id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
926f7a3a3a06-4 | def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
embed_tuple_index = tuple(column_names).index(
self.config.column_map["embedding"]
)
_data = []
for n in transac:
n = ",".join(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
926f7a3a3a06-5 | 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 [sha1(t.encode("utf-8")).hexdigest() for t in texts]
colmap_ = self.config.column_map
transac = []
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
926f7a3a3a06-6 | return []
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
config: Optional[StarRocksSettings] = None,
text_ids: Optional[Iterable[str]] = None,
batch_size: int = 32,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
926f7a3a3a06-7 | _repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n"
width = 25
fields = 3
_repr += "-" * (width * fields + 1) + "\n"
columns = ["name", "type", "key"]
_repr += f"|\033[94m{columns[0]:24s}\033[0m|\033[96m{columns[1]:24s}"
_repr += f"\033[0m|\033[9... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
926f7a3a3a06-8 | q_str = f"""
SELECT {self.config.column_map['document']},
{self.config.column_map['metadata']},
cosine_similarity_norm(array<float>[{q_emb_str}],
{self.config.column_map['embedding']}) as dist
FROM {self.config.database}.{self.config.table}
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
926f7a3a3a06-9 | """Perform a similarity search with StarRocks by vectors
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 No... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
926f7a3a3a06-10 | where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use `{self.metadata... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
bf9be4fbeeec-0 | Source code for langchain.vectorstores.awadb
"""Wrapper around AwaDB for embedding vectors"""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
bf9be4fbeeec-1 | self.table2embeddings: dict[str, Embeddings] = {}
if embedding is not None:
self.table2embeddings[table_name] = embedding
self.using_table_name = table_name
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
is_duplica... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
bf9be4fbeeec-2 | [docs] def similarity_search(
self,
query: str,
k: int = DEFAULT_TOPN,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
embedding = None
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
bf9be4fbeeec-3 | retrieval_docs = self.similarity_search_by_vector(embedding, k, scores)
L2_Norm = 0.0
for score in scores:
L2_Norm = L2_Norm + score * score
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - (scores[doc_no] / L2_Norm))... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
bf9be4fbeeec-4 | L2_Norm = L2_Norm + score * score
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - scores[doc_no] / L2_Norm)
results.append(doc_tuple)
doc_no = doc_no + 1
return results
[docs] def similarity_search_by_vector(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
bf9be4fbeeec-5 | content = item_detail[item_key]
elif (
item_key == "Field@1" or item_key == "text_embedding"
): # embedding field for the document
continue
elif item_key == "score": # L2 distance
if scores is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
bf9be4fbeeec-6 | ) -> str:
"""Get the current table."""
return self.using_table_name
[docs] @classmethod
def from_texts(
cls: Type[AwaDB],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
table_name: str = _DEFAULT_TABLE_NAME... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
bf9be4fbeeec-7 | table_name: str = _DEFAULT_TABLE_NAME,
log_and_data_dir: Optional[str] = None,
client: Optional[awadb.Client] = None,
**kwargs: Any,
) -> AwaDB:
"""Create an AwaDB vectorstore from a list of documents.
If a log_and_data_dir specified, the table will be persisted there.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
862f01a7db39-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-1 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-2 | )
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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-3 | if self._embedding is not None:
vector = self._embedding.embed_documents([text])[0]
else:
vector = None
batch.add_data_object(
data_object=data_properties,
class_name=self._index_name,
uui... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-4 | if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("additi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-5 | docs.append(Document(page_content=text, metadata=res))
return docs
[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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-6 | **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:
embedding: Embedding to look up documents similar to.
k... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-7 | return docs
[docs] def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-8 | return docs_and_scores
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-9 | weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
"""
client = _create_weaviate_client(**kwargs)
from weaviate.util import get_valid_uuid
index_name = kwargs.get("index_nam... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
862f01a7db39-10 | "class_name": index_name,
}
if embeddings is not None:
params["vector"] = embeddings[i]
batch.add_data_object(**params)
batch.flush()
relevance_score_fn = kwargs.get("relevance_score_fn")
by_text: bool = kwargs.get("by_text"... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
7abb230f4ea9-0 | 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 |
7abb230f4ea9-1 | client: Any,
embeddings: Embeddings,
collection_name: str,
text_key: str,
embedding_key: str,
):
"""Initialize with Rockset client.
Args:
client: Rockset client object
collection: Rockset collection to insert docs / query
embeddings... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
7abb230f4ea9-2 | """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 to associate with the texts.
batch_si... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
7abb230f4ea9-3 | ) -> 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, "Collection name cannot be empty"
assert text_ke... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
7abb230f4ea9-4 | 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 do not let end-user to fill this ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
7abb230f4ea9-5 | """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 |
7abb230f4ea9-6 | 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 |
7abb230f4ea9-7 | collection=self._collection_name, data=batch
)
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 import DeleteDocumentsRequestDa... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
d4f44340bf93-0 | 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 |
d4f44340bf93-1 | 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 |
d4f44340bf93-2 | constraints and even sub-queries.
For more information, please visit
[myscale official site](https://docs.myscale.com/en/overview/)
"""
def __init__(
self,
embedding: Embeddings,
config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScal... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
d4f44340bf93-3 | 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 |
d4f44340bf93-4 | def _build_istr(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
_data = []
for n in transac:
n = ",".join([f"'{self.escape_str(str(_n))}'" for _n in n])
_data.append(f"({n})")
i_str = f"""
INSERT INTO TABLE... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
d4f44340bf93-5 | column_names = {
colmap_["id"]: ids,
colmap_["text"]: texts,
colmap_["vector"]: map(self.embedding_function, texts),
}
metadatas = metadatas or [{} for _ in texts]
column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) -... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
d4f44340bf93-6 | batch_size: int = 32,
**kwargs: Any,
) -> MyScale:
"""Create Myscale wrapper with existing texts
Args:
embedding_function (Embeddings): Function to extract text embedding
texts (Iterable[str]): List or tuple of strings to be added
config (MyScaleSettings, ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
d4f44340bf93-7 | 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['type']:24s}\033[0m|\n"
)
_repr += "-" * 51 + "\n"
return _repr
def _build_qstr(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
d4f44340bf93-8 | NOTE: Please do not let end-user to fill this and always be aware
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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
d4f44340bf93-9 | ]
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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
d4f44340bf93-10 | ]
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:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
35778753d1dd-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
35778753d1dd-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
35778753d1dd-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
35778753d1dd-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
35778753d1dd-4 | """
if self._embedding_function is None:
raise NotImplementedError(
"AtlasDB requires an embedding_function for text similarity search!"
)
_embedding = self._embedding_function.embed_documents([query])[0]
embedding = np.array(_embedding).reshape(1, -1)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
35778753d1dd-5 | ids (Optional[List[str]]): Optional list of document IDs. If None,
ids will be auto created
description (str): A description for your project.
is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
35778753d1dd-6 | ids: Optional[List[str]] = None,
name: Optional[str] = None,
api_key: Optional[str] = None,
persist_directory: Optional[str] = None,
description: str = "A description for your project",
is_public: bool = True,
reset_project_if_exists: bool = False,
index_kwargs: O... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
35778753d1dd-7 | return cls.from_texts(
name=name,
api_key=api_key,
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
description=description,
is_public=is_public,
reset_project_if_exists=reset_project_if_exists,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
84c33472e2d3-0 | 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 |
84c33472e2d3-1 | 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 |
84c33472e2d3-2 | 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 |
84c33472e2d3-3 | 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 |
84c33472e2d3-4 | """
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 |
84c33472e2d3-5 | [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 embeddings and add to the VectorStore.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
84c33472e2d3-6 | 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:
if t:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
84c33472e2d3-7 | Returns:
ClickHouse Index
"""
ctx = cls(embedding, config, **kwargs)
ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas)
return ctx
def __repr__(self) -> str:
"""Text representation for ClickHouse Vector Store, prints backends, username
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
84c33472e2d3-8 | 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]}")
q_str = f"""
SELECT {self.config.column_map['document']... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
84c33472e2d3-9 | self,
embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search with ClickHouse by vectors
Args:
query (str): query string
k (int, optional): Top K neighbors to retri... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
84c33472e2d3-10 | 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/clickhouse.html |
6148901c39e6-0 | 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, Dict, Iterable, List, Optional, Sequence, Tuple, Type
from sqlalchemy import REAL, Column, String, Table, create_engine, ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
6148901c39e6-1 | - Useful for testing.
"""
def __init__(
self,
connection_string: str,
embedding_function: Embeddings,
embedding_dimension: int = _LANGCHAIN_DEFAULT_EMBEDDING_DIM,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
pre_delete_collection: bool = False,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
6148901c39e6-2 | """
)
result = conn.execute(index_query).scalar()
# Create the index if it doesn't exist
if not result:
index_statement = text(
f"""
CREATE INDEX {index_name}
ON {s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
6148901c39e6-3 | if not metadatas:
metadatas = [{} for _ in texts]
# Define the table schema
chunks_table = Table(
self.collection_name,
Base.metadata,
Column("id", TEXT, primary_key=True),
Column("embedding", ARRAY(REAL)),
Column("document", String... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
6148901c39e6-4 | k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding_function.embed_query(text=query)
return self.similari... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
6148901c39e6-5 | **kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
return self.si... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
6148901c39e6-6 | )
for result in results
]
return documents_with_scores
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to em... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
6148901c39e6-7 | connection_string=connection_string,
collection_name=collection_name,
embedding_function=embedding,
embedding_dimension=embedding_dimension,
pre_delete_collection=pre_delete_collection,
)
store.add_texts(texts=texts, metadatas=metadatas, ids=ids, **kwargs)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
6148901c39e6-8 | return cls.from_texts(
texts=texts,
pre_delete_collection=pre_delete_collection,
embedding=embedding,
embedding_dimension=embedding_dimension,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
**kwargs,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
371726e38a5b-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
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.bas... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
371726e38a5b-1 | f"client should be an instance of pinecone.index.Index, "
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: Itera... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
371726e38a5b-2 | self,
query: str,
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.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
371726e38a5b-3 | """Return pinecone documents most similar to query.
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 i... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
371726e38a5b-4 | lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
371726e38a5b-5 | k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
371726e38a5b-6 | embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
"""
try:
import pinecone
except ImportError:
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
371726e38a5b-7 | else:
metadata = [{} for _ in range(i, i_end)]
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)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
90d57b75adf4-0 | Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
try:
import deeplake
from deeplake.core.fast_forwarding import version_co... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-1 | 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,
embedding_function: Optional[Embeddings] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-2 | read_only (bool): Open dataset in read-only mode. Default is False.
ingestion_batch_size (int): During data ingestion, data is divided
into batches. Batch size is the size of each batch.
Default is 1000.
num_workers (int): Number of workers to use during data inge... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-3 | "Please install it with `pip install deeplake`."
)
if version_compare(deeplake.__version__, "3.6.2") == -1:
raise ValueError(
"deeplake version should be >= 3.6.3, but you've installed"
f" {deeplake.__version__}. Consider upgrading deeplake version \
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-4 | ids (Optional[List[str]], optional): Optional list of IDs.
**kwargs: other optional keyword arguments.
Returns:
List[str]: List of IDs of the added texts.
"""
kwargs = {}
if ids:
if self._id_tensor_name == "ids": # for backwards compatibility
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-5 | Engine for the client. Not for in-memory or local datasets.
- ``tensor_db`` - Hosted Managed Tensor Database for storage
and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
re... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-6 | """
Return docs similar to query.
Args:
query (str, optional): Text to look up similar docs.
embedding (Union[List[float], np.ndarray], optional): Query's embedding.
embedding_function (Callable, optional): Function to convert `query`
into embedding.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-7 | and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
**kwargs: Additional keyword arguments.
Returns:
List of Documents by the specified distance metric,
if return_score True, return a... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-8 | )
scores = result["score"]
embeddings = result["embedding"]
metadatas = result["metadata"]
texts = result["text"]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance( # type: ignore
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-9 | ... exec_option="compute_engine",
... )
Args:
k (int): Number of Documents to return. Defaults to 4.
query (str): Text to look up similar documents.
**kwargs: Additional keyword arguments include:
embedding (Callable): Embedding function to use... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-10 | k=k,
use_maximal_marginal_relevance=False,
return_score=False,
**kwargs,
)
[docs] def similarity_search_by_vector(
self,
embedding: Union[List[float], np.ndarray],
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""
Retur... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-11 | - "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be
used with in-memory or local datasets.
- "tens... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-12 | ... )
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
**kwargs: Additional keyword arguments. Some of these arguments are:
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-13 | text with distance in float."""
return self._search(
query=query,
k=k,
return_score=True,
**kwargs,
)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-14 | option with big datasets is discouraged due to potential
memory issues.
- "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-15 | ... embedding_function = <embedding_function_for_query>,
... k = <number_of_items_to_return>,
... exec_option = <preferred_exec_option>,
... )
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-16 | "For MMR search, you must specify an embedding function on"
" `creation` or during add call."
)
return self._search(
query=query,
k=k,
fetch_k=fetch_k,
use_maximal_marginal_relevance=True,
lambda_mult=lambda_mult,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-17 | (use 'activeloop login' from command line)
- AWS S3 path of the form ``s3://bucketname/path/to/dataset``.
Credentials are required in either the environment
- Google Cloud Storage path of the form
``gcs://bucketname/path/to/dataset`` Credentials ar... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
90d57b75adf4-18 | metadatas=metadatas,
ids=ids,
embedding_function=embedding.embed_documents, # type: ignore
)
return deeplake_dataset
[docs] def delete(
self,
ids: Any[List[str], None] = None,
filter: Any[Dict[str, str], None] = None,
delete_all: Any[bool, None... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
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