id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 50 116 |
|---|---|---|
9dd0ae5d44fd-6 | ) as response:
if not response.ok:
raise ValueError("Searx API returned an error: ", response.text)
result = SearxResults(await response.text())
self._result = result
else:
async with self.aiosession.get(
... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
9dd0ae5d44fd-7 | searx.run("what is the weather in France ?", engine="qwant")
# the same result can be achieved using the `!` syntax of searx
# to select the engine using `query_suffix`
searx.run("what is the weather in France ?", query_suffix="!qwant")
"""
_params = {
... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
9dd0ae5d44fd-8 | ) -> str:
"""Asynchronously version of `run`."""
_params = {
"q": query,
}
params = {**self.params, **_params, **kwargs}
if self.query_suffix and len(self.query_suffix) > 0:
params["q"] += " " + self.query_suffix
if isinstance(query_suffix, str) an... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
9dd0ae5d44fd-9 | categories: List of categories to use for the query.
**kwargs: extra parameters to pass to the searx API.
Returns:
Dict with the following keys:
{
snippet: The description of the result.
title: The title of the result.
link: T... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
9dd0ae5d44fd-10 | self,
query: str,
num_results: int,
engines: Optional[List[str]] = None,
query_suffix: Optional[str] = "",
**kwargs: Any,
) -> List[Dict]:
"""Asynchronously query with json results.
Uses aiohttp. See `results` for more info.
"""
_params = {
... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
ec9608d72fe9-0 | 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 |
ec9608d72fe9-1 | - 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 |
ec9608d72fe9-2 | from googleapiclient.discovery import build
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=go... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/google_search.html |
ec9608d72fe9-3 | if "snippet" in result:
metadata_result["snippet"] = result["snippet"]
metadata_results.append(metadata_result)
return metadata_results
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/utilities/google_search.html |
ab9c094b5bba-0 | Source code for langchain.utilities.wikipedia
"""Util that calls Wikipedia."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
WIKIPEDIA_MAX_QUERY_LENGTH = 300
[docs]class WikipediaAPIWrapper(BaseModel):
"""Wrapper around WikipediaAPI.
To use, you should have the ``w... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/wikipedia.html |
ab9c094b5bba-1 | summary = self.fetch_formatted_page_summary(search_results[i])
if summary is not None:
summaries.append(summary)
return "\n\n".join(summaries)
[docs] def fetch_formatted_page_summary(self, page: str) -> Optional[str]:
try:
wiki_page = self.wiki_client.page(titl... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/wikipedia.html |
cf04dfff9faf-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 |
cf04dfff9faf-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 |
cc4af407f36c-0 | Source code for langchain.utilities.google_serper
"""Util that calls Google Search using the Serper.dev API."""
from typing import Dict, Optional
import requests
from pydantic.class_validators import root_validator
from pydantic.main import BaseModel
from langchain.utils import get_from_dict_or_env
[docs]class GoogleSe... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
cc4af407f36c-1 | snippets = []
if results.get("answerBox"):
answer_box = results.get("answerBox", {})
if answer_box.get("answer"):
return answer_box.get("answer")
elif answer_box.get("snippet"):
return answer_box.get("snippet").replace("\n", " ")
el... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
cc4af407f36c-2 | )
response.raise_for_status()
search_results = response.json()
return search_results
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
512b6d37fded-0 | Source code for langchain.utilities.wolfram_alpha
"""Util that calls WolframAlpha."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class WolframAlphaAPIWrapper(BaseModel):
"""Wrapper for Wolfram Alpha.
Docs fo... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/wolfram_alpha.html |
512b6d37fded-1 | res = self.wolfram_client.query(query)
try:
assumption = next(res.pods).text
answer = next(res.results).text
except StopIteration:
return "Wolfram Alpha wasn't able to answer it"
if answer is None or answer == "":
# We don't want to return the assu... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/wolfram_alpha.html |
aaf05089460b-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 |
aaf05089460b-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 |
aaf05089460b-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 May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/utilities/bing_search.html |
c602dceabb1a-0 | Source code for langchain.utilities.powerbi
"""Wrapper around a Power BI endpoint."""
from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
import aiohttp
import requests
from aiohttp import ServerTimeoutError
from pydantic import BaseMo... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
c602dceabb1a-1 | arbitrary_types_allowed = True
@root_validator(pre=True, allow_reuse=True)
def token_or_credential_present(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate that at least one of token and credentials is present."""
if "token" in values or "credential" in values:
return valu... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
c602dceabb1a-2 | """Get names of tables available."""
return self.table_names
[docs] def get_schemas(self) -> str:
"""Get the available schema's."""
if self.schemas:
return ", ".join([f"{key}: {value}" for key, value in self.schemas.items()])
return "No known schema's yet. Use the schema_p... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
c602dceabb1a-3 | ) -> str:
"""Get information about specified tables."""
tables_requested = self._get_tables_to_query(table_names)
tables_todo = self._get_tables_todo(tables_requested)
for table in tables_todo:
try:
result = self.run(
f"EVALUATE TOPN({self.... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
c602dceabb1a-4 | if "bad request" in str(exc).lower():
return SCHEMA_ERROR_RESPONSE
if "unauthorized" in str(exc).lower():
return UNAUTHORIZED_RESPONSE
return str(exc)
self.schemas[table] = json_to_md(result["results"][0]["tables"][0]["rows"])
r... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
c602dceabb1a-5 | ) as response:
response.raise_for_status()
response_json = await response.json()
return response_json
def json_to_md(
json_contents: List[Dict[str, Union[str, int, float]]],
table_name: Optional[str] = None,
) -> str:
"""Converts a JSON object to a markdown ta... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
6c50f17d7abe-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 |
6c50f17d7abe-1 | temperature = w.temperature("celsius")
rain = w.rain
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... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/openweathermap.html |
53ab34c44db7-0 | Source code for langchain.utilities.serpapi
"""Chain that calls SerpAPI.
Heavily borrowed from https://github.com/ofirpress/self-ask
"""
import os
import sys
from typing import Any, Dict, Optional, Tuple
import aiohttp
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.utils import get_from_dic... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
53ab34c44db7-1 | aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python packag... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
53ab34c44db7-2 | """Use aiohttp to run query through SerpAPI and return the results async."""
def construct_url_and_params() -> Tuple[str, Dict[str, str]]:
params = self.get_params(query)
params["source"] = "python"
if self.serpapi_api_key:
params["serp_api_key"] = self.serpap... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
53ab34c44db7-3 | toret = res["answer_box"]["snippet"]
elif (
"answer_box" in res.keys()
and "snippet_highlighted_words" in res["answer_box"].keys()
):
toret = res["answer_box"]["snippet_highlighted_words"][0]
elif (
"sports_results" in res.keys()
and "g... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
223c6275e671-0 | Source code for langchain.utilities.arxiv
"""Util that calls Arxiv."""
import logging
from typing import Any, Dict, List
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
[docs]class ArxivAPIWrapper(BaseModel):
"""Wrapper around ArxivAPI... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
223c6275e671-1 | """Validate that the python package exists in environment."""
try:
import arxiv
values["arxiv_search"] = arxiv.Search
values["arxiv_exceptions"] = (
arxiv.ArxivError,
arxiv.UnexpectedEmptyPageError,
arxiv.HTTPError,
... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
223c6275e671-2 | """
Run Arxiv search and get the PDF documents plus the meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
Returns: a list of documents with the document.page_content in PDF format
"""
try:
import fitz
except ImportError:
raise... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
223c6275e671-3 | **add_meta,
}
),
)
docs.append(doc)
except FileNotFoundError as f_ex:
logger.debug(f_ex)
return docs
except self.arxiv_exceptions as ex:
logger.... | https:///python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
d55e98ec5bee-0 | Source code for langchain.vectorstores.opensearch_vector_search
"""Wrapper around OpenSearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Dict, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from la... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-1 | try:
opensearch = _import_opensearch()
client = opensearch(opensearch_url, **kwargs)
except ValueError as e:
raise ValueError(
f"OpenSearch client string provided is not in proper format. "
f"Got error: {e} "
)
return client
def _validate_embeddings_and_bu... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-2 | request = {
"_op_type": "index",
"_index": index_name,
vector_field: embeddings[i],
text_field: text,
"metadata": metadata,
"_id": _id,
}
requests.append(request)
ids.append(_id)
bulk(client, requests)
client.indices... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-3 | "parameters": {"ef_construction": ef_construction, "m": m},
},
}
}
},
}
def _default_approximate_search_query(
query_vector: List[float],
size: int = 4,
k: int = 4,
vector_field: str = "vector_field",
) -> Dict:
"""For Approximate k-NN Sear... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-4 | query_vector, size, k, vector_field
)
search_query["query"]["knn"][vector_field]["filter"] = lucene_filter
return search_query
def _default_script_query(
query_vector: List[float],
space_type: str = "l2",
pre_filter: Dict = MATCH_ALL_QUERY,
vector_field: str = "vector_field",
) -> Dict:
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-5 | vector_field: str = "vector_field",
) -> Dict:
"""For Painless Scripting Search, this is the default query."""
source = __get_painless_scripting_source(space_type, query_vector)
return {
"query": {
"script_score": {
"query": pre_filter,
"script": {
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-6 | bulk_size: int = 500,
**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.
bulk_size... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-7 | texts,
metadatas,
vector_field,
text_field,
mapping,
)
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
By default supports Approximate Search.
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-8 | search_type: "script_scoring"; default: "approximate_search"
space_type: "l2", "l1", "linf", "cosinesimil", "innerproduct",
"hammingbit"; default: "l2"
pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {"match_all": {}}
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-9 | "is invalid"
)
if boolean_filter != {}:
search_query = _approximate_search_query_with_boolean_filter(
embedding, boolean_filter, size, k, vector_field, subquery_clause
)
elif lucene_filter != {}:
search_query = _... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-10 | for hit in hits
]
return documents
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
bulk_size: int = 500,
**kwargs: Any,
) -> OpenSearchVectorSearch:
"""Construct O... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-11 | ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 10... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
d55e98ec5bee-12 | if is_appx_search:
engine = _get_kwargs_value(kwargs, "engine", "nmslib")
space_type = _get_kwargs_value(kwargs, "space_type", "l2")
ef_search = _get_kwargs_value(kwargs, "ef_search", 512)
ef_construction = _get_kwargs_value(kwargs, "ef_construction", 512)
m =... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
ac1e8e89d96c-0 | Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import Addabl... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-1 | [docs]class FAISS(VectorStore):
"""Wrapper around FAISS vector database.
To use, you should have the ``faiss`` python package installed.
Example:
.. code-block:: python
from langchain import FAISS
faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
""... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-2 | starting_len = len(self.index_to_docstore_id)
self.index.add(np.array(embeddings, dtype=np.float32))
# Get list of index, id, and docs.
full_info = [
(starting_len + i, str(uuid.uuid4()), doc)
for i, doc in enumerate(documents)
]
# Add information to docst... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-3 | self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
text_embeddings: Iterable pairs of string and embedding to
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-4 | # This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-5 | """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 to the query.
"""
docs_and_scores = self.similarity_search_with_score(quer... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-6 | np.array([embedding], dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
selected_indices = [indices[0][i] for i in mmr_selected]
docs = []
for i in selected_indices:
if i == -1:
# This happens when not enough do... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-7 | embedding, k, fetch_k, lambda_mult=lambda_mult
)
return docs
[docs] def merge_from(self, target: FAISS) -> None:
"""Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the curre... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-8 | ) -> FAISS:
faiss = dependable_faiss_import()
index = faiss.IndexFlatL2(len(embeddings[0]))
index.add(np.array(embeddings, dtype=np.float32))
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Docu... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-9 | metadatas,
**kwargs,
)
[docs] @classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from ra... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-10 | path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
# save index separately since it is not picklable
faiss = dependable_faiss_import()
faiss.write_index(
self.index, str(path / "{index_name}.faiss".format(index_name=index_name))
)
# save docstore... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
ac1e8e89d96c-11 | self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
if self.relevance_score_fn is None:
raise ValueError(
"normalize_score_fn must be provided to"
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
dfdeefff4be2-0 | 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 |
dfdeefff4be2-1 | 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 |
dfdeefff4be2-2 | 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 |
dfdeefff4be2-3 | 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 |
dfdeefff4be2-4 | """
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 |
dfdeefff4be2-5 | 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 |
dfdeefff4be2-6 | 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 |
dfdeefff4be2-7 | 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 |
799c5787f375-0 | Source code for langchain.vectorstores.zilliz
from __future__ import annotations
import logging
from typing import Any, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.milvus import Milvus
logger = logging.getLogger(__name__)
[docs]class Zilliz(Milvus):
def _create_index(... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
799c5787f375-1 | "Failed to create an index on collection: %s", self.collection_name
)
raise e
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = "LangChainCollecti... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
799c5787f375-2 | Zilliz: Zilliz Vector Store
"""
vector_db = cls(
embedding_function=embedding,
collection_name=collection_name,
connection_args=connection_args,
consistency_level=consistency_level,
index_params=index_params,
search_params=search_pa... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
7c3ca04c2244-0 | Source code for langchain.vectorstores.base
"""Interface for vector stores."""
from __future__ import annotations
import asyncio
from abc import ABC, abstractmethod
from functools import partial
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TypeVar
from pydantic import BaseModel, Field, root_vali... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
7c3ca04c2244-1 | 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]
metadatas = [doc.metada... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
7c3ca04c2244-2 | ) -> 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 self.amax_marginal_relevance_search(query, **kwargs)
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
7c3ca04c2244-3 | 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.
"""
raise NotImplementedError
[docs] async def asimilarity_search(
self, query: str, k: int = 4... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
7c3ca04c2244-4 | # asynchronous in the vector store implementations.
func = partial(self.similarity_search_by_vector, embedding, k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
7c3ca04c2244-5 | # asynchronous in the vector store implementations.
func = partial(
self.max_marginal_relevance_search, query, k, fetch_k, lambda_mult, **kwargs
)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def max_marginal_relevance_search_by_vector(
self,
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
7c3ca04c2244-6 | [docs] @classmethod
def from_documents(
cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
texts = [d.page_content for d in documents]
metadatas ... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
7c3ca04c2244-7 | """Return VectorStore initialized from texts and embeddings."""
raise NotImplementedError
[docs] def as_retriever(self, **kwargs: Any) -> BaseRetriever:
return VectorStoreRetriever(vectorstore=self, **kwargs)
class VectorStoreRetriever(BaseRetriever, BaseModel):
vectorstore: VectorStore
searc... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
7c3ca04c2244-8 | docs = await self.vectorstore.amax_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
ee8f39f95e54-0 | 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 |
ee8f39f95e54-1 | 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 |
ee8f39f95e54-2 | """
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 |
8265c4c4da77-0 | Source code for langchain.vectorstores.annoy
"""Wrapper around Annoy vector database."""
from __future__ import annotations
import os
import pickle
import uuid
from configparser import ConfigParser
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from l... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-1 | ):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
self.index = index
self.metric = metric
self.docstore = docstore
self.index_to_docstore_id = index_to_docstore_id
[docs] def add_texts(
self,
texts: Iterable[str... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-2 | Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score ... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-3 | k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function(query)
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-4 | Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_index(
docstore_index, k, search_k
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search(
self, query: str, k: int =... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-5 | 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.
"""
idxs = self.index.get_nns_by_vector(
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-6 | 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:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-7 | documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
docstore = InMemoryDocstore(
{inde... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-8 | from langchain import Annoy
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index = Annoy.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts, embedd... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-9 | embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = Annoy.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
em... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
8265c4c4da77-10 | Args:
folder_path: folder path to load index, docstore,
and index_to_docstore_id from.
embeddings: Embeddings to use when generating queries.
"""
path = Path(folder_path)
# load index separately since it is not picklable
annoy = dependable_annoy_im... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
ae72e5742c75-0 | Source code for langchain.vectorstores.redis
"""Wrapper around Redis vector database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Type,
)
import num... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
ae72e5742c75-1 | "Please refer to Redis Stack docs: https://redis.io/docs/stack/"
)
logging.error(error_message)
raise ValueError(error_message)
def _check_index_exists(client: RedisType, index_name: str) -> bool:
"""Check if Redis index exists."""
try:
client.ft(index_name).info()
except: # noqa: E722
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
ae72e5742c75-2 | vector_key: str = "content_vector",
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_relevance_score,
**kwargs: Any,
):
"""Initialize with necessary components."""
try:
import redis
except ImportError:
raise Value... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
ae72e5742c75-3 | schema = (
TextField(name=self.content_key),
TextField(name=self.metadata_key),
VectorField(
self.vector_key,
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": dim,
... | https:///python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
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