id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
6b5de2d557db-18 | self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
6b5de2d557db-19 | output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return [(output, observation)]
# If the tool chosen is the finishing tool, then we end and... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
6b5de2d557db-20 | **tool_run_kwargs,
)
return agent_action, observation
# Use asyncio.gather to run multiple tool.arun() calls concurrently
result = await asyncio.gather(
*[_aperform_agent_action(agent_action) for agent_action in actions]
)
return list(result)
d... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
6b5de2d557db-21 | next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(
tool_return, intermediate_steps, run_manager=run_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
6b5de2d557db-22 | color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return await self._areturn(
next_step_ou... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
6b5de2d557db-23 | if agent_action.tool in name_to_tool_map:
if name_to_tool_map[agent_action.tool].return_direct:
return AgentFinish(
{self.agent.return_values[0]: observation},
"",
)
return None | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
ba2db4abada0-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple
from mypy_extensions import Arg, KwArg
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base im... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-1 | from langchain.tools.shell.tool import ShellTool
from langchain.tools.sleep.tool import SleepTool
from langchain.tools.wikipedia.tool import WikipediaQueryRun
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.tools.openweathermap.tool import OpenWeatherMapQueryRun
from langchain.utiliti... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-2 | def _get_tools_requests_delete() -> BaseTool:
return RequestsDeleteTool(requests_wrapper=TextRequestsWrapper())
def _get_terminal() -> BaseTool:
return ShellTool()
def _get_sleep() -> BaseTool:
return SleepTool()
_BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
"python_repl": _get_python_repl,
"re... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-3 | return Tool(
name="Calculator",
description="Useful for when you need to answer questions about math.",
func=LLMMathChain.from_llm(llm=llm).run,
coroutine=LLMMathChain.from_llm(llm=llm).arun,
)
def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
chain = APIChain.from_llm... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-4 | func=chain.run,
)
def _get_tmdb_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
tmdb_bearer_token = kwargs["tmdb_bearer_token"]
chain = APIChain.from_llm_and_api_docs(
llm,
tmdb_docs.TMDB_DOCS,
headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
)
return Tool(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-5 | def _get_google_search(**kwargs: Any) -> BaseTool:
return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_wikipedia(**kwargs: Any) -> BaseTool:
return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))
def _get_arxiv(**kwargs: Any) -> BaseTool:
return ArxivQueryRun(api_wrapp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-6 | )
def _get_searx_search(**kwargs: Any) -> BaseTool:
return SearxSearchRun(wrapper=SearxSearchWrapper(**kwargs))
def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
wrapper_kwargs = {k: v for k, v in kwargs.items() if k != "num_results"}
return SearxSearchResults(wrapper=SearxSearchWrapper(**wrapp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-7 | ] = {
"news-api": (_get_news_api, ["news_api_key"]),
"tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
"podcast-api": (_get_podcast_api, ["listen_api_key"]),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
"wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-8 | "searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
"wikipedia": (_get_wikipedia, ["top_k_results", "lang"]),
"arxiv": (
_get_arxiv,
["top_k_results", "load_max_docs", "load_all_available_meta"],
),
"pupmed": (
_get_pupmed,
["top_k_results", "loa... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-9 | **kwargs: Any,
) -> BaseTool:
"""Loads a tool from the HuggingFace Hub.
Args:
task_or_repo_id: Task or model repo id.
model_repo_id: Optional model repo id.
token: Optional token.
remote: Optional remote. Defaults to False.
**kwargs:
Returns:
A tool.
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-10 | Args:
tool_names: name of tools to load.
llm: Optional language model, may be needed to initialize certain tools.
callbacks: Optional callback manager or list of callback handlers.
If not provided, default global callback manager will be used.
Returns:
List of tools.
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
ba2db4abada0-11 | f"provided: {missing_keys}"
)
sub_kwargs = {k: kwargs[k] for k in extra_keys}
tool = _get_llm_tool_func(llm=llm, **sub_kwargs)
tools.append(tool)
elif name in _EXTRA_OPTIONAL_TOOLS:
_get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
bf66f1023f60-0 | Source code for langchain.agents.schema
from typing import Any, Dict, List, Tuple
from langchain.prompts.chat import ChatPromptTemplate
from langchain.schema import AgentAction
[docs]class AgentScratchPadChatPromptTemplate(ChatPromptTemplate):
def _construct_agent_scratchpad(
self, intermediate_steps: List[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/schema.html |
1318ea267389-0 | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import BaseMultiActionAgent, BaseSingleActionAgent
from langchain.agents.tools import Tool
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
1318ea267389-1 | if load_from_tools:
if llm is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
"then LLM must be provided"
)
if tools is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
1318ea267389-2 | if hub_result := try_load_from_hub(
path, _load_agent_from_file, "agents", {"json", "yaml"}
):
return hub_result
else:
return _load_agent_from_file(path, **kwargs)
def _load_agent_from_file(
file: Union[str, Path], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
a5ec93eb700d-0 | Source code for langchain.agents.tools
"""Interface for tools."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool, Tool, tool
[docs]class InvalidTool(BaseTool):
"""Tool that is run wh... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
3336977c804f-0 | Source code for langchain.agents.self_ask_with_search.output_parser
from typing import Sequence, Union
from langchain.agents.agent import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish, OutputParserException
[docs]class SelfAskOutputParser(AgentOutputParser):
followups: Sequence[str] = ("Fo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/output_parser.html |
a31343aedd2b-0 | Source code for langchain.agents.self_ask_with_search.base
"""Chain that does self ask with search."""
from typing import Any, Sequence, Union
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.se... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
a31343aedd2b-1 | raise ValueError(f"Exactly one tool must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Intermediate Answer"}:
raise ValueError(
f"Tool name should be Intermediate Answer, got {tool_names}"
)
@property
def obs... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
e559f5cce58c-0 | Source code for langchain.agents.structured_chat.output_parser
from __future__ import annotations
import json
import logging
import re
from typing import Optional, Union
from pydantic import Field
from langchain.agents.agent import AgentOutputParser
from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTION... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/output_parser.html |
e559f5cce58c-1 | def _type(self) -> str:
return "structured_chat"
[docs]class StructuredChatOutputParserWithRetries(AgentOutputParser):
base_parser: AgentOutputParser = Field(default_factory=StructuredChatOutputParser)
output_fixing_parser: Optional[OutputFixingParser] = None
[docs] def get_format_instructions(self) ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/output_parser.html |
27f62269828b-0 | Source code for langchain.agents.structured_chat.base
import re
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.structured_chat.output_parser import (
StructuredChatOutputParserWithRetries,
)
from la... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
27f62269828b-1 | return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _validate_tools(cls, tools: Sequence[Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
27f62269828b-2 | template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
_memory_prompts = memory_prompts or []
messages = [
SystemMessagePromptTemplate.from_template(template),
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
27f62269828b-3 | )
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser(llm=llm)
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
@property
de... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
3b0941723010-0 | Source code for langchain.agents.agent_toolkits.base
"""Toolkits for agents."""
from abc import abstractmethod
from typing import List
from pydantic import BaseModel
from langchain.tools import BaseTool
[docs]class BaseToolkit(BaseModel):
"""Class responsible for defining a collection of related tools."""
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/base.html |
b3c1a8f3e65c-0 | Source code for langchain.agents.agent_toolkits.zapier.toolkit
"""Zapier Toolkit."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.zapier.tool import ZapierNLARunAction
from langchain.utilities.zapier import ZapierNLAWrappe... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
b3c1a8f3e65c-1 | for action in actions
]
return cls(tools=tools)
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return self.tools | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
e920c5fa5153-0 | Source code for langchain.agents.agent_toolkits.python.base
"""Python agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.python.prompt import PREFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langch... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
e920c5fa5153-1 | elif agent_type == AgentType.OPENAI_FUNCTIONS:
system_message = SystemMessage(content=prefix)
_prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_m... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
97794e010498-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
97794e010498-1 | 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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
c89fa1b48311-0 | 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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
c89fa1b48311-1 | )
[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]... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
000f9a622f77-0 | Source code for langchain.agents.agent_toolkits.gmail.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.gmail.create_draft import GmailCreateD... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/gmail/toolkit.html |
0ee102cb8d43-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
0ee102cb8d43-1 | "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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
1e13edb219ee-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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
1e13edb219ee-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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
1e13edb219ee-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 {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
c7edee8a2f6d-0 | Source code for langchain.agents.agent_toolkits.spark_sql.toolkit
"""Toolkit for interacting with Spark SQL."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.tools import BaseTool
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/toolkit.html |
5e3822023f11-0 | Source code for langchain.agents.agent_toolkits.spark_sql.base
"""Spark SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark_sql.prompt import SQL_PREFIX, SQL_SUFFIX
from langchain.agents.agent_toolkits.spark_sql.toolkit i... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
5e3822023f11-1 | 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_and_tools(
agent=agent,
too... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
f28b6b13c7f7-0 | Source code for langchain.agents.agent_toolkits.nla.tool
"""Tool for interacting with a single API with natural language efinition."""
from typing import Any, Optional
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.chains.api.openapi.chain import OpenAPIEndp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/tool.html |
f28b6b13c7f7-1 | api_operation = APIOperation.from_openapi_spec(spec, path, method)
chain = OpenAPIEndpointChain.from_api_operation(
api_operation,
llm,
requests=requests,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
**kwargs,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/tool.html |
b6e594f8983c-0 | Source code for langchain.agents.agent_toolkits.nla.toolkit
"""Toolkit for interacting with API's using natural language."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.a... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
b6e594f8983c-1 | )
http_operation_tools.append(endpoint_tool)
return http_operation_tools
[docs] @classmethod
def from_llm_and_spec(
cls,
llm: BaseLanguageModel,
spec: OpenAPISpec,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
b6e594f8983c-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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
6487b9074530-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
6487b9074530-1 | ) -> 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
cad7b8cb5305-0 | Source code for langchain.agents.agent_toolkits.json.toolkit
"""Toolkit for interacting with a JSON spec."""
from __future__ import annotations
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.json.tool import JsonGetValueTool... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/toolkit.html |
2fc5c75e6373-0 | Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
2fc5c75e6373-1 | return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
92f9478dd6ed-0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional, Tuple
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.pandas.prompt import (
FUNCTIONS_WITH_DF,
FUNC... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
92f9478dd6ed-1 | include_dfs_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad", "num_dfs"]
if include_dfs_head:
input_variables += ["dfs_head"]
if prefix is None:
prefix = MULTI_DF_PREFIX
df_locals = {}
for i, dataframe in enumerate(dfs):
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
92f9478dd6ed-2 | include_df_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
if include_df_head:
input_variables += ["df_head"]
if prefix is None:
prefix = PREFIX
tools = [PythonAstREPLTool(locals={"df": df})]
prompt = ZeroShotAgent.create_promp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
92f9478dd6ed-3 | include_df_in_prompt=include_df_in_prompt,
)
else:
if not isinstance(df, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_single_prompt(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_v... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
92f9478dd6ed-4 | suffix_to_use = suffix
if include_df_in_prompt:
dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
suffix_to_use = suffix_to_use.format(
dfs_head=dfs_head,
)
elif include_df_in_prompt:
dfs_head = "\n\n".join([d.head().to_markdown() for d... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
92f9478dd6ed-5 | if include_df_in_prompt is not None and suffix is not None:
raise ValueError("If suffix is specified, include_df_in_prompt should not be.")
if isinstance(df, list):
for item in df:
if not isinstance(item, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
92f9478dd6ed-6 | agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt, tools = _get_prompt_and_tools(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_prompt=include_df_in_prompt,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
665bcb7a0f89-0 | Source code for langchain.agents.agent_toolkits.csv.base
"""Agent for working with csvs."""
from typing import Any, List, Optional, Union
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.base_language import BaseLanguag... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
89b0ad1e015c-0 | Source code for langchain.agents.agent_toolkits.azure_cognitive_services.toolkit
from __future__ import annotations
import sys
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools.azure_cognitive_services import (
AzureCogsFormRecognizerTool,
AzureCogsImageAn... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/azure_cognitive_services/toolkit.html |
f53ea2be9a63-0 | Source code for langchain.agents.agent_toolkits.openapi.planner
"""Agent that interacts with OpenAPI APIs via a hierarchical planning approach."""
import json
import re
from functools import partial
from typing import Any, Callable, Dict, List, Optional
import yaml
from pydantic import Field
from langchain.agents.agent... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f53ea2be9a63-1 | # information in the response.
# However, the goal for now is to have only a single inference step.
MAX_RESPONSE_LENGTH = 5000
def _get_default_llm_chain(prompt: BasePromptTemplate) -> LLMChain:
return LLMChain(
llm=OpenAI(),
prompt=prompt,
)
def _get_default_llm_chain_factory(
prompt: BaseP... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f53ea2be9a63-2 | llm_chain: LLMChain = Field(
default_factory=_get_default_llm_chain_factory(PARSING_POST_PROMPT)
)
def _run(self, text: str) -> str:
try:
data = json.loads(text)
except json.JSONDecodeError as e:
raise e
response = self.requests_wrapper.post(data["url"], d... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f53ea2be9a63-3 | llm_chain: LLMChain = Field(
default_factory=_get_default_llm_chain_factory(PARSING_DELETE_PROMPT)
)
def _run(self, text: str) -> str:
try:
data = json.loads(text)
except json.JSONDecodeError as e:
raise e
response = self.requests_wrapper.delete(data["url"... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f53ea2be9a63-4 | post_llm_chain = LLMChain(llm=llm, prompt=PARSING_POST_PROMPT)
tools: List[BaseTool] = [
RequestsGetToolWithParsing(
requests_wrapper=requests_wrapper, llm_chain=get_llm_chain
),
RequestsPostToolWithParsing(
requests_wrapper=requests_wrapper, llm_chain=post_llm_chain
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f53ea2be9a63-5 | def _create_and_run_api_controller_agent(plan_str: str) -> str:
pattern = r"\b(GET|POST|PATCH|DELETE)\s+(/\S+)*"
matches = re.findall(pattern, plan_str)
endpoint_names = [
"{method} {route}".format(method=method, route=route.split("?")[0])
for method, route in matches
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f53ea2be9a63-6 | We use a top-level "orchestrator" agent to invoke the planner and controller,
rather than a top-level planner
that invokes a controller with its plan. This is to keep the planner simple.
"""
tools = [
_create_api_planner_tool(api_spec, llm),
_create_api_controller_tool(api_spec, requests... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
01bfc05430bc-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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
01bfc05430bc-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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
b52b700f4707-0 | Source code for langchain.agents.agent_toolkits.openapi.spec
"""Quick and dirty representation for OpenAPI specs."""
from dataclasses import dataclass
from typing import Any, Dict, List, Tuple, Union
[docs]def dereference_refs(spec_obj: dict, full_spec: dict) -> Union[dict, list]:
"""Try to substitute $refs.
Th... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/spec.html |
b52b700f4707-1 | obj_out[k] = _dereference_refs(v)
else:
obj_out[k] = v
return obj_out
elif isinstance(obj, list):
return [_dereference_refs(el) for el in obj]
else:
return obj
return _dereference_refs(spec_obj)
@dataclass(frozen=True)
class Red... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/spec.html |
b52b700f4707-2 | # 3. Strip docs down to required request args + happy path response.
def reduce_endpoint_docs(docs: dict) -> dict:
out = {}
if docs.get("description"):
out["description"] = docs.get("description")
if docs.get("parameters"):
out["parameters"] = [
parame... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/spec.html |
83fb68d29a51-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
83fb68d29a51-1 | 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_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
a2e44e03c318-0 | Source code for langchain.agents.agent_toolkits.playwright.toolkit
"""Playwright web browser toolkit."""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Type, cast
from pydantic import Extra, root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
a2e44e03c318-1 | """Check that the arguments are valid."""
lazy_import_playwright_browsers()
if values.get("async_browser") is None and values.get("sync_browser") is None:
raise ValueError("Either async_browser or sync_browser must be specified.")
return values
[docs] def get_tools(self) -> List[B... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
8bb1746b1d68-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
8bb1746b1d68-1 | )
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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
992712e0cf61-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
5277915715ed-0 | Source code for langchain.agents.agent_toolkits.office365.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.office365.create_draft_message imp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/office365/toolkit.html |
4734b444ed85-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
4734b444ed85-1 | """
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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
5702387e309c-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.... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
5702387e309c-1 | prompt=PromptTemplate(
template=QUESTION_TO_QUERY,
input_variables=["tool_input", "tables", "schemas", "examples"],
),
)
return [
QueryPowerBITool(
llm_chain=chain,
powerbi=self.powerbi,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
6cbfe80e8bd1-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
6cbfe80e8bd1-1 | 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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
131693acf272-0 | Source code for langchain.agents.mrkl.output_parser
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
FINAL_ANSWER_ACTION = "Final Answer:"
[doc... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/output_parser.html |
131693acf272-1 | raise OutputParserException(
f"Could not parse LLM output: `{text}`",
observation="Invalid Format: Missing 'Action:' after 'Thought:'",
llm_output=text,
send_to_llm=True,
)
elif not re.search(
r"[\s]*Action\s*\d*\s*Input\s*\... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/output_parser.html |
04835fe85928-0 | Source code for langchain.agents.mrkl.base
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
from typing import Any, Callable, List, NamedTuple, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, A... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
04835fe85928-1 | @property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
04835fe85928-2 | llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variable... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
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