id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
c3da804ad8d4-1 | """
Get or create a collection.
Returns [Collection, bool] where the bool is True if the collection was created.
"""
created = False
collection = cls.get_by_name(session, name)
if collection:
return collection, created
collection = cls(name=name, cmeta... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
c3da804ad8d4-2 | """
VectorStore implementation using AnalyticDB.
AnalyticDB is a distributed full PostgresSQL syntax cloud-native database.
- `connection_string` is a postgres connection string.
- `embedding_function` any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
- `c... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
c3da804ad8d4-3 | engine = sqlalchemy.create_engine(self.connection_string)
conn = engine.connect()
return conn
[docs] def create_tables_if_not_exists(self) -> None:
Base.metadata.create_all(self._conn)
[docs] def drop_tables(self) -> None:
Base.metadata.drop_all(self._conn)
[docs] def create_col... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
c3da804ad8d4-4 | """
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
embeddings = self.embedding_function.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
with Session(self._conn) as session:
collection = self.get_collection(sessi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
c3da804ad8d4-5 | self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
) -> 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.
filter... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
c3da804ad8d4-6 | )
.filter(filter_by)
.order_by(EmbeddingStore.embedding.op("<->")(embedding))
.join(
CollectionStore,
EmbeddingStore.collection_id == CollectionStore.uuid,
)
.limit(k)
.all()
)
docs = [
(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
c3da804ad8d4-7 | pre_delete_collection: bool = False,
**kwargs: Any,
) -> AnalyticDB:
"""
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
c3da804ad8d4-8 | or set the PGVECTOR_CONNECTION_STRING environment variable.
"""
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_te... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
8e9ca2bfc256-0 | Source code for langchain.vectorstores.pinecone
"""Wrapper around Pinecone vector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Callable, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
8e9ca2bfc256-1 | f"got {type(index)}"
)
self._index = index
self._embedding_function = embedding_function
self._text_key = text_key
self._namespace = namespace
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Opt... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
8e9ca2bfc256-2 | k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to r... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
8e9ca2bfc256-3 | Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documen... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
8e9ca2bfc256-4 | pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
"""
try:
import pinecone
except ImportError:
raise ValueError(
"Could not import pinecone python pa... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
8e9ca2bfc256-5 | for j, line in enumerate(lines_batch):
metadata[j][text_key] = line
to_upsert = zip(ids_batch, embeds, metadata)
# upsert to Pinecone
index.upsert(vectors=list(to_upsert), namespace=namespace)
return cls(index, embedding.embed_query, text_key, namespace)
[docs... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
4a78a37cded8-0 | Source code for langchain.vectorstores.myscale
"""Wrapper around MyScale vector database."""
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from pydantic import BaseSettings
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-1 | .. code-block:: python
{
'id': 'text_id',
'vector': 'text_embedding',
'text': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-2 | config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScale Wrapper to LangChain
embedding_function (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-3 | CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map['id']} String,
{self.config.column_map['text']} String,
{self.config.column_map['vector']} Array(Float32),
{self.config.column_map['metadata']} JSON,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-4 | _data.append(f"({n})")
i_str = f"""
INSERT INTO TABLE
{self.config.database}.{self.config.table}({ks})
VALUES
{','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> N... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-5 | column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) - set(column_names)) >= 0
keys, values = zip(*column_names.items())
try:
t = None
for v in self.pgbar(
zip(*values), desc="Inserting data...", total=len(metadatas)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-6 | texts (Iterable[str]): List or tuple of strings to be added
config (MyScaleSettings, Optional): Myscale configuration
text_ids (Optional[Iterable], optional): IDs for the texts.
Defaults to None.
batch_size (int, optional): Batchsi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-7 | ).named_results():
_repr += (
f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n"
)
_repr += "-" * 51 + "\n"
return _repr
def _build_qstr(
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
) -> str:
q_emb... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-8 | of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of Documents
"""
return self.similarity_search_by_v... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-9 | ]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] def similarity_search_with_relevance_scores(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
4a78a37cded8-10 | return []
[docs] def drop(self) -> None:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}"
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
ae0a6cc91d2e-0 | Source code for langchain.vectorstores.atlas
"""Wrapper around Atlas by Nomic."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
ae0a6cc91d2e-1 | is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool): Whether to reset this project if it
already exists. Default False.
Generally userful during development and testing.
"""
try:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
ae0a6cc91d2e-2 | metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]]): An optional list of ids.
refresh(bool): Whether or not to refresh indices with the updated data.
Default True.
Returns:
List[str]: List of IDs of the added texts... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
ae0a6cc91d2e-3 | else:
if metadatas is None:
data = [
{"text": text, AtlasDB._ATLAS_DEFAULT_ID_FIELD: ids[i]}
for i, text in enumerate(texts)
]
else:
for i, text in enumerate(texts):
metadatas[i]["text"] =... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
ae0a6cc91d2e-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://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
ae0a6cc91d2e-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://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
ae0a6cc91d2e-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://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
ae0a6cc91d2e-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://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e4522d97411b-0 | Source code for langchain.vectorstores.typesense
"""Wrapper around Typesense vector search"""
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
fro... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
e4522d97411b-1 | *,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ValueError(
"Could not import typesense python package. "... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
e4522d97411b-2 | ]
def _create_collection(self, num_dim: int) -> None:
fields = [
{"name": "vec", "type": "float[]", "num_dim": num_dim},
{"name": f"{self._text_key}", "type": "string"},
{"name": ".*", "type": "auto"},
]
self._typesense_client.collections.create(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
e4522d97411b-3 | self,
query: str,
k: int = 4,
filter: Optional[str] = "",
) -> List[Tuple[Document, float]]:
"""Return typesense documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. De... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
e4522d97411b-4 | k: Number of Documents to return. Defaults to 4.
filter: typesense filter_by expression to filter documents on
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_score = self.similarity_search_with_score(query, k=k, filter=filter)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
e4522d97411b-5 | }
typesense_api_key = typesense_api_key or get_from_env(
"typesense_api_key", "TYPESENSE_API_KEY"
)
client_config = {
"nodes": [node],
"api_key": typesense_api_key,
"connection_timeout_seconds": connection_timeout_seconds,
}
return ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
75098c4652e7-0 | Source code for langchain.vectorstores.base
"""Interface for vector stores."""
from __future__ import annotations
import asyncio
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TypeVar
from pydantic import BaseModel, ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-1 | Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the Collection
texts = [doc.page_content for doc in documents]
metad... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-2 | self, query: str, search_type: str, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_type == "mmr":
return await se... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-3 | query, k=k, **kwargs
)
if any(
similarity < 0.0 or similarity > 1.0
for _, similarity in docs_and_similarities
):
warnings.warn(
"Relevance scores must be between"
f" 0 and 1, got {docs_and_similarities}"
)
s... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-4 | return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
# This is a temporary workaround to make the similarity search
# asynchr... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-5 | self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
amon... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-6 | [docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marg... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-7 | texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs] @classmethod
async def afrom_documents(
cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-8 | vectorstore: VectorStore
search_type: str = "similarity"
search_kwargs: dict = Field(default_factory=dict)
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Vali... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
75098c4652e7-9 | raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def aget_relevant_documents(self, query: str) -> List[Document]:
if self.search_type == "similarity":
docs = await self.vectorstore.asimilarity_search(
query, **self.search_kwargs
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
8d47fcdc9b0a-0 | Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
import uuid
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple
import numpy as np
from langchain.docstore.document imp... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-1 | returns:
nearest_indices: List, indices of nearest neighbors
"""
if data_vectors.shape[0] == 0:
return [], []
# Calculate the distance between the query_vector and all data_vectors
distances = distance_metric_map[distance_metric](query_embedding, data_vectors)
nearest_indices = np.ar... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-2 | embeddings = OpenAIEmbeddings()
vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedd... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-3 | if self.verbose:
print(
f"Deep Lake Dataset in {dataset_path} already exists, "
f"loading from the storage"
)
self.ds.summary()
else:
if "overwrite" in kwargs:
del kwargs["overwrite"]
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-4 | **kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], opti... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-5 | if batch_size == 0:
return []
batched = [
elements[i : i + batch_size] for i in range(0, len(elements), batch_size)
]
ingest().eval(
batched,
self.ds,
num_workers=min(self.num_workers, len(batched) // max(self.num_workers, 1)),
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-6 | take [Deep Lake filter]
(https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)
Defaults to None.
maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-7 | distance_metric=distance_metric.lower(),
)
view = view[indices]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance(
query_emb,
embeddings[indices]... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-8 | maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
Lis... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-9 | k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
return self._s... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-10 | )
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optim... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-11 | **kwargs: Any,
) -> DeepLake:
"""Create a Deep Lake dataset from a raw documents.
If a dataset_path is specified, the dataset will be persisted in that location,
otherwise by default at `./deeplake`
Args:
path (str, pathlib.Path): - The full path to the dataset. Can be:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-12 | dataset_path=dataset_path, embedding_function=embedding, **kwargs
)
deeplake_dataset.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return deeplake_dataset
[docs] def delete(
self,
ids: Any[List[str], None] = None,
filter: Any[Dict[str, str], None] = None,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
8d47fcdc9b0a-13 | try:
import deeplake
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake`."
)
deeplake.delete(path, large_ok=True, force=True)
[docs] def delete_dataset(sel... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
2dd424202175-0 | Source code for langchain.vectorstores.lancedb
"""Wrapper around LanceDB vector database"""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base i... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
2dd424202175-1 | self._id_key = id_key
self._text_key = text_key
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Turn texts into embedding and add it to the database... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
2dd424202175-2 | """
embedding = self._embedding.embed_query(query)
docs = self._connection.search(embedding).limit(k).to_df()
return [
Document(
page_content=row[self._text_key],
metadata=row[docs.columns != self._text_key],
)
for _, row in doc... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
32b203bf2841-0 | Source code for langchain.vectorstores.sklearn
""" Wrapper around scikit-learn NearestNeighbors implementation.
The vector store can be persisted in json, bson or parquet format.
"""
import json
import math
import os
from abc import ABC, abstractmethod
from typing import Any, Dict, Iterable, List, Literal, Optional, Tu... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
32b203bf2841-1 | with open(self.persist_path, "r") as fp:
return json.load(fp)
class BsonSerializer(BaseSerializer):
"""Serializes data in binary json using the bson python package."""
def __init__(self, persist_path: str) -> None:
super().__init__(persist_path)
self.bson = guard_import("bson")
@... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
32b203bf2841-2 | raise exc
else:
os.remove(backup_path)
else:
self.pq.write_table(table, self.persist_path)
def load(self) -> Any:
table = self.pq.read_table(self.persist_path)
df = table.to_pandas()
return {col: series.tolist() for col, series in df.items()}
S... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
32b203bf2841-3 | # data properties
self._embeddings: List[List[float]] = []
self._texts: List[str] = []
self._metadatas: List[dict] = []
self._ids: List[str] = []
# cache properties
self._embeddings_np: Any = np.asarray([])
if self._persist_path is not None and os.path.isfile(self... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
32b203bf2841-4 | ) -> List[str]:
_texts = list(texts)
_ids = ids or [str(uuid4()) for _ in _texts]
self._texts.extend(_texts)
self._embeddings.extend(self._embedding_function.embed_documents(_texts))
self._metadatas.extend(metadatas or ([{}] * len(_texts)))
self._ids.extend(_ids)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
32b203bf2841-5 | query_embedding = self._embedding_function.embed_query(query)
indices_dists = self._similarity_index_search_with_score(
query_embedding, k=k, **kwargs
)
return [
(
Document(
page_content=self._texts[idx],
metadata={"... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
32b203bf2841-6 | Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
32b203bf2841-7 | among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
7208e2189b9e-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.embeddings.base import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_doc... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html |
7208e2189b9e-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://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html |
eadfa296f8f3-0 | Source code for langchain.vectorstores.docarray.hnsw
"""Wrapper around Hnswlib store."""
from __future__ import annotations
from typing import Any, List, Literal, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_docarray_import,
)... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
eadfa296f8f3-1 | "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://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
eadfa296f8f3-2 | 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://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
bb95ba0f0019-0 | Source code for langchain.utilities.google_search
"""Util that calls Google Search."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class GoogleSearchAPIWrapper(BaseModel):
"""Wrapper for Google Search API.
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_search.html |
bb95ba0f0019-1 | - Under Search engine ID you’ll find the search-engine-ID.
4. Enable the Custom Search API
- Navigate to the APIs & Services→Dashboard panel in Cloud Console.
- Click Enable APIs and Services.
- Search for Custom Search API and click on it.
- Click Enable.
URL for it: https://console.cloud.googl... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_search.html |
bb95ba0f0019-2 | except ImportError:
raise ImportError(
"google-api-python-client is not installed. "
"Please install it with `pip install google-api-python-client`"
)
service = build("customsearch", "v1", developerKey=google_api_key)
values["search_engine"] = serv... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_search.html |
bb95ba0f0019-3 | metadata_result["snippet"] = result["snippet"]
metadata_results.append(metadata_result)
return metadata_results
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_search.html |
a62bf6257e32-0 | Source code for langchain.utilities.metaphor_search
"""Util that calls Metaphor Search API.
In order to set this up, follow instructions at:
"""
import json
from typing import Dict, List
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env... | https://python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html |
a62bf6257e32-1 | """Run query through Metaphor Search and return metadata.
Args:
query: The query to search for.
num_results: The number of results to return.
Returns:
A list of dictionaries with the following keys:
title - The title of the
url - The ur... | https://python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html |
a62bf6257e32-2 | for result in raw_search_results:
cleaned_results.append(
{
"title": result["title"],
"url": result["url"],
"author": result["author"],
"date_created": result["dateCreated"],
}
)
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html |
848d134415ca-0 | Source code for langchain.utilities.awslambda
"""Util that calls Lambda."""
import json
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
[docs]class LambdaWrapper(BaseModel):
"""Wrapper for AWS Lambda SDK.
Docs for using:
1. pip install boto3
2. Create a lambd... | https://python.langchain.com/en/latest/_modules/langchain/utilities/awslambda.html |
848d134415ca-1 | answer = json.loads(payload_string)["body"]
except StopIteration:
return "Failed to parse response from Lambda"
if answer is None or answer == "":
# We don't want to return the assumption alone if answer is empty
return "Request failed."
else:
retu... | https://python.langchain.com/en/latest/_modules/langchain/utilities/awslambda.html |
eb4dbeac9a03-0 | Source code for langchain.utilities.google_places_api
"""Chain that calls Google Places API.
"""
import logging
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class GooglePlacesAPIWrapper(BaseModel):
"""Wrapper arou... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
eb4dbeac9a03-1 | except ImportError:
raise ImportError(
"Could not import googlemaps python package. "
"Please install it with `pip install googlemaps`."
)
return values
[docs] def run(self, query: str) -> str:
"""Run Places search and get k number of places tha... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
eb4dbeac9a03-2 | "formatted_address", "Unknown"
)
phone_number = place_details.get("result", {}).get(
"formatted_phone_number", "Unknown"
)
website = place_details.get("result", {}).get("website", "Unknown")
formatted_details = (
f"{name}\nAddre... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
0f3b0510e408-0 | Source code for langchain.utilities.bing_search
"""Util that calls Bing Search.
In order to set this up, follow instructions at:
https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e
"""
from typing import Dict, List
import requests
from pydantic import BaseModel, Extra, ro... | https://python.langchain.com/en/latest/_modules/langchain/utilities/bing_search.html |
0f3b0510e408-1 | bing_subscription_key = get_from_dict_or_env(
values, "bing_subscription_key", "BING_SUBSCRIPTION_KEY"
)
values["bing_subscription_key"] = bing_subscription_key
bing_search_url = get_from_dict_or_env(
values,
"bing_search_url",
"BING_SEARCH_URL",
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/bing_search.html |
0f3b0510e408-2 | "snippet": result["snippet"],
"title": result["name"],
"link": result["url"],
}
metadata_results.append(metadata_result)
return metadata_results
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/utilities/bing_search.html |
d1cee340e36e-0 | Source code for langchain.utilities.openweathermap
"""Util that calls OpenWeatherMap using PyOWM."""
from typing import Any, Dict, Optional
from pydantic import Extra, root_validator
from langchain.tools.base import BaseModel
from langchain.utils import get_from_dict_or_env
[docs]class OpenWeatherMapAPIWrapper(BaseMode... | https://python.langchain.com/en/latest/_modules/langchain/utilities/openweathermap.html |
d1cee340e36e-1 | heat_index = w.heat_index
clouds = w.clouds
return (
f"In {location}, the current weather is as follows:\n"
f"Detailed status: {detailed_status}\n"
f"Wind speed: {wind['speed']} m/s, direction: {wind['deg']}°\n"
f"Humidity: {humidity}%\n"
f"Tem... | https://python.langchain.com/en/latest/_modules/langchain/utilities/openweathermap.html |
e5b2d309cf90-0 | Source code for langchain.utilities.searx_search
"""Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
`multiple search engines
<https://docs.searxng.org/admin/engines/configured_engines.html>`_ and databases and
supports the `OpenSearch
<https:/... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
e5b2d309cf90-1 | :class:`SearxResults` is a convenience wrapper around the raw json result.
Example usage of the ``run`` method to make a search:
.. code-block:: python
s.run(query="what is the best search engine?")
Engine Parameters
-----------------
You can pass any `accepted searx search API
<https://docs.searxng.org/dev... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
e5b2d309cf90-2 | .. code-block:: python
# select the github engine and pass the search suffix
s = SearchWrapper("langchain library", query_suffix="!gh")
s = SearchWrapper("langchain library")
# select github the conventional google search syntax
s.run("large language models", query_suffix="site:g... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
e5b2d309cf90-3 | return {"language": "en", "format": "json"}
[docs]class SearxResults(dict):
"""Dict like wrapper around search api results."""
_data = ""
def __init__(self, data: str):
"""Take a raw result from Searx and make it into a dict like object."""
json_data = json.loads(data)
super().__init... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
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