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ebb015d4a065-0
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Source code for langchain.utilities.pubmed
import json
import logging
import time
import urllib.error
import urllib.request
from typing import Any, Dict, Iterator, List
from pydantic import BaseModel
from pydantic.class_validators import root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
[docs]class PubMedAPIWrapper(BaseModel):
"""
Wrapper around PubMed API.
This wrapper will use the PubMed API to conduct searches and fetch
document summaries. By default, it will return the document summaries
of the top-k results of an input search.
Parameters:
top_k_results: number of the top-scored document used for the PubMed tool
MAX_QUERY_LENGTH: maximum length of the query.
Default is 300 characters.
doc_content_chars_max: maximum length of the document content.
Content will be truncated if it exceeds this length.
Default is 2000 characters.
max_retry: maximum number of retries for a request. Default is 5.
sleep_time: time to wait between retries.
Default is 0.2 seconds.
email: email address to be used for the PubMed API.
"""
parse: Any #: :meta private:
base_url_esearch = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?"
base_url_efetch = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?"
max_retry = 5
sleep_time = 0.2
# Default values for the parameters
top_k_results: int = 3
MAX_QUERY_LENGTH = 300
doc_content_chars_max: int = 2000
email: str = "your_email@example.com"
@root_validator()
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email: str = "your_email@example.com"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in environment."""
try:
import xmltodict
values["parse"] = xmltodict.parse
except ImportError:
raise ImportError(
"Could not import xmltodict python package. "
"Please install it with `pip install xmltodict`."
)
return values
[docs] def run(self, query: str) -> str:
"""
Run PubMed search and get the article meta information.
See https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch
It uses only the most informative fields of article meta information.
"""
try:
# Retrieve the top-k results for the query
docs = [
f"Published: {result['Published']}\n"
f"Title: {result['Title']}\n"
f"Copyright Information: {result['Copyright Information']}\n"
f"Summary::\n{result['Summary']}"
for result in self.load(query[: self.MAX_QUERY_LENGTH])
]
# Join the results and limit the character count
return (
"\n\n".join(docs)[: self.doc_content_chars_max]
if docs
else "No good PubMed Result was found"
)
except Exception as ex:
return f"PubMed exception: {ex}"
[docs] def lazy_load(self, query: str) -> Iterator[dict]:
"""
Search PubMed for documents matching the query.
Return an iterator of dictionaries containing the document metadata.
"""
url = (
self.base_url_esearch
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"""
url = (
self.base_url_esearch
+ "db=pubmed&term="
+ str({urllib.parse.quote(query)})
+ f"&retmode=json&retmax={self.top_k_results}&usehistory=y"
)
result = urllib.request.urlopen(url)
text = result.read().decode("utf-8")
json_text = json.loads(text)
webenv = json_text["esearchresult"]["webenv"]
for uid in json_text["esearchresult"]["idlist"]:
yield self.retrieve_article(uid, webenv)
[docs] def load(self, query: str) -> List[dict]:
"""
Search PubMed for documents matching the query.
Return a list of dictionaries containing the document metadata.
"""
return list(self.lazy_load(query))
def _dict2document(self, doc: dict) -> Document:
summary = doc.pop("Summary")
return Document(page_content=summary, metadata=doc)
[docs] def lazy_load_docs(self, query: str) -> Iterator[Document]:
for d in self.lazy_load(query=query):
yield self._dict2document(d)
[docs] def load_docs(self, query: str) -> List[Document]:
return list(self.lazy_load_docs(query=query))
[docs] def retrieve_article(self, uid: str, webenv: str) -> dict:
url = (
self.base_url_efetch
+ "db=pubmed&retmode=xml&id="
+ uid
+ "&webenv="
+ webenv
)
retry = 0
while True:
try:
result = urllib.request.urlopen(url)
break
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try:
result = urllib.request.urlopen(url)
break
except urllib.error.HTTPError as e:
if e.code == 429 and retry < self.max_retry:
# Too Many Requests errors
# wait for an exponentially increasing amount of time
print(
f"Too Many Requests, "
f"waiting for {self.sleep_time:.2f} seconds..."
)
time.sleep(self.sleep_time)
self.sleep_time *= 2
retry += 1
else:
raise e
xml_text = result.read().decode("utf-8")
text_dict = self.parse(xml_text)
return self._parse_article(uid, text_dict)
def _parse_article(self, uid: str, text_dict: dict) -> dict:
ar = text_dict["PubmedArticleSet"]["PubmedArticle"]["MedlineCitation"][
"Article"
]
summary = "\n".join(
[
f"{txt['@Label']}: {txt['#text']}"
for txt in ar.get("Abstract", {}).get("AbstractText", [])
if "#text" in txt and "@Label" in txt
]
)
a_d = ar.get("ArticleDate", {})
pub_date = "-".join(
[a_d.get("Year", ""), a_d.get("Month", ""), a_d.get("Day", "")]
)
return {
"uid": uid,
"Title": ar.get("ArticleTitle", ""),
"Published": pub_date,
"Copyright Information": ar.get("Abstract", {}).get(
"CopyrightInformation", ""
),
"Summary": summary,
}
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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 for using:
1. Go to wolfram alpha and sign up for a developer account
2. Create an app and get your APP ID
3. Save your APP ID into WOLFRAM_ALPHA_APPID env variable
4. pip install wolframalpha
"""
wolfram_client: Any #: :meta private:
wolfram_alpha_appid: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
wolfram_alpha_appid = get_from_dict_or_env(
values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID"
)
values["wolfram_alpha_appid"] = wolfram_alpha_appid
try:
import wolframalpha
except ImportError:
raise ImportError(
"wolframalpha is not installed. "
"Please install it with `pip install wolframalpha`"
)
client = wolframalpha.Client(wolfram_alpha_appid)
values["wolfram_client"] = client
return values
[docs] def run(self, query: str) -> str:
"""Run query through WolframAlpha and parse result."""
res = self.wolfram_client.query(query)
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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 assumption alone if answer is empty
return "No good Wolfram Alpha Result was found"
else:
return f"Assumption: {assumption} \nAnswer: {answer}"
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|
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Source code for langchain.utilities.spark_sql
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Iterable, List, Optional
if TYPE_CHECKING:
from pyspark.sql import DataFrame, Row, SparkSession
[docs]class SparkSQL:
[docs] def __init__(
self,
spark_session: Optional[SparkSession] = None,
catalog: Optional[str] = None,
schema: Optional[str] = None,
ignore_tables: Optional[List[str]] = None,
include_tables: Optional[List[str]] = None,
sample_rows_in_table_info: int = 3,
):
try:
from pyspark.sql import SparkSession
except ImportError:
raise ValueError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
self._spark = (
spark_session if spark_session else SparkSession.builder.getOrCreate()
)
if catalog is not None:
self._spark.catalog.setCurrentCatalog(catalog)
if schema is not None:
self._spark.catalog.setCurrentDatabase(schema)
self._all_tables = set(self._get_all_table_names())
self._include_tables = set(include_tables) if include_tables else set()
if self._include_tables:
missing_tables = self._include_tables - self._all_tables
if missing_tables:
raise ValueError(
f"include_tables {missing_tables} not found in database"
)
self._ignore_tables = set(ignore_tables) if ignore_tables else set()
if self._ignore_tables:
missing_tables = self._ignore_tables - self._all_tables
if missing_tables:
raise ValueError(
f"ignore_tables {missing_tables} not found in database"
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raise ValueError(
f"ignore_tables {missing_tables} not found in database"
)
usable_tables = self.get_usable_table_names()
self._usable_tables = set(usable_tables) if usable_tables else self._all_tables
if not isinstance(sample_rows_in_table_info, int):
raise TypeError("sample_rows_in_table_info must be an integer")
self._sample_rows_in_table_info = sample_rows_in_table_info
[docs] @classmethod
def from_uri(
cls, database_uri: str, engine_args: Optional[dict] = None, **kwargs: Any
) -> SparkSQL:
"""Creating a remote Spark Session via Spark connect.
For example: SparkSQL.from_uri("sc://localhost:15002")
"""
try:
from pyspark.sql import SparkSession
except ImportError:
raise ValueError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
spark = SparkSession.builder.remote(database_uri).getOrCreate()
return cls(spark, **kwargs)
[docs] def get_usable_table_names(self) -> Iterable[str]:
"""Get names of tables available."""
if self._include_tables:
return self._include_tables
# sorting the result can help LLM understanding it.
return sorted(self._all_tables - self._ignore_tables)
def _get_all_table_names(self) -> Iterable[str]:
rows = self._spark.sql("SHOW TABLES").select("tableName").collect()
return list(map(lambda row: row.tableName, rows))
def _get_create_table_stmt(self, table: str) -> str:
statement = (
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statement = (
self._spark.sql(f"SHOW CREATE TABLE {table}").collect()[0].createtab_stmt
)
# Ignore the data source provider and options to reduce the number of tokens.
using_clause_index = statement.find("USING")
return statement[:using_clause_index] + ";"
[docs] def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
all_table_names = self.get_usable_table_names()
if table_names is not None:
missing_tables = set(table_names).difference(all_table_names)
if missing_tables:
raise ValueError(f"table_names {missing_tables} not found in database")
all_table_names = table_names
tables = []
for table_name in all_table_names:
table_info = self._get_create_table_stmt(table_name)
if self._sample_rows_in_table_info:
table_info += "\n\n/*"
table_info += f"\n{self._get_sample_spark_rows(table_name)}\n"
table_info += "*/"
tables.append(table_info)
final_str = "\n\n".join(tables)
return final_str
def _get_sample_spark_rows(self, table: str) -> str:
query = f"SELECT * FROM {table} LIMIT {self._sample_rows_in_table_info}"
df = self._spark.sql(query)
columns_str = "\t".join(list(map(lambda f: f.name, df.schema.fields)))
try:
sample_rows = self._get_dataframe_results(df)
# save the sample rows in string format
sample_rows_str = "\n".join(["\t".join(row) for row in sample_rows])
except Exception:
sample_rows_str = ""
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except Exception:
sample_rows_str = ""
return (
f"{self._sample_rows_in_table_info} rows from {table} table:\n"
f"{columns_str}\n"
f"{sample_rows_str}"
)
def _convert_row_as_tuple(self, row: Row) -> tuple:
return tuple(map(str, row.asDict().values()))
def _get_dataframe_results(self, df: DataFrame) -> list:
return list(map(self._convert_row_as_tuple, df.collect()))
[docs] def run(self, command: str, fetch: str = "all") -> str:
df = self._spark.sql(command)
if fetch == "one":
df = df.limit(1)
return str(self._get_dataframe_results(df))
[docs] def get_table_info_no_throw(self, table_names: Optional[List[str]] = None) -> str:
"""Get information about specified tables.
Follows best practices as specified in: Rajkumar et al, 2022
(https://arxiv.org/abs/2204.00498)
If `sample_rows_in_table_info`, the specified number of sample rows will be
appended to each table description. This can increase performance as
demonstrated in the paper.
"""
try:
return self.get_table_info(table_names)
except ValueError as e:
"""Format the error message"""
return f"Error: {e}"
[docs] def run_no_throw(self, command: str, fetch: str = "all") -> str:
"""Execute a SQL command and return a string representing the results.
If the statement returns rows, a string of the results is returned.
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If the statement returns rows, a string of the results is returned.
If the statement returns no rows, an empty string is returned.
If the statement throws an error, the error message is returned.
"""
try:
from pyspark.errors import PySparkException
except ImportError:
raise ValueError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
try:
return self.run(command, fetch)
except PySparkException as e:
"""Format the error message"""
return f"Error: {e}"
|
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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.
Adapted from: Instructions adapted from https://stackoverflow.com/questions/
37083058/
programmatically-searching-google-in-python-using-custom-search
TODO: DOCS for using it
1. Install google-api-python-client
- If you don't already have a Google account, sign up.
- If you have never created a Google APIs Console project,
read the Managing Projects page and create a project in the Google API Console.
- Install the library using pip install google-api-python-client
The current version of the library is 2.70.0 at this time
2. To create an API key:
- Navigate to the APIs & Services→Credentials panel in Cloud Console.
- Select Create credentials, then select API key from the drop-down menu.
- The API key created dialog box displays your newly created key.
- You now have an API_KEY
3. Setup Custom Search Engine so you can search the entire web
- Create a custom search engine in this link.
- In Sites to search, add any valid URL (i.e. www.stackoverflow.com).
- That’s all you have to fill up, the rest doesn’t matter.
In the left-side menu, click Edit search engine → {your search engine name}
→ Setup Set Search the entire web to ON. Remove the URL you added from
the list of Sites to search.
- Under Search engine ID you’ll find the search-engine-ID.
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- 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.google.com/apis/library/customsearch.googleapis
.com
"""
search_engine: Any #: :meta private:
google_api_key: Optional[str] = None
google_cse_id: Optional[str] = None
k: int = 10
siterestrict: bool = False
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def _google_search_results(self, search_term: str, **kwargs: Any) -> List[dict]:
cse = self.search_engine.cse()
if self.siterestrict:
cse = cse.siterestrict()
res = cse.list(q=search_term, cx=self.google_cse_id, **kwargs).execute()
return res.get("items", [])
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
google_api_key = get_from_dict_or_env(
values, "google_api_key", "GOOGLE_API_KEY"
)
values["google_api_key"] = google_api_key
google_cse_id = get_from_dict_or_env(values, "google_cse_id", "GOOGLE_CSE_ID")
values["google_cse_id"] = google_cse_id
try:
from googleapiclient.discovery import build
except ImportError:
raise ImportError(
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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"] = service
return values
[docs] def run(self, query: str) -> str:
"""Run query through GoogleSearch and parse result."""
snippets = []
results = self._google_search_results(query, num=self.k)
if len(results) == 0:
return "No good Google Search Result was found"
for result in results:
if "snippet" in result:
snippets.append(result["snippet"])
return " ".join(snippets)
[docs] def results(
self,
query: str,
num_results: int,
search_params: Optional[Dict[str, str]] = None,
) -> List[Dict]:
"""Run query through GoogleSearch and return metadata.
Args:
query: The query to search for.
num_results: The number of results to return.
search_params: Parameters to be passed on search
Returns:
A list of dictionaries with the following keys:
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
"""
metadata_results = []
results = self._google_search_results(
query, num=num_results, **(search_params or {})
)
if len(results) == 0:
return [{"Result": "No good Google Search Result was found"}]
for result in results:
metadata_result = {
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for result in results:
metadata_result = {
"title": result["title"],
"link": result["link"],
}
if "snippet" in result:
metadata_result["snippet"] = result["snippet"]
metadata_results.append(metadata_result)
return metadata_results
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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 langchain.utils import get_from_dict_or_env
[docs]class GoogleSerperAPIWrapper(BaseModel):
"""Wrapper around the Serper.dev Google Search API.
You can create a free API key at https://serper.dev.
To use, you should have the environment variable ``SERPER_API_KEY``
set with your API key, or pass `serper_api_key` as a named parameter
to the constructor.
Example:
.. code-block:: python
from langchain import GoogleSerperAPIWrapper
google_serper = GoogleSerperAPIWrapper()
"""
k: int = 10
gl: str = "us"
hl: str = "en"
# "places" and "images" is available from Serper but not implemented in the
# parser of run(). They can be used in results()
type: Literal["news", "search", "places", "images"] = "search"
result_key_for_type = {
"news": "news",
"places": "places",
"images": "images",
"search": "organic",
}
tbs: Optional[str] = None
serper_api_key: Optional[str] = None
aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
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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"] = serper_api_key
return values
[docs] def results(self, query: str, **kwargs: Any) -> Dict:
"""Run query through GoogleSearch."""
return self._google_serper_api_results(
query,
gl=self.gl,
hl=self.hl,
num=self.k,
tbs=self.tbs,
search_type=self.type,
**kwargs,
)
[docs] def run(self, query: str, **kwargs: Any) -> str:
"""Run query through GoogleSearch and parse result."""
results = self._google_serper_api_results(
query,
gl=self.gl,
hl=self.hl,
num=self.k,
tbs=self.tbs,
search_type=self.type,
**kwargs,
)
return self._parse_results(results)
[docs] async def aresults(self, query: str, **kwargs: Any) -> Dict:
"""Run query through GoogleSearch."""
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,
)
return results
[docs] async def arun(self, query: str, **kwargs: Any) -> str:
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"""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,
)
return self._parse_results(results)
def _parse_snippets(self, results: dict) -> List[str]:
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", " ")]
elif answer_box.get("snippetHighlighted"):
return answer_box.get("snippetHighlighted")
if results.get("knowledgeGraph"):
kg = results.get("knowledgeGraph", {})
title = kg.get("title")
entity_type = kg.get("type")
if entity_type:
snippets.append(f"{title}: {entity_type}.")
description = kg.get("description")
if description:
snippets.append(description)
for attribute, value in kg.get("attributes", {}).items():
snippets.append(f"{title} {attribute}: {value}.")
for result in results[self.result_key_for_type[self.type]][: self.k]:
if "snippet" in result:
snippets.append(result["snippet"])
for attribute, value in result.get("attributes", {}).items():
snippets.append(f"{attribute}: {value}.")
if len(snippets) == 0:
return ["No good Google Search Result was found"]
return snippets
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return ["No good Google Search Result was found"]
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 "",
"Content-Type": "application/json",
}
params = {
"q": search_term,
**{key: value for key, value in kwargs.items() if value is not None},
}
response = requests.post(
f"https://google.serper.dev/{search_type}", headers=headers, params=params
)
response.raise_for_status()
search_results = response.json()
return search_results
async def _async_google_serper_search_results(
self, search_term: str, search_type: str = "search", **kwargs: Any
) -> dict:
headers = {
"X-API-KEY": self.serper_api_key or "",
"Content-Type": "application/json",
}
url = f"https://google.serper.dev/{search_type}"
params = {
"q": search_term,
**{key: value for key, value in kwargs.items() if value is not None},
}
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.post(
url, params=params, headers=headers, raise_for_status=False
) as response:
search_results = await response.json()
else:
async with self.aiosession.post(
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else:
async with self.aiosession.post(
url, params=params, headers=headers, raise_for_status=True
) as response:
search_results = await response.json()
return search_results
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Source code for langchain.utilities.requests
"""Lightweight wrapper around requests library, with async support."""
from contextlib import asynccontextmanager
from typing import Any, AsyncGenerator, Dict, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra
[docs]class Requests(BaseModel):
"""Wrapper around requests to handle auth and async.
The main purpose of this wrapper is to handle authentication (by saving
headers) and enable easy async methods on the same base object.
"""
headers: Optional[Dict[str, str]] = None
aiosession: Optional[aiohttp.ClientSession] = None
auth: Optional[Any] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
[docs] def get(self, url: str, **kwargs: Any) -> requests.Response:
"""GET the URL and return the text."""
return requests.get(url, headers=self.headers, auth=self.auth, **kwargs)
[docs] def post(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""POST to the URL and return the text."""
return requests.post(
url, json=data, headers=self.headers, auth=self.auth, **kwargs
)
[docs] def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""PATCH the URL and return the text."""
return requests.patch(
url, json=data, headers=self.headers, auth=self.auth, **kwargs
)
[docs] def put(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
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"""PUT the URL and return the text."""
return requests.put(
url, json=data, headers=self.headers, auth=self.auth, **kwargs
)
[docs] def delete(self, url: str, **kwargs: Any) -> requests.Response:
"""DELETE the URL and return the text."""
return requests.delete(url, headers=self.headers, auth=self.auth, **kwargs)
@asynccontextmanager
async def _arequest(
self, method: str, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""Make an async request."""
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.request(
method, url, headers=self.headers, auth=self.auth, **kwargs
) as response:
yield response
else:
async with self.aiosession.request(
method, url, headers=self.headers, auth=self.auth, **kwargs
) as response:
yield response
[docs] @asynccontextmanager
async def aget(
self, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""GET the URL and return the text asynchronously."""
async with self._arequest("GET", url, auth=self.auth, **kwargs) as response:
yield response
[docs] @asynccontextmanager
async def apost(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""POST to the URL and return the text asynchronously."""
async with self._arequest(
|
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|
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async with self._arequest(
"POST", url, json=data, auth=self.auth, **kwargs
) as response:
yield response
[docs] @asynccontextmanager
async def apatch(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""PATCH the URL and return the text asynchronously."""
async with self._arequest(
"PATCH", url, json=data, auth=self.auth, **kwargs
) as response:
yield response
[docs] @asynccontextmanager
async def aput(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""PUT the URL and return the text asynchronously."""
async with self._arequest(
"PUT", url, json=data, auth=self.auth, **kwargs
) as response:
yield response
[docs] @asynccontextmanager
async def adelete(
self, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""DELETE the URL and return the text asynchronously."""
async with self._arequest("DELETE", url, auth=self.auth, **kwargs) as response:
yield response
[docs]class TextRequestsWrapper(BaseModel):
"""Lightweight wrapper around requests library.
The main purpose of this wrapper is to always return a text output.
"""
headers: Optional[Dict[str, str]] = None
aiosession: Optional[aiohttp.ClientSession] = None
auth: Optional[Any] = None
class Config:
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auth: Optional[Any] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def requests(self) -> Requests:
return Requests(
headers=self.headers, aiosession=self.aiosession, auth=self.auth
)
[docs] def get(self, url: str, **kwargs: Any) -> str:
"""GET the URL and return the text."""
return self.requests.get(url, **kwargs).text
[docs] def post(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""POST to the URL and return the text."""
return self.requests.post(url, data, **kwargs).text
[docs] def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PATCH the URL and return the text."""
return self.requests.patch(url, data, **kwargs).text
[docs] def put(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PUT the URL and return the text."""
return self.requests.put(url, data, **kwargs).text
[docs] def delete(self, url: str, **kwargs: Any) -> str:
"""DELETE the URL and return the text."""
return self.requests.delete(url, **kwargs).text
[docs] async def aget(self, url: str, **kwargs: Any) -> str:
"""GET the URL and return the text asynchronously."""
async with self.requests.aget(url, **kwargs) as response:
return await response.text()
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|
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return await response.text()
[docs] async def apost(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""POST to the URL and return the text asynchronously."""
async with self.requests.apost(url, data, **kwargs) as response:
return await response.text()
[docs] async def apatch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PATCH the URL and return the text asynchronously."""
async with self.requests.apatch(url, data, **kwargs) as response:
return await response.text()
[docs] async def aput(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PUT the URL and return the text asynchronously."""
async with self.requests.aput(url, data, **kwargs) as response:
return await response.text()
[docs] async def adelete(self, url: str, **kwargs: Any) -> str:
"""DELETE the URL and return the text asynchronously."""
async with self.requests.adelete(url, **kwargs) as response:
return await response.text()
# For backwards compatibility
RequestsWrapper = TextRequestsWrapper
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Source code for langchain.utilities.python
import functools
import logging
import multiprocessing
import sys
from io import StringIO
from typing import Dict, Optional
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
@functools.lru_cache(maxsize=None)
def warn_once() -> None:
"""Warn once about the dangers of PythonREPL."""
logger.warning("Python REPL can execute arbitrary code. Use with caution.")
[docs]class PythonREPL(BaseModel):
"""Simulates a standalone Python REPL."""
globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")
locals: Optional[Dict] = Field(default_factory=dict, alias="_locals")
[docs] @classmethod
def worker(
cls,
command: str,
globals: Optional[Dict],
locals: Optional[Dict],
queue: multiprocessing.Queue,
) -> None:
old_stdout = sys.stdout
sys.stdout = mystdout = StringIO()
try:
exec(command, globals, locals)
sys.stdout = old_stdout
queue.put(mystdout.getvalue())
except Exception as e:
sys.stdout = old_stdout
queue.put(repr(e))
[docs] def run(self, command: str, timeout: Optional[int] = None) -> str:
"""Run command with own globals/locals and returns anything printed.
Timeout after the specified number of seconds."""
# Warn against dangers of PythonREPL
warn_once()
queue: multiprocessing.Queue = multiprocessing.Queue()
# Only use multiprocessing if we are enforcing a timeout
if timeout is not None:
# create a Process
p = multiprocessing.Process(
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# create a Process
p = multiprocessing.Process(
target=self.worker, args=(command, self.globals, self.locals, queue)
)
# start it
p.start()
# wait for the process to finish or kill it after timeout seconds
p.join(timeout)
if p.is_alive():
p.terminate()
return "Execution timed out"
else:
self.worker(command, self.globals, self.locals, queue)
# get the result from the worker function
return queue.get()
|
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|
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|
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, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class BingSearchAPIWrapper(BaseModel):
"""Wrapper for Bing Search API.
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
"""
bing_subscription_key: str
bing_search_url: str
k: int = 10
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def _bing_search_results(self, search_term: str, count: int) -> List[dict]:
headers = {"Ocp-Apim-Subscription-Key": self.bing_subscription_key}
params = {
"q": search_term,
"count": count,
"textDecorations": True,
"textFormat": "HTML",
}
response = requests.get(
self.bing_search_url, headers=headers, params=params # type: ignore
)
response.raise_for_status()
search_results = response.json()
return search_results["webPages"]["value"]
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
bing_subscription_key = get_from_dict_or_env(
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|
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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",
# default="https://api.bing.microsoft.com/v7.0/search",
)
values["bing_search_url"] = bing_search_url
return values
[docs] def run(self, query: str) -> str:
"""Run query through BingSearch and parse result."""
snippets = []
results = self._bing_search_results(query, count=self.k)
if len(results) == 0:
return "No good Bing Search Result was found"
for result in results:
snippets.append(result["snippet"])
return " ".join(snippets)
[docs] def results(self, query: str, num_results: int) -> List[Dict]:
"""Run query through BingSearch 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:
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
"""
metadata_results = []
results = self._bing_search_results(query, count=num_results)
if len(results) == 0:
return [{"Result": "No good Bing Search Result was found"}]
for result in results:
metadata_result = {
"snippet": result["snippet"],
"title": result["name"],
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|
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"snippet": result["snippet"],
"title": result["name"],
"link": result["url"],
}
metadata_results.append(metadata_result)
return metadata_results
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|
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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 around Google Places API.
To use, you should have the ``googlemaps`` python package installed,
**an API key for the google maps platform**,
and the environment variable ''GPLACES_API_KEY''
set with your API key , or pass 'gplaces_api_key'
as a named parameter to the constructor.
By default, this will return the all the results on the input query.
You can use the top_k_results argument to limit the number of results.
Example:
.. code-block:: python
from langchain import GooglePlacesAPIWrapper
gplaceapi = GooglePlacesAPIWrapper()
"""
gplaces_api_key: Optional[str] = None
google_map_client: Any #: :meta private:
top_k_results: Optional[int] = 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 is in your environment variable."""
gplaces_api_key = get_from_dict_or_env(
values, "gplaces_api_key", "GPLACES_API_KEY"
)
values["gplaces_api_key"] = gplaces_api_key
try:
import googlemaps
values["google_map_client"] = googlemaps.Client(gplaces_api_key)
except ImportError:
raise ImportError(
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|
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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 that exists that match."""
search_results = self.google_map_client.places(query)["results"]
num_to_return = len(search_results)
places = []
if num_to_return == 0:
return "Google Places did not find any places that match the description"
num_to_return = (
num_to_return
if self.top_k_results is None
else min(num_to_return, self.top_k_results)
)
for i in range(num_to_return):
result = search_results[i]
details = self.fetch_place_details(result["place_id"])
if details is not None:
places.append(details)
return "\n".join([f"{i+1}. {item}" for i, item in enumerate(places)])
[docs] def fetch_place_details(self, place_id: str) -> Optional[str]:
try:
place_details = self.google_map_client.place(place_id)
place_details["place_id"] = place_id
formatted_details = self.format_place_details(place_details)
return formatted_details
except Exception as e:
logging.error(f"An Error occurred while fetching place details: {e}")
return None
[docs] def format_place_details(self, place_details: Dict[str, Any]) -> Optional[str]:
try:
name = place_details.get("result", {}).get("name", "Unknown")
address = place_details.get("result", {}).get(
"formatted_address", "Unknown"
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|
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"formatted_address", "Unknown"
)
phone_number = place_details.get("result", {}).get(
"formatted_phone_number", "Unknown"
)
website = place_details.get("result", {}).get("website", "Unknown")
place_id = place_details.get("result", {}).get("place_id", "Unknown")
formatted_details = (
f"{name}\nAddress: {address}\n"
f"Google place ID: {place_id}\n"
f"Phone: {phone_number}\nWebsite: {website}\n\n"
)
return formatted_details
except Exception as e:
logging.error(f"An error occurred while formatting place details: {e}")
return None
|
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|
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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.
To use, you should have the ``boto3`` package installed
and a lambda functions built from the AWS Console or
CLI. Set up your AWS credentials with ``aws configure``
Example:
.. code-block:: bash
pip install boto3
aws configure
"""
lambda_client: Any #: :meta private:
"""The configured boto3 client"""
function_name: Optional[str] = None
"""The name of your lambda function"""
awslambda_tool_name: Optional[str] = None
"""If passing to an agent as a tool, the tool name"""
awslambda_tool_description: Optional[str] = None
"""If passing to an agent as a tool, the description"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that python package exists in environment."""
try:
import boto3
except ImportError:
raise ImportError(
"boto3 is not installed. Please install it with `pip install boto3`"
)
values["lambda_client"] = boto3.client("lambda")
values["function_name"] = values["function_name"]
return values
[docs] def run(self, query: str) -> str:
"""
Invokes the lambda function and returns the
result.
Args:
query: an input to passed to the lambda
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|
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|
result.
Args:
query: an input to passed to the lambda
function as the ``body`` of a JSON
object.
""" # noqa: E501
res = self.lambda_client.invoke(
FunctionName=self.function_name,
InvocationType="RequestResponse",
Payload=json.dumps({"body": query}),
)
try:
payload_stream = res["Payload"]
payload_string = payload_stream.read().decode("utf-8")
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:
return f"Result: {answer}"
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Source code for langchain.utilities.wikipedia
"""Util that calls Wikipedia."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
WIKIPEDIA_MAX_QUERY_LENGTH = 300
[docs]class WikipediaAPIWrapper(BaseModel):
"""Wrapper around WikipediaAPI.
To use, you should have the ``wikipedia`` python package installed.
This wrapper will use the Wikipedia API to conduct searches and
fetch page summaries. By default, it will return the page summaries
of the top-k results.
It limits the Document content by doc_content_chars_max.
"""
wiki_client: Any #: :meta private:
top_k_results: int = 3
lang: str = "en"
load_all_available_meta: bool = False
doc_content_chars_max: int = 4000
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in environment."""
try:
import wikipedia
wikipedia.set_lang(values["lang"])
values["wiki_client"] = wikipedia
except ImportError:
raise ImportError(
"Could not import wikipedia python package. "
"Please install it with `pip install wikipedia`."
)
return values
[docs] def run(self, query: str) -> str:
"""Run Wikipedia search and get page summaries."""
page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
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):
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if summary := self._formatted_page_summary(page_title, wiki_page):
summaries.append(summary)
if not summaries:
return "No good Wikipedia Search Result was found"
return "\n\n".join(summaries)[: self.doc_content_chars_max]
@staticmethod
def _formatted_page_summary(page_title: str, wiki_page: Any) -> Optional[str]:
return f"Page: {page_title}\nSummary: {wiki_page.summary}"
def _page_to_document(self, page_title: str, wiki_page: Any) -> Document:
main_meta = {
"title": page_title,
"summary": wiki_page.summary,
"source": wiki_page.url,
}
add_meta = (
{
"categories": wiki_page.categories,
"page_url": wiki_page.url,
"image_urls": wiki_page.images,
"related_titles": wiki_page.links,
"parent_id": wiki_page.parent_id,
"references": wiki_page.references,
"revision_id": wiki_page.revision_id,
"sections": wiki_page.sections,
}
if self.load_all_available_meta
else {}
)
doc = Document(
page_content=wiki_page.content[: self.doc_content_chars_max],
metadata={
**main_meta,
**add_meta,
},
)
return doc
def _fetch_page(self, page: str) -> Optional[str]:
try:
return self.wiki_client.page(title=page, auto_suggest=False)
except (
self.wiki_client.exceptions.PageError,
self.wiki_client.exceptions.DisambiguationError,
):
return None
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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
Returns: a list of documents.
"""
page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
docs = []
for page_title in page_titles[: self.top_k_results]:
if wiki_page := self._fetch_page(page_title):
if doc := self._page_to_document(page_title, wiki_page):
docs.append(doc)
return docs
|
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Source code for langchain.utilities.loading
"""Utilities for loading configurations from langchain-hub."""
import os
import re
import tempfile
from pathlib import Path, PurePosixPath
from typing import Any, Callable, Optional, Set, TypeVar, Union
from urllib.parse import urljoin
import requests
DEFAULT_REF = os.environ.get("LANGCHAIN_HUB_DEFAULT_REF", "master")
URL_BASE = os.environ.get(
"LANGCHAIN_HUB_URL_BASE",
"https://raw.githubusercontent.com/hwchase17/langchain-hub/{ref}/",
)
HUB_PATH_RE = re.compile(r"lc(?P<ref>@[^:]+)?://(?P<path>.*)")
T = TypeVar("T")
[docs]def try_load_from_hub(
path: Union[str, Path],
loader: Callable[[str], T],
valid_prefix: str,
valid_suffixes: Set[str],
**kwargs: Any,
) -> Optional[T]:
"""Load configuration from hub. Returns None if path is not a hub path."""
if not isinstance(path, str) or not (match := HUB_PATH_RE.match(path)):
return None
ref, remote_path_str = match.groups()
ref = ref[1:] if ref else DEFAULT_REF
remote_path = Path(remote_path_str)
if remote_path.parts[0] != valid_prefix:
return None
if remote_path.suffix[1:] not in valid_suffixes:
raise ValueError("Unsupported file type.")
# Using Path with URLs is not recommended, because on Windows
# the backslash is used as the path separator, which can cause issues
# when working with URLs that use forward slashes as the path separator.
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# when working with URLs that use forward slashes as the path separator.
# Instead, use PurePosixPath to ensure that forward slashes are used as the
# path separator, regardless of the operating system.
full_url = urljoin(URL_BASE.format(ref=ref), PurePosixPath(remote_path).__str__())
r = requests.get(full_url, timeout=5)
if r.status_code != 200:
raise ValueError(f"Could not find file at {full_url}")
with tempfile.TemporaryDirectory() as tmpdirname:
file = Path(tmpdirname) / remote_path.name
with open(file, "wb") as f:
f.write(r.content)
return loader(str(file), **kwargs)
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https://api.python.langchain.com/en/latest/_modules/langchain/utilities/loading.html
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Source code for langchain.utilities.duckduckgo_search
"""Util that calls DuckDuckGo Search.
No setup required. Free.
https://pypi.org/project/duckduckgo-search/
"""
from typing import Dict, List, Optional
from pydantic import BaseModel, Extra
from pydantic.class_validators import root_validator
[docs]class DuckDuckGoSearchAPIWrapper(BaseModel):
"""Wrapper for DuckDuckGo Search API.
Free and does not require any setup.
"""
region: Optional[str] = "wt-wt"
safesearch: str = "moderate"
time: Optional[str] = "y"
max_results: int = 5
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that python package exists in environment."""
try:
from duckduckgo_search import DDGS # noqa: F401
except ImportError:
raise ValueError(
"Could not import duckduckgo-search python package. "
"Please install it with `pip install duckduckgo-search`."
)
return values
[docs] def get_snippets(self, query: str) -> List[str]:
"""Run query through DuckDuckGo and return concatenated results."""
from duckduckgo_search import DDGS
with DDGS() as ddgs:
results = ddgs.text(
query,
region=self.region,
safesearch=self.safesearch,
timelimit=self.time,
)
if results is None:
return ["No good DuckDuckGo Search Result was found"]
snippets = []
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return ["No good DuckDuckGo Search Result was found"]
snippets = []
for i, res in enumerate(results, 1):
if res is not None:
snippets.append(res["body"])
if len(snippets) == self.max_results:
break
return snippets
[docs] def run(self, query: str) -> str:
snippets = self.get_snippets(query)
return " ".join(snippets)
[docs] def results(
self, query: str, num_results: int, backend: str = "api"
) -> List[Dict[str, str]]:
"""Run query through DuckDuckGo 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:
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
"""
from duckduckgo_search import DDGS
with DDGS() as ddgs:
results = ddgs.text(
query,
region=self.region,
safesearch=self.safesearch,
timelimit=self.time,
backend=backend,
)
if results is None:
return [{"Result": "No good DuckDuckGo Search Result was found"}]
def to_metadata(result: Dict) -> Dict[str, str]:
if backend == "news":
return {
"date": result["date"],
"title": result["title"],
"snippet": result["body"],
"source": result["source"],
"link": result["url"],
}
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"link": result["url"],
}
return {
"snippet": result["body"],
"title": result["title"],
"link": result["href"],
}
formatted_results = []
for i, res in enumerate(results, 1):
if res is not None:
formatted_results.append(to_metadata(res))
if len(formatted_results) == num_results:
break
return formatted_results
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https://api.python.langchain.com/en/latest/_modules/langchain/utilities/duckduckgo_search.html
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Source code for langchain.utilities.redis
from __future__ import annotations
import logging
from typing import (
TYPE_CHECKING,
Any,
)
from urllib.parse import urlparse
if TYPE_CHECKING:
from redis.client import Redis as RedisType
logger = logging.getLogger(__name__)
[docs]def get_client(redis_url: str, **kwargs: Any) -> RedisType:
"""Get a redis client from the connection url given. This helper accepts
urls for Redis server (TCP with/without TLS or UnixSocket) as well as
Redis Sentinel connections.
Redis Cluster is not supported.
Before creating a connection the existence of the database driver is checked
an and ValueError raised otherwise
To use, you should have the ``redis`` python package installed.
Example:
.. code-block:: python
from langchain.utilities.redis import get_client
redis_client = get_client(
redis_url="redis://username:password@localhost:6379"
index_name="my-index",
embedding_function=embeddings.embed_query,
)
To use a redis replication setup with multiple redis server and redis sentinels
set "redis_url" to "redis+sentinel://" scheme. With this url format a path is
needed holding the name of the redis service within the sentinels to get the
correct redis server connection. The default service name is "mymaster". The
optional second part of the path is the redis db number to connect to.
An optional username or password is used for booth connections to the rediserver
and the sentinel, different passwords for server and sentinel are not supported.
And as another constraint only one sentinel instance can be given:
Example:
.. code-block:: python
from langchain.utilities.redis import get_client
|
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.. code-block:: python
from langchain.utilities.redis import get_client
redis_client = get_client(
redis_url="redis+sentinel://username:password@sentinelhost:26379/mymaster/0"
index_name="my-index",
embedding_function=embeddings.embed_query,
)
"""
# Initialize with necessary components.
try:
import redis
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis>=4.1.0`."
)
# check if normal redis:// or redis+sentinel:// url
if redis_url.startswith("redis+sentinel"):
redis_client = _redis_sentinel_client(redis_url, **kwargs)
if redis_url.startswith("rediss+sentinel"): # sentinel with TLS support enables
kwargs["ssl"] = True
if "ssl_cert_reqs" not in kwargs:
kwargs["ssl_cert_reqs"] = "none"
redis_client = _redis_sentinel_client(redis_url, **kwargs)
else:
# connect to redis server from url
redis_client = redis.from_url(redis_url, **kwargs)
return redis_client
def _redis_sentinel_client(redis_url: str, **kwargs: Any) -> RedisType:
"""helper method to parse an (un-official) redis+sentinel url
and create a Sentinel connection to fetch the final redis client
connection to a replica-master for read-write operations.
If username and/or password for authentication is given the
same credentials are used for the Redis Sentinel as well as Redis Server.
With this implementation using a redis url only it is not possible
to use different data for authentication on booth systems.
"""
|
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|
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|
to use different data for authentication on booth systems.
"""
import redis
parsed_url = urlparse(redis_url)
# sentinel needs list with (host, port) tuple, use default port if none available
sentinel_list = [(parsed_url.hostname or "localhost", parsed_url.port or 26379)]
if parsed_url.path:
# "/mymaster/0" first part is service name, optional second part is db number
path_parts = parsed_url.path.split("/")
service_name = path_parts[1] or "mymaster"
if len(path_parts) > 2:
kwargs["db"] = path_parts[2]
else:
service_name = "mymaster"
sentinel_args = {}
if parsed_url.password:
sentinel_args["password"] = parsed_url.password
kwargs["password"] = parsed_url.password
if parsed_url.username:
sentinel_args["username"] = parsed_url.username
kwargs["username"] = parsed_url.username
# check for all SSL related properties and copy them into sentinel_kwargs too,
# add client_name also
for arg in kwargs:
if arg.startswith("ssl") or arg == "client_name":
sentinel_args[arg] = kwargs[arg]
# sentinel user/pass is part of sentinel_kwargs, user/pass for redis server
# connection as direct parameter in kwargs
sentinel_client = redis.sentinel.Sentinel(
sentinel_list, sentinel_kwargs=sentinel_args, **kwargs
)
# redis server might have password but not sentinel - fetch this error and try
# again without pass, everything else cannot be handled here -> user needed
try:
sentinel_client.execute_command("ping")
except redis.exceptions.AuthenticationError as ae:
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|
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|
sentinel_client.execute_command("ping")
except redis.exceptions.AuthenticationError as ae:
if "no password is set" in ae.args[0]:
logger.warning(
"Redis sentinel connection configured with password but Sentinel \
answered NO PASSWORD NEEDED - Please check Sentinel configuration"
)
sentinel_client = redis.sentinel.Sentinel(sentinel_list, **kwargs)
else:
raise ae
return sentinel_client.master_for(service_name)
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/redis.html
|
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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, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
METAPHOR_API_URL = "https://api.metaphor.systems"
[docs]class MetaphorSearchAPIWrapper(BaseModel):
"""Wrapper for Metaphor Search API."""
metaphor_api_key: str
k: int = 10
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def _metaphor_search_results(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
use_autoprompt: Optional[bool] = None,
) -> List[dict]:
headers = {"X-Api-Key": self.metaphor_api_key}
params = {
"numResults": num_results,
"query": query,
"includeDomains": include_domains,
"excludeDomains": exclude_domains,
"startCrawlDate": start_crawl_date,
"endCrawlDate": end_crawl_date,
"startPublishedDate": start_published_date,
"endPublishedDate": end_published_date,
"useAutoprompt": use_autoprompt,
}
|
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|
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"useAutoprompt": use_autoprompt,
}
response = requests.post(
# type: ignore
f"{METAPHOR_API_URL}/search",
headers=headers,
json=params,
)
response.raise_for_status()
search_results = response.json()
return search_results["results"]
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
metaphor_api_key = get_from_dict_or_env(
values, "metaphor_api_key", "METAPHOR_API_KEY"
)
values["metaphor_api_key"] = metaphor_api_key
return values
[docs] def results(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
use_autoprompt: Optional[bool] = None,
) -> List[Dict]:
"""Run query through Metaphor Search and return metadata.
Args:
query: The query to search for.
num_results: The number of results to return.
include_domains: A list of domains to include in the search. Only one of include_domains and exclude_domains should be defined.
exclude_domains: A list of domains to exclude from the search. Only one of include_domains and exclude_domains should be defined.
|
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|
start_crawl_date: If specified, only pages we crawled after start_crawl_date will be returned.
end_crawl_date: If specified, only pages we crawled before end_crawl_date will be returned.
start_published_date: If specified, only pages published after start_published_date will be returned.
end_published_date: If specified, only pages published before end_published_date will be returned.
use_autoprompt: If true, we turn your query into a more Metaphor-friendly query. Adds latency.
Returns:
A list of dictionaries with the following keys:
title - The title of the page
url - The url
author - Author of the content, if applicable. Otherwise, None.
published_date - Estimated date published
in YYYY-MM-DD format. Otherwise, None.
""" # noqa: E501
raw_search_results = self._metaphor_search_results(
query,
num_results=num_results,
include_domains=include_domains,
exclude_domains=exclude_domains,
start_crawl_date=start_crawl_date,
end_crawl_date=end_crawl_date,
start_published_date=start_published_date,
end_published_date=end_published_date,
use_autoprompt=use_autoprompt,
)
return self._clean_results(raw_search_results)
[docs] async def results_async(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
|
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|
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|
end_published_date: Optional[str] = None,
use_autoprompt: Optional[bool] = None,
) -> List[Dict]:
"""Get results from the Metaphor Search API asynchronously."""
# Function to perform the API call
async def fetch() -> str:
headers = {"X-Api-Key": self.metaphor_api_key}
params = {
"numResults": num_results,
"query": query,
"includeDomains": include_domains,
"excludeDomains": exclude_domains,
"startCrawlDate": start_crawl_date,
"endCrawlDate": end_crawl_date,
"startPublishedDate": start_published_date,
"endPublishedDate": end_published_date,
"useAutoprompt": use_autoprompt,
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{METAPHOR_API_URL}/search", json=params, headers=headers
) as res:
if res.status == 200:
data = await res.text()
return data
else:
raise Exception(f"Error {res.status}: {res.reason}")
results_json_str = await fetch()
results_json = json.loads(results_json_str)
return self._clean_results(results_json["results"])
def _clean_results(self, raw_search_results: List[Dict]) -> List[Dict]:
cleaned_results = []
for result in raw_search_results:
cleaned_results.append(
{
"title": result["title"],
"url": result["url"],
"author": result["author"],
"published_date": result["publishedDate"],
}
)
return cleaned_results
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html
|
d616531287d9-0
|
Source code for langchain.utilities.vertexai
"""Utilities to init Vertex AI."""
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from google.auth.credentials import Credentials
[docs]def raise_vertex_import_error(minimum_expected_version: str = "1.26.1") -> None:
"""Raise ImportError related to Vertex SDK being not available.
Args:
minimum_expected_version: The lowest expected version of the SDK.
Raises:
ImportError: an ImportError that mentions a required version of the SDK.
"""
raise ImportError(
"Could not import VertexAI. Please, install it with "
f"pip install google-cloud-aiplatform>={minimum_expected_version}"
)
[docs]def init_vertexai(
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional["Credentials"] = None,
) -> None:
"""Init vertexai.
Args:
project: The default GCP project to use when making Vertex API calls.
location: The default location to use when making API calls.
credentials: The default custom
credentials to use when making API calls. If not provided credentials
will be ascertained from the environment.
Raises:
ImportError: If importing vertexai SDK did not succeed.
"""
try:
import vertexai
except ImportError:
raise_vertex_import_error()
vertexai.init(
project=project,
location=location,
credentials=credentials,
)
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/vertexai.html
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dba00f94a383-0
|
Source code for langchain.utilities.golden_query
"""Util that calls Golden."""
import json
from typing import Dict, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
GOLDEN_BASE_URL = "https://golden.com"
GOLDEN_TIMEOUT = 5000
[docs]class GoldenQueryAPIWrapper(BaseModel):
"""Wrapper for Golden.
Docs for using:
1. Go to https://golden.com and sign up for an account
2. Get your API Key from https://golden.com/settings/api
3. Save your API Key into GOLDEN_API_KEY env variable
"""
golden_api_key: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
golden_api_key = get_from_dict_or_env(
values, "golden_api_key", "GOLDEN_API_KEY"
)
values["golden_api_key"] = golden_api_key
return values
[docs] def run(self, query: str) -> str:
"""Run query through Golden Query API and return the JSON raw result."""
headers = {"apikey": self.golden_api_key or ""}
response = requests.post(
f"{GOLDEN_BASE_URL}/api/v2/public/queries/",
json={"prompt": query},
headers=headers,
timeout=GOLDEN_TIMEOUT,
)
if response.status_code != 201:
return response.text
content = json.loads(response.content)
query_id = content["id"]
|
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|
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|
content = json.loads(response.content)
query_id = content["id"]
response = requests.get(
(
f"{GOLDEN_BASE_URL}/api/v2/public/queries/{query_id}/results/"
"?pageSize=10"
),
headers=headers,
timeout=GOLDEN_TIMEOUT,
)
return response.text
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/golden_query.html
|
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|
Source code for langchain.utilities.scenexplain
"""Util that calls SceneXplain.
In order to set this up, you need API key for the SceneXplain API.
You can obtain a key by following the steps below.
- Sign up for a free account at https://scenex.jina.ai/.
- Navigate to the API Access page (https://scenex.jina.ai/api) and create a new API key.
"""
from typing import Dict
import requests
from pydantic import BaseModel, BaseSettings, Field, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class SceneXplainAPIWrapper(BaseSettings, BaseModel):
"""Wrapper for SceneXplain API.
In order to set this up, you need API key for the SceneXplain API.
You can obtain a key by following the steps below.
- Sign up for a free account at https://scenex.jina.ai/.
- Navigate to the API Access page (https://scenex.jina.ai/api)
and create a new API key.
"""
scenex_api_key: str = Field(..., env="SCENEX_API_KEY")
scenex_api_url: str = "https://api.scenex.jina.ai/v1/describe"
def _describe_image(self, image: str) -> str:
headers = {
"x-api-key": f"token {self.scenex_api_key}",
"content-type": "application/json",
}
payload = {
"data": [
{
"image": image,
"algorithm": "Ember",
"languages": ["en"],
}
]
}
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"languages": ["en"],
}
]
}
response = requests.post(self.scenex_api_url, headers=headers, json=payload)
response.raise_for_status()
result = response.json().get("result", [])
img = result[0] if result else {}
return img.get("text", "")
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
scenex_api_key = get_from_dict_or_env(
values, "scenex_api_key", "SCENEX_API_KEY"
)
values["scenex_api_key"] = scenex_api_key
return values
[docs] def run(self, image: str) -> str:
"""Run SceneXplain image explainer."""
description = self._describe_image(image)
if not description:
return "No description found."
return description
|
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Source code for langchain.utilities.max_compute
from __future__ import annotations
from typing import TYPE_CHECKING, Iterator, List, Optional
from langchain.utils import get_from_env
if TYPE_CHECKING:
from odps import ODPS
[docs]class MaxComputeAPIWrapper:
"""Interface for querying Alibaba Cloud MaxCompute tables."""
[docs] def __init__(self, client: ODPS):
"""Initialize MaxCompute document loader.
Args:
client: odps.ODPS MaxCompute client object.
"""
self.client = client
[docs] @classmethod
def from_params(
cls,
endpoint: str,
project: str,
*,
access_id: Optional[str] = None,
secret_access_key: Optional[str] = None,
) -> MaxComputeAPIWrapper:
"""Convenience constructor that builds the odsp.ODPS MaxCompute client from
given parameters.
Args:
endpoint: MaxCompute endpoint.
project: A project is a basic organizational unit of MaxCompute, which is
similar to a database.
access_id: MaxCompute access ID. Should be passed in directly or set as the
environment variable `MAX_COMPUTE_ACCESS_ID`.
secret_access_key: MaxCompute secret access key. Should be passed in
directly or set as the environment variable
`MAX_COMPUTE_SECRET_ACCESS_KEY`.
"""
try:
from odps import ODPS
except ImportError as ex:
raise ImportError(
"Could not import pyodps python package. "
"Please install it with `pip install pyodps` or refer to "
"https://pyodps.readthedocs.io/."
) from ex
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"https://pyodps.readthedocs.io/."
) from ex
access_id = access_id or get_from_env("access_id", "MAX_COMPUTE_ACCESS_ID")
secret_access_key = secret_access_key or get_from_env(
"secret_access_key", "MAX_COMPUTE_SECRET_ACCESS_KEY"
)
client = ODPS(
access_id=access_id,
secret_access_key=secret_access_key,
project=project,
endpoint=endpoint,
)
if not client.exist_project(project):
raise ValueError(f'The project "{project}" does not exist.')
return cls(client)
[docs] def lazy_query(self, query: str) -> Iterator[dict]:
# Execute SQL query.
with self.client.execute_sql(query).open_reader() as reader:
if reader.count == 0:
raise ValueError("Table contains no data.")
for record in reader:
yield {k: v for k, v in record}
[docs] def query(self, query: str) -> List[dict]:
return list(self.lazy_query(query))
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Source code for langchain.utilities.dataforseo_api_search
import base64
from typing import Dict, Optional
from urllib.parse import quote
import aiohttp
import requests
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class DataForSeoAPIWrapper(BaseModel):
"""Wrapper around the DataForSeo API."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
default_params: dict = Field(
default={
"location_name": "United States",
"language_code": "en",
"depth": 10,
"se_name": "google",
"se_type": "organic",
}
)
"""Default parameters to use for the DataForSEO SERP API."""
params: dict = Field(default={})
"""Additional parameters to pass to the DataForSEO SERP API."""
api_login: Optional[str] = None
"""The API login to use for the DataForSEO SERP API."""
api_password: Optional[str] = None
"""The API password to use for the DataForSEO SERP API."""
json_result_types: Optional[list] = None
"""The JSON result types."""
json_result_fields: Optional[list] = None
"""The JSON result fields."""
top_count: Optional[int] = None
"""The number of top results to return."""
aiosession: Optional[aiohttp.ClientSession] = None
"""The aiohttp session to use for the DataForSEO SERP API."""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that login and password exists in environment."""
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"""Validate that login and password exists in environment."""
login = get_from_dict_or_env(values, "api_login", "DATAFORSEO_LOGIN")
password = get_from_dict_or_env(values, "api_password", "DATAFORSEO_PASSWORD")
values["api_login"] = login
values["api_password"] = password
return values
[docs] async def arun(self, url: str) -> str:
"""Run request to DataForSEO SERP API and parse result async."""
return self._process_response(await self._aresponse_json(url))
[docs] def run(self, url: str) -> str:
"""Run request to DataForSEO SERP API and parse result async."""
return self._process_response(self._response_json(url))
[docs] def results(self, url: str) -> list:
res = self._response_json(url)
return self._filter_results(res)
[docs] async def aresults(self, url: str) -> list:
res = await self._aresponse_json(url)
return self._filter_results(res)
def _prepare_request(self, keyword: str) -> dict:
"""Prepare the request details for the DataForSEO SERP API."""
if self.api_login is None or self.api_password is None:
raise ValueError("api_login or api_password is not provided")
cred = base64.b64encode(
f"{self.api_login}:{self.api_password}".encode("utf-8")
).decode("utf-8")
headers = {"Authorization": f"Basic {cred}", "Content-Type": "application/json"}
obj = {"keyword": quote(keyword)}
obj = {**obj, **self.default_params, **self.params}
data = [obj]
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data = [obj]
_url = (
f"https://api.dataforseo.com/v3/serp/{obj['se_name']}"
f"/{obj['se_type']}/live/advanced"
)
return {
"url": _url,
"headers": headers,
"data": data,
}
def _check_response(self, response: dict) -> dict:
"""Check the response from the DataForSEO SERP API for errors."""
if response.get("status_code") != 20000:
raise ValueError(
f"Got error from DataForSEO SERP API: {response.get('status_message')}"
)
return response
def _response_json(self, url: str) -> dict:
"""Use requests to run request to DataForSEO SERP API and return results."""
request_details = self._prepare_request(url)
response = requests.post(
request_details["url"],
headers=request_details["headers"],
json=request_details["data"],
)
response.raise_for_status()
return self._check_response(response.json())
async def _aresponse_json(self, url: str) -> dict:
"""Use aiohttp to request DataForSEO SERP API and return results async."""
request_details = self._prepare_request(url)
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.post(
request_details["url"],
headers=request_details["headers"],
json=request_details["data"],
) as response:
res = await response.json()
else:
async with self.aiosession.post(
request_details["url"],
headers=request_details["headers"],
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request_details["url"],
headers=request_details["headers"],
json=request_details["data"],
) as response:
res = await response.json()
return self._check_response(res)
def _filter_results(self, res: dict) -> list:
output = []
types = self.json_result_types if self.json_result_types is not None else []
for task in res.get("tasks", []):
for result in task.get("result", []):
for item in result.get("items", []):
if len(types) == 0 or item.get("type", "") in types:
self._cleanup_unnecessary_items(item)
if len(item) != 0:
output.append(item)
if self.top_count is not None and len(output) >= self.top_count:
break
return output
def _cleanup_unnecessary_items(self, d: dict) -> dict:
fields = self.json_result_fields if self.json_result_fields is not None else []
if len(fields) > 0:
for k, v in list(d.items()):
if isinstance(v, dict):
self._cleanup_unnecessary_items(v)
if len(v) == 0:
del d[k]
elif k not in fields:
del d[k]
if "xpath" in d:
del d["xpath"]
if "position" in d:
del d["position"]
if "rectangle" in d:
del d["rectangle"]
for k, v in list(d.items()):
if isinstance(v, dict):
self._cleanup_unnecessary_items(v)
return d
def _process_response(self, res: dict) -> str:
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return d
def _process_response(self, res: dict) -> str:
"""Process response from DataForSEO SERP API."""
toret = "No good search result found"
for task in res.get("tasks", []):
for result in task.get("result", []):
item_types = result.get("item_types")
items = result.get("items", [])
if "answer_box" in item_types:
toret = next(
item for item in items if item.get("type") == "answer_box"
).get("text")
elif "knowledge_graph" in item_types:
toret = next(
item for item in items if item.get("type") == "knowledge_graph"
).get("description")
elif "featured_snippet" in item_types:
toret = next(
item for item in items if item.get("type") == "featured_snippet"
).get("description")
elif "shopping" in item_types:
toret = next(
item for item in items if item.get("type") == "shopping"
).get("price")
elif "organic" in item_types:
toret = next(
item for item in items if item.get("type") == "organic"
).get("description")
if toret:
break
return toret
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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):
"""Messaging Client using Twilio.
To use, you should have the ``twilio`` python package installed,
and the environment variables ``TWILIO_ACCOUNT_SID``, ``TWILIO_AUTH_TOKEN``, and
``TWILIO_FROM_NUMBER``, or pass `account_sid`, `auth_token`, and `from_number` as
named parameters to the constructor.
Example:
.. code-block:: python
from langchain.utilities.twilio import TwilioAPIWrapper
twilio = TwilioAPIWrapper(
account_sid="ACxxx",
auth_token="xxx",
from_number="+10123456789"
)
twilio.run('test', '+12484345508')
"""
client: Any #: :meta private:
account_sid: Optional[str] = None
"""Twilio account string identifier."""
auth_token: Optional[str] = None
"""Twilio auth token."""
from_number: Optional[str] = None
"""A Twilio phone number in [E.164](https://www.twilio.com/docs/glossary/what-e164)
format, an
[alphanumeric sender ID](https://www.twilio.com/docs/sms/send-messages#use-an-alphanumeric-sender-id),
or a [Channel Endpoint address](https://www.twilio.com/docs/sms/channels#channel-addresses)
that is enabled for the type of message you want to send. Phone numbers or
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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`, this parameter
must be empty.
""" # noqa: E501
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = False
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from twilio.rest import Client
except ImportError:
raise ImportError(
"Could not import twilio python package. "
"Please install it with `pip install twilio`."
)
account_sid = get_from_dict_or_env(values, "account_sid", "TWILIO_ACCOUNT_SID")
auth_token = get_from_dict_or_env(values, "auth_token", "TWILIO_AUTH_TOKEN")
values["from_number"] = get_from_dict_or_env(
values, "from_number", "TWILIO_FROM_NUMBER"
)
values["client"] = Client(account_sid, auth_token)
return values
[docs] def run(self, body: str, to: str) -> str:
"""Run body through Twilio and respond with message sid.
Args:
body: The text of the message you want to send. Can be up to 1,600
characters in length.
to: The destination phone number in
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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 channels.
""" # noqa: E501
message = self.client.messages.create(to, from_=self.from_number, body=body)
return message.sid
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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_dict_or_env
[docs]class HiddenPrints:
"""Context manager to hide prints."""
def __enter__(self) -> None:
"""Open file to pipe stdout to."""
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
def __exit__(self, *_: Any) -> None:
"""Close file that stdout was piped to."""
sys.stdout.close()
sys.stdout = self._original_stdout
[docs]class SerpAPIWrapper(BaseModel):
"""Wrapper around SerpAPI.
To use, you should have the ``google-search-results`` python package installed,
and the environment variable ``SERPAPI_API_KEY`` set with your API key, or pass
`serpapi_api_key` as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.utilities import SerpAPIWrapper
serpapi = SerpAPIWrapper()
"""
search_engine: Any #: :meta private:
params: dict = Field(
default={
"engine": "google",
"google_domain": "google.com",
"gl": "us",
"hl": "en",
}
)
serpapi_api_key: Optional[str] = None
aiosession: Optional[aiohttp.ClientSession] = None
class Config:
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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 package exists in environment."""
serpapi_api_key = get_from_dict_or_env(
values, "serpapi_api_key", "SERPAPI_API_KEY"
)
values["serpapi_api_key"] = serpapi_api_key
try:
from serpapi import GoogleSearch
values["search_engine"] = GoogleSearch
except ImportError:
raise ValueError(
"Could not import serpapi python package. "
"Please install it with `pip install google-search-results`."
)
return values
[docs] async def arun(self, query: str, **kwargs: Any) -> str:
"""Run query through SerpAPI and parse result async."""
return self._process_response(await self.aresults(query))
[docs] def run(self, query: str, **kwargs: Any) -> str:
"""Run query through SerpAPI and parse result."""
return self._process_response(self.results(query))
[docs] def results(self, query: str) -> dict:
"""Run query through SerpAPI and return the raw result."""
params = self.get_params(query)
with HiddenPrints():
search = self.search_engine(params)
res = search.get_dict()
return res
[docs] async def aresults(self, query: str) -> dict:
"""Use aiohttp to run query through SerpAPI and return the results async."""
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"""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.serpapi_api_key
params["output"] = "json"
url = "https://serpapi.com/search"
return url, params
url, params = construct_url_and_params()
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.get(url, params=params) as response:
res = await response.json()
else:
async with self.aiosession.get(url, params=params) as response:
res = await response.json()
return res
[docs] def get_params(self, query: str) -> Dict[str, str]:
"""Get parameters for SerpAPI."""
_params = {
"api_key": self.serpapi_api_key,
"q": query,
}
params = {**self.params, **_params}
return params
@staticmethod
def _process_response(res: dict) -> str:
"""Process response from SerpAPI."""
if "error" in res.keys():
raise ValueError(f"Got error from SerpAPI: {res['error']}")
if "answer_box" in res.keys() and type(res["answer_box"]) == list:
res["answer_box"] = res["answer_box"][0]
if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
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toret = res["answer_box"]["answer"]
elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
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 "game_spotlight" in res["sports_results"].keys()
):
toret = res["sports_results"]["game_spotlight"]
elif (
"shopping_results" in res.keys()
and "title" in res["shopping_results"][0].keys()
):
toret = res["shopping_results"][:3]
elif (
"knowledge_graph" in res.keys()
and "description" in res["knowledge_graph"].keys()
):
toret = res["knowledge_graph"]["description"]
elif "snippet" in res["organic_results"][0].keys():
toret = res["organic_results"][0]["snippet"]
elif "link" in res["organic_results"][0].keys():
toret = res["organic_results"][0]["link"]
elif (
"images_results" in res.keys()
and "thumbnail" in res["images_results"][0].keys()
):
thumbnails = [item["thumbnail"] for item in res["images_results"][:10]]
toret = thumbnails
else:
toret = "No good search result found"
return toret
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Source code for langchain.utilities.brave_search
import json
from typing import List
import requests
from pydantic import BaseModel, Field
from langchain.schema import Document
[docs]class BraveSearchWrapper(BaseModel):
"""Wrapper around the Brave search engine."""
api_key: str
"""The API key to use for the Brave search engine."""
search_kwargs: dict = Field(default_factory=dict)
"""Additional keyword arguments to pass to the search request."""
base_url = "https://api.search.brave.com/res/v1/web/search"
"""The base URL for the Brave search engine."""
[docs] def run(self, query: str) -> str:
"""Query the Brave search engine and return the results as a JSON string.
Args:
query: The query to search for.
Returns: The results as a JSON string.
"""
web_search_results = self._search_request(query=query)
final_results = [
{
"title": item.get("title"),
"link": item.get("url"),
"snippet": item.get("description"),
}
for item in web_search_results
]
return json.dumps(final_results)
[docs] def download_documents(self, query: str) -> List[Document]:
"""Query the Brave search engine and return the results as a list of Documents.
Args:
query: The query to search for.
Returns: The results as a list of Documents.
"""
results = self._search_request(query)
return [
Document(
page_content=item.get("description"),
metadata={"title": item.get("title"), "link": item.get("url")},
)
for item in results
]
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)
for item in results
]
def _search_request(self, query: str) -> List[dict]:
headers = {
"X-Subscription-Token": self.api_key,
"Accept": "application/json",
}
req = requests.PreparedRequest()
params = {**self.search_kwargs, **{"q": query}}
req.prepare_url(self.base_url, params)
if req.url is None:
raise ValueError("prepared url is None, this should not happen")
response = requests.get(req.url, headers=headers)
if not response.ok:
raise Exception(f"HTTP error {response.status_code}")
return response.json().get("web", {}).get("results", [])
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Source code for langchain.utilities.zapier
"""Util that can interact with Zapier NLA.
Full docs here: https://nla.zapier.com/start/
Note: this wrapper currently only implemented the `api_key` auth method for testing
and server-side production use cases (using the developer's connected accounts on
Zapier.com)
For use-cases where LangChain + Zapier NLA is powering a user-facing application, and
LangChain needs access to the end-user's connected accounts on Zapier.com, you'll need
to use oauth. Review the full docs above and reach out to nla@zapier.com for
developer support.
"""
import json
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from requests import Request, Session
from langchain.utils import get_from_dict_or_env
[docs]class ZapierNLAWrapper(BaseModel):
"""Wrapper for Zapier NLA.
Full docs here: https://nla.zapier.com/start/
This wrapper supports both API Key and OAuth Credential auth methods. API Key
is the fastest way to get started using this wrapper.
Call this wrapper with either `zapier_nla_api_key` or
`zapier_nla_oauth_access_token` arguments, or set the `ZAPIER_NLA_API_KEY`
environment variable. If both arguments are set, the Access Token will take
precedence.
For use-cases where LangChain + Zapier NLA is powering a user-facing application,
and LangChain needs access to the end-user's connected accounts on Zapier.com,
you'll need to use OAuth. Review the full docs above to learn how to create
your own provider and generate credentials.
"""
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your own provider and generate credentials.
"""
zapier_nla_api_key: str
zapier_nla_oauth_access_token: str
zapier_nla_api_base: str = "https://nla.zapier.com/api/v1/"
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def _format_headers(self) -> Dict[str, str]:
"""Format headers for requests."""
headers = {
"Accept": "application/json",
"Content-Type": "application/json",
}
if self.zapier_nla_oauth_access_token:
headers.update(
{"Authorization": f"Bearer {self.zapier_nla_oauth_access_token}"}
)
else:
headers.update({"X-API-Key": self.zapier_nla_api_key})
return headers
def _get_session(self) -> Session:
session = requests.Session()
session.headers.update(self._format_headers())
return session
async def _arequest(self, method: str, url: str, **kwargs: Any) -> Dict[str, Any]:
"""Make an async request."""
async with aiohttp.ClientSession(headers=self._format_headers()) as session:
async with session.request(method, url, **kwargs) as response:
response.raise_for_status()
return await response.json()
def _create_action_payload( # type: ignore[no-untyped-def]
self, instructions: str, params: Optional[Dict] = None, preview_only=False
) -> Dict:
"""Create a payload for an action."""
data = params if params else {}
data.update(
{
"instructions": instructions,
}
)
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{
"instructions": instructions,
}
)
if preview_only:
data.update({"preview_only": True})
return data
def _create_action_url(self, action_id: str) -> str:
"""Create a url for an action."""
return self.zapier_nla_api_base + f"exposed/{action_id}/execute/"
def _create_action_request( # type: ignore[no-untyped-def]
self,
action_id: str,
instructions: str,
params: Optional[Dict] = None,
preview_only=False,
) -> Request:
data = self._create_action_payload(instructions, params, preview_only)
return Request(
"POST",
self._create_action_url(action_id),
json=data,
)
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
zapier_nla_api_key_default = None
# If there is a oauth_access_key passed in the values
# we don't need a nla_api_key it can be blank
if "zapier_nla_oauth_access_token" in values:
zapier_nla_api_key_default = ""
else:
values["zapier_nla_oauth_access_token"] = ""
# we require at least one API Key
zapier_nla_api_key = get_from_dict_or_env(
values,
"zapier_nla_api_key",
"ZAPIER_NLA_API_KEY",
zapier_nla_api_key_default,
)
values["zapier_nla_api_key"] = zapier_nla_api_key
return values
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return values
[docs] async def alist(self) -> List[Dict]:
"""Returns a list of all exposed (enabled) actions associated with
current user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{
"id": str,
"description": str,
"params": Dict[str, str]
}]
`params` will always contain an `instructions` key, the only required
param. All others optional and if provided will override any AI guesses
(see "understanding the AI guessing flow" here:
https://nla.zapier.com/api/v1/docs)
"""
response = await self._arequest("GET", self.zapier_nla_api_base + "exposed/")
return response["results"]
[docs] def list(self) -> List[Dict]:
"""Returns a list of all exposed (enabled) actions associated with
current user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{
"id": str,
"description": str,
"params": Dict[str, str]
}]
`params` will always contain an `instructions` key, the only required
param. All others optional and if provided will override any AI guesses
(see "understanding the AI guessing flow" here:
https://nla.zapier.com/docs/using-the-api#ai-guessing)
"""
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"""
session = self._get_session()
try:
response = session.get(self.zapier_nla_api_base + "exposed/")
response.raise_for_status()
except requests.HTTPError as http_err:
if response.status_code == 401:
if self.zapier_nla_oauth_access_token:
raise requests.HTTPError(
f"An unauthorized response occurred. Check that your "
f"access token is correct and doesn't need to be "
f"refreshed. Err: {http_err}"
)
raise requests.HTTPError(
f"An unauthorized response occurred. Check that your api "
f"key is correct. Err: {http_err}"
)
raise http_err
return response.json()["results"]
[docs] def run(
self, action_id: str, instructions: str, params: Optional[Dict] = None
) -> Dict:
"""Executes an action that is identified by action_id, must be exposed
(enabled) by the current user (associated with the set api_key). Change
your exposed actions here: https://nla.zapier.com/demo/start/
The return JSON is guaranteed to be less than ~500 words (350
tokens) making it safe to inject into the prompt of another LLM
call.
"""
session = self._get_session()
request = self._create_action_request(action_id, instructions, params)
response = session.send(session.prepare_request(request))
response.raise_for_status()
return response.json()["result"]
[docs] async def arun(
self, action_id: str, instructions: str, params: Optional[Dict] = None
) -> Dict:
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) -> Dict:
"""Executes an action that is identified by action_id, must be exposed
(enabled) by the current user (associated with the set api_key). Change
your exposed actions here: https://nla.zapier.com/demo/start/
The return JSON is guaranteed to be less than ~500 words (350
tokens) making it safe to inject into the prompt of another LLM
call.
"""
response = await self._arequest(
"POST",
self._create_action_url(action_id),
json=self._create_action_payload(instructions, params),
)
return response["result"]
[docs] def preview(
self, action_id: str, instructions: str, params: Optional[Dict] = None
) -> Dict:
"""Same as run, but instead of actually executing the action, will
instead return a preview of params that have been guessed by the AI in
case you need to explicitly review before executing."""
session = self._get_session()
params = params if params else {}
params.update({"preview_only": True})
request = self._create_action_request(action_id, instructions, params, True)
response = session.send(session.prepare_request(request))
response.raise_for_status()
return response.json()["input_params"]
[docs] async def apreview(
self, action_id: str, instructions: str, params: Optional[Dict] = None
) -> Dict:
"""Same as run, but instead of actually executing the action, will
instead return a preview of params that have been guessed by the AI in
case you need to explicitly review before executing."""
response = await self._arequest(
"POST",
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response = await self._arequest(
"POST",
self._create_action_url(action_id),
json=self._create_action_payload(instructions, params, preview_only=True),
)
return response["result"]
[docs] def run_as_str(self, *args, **kwargs) -> str: # type: ignore[no-untyped-def]
"""Same as run, but returns a stringified version of the JSON for
insertting back into an LLM."""
data = self.run(*args, **kwargs)
return json.dumps(data)
[docs] async def arun_as_str(self, *args, **kwargs) -> str: # type: ignore[no-untyped-def]
"""Same as run, but returns a stringified version of the JSON for
insertting back into an LLM."""
data = await self.arun(*args, **kwargs)
return json.dumps(data)
[docs] def preview_as_str(self, *args, **kwargs) -> str: # type: ignore[no-untyped-def]
"""Same as preview, but returns a stringified version of the JSON for
insertting back into an LLM."""
data = self.preview(*args, **kwargs)
return json.dumps(data)
[docs] async def apreview_as_str( # type: ignore[no-untyped-def]
self, *args, **kwargs
) -> str:
"""Same as preview, but returns a stringified version of the JSON for
insertting back into an LLM."""
data = await self.apreview(*args, **kwargs)
return json.dumps(data)
[docs] def list_as_str(self) -> str: # type: ignore[no-untyped-def]
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"""Same as list, but returns a stringified version of the JSON for
insertting back into an LLM."""
actions = self.list()
return json.dumps(actions)
[docs] async def alist_as_str(self) -> str: # type: ignore[no-untyped-def]
"""Same as list, but returns a stringified version of the JSON for
insertting back into an LLM."""
actions = await self.alist()
return json.dumps(actions)
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Source code for langchain.utilities.bash
"""Wrapper around subprocess to run commands."""
from __future__ import annotations
import platform
import re
import subprocess
from typing import TYPE_CHECKING, List, Union
from uuid import uuid4
if TYPE_CHECKING:
import pexpect
[docs]class BashProcess:
"""
Wrapper class for starting subprocesses.
Uses the python built-in subprocesses.run()
Persistent processes are **not** available
on Windows systems, as pexpect makes use of
Unix pseudoterminals (ptys). MacOS and Linux
are okay.
Example:
.. code-block:: python
from langchain.utilities.bash import BashProcess
bash = BashProcess(
strip_newlines = False,
return_err_output = False,
persistent = False
)
bash.run('echo \'hello world\'')
"""
strip_newlines: bool = False
"""Whether or not to run .strip() on the output"""
return_err_output: bool = False
"""Whether or not to return the output of a failed
command, or just the error message and stacktrace"""
persistent: bool = False
"""Whether or not to spawn a persistent session
NOTE: Unavailable for Windows environments"""
[docs] def __init__(
self,
strip_newlines: bool = False,
return_err_output: bool = False,
persistent: bool = False,
):
"""
Initializes with default settings
"""
self.strip_newlines = strip_newlines
self.return_err_output = return_err_output
self.prompt = ""
self.process = None
if persistent:
self.prompt = str(uuid4())
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self.process = None
if persistent:
self.prompt = str(uuid4())
self.process = self._initialize_persistent_process(self, self.prompt)
@staticmethod
def _lazy_import_pexpect() -> pexpect:
"""Import pexpect only when needed."""
if platform.system() == "Windows":
raise ValueError(
"Persistent bash processes are not yet supported on Windows."
)
try:
import pexpect
except ImportError:
raise ImportError(
"pexpect required for persistent bash processes."
" To install, run `pip install pexpect`."
)
return pexpect
@staticmethod
def _initialize_persistent_process(self: BashProcess, prompt: str) -> pexpect.spawn:
# Start bash in a clean environment
# Doesn't work on windows
"""
Initializes a persistent bash setting in a
clean environment.
NOTE: Unavailable on Windows
Args:
Prompt(str): the bash command to execute
""" # noqa: E501
pexpect = self._lazy_import_pexpect()
process = pexpect.spawn(
"env", ["-i", "bash", "--norc", "--noprofile"], encoding="utf-8"
)
# Set the custom prompt
process.sendline("PS1=" + prompt)
process.expect_exact(prompt, timeout=10)
return process
[docs] def run(self, commands: Union[str, List[str]]) -> str:
"""
Run commands in either an existing persistent
subprocess or on in a new subprocess environment.
Args:
commands(List[str]): a list of commands to
execute in the session
""" # noqa: E501
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execute in the session
""" # noqa: E501
if isinstance(commands, str):
commands = [commands]
commands = ";".join(commands)
if self.process is not None:
return self._run_persistent(
commands,
)
else:
return self._run(commands)
def _run(self, command: str) -> str:
"""
Runs a command in a subprocess and returns
the output.
Args:
command: The command to run
""" # noqa: E501
try:
output = subprocess.run(
command,
shell=True,
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
).stdout.decode()
except subprocess.CalledProcessError as error:
if self.return_err_output:
return error.stdout.decode()
return str(error)
if self.strip_newlines:
output = output.strip()
return output
[docs] def process_output(self, output: str, command: str) -> str:
"""
Uses regex to remove the command from the output
Args:
output: a process' output string
command: the executed command
""" # noqa: E501
pattern = re.escape(command) + r"\s*\n"
output = re.sub(pattern, "", output, count=1)
return output.strip()
def _run_persistent(self, command: str) -> str:
"""
Runs commands in a persistent environment
and returns the output.
Args:
command: the command to execute
""" # noqa: E501
pexpect = self._lazy_import_pexpect()
if self.process is None:
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pexpect = self._lazy_import_pexpect()
if self.process is None:
raise ValueError("Process not initialized")
self.process.sendline(command)
# Clear the output with an empty string
self.process.expect(self.prompt, timeout=10)
self.process.sendline("")
try:
self.process.expect([self.prompt, pexpect.EOF], timeout=10)
except pexpect.TIMEOUT:
return f"Timeout error while executing command {command}"
if self.process.after == pexpect.EOF:
return f"Exited with error status: {self.process.exitstatus}"
output = self.process.before
output = self.process_output(output, command)
if self.strip_newlines:
return output.strip()
return output
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Source code for langchain.utilities.jira
"""Util that calls Jira."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
# TODO: think about error handling, more specific api specs, and jql/project limits
[docs]class JiraAPIWrapper(BaseModel):
"""Wrapper for Jira API."""
jira: Any #: :meta private:
confluence: Any
jira_username: Optional[str] = None
jira_api_token: Optional[str] = None
jira_instance_url: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
jira_username = get_from_dict_or_env(values, "jira_username", "JIRA_USERNAME")
values["jira_username"] = jira_username
jira_api_token = get_from_dict_or_env(
values, "jira_api_token", "JIRA_API_TOKEN"
)
values["jira_api_token"] = jira_api_token
jira_instance_url = get_from_dict_or_env(
values, "jira_instance_url", "JIRA_INSTANCE_URL"
)
values["jira_instance_url"] = jira_instance_url
try:
from atlassian import Confluence, Jira
except ImportError:
raise ImportError(
"atlassian-python-api is not installed. "
"Please install it with `pip install atlassian-python-api`"
)
jira = Jira(
url=jira_instance_url,
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)
jira = Jira(
url=jira_instance_url,
username=jira_username,
password=jira_api_token,
cloud=True,
)
confluence = Confluence(
url=jira_instance_url,
username=jira_username,
password=jira_api_token,
cloud=True,
)
values["jira"] = jira
values["confluence"] = confluence
return values
[docs] def parse_issues(self, issues: Dict) -> List[dict]:
parsed = []
for issue in issues["issues"]:
key = issue["key"]
summary = issue["fields"]["summary"]
created = issue["fields"]["created"][0:10]
priority = issue["fields"]["priority"]["name"]
status = issue["fields"]["status"]["name"]
try:
assignee = issue["fields"]["assignee"]["displayName"]
except Exception:
assignee = "None"
rel_issues = {}
for related_issue in issue["fields"]["issuelinks"]:
if "inwardIssue" in related_issue.keys():
rel_type = related_issue["type"]["inward"]
rel_key = related_issue["inwardIssue"]["key"]
rel_summary = related_issue["inwardIssue"]["fields"]["summary"]
if "outwardIssue" in related_issue.keys():
rel_type = related_issue["type"]["outward"]
rel_key = related_issue["outwardIssue"]["key"]
rel_summary = related_issue["outwardIssue"]["fields"]["summary"]
rel_issues = {"type": rel_type, "key": rel_key, "summary": rel_summary}
parsed.append(
{
"key": key,
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parsed.append(
{
"key": key,
"summary": summary,
"created": created,
"assignee": assignee,
"priority": priority,
"status": status,
"related_issues": rel_issues,
}
)
return parsed
[docs] def parse_projects(self, projects: List[dict]) -> List[dict]:
parsed = []
for project in projects:
id = project["id"]
key = project["key"]
name = project["name"]
type = project["projectTypeKey"]
style = project["style"]
parsed.append(
{"id": id, "key": key, "name": name, "type": type, "style": style}
)
return parsed
[docs] def search(self, query: str) -> str:
issues = self.jira.jql(query)
parsed_issues = self.parse_issues(issues)
parsed_issues_str = (
"Found " + str(len(parsed_issues)) + " issues:\n" + str(parsed_issues)
)
return parsed_issues_str
[docs] def project(self) -> str:
projects = self.jira.projects()
parsed_projects = self.parse_projects(projects)
parsed_projects_str = (
"Found " + str(len(parsed_projects)) + " projects:\n" + str(parsed_projects)
)
return parsed_projects_str
[docs] def issue_create(self, query: str) -> str:
try:
import json
except ImportError:
raise ImportError(
"json is not installed. Please install it with `pip install json`"
)
params = json.loads(query)
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)
params = json.loads(query)
return self.jira.issue_create(fields=dict(params))
[docs] def page_create(self, query: str) -> str:
try:
import json
except ImportError:
raise ImportError(
"json is not installed. Please install it with `pip install json`"
)
params = json.loads(query)
return self.confluence.create_page(**dict(params))
[docs] def other(self, query: str) -> str:
try:
import json
except ImportError:
raise ImportError(
"json is not installed. Please install it with `pip install json`"
)
params = json.loads(query)
jira_function = getattr(self.jira, params["function"])
return jira_function(*params.get("args", []), **params.get("kwargs", {}))
[docs] def run(self, mode: str, query: str) -> str:
if mode == "jql":
return self.search(query)
elif mode == "get_projects":
return self.project()
elif mode == "create_issue":
return self.issue_create(query)
elif mode == "other":
return self.other(query)
elif mode == "create_page":
return self.page_create(query)
else:
raise ValueError(f"Got unexpected mode {mode}")
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Source code for langchain.utilities.github
"""Util that calls GitHub."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
from github.Issue import Issue
[docs]class GitHubAPIWrapper(BaseModel):
"""Wrapper for GitHub API."""
github: Any #: :meta private:
github_repo_instance: Any #: :meta private:
github_repository: Optional[str] = None
github_app_id: Optional[str] = None
github_app_private_key: Optional[str] = None
github_branch: Optional[str] = None
github_base_branch: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
github_repository = get_from_dict_or_env(
values, "github_repository", "GITHUB_REPOSITORY"
)
github_app_id = get_from_dict_or_env(values, "github_app_id", "GITHUB_APP_ID")
github_app_private_key = get_from_dict_or_env(
values, "github_app_private_key", "GITHUB_APP_PRIVATE_KEY"
)
github_branch = get_from_dict_or_env(
values, "github_branch", "GITHUB_BRANCH", default="master"
)
github_base_branch = get_from_dict_or_env(
values, "github_base_branch", "GITHUB_BASE_BRANCH", default="master"
)
try:
from github import Auth, GithubIntegration
except ImportError:
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try:
from github import Auth, GithubIntegration
except ImportError:
raise ImportError(
"PyGithub is not installed. "
"Please install it with `pip install PyGithub`"
)
with open(github_app_private_key, "r") as f:
private_key = f.read()
auth = Auth.AppAuth(
github_app_id,
private_key,
)
gi = GithubIntegration(auth=auth)
installation = gi.get_installations()[0]
# create a GitHub instance:
g = installation.get_github_for_installation()
values["github"] = g
values["github_repo_instance"] = g.get_repo(github_repository)
values["github_repository"] = github_repository
values["github_app_id"] = github_app_id
values["github_app_private_key"] = github_app_private_key
values["github_branch"] = github_branch
values["github_base_branch"] = github_base_branch
return values
[docs] def parse_issues(self, issues: List[Issue]) -> List[dict]:
"""
Extracts title and number from each Issue and puts them in a dictionary
Parameters:
issues(List[Issue]): A list of Github Issue objects
Returns:
List[dict]: A dictionary of issue titles and numbers
"""
parsed = []
for issue in issues:
title = issue.title
number = issue.number
parsed.append({"title": title, "number": number})
return parsed
[docs] def get_issues(self) -> str:
"""
Fetches all open issues from the repo
Returns:
str: A plaintext report containing the number of issues
and each issue's title and number.
"""
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and each issue's title and number.
"""
issues = self.github_repo_instance.get_issues(state="open")
if issues.totalCount > 0:
parsed_issues = self.parse_issues(issues)
parsed_issues_str = (
"Found " + str(len(parsed_issues)) + " issues:\n" + str(parsed_issues)
)
return parsed_issues_str
else:
return "No open issues available"
[docs] def get_issue(self, issue_number: int) -> Dict[str, Any]:
"""
Fetches a specific issue and its first 10 comments
Parameters:
issue_number(int): The number for the github issue
Returns:
dict: A doctionary containing the issue's title,
body, and comments as a string
"""
issue = self.github_repo_instance.get_issue(number=issue_number)
page = 0
comments: List[dict] = []
while len(comments) <= 10:
comments_page = issue.get_comments().get_page(page)
if len(comments_page) == 0:
break
for comment in comments_page:
comments.append({"body": comment.body, "user": comment.user.login})
page += 1
return {
"title": issue.title,
"body": issue.body,
"comments": str(comments),
}
[docs] def create_pull_request(self, pr_query: str) -> str:
"""
Makes a pull request from the bot's branch to the base branch
Parameters:
pr_query(str): a string which contains the PR title
and the PR body. The title is the first line
in the string, and the body are the rest of the string.
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in the string, and the body are the rest of the string.
For example, "Updated README\nmade changes to add info"
Returns:
str: A success or failure message
"""
if self.github_base_branch == self.github_branch:
return """Cannot make a pull request because
commits are already in the master branch"""
else:
try:
title = pr_query.split("\n")[0]
body = pr_query[len(title) + 2 :]
pr = self.github_repo_instance.create_pull(
title=title,
body=body,
head=self.github_branch,
base=self.github_base_branch,
)
return f"Successfully created PR number {pr.number}"
except Exception as e:
return "Unable to make pull request due to error:\n" + str(e)
[docs] def comment_on_issue(self, comment_query: str) -> str:
"""
Adds a comment to a github issue
Parameters:
comment_query(str): a string which contains the issue number,
two newlines, and the comment.
for example: "1\n\nWorking on it now"
adds the comment "working on it now" to issue 1
Returns:
str: A success or failure message
"""
issue_number = int(comment_query.split("\n\n")[0])
comment = comment_query[len(str(issue_number)) + 2 :]
try:
issue = self.github_repo_instance.get_issue(number=issue_number)
issue.create_comment(comment)
return "Commented on issue " + str(issue_number)
except Exception as e:
return "Unable to make comment due to error:\n" + str(e)
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/github.html
|
09de49cb97ce-4
|
return "Unable to make comment due to error:\n" + str(e)
[docs] def create_file(self, file_query: str) -> str:
"""
Creates a new file on the Github repo
Parameters:
file_query(str): a string which contains the file path
and the file contents. The file path is the first line
in the string, and the contents are the rest of the string.
For example, "hello_world.md\n# Hello World!"
Returns:
str: A success or failure message
"""
file_path = file_query.split("\n")[0]
file_contents = file_query[len(file_path) + 2 :]
try:
exists = self.github_repo_instance.get_contents(file_path)
if exists is None:
self.github_repo_instance.create_file(
path=file_path,
message="Create " + file_path,
content=file_contents,
branch=self.github_branch,
)
return "Created file " + file_path
else:
return f"File already exists at {file_path}. Use update_file instead"
except Exception as e:
return "Unable to make file due to error:\n" + str(e)
[docs] def read_file(self, file_path: str) -> str:
"""
Reads a file from the github repo
Parameters:
file_path(str): the file path
Returns:
str: The file decoded as a string
"""
file = self.github_repo_instance.get_contents(file_path)
return file.decoded_content.decode("utf-8")
[docs] def update_file(self, file_query: str) -> str:
"""
Updates a file with new content.
Parameters:
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/github.html
|
09de49cb97ce-5
|
"""
Updates a file with new content.
Parameters:
file_query(str): Contains the file path and the file contents.
The old file contents is wrapped in OLD <<<< and >>>> OLD
The new file contents is wrapped in NEW <<<< and >>>> NEW
For example:
/test/hello.txt
OLD <<<<
Hello Earth!
>>>> OLD
NEW <<<<
Hello Mars!
>>>> NEW
Returns:
A success or failure message
"""
try:
file_path = file_query.split("\n")[0]
old_file_contents = (
file_query.split("OLD <<<<")[1].split(">>>> OLD")[0].strip()
)
new_file_contents = (
file_query.split("NEW <<<<")[1].split(">>>> NEW")[0].strip()
)
file_content = self.read_file(file_path)
updated_file_content = file_content.replace(
old_file_contents, new_file_contents
)
if file_content == updated_file_content:
return (
"File content was not updated because old content was not found."
"It may be helpful to use the read_file action to get "
"the current file contents."
)
self.github_repo_instance.update_file(
path=file_path,
message="Update " + file_path,
content=updated_file_content,
branch=self.github_branch,
sha=self.github_repo_instance.get_contents(file_path).sha,
)
return "Updated file " + file_path
except Exception as e:
return "Unable to update file due to error:\n" + str(e)
[docs] def delete_file(self, file_path: str) -> str:
"""
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/github.html
|
09de49cb97ce-6
|
"""
Deletes a file from the repo
Parameters:
file_path(str): Where the file is
Returns:
str: Success or failure message
"""
try:
file = self.github_repo_instance.get_contents(file_path)
self.github_repo_instance.delete_file(
path=file_path,
message="Delete " + file_path,
branch=self.github_branch,
sha=file.sha,
)
return "Deleted file " + file_path
except Exception as e:
return "Unable to delete file due to error:\n" + str(e)
[docs] def run(self, mode: str, query: str) -> str:
if mode == "get_issues":
return self.get_issues()
elif mode == "get_issue":
return json.dumps(self.get_issue(int(query)))
elif mode == "comment_on_issue":
return self.comment_on_issue(query)
elif mode == "create_file":
return self.create_file(query)
elif mode == "create_pull_request":
return self.create_pull_request(query)
elif mode == "read_file":
return self.read_file(query)
elif mode == "update_file":
return self.update_file(query)
elif mode == "delete_file":
return self.delete_file(query)
else:
raise ValueError("Invalid mode" + mode)
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/github.html
|
1396f7bbe358-0
|
Source code for langchain.utilities.openweathermap
"""Util that calls OpenWeatherMap using PyOWM."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class OpenWeatherMapAPIWrapper(BaseModel):
"""Wrapper for OpenWeatherMap API using PyOWM.
Docs for using:
1. Go to OpenWeatherMap and sign up for an API key
2. Save your API KEY into OPENWEATHERMAP_API_KEY env variable
3. pip install pyowm
"""
owm: Any
openweathermap_api_key: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
openweathermap_api_key = get_from_dict_or_env(
values, "openweathermap_api_key", "OPENWEATHERMAP_API_KEY"
)
try:
import pyowm
except ImportError:
raise ImportError(
"pyowm is not installed. Please install it with `pip install pyowm`"
)
owm = pyowm.OWM(openweathermap_api_key)
values["owm"] = owm
return values
def _format_weather_info(self, location: str, w: Any) -> str:
detailed_status = w.detailed_status
wind = w.wind()
humidity = w.humidity
temperature = w.temperature("celsius")
rain = w.rain
heat_index = w.heat_index
clouds = w.clouds
return (
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/openweathermap.html
|
1396f7bbe358-1
|
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"Temperature: \n"
f" - Current: {temperature['temp']}°C\n"
f" - High: {temperature['temp_max']}°C\n"
f" - Low: {temperature['temp_min']}°C\n"
f" - Feels like: {temperature['feels_like']}°C\n"
f"Rain: {rain}\n"
f"Heat index: {heat_index}\n"
f"Cloud cover: {clouds}%"
)
[docs] def run(self, location: str) -> str:
"""Get the current weather information for a specified location."""
mgr = self.owm.weather_manager()
observation = mgr.weather_at_place(location)
w = observation.weather
return self._format_weather_info(location, w)
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/openweathermap.html
|
d95854e69880-0
|
Source code for langchain.utilities.portkey
import json
import os
from typing import Dict, Optional
[docs]class Portkey:
base = "https://api.portkey.ai/v1/proxy"
[docs] @staticmethod
def Config(
api_key: str,
trace_id: Optional[str] = None,
environment: Optional[str] = None,
user: Optional[str] = None,
organisation: Optional[str] = None,
prompt: Optional[str] = None,
retry_count: Optional[int] = None,
cache: Optional[str] = None,
cache_force_refresh: Optional[str] = None,
cache_age: Optional[int] = None,
) -> Dict[str, str]:
assert retry_count is None or retry_count in range(
1, 6
), "retry_count must be an integer and in range [1, 2, 3, 4, 5]"
assert cache is None or cache in [
"simple",
"semantic",
], "cache must be 'simple' or 'semantic'"
assert cache_force_refresh is None or (
isinstance(cache_force_refresh, str)
and cache_force_refresh in ["True", "False"]
), "cache_force_refresh must be 'True' or 'False'"
assert cache_age is None or isinstance(
cache_age, int
), "cache_age must be an integer"
os.environ["OPENAI_API_BASE"] = Portkey.base
headers = {
"x-portkey-api-key": api_key,
"x-portkey-mode": "proxy openai",
}
if trace_id:
headers["x-portkey-trace-id"] = trace_id
if retry_count:
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/portkey.html
|
d95854e69880-1
|
headers["x-portkey-trace-id"] = trace_id
if retry_count:
headers["x-portkey-retry-count"] = str(retry_count)
if cache:
headers["x-portkey-cache"] = cache
if cache_force_refresh:
headers["x-portkey-cache-force-refresh"] = cache_force_refresh
if cache_age:
headers["Cache-Control"] = f"max-age:{str(cache_age)}"
metadata = {}
if environment:
metadata["_environment"] = environment
if user:
metadata["_user"] = user
if organisation:
metadata["_organisation"] = organisation
if prompt:
metadata["_prompt"] = prompt
if metadata:
headers.update({"x-portkey-metadata": json.dumps(metadata)})
return headers
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/portkey.html
|
d25444482d96-0
|
Source code for langchain.utilities.tensorflow_datasets
import logging
from typing import Any, Callable, Dict, Iterator, List, Optional
from pydantic import BaseModel, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
[docs]class TensorflowDatasets(BaseModel):
"""Access to the TensorFlow Datasets.
The Current implementation can work only with datasets that fit in a memory.
`TensorFlow Datasets` is a collection of datasets ready to use, with TensorFlow
or other Python ML frameworks, such as Jax. All datasets are exposed
as `tf.data.Datasets`.
To get started see the Guide: https://www.tensorflow.org/datasets/overview and
the list of datasets: https://www.tensorflow.org/datasets/catalog/
overview#all_datasets
You have to provide the sample_to_document_function: a function that
a sample from the dataset-specific format to the Document.
Attributes:
dataset_name: the name of the dataset to load
split_name: the name of the split to load. Defaults to "train".
load_max_docs: a limit to the number of loaded documents. Defaults to 100.
sample_to_document_function: a function that converts a dataset sample
to a Document
Example:
.. code-block:: python
from langchain.utilities import TensorflowDatasets
def mlqaen_example_to_document(example: dict) -> Document:
return Document(
page_content=decode_to_str(example["context"]),
metadata={
"id": decode_to_str(example["id"]),
"title": decode_to_str(example["title"]),
"question": decode_to_str(example["question"]),
"answer": decode_to_str(example["answers"]["text"][0]),
},
)
|
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/tensorflow_datasets.html
|
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