id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
d1465df74ebb-2 | spec = OpenAPISpec.from_url(ai_plugin.api.url)
# TODO: Merge optional Auth information with the `requests` argument
return cls.from_llm_and_spec(
llm=llm,
spec=spec,
requests=requests,
verbose=verbose,
**kwargs,
)
[docs] @classmethod... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
16681484bc59-0 | Source code for langchain.agents.agent_toolkits.file_management.toolkit
"""Toolkit for interacting with the local filesystem."""
from __future__ import annotations
from typing import List, Optional
from pydantic import root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools impo... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
16681484bc59-1 | )
return values
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
allowed_tools = self.selected_tools or _FILE_TOOLS.keys()
tools: List[BaseTool] = []
for tool in allowed_tools:
tool_cls = _FILE_TOOLS[tool]
tools.append(t... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
dadeb0cfa25b-0 | Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
dadeb0cfa25b-1 | )
[docs]def create_vectorstore_router_agent(
llm: BaseLanguageModel,
toolkit: VectorStoreRouterToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = ROUTER_PREFIX,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any]... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
1fa354a244c4-0 | Source code for langchain.agents.agent_toolkits.vectorstore.toolkit
"""Toolkit for interacting with a vector store."""
from typing import List
from pydantic import BaseModel, Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.llms.open... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
1fa354a244c4-1 | self.vectorstore_info.name, self.vectorstore_info.description
)
qa_with_sources_tool = VectorStoreQAWithSourcesTool(
name=f"{self.vectorstore_info.name}_with_sources",
description=description,
vectorstore=self.vectorstore_info.vectorstore,
llm=self.llm,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
7caeb0a0ca5a-0 | Source code for langchain.agents.agent_toolkits.jira.toolkit
"""Jira Toolkit."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.jira.tool import JiraAction
from langchain.utilities.jira import JiraAPIWrapper
[docs]class Jira... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
6b6351e404ac-0 | Source code for langchain.agents.agent_toolkits.powerbi.base
"""Power BI agent."""
from typing import Any, Dict, List, Optional
from langchain.agents import AgentExecutor
from langchain.agents.agent_toolkits.powerbi.prompt import (
POWERBI_PREFIX,
POWERBI_SUFFIX,
)
from langchain.agents.agent_toolkits.powerbi.t... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
6b6351e404ac-1 | tools = toolkit.get_tools()
agent = ZeroShotAgent(
llm_chain=LLMChain(
llm=llm,
prompt=ZeroShotAgent.create_prompt(
tools,
prefix=prefix.format(top_k=top_k),
suffix=suffix,
format_instructions=format_instructions,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
2e342796f126-0 | Source code for langchain.agents.agent_toolkits.powerbi.toolkit
"""Toolkit for interacting with a Power BI dataset."""
from typing import List, Optional
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
2e342796f126-1 | prompt=PromptTemplate(
template=QUESTION_TO_QUERY,
input_variables=["tool_input", "tables", "schemas", "examples"],
),
)
return [
QueryPowerBITool(
llm_chain=chain,
powerbi=self.powerbi,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
3720ef0fa70b-0 | Source code for langchain.agents.agent_toolkits.powerbi.chat_base
"""Power BI agent."""
from typing import Any, Dict, List, Optional
from langchain.agents import AgentExecutor
from langchain.agents.agent import AgentOutputParser
from langchain.agents.agent_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
P... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
3720ef0fa70b-1 | """
if toolkit is None:
if powerbi is None:
raise ValueError("Must provide either a toolkit or powerbi dataset")
toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
tools = toolkit.get_tools()
agent = ConversationalChatAgent.from_llm_and_tools(
llm=llm,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
038d5e48e78b-0 | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/openapi/base.html |
038d5e48e78b-1 | input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/openapi/base.html |
38984c9a68ae-0 | Source code for langchain.agents.agent_toolkits.openapi.toolkit
"""Requests toolkit."""
from __future__ import annotations
from typing import Any, List
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.agent_toolkits.json.base import crea... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
38984c9a68ae-1 | func=self.json_agent.run,
description=DESCRIPTION,
)
request_toolkit = RequestsToolkit(requests_wrapper=self.requests_wrapper)
return [*request_toolkit.get_tools(), json_agent_tool]
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
json_spe... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
d2f35fc7c613-0 | Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
from langcha... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/base.html |
d2f35fc7c613-1 | **kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a sql agent from an LLM and tools."""
tools = toolkit.get_tools()
prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt = ZeroShotAgen... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/base.html |
d2f35fc7c613-2 | tools=tools,
callback_manager=callback_manager,
verbose=verbose,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
)
By Harrison Chase
© Copyright 2023, Harris... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/base.html |
f478103f116c-0 | Source code for langchain.agents.agent_toolkits.sql.toolkit
"""Toolkit for interacting with a SQL database."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.sql_database import SQLDatab... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
f478103f116c-1 | "schema and sample rows for those tables. "
"Be sure that the tables actually exist by calling list_tables_sql_db "
"first! Example Input: 'table1, table2, table3'"
)
return [
QuerySQLDataBaseTool(
db=self.db, description=query_sql_database_tool_descri... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
4c8501407393-0 | Source code for langchain.agents.agent_toolkits.spark.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAge... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark/base.html |
4c8501407393-1 | ) -> AgentExecutor:
"""Construct a spark agent from an LLM and dataframe."""
if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
raise ValueError("Spark is not installed. run `pip install pyspark`.")
if input_variables is None:
input_variables = ["df", "input", "agent_scrat... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark/base.html |
43c5777a5f07-0 | Source code for langchain.experimental.generative_agents.generative_agent
import re
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.experimental.gen... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
43c5777a5f07-1 | arbitrary_types_allowed = True
# LLM-related methods
@staticmethod
def _parse_list(text: str) -> List[str]:
"""Parse a newline-separated string into a list of strings."""
lines = re.split(r"\n", text.strip())
return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
de... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
43c5777a5f07-2 | entity_action = self._get_entity_action(observation, entity_name)
q1 = f"What is the relationship between {self.name} and {entity_name}"
q2 = f"{entity_name} is {entity_action}"
return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
def _generate_reaction(
self, observ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
43c5777a5f07-3 | )
consumed_tokens = self.llm.get_num_tokens(
prompt.format(most_recent_memories="", **kwargs)
)
kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
return self.chain(prompt=prompt).run(**kwargs).strip()
def _clean_response(self, text: str) -> str:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
43c5777a5f07-4 | if "SAY:" in result:
said_value = self._clean_response(result.split("SAY:")[-1])
return True, f"{self.name} said {said_value}"
else:
return False, result
[docs] def generate_dialogue_response(
self, observation: str, now: Optional[datetime] = None
) -> Tuple[bo... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
43c5777a5f07-5 | )
return True, f"{self.name} said {response_text}"
else:
return False, result
######################################################
# Agent stateful' summary methods. #
# Each dialog or response prompt includes a header #
# summarizing the agent's sel... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
43c5777a5f07-6 | + f"\nInnate traits: {self.traits}"
+ f"\n{self.summary}"
)
[docs] def get_full_header(
self, force_refresh: bool = False, now: Optional[datetime] = None
) -> str:
"""Return a full header of the agent's status, summary, and current time."""
now = datetime.now() if now ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/generative_agent.html |
3aeb4e560610-0 | Source code for langchain.experimental.generative_agents.memory
import logging
import re
from datetime import datetime
from typing import Any, Dict, List, Optional
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.prompts import PromptTemplate
from langchain.retrievers ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
3aeb4e560610-1 | # output keys
relevant_memories_key: str = "relevant_memories"
relevant_memories_simple_key: str = "relevant_memories_simple"
most_recent_memories_key: str = "most_recent_memories"
now_key: str = "now"
reflecting: bool = False
def chain(self, prompt: PromptTemplate) -> LLMChain:
return L... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
3aeb4e560610-2 | self, topic: str, now: Optional[datetime] = None
) -> List[str]:
"""Generate 'insights' on a topic of reflection, based on pertinent memories."""
prompt = PromptTemplate.from_template(
"Statements relevant to: '{topic}'\n"
"---\n"
"{related_statements}\n"
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
3aeb4e560610-3 | insights = self._get_insights_on_topic(topic, now=now)
for insight in insights:
self.add_memory(insight, now=now)
new_insights.extend(insights)
return new_insights
def _score_memory_importance(self, memory_content: str) -> float:
"""Score the absolute importan... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
3aeb4e560610-4 | + " acceptance), rate the likely poignancy of the"
+ " following piece of memory. Always answer with only a list of numbers."
+ " If just given one memory still respond in a list."
+ " Memories are separated by semi colans (;)"
+ "\Memories: {memory_content}"
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
3aeb4e560610-5 | and not self.reflecting
):
self.reflecting = True
self.pause_to_reflect(now=now)
# Hack to clear the importance from reflection
self.aggregate_importance = 0.0
self.reflecting = False
return result
[docs] def add_memory(
self, memory... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
3aeb4e560610-6 | else:
return self.memory_retriever.get_relevant_documents(observation)
def format_memories_detail(self, relevant_memories: List[Document]) -> str:
content = []
for mem in relevant_memories:
content.append(self._format_memory_detail(mem, prefix="- "))
return "\n".join(... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
3aeb4e560610-7 | now = inputs.get(self.now_key)
if queries is not None:
relevant_memories = [
mem for query in queries for mem in self.fetch_memories(query, now=now)
]
return {
self.relevant_memories_key: self.format_memories_detail(
relevan... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/generative_agents/memory.html |
ea0ac6f5fb34-0 | Source code for langchain.experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.au... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
ea0ac6f5fb34-1 | @classmethod
def from_llm_and_tools(
cls,
ai_name: str,
ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
chat_histor... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
ea0ac6f5fb34-2 | user_input=user_input,
)
# Print Assistant thoughts
print(assistant_reply)
self.chat_history_memory.add_message(HumanMessage(content=user_input))
self.chat_history_memory.add_message(AIMessage(content=assistant_reply))
# Get command name and argume... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
ea0ac6f5fb34-3 | return "EXITING"
memory_to_add += feedback
self.memory.add_documents([Document(page_content=memory_to_add)])
self.chat_history_memory.add_message(SystemMessage(content=result))
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
1a5086a44589-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
"""BabyAGI agent."""
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerFo... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
1a5086a44589-1 | print(str(t["task_id"]) + ": " + t["task_name"])
def print_next_task(self, task: Dict) -> None:
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
print(str(task["task_id"]) + ": " + task["task_name"])
def print_task_result(self, result: str) -> None:
print("\033[93m... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
1a5086a44589-2 | next_task_id = int(this_task_id) + 1
response = self.task_prioritization_chain.run(
task_names=", ".join(task_names),
next_task_id=str(next_task_id),
objective=objective,
)
new_tasks = response.split("\n")
prioritized_task_list = []
for task_st... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
1a5086a44589-3 | """Run the agent."""
objective = inputs["objective"]
first_task = inputs.get("first_task", "Make a todo list")
self.add_task({"task_id": 1, "task_name": first_task})
num_iters = 0
while True:
if self.task_list:
self.print_task_list()
# ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
1a5086a44589-4 | return {}
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
verbose: bool = False,
task_execution_chain: Optional[Chain] = None,
**kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initialize the BabyAGI Controller."""
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
07f828686f35-0 | Source code for langchain.tools.base
"""Base implementation for tools or skills."""
from __future__ import annotations
import warnings
from abc import ABC, abstractmethod
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, Optional, Tuple, Type, Union
from pydantic import (
BaseModel,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-1 | ...
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.\n"
f"Expected annotation of 'Type[... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-2 | """Create a pydantic schema from a function's signature.
Args:
model_name: Name to assign to the generated pydandic schema
func: Function to generate the schema from
Returns:
A pydantic model with the same arguments as the function
"""
# https://docs.pydantic.dev/latest/usage/val... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-3 | """Pydantic model class to validate and parse the tool's input arguments."""
return_direct: bool = False
"""Whether to return the tool's output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
"""
verbose: bool = False
"""Whether to lo... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-4 | if isinstance(tool_input, str):
if input_args is not None:
key_ = next(iter(input_args.__fields__.keys()))
input_args.validate({key_: tool_input})
return tool_input
else:
if input_args is not None:
result = input_args.parse_obj(... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-5 | # pass as a positional argument.
if isinstance(tool_input, str):
return (tool_input,), {}
else:
return (), tool_input
[docs] def run(
self,
tool_input: Union[str, Dict],
verbose: Optional[bool] = None,
start_color: Optional[str] = "green",
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-6 | if e.args:
observation = e.args[0]
else:
observation = "Tool execution error"
elif isinstance(self.handle_tool_error, str):
observation = self.handle_tool_error
elif callable(self.handle_tool_error):
observat... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-7 | {"name": self.name, "description": self.description},
tool_input if isinstance(tool_input, str) else str(tool_input),
color=start_color,
**kwargs,
)
try:
# We then call the tool on the tool input to get an observation
tool_args, tool_kwargs = s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-8 | """Make tool callable."""
return self.run(tool_input, callbacks=callbacks)
[docs]class Tool(BaseTool):
"""Tool that takes in function or coroutine directly."""
description: str = ""
func: Callable[..., str]
"""The function to run when the tool is called."""
coroutine: Optional[Callable[..., ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-9 | return (
self.func(
*args,
callbacks=run_manager.get_child() if run_manager else None,
**kwargs,
)
if new_argument_supported
else self.func(*args, **kwargs)
)
async def _arun(
self,
*args: Any,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-10 | """Initialize tool from a function."""
return cls(
name=name,
func=func,
description=description,
return_direct=return_direct,
args_schema=args_schema,
**kwargs,
)
[docs]class StructuredTool(BaseTool):
"""Tool that can operate o... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-11 | new_argument_supported = signature(self.coroutine).parameters.get(
"callbacks"
)
return (
await self.coroutine(
*args,
callbacks=run_manager.get_child() if run_manager else None,
**kwargs,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-12 | """
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:
# search_api(query: str) - Searches the API for the query.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-13 | # Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the query.
return
"""
def _make_with_name(tool_name: str) -> Callable:
def _make_tool(func: Calla... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
07f828686f35-14 | # Example usage: @tool(return_direct=True)
def _partial(func: Callable[[str], str]) -> BaseTool:
return _make_with_name(func.__name__)(func)
return _partial
else:
raise ValueError("Too many arguments for tool decorator")
By Harrison Chase
© Copyright 2023, Harrison Cha... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/base.html |
85bc7760a83a-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/plugin.html |
85bc7760a83a-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? "
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/plugin.html |
a6573cdc7c2a-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/ifttt.html |
a6573cdc7c2a-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/ifttt.html |
b3c788a46096-0 | Source code for langchain.tools.convert_to_openai
from typing import TypedDict
from langchain.tools import BaseTool, StructuredTool
class FunctionDescription(TypedDict):
"""Representation of a callable function to the OpenAI API."""
name: str
"""The name of the function."""
description: str
"""A des... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/convert_to_openai.html |
b3c788a46096-1 | "parameters": parameters,
}
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/convert_to_openai.html |
ebe2de81fad0-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/scenexplain/tool.html |
7d4159f7709a-0 | Source code for langchain.tools.gmail.get_message
import base64
import email
from typing import Dict, Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.gmail.base import GmailBaseTool
f... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/get_message.html |
7d4159f7709a-1 | "snippet": message_data["snippet"],
"body": body,
"subject": subject,
"sender": sender,
}
async def _arun(
self,
message_id: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> Dict:
"""Run the tool."""
raise... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/get_message.html |
3303a0e92cd1-0 | Source code for langchain.tools.gmail.send_message
"""Send Gmail messages."""
import base64
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackMa... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/send_message.html |
3303a0e92cd1-1 | mime_message["To"] = ", ".join(to)
mime_message["Subject"] = subject
if cc is not None:
mime_message["Cc"] = ", ".join(cc)
if bcc is not None:
mime_message["Bcc"] = ", ".join(bcc)
encoded_message = base64.urlsafe_b64encode(mime_message.as_bytes()).decode()
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/send_message.html |
3303a0e92cd1-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/send_message.html |
8db032df4989-0 | Source code for langchain.tools.gmail.get_thread
from typing import Dict, Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.gmail.base import GmailBaseTool
class GetThreadSchema(BaseMod... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/get_thread.html |
8db032df4989-1 | )
return thread_data
async def _arun(
self,
thread_id: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> Dict:
"""Run the tool."""
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/get_thread.html |
cf418e1b93d3-0 | Source code for langchain.tools.gmail.search
import base64
import email
from enum import Enum
from typing import Any, Dict, List, Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.gmail... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/search.html |
cf418e1b93d3-1 | name: str = "search_gmail"
description: str = (
"Use this tool to search for email messages or threads."
" The input must be a valid Gmail query."
" The output is a JSON list of the requested resource."
)
args_schema: Type[SearchArgsSchema] = SearchArgsSchema
def _parse_threads(s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/search.html |
cf418e1b93d3-2 | body = clean_email_body(message_body)
results.append(
{
"id": message["id"],
"threadId": message_data["threadId"],
"snippet": message_data["snippet"],
"body": body,
"subject": subject,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/search.html |
e069f4b620d7-0 | Source code for langchain.tools.gmail.create_draft
import base64
from email.message import EmailMessage
from typing import List, Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.gmail.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/create_draft.html |
e069f4b620d7-1 | draft_message["Subject"] = subject
if cc is not None:
draft_message["Cc"] = ", ".join(cc)
if bcc is not None:
draft_message["Bcc"] = ", ".join(bcc)
encoded_message = base64.urlsafe_b64encode(draft_message.as_bytes()).decode()
return {"message": {"raw": encoded_mes... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/gmail/create_draft.html |
1ba5028ac2d2-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/click.html |
1ba5028ac2d2-1 | # Navigate to the desired webpage before using this tool
selector_effective = self._selector_effective(selector=selector)
from playwright.sync_api import TimeoutError as PlaywrightTimeoutError
try:
page.click(
selector_effective,
strict=self.playwright... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/click.html |
3e5c63896784-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/navigate_back.html |
3e5c63896784-1 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/navigate_back.html |
f9381b8f134e-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/get_elements.html |
f9381b8f134e-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/get_elements.html |
f9381b8f134e-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, ensure_ascii=False)
By H... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/get_elements.html |
353e9877096b-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/current_page.html |
63e9f8824711-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/navigate.html |
63e9f8824711-1 | 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 Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/navigate.html |
622ad6cf94ac-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/extract_hyperlinks.html |
622ad6cf94ac-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/extract_hyperlinks.html |
737e97d5ccda-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 ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/extract_text.html |
737e97d5ccda-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/playwright/extract_text.html |
8e386b9b80fc-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/wolfram_alpha/tool.html |
c816c78470fd-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/google_search/tool.html |
c816c78470fd-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,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/tools/google_search/tool.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.