id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
fe9d545ce648-1 | *,
build: Optional[str] = None,
memory_mbytes: Optional[int] = None,
timeout_secs: Optional[int] = None,
) -> ApifyDatasetLoader:
"""Run an Actor on the Apify platform and wait for results to be ready.
Args:
actor_id (str): The ID or name of the Actor on the Apify... | https://python.langchain.com/en/latest/_modules/langchain/utilities/apify.html |
fe9d545ce648-2 | memory_mbytes: Optional[int] = None,
timeout_secs: Optional[int] = None,
) -> ApifyDatasetLoader:
"""Run an Actor on the Apify platform and wait for results to be ready.
Args:
actor_id (str): The ID or name of the Actor on the Apify platform.
run_input (Dict): The inp... | https://python.langchain.com/en/latest/_modules/langchain/utilities/apify.html |
7fdf084c9a19-0 | Source code for langchain.utilities.twilio
"""Util that calls Twilio."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class TwilioAPIWrapper(BaseModel):
"""Sms Client using Twilio.
To use, you should have the ... | https://python.langchain.com/en/latest/_modules/langchain/utilities/twilio.html |
7fdf084c9a19-1 | that is enabled for the type of message you want to send. Phone numbers or
[short codes](https://www.twilio.com/docs/sms/api/short-code) purchased from
Twilio also work here. You cannot, for example, spoof messages from a private
cell phone number. If you are using `messaging_service_sid`, th... | https://python.langchain.com/en/latest/_modules/langchain/utilities/twilio.html |
7fdf084c9a19-2 | characters in length.
to: The destination phone number in
[E.164](https://www.twilio.com/docs/glossary/what-e164) format for
SMS/MMS or
[Channel user address](https://www.twilio.com/docs/sms/channels#channel-addresses)
for other 3rd-party chann... | https://python.langchain.com/en/latest/_modules/langchain/utilities/twilio.html |
f6b5ad050170-0 | 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 |
f6b5ad050170-1 | Other methods are are available for convenience.
: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 `accept... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
f6b5ad050170-2 | .. 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 |
f6b5ad050170-3 | 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 |
f6b5ad050170-4 | .. code-block:: python
from langchain.utilities import SearxSearchWrapper
# note the unsecure parameter is not needed if you pass the url scheme as
# http
searx = SearxSearchWrapper(searx_host="http://localhost:8888",
un... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
f6b5ad050170-5 | if categories:
values["params"]["categories"] = ",".join(categories)
searx_host = get_from_dict_or_env(values, "searx_host", "SEARX_HOST")
if not searx_host.startswith("http"):
print(
f"Warning: missing the url scheme on host \
! assuming secure ht... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
f6b5ad050170-6 | ) 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 |
f6b5ad050170-7 | 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 |
f6b5ad050170-8 | ) -> 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 |
f6b5ad050170-9 | 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 |
f6b5ad050170-10 | 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 |
9a93ade63c1e-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 |
9a93ade63c1e-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 |
9a93ade63c1e-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 |
9a93ade63c1e-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 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_search.html |
467341b6206f-0 | Source code for langchain.utilities.wikipedia
"""Util that calls Wikipedia."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
WIKIPEDIA_MAX_QUERY_LENGTH = 300
[docs]class Wikiped... | https://python.langchain.com/en/latest/_modules/langchain/utilities/wikipedia.html |
467341b6206f-1 | summaries = []
for page_title in page_titles[: self.top_k_results]:
if wiki_page := self._fetch_page(page_title):
if summary := self._formatted_page_summary(page_title, wiki_page):
summaries.append(summary)
if not summaries:
return "No good Wik... | https://python.langchain.com/en/latest/_modules/langchain/utilities/wikipedia.html |
467341b6206f-2 | except (
self.wiki_client.exceptions.PageError,
self.wiki_client.exceptions.DisambiguationError,
):
return None
[docs] def load(self, query: str) -> List[Document]:
"""
Run Wikipedia search and get the article text plus the meta information.
See
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/wikipedia.html |
460c719e5711-0 | 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 |
460c719e5711-1 | 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 |
5fff2bc12059-0 | Source code for langchain.utilities.google_serper
"""Util that calls Google Search using the Serper.dev API."""
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from pydantic.class_validators import root_validator
from pydantic.main import BaseModel
from typing_extensions import Literal
from ... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
5fff2bc12059-1 | arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
serper_api_key = get_from_dict_or_env(
values, "serper_api_key", "SERPER_API_KEY"
)
values["serper_api_key"] = serp... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
5fff2bc12059-2 | """Run query through GoogleSearch and parse result async."""
results = await self._async_google_serper_search_results(
query,
gl=self.gl,
hl=self.hl,
num=self.k,
search_type=self.type,
tbs=self.tbs,
**kwargs,
)
r... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
5fff2bc12059-3 | return snippets
def _parse_results(self, results: dict) -> str:
return " ".join(self._parse_snippets(results))
def _google_serper_api_results(
self, search_term: str, search_type: str = "search", **kwargs: Any
) -> dict:
headers = {
"X-API-KEY": self.serper_api_key or "",... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
5fff2bc12059-4 | url, params=params, headers=headers, raise_for_status=True
) as response:
search_results = await response.json()
return search_results
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
f60f3b36a9de-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 |
f60f3b36a9de-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 |
95563a2d99d6-0 | 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 |
95563a2d99d6-1 | 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 |
95563a2d99d6-2 | "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 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/utilities/bing_search.html |
64377c3bb793-0 | Source code for langchain.utilities.powerbi
"""Wrapper around a Power BI endpoint."""
from __future__ import annotations
import asyncio
import logging
import os
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
import aiohttp
import requests
from aiohttp import ServerTimeoutError
from pydanti... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
64377c3bb793-1 | """Fix the table names."""
return [fix_table_name(table) for table in table_names]
@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" ... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
64377c3bb793-2 | "Could not get a token from the supplied credentials."
) from exc
raise ClientAuthenticationError("No credential or token supplied.")
[docs] def get_table_names(self) -> Iterable[str]:
"""Get names of tables available."""
return self.table_names
[docs] def get_schemas(self)... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
64377c3bb793-3 | if table_names not in self.table_names:
_LOGGER.warning("Table %s not found in dataset.", table_names)
return None
return [fix_table_name(table_names)]
return self.table_names
def _get_tables_todo(self, tables_todo: List[str]) -> List[str]:
"""... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
64377c3bb793-4 | await asyncio.gather(*[self._aget_schema(table) for table in tables_todo])
return self._get_schema_for_tables(tables_requested)
def _get_schema(self, table: str) -> None:
"""Get the schema for a table."""
try:
result = self.run(
f"EVALUATE TOPN({self.sample_rows_i... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
64377c3bb793-5 | def _create_json_content(self, command: str) -> dict[str, Any]:
"""Create the json content for the request."""
return {
"queries": [{"query": rf"{command}"}],
"impersonatedUserName": self.impersonated_user_name,
"serializerSettings": {"includeNulls": True},
}
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
64377c3bb793-6 | ) -> str:
"""Converts a JSON object to a markdown table."""
output_md = ""
headers = json_contents[0].keys()
for header in headers:
header.replace("[", ".").replace("]", "")
if table_name:
header.replace(f"{table_name}.", "")
output_md += f"| {header} "
output_md ... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
b6c20e01b7c3-0 | 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 |
b6c20e01b7c3-1 | 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 |
b298705f4e34-0 | 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 |
b298705f4e34-1 | """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 |
b298705f4e34-2 | 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 |
921a4674d2f5-0 | 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 |
921a4674d2f5-1 | 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 |
921a4674d2f5-2 | """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 |
921a4674d2f5-3 | 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 |
9c0e129e090f-0 | Source code for langchain.utilities.arxiv
"""Util that calls Arxiv."""
import logging
import os
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 aroun... | https://python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
9c0e129e090f-1 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in environment."""
try:
import arxiv
values["arxiv_search"] =... | https://python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
9c0e129e090f-2 | for result in results
]
if docs:
return "\n\n".join(docs)[: self.doc_content_chars_max]
else:
return "No good Arxiv Result was found"
[docs] def load(self, query: str) -> List[Document]:
"""
Run Arxiv search and get the article texts plus the article me... | https://python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
9c0e129e090f-3 | "doi": result.doi,
"primary_category": result.primary_category,
"categories": result.categories,
"links": [link.href for link in result.links],
}
else:
extra_metadata = {}
metadata = {
"Pu... | https://python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
c0493d7648ec-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, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-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 |
c0493d7648ec-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 |
c0493d7648ec-3 | "parameters": {"ef_construction": ef_construction, "m": m},
},
}
}
},
}
def _default_approximate_search_query(
query_vector: List[float],
k: int = 4,
vector_field: str = "vector_field",
) -> Dict:
"""For Approximate k-NN Search, this is the def... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-4 | 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:
"""For Script Scoring Search, this is the default query."""
return {
"query": {
"script_score":... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-5 | source = __get_painless_scripting_source(space_type, query_vector)
return {
"query": {
"script_score": {
"query": pre_filter,
"script": {
"source": source,
"params": {
"field": vector_field,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-6 | """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: Bulk API request count; Default: 500
Returns:
List ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-7 | 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.
Also supports Script Scoring and Painless Scripting.
Args... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-8 | pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {"match_all": {}}
Optional Args for Painless Scripting Search:
search_type: "painless_scripting"; default: "approximate_search"
space_type: "l2Squared", "l1Norm", "cosineSimi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-9 | if search_type == "approximate_search":
boolean_filter = _get_kwargs_value(kwargs, "boolean_filter", {})
subquery_clause = _get_kwargs_value(kwargs, "subquery_clause", "must")
lucene_filter = _get_kwargs_value(kwargs, "lucene_filter", {})
if boolean_filter != {} and lucen... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-10 | embedding, space_type, pre_filter, vector_field
)
else:
raise ValueError("Invalid `search_type` provided as an argument")
response = self.client.search(index=self.index_name, body=search_query)
hits = [hit for hit in response["hits"]["hits"][:k]]
documents_with_sc... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-11 | vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
Optional Keyword Args for Approximate Search:
engine: "nmslib", "faiss", "lucene"; default: "nm... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-12 | _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
dim = len(embeddings[0])
# Get the index name from either from kwargs or ENV Variable
# before falling back to random generation
index_name = get_from_dict_or_env(
kwargs, "index_name", "OPENSEARCH_INDEX_NAME", de... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
c0493d7648ec-13 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
1e3993815549-0 | Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import os
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 imp... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-1 | return faiss
def _default_relevance_score_fn(score: float) -> float:
"""Return a similarity score on a scale [0, 1]."""
# The 'correct' relevance function
# may differ depending on a few things, including:
# - the distance / similarity metric used by the VectorStore
# - the scale of your embeddings ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-2 | self._normalize_L2 = normalize_L2
def __add(
self,
texts: Iterable[str],
embeddings: Iterable[List[float]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
if not isinstance(self.docstore, AddableMixi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-3 | return [_id for _, _id, _ in full_info]
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-4 | ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"add... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-5 | docs.append((doc, scores[0][j]))
return docs
[docs] def similarity_search_with_score(
self, query: str, k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Docum... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-6 | """
docs_and_scores = self.similarity_search_with_score(query, k)
return [doc for doc, _ in docs_and_scores]
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwa... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-7 | docs = []
for i in selected_indices:
if i == -1:
# 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 V... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-8 | Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the current one
Returns:
None.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError("Cannot merge with this type of docstore")
# Numerica... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-9 | vector = np.array(embeddings, dtype=np.float32)
if normalize_L2:
faiss.normalize_L2(vector)
index.add(vector)
documents = []
if ids is None:
ids = [str(uuid.uuid4()) for _ in texts]
for i, text in enumerate(texts):
metadata = metadatas[i] if me... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-10 | return cls.__from(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
**kwargs,
)
[docs] @classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-11 | Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
# save index separately since it is not... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
1e3993815549-12 | docstore, index_to_docstore_id = pickle.load(f)
return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs a... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9358d8948c4c-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 |
9358d8948c4c-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 |
9358d8948c4c-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 |
9358d8948c4c-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 |
9358d8948c4c-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 |
9358d8948c4c-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 |
9358d8948c4c-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 |
9358d8948c4c-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 |
00a02e02c1f5-0 | 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 importlib
import json
import math
import os
from abc import ABC, abstractmethod
from typing import Any, Dict, Iterable, List, Lite... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
00a02e02c1f5-1 | """Serializes data in json using the json package from python standard library."""
@classmethod
def extension(cls) -> str:
return "json"
def save(self, data: Any) -> None:
with open(self.persist_path, "w") as fp:
json.dump(data, fp)
def load(self) -> Any:
with open(se... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
00a02e02c1f5-2 | if os.path.exists(self.persist_path):
backup_path = str(self.persist_path) + "-backup"
os.rename(self.persist_path, backup_path)
try:
self.pq.write_table(table, self.persist_path)
except Exception as exc:
os.rename(backup_path, self.persist... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
00a02e02c1f5-3 | self._neighbors_fitted = False
self._embedding_function = embedding
self._persist_path = persist_path
self._serializer: Optional[BaseSerializer] = None
if self._persist_path is not None:
serializer_cls = SERIALIZER_MAP[serializer]
self._serializer = serializer_cls... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
00a02e02c1f5-4 | self._ids = data["ids"]
self._update_neighbors()
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
_texts = list(texts)
_ids = ids or [str(uuid4(... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
00a02e02c1f5-5 | for idx, dist in zip(neigh_idxs[0], neigh_dists[0]):
_idx = int(idx)
metadata = {"id": self._ids[_idx], **self._metadatas[_idx]}
doc = Document(page_content=self._texts[_idx], metadata=metadata)
res.append((doc, dist))
return res
[docs] def similarity_search(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
00a02e02c1f5-6 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/sklearn.html |
658aeb7f3185-0 | Source code for langchain.vectorstores.vectara
"""Wrapper around Vectara vector database."""
from __future__ import annotations
import json
import logging
import os
from hashlib import md5
from typing import Any, Iterable, List, Optional, Tuple, Type
import requests
from pydantic import Field
from langchain.embeddings.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
658aeb7f3185-1 | or self._vectara_api_key is None
):
logging.warning(
"Cant find Vectara credentials, customer_id or corpus_id in "
"environment."
)
else:
logging.debug(f"Using corpus id {self._vectara_corpus_id}")
self._session = requests.Sessi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
658aeb7f3185-2 | f"{response.text}"
)
return False
return True
def _index_doc(self, doc_id: str, text: str, metadata: dict) -> bool:
request: dict[str, Any] = {}
request["customer_id"] = self._vectara_customer_id
request["corpus_id"] = self._vectara_corpus_id
request["... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
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