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ef517a05d19f-0
|
Source code for langchain.tools.youtube.search
"""
Adapted from https://github.com/venuv/langchain_yt_tools
CustomYTSearchTool searches YouTube videos related to a person
and returns a specified number of video URLs.
Input to this tool should be a comma separated list,
- the first part contains a person name
- and the second(optional) a number that is the
maximum number of video results to return
"""
import json
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools import BaseTool
[docs]class YouTubeSearchTool(BaseTool):
name = "youtube_search"
description = (
"search for youtube videos associated with a person. "
"the input to this tool should be a comma separated list, "
"the first part contains a person name and the second a "
"number that is the maximum number of video results "
"to return aka num_results. the second part is optional"
)
def _search(self, person: str, num_results: int) -> str:
from youtube_search import YoutubeSearch
results = YoutubeSearch(person, num_results).to_json()
data = json.loads(results)
url_suffix_list = [video["url_suffix"] for video in data["videos"]]
return str(url_suffix_list)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
values = query.split(",")
person = values[0]
if len(values) > 1:
num_results = int(values[1])
else:
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/youtube/search.html
|
ef517a05d19f-1
|
num_results = int(values[1])
else:
num_results = 2
return self._search(person, num_results)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("YouTubeSearchTool does not yet support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/youtube/search.html
|
160c2293fad8-0
|
Source code for langchain.tools.openweathermap.tool
"""Tool for the OpenWeatherMap API."""
from typing import Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities import OpenWeatherMapAPIWrapper
[docs]class OpenWeatherMapQueryRun(BaseTool):
"""Tool that adds the capability to query using the OpenWeatherMap API."""
api_wrapper: OpenWeatherMapAPIWrapper = Field(
default_factory=OpenWeatherMapAPIWrapper
)
name = "OpenWeatherMap"
description = (
"A wrapper around OpenWeatherMap API. "
"Useful for fetching current weather information for a specified location. "
"Input should be a location string (e.g. London,GB)."
)
def _run(
self, location: str, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
"""Use the OpenWeatherMap tool."""
return self.api_wrapper.run(location)
async def _arun(
self,
location: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the OpenWeatherMap tool asynchronously."""
raise NotImplementedError("OpenWeatherMapQueryRun does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/openweathermap/tool.html
|
c4cf14d4b3b6-0
|
Source code for langchain.tools.zapier.tool
"""## Zapier Natural Language Actions API
\
Full docs here: https://nla.zapier.com/api/v1/docs
**Zapier Natural Language Actions** gives you access to the 5k+ apps, 20k+ actions
on Zapier's platform through a natural language API interface.
NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets,
Microsoft Teams, and thousands more apps: https://zapier.com/apps
Zapier NLA handles ALL the underlying API auth and translation from
natural language --> underlying API call --> return simplified output for LLMs
The key idea is you, or your users, expose a set of actions via an oauth-like setup
window, which you can then query and execute via a REST API.
NLA offers both API Key and OAuth for signing NLA API requests.
1. Server-side (API Key): for quickly getting started, testing, and production scenarios
where LangChain will only use actions exposed in the developer's Zapier account
(and will use the developer's connected accounts on Zapier.com)
2. User-facing (Oauth): for production scenarios where you are deploying an end-user
facing application and LangChain needs access to end-user's exposed actions and
connected accounts on Zapier.com
This quick start will focus on the server-side use case for brevity.
Review [full docs](https://nla.zapier.com/api/v1/docs) or reach out to
[email protected] for user-facing oauth developer support.
Typically, you'd use SequentialChain, here's a basic example:
1. Use NLA to find an email in Gmail
2. Use LLMChain to generate a draft reply to (1)
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/zapier/tool.html
|
c4cf14d4b3b6-1
|
2. Use LLMChain to generate a draft reply to (1)
3. Use NLA to send the draft reply (2) to someone in Slack via direct message
In code, below:
```python
import os
# get from https://platform.openai.com/
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")
# get from https://nla.zapier.com/demo/provider/debug
# (under User Information, after logging in):
os.environ["ZAPIER_NLA_API_KEY"] = os.environ.get("ZAPIER_NLA_API_KEY", "")
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.utilities.zapier import ZapierNLAWrapper
## step 0. expose gmail 'find email' and slack 'send channel message' actions
# first go here, log in, expose (enable) the two actions:
# https://nla.zapier.com/demo/start
# -- for this example, can leave all fields "Have AI guess"
# in an oauth scenario, you'd get your own <provider> id (instead of 'demo')
# which you route your users through first
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
## To leverage a nla_oauth_access_token you may pass the value to the ZapierNLAWrapper
## If you do this there is no need to initialize the ZAPIER_NLA_API_KEY env variable
# zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token="TOKEN_HERE")
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(
toolkit.get_tools(),
|
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|
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|
agent = initialize_agent(
toolkit.get_tools(),
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
agent.run(("Summarize the last email I received regarding Silicon Valley Bank. "
"Send the summary to the #test-zapier channel in slack."))
```
"""
from typing import Any, Dict, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.zapier.prompt import BASE_ZAPIER_TOOL_PROMPT
from langchain.utilities.zapier import ZapierNLAWrapper
[docs]class ZapierNLARunAction(BaseTool):
"""
Args:
action_id: a specific action ID (from list actions) of the action to execute
(the set api_key must be associated with the action owner)
instructions: a natural language instruction string for using the action
(eg. "get the latest email from Mike Knoop" for "Gmail: find email" action)
params: a dict, optional. Any params provided will *override* AI guesses
from `instructions` (see "understanding the AI guessing flow" here:
https://nla.zapier.com/api/v1/docs)
"""
api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)
action_id: str
params: Optional[dict] = None
base_prompt: str = BASE_ZAPIER_TOOL_PROMPT
zapier_description: str
params_schema: Dict[str, str] = Field(default_factory=dict)
name = ""
description = ""
@root_validator
|
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|
c4cf14d4b3b6-3
|
name = ""
description = ""
@root_validator
def set_name_description(cls, values: Dict[str, Any]) -> Dict[str, Any]:
zapier_description = values["zapier_description"]
params_schema = values["params_schema"]
if "instructions" in params_schema:
del params_schema["instructions"]
# Ensure base prompt (if overrided) contains necessary input fields
necessary_fields = {"{zapier_description}", "{params}"}
if not all(field in values["base_prompt"] for field in necessary_fields):
raise ValueError(
"Your custom base Zapier prompt must contain input fields for "
"{zapier_description} and {params}."
)
values["name"] = zapier_description
values["description"] = values["base_prompt"].format(
zapier_description=zapier_description,
params=str(list(params_schema.keys())),
)
return values
def _run(
self, instructions: str, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
"""Use the Zapier NLA tool to return a list of all exposed user actions."""
return self.api_wrapper.run_as_str(self.action_id, instructions, self.params)
async def _arun(
self,
_: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the Zapier NLA tool to return a list of all exposed user actions."""
raise NotImplementedError("ZapierNLAListActions does not support async")
ZapierNLARunAction.__doc__ = (
ZapierNLAWrapper.run.__doc__ + ZapierNLARunAction.__doc__ # type: ignore
)
|
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|
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|
)
# other useful actions
[docs]class ZapierNLAListActions(BaseTool):
"""
Args:
None
"""
name = "ZapierNLA_list_actions"
description = BASE_ZAPIER_TOOL_PROMPT + (
"This tool returns a list of the user's exposed actions."
)
api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)
def _run(
self,
_: str = "",
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the Zapier NLA tool to return a list of all exposed user actions."""
return self.api_wrapper.list_as_str()
async def _arun(
self,
_: str = "",
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the Zapier NLA tool to return a list of all exposed user actions."""
raise NotImplementedError("ZapierNLAListActions does not support async")
ZapierNLAListActions.__doc__ = (
ZapierNLAWrapper.list.__doc__ + ZapierNLAListActions.__doc__ # type: ignore
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/zapier/tool.html
|
82ddacd1c630-0
|
Source code for langchain.tools.steamship_image_generation.tool
"""This tool allows agents to generate images using Steamship.
Steamship offers access to different third party image generation APIs
using a single API key.
Today the following models are supported:
- Dall-E
- Stable Diffusion
To use this tool, you must first set as environment variables:
STEAMSHIP_API_KEY
```
"""
from __future__ import annotations
from enum import Enum
from typing import TYPE_CHECKING, Dict, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools import BaseTool
from langchain.tools.steamship_image_generation.utils import make_image_public
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
pass
class ModelName(str, Enum):
"""Supported Image Models for generation."""
DALL_E = "dall-e"
STABLE_DIFFUSION = "stable-diffusion"
SUPPORTED_IMAGE_SIZES = {
ModelName.DALL_E: ("256x256", "512x512", "1024x1024"),
ModelName.STABLE_DIFFUSION: ("512x512", "768x768"),
}
[docs]class SteamshipImageGenerationTool(BaseTool):
try:
from steamship import Steamship
except ImportError:
pass
"""Tool used to generate images from a text-prompt."""
model_name: ModelName
size: Optional[str] = "512x512"
steamship: Steamship
return_urls: Optional[bool] = False
name = "GenerateImage"
description = (
"Useful for when you need to generate an image."
|
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|
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|
description = (
"Useful for when you need to generate an image."
"Input: A detailed text-2-image prompt describing an image"
"Output: the UUID of a generated image"
)
@root_validator(pre=True)
def validate_size(cls, values: Dict) -> Dict:
if "size" in values:
size = values["size"]
model_name = values["model_name"]
if size not in SUPPORTED_IMAGE_SIZES[model_name]:
raise RuntimeError(f"size {size} is not supported by {model_name}")
return values
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
steamship_api_key = get_from_dict_or_env(
values, "steamship_api_key", "STEAMSHIP_API_KEY"
)
try:
from steamship import Steamship
except ImportError:
raise ImportError(
"steamship is not installed. "
"Please install it with `pip install steamship`"
)
steamship = Steamship(
api_key=steamship_api_key,
)
values["steamship"] = steamship
if "steamship_api_key" in values:
del values["steamship_api_key"]
return values
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
image_generator = self.steamship.use_plugin(
plugin_handle=self.model_name.value, config={"n": 1, "size": self.size}
)
|
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|
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|
)
task = image_generator.generate(text=query, append_output_to_file=True)
task.wait()
blocks = task.output.blocks
if len(blocks) > 0:
if self.return_urls:
return make_image_public(self.steamship, blocks[0])
else:
return blocks[0].id
raise RuntimeError(f"[{self.name}] Tool unable to generate image!")
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("GenerateImageTool does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/steamship_image_generation/tool.html
|
4a32f9916450-0
|
Source code for langchain.tools.azure_cognitive_services.text2speech
from __future__ import annotations
import logging
import tempfile
from typing import Any, Dict, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class AzureCogsText2SpeechTool(BaseTool):
"""Tool that queries the Azure Cognitive Services Text2Speech API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/get-started-text-to-speech?pivots=programming-language-python
"""
azure_cogs_key: str = "" #: :meta private:
azure_cogs_region: str = "" #: :meta private:
speech_language: str = "en-US" #: :meta private:
speech_config: Any #: :meta private:
name = "azure_cognitive_services_text2speech"
description = (
"A wrapper around Azure Cognitive Services Text2Speech. "
"Useful for when you need to convert text to speech. "
)
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
azure_cogs_key = get_from_dict_or_env(
values, "azure_cogs_key", "AZURE_COGS_KEY"
)
azure_cogs_region = get_from_dict_or_env(
values, "azure_cogs_region", "AZURE_COGS_REGION"
)
try:
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/azure_cognitive_services/text2speech.html
|
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|
)
try:
import azure.cognitiveservices.speech as speechsdk
values["speech_config"] = speechsdk.SpeechConfig(
subscription=azure_cogs_key, region=azure_cogs_region
)
except ImportError:
raise ImportError(
"azure-cognitiveservices-speech is not installed. "
"Run `pip install azure-cognitiveservices-speech` to install."
)
return values
def _text2speech(self, text: str, speech_language: str) -> str:
try:
import azure.cognitiveservices.speech as speechsdk
except ImportError:
pass
self.speech_config.speech_synthesis_language = speech_language
speech_synthesizer = speechsdk.SpeechSynthesizer(
speech_config=self.speech_config, audio_config=None
)
result = speech_synthesizer.speak_text(text)
if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
stream = speechsdk.AudioDataStream(result)
with tempfile.NamedTemporaryFile(
mode="wb", suffix=".wav", delete=False
) as f:
stream.save_to_wav_file(f.name)
return f.name
elif result.reason == speechsdk.ResultReason.Canceled:
cancellation_details = result.cancellation_details
logger.debug(f"Speech synthesis canceled: {cancellation_details.reason}")
if cancellation_details.reason == speechsdk.CancellationReason.Error:
raise RuntimeError(
f"Speech synthesis error: {cancellation_details.error_details}"
)
return "Speech synthesis canceled."
else:
return f"Speech synthesis failed: {result.reason}"
def _run(
self,
query: str,
|
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|
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|
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
try:
speech_file = self._text2speech(query, self.speech_language)
return speech_file
except Exception as e:
raise RuntimeError(f"Error while running AzureCogsText2SpeechTool: {e}")
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("AzureCogsText2SpeechTool does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/azure_cognitive_services/text2speech.html
|
8508b46e7a63-0
|
Source code for langchain.tools.azure_cognitive_services.image_analysis
from __future__ import annotations
import logging
from typing import Any, Dict, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.azure_cognitive_services.utils import detect_file_src_type
from langchain.tools.base import BaseTool
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class AzureCogsImageAnalysisTool(BaseTool):
"""Tool that queries the Azure Cognitive Services Image Analysis API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40
"""
azure_cogs_key: str = "" #: :meta private:
azure_cogs_endpoint: str = "" #: :meta private:
vision_service: Any #: :meta private:
analysis_options: Any #: :meta private:
name = "azure_cognitive_services_image_analysis"
description = (
"A wrapper around Azure Cognitive Services Image Analysis. "
"Useful for when you need to analyze images. "
"Input should be a url to an image."
)
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
azure_cogs_key = get_from_dict_or_env(
values, "azure_cogs_key", "AZURE_COGS_KEY"
)
azure_cogs_endpoint = get_from_dict_or_env(
values, "azure_cogs_endpoint", "AZURE_COGS_ENDPOINT"
)
|
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|
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|
)
try:
import azure.ai.vision as sdk
values["vision_service"] = sdk.VisionServiceOptions(
endpoint=azure_cogs_endpoint, key=azure_cogs_key
)
values["analysis_options"] = sdk.ImageAnalysisOptions()
values["analysis_options"].features = (
sdk.ImageAnalysisFeature.CAPTION
| sdk.ImageAnalysisFeature.OBJECTS
| sdk.ImageAnalysisFeature.TAGS
| sdk.ImageAnalysisFeature.TEXT
)
except ImportError:
raise ImportError(
"azure-ai-vision is not installed. "
"Run `pip install azure-ai-vision` to install."
)
return values
def _image_analysis(self, image_path: str) -> Dict:
try:
import azure.ai.vision as sdk
except ImportError:
pass
image_src_type = detect_file_src_type(image_path)
if image_src_type == "local":
vision_source = sdk.VisionSource(filename=image_path)
elif image_src_type == "remote":
vision_source = sdk.VisionSource(url=image_path)
else:
raise ValueError(f"Invalid image path: {image_path}")
image_analyzer = sdk.ImageAnalyzer(
self.vision_service, vision_source, self.analysis_options
)
result = image_analyzer.analyze()
res_dict = {}
if result.reason == sdk.ImageAnalysisResultReason.ANALYZED:
if result.caption is not None:
res_dict["caption"] = result.caption.content
if result.objects is not None:
res_dict["objects"] = [obj.name for obj in result.objects]
if result.tags is not None:
res_dict["tags"] = [tag.name for tag in result.tags]
|
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|
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|
res_dict["tags"] = [tag.name for tag in result.tags]
if result.text is not None:
res_dict["text"] = [line.content for line in result.text.lines]
else:
error_details = sdk.ImageAnalysisErrorDetails.from_result(result)
raise RuntimeError(
f"Image analysis failed.\n"
f"Reason: {error_details.reason}\n"
f"Details: {error_details.message}"
)
return res_dict
def _format_image_analysis_result(self, image_analysis_result: Dict) -> str:
formatted_result = []
if "caption" in image_analysis_result:
formatted_result.append("Caption: " + image_analysis_result["caption"])
if (
"objects" in image_analysis_result
and len(image_analysis_result["objects"]) > 0
):
formatted_result.append(
"Objects: " + ", ".join(image_analysis_result["objects"])
)
if "tags" in image_analysis_result and len(image_analysis_result["tags"]) > 0:
formatted_result.append("Tags: " + ", ".join(image_analysis_result["tags"]))
if "text" in image_analysis_result and len(image_analysis_result["text"]) > 0:
formatted_result.append("Text: " + ", ".join(image_analysis_result["text"]))
return "\n".join(formatted_result)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
try:
image_analysis_result = self._image_analysis(query)
if not image_analysis_result:
return "No good image analysis result was found"
|
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|
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|
if not image_analysis_result:
return "No good image analysis result was found"
return self._format_image_analysis_result(image_analysis_result)
except Exception as e:
raise RuntimeError(f"Error while running AzureCogsImageAnalysisTool: {e}")
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("AzureCogsImageAnalysisTool does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/azure_cognitive_services/image_analysis.html
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84f8c5a179f7-0
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Source code for langchain.tools.azure_cognitive_services.speech2text
from __future__ import annotations
import logging
import time
from typing import Any, Dict, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.azure_cognitive_services.utils import (
detect_file_src_type,
download_audio_from_url,
)
from langchain.tools.base import BaseTool
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class AzureCogsSpeech2TextTool(BaseTool):
"""Tool that queries the Azure Cognitive Services Speech2Text API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/get-started-speech-to-text?pivots=programming-language-python
"""
azure_cogs_key: str = "" #: :meta private:
azure_cogs_region: str = "" #: :meta private:
speech_language: str = "en-US" #: :meta private:
speech_config: Any #: :meta private:
name = "azure_cognitive_services_speech2text"
description = (
"A wrapper around Azure Cognitive Services Speech2Text. "
"Useful for when you need to transcribe audio to text. "
"Input should be a url to an audio file."
)
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
azure_cogs_key = get_from_dict_or_env(
values, "azure_cogs_key", "AZURE_COGS_KEY"
)
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)
azure_cogs_region = get_from_dict_or_env(
values, "azure_cogs_region", "AZURE_COGS_REGION"
)
try:
import azure.cognitiveservices.speech as speechsdk
values["speech_config"] = speechsdk.SpeechConfig(
subscription=azure_cogs_key, region=azure_cogs_region
)
except ImportError:
raise ImportError(
"azure-cognitiveservices-speech is not installed. "
"Run `pip install azure-cognitiveservices-speech` to install."
)
return values
def _continuous_recognize(self, speech_recognizer: Any) -> str:
done = False
text = ""
def stop_cb(evt: Any) -> None:
"""callback that stop continuous recognition"""
speech_recognizer.stop_continuous_recognition_async()
nonlocal done
done = True
def retrieve_cb(evt: Any) -> None:
"""callback that retrieves the intermediate recognition results"""
nonlocal text
text += evt.result.text
# retrieve text on recognized events
speech_recognizer.recognized.connect(retrieve_cb)
# stop continuous recognition on either session stopped or canceled events
speech_recognizer.session_stopped.connect(stop_cb)
speech_recognizer.canceled.connect(stop_cb)
# Start continuous speech recognition
speech_recognizer.start_continuous_recognition_async()
while not done:
time.sleep(0.5)
return text
def _speech2text(self, audio_path: str, speech_language: str) -> str:
try:
import azure.cognitiveservices.speech as speechsdk
except ImportError:
pass
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except ImportError:
pass
audio_src_type = detect_file_src_type(audio_path)
if audio_src_type == "local":
audio_config = speechsdk.AudioConfig(filename=audio_path)
elif audio_src_type == "remote":
tmp_audio_path = download_audio_from_url(audio_path)
audio_config = speechsdk.AudioConfig(filename=tmp_audio_path)
else:
raise ValueError(f"Invalid audio path: {audio_path}")
self.speech_config.speech_recognition_language = speech_language
speech_recognizer = speechsdk.SpeechRecognizer(self.speech_config, audio_config)
return self._continuous_recognize(speech_recognizer)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
try:
text = self._speech2text(query, self.speech_language)
return text
except Exception as e:
raise RuntimeError(f"Error while running AzureCogsSpeech2TextTool: {e}")
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("AzureCogsSpeech2TextTool does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/azure_cognitive_services/speech2text.html
|
e8362078bc22-0
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Source code for langchain.tools.azure_cognitive_services.form_recognizer
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.azure_cognitive_services.utils import detect_file_src_type
from langchain.tools.base import BaseTool
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class AzureCogsFormRecognizerTool(BaseTool):
"""Tool that queries the Azure Cognitive Services Form Recognizer API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/quickstarts/get-started-sdks-rest-api?view=form-recog-3.0.0&pivots=programming-language-python
"""
azure_cogs_key: str = "" #: :meta private:
azure_cogs_endpoint: str = "" #: :meta private:
doc_analysis_client: Any #: :meta private:
name = "azure_cognitive_services_form_recognizer"
description = (
"A wrapper around Azure Cognitive Services Form Recognizer. "
"Useful for when you need to "
"extract text, tables, and key-value pairs from documents. "
"Input should be a url to a document."
)
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
azure_cogs_key = get_from_dict_or_env(
values, "azure_cogs_key", "AZURE_COGS_KEY"
)
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)
azure_cogs_endpoint = get_from_dict_or_env(
values, "azure_cogs_endpoint", "AZURE_COGS_ENDPOINT"
)
try:
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential
values["doc_analysis_client"] = DocumentAnalysisClient(
endpoint=azure_cogs_endpoint,
credential=AzureKeyCredential(azure_cogs_key),
)
except ImportError:
raise ImportError(
"azure-ai-formrecognizer is not installed. "
"Run `pip install azure-ai-formrecognizer` to install."
)
return values
def _parse_tables(self, tables: List[Any]) -> List[Any]:
result = []
for table in tables:
rc, cc = table.row_count, table.column_count
_table = [["" for _ in range(cc)] for _ in range(rc)]
for cell in table.cells:
_table[cell.row_index][cell.column_index] = cell.content
result.append(_table)
return result
def _parse_kv_pairs(self, kv_pairs: List[Any]) -> List[Any]:
result = []
for kv_pair in kv_pairs:
key = kv_pair.key.content if kv_pair.key else ""
value = kv_pair.value.content if kv_pair.value else ""
result.append((key, value))
return result
def _document_analysis(self, document_path: str) -> Dict:
document_src_type = detect_file_src_type(document_path)
if document_src_type == "local":
with open(document_path, "rb") as document:
poller = self.doc_analysis_client.begin_analyze_document(
"prebuilt-document", document
)
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"prebuilt-document", document
)
elif document_src_type == "remote":
poller = self.doc_analysis_client.begin_analyze_document_from_url(
"prebuilt-document", document_path
)
else:
raise ValueError(f"Invalid document path: {document_path}")
result = poller.result()
res_dict = {}
if result.content is not None:
res_dict["content"] = result.content
if result.tables is not None:
res_dict["tables"] = self._parse_tables(result.tables)
if result.key_value_pairs is not None:
res_dict["key_value_pairs"] = self._parse_kv_pairs(result.key_value_pairs)
return res_dict
def _format_document_analysis_result(self, document_analysis_result: Dict) -> str:
formatted_result = []
if "content" in document_analysis_result:
formatted_result.append(
f"Content: {document_analysis_result['content']}".replace("\n", " ")
)
if "tables" in document_analysis_result:
for i, table in enumerate(document_analysis_result["tables"]):
formatted_result.append(f"Table {i}: {table}".replace("\n", " "))
if "key_value_pairs" in document_analysis_result:
for kv_pair in document_analysis_result["key_value_pairs"]:
formatted_result.append(
f"{kv_pair[0]}: {kv_pair[1]}".replace("\n", " ")
)
return "\n".join(formatted_result)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
try:
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|
e8362078bc22-3
|
) -> str:
"""Use the tool."""
try:
document_analysis_result = self._document_analysis(query)
if not document_analysis_result:
return "No good document analysis result was found"
return self._format_document_analysis_result(document_analysis_result)
except Exception as e:
raise RuntimeError(f"Error while running AzureCogsFormRecognizerTool: {e}")
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("AzureCogsFormRecognizerTool does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/azure_cognitive_services/form_recognizer.html
|
a78f210a83b4-0
|
Source code for langchain.tools.file_management.move
import shutil
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
class FileMoveInput(BaseModel):
"""Input for MoveFileTool."""
source_path: str = Field(..., description="Path of the file to move")
destination_path: str = Field(..., description="New path for the moved file")
[docs]class MoveFileTool(BaseFileToolMixin, BaseTool):
name: str = "move_file"
args_schema: Type[BaseModel] = FileMoveInput
description: str = "Move or rename a file from one location to another"
def _run(
self,
source_path: str,
destination_path: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
source_path_ = self.get_relative_path(source_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(
arg_name="source_path", value=source_path
)
try:
destination_path_ = self.get_relative_path(destination_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(
arg_name="destination_path_", value=destination_path_
)
if not source_path_.exists():
return f"Error: no such file or directory {source_path}"
try:
# shutil.move expects str args in 3.8
shutil.move(str(source_path_), destination_path_)
|
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shutil.move(str(source_path_), destination_path_)
return f"File moved successfully from {source_path} to {destination_path}."
except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
source_path: str,
destination_path: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
# TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/move.html
|
04822f3b9741-0
|
Source code for langchain.tools.file_management.read
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
class ReadFileInput(BaseModel):
"""Input for ReadFileTool."""
file_path: str = Field(..., description="name of file")
[docs]class ReadFileTool(BaseFileToolMixin, BaseTool):
name: str = "read_file"
args_schema: Type[BaseModel] = ReadFileInput
description: str = "Read file from disk"
def _run(
self,
file_path: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
read_path = self.get_relative_path(file_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(arg_name="file_path", value=file_path)
if not read_path.exists():
return f"Error: no such file or directory: {file_path}"
try:
with read_path.open("r", encoding="utf-8") as f:
content = f.read()
return content
except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
file_path: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
# TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/read.html
|
04822f3b9741-1
|
# TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/read.html
|
b008b381d486-0
|
Source code for langchain.tools.file_management.file_search
import fnmatch
import os
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
class FileSearchInput(BaseModel):
"""Input for FileSearchTool."""
dir_path: str = Field(
default=".",
description="Subdirectory to search in.",
)
pattern: str = Field(
...,
description="Unix shell regex, where * matches everything.",
)
[docs]class FileSearchTool(BaseFileToolMixin, BaseTool):
name: str = "file_search"
args_schema: Type[BaseModel] = FileSearchInput
description: str = (
"Recursively search for files in a subdirectory that match the regex pattern"
)
def _run(
self,
pattern: str,
dir_path: str = ".",
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
dir_path_ = self.get_relative_path(dir_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(arg_name="dir_path", value=dir_path)
matches = []
try:
for root, _, filenames in os.walk(dir_path_):
for filename in fnmatch.filter(filenames, pattern):
absolute_path = os.path.join(root, filename)
relative_path = os.path.relpath(absolute_path, dir_path_)
matches.append(relative_path)
if matches:
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matches.append(relative_path)
if matches:
return "\n".join(matches)
else:
return f"No files found for pattern {pattern} in directory {dir_path}"
except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
dir_path: str,
pattern: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
# TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/file_search.html
|
fc4c248b393a-0
|
Source code for langchain.tools.file_management.write
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
class WriteFileInput(BaseModel):
"""Input for WriteFileTool."""
file_path: str = Field(..., description="name of file")
text: str = Field(..., description="text to write to file")
append: bool = Field(
default=False, description="Whether to append to an existing file."
)
[docs]class WriteFileTool(BaseFileToolMixin, BaseTool):
name: str = "write_file"
args_schema: Type[BaseModel] = WriteFileInput
description: str = "Write file to disk"
def _run(
self,
file_path: str,
text: str,
append: bool = False,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
write_path = self.get_relative_path(file_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(arg_name="file_path", value=file_path)
try:
write_path.parent.mkdir(exist_ok=True, parents=False)
mode = "a" if append else "w"
with write_path.open(mode, encoding="utf-8") as f:
f.write(text)
return f"File written successfully to {file_path}."
except Exception as e:
return "Error: " + str(e)
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|
fc4c248b393a-1
|
except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
file_path: str,
text: str,
append: bool = False,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
# TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/write.html
|
378e4ca07e2a-0
|
Source code for langchain.tools.file_management.copy
import shutil
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
class FileCopyInput(BaseModel):
"""Input for CopyFileTool."""
source_path: str = Field(..., description="Path of the file to copy")
destination_path: str = Field(..., description="Path to save the copied file")
[docs]class CopyFileTool(BaseFileToolMixin, BaseTool):
name: str = "copy_file"
args_schema: Type[BaseModel] = FileCopyInput
description: str = "Create a copy of a file in a specified location"
def _run(
self,
source_path: str,
destination_path: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
source_path_ = self.get_relative_path(source_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(
arg_name="source_path", value=source_path
)
try:
destination_path_ = self.get_relative_path(destination_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(
arg_name="destination_path", value=destination_path
)
try:
shutil.copy2(source_path_, destination_path_, follow_symlinks=False)
return f"File copied successfully from {source_path} to {destination_path}."
except Exception as e:
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|
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|
except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
source_path: str,
destination_path: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
# TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/copy.html
|
9422e148225c-0
|
Source code for langchain.tools.file_management.delete
import os
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
class FileDeleteInput(BaseModel):
"""Input for DeleteFileTool."""
file_path: str = Field(..., description="Path of the file to delete")
[docs]class DeleteFileTool(BaseFileToolMixin, BaseTool):
name: str = "file_delete"
args_schema: Type[BaseModel] = FileDeleteInput
description: str = "Delete a file"
def _run(
self,
file_path: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
file_path_ = self.get_relative_path(file_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(arg_name="file_path", value=file_path)
if not file_path_.exists():
return f"Error: no such file or directory: {file_path}"
try:
os.remove(file_path_)
return f"File deleted successfully: {file_path}."
except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
file_path: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
# TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/delete.html
|
9422e148225c-1
|
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/delete.html
|
83f93b478853-0
|
Source code for langchain.tools.file_management.list_dir
import os
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
class DirectoryListingInput(BaseModel):
"""Input for ListDirectoryTool."""
dir_path: str = Field(default=".", description="Subdirectory to list.")
[docs]class ListDirectoryTool(BaseFileToolMixin, BaseTool):
name: str = "list_directory"
args_schema: Type[BaseModel] = DirectoryListingInput
description: str = "List files and directories in a specified folder"
def _run(
self,
dir_path: str = ".",
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
dir_path_ = self.get_relative_path(dir_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(arg_name="dir_path", value=dir_path)
try:
entries = os.listdir(dir_path_)
if entries:
return "\n".join(entries)
else:
return f"No files found in directory {dir_path}"
except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
dir_path: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
# TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/list_dir.html
|
83f93b478853-1
|
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/file_management/list_dir.html
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ee89fb03f74c-0
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Source code for langchain.tools.metaphor_search.tool
"""Tool for the Metaphor search API."""
from typing import Dict, List, Optional, Union
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.metaphor_search import MetaphorSearchAPIWrapper
[docs]class MetaphorSearchResults(BaseTool):
"""Tool that has capability to query the Metaphor Search API and get back json."""
name = "metaphor_search_results_json"
description = (
"A wrapper around Metaphor Search. "
"Input should be a Metaphor-optimized query. "
"Output is a JSON array of the query results"
)
api_wrapper: MetaphorSearchAPIWrapper
def _run(
self,
query: str,
num_results: int,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> Union[List[Dict], str]:
"""Use the tool."""
try:
return self.api_wrapper.results(query, num_results)
except Exception as e:
return repr(e)
async def _arun(
self,
query: str,
num_results: int,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> Union[List[Dict], str]:
"""Use the tool asynchronously."""
try:
return await self.api_wrapper.results_async(query, num_results)
except Exception as e:
return repr(e)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/metaphor_search/tool.html
|
ba5e8a60d287-0
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Source code for langchain.tools.shell.tool
import asyncio
import platform
import warnings
from typing import List, Optional, Type, Union
from pydantic import BaseModel, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.bash import BashProcess
class ShellInput(BaseModel):
"""Commands for the Bash Shell tool."""
commands: Union[str, List[str]] = Field(
...,
description="List of shell commands to run. Deserialized using json.loads",
)
"""List of shell commands to run."""
@root_validator
def _validate_commands(cls, values: dict) -> dict:
"""Validate commands."""
# TODO: Add real validators
commands = values.get("commands")
if not isinstance(commands, list):
values["commands"] = [commands]
# Warn that the bash tool is not safe
warnings.warn(
"The shell tool has no safeguards by default. Use at your own risk."
)
return values
def _get_default_bash_processs() -> BashProcess:
"""Get file path from string."""
return BashProcess(return_err_output=True)
def _get_platform() -> str:
"""Get platform."""
system = platform.system()
if system == "Darwin":
return "MacOS"
return system
[docs]class ShellTool(BaseTool):
"""Tool to run shell commands."""
process: BashProcess = Field(default_factory=_get_default_bash_processs)
"""Bash process to run commands."""
name: str = "terminal"
"""Name of tool."""
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name: str = "terminal"
"""Name of tool."""
description: str = f"Run shell commands on this {_get_platform()} machine."
"""Description of tool."""
args_schema: Type[BaseModel] = ShellInput
"""Schema for input arguments."""
def _run(
self,
commands: Union[str, List[str]],
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Run commands and return final output."""
return self.process.run(commands)
async def _arun(
self,
commands: Union[str, List[str]],
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Run commands asynchronously and return final output."""
return await asyncio.get_event_loop().run_in_executor(
None, self.process.run, commands
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/shell/tool.html
|
5e5988edabf3-0
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Source code for langchain.tools.wikipedia.tool
"""Tool for the Wikipedia API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.wikipedia import WikipediaAPIWrapper
[docs]class WikipediaQueryRun(BaseTool):
"""Tool that adds the capability to search using the Wikipedia API."""
name = "Wikipedia"
description = (
"A wrapper around Wikipedia. "
"Useful for when you need to answer general questions about "
"people, places, companies, facts, historical events, or other subjects. "
"Input should be a search query."
)
api_wrapper: WikipediaAPIWrapper
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the Wikipedia tool."""
return self.api_wrapper.run(query)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the Wikipedia tool asynchronously."""
raise NotImplementedError("WikipediaQueryRun does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/wikipedia/tool.html
|
b15b0a46e40d-0
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Source code for langchain.tools.vectorstore.tool
"""Tools for interacting with vectorstores."""
import json
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain
from langchain.llms.openai import OpenAI
from langchain.tools.base import BaseTool
from langchain.vectorstores.base import VectorStore
class BaseVectorStoreTool(BaseModel):
"""Base class for tools that use a VectorStore."""
vectorstore: VectorStore = Field(exclude=True)
llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0))
class Config(BaseTool.Config):
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _create_description_from_template(values: Dict[str, Any]) -> Dict[str, Any]:
values["description"] = values["template"].format(name=values["name"])
return values
[docs]class VectorStoreQATool(BaseVectorStoreTool, BaseTool):
"""Tool for the VectorDBQA chain. To be initialized with name and chain."""
[docs] @staticmethod
def get_description(name: str, description: str) -> str:
template: str = (
"Useful for when you need to answer questions about {name}. "
"Whenever you need information about {description} "
"you should ALWAYS use this. "
"Input should be a fully formed question."
)
return template.format(name=name, description=description)
def _run(
self,
query: str,
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def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
chain = RetrievalQA.from_chain_type(
self.llm, retriever=self.vectorstore.as_retriever()
)
return chain.run(query)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("VectorStoreQATool does not support async")
[docs]class VectorStoreQAWithSourcesTool(BaseVectorStoreTool, BaseTool):
"""Tool for the VectorDBQAWithSources chain."""
[docs] @staticmethod
def get_description(name: str, description: str) -> str:
template: str = (
"Useful for when you need to answer questions about {name} and the sources "
"used to construct the answer. "
"Whenever you need information about {description} "
"you should ALWAYS use this. "
" Input should be a fully formed question. "
"Output is a json serialized dictionary with keys `answer` and `sources`. "
"Only use this tool if the user explicitly asks for sources."
)
return template.format(name=name, description=description)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
chain = RetrievalQAWithSourcesChain.from_chain_type(
self.llm, retriever=self.vectorstore.as_retriever()
)
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self.llm, retriever=self.vectorstore.as_retriever()
)
return json.dumps(chain({chain.question_key: query}, return_only_outputs=True))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("VectorStoreQAWithSourcesTool does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/vectorstore/tool.html
|
20cab57c9231-0
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Source code for langchain.tools.powerbi.tool
"""Tools for interacting with a Power BI dataset."""
import logging
from typing import Any, Dict, Optional, Tuple
from pydantic import Field, validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.chains.llm import LLMChain
from langchain.tools.base import BaseTool
from langchain.tools.powerbi.prompt import (
BAD_REQUEST_RESPONSE,
DEFAULT_FEWSHOT_EXAMPLES,
QUESTION_TO_QUERY,
RETRY_RESPONSE,
)
from langchain.utilities.powerbi import PowerBIDataset, json_to_md
logger = logging.getLogger(__name__)
[docs]class QueryPowerBITool(BaseTool):
"""Tool for querying a Power BI Dataset."""
name = "query_powerbi"
description = """
Input to this tool is a detailed question about the dataset, output is a result from the dataset. It will try to answer the question using the dataset, and if it cannot, it will ask for clarification.
Example Input: "How many rows are in table1?"
""" # noqa: E501
llm_chain: LLMChain
powerbi: PowerBIDataset = Field(exclude=True)
template: Optional[str] = QUESTION_TO_QUERY
examples: Optional[str] = DEFAULT_FEWSHOT_EXAMPLES
session_cache: Dict[str, Any] = Field(default_factory=dict, exclude=True)
max_iterations: int = 5
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@validator("llm_chain")
def validate_llm_chain_input_variables( # pylint: disable=E0213
cls, llm_chain: LLMChain
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cls, llm_chain: LLMChain
) -> LLMChain:
"""Make sure the LLM chain has the correct input variables."""
if llm_chain.prompt.input_variables != [
"tool_input",
"tables",
"schemas",
"examples",
]:
raise ValueError(
"LLM chain for QueryPowerBITool must have input variables ['tool_input', 'tables', 'schemas', 'examples'], found %s", # noqa: C0301 E501 # pylint: disable=C0301
llm_chain.prompt.input_variables,
)
return llm_chain
def _check_cache(self, tool_input: str) -> Optional[str]:
"""Check if the input is present in the cache.
If the value is a bad request, overwrite with the escalated version,
if not present return None."""
if tool_input not in self.session_cache:
return None
return self.session_cache[tool_input]
def _run(
self,
tool_input: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
**kwargs: Any,
) -> str:
"""Execute the query, return the results or an error message."""
if cache := self._check_cache(tool_input):
logger.debug("Found cached result for %s: %s", tool_input, cache)
return cache
try:
logger.info("Running PBI Query Tool with input: %s", tool_input)
query = self.llm_chain.predict(
tool_input=tool_input,
tables=self.powerbi.get_table_names(),
schemas=self.powerbi.get_schemas(),
examples=self.examples,
)
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schemas=self.powerbi.get_schemas(),
examples=self.examples,
)
except Exception as exc: # pylint: disable=broad-except
self.session_cache[tool_input] = f"Error on call to LLM: {exc}"
return self.session_cache[tool_input]
if query == "I cannot answer this":
self.session_cache[tool_input] = query
return self.session_cache[tool_input]
logger.info("Query: %s", query)
pbi_result = self.powerbi.run(command=query)
result, error = self._parse_output(pbi_result)
iterations = kwargs.get("iterations", 0)
if error and iterations < self.max_iterations:
return self._run(
tool_input=RETRY_RESPONSE.format(
tool_input=tool_input, query=query, error=error
),
run_manager=run_manager,
iterations=iterations + 1,
)
self.session_cache[tool_input] = (
result if result else BAD_REQUEST_RESPONSE.format(error=error)
)
return self.session_cache[tool_input]
async def _arun(
self,
tool_input: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
**kwargs: Any,
) -> str:
"""Execute the query, return the results or an error message."""
if cache := self._check_cache(tool_input):
logger.debug("Found cached result for %s: %s", tool_input, cache)
return cache
try:
logger.info("Running PBI Query Tool with input: %s", tool_input)
query = await self.llm_chain.apredict(
tool_input=tool_input,
|
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query = await self.llm_chain.apredict(
tool_input=tool_input,
tables=self.powerbi.get_table_names(),
schemas=self.powerbi.get_schemas(),
examples=self.examples,
)
except Exception as exc: # pylint: disable=broad-except
self.session_cache[tool_input] = f"Error on call to LLM: {exc}"
return self.session_cache[tool_input]
if query == "I cannot answer this":
self.session_cache[tool_input] = query
return self.session_cache[tool_input]
logger.info("Query: %s", query)
pbi_result = await self.powerbi.arun(command=query)
result, error = self._parse_output(pbi_result)
iterations = kwargs.get("iterations", 0)
if error and iterations < self.max_iterations:
return await self._arun(
tool_input=RETRY_RESPONSE.format(
tool_input=tool_input, query=query, error=error
),
run_manager=run_manager,
iterations=iterations + 1,
)
self.session_cache[tool_input] = (
result if result else BAD_REQUEST_RESPONSE.format(error=error)
)
return self.session_cache[tool_input]
def _parse_output(
self, pbi_result: Dict[str, Any]
) -> Tuple[Optional[str], Optional[str]]:
"""Parse the output of the query to a markdown table."""
if "results" in pbi_result:
return json_to_md(pbi_result["results"][0]["tables"][0]["rows"]), None
if "error" in pbi_result:
if (
"pbi.error" in pbi_result["error"]
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if (
"pbi.error" in pbi_result["error"]
and "details" in pbi_result["error"]["pbi.error"]
):
return None, pbi_result["error"]["pbi.error"]["details"][0]["detail"]
return None, pbi_result["error"]
return None, "Unknown error"
[docs]class InfoPowerBITool(BaseTool):
"""Tool for getting metadata about a PowerBI Dataset."""
name = "schema_powerbi"
description = """
Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.
Be sure that the tables actually exist by calling list_tables_powerbi first!
Example Input: "table1, table2, table3"
""" # noqa: E501
powerbi: PowerBIDataset = Field(exclude=True)
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _run(
self,
tool_input: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Get the schema for tables in a comma-separated list."""
return self.powerbi.get_table_info(tool_input.split(", "))
async def _arun(
self,
tool_input: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
return await self.powerbi.aget_table_info(tool_input.split(", "))
[docs]class ListPowerBITool(BaseTool):
"""Tool for getting tables names."""
name = "list_tables_powerbi"
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"""Tool for getting tables names."""
name = "list_tables_powerbi"
description = "Input is an empty string, output is a comma separated list of tables in the database." # noqa: E501 # pylint: disable=C0301
powerbi: PowerBIDataset = Field(exclude=True)
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _run(
self,
tool_input: Optional[str] = None,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Get the names of the tables."""
return ", ".join(self.powerbi.get_table_names())
async def _arun(
self,
tool_input: Optional[str] = None,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Get the names of the tables."""
return ", ".join(self.powerbi.get_table_names())
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/powerbi/tool.html
|
645502f2aeee-0
|
Source code for langchain.tools.openapi.utils.api_models
"""Pydantic models for parsing an OpenAPI spec."""
import logging
from enum import Enum
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, Union
from openapi_schema_pydantic import MediaType, Parameter, Reference, RequestBody, Schema
from pydantic import BaseModel, Field
from langchain.tools.openapi.utils.openapi_utils import HTTPVerb, OpenAPISpec
logger = logging.getLogger(__name__)
PRIMITIVE_TYPES = {
"integer": int,
"number": float,
"string": str,
"boolean": bool,
"array": List,
"object": Dict,
"null": None,
}
# See https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.1.0.md#parameterIn
# for more info.
class APIPropertyLocation(Enum):
"""The location of the property."""
QUERY = "query"
PATH = "path"
HEADER = "header"
COOKIE = "cookie" # Not yet supported
@classmethod
def from_str(cls, location: str) -> "APIPropertyLocation":
"""Parse an APIPropertyLocation."""
try:
return cls(location)
except ValueError:
raise ValueError(
f"Invalid APIPropertyLocation. Valid values are {cls.__members__}"
)
_SUPPORTED_MEDIA_TYPES = ("application/json",)
SUPPORTED_LOCATIONS = {
APIPropertyLocation.QUERY,
APIPropertyLocation.PATH,
}
INVALID_LOCATION_TEMPL = (
'Unsupported APIPropertyLocation "{location}"'
" for parameter {name}. "
+ f"Valid values are {[loc.value for loc in SUPPORTED_LOCATIONS]}"
)
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)
SCHEMA_TYPE = Union[str, Type, tuple, None, Enum]
class APIPropertyBase(BaseModel):
"""Base model for an API property."""
# The name of the parameter is required and is case sensitive.
# If "in" is "path", the "name" field must correspond to a template expression
# within the path field in the Paths Object.
# If "in" is "header" and the "name" field is "Accept", "Content-Type",
# or "Authorization", the parameter definition is ignored.
# For all other cases, the "name" corresponds to the parameter
# name used by the "in" property.
name: str = Field(alias="name")
"""The name of the property."""
required: bool = Field(alias="required")
"""Whether the property is required."""
type: SCHEMA_TYPE = Field(alias="type")
"""The type of the property.
Either a primitive type, a component/parameter type,
or an array or 'object' (dict) of the above."""
default: Optional[Any] = Field(alias="default", default=None)
"""The default value of the property."""
description: Optional[str] = Field(alias="description", default=None)
"""The description of the property."""
class APIProperty(APIPropertyBase):
"""A model for a property in the query, path, header, or cookie params."""
location: APIPropertyLocation = Field(alias="location")
"""The path/how it's being passed to the endpoint."""
@staticmethod
def _cast_schema_list_type(schema: Schema) -> Optional[Union[str, Tuple[str, ...]]]:
type_ = schema.type
if not isinstance(type_, list):
return type_
|
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if not isinstance(type_, list):
return type_
else:
return tuple(type_)
@staticmethod
def _get_schema_type_for_enum(parameter: Parameter, schema: Schema) -> Enum:
"""Get the schema type when the parameter is an enum."""
param_name = f"{parameter.name}Enum"
return Enum(param_name, {str(v): v for v in schema.enum})
@staticmethod
def _get_schema_type_for_array(
schema: Schema,
) -> Optional[Union[str, Tuple[str, ...]]]:
items = schema.items
if isinstance(items, Schema):
schema_type = APIProperty._cast_schema_list_type(items)
elif isinstance(items, Reference):
ref_name = items.ref.split("/")[-1]
schema_type = ref_name # TODO: Add ref definitions to make his valid
else:
raise ValueError(f"Unsupported array items: {items}")
if isinstance(schema_type, str):
# TODO: recurse
schema_type = (schema_type,)
return schema_type
@staticmethod
def _get_schema_type(parameter: Parameter, schema: Optional[Schema]) -> SCHEMA_TYPE:
if schema is None:
return None
schema_type: SCHEMA_TYPE = APIProperty._cast_schema_list_type(schema)
if schema_type == "array":
schema_type = APIProperty._get_schema_type_for_array(schema)
elif schema_type == "object":
# TODO: Resolve array and object types to components.
raise NotImplementedError("Objects not yet supported")
elif schema_type in PRIMITIVE_TYPES:
if schema.enum:
schema_type = APIProperty._get_schema_type_for_enum(parameter, schema)
else:
# Directly use the primitive type
pass
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else:
# Directly use the primitive type
pass
else:
raise NotImplementedError(f"Unsupported type: {schema_type}")
return schema_type
@staticmethod
def _validate_location(location: APIPropertyLocation, name: str) -> None:
if location not in SUPPORTED_LOCATIONS:
raise NotImplementedError(
INVALID_LOCATION_TEMPL.format(location=location, name=name)
)
@staticmethod
def _validate_content(content: Optional[Dict[str, MediaType]]) -> None:
if content:
raise ValueError(
"API Properties with media content not supported. "
"Media content only supported within APIRequestBodyProperty's"
)
@staticmethod
def _get_schema(parameter: Parameter, spec: OpenAPISpec) -> Optional[Schema]:
schema = parameter.param_schema
if isinstance(schema, Reference):
schema = spec.get_referenced_schema(schema)
elif schema is None:
return None
elif not isinstance(schema, Schema):
raise ValueError(f"Error dereferencing schema: {schema}")
return schema
@staticmethod
def is_supported_location(location: str) -> bool:
"""Return whether the provided location is supported."""
try:
return APIPropertyLocation.from_str(location) in SUPPORTED_LOCATIONS
except ValueError:
return False
@classmethod
def from_parameter(cls, parameter: Parameter, spec: OpenAPISpec) -> "APIProperty":
"""Instantiate from an OpenAPI Parameter."""
location = APIPropertyLocation.from_str(parameter.param_in)
cls._validate_location(
location,
parameter.name,
)
cls._validate_content(parameter.content)
schema = cls._get_schema(parameter, spec)
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schema = cls._get_schema(parameter, spec)
schema_type = cls._get_schema_type(parameter, schema)
default_val = schema.default if schema is not None else None
return cls(
name=parameter.name,
location=location,
default=default_val,
description=parameter.description,
required=parameter.required,
type=schema_type,
)
class APIRequestBodyProperty(APIPropertyBase):
"""A model for a request body property."""
properties: List["APIRequestBodyProperty"] = Field(alias="properties")
"""The sub-properties of the property."""
# This is useful for handling nested property cycles.
# We can define separate types in that case.
references_used: List[str] = Field(alias="references_used")
"""The references used by the property."""
@classmethod
def _process_object_schema(
cls, schema: Schema, spec: OpenAPISpec, references_used: List[str]
) -> Tuple[Union[str, List[str], None], List["APIRequestBodyProperty"]]:
properties = []
required_props = schema.required or []
if schema.properties is None:
raise ValueError(
f"No properties found when processing object schema: {schema}"
)
for prop_name, prop_schema in schema.properties.items():
if isinstance(prop_schema, Reference):
ref_name = prop_schema.ref.split("/")[-1]
if ref_name not in references_used:
references_used.append(ref_name)
prop_schema = spec.get_referenced_schema(prop_schema)
else:
continue
properties.append(
cls.from_schema(
schema=prop_schema,
name=prop_name,
required=prop_name in required_props,
spec=spec,
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required=prop_name in required_props,
spec=spec,
references_used=references_used,
)
)
return schema.type, properties
@classmethod
def _process_array_schema(
cls, schema: Schema, name: str, spec: OpenAPISpec, references_used: List[str]
) -> str:
items = schema.items
if items is not None:
if isinstance(items, Reference):
ref_name = items.ref.split("/")[-1]
if ref_name not in references_used:
references_used.append(ref_name)
items = spec.get_referenced_schema(items)
else:
pass
return f"Array<{ref_name}>"
else:
pass
if isinstance(items, Schema):
array_type = cls.from_schema(
schema=items,
name=f"{name}Item",
required=True, # TODO: Add required
spec=spec,
references_used=references_used,
)
return f"Array<{array_type.type}>"
return "array"
@classmethod
def from_schema(
cls,
schema: Schema,
name: str,
required: bool,
spec: OpenAPISpec,
references_used: Optional[List[str]] = None,
) -> "APIRequestBodyProperty":
"""Recursively populate from an OpenAPI Schema."""
if references_used is None:
references_used = []
schema_type = schema.type
properties: List[APIRequestBodyProperty] = []
if schema_type == "object" and schema.properties:
schema_type, properties = cls._process_object_schema(
schema, spec, references_used
)
elif schema_type == "array":
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schema, spec, references_used
)
elif schema_type == "array":
schema_type = cls._process_array_schema(schema, name, spec, references_used)
elif schema_type in PRIMITIVE_TYPES:
# Use the primitive type directly
pass
elif schema_type is None:
# No typing specified/parsed. WIll map to 'any'
pass
else:
raise ValueError(f"Unsupported type: {schema_type}")
return cls(
name=name,
required=required,
type=schema_type,
default=schema.default,
description=schema.description,
properties=properties,
references_used=references_used,
)
class APIRequestBody(BaseModel):
"""A model for a request body."""
description: Optional[str] = Field(alias="description")
"""The description of the request body."""
properties: List[APIRequestBodyProperty] = Field(alias="properties")
# E.g., application/json - we only support JSON at the moment.
media_type: str = Field(alias="media_type")
"""The media type of the request body."""
@classmethod
def _process_supported_media_type(
cls,
media_type_obj: MediaType,
spec: OpenAPISpec,
) -> List[APIRequestBodyProperty]:
"""Process the media type of the request body."""
references_used = []
schema = media_type_obj.media_type_schema
if isinstance(schema, Reference):
references_used.append(schema.ref.split("/")[-1])
schema = spec.get_referenced_schema(schema)
if schema is None:
raise ValueError(
f"Could not resolve schema for media type: {media_type_obj}"
)
api_request_body_properties = []
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)
api_request_body_properties = []
required_properties = schema.required or []
if schema.type == "object" and schema.properties:
for prop_name, prop_schema in schema.properties.items():
if isinstance(prop_schema, Reference):
prop_schema = spec.get_referenced_schema(prop_schema)
api_request_body_properties.append(
APIRequestBodyProperty.from_schema(
schema=prop_schema,
name=prop_name,
required=prop_name in required_properties,
spec=spec,
)
)
else:
api_request_body_properties.append(
APIRequestBodyProperty(
name="body",
required=True,
type=schema.type,
default=schema.default,
description=schema.description,
properties=[],
references_used=references_used,
)
)
return api_request_body_properties
@classmethod
def from_request_body(
cls, request_body: RequestBody, spec: OpenAPISpec
) -> "APIRequestBody":
"""Instantiate from an OpenAPI RequestBody."""
properties = []
for media_type, media_type_obj in request_body.content.items():
if media_type not in _SUPPORTED_MEDIA_TYPES:
continue
api_request_body_properties = cls._process_supported_media_type(
media_type_obj,
spec,
)
properties.extend(api_request_body_properties)
return cls(
description=request_body.description,
properties=properties,
media_type=media_type,
)
[docs]class APIOperation(BaseModel):
"""A model for a single API operation."""
operation_id: str = Field(alias="operation_id")
"""The unique identifier of the operation."""
description: Optional[str] = Field(alias="description")
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description: Optional[str] = Field(alias="description")
"""The description of the operation."""
base_url: str = Field(alias="base_url")
"""The base URL of the operation."""
path: str = Field(alias="path")
"""The path of the operation."""
method: HTTPVerb = Field(alias="method")
"""The HTTP method of the operation."""
properties: Sequence[APIProperty] = Field(alias="properties")
# TODO: Add parse in used components to be able to specify what type of
# referenced object it is.
# """The properties of the operation."""
# components: Dict[str, BaseModel] = Field(alias="components")
request_body: Optional[APIRequestBody] = Field(alias="request_body")
"""The request body of the operation."""
@staticmethod
def _get_properties_from_parameters(
parameters: List[Parameter], spec: OpenAPISpec
) -> List[APIProperty]:
"""Get the properties of the operation."""
properties = []
for param in parameters:
if APIProperty.is_supported_location(param.param_in):
properties.append(APIProperty.from_parameter(param, spec))
elif param.required:
raise ValueError(
INVALID_LOCATION_TEMPL.format(
location=param.param_in, name=param.name
)
)
else:
logger.warning(
INVALID_LOCATION_TEMPL.format(
location=param.param_in, name=param.name
)
+ " Ignoring optional parameter"
)
pass
return properties
[docs] @classmethod
def from_openapi_url(
cls,
spec_url: str,
path: str,
method: str,
) -> "APIOperation":
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path: str,
method: str,
) -> "APIOperation":
"""Create an APIOperation from an OpenAPI URL."""
spec = OpenAPISpec.from_url(spec_url)
return cls.from_openapi_spec(spec, path, method)
[docs] @classmethod
def from_openapi_spec(
cls,
spec: OpenAPISpec,
path: str,
method: str,
) -> "APIOperation":
"""Create an APIOperation from an OpenAPI spec."""
operation = spec.get_operation(path, method)
parameters = spec.get_parameters_for_operation(operation)
properties = cls._get_properties_from_parameters(parameters, spec)
operation_id = OpenAPISpec.get_cleaned_operation_id(operation, path, method)
request_body = spec.get_request_body_for_operation(operation)
api_request_body = (
APIRequestBody.from_request_body(request_body, spec)
if request_body is not None
else None
)
description = operation.description or operation.summary
if not description and spec.paths is not None:
description = spec.paths[path].description or spec.paths[path].summary
return cls(
operation_id=operation_id,
description=description,
base_url=spec.base_url,
path=path,
method=method,
properties=properties,
request_body=api_request_body,
)
[docs] @staticmethod
def ts_type_from_python(type_: SCHEMA_TYPE) -> str:
if type_ is None:
# TODO: Handle Nones better. These often result when
# parsing specs that are < v3
return "any"
elif isinstance(type_, str):
return {
"str": "string",
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elif isinstance(type_, str):
return {
"str": "string",
"integer": "number",
"float": "number",
"date-time": "string",
}.get(type_, type_)
elif isinstance(type_, tuple):
return f"Array<{APIOperation.ts_type_from_python(type_[0])}>"
elif isinstance(type_, type) and issubclass(type_, Enum):
return " | ".join([f"'{e.value}'" for e in type_])
else:
return str(type_)
def _format_nested_properties(
self, properties: List[APIRequestBodyProperty], indent: int = 2
) -> str:
"""Format nested properties."""
formatted_props = []
for prop in properties:
prop_name = prop.name
prop_type = self.ts_type_from_python(prop.type)
prop_required = "" if prop.required else "?"
prop_desc = f"/* {prop.description} */" if prop.description else ""
if prop.properties:
nested_props = self._format_nested_properties(
prop.properties, indent + 2
)
prop_type = f"{{\n{nested_props}\n{' ' * indent}}}"
formatted_props.append(
f"{prop_desc}\n{' ' * indent}{prop_name}{prop_required}: {prop_type},"
)
return "\n".join(formatted_props)
[docs] def to_typescript(self) -> str:
"""Get typescript string representation of the operation."""
operation_name = self.operation_id
params = []
if self.request_body:
formatted_request_body_props = self._format_nested_properties(
self.request_body.properties
)
params.append(formatted_request_body_props)
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self.request_body.properties
)
params.append(formatted_request_body_props)
for prop in self.properties:
prop_name = prop.name
prop_type = self.ts_type_from_python(prop.type)
prop_required = "" if prop.required else "?"
prop_desc = f"/* {prop.description} */" if prop.description else ""
params.append(f"{prop_desc}\n\t\t{prop_name}{prop_required}: {prop_type},")
formatted_params = "\n".join(params).strip()
description_str = f"/* {self.description} */" if self.description else ""
typescript_definition = f"""
{description_str}
type {operation_name} = (_: {{
{formatted_params}
}}) => any;
"""
return typescript_definition.strip()
@property
def query_params(self) -> List[str]:
return [
property.name
for property in self.properties
if property.location == APIPropertyLocation.QUERY
]
@property
def path_params(self) -> List[str]:
return [
property.name
for property in self.properties
if property.location == APIPropertyLocation.PATH
]
@property
def body_params(self) -> List[str]:
if self.request_body is None:
return []
return [prop.name for prop in self.request_body.properties]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
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|
c28e50cb75a6-0
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Source code for langchain.tools.openapi.utils.openapi_utils
"""Utility functions for parsing an OpenAPI spec."""
import copy
import json
import logging
import re
from enum import Enum
from pathlib import Path
from typing import Dict, List, Optional, Union
import requests
import yaml
from openapi_schema_pydantic import (
Components,
OpenAPI,
Operation,
Parameter,
PathItem,
Paths,
Reference,
RequestBody,
Schema,
)
from pydantic import ValidationError
logger = logging.getLogger(__name__)
class HTTPVerb(str, Enum):
"""HTTP verbs."""
GET = "get"
PUT = "put"
POST = "post"
DELETE = "delete"
OPTIONS = "options"
HEAD = "head"
PATCH = "patch"
TRACE = "trace"
@classmethod
def from_str(cls, verb: str) -> "HTTPVerb":
"""Parse an HTTP verb."""
try:
return cls(verb)
except ValueError:
raise ValueError(f"Invalid HTTP verb. Valid values are {cls.__members__}")
[docs]class OpenAPISpec(OpenAPI):
"""OpenAPI Model that removes misformatted parts of the spec."""
@property
def _paths_strict(self) -> Paths:
if not self.paths:
raise ValueError("No paths found in spec")
return self.paths
def _get_path_strict(self, path: str) -> PathItem:
path_item = self._paths_strict.get(path)
if not path_item:
raise ValueError(f"No path found for {path}")
return path_item
@property
def _components_strict(self) -> Components:
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@property
def _components_strict(self) -> Components:
"""Get components or err."""
if self.components is None:
raise ValueError("No components found in spec. ")
return self.components
@property
def _parameters_strict(self) -> Dict[str, Union[Parameter, Reference]]:
"""Get parameters or err."""
parameters = self._components_strict.parameters
if parameters is None:
raise ValueError("No parameters found in spec. ")
return parameters
@property
def _schemas_strict(self) -> Dict[str, Schema]:
"""Get the dictionary of schemas or err."""
schemas = self._components_strict.schemas
if schemas is None:
raise ValueError("No schemas found in spec. ")
return schemas
@property
def _request_bodies_strict(self) -> Dict[str, Union[RequestBody, Reference]]:
"""Get the request body or err."""
request_bodies = self._components_strict.requestBodies
if request_bodies is None:
raise ValueError("No request body found in spec. ")
return request_bodies
def _get_referenced_parameter(self, ref: Reference) -> Union[Parameter, Reference]:
"""Get a parameter (or nested reference) or err."""
ref_name = ref.ref.split("/")[-1]
parameters = self._parameters_strict
if ref_name not in parameters:
raise ValueError(f"No parameter found for {ref_name}")
return parameters[ref_name]
def _get_root_referenced_parameter(self, ref: Reference) -> Parameter:
"""Get the root reference or err."""
parameter = self._get_referenced_parameter(ref)
while isinstance(parameter, Reference):
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parameter = self._get_referenced_parameter(ref)
while isinstance(parameter, Reference):
parameter = self._get_referenced_parameter(parameter)
return parameter
[docs] def get_referenced_schema(self, ref: Reference) -> Schema:
"""Get a schema (or nested reference) or err."""
ref_name = ref.ref.split("/")[-1]
schemas = self._schemas_strict
if ref_name not in schemas:
raise ValueError(f"No schema found for {ref_name}")
return schemas[ref_name]
def _get_root_referenced_schema(self, ref: Reference) -> Schema:
"""Get the root reference or err."""
schema = self.get_referenced_schema(ref)
while isinstance(schema, Reference):
schema = self.get_referenced_schema(schema)
return schema
def _get_referenced_request_body(
self, ref: Reference
) -> Optional[Union[Reference, RequestBody]]:
"""Get a request body (or nested reference) or err."""
ref_name = ref.ref.split("/")[-1]
request_bodies = self._request_bodies_strict
if ref_name not in request_bodies:
raise ValueError(f"No request body found for {ref_name}")
return request_bodies[ref_name]
def _get_root_referenced_request_body(
self, ref: Reference
) -> Optional[RequestBody]:
"""Get the root request Body or err."""
request_body = self._get_referenced_request_body(ref)
while isinstance(request_body, Reference):
request_body = self._get_referenced_request_body(request_body)
return request_body
@staticmethod
def _alert_unsupported_spec(obj: dict) -> None:
"""Alert if the spec is not supported."""
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"""Alert if the spec is not supported."""
warning_message = (
" This may result in degraded performance."
+ " Convert your OpenAPI spec to 3.1.* spec"
+ " for better support."
)
swagger_version = obj.get("swagger")
openapi_version = obj.get("openapi")
if isinstance(openapi_version, str):
if openapi_version != "3.1.0":
logger.warning(
f"Attempting to load an OpenAPI {openapi_version}"
f" spec. {warning_message}"
)
else:
pass
elif isinstance(swagger_version, str):
logger.warning(
f"Attempting to load a Swagger {swagger_version}"
f" spec. {warning_message}"
)
else:
raise ValueError(
"Attempting to load an unsupported spec:"
f"\n\n{obj}\n{warning_message}"
)
[docs] @classmethod
def parse_obj(cls, obj: dict) -> "OpenAPISpec":
try:
cls._alert_unsupported_spec(obj)
return super().parse_obj(obj)
except ValidationError as e:
# We are handling possibly misconfigured specs and want to do a best-effort
# job to get a reasonable interface out of it.
new_obj = copy.deepcopy(obj)
for error in e.errors():
keys = error["loc"]
item = new_obj
for key in keys[:-1]:
item = item[key]
item.pop(keys[-1], None)
return cls.parse_obj(new_obj)
[docs] @classmethod
def from_spec_dict(cls, spec_dict: dict) -> "OpenAPISpec":
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|
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def from_spec_dict(cls, spec_dict: dict) -> "OpenAPISpec":
"""Get an OpenAPI spec from a dict."""
return cls.parse_obj(spec_dict)
[docs] @classmethod
def from_text(cls, text: str) -> "OpenAPISpec":
"""Get an OpenAPI spec from a text."""
try:
spec_dict = json.loads(text)
except json.JSONDecodeError:
spec_dict = yaml.safe_load(text)
return cls.from_spec_dict(spec_dict)
[docs] @classmethod
def from_file(cls, path: Union[str, Path]) -> "OpenAPISpec":
"""Get an OpenAPI spec from a file path."""
path_ = path if isinstance(path, Path) else Path(path)
if not path_.exists():
raise FileNotFoundError(f"{path} does not exist")
with path_.open("r") as f:
return cls.from_text(f.read())
[docs] @classmethod
def from_url(cls, url: str) -> "OpenAPISpec":
"""Get an OpenAPI spec from a URL."""
response = requests.get(url)
return cls.from_text(response.text)
@property
def base_url(self) -> str:
"""Get the base url."""
return self.servers[0].url
[docs] def get_methods_for_path(self, path: str) -> List[str]:
"""Return a list of valid methods for the specified path."""
path_item = self._get_path_strict(path)
results = []
for method in HTTPVerb:
operation = getattr(path_item, method.value, None)
if isinstance(operation, Operation):
results.append(method.value)
return results
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if isinstance(operation, Operation):
results.append(method.value)
return results
[docs] def get_operation(self, path: str, method: str) -> Operation:
"""Get the operation object for a given path and HTTP method."""
path_item = self._get_path_strict(path)
operation_obj = getattr(path_item, method, None)
if not isinstance(operation_obj, Operation):
raise ValueError(f"No {method} method found for {path}")
return operation_obj
[docs] def get_parameters_for_operation(self, operation: Operation) -> List[Parameter]:
"""Get the components for a given operation."""
parameters = []
if operation.parameters:
for parameter in operation.parameters:
if isinstance(parameter, Reference):
parameter = self._get_root_referenced_parameter(parameter)
parameters.append(parameter)
return parameters
[docs] def get_request_body_for_operation(
self, operation: Operation
) -> Optional[RequestBody]:
"""Get the request body for a given operation."""
request_body = operation.requestBody
if isinstance(request_body, Reference):
request_body = self._get_root_referenced_request_body(request_body)
return request_body
[docs] @staticmethod
def get_cleaned_operation_id(operation: Operation, path: str, method: str) -> str:
"""Get a cleaned operation id from an operation id."""
operation_id = operation.operationId
if operation_id is None:
# Replace all punctuation of any kind with underscore
path = re.sub(r"[^a-zA-Z0-9]", "_", path.lstrip("/"))
operation_id = f"{path}_{method}"
return operation_id.replace("-", "_").replace(".", "_").replace("/", "_")
By Harrison Chase
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/openapi/utils/openapi_utils.html
|
c28e50cb75a6-6
|
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/openapi/utils/openapi_utils.html
|
1aa44c74cb68-0
|
Source code for langchain.tools.pubmed.tool
"""Tool for the Pubmed API."""
from typing import Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.pupmed import PubMedAPIWrapper
[docs]class PubmedQueryRun(BaseTool):
"""Tool that adds the capability to search using the PubMed API."""
name = "PubMed"
description = (
"A wrapper around PubMed.org "
"Useful for when you need to answer questions about Physics, Mathematics, "
"Computer Science, Quantitative Biology, Quantitative Finance, Statistics, "
"Electrical Engineering, and Economics "
"from scientific articles on PubMed.org. "
"Input should be a search query."
)
api_wrapper: PubMedAPIWrapper = Field(default_factory=PubMedAPIWrapper)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the Arxiv tool."""
return self.api_wrapper.run(query)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the PubMed tool asynchronously."""
raise NotImplementedError("PubMedAPIWrapper does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/pubmed/tool.html
|
f521d5a72594-0
|
Source code for langchain.tools.brave_search.tool
from __future__ import annotations
from typing import Any, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.brave_search import BraveSearchWrapper
[docs]class BraveSearch(BaseTool):
name = "brave_search"
description = (
"a search engine. "
"useful for when you need to answer questions about current events."
" input should be a search query."
)
search_wrapper: BraveSearchWrapper
[docs] @classmethod
def from_api_key(
cls, api_key: str, search_kwargs: Optional[dict] = None, **kwargs: Any
) -> BraveSearch:
wrapper = BraveSearchWrapper(api_key=api_key, search_kwargs=search_kwargs or {})
return cls(search_wrapper=wrapper, **kwargs)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return self.search_wrapper.run(query)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("BraveSearch does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/brave_search/tool.html
|
80ce32ad739f-0
|
Source code for langchain.tools.google_serper.tool
"""Tool for the Serper.dev Google Search API."""
from typing import Optional
from pydantic.fields import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
[docs]class GoogleSerperRun(BaseTool):
"""Tool that adds the capability to query the Serper.dev Google search API."""
name = "google_serper"
description = (
"A low-cost Google Search API."
"Useful for when you need to answer questions about current events."
"Input should be a search query."
)
api_wrapper: GoogleSerperAPIWrapper
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.api_wrapper.run(query))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
return (await self.api_wrapper.arun(query)).__str__()
[docs]class GoogleSerperResults(BaseTool):
"""Tool that has capability to query the Serper.dev Google Search API
and get back json."""
name = "Google Serrper Results JSON"
description = (
"A low-cost Google Search API."
"Useful for when you need to answer questions about current events."
"Input should be a search query. Output is a JSON object of the query results"
)
|
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|
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)
api_wrapper: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.api_wrapper.results(query))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
return (await self.api_wrapper.aresults(query)).__str__()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/google_serper/tool.html
|
c047eddad7db-0
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Source code for langchain.tools.human.tool
"""Tool for asking human input."""
from typing import Callable, Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
def _print_func(text: str) -> None:
print("\n")
print(text)
[docs]class HumanInputRun(BaseTool):
"""Tool that adds the capability to ask user for input."""
name = "human"
description = (
"You can ask a human for guidance when you think you "
"got stuck or you are not sure what to do next. "
"The input should be a question for the human."
)
prompt_func: Callable[[str], None] = Field(default_factory=lambda: _print_func)
input_func: Callable = Field(default_factory=lambda: input)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the Human input tool."""
self.prompt_func(query)
return self.input_func()
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the Human tool asynchronously."""
raise NotImplementedError("Human tool does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/human/tool.html
|
bd9c284cf6d6-0
|
Source code for langchain.tools.ddg_search.tool
"""Tool for the DuckDuckGo search API."""
import warnings
from typing import Any, Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
[docs]class DuckDuckGoSearchRun(BaseTool):
"""Tool that adds the capability to query the DuckDuckGo search API."""
name = "duckduckgo_search"
description = (
"A wrapper around DuckDuckGo Search. "
"Useful for when you need to answer questions about current events. "
"Input should be a search query."
)
api_wrapper: DuckDuckGoSearchAPIWrapper = Field(
default_factory=DuckDuckGoSearchAPIWrapper
)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return self.api_wrapper.run(query)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("DuckDuckGoSearch does not support async")
[docs]class DuckDuckGoSearchResults(BaseTool):
"""Tool that queries the Duck Duck Go Search API and get back json."""
name = "DuckDuckGo Results JSON"
description = (
"A wrapper around Duck Duck Go Search. "
|
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description = (
"A wrapper around Duck Duck Go Search. "
"Useful for when you need to answer questions about current events. "
"Input should be a search query. Output is a JSON array of the query results"
)
num_results: int = 4
api_wrapper: DuckDuckGoSearchAPIWrapper = Field(
default_factory=DuckDuckGoSearchAPIWrapper
)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.api_wrapper.results(query, self.num_results))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("DuckDuckGoSearchResults does not support async")
def DuckDuckGoSearchTool(*args: Any, **kwargs: Any) -> DuckDuckGoSearchRun:
warnings.warn(
"DuckDuckGoSearchTool will be deprecated in the future. "
"Please use DuckDuckGoSearchRun instead.",
DeprecationWarning,
)
return DuckDuckGoSearchRun(*args, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/ddg_search/tool.html
|
77e07fc881c1-0
|
Source code for langchain.tools.bing_search.tool
"""Tool for the Bing search API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.bing_search import BingSearchAPIWrapper
[docs]class BingSearchRun(BaseTool):
"""Tool that adds the capability to query the Bing search API."""
name = "bing_search"
description = (
"A wrapper around Bing Search. "
"Useful for when you need to answer questions about current events. "
"Input should be a search query."
)
api_wrapper: BingSearchAPIWrapper
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return self.api_wrapper.run(query)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("BingSearchRun does not support async")
[docs]class BingSearchResults(BaseTool):
"""Tool that has capability to query the Bing Search API and get back json."""
name = "Bing Search Results JSON"
description = (
"A wrapper around Bing Search. "
"Useful for when you need to answer questions about current events. "
"Input should be a search query. Output is a JSON array of the query results"
)
num_results: int = 4
api_wrapper: BingSearchAPIWrapper
def _run(
self,
|
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|
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|
api_wrapper: BingSearchAPIWrapper
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.api_wrapper.results(query, self.num_results))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("BingSearchResults does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/bing_search/tool.html
|
a841d48ef9ec-0
|
Source code for langchain.tools.google_places.tool
"""Tool for the Google search API."""
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.google_places_api import GooglePlacesAPIWrapper
class GooglePlacesSchema(BaseModel):
query: str = Field(..., description="Query for google maps")
[docs]class GooglePlacesTool(BaseTool):
"""Tool that adds the capability to query the Google places API."""
name = "google_places"
description = (
"A wrapper around Google Places. "
"Useful for when you need to validate or "
"discover addressed from ambiguous text. "
"Input should be a search query."
)
api_wrapper: GooglePlacesAPIWrapper = Field(default_factory=GooglePlacesAPIWrapper)
args_schema: Type[BaseModel] = GooglePlacesSchema
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return self.api_wrapper.run(query)
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("GooglePlacesRun does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/google_places/tool.html
|
710b3df751b9-0
|
Source code for langchain.embeddings.deepinfra
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32"
[docs]class DeepInfraEmbeddings(BaseModel, Embeddings):
"""Wrapper around Deep Infra's embedding inference service.
To use, you should have the
environment variable ``DEEPINFRA_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
There are multiple embeddings models available,
see https://deepinfra.com/models?type=embeddings.
Example:
.. code-block:: python
from langchain.embeddings import DeepInfraEmbeddings
deepinfra_emb = DeepInfraEmbeddings(
model_id="sentence-transformers/clip-ViT-B-32",
deepinfra_api_token="my-api-key"
)
r1 = deepinfra_emb.embed_documents(
[
"Alpha is the first letter of Greek alphabet",
"Beta is the second letter of Greek alphabet",
]
)
r2 = deepinfra_emb.embed_query(
"What is the second letter of Greek alphabet"
)
"""
model_id: str = DEFAULT_MODEL_ID
"""Embeddings model to use."""
normalize: bool = False
"""whether to normalize the computed embeddings"""
embed_instruction: str = "passage: "
"""Instruction used to embed documents."""
query_instruction: str = "query: "
"""Instruction used to embed the query."""
model_kwargs: Optional[dict] = None
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model_kwargs: Optional[dict] = None
"""Other model keyword args"""
deepinfra_api_token: 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."""
deepinfra_api_token = get_from_dict_or_env(
values, "deepinfra_api_token", "DEEPINFRA_API_TOKEN"
)
values["deepinfra_api_token"] = deepinfra_api_token
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"model_id": self.model_id}
def _embed(self, input: List[str]) -> List[List[float]]:
_model_kwargs = self.model_kwargs or {}
# HTTP headers for authorization
headers = {
"Authorization": f"bearer {self.deepinfra_api_token}",
"Content-Type": "application/json",
}
# send request
try:
res = requests.post(
f"https://api.deepinfra.com/v1/inference/{self.model_id}",
headers=headers,
json={"inputs": input, "normalize": self.normalize, **_model_kwargs},
)
except requests.exceptions.RequestException as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
if res.status_code != 200:
raise ValueError(
"Error raised by inference API HTTP code: %s, %s"
% (res.status_code, res.text)
)
try:
t = res.json()
embeddings = t["embeddings"]
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try:
t = res.json()
embeddings = t["embeddings"]
except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f"Error raised by inference API: {e}.\nResponse: {res.text}"
)
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a Deep Infra deployed embedding model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
instruction_pairs = [f"{self.query_instruction}{text}" for text in texts]
embeddings = self._embed(instruction_pairs)
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using a Deep Infra deployed embedding model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = f"{self.query_instruction}{text}"
embedding = self._embed([instruction_pair])[0]
return embedding
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/deepinfra.html
|
dec942a12f89-0
|
Source code for langchain.embeddings.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
class EmbeddingsContentHandler(ContentHandlerBase[List[str], List[List[float]]]):
"""Content handler for LLM class."""
[docs]class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
"""Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
"""
"""
Example:
.. code-block:: python
from langchain.embeddings import SagemakerEndpointEmbeddings
endpoint_name = (
"my-endpoint-name"
)
region_name = (
"us-west-2"
)
credentials_profile_name = (
"default"
)
se = SagemakerEndpointEmbeddings(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
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credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
region_name: str = ""
"""The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
credentials_profile_name: Optional[str] = None
"""The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
"""
content_handler: EmbeddingsContentHandler
"""The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
"""
"""
Example:
.. code-block:: python
from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
class ContentHandler(EmbeddingsContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompts: List[str], model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompts: prompts, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> List[List[float]]:
response_json = json.loads(output.read().decode("utf-8"))
return response_json["vectors"]
""" # noqa: E501
model_kwargs: Optional[Dict] = None
|
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""" # noqa: E501
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
endpoint_kwargs: Optional[Dict] = None
"""Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
"""
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 AWS credentials to and python package exists in environment."""
try:
import boto3
try:
if values["credentials_profile_name"] is not None:
session = boto3.Session(
profile_name=values["credentials_profile_name"]
)
else:
# use default credentials
session = boto3.Session()
values["client"] = session.client(
"sagemaker-runtime", region_name=values["region_name"]
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
) from e
except ImportError:
raise ValueError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
return values
def _embedding_func(self, texts: List[str]) -> List[List[float]]:
"""Call out to SageMaker Inference embedding endpoint."""
# replace newlines, which can negatively affect performance.
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# replace newlines, which can negatively affect performance.
texts = list(map(lambda x: x.replace("\n", " "), texts))
_model_kwargs = self.model_kwargs or {}
_endpoint_kwargs = self.endpoint_kwargs or {}
body = self.content_handler.transform_input(texts, _model_kwargs)
content_type = self.content_handler.content_type
accepts = self.content_handler.accepts
# send request
try:
response = self.client.invoke_endpoint(
EndpointName=self.endpoint_name,
Body=body,
ContentType=content_type,
Accept=accepts,
**_endpoint_kwargs,
)
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
return self.content_handler.transform_output(response["Body"])
[docs] def embed_documents(
self, texts: List[str], chunk_size: int = 64
) -> List[List[float]]:
"""Compute doc embeddings using a SageMaker Inference Endpoint.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size defines how many input texts will
be grouped together as request. If None, will use the
chunk size specified by the class.
Returns:
List of embeddings, one for each text.
"""
results = []
_chunk_size = len(texts) if chunk_size > len(texts) else chunk_size
for i in range(0, len(texts), _chunk_size):
response = self._embedding_func(texts[i : i + _chunk_size])
results.extend(response)
return results
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a SageMaker inference endpoint.
|
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|
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|
"""Compute query embeddings using a SageMaker inference endpoint.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self._embedding_func([text])[0]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/sagemaker_endpoint.html
|
42c9539dca1e-0
|
Source code for langchain.embeddings.mosaicml
"""Wrapper around MosaicML APIs."""
from typing import Any, Dict, List, Mapping, Optional, Tuple
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class MosaicMLInstructorEmbeddings(BaseModel, Embeddings):
"""Wrapper around MosaicML's embedding inference service.
To use, you should have the
environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.llms import MosaicMLInstructorEmbeddings
endpoint_url = (
"https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
)
mosaic_llm = MosaicMLInstructorEmbeddings(
endpoint_url=endpoint_url,
mosaicml_api_token="my-api-key"
)
"""
endpoint_url: str = (
"https://models.hosted-on.mosaicml.hosting/instructor-xl/v1/predict"
)
"""Endpoint URL to use."""
embed_instruction: str = "Represent the document for retrieval: "
"""Instruction used to embed documents."""
query_instruction: str = (
"Represent the question for retrieving supporting documents: "
)
"""Instruction used to embed the query."""
retry_sleep: float = 1.0
"""How long to try sleeping for if a rate limit is encountered"""
mosaicml_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
|
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|
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|
"""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."""
mosaicml_api_token = get_from_dict_or_env(
values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
)
values["mosaicml_api_token"] = mosaicml_api_token
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"endpoint_url": self.endpoint_url}
def _embed(
self, input: List[Tuple[str, str]], is_retry: bool = False
) -> List[List[float]]:
payload = {"input_strings": input}
# HTTP headers for authorization
headers = {
"Authorization": f"{self.mosaicml_api_token}",
"Content-Type": "application/json",
}
# send request
try:
response = requests.post(self.endpoint_url, headers=headers, json=payload)
except requests.exceptions.RequestException as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
try:
parsed_response = response.json()
if "error" in parsed_response:
# if we get rate limited, try sleeping for 1 second
if (
not is_retry
and "rate limit exceeded" in parsed_response["error"].lower()
):
import time
time.sleep(self.retry_sleep)
return self._embed(input, is_retry=True)
raise ValueError(
f"Error raised by inference API: {parsed_response['error']}"
)
|
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|
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|
f"Error raised by inference API: {parsed_response['error']}"
)
# The inference API has changed a couple of times, so we add some handling
# to be robust to multiple response formats.
if isinstance(parsed_response, dict):
if "data" in parsed_response:
output_item = parsed_response["data"]
elif "output" in parsed_response:
output_item = parsed_response["output"]
else:
raise ValueError(
f"No key data or output in response: {parsed_response}"
)
if isinstance(output_item, list) and isinstance(output_item[0], list):
embeddings = output_item
else:
embeddings = [output_item]
elif isinstance(parsed_response, list):
first_item = parsed_response[0]
if isinstance(first_item, list):
embeddings = parsed_response
elif isinstance(first_item, dict):
if "output" in first_item:
embeddings = [item["output"] for item in parsed_response]
else:
raise ValueError(
f"No key data or output in response: {parsed_response}"
)
else:
raise ValueError(f"Unexpected response format: {parsed_response}")
else:
raise ValueError(f"Unexpected response type: {parsed_response}")
except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f"Error raised by inference API: {e}.\nResponse: {response.text}"
)
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a MosaicML deployed instructor embedding model.
Args:
texts: The list of texts to embed.
Returns:
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/mosaicml.html
|
42c9539dca1e-3
|
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
instruction_pairs = [(self.embed_instruction, text) for text in texts]
embeddings = self._embed(instruction_pairs)
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using a MosaicML deployed instructor embedding model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = (self.query_instruction, text)
embedding = self._embed([instruction_pair])[0]
return embedding
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/mosaicml.html
|
8ea8f222abb5-0
|
Source code for langchain.embeddings.self_hosted_hugging_face
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
"Represent the question for retrieving supporting documents: "
)
logger = logging.getLogger(__name__)
def _embed_documents(client: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
"""Inference function to send to the remote hardware.
Accepts a sentence_transformer model_id and
returns a list of embeddings for each document in the batch.
"""
return client.encode(*args, **kwargs)
def load_embedding_model(model_id: str, instruct: bool = False, device: int = 0) -> Any:
"""Load the embedding model."""
if not instruct:
import sentence_transformers
client = sentence_transformers.SentenceTransformer(model_id)
else:
from InstructorEmbedding import INSTRUCTOR
client = INSTRUCTOR(model_id)
if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device < 0 and cuda_device_count > 0:
logger.warning(
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/self_hosted_hugging_face.html
|
8ea8f222abb5-1
|
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
client = client.to(device)
return client
[docs]class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings):
"""Runs sentence_transformers embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.).
To use, you should have the ``runhouse`` python package installed.
Example:
.. code-block:: python
from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
import runhouse as rh
model_name = "sentence-transformers/all-mpnet-base-v2"
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
"""
client: Any #: :meta private:
model_id: str = DEFAULT_MODEL_NAME
"""Model name to use."""
model_reqs: List[str] = ["./", "sentence_transformers", "torch"]
"""Requirements to install on hardware to inference the model."""
hardware: Any
"""Remote hardware to send the inference function to."""
model_load_fn: Callable = load_embedding_model
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/self_hosted_hugging_face.html
|
8ea8f222abb5-2
|
model_load_fn: Callable = load_embedding_model
"""Function to load the model remotely on the server."""
load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
inference_fn: Callable = _embed_documents
"""Inference function to extract the embeddings."""
def __init__(self, **kwargs: Any):
"""Initialize the remote inference function."""
load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
load_fn_kwargs["model_id"] = load_fn_kwargs.get("model_id", DEFAULT_MODEL_NAME)
load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", False)
load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs]class SelfHostedHuggingFaceInstructEmbeddings(SelfHostedHuggingFaceEmbeddings):
"""Runs InstructorEmbedding embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the ``runhouse`` python package installed.
Example:
.. code-block:: python
from langchain.embeddings import SelfHostedHuggingFaceInstructEmbeddings
import runhouse as rh
model_name = "hkunlp/instructor-large"
gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
hf = SelfHostedHuggingFaceInstructEmbeddings(
model_name=model_name, hardware=gpu)
"""
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/self_hosted_hugging_face.html
|
8ea8f222abb5-3
|
model_name=model_name, hardware=gpu)
"""
model_id: str = DEFAULT_INSTRUCT_MODEL
"""Model name to use."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding query."""
model_reqs: List[str] = ["./", "InstructorEmbedding", "torch"]
"""Requirements to install on hardware to inference the model."""
def __init__(self, **kwargs: Any):
"""Initialize the remote inference function."""
load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
load_fn_kwargs["model_id"] = load_fn_kwargs.get(
"model_id", DEFAULT_INSTRUCT_MODEL
)
load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", True)
load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace instruct model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
instruction_pairs = []
for text in texts:
instruction_pairs.append([self.embed_instruction, text])
embeddings = self.client(self.pipeline_ref, instruction_pairs)
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace instruct model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/self_hosted_hugging_face.html
|
8ea8f222abb5-4
|
Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
return embedding.tolist()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/self_hosted_hugging_face.html
|
1323debf1f02-0
|
Source code for langchain.embeddings.embaas
"""Wrapper around embaas embeddings API."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from typing_extensions import NotRequired, TypedDict
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
# Currently supported maximum batch size for embedding requests
MAX_BATCH_SIZE = 256
EMBAAS_API_URL = "https://api.embaas.io/v1/embeddings/"
class EmbaasEmbeddingsPayload(TypedDict):
"""Payload for the embaas embeddings API."""
model: str
texts: List[str]
instruction: NotRequired[str]
[docs]class EmbaasEmbeddings(BaseModel, Embeddings):
"""Wrapper around embaas's embedding service.
To use, you should have the
environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
# Initialise with default model and instruction
from langchain.embeddings import EmbaasEmbeddings
emb = EmbaasEmbeddings()
# Initialise with custom model and instruction
from langchain.embeddings import EmbaasEmbeddings
emb_model = "instructor-large"
emb_inst = "Represent the Wikipedia document for retrieval"
emb = EmbaasEmbeddings(
model=emb_model,
instruction=emb_inst
)
"""
model: str = "e5-large-v2"
"""The model used for embeddings."""
instruction: Optional[str] = None
"""Instruction used for domain-specific embeddings."""
api_url: str = EMBAAS_API_URL
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/embaas.html
|
1323debf1f02-1
|
api_url: str = EMBAAS_API_URL
"""The URL for the embaas embeddings API."""
embaas_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."""
embaas_api_key = get_from_dict_or_env(
values, "embaas_api_key", "EMBAAS_API_KEY"
)
values["embaas_api_key"] = embaas_api_key
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying params."""
return {"model": self.model, "instruction": self.instruction}
def _generate_payload(self, texts: List[str]) -> EmbaasEmbeddingsPayload:
"""Generates payload for the API request."""
payload = EmbaasEmbeddingsPayload(texts=texts, model=self.model)
if self.instruction:
payload["instruction"] = self.instruction
return payload
def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]:
"""Sends a request to the Embaas API and handles the response."""
headers = {
"Authorization": f"Bearer {self.embaas_api_key}",
"Content-Type": "application/json",
}
response = requests.post(self.api_url, headers=headers, json=payload)
response.raise_for_status()
parsed_response = response.json()
embeddings = [item["embedding"] for item in parsed_response["data"]]
return embeddings
|
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/embaas.html
|
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