = {
"host": host,
"port": str(port),
"protocol": protocol,
}
typesense_api_key = typesense_api_key or get_from_env(
"typesense_api_key", "TYPESENSE_API_KEY"
)
clie... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
de5e8df2488c-0 | Source code for langchain.vectorstores.myscale
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 langchain.docstore.document import Document
from langchain.pydantic_v1 import BaseSettings... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
de5e8df2488c-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 |
de5e8df2488c-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 |
de5e8df2488c-3 | logger.warning(
"Lower case metric types will be deprecated "
"the future. Please use one of ('IP', 'Cosine', 'L2')"
)
# initialize the schema
dim = len(embedding.embed_query("try this out"))
index_params = (
", " + ",".join([f"'{k}={v}'" f... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
de5e8df2488c-4 | password=self.config.password,
**kwargs,
)
self.client.command("SET allow_experimental_object_type=1")
self.client.command(schema_)
@property
def embeddings(self) -> Embeddings:
return self._embeddings
[docs] def escape_str(self, value: str) -> str:
return ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
de5e8df2488c-5 | ids: Optional list of ids to associate with the texts.
batch_size: Batch size of insertion
metadata: Optional column data to be inserted
Returns:
List of ids from adding the texts into the vectorstore.
"""
# Embed and create the documents
ids = ids or ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
de5e8df2488c-6 | return [i for i in ids]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] @classmethod
def from_texts(
cls,
texts: Iterable[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
de5e8df2488c-7 | """Text representation for myscale, prints backends, username and schemas.
Easy to use with `str(Myscale())`
Returns:
repr: string to show connection info and data schema
"""
_repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ "
_repr += f"{self.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
de5e8df2488c-8 | AS dist {self.dist_order}
LIMIT {topk}
"""
return q_str
[docs] def similarity_search(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Document]:
"""Perform a similarity search with MyScale
Args:
query (str)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
de5e8df2488c-9 | 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 (Document, similarity)
"""
q_str = self._build_q... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
de5e8df2488c-10 | and cosine distance in float for each.
Lower score represents more similarity.
"""
q_str = self._build_qstr(self._embeddings.embed_query(query), k, where_str)
try:
return [
(
Document(
page_content=r[self.config.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9b1ffce7c4dd-0 | Source code for langchain.vectorstores.rocksetdb
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.schema.embeddings import Embeddings
from langchain.schema.vectorstore import Ve... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
9b1ffce7c4dd-1 | 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 Embeddings object to use to generat... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
9b1ffce7c4dd-2 | ids: Optional[List[str]] = None,
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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
9b1ffce7c4dd-3 | embedding_key: str = "",
ids: Optional[List[str]] = None,
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 Non... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
9b1ffce7c4dd-4 | distance_func (DistanceFunction): how to compute distance between two
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 N... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
9b1ffce7c4dd-5 | **kwargs: Any,
) -> List[Document]:
"""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 fo... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
9b1ffce7c4dd-6 | But found: `{}`".format(
self._text_key, type(v)
)
page_content = v
elif k == "dist":
assert isinstance(
v, float
), "Computed distance between vectors must of type `float`. \
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
9b1ffce7c4dd-7 | add_doc_res = self._client.Documents.add_documents(
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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
cbe4dbd9f809-0 | Source code for langchain.vectorstores.chroma
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 langchain.docstore.document import Document
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-1 | from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma("langchain_store", embeddings)
"""
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
[docs] def __init__(
self,
collection_name: str = _LANGCHAIN_DEFAUL... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-2 | 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 with chromadb < 0.4.0
major, minor, _ = chromadb.__ve... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-3 | where: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Query the chroma collection."""
try:
import chromadb # noqa: F401
except ImportError:
raise ValueError(
"Co... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-4 | embeddings = self._embedding_function.embed_documents(texts)
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 + [{}]... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-5 | embeddings_without_metadatas = (
[embeddings[j] for j in empty_ids] if embeddings else None
)
ids_without_metadatas = [ids[j] for j in empty_ids]
self._collection.upsert(
embeddings=embeddings_without_metadatas,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-6 | """Return docs most similar to embedding vector.
Args:
embedding (List[float]): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-7 | [docs] def similarity_search_with_score(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma w... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-8 | - embedding dimensionality
- etc.
"""
if self.override_relevance_score_fn:
return self.override_relevance_score_fn
distance = "l2"
distance_key = "hnsw:space"
metadata = self._collection.metadata
if metadata and distance_key in metadata:
di... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-9 | of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal ma... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-10 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-11 | limit: The number of documents to return. Optional.
offset: The offset to start returning results from.
Useful for paging results with limit. Optional.
where_document: A WhereDocument type dict used to filter by the documents.
E.g. `{$contains: "he... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-12 | Args:
document_id (str): ID of the document to update.
document (Document): Document to update.
"""
return self.update_documents([document_id], [document])
[docs] def update_documents(self, ids: List[str], documents: List[Document]) -> None:
"""Update a document in the... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-13 | If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
texts (List[str]): List of texts to add to the collection.
collection_name (str): Name of the collection to create.
persist_directory (O... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
cbe4dbd9f809-14 | collection_metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
col... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
972480929cb6-0 | Source code for langchain.vectorstores.usearch
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemory... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/usearch.html |
972480929cb6-1 | Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(se... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/usearch.html |
972480929cb6-2 | matches = self.index.search(np.array(query_embedding), k)
docs_with_scores: List[Tuple[Document, float]] = []
for id, score in zip(matches.keys, matches.distances):
doc = self.docstore.search(str(id))
if not isinstance(doc, Document):
raise ValueError(f"Could not ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/usearch.html |
972480929cb6-3 | This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the USearch database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/usearch.html |
98554b528cc0-0 | Source code for langchain.vectorstores.docarray.in_memory
"""Wrapper around in-memory storage."""
from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_d... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html |
98554b528cc0-1 | [docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> DocArrayInMemorySearch:
"""Create an DocArrayInMemorySearch store and insert data.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html |
9068ae2fd985-0 | Source code for langchain.vectorstores.docarray.hnsw
from __future__ import annotations
from typing import Any, List, Literal, Optional
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_docarray_import,
)
[docs]class DocArrayHnswSearch(Do... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
9068ae2fd985-1 | "cosine", "ip", and "l2". Defaults to "cosine".
max_elements (int): Maximum number of vectors that can be stored.
Defaults to 1024.
index (bool): Whether an index should be built for this field.
Defaults to True.
ef_construction (int): defines a constr... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
9068ae2fd985-2 | work_dir: Optional[str] = None,
n_dim: Optional[int] = None,
**kwargs: Any,
) -> DocArrayHnswSearch:
"""Create an DocArrayHnswSearch store and insert data.
Args:
texts (List[str]): Text data.
embedding (Embeddings): Embedding function.
metadatas (O... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
58565e88c920-0 | Source code for langchain.vectorstores.docarray.base
from abc import ABC
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain.pydantic_v1 import Field
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.vectors... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
58565e88c920-1 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
58565e88c920-2 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
58565e88c920-3 | """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 m... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
58565e88c920-4 | 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)
que... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
7d3533edde39-0 | Source code for langchain.vectorstores.redis.filters
from enum import Enum
from functools import wraps
from numbers import Number
from typing import Any, Callable, Dict, List, Optional, Union
from langchain.utilities.redis import TokenEscaper
# disable mypy error for dunder method overrides
# mypy: disable-error-code="... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-1 | ) -> None:
# check that the operator is supported by this class
if operator not in self.OPERATORS:
raise ValueError(
f"Operator {operator} not supported by {self.__class__.__name__}. "
+ f"Supported operators are {self.OPERATORS.values()}."
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-2 | RedisFilterOperator.EQ: "==",
RedisFilterOperator.NE: "!=",
RedisFilterOperator.IN: "==",
}
OPERATOR_MAP: Dict[RedisFilterOperator, str] = {
RedisFilterOperator.EQ: "@%s:{%s}",
RedisFilterOperator.NE: "(-@%s:{%s})",
RedisFilterOperator.IN: "@%s:{%s}",
}
[docs] def ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-3 | """Create a RedisTag inequality filter expression
Args:
other (Union[List[str], str]): The tag(s) to filter on.
Example:
>>> from langchain.vectorstores.redis import RedisTag
>>> filter = RedisTag("brand") != "nike"
"""
self._set_tag_value(other, Redis... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-4 | RedisFilterOperator.NE: "(-@%s:[%f %f])",
RedisFilterOperator.GT: "@%s:[(%f +inf]",
RedisFilterOperator.LT: "@%s:[-inf (%f]",
RedisFilterOperator.GE: "@%s:[%f +inf]",
RedisFilterOperator.LE: "@%s:[-inf %f]",
}
def __str__(self) -> str:
"""Return the Redis Query syntax for... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-5 | """Create a Numeric inequality filter expression
Args:
other (Number): The value to filter on.
Example:
>>> from langchain.vectorstores.redis import RedisNum
>>> filter = RedisNum("zipcode") != 90210
"""
self._set_value(other, Number, RedisFilterOperat... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-6 | return RedisFilterExpression(str(self))
def __le__(self, other: Union[int, float]) -> "RedisFilterExpression":
"""Create a Numeric less than or equal to filter expression
Args:
other (Number): The value to filter on.
Example:
>>> from langchain.vectorstores.redis impo... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-7 | """Create a RedisText inequality filter expression
Args:
other (str): The text value to filter on.
Example:
>>> from langchain.vectorstores.redis import RedisText
>>> filter = RedisText("job") != "engineer"
"""
self._set_value(other, str, RedisFilterOp... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-8 | by combining RedisFilterFields using the & and | operators.
Examples:
>>> from langchain.vectorstores.redis import RedisTag, RedisNum
>>> brand_is_nike = RedisTag("brand") == "nike"
>>> price_is_under_100 = RedisNum("price") < 100
>>> filter = brand_is_nike & price_is_under_100
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
7d3533edde39-9 | operator_str = " | " if self._operator == RedisFilterOperator.OR else " "
return f"({str(self._left)}{operator_str}{str(self._right)})"
# check that base case, the filter is set
if not self._filter:
raise ValueError("Improperly initialized RedisFilterExpression")
return s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
32a612515e5d-0 | Source code for langchain.vectorstores.redis.base
"""Wrapper around Redis vector database."""
from __future__ import annotations
import logging
import os
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Type,
Union... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/base.html |
32a612515e5d-1 | def _default_relevance_score(val: float) -> float:
return 1 - val
[docs]def check_index_exists(client: RedisType, index_name: str) -> bool:
"""Check if Redis index exists."""
try:
client.ft(index_name).info()
except: # noqa: E722
logger.info("Index does not exist")
return False
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/base.html |
32a612515e5d-2 | .. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
Initialize, create index, and load Documents
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbedd... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/base.html |
32a612515e5d-3 | rds = Redis.from_existing_index(
embeddings, # an Embeddings object
index_name="my-index",
redis_url="redis://localhost:6379",
)
Advanced examples:
Custom vector schema can be supplied to change the way that
Redis creates the underlying vector sche... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/base.html |
32a612515e5d-4 | tag:
- name: credit_score
text:
- name: user
- name: job
Typically, the ``credit_score`` field would be a text field since it's a string,
however, we can override this behavior by specifying the field type as shown with
the yaml config (can also be... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/base.html |
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