id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 50 116 |
|---|---|---|
3fbe180ab87f-1 | doc.metadata["relevance_score"] = r.relevance_score
final_results.append(doc)
return final_results
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
raise NotImplementedError
By Harrison Chase
© Copyright ... | https:///python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
caa9398ee407-0 | Source code for langchain.retrievers.document_compressors.chain_extract
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
import asyncio
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import LLMChain, PromptTemplate
from... | https:///python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
caa9398ee407-1 | [docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress page content of raw documents."""
compressed_docs = []
for doc in documents:
_input = self.get_input(query, doc)
output = self.llm_chain.pred... | https:///python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
caa9398ee407-2 | _get_input = get_input if get_input is not None else default_get_input
llm_chain = LLMChain(llm=llm, prompt=_prompt, **(llm_chain_kwargs or {}))
return cls(llm_chain=llm_chain, get_input=_get_input)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
53df88743dfb-0 | Source code for langchain.tools.base
"""Base implementation for tools or skills."""
from __future__ import annotations
import warnings
from abc import ABC, abstractmethod
from functools import partial
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, Optional, Tuple, Type, Union
from pyda... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-1 | typehint_mandate = """
class ChildTool(BaseTool):
...
args_schema: Type[BaseModel] = SchemaClass
..."""
raise SchemaAnnotationError(
f"Tool definition for {name} must include valid type annotations"
f" for argument 'args_schema' to behave as expected.\... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-2 | arbitrary_types_allowed = True
def create_schema_from_function(
model_name: str,
func: Callable,
) -> Type[BaseModel]:
"""Create a pydantic schema from a function's signature."""
validated = validate_arguments(func, config=_SchemaConfig) # type: ignore
inferred_model = validated.model # type: igno... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-3 | """Deprecated. Please use callbacks instead."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def is_single_input(self) -> bool:
"""Whether the tool only accepts a single input."""
return len(... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-4 | Add run_manager: Optional[CallbackManagerForToolRun] = None
to child implementations to enable tracing,
"""
@abstractmethod
async def _arun(
self,
*args: Any,
**kwargs: Any,
) -> Any:
"""Use the tool asynchronously.
Add run_manager: Optional[AsyncCallb... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-5 | tool_input if isinstance(tool_input, str) else str(tool_input),
color=start_color,
**kwargs,
)
try:
tool_args, tool_kwargs = self._to_args_and_kwargs(tool_input)
observation = (
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-6 | try:
# We then call the tool on the tool input to get an observation
tool_args, tool_kwargs = self._to_args_and_kwargs(tool_input)
observation = (
await self._arun(*tool_args, run_manager=run_manager, **tool_kwargs)
if new_arg_supported
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-7 | # assume it takes a single string input.
return {"tool_input": {"type": "string"}}
def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
"""Convert tool input to pydantic model."""
args, kwargs = super()._to_args_and_kwargs(tool_input)
# For backwards com... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-8 | *args,
callbacks=run_manager.get_child() if run_manager else None,
**kwargs,
)
if new_argument_supported
else await self.coroutine(*args, **kwargs)
)
raise NotImplementedError("Tool does not support async")
#... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-9 | @property
def args(self) -> dict:
"""The tool's input arguments."""
return self.args_schema.schema()["properties"]
def _run(
self,
*args: Any,
run_manager: Optional[CallbackManagerForToolRun] = None,
**kwargs: Any,
) -> Any:
"""Use the tool."""
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-10 | infer_schema: bool = True,
**kwargs: Any,
) -> StructuredTool:
name = name or func.__name__
description = description or func.__doc__
assert (
description is not None
), "Function must have a docstring if description not provided."
# Description example:
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-11 | Examples:
.. code-block:: python
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the que... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
53df88743dfb-12 | elif len(args) == 0:
# if there are no arguments, then we use the function name as the tool name
# Example usage: @tool(return_direct=True)
def _partial(func: Callable[[str], str]) -> BaseTool:
return _make_with_name(func.__name__)(func)
return _partial
else:
rais... | https:///python.langchain.com/en/latest/_modules/langchain/tools/base.html |
943d1ef0fba4-0 | Source code for langchain.tools.plugin
from __future__ import annotations
import json
from typing import Optional, Type
import requests
import yaml
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base impo... | https:///python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
943d1ef0fba4-1 | plugin = AIPlugin.from_url(url)
description = (
f"Call this tool to get the OpenAPI spec (and usage guide) "
f"for interacting with the {plugin.name_for_human} API. "
f"You should only call this ONCE! What is the "
f"{plugin.name_for_human} API useful for? "
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
4dda54dd2775-0 | Source code for langchain.tools.ifttt
"""From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.
# Creating a webhook
- Go to https://ifttt.com/create
# Configuring the "If This"
- Click on the "If This" button in the IFTTT interface.
- Search for "Webhooks" in the search bar.
- Choose the first... | https:///python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
4dda54dd2775-1 | - To get your webhook URL go to https://ifttt.com/maker_webhooks/settings
- Copy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.
"""
from typing import Optional
import requests
from langchain.callbacks.manager import (
AsyncCallbackMa... | https:///python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
8bf60f7985c4-0 | 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 WikipediaAPIWrap... | https:///python.langchain.com/en/latest/_modules/langchain/tools/wikipedia/tool.html |
b7594cf46412-0 | Source code for langchain.tools.shell.tool
import asyncio
import platform
import warnings
from typing import List, Optional, Type
from pydantic import BaseModel, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base... | https:///python.langchain.com/en/latest/_modules/langchain/tools/shell/tool.html |
b7594cf46412-1 | 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: List[str],
run_manager: ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/shell/tool.html |
9adfd87fb897-0 | 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, Sales... | https:///python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
9adfd87fb897-1 | 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/provid... | https:///python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
9adfd87fb897-2 | 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
f... | https:///python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
9adfd87fb897-3 | 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["instruction... | https:///python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
9adfd87fb897-4 | "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 ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
bfe817b161de-0 | Source code for langchain.tools.google_places.tool
"""Tool for the Google search API."""
from typing import Optional
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langcha... | https:///python.langchain.com/en/latest/_modules/langchain/tools/google_places/tool.html |
6d11fcf6fc37-0 | 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... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/read.html |
6d11fcf6fc37-1 | # TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/read.html |
c4aaf86b3ed0-0 | 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 langc... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/file_search.html |
c4aaf86b3ed0-1 | 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_pat... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/file_search.html |
072b9f89f9a9-0 | 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_ma... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/list_dir.html |
072b9f89f9a9-1 | raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/list_dir.html |
62ad9339838d-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.util... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/write.html |
62ad9339838d-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 NotImplementedErr... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/write.html |
7d860ffc0308-0 | 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_ma... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/copy.html |
7d860ffc0308-1 | 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 C... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/copy.html |
436200cf227e-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_ma... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/move.html |
436200cf227e-1 | 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: ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/move.html |
3d8f3acff7bf-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_mana... | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/delete.html |
3d8f3acff7bf-1 | raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/tools/file_management/delete.html |
50438994f997-0 | Source code for langchain.tools.playwright.navigate_back
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrow... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate_back.html |
50438994f997-1 | response = await page.go_back()
if response:
return (
f"Navigated back to the previous page with URL '{response.url}'."
f" Status code {response.status}"
)
else:
return "Unable to navigate back; no previous page in the history"
By Harri... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate_back.html |
057d86b1cbad-0 | Source code for langchain.tools.playwright.navigate
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBr... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate.html |
057d86b1cbad-1 | response = await page.goto(url)
status = response.status if response else "unknown"
return f"Navigating to {url} returned status code {status}"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate.html |
1f155ae1a204-0 | Source code for langchain.tools.playwright.extract_hyperlinks
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Optional, Type
from pydantic import BaseModel, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToo... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_hyperlinks.html |
1f155ae1a204-1 | # Find all the anchor elements and extract their href attributes
anchors = soup.find_all("a")
if absolute_urls:
base_url = page.url
links = [urljoin(base_url, anchor.get("href", "")) for anchor in anchors]
else:
links = [anchor.get("href", "") for anchor in an... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_hyperlinks.html |
8e5c8242ed3a-0 | Source code for langchain.tools.playwright.current_page
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrows... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/current_page.html |
10076e66a24a-0 | Source code for langchain.tools.playwright.click
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrows... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/click.html |
10076e66a24a-1 | # Navigate to the desired webpage before using this tool
await page.click(selector)
return f"Clicked element '{selector}'"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/click.html |
69c9316f1b9a-0 | Source code for langchain.tools.playwright.extract_text
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_text.html |
69c9316f1b9a-1 | self, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
) -> str:
"""Use the tool."""
if self.async_browser is None:
raise ValueError(f"Asynchronous browser not provided to {self.name}")
# Use Beautiful Soup since it's faster than looping through the elements
f... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_text.html |
19ba288bcef2-0 | Source code for langchain.tools.playwright.get_elements
from __future__ import annotations
import json
from typing import TYPE_CHECKING, List, Optional, Sequence, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
fro... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
19ba288bcef2-1 | ) -> List[dict]:
"""Get elements matching the given CSS selector."""
elements = page.query_selector_all(selector)
results = []
for element in elements:
result = {}
for attribute in attributes:
if attribute == "innerText":
val: Optional[str] = element.inner_tex... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
19ba288bcef2-2 | raise ValueError(f"Asynchronous browser not provided to {self.name}")
page = await aget_current_page(self.async_browser)
# Navigate to the desired webpage before using this tool
results = await _aget_elements(page, selector, attributes)
return json.dumps(results)
By Harrison Chase
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
dda22c251306-0 | 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 pydant... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-1 | + f"Valid values are {[loc.value for loc in SUPPORTED_LOCATIONS]}"
)
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 ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-2 | type_ = schema.type
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"{p... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-3 | schema_type = APIProperty._get_schema_type_for_enum(parameter, schema)
else:
# Directly use the primitive type
pass
else:
raise NotImplementedError(f"Unsupported type: {schema_type}")
return schema_type
@staticmethod
def _validate_location(... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-4 | location,
parameter.name,
)
cls._validate_content(parameter.content)
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=param... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-5 | cls.from_schema(
schema=prop_schema,
name=prop_name,
required=prop_name in required_props,
spec=spec,
references_used=references_used,
)
)
return schema.type, properties
@classmeth... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-6 | schema_type, properties = cls._process_object_schema(
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 typ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-7 | f"Could not resolve schema for media type: {media_type_obj}"
)
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 isinst... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-8 | operation_id: str = Field(alias="operation_id")
"""The unique identifier of the operation."""
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... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-9 | def from_openapi_url(
cls,
spec_url: str,
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_... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-10 | # parsing specs that are < v3
return "any"
elif isinstance(type_, str):
return {
"str": "string",
"integer": "number",
"float": "number",
"date-time": "string",
}.get(type_, type_)
elif isinstance(type_, ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
dda22c251306-11 | if self.request_body:
formatted_request_body_props = self._format_nested_properties(
self.request_body.properties
)
params.append(formatted_request_body_props)
for prop in self.properties:
prop_name = prop.name
prop_type = self.ts_type_... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
13874158dc07-0 | 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 ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
13874158dc07-1 | return path_item
@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... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
13874158dc07-2 | 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."""
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
13874158dc07-3 | """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")
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
13874158dc07-4 | 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... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
13874158dc07-5 | 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 ... | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
13874158dc07-6 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
069843e0877f-0 | Source code for langchain.tools.google_search.tool
"""Tool for the Google search API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.google_search import Goog... | https:///python.langchain.com/en/latest/_modules/langchain/tools/google_search/tool.html |
069843e0877f-1 | api_wrapper: GoogleSearchAPIWrapper
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,
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/google_search/tool.html |
6c60263c9675-0 | Source code for langchain.tools.wolfram_alpha.tool
"""Tool for the Wolfram Alpha API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.wolfram_alpha import Wolf... | https:///python.langchain.com/en/latest/_modules/langchain/tools/wolfram_alpha/tool.html |
f543fb827b81-0 | 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
f... | https:///python.langchain.com/en/latest/_modules/langchain/tools/ddg_search/tool.html |
f543fb827b81-1 | 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(
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/ddg_search/tool.html |
e85be1e6fc47-0 | 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 BingSearch... | https:///python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
e85be1e6fc47-1 | 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,
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
a5dbb5e71334-0 | Source code for langchain.tools.scenexplain.tool
"""Tool for the SceneXplain API."""
from typing import Optional
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.u... | https:///python.langchain.com/en/latest/_modules/langchain/tools/scenexplain/tool.html |
7e92e294efe6-0 | 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: st... | https:///python.langchain.com/en/latest/_modules/langchain/tools/human/tool.html |
36844e29b7b6-0 | 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,
... | https:///python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
36844e29b7b6-1 | 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 _aru... | https:///python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
36844e29b7b6-2 | 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 asynchr... | https:///python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
f0c5cd47cf1b-0 | Source code for langchain.embeddings.huggingface_hub
"""Wrapper around HuggingFace Hub embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
f0c5cd47cf1b-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
f0c5cd47cf1b-2 | texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
responses = self.client(inputs=texts, params=_model_kwargs)
return responses
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to HuggingFaceHub's embedding endpoint for embed... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
6ffc2b4d2321-0 | Source code for langchain.embeddings.self_hosted
"""Running custom embedding models on self-hosted remote hardware."""
from typing import Any, Callable, List
from pydantic import Extra
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
def _embed_documents(pipeline: Any, *arg... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
6ffc2b4d2321-1 | model_load_fn=get_pipeline,
hardware=gpu
model_reqs=["./", "torch", "transformers"],
)
Example passing in a pipeline path:
.. code-block:: python
from langchain.embeddings import SelfHostedHFEmbeddings
import runhouse as rh
from... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
6ffc2b4d2321-2 | [docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embeddings = self.clie... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
569c98686fa9-0 | 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
... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
569c98686fa9-1 | 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 ... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
569c98686fa9-2 | """ # 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/ap... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
569c98686fa9-3 | # 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_ty... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
569c98686fa9-4 | """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 Ma... | https:///python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
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