id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
fbe2ff5864f3-34 | if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions = [output]
else:
actions = output
result = []
for agent_action in actions:
if run_manager:
run_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-35 | verbose=self.verbose,
color=color,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
else:
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = InvalidTo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-36 | ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
# Call the LLM to see what to do.
output = await self.agent.apl... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-37 | observation = str(e.observation)
text = str(e.llm_output)
else:
observation = "Invalid or incomplete response"
elif isinstance(self.handle_parsing_errors, str):
observation = self.handle_parsing_errors
elif callable(self.han... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-38 | if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions = [output]
else:
actions = output
async def _aperform_agent_action(
agent_action: AgentAction,
) -> Tuple[Agent... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-39 | # We then call the tool on the tool input to get an observation
observation = await tool.arun(
agent_action.tool_input,
verbose=self.verbose,
color=color,
callbacks=run_manager.get_child() if run_manager else None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-40 | )
return list(result)
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run text through and get agent response."""
# Construct a mapping of tool name to tool for easy lookup
name_to_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-41 | start_time = time.time()
# We now enter the agent loop (until it returns something).
while self._should_continue(iterations, time_elapsed):
next_step_output = self._take_next_step(
name_to_tool_map,
color_mapping,
inputs,
interm... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-42 | )
iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return self._return(output, intermediate_steps, run_manager=run_manager)
async def _acall(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-43 | )
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Let's start tracking the number of iterations and time elapsed
iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
async with asyncio_time... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-44 | intermediate_steps.extend(next_step_output)
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
fbe2ff5864f3-45 | self.early_stopping_method, intermediate_steps, **inputs
)
return await self._areturn(
output, intermediate_steps, run_manager=run_manager
)
def _get_tool_return(
self, next_step_output: Tuple[AgentAction, str]
) -> Optional[AgentFinish... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
94ec7979f5bc-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 |
94ec7979f5bc-1 | [docs]class StructuredChatAgent(Agent):
output_parser: AgentOutputParser = Field(
default_factory=StructuredChatOutputParserWithRetries
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_p... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
94ec7979f5bc-2 | 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[BaseTool]) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
94ec7979f5bc-3 | tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
memory_prompts: Optional[List[BasePromptTemplate]] = N... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
94ec7979f5bc-4 | 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 |
94ec7979f5bc-5 | suffix: str = SUFFIX,
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
memory_prompts: Optional[List[BasePromptTemplate]] = None,
**kwargs: Any,
) -> Agent:
"""Construct ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
94ec7979f5bc-6 | )
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 |
990de165640f-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 |
990de165640f-1 | ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
from langchain.schema import AIMessage, SystemMessage
[docs]def create_sql_agent(
llm: BaseLanguageModel,
toolkit: SQLDatabaseToolkit,
agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callback_manager: Optional[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
990de165640f-2 | **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 |
990de165640f-3 | elif agent_type == AgentType.OPENAI_FUNCTIONS:
messages = [
SystemMessage(content=prefix),
HumanMessagePromptTemplate.from_template("{input}"),
AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
990de165640f-4 | 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 |
839b4df8b5ad-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 |
839b4df8b5ad-1 | class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
query_sql_database_tool_description = (
"Input to this tool is a detailed and correct SQL query, out... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
839b4df8b5ad-2 | "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 |
8704531ee2d8-0 | 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 |
8704531ee2d8-1 | callback_manager: Optional[BaseCallbackManager] = None,
verbose: bool = False,
prefix: str = PREFIX,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a python agent from an LLM and tool."""
tools = [tool]
agent: BaseSingleActi... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
8704531ee2d8-2 | 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 |
f9e576155924-0 | 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 |
f9e576155924-1 | """Get the tools for all the API operations."""
return list(self.nla_tools)
@staticmethod
def _get_http_operation_tools(
llm: BaseLanguageModel,
spec: OpenAPISpec,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
) -> List[NLATool]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
f9e576155924-2 | **kwargs,
)
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,... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
f9e576155924-3 | open_api_url: str,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
) -> NLAToolkit:
"""Instantiate the toolkit from an OpenAPI Spec URL"""
spec = OpenAPISpec.from_url(open_api_url)
return cls.from_llm_and_spec(
llm=llm, spec=spec... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
f9e576155924-4 | 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 |
f9e576155924-5 | llm=llm, ai_plugin=plugin, requests=requests, verbose=verbose, **kwargs
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
e8dd8daeb092-0 | 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 |
e8dd8daeb092-1 | powerbi: Optional[PowerBIDataset] = None,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = POWERBI_PREFIX,
suffix: str = POWERBI_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
examples: Optional[str] = None,
input_variables: Optional[List[str]] = None,
top_k: in... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
e8dd8daeb092-2 | toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
tools = toolkit.get_tools()
agent = ZeroShotAgent(
llm_chain=LLMChain(
llm=llm,
prompt=ZeroShotAgent.create_prompt(
tools,
prefix=prefix.format(top_k=top_k),
suff... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
a1390202d518-0 | 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 |
a1390202d518-1 | llm: BaseLanguageModel = Field(exclude=True)
examples: Optional[str] = None
max_iterations: int = 5
callback_manager: Optional[BaseCallbackManager] = None
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[Base... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
a1390202d518-2 | template=QUESTION_TO_QUERY,
input_variables=["tool_input", "tables", "schemas", "examples"],
),
)
return [
QueryPowerBITool(
llm_chain=chain,
powerbi=self.powerbi,
examples=self.examples,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
38defe0ffbd9-0 | 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 |
38defe0ffbd9-1 | powerbi: Optional[PowerBIDataset] = None,
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = POWERBI_CHAT_PREFIX,
suffix: str = POWERBI_CHAT_SUFFIX,
examples: Optional[str] = None,
input_variables: Optional[List[str]] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
38defe0ffbd9-2 | """
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 |
38defe0ffbd9-3 | verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
9050b8450d27-0 | 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 |
9050b8450d27-1 | input_variables: Optional[List[str]] = None,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a json agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
9050b8450d27-2 | 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 |
d6b7b2606841-0 | 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 |
be9938903c19-0 | 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 |
be9938903c19-1 | from langchain.prompts.base import BasePromptTemplate
from langchain.schema import SystemMessage
from langchain.tools.python.tool import PythonAstREPLTool
def _get_multi_prompt(
dfs: List[Any],
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
i... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-2 | 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):
df_locals[f"df{i + 1}"] = dat... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-3 | if "num_dfs" in input_variables:
partial_prompt = partial_prompt.partial(num_dfs=str(num_dfs))
return partial_prompt, tools
def _get_single_prompt(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
include_df_in_prompt: Opti... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-4 | 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_prompt(
tools, prefix=pre... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-5 | include_df_in_prompt: Optional[bool] = True,
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
try:
import pandas as pd
except ImportError:
raise ValueError(
"pandas package not found, please install with `pip install pandas`"
)
if include_df_in_prompt is not None ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-6 | )
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_variables,
include_df_in_prompt=include_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-7 | elif include_df_in_prompt:
suffix_to_use = FUNCTIONS_WITH_DF.format(df_head=str(df.head().to_markdown()))
else:
suffix_to_use = ""
if prefix is None:
prefix = PREFIX_FUNCTIONS
tools = [PythonAstREPLTool(locals={"df": df})]
system_message = SystemMessage(content=prefix + suffix_to... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-8 | 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 in dfs])
suffix_to_use... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-9 | system_message = SystemMessage(content=prefix + suffix_to_use)
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
return prompt, tools
def _get_functions_prompt_and_tools(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-10 | 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(df)}")
return _get_functions_multi_prompt(
df,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-11 | df: Any,
agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
verbose: bool = False,
return_intermediate_steps: bool = Fa... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
be9938903c19-12 | 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 |
be9938903c19-13 | df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_prompt=include_df_in_prompt,
)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_manager=callback_ma... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
90313a315de5-0 | 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 |
90313a315de5-1 | except ImportError:
pass
SCOPES = ["https://mail.google.com/"]
[docs]class GmailToolkit(BaseToolkit):
"""Toolkit for interacting with Gmail."""
api_resource: Resource = Field(default_factory=build_resource_service)
class Config:
"""Pydantic config."""
arbitrary_types_allowed = True
[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/gmail/toolkit.html |
16e3432a61f6-0 | 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 |
16e3432a61f6-1 | verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a vectorstore agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
16e3432a61f6-2 | )
[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 |
16e3432a61f6-3 | agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
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/vectorstore/base.html |
a66e2b9e1216-0 | 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 |
a66e2b9e1216-1 | """Toolkit for interacting with a vector store."""
vectorstore_info: VectorStoreInfo = Field(exclude=True)
llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0))
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def ge... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
a66e2b9e1216-2 | )
qa_with_sources_tool = VectorStoreQAWithSourcesTool(
name=f"{self.vectorstore_info.name}_with_sources",
description=description,
vectorstore=self.vectorstore_info.vectorstore,
llm=self.llm,
)
return [qa_tool, qa_with_sources_tool]
[docs]class Vec... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
a66e2b9e1216-3 | for vectorstore_info in self.vectorstores:
description = VectorStoreQATool.get_description(
vectorstore_info.name, vectorstore_info.description
)
qa_tool = VectorStoreQATool(
name=vectorstore_info.name,
description=description,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
1da169999ffb-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 |
1da169999ffb-1 | try:
from pyspark.sql.connect.dataframe import DataFrame as SparkConnectDataFrame
return isinstance(df, SparkConnectDataFrame)
except ImportError:
return False
[docs]def create_spark_dataframe_agent(
llm: BaseLLM,
df: Any,
callback_manager: Optional[BaseCallbackManager] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
1da169999ffb-2 | ) -> 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 |
1da169999ffb-3 | agent = ZeroShotAgent(
llm_chain=llm_chain,
allowed_tools=tool_names,
callback_manager=callback_manager,
**kwargs,
)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
re... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
49c3e1452655-0 | 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 |
49c3e1452655-1 | if TYPE_CHECKING:
from playwright.async_api import Browser as AsyncBrowser
from playwright.sync_api import Browser as SyncBrowser
else:
try:
# We do this so pydantic can resolve the types when instantiating
from playwright.async_api import Browser as AsyncBrowser
from playwright.sync... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
49c3e1452655-2 | """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 |
49c3e1452655-3 | [docs] @classmethod
def from_browser(
cls,
sync_browser: Optional[SyncBrowser] = None,
async_browser: Optional[AsyncBrowser] = None,
) -> PlayWrightBrowserToolkit:
"""Instantiate the toolkit."""
# This is to raise a better error than the forward ref ones Pydantic would... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
8321869bf88e-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 |
8321869bf88e-1 | if isinstance(path, str):
df = pd.read_csv(path, **_kwargs)
elif isinstance(path, list):
df = []
for item in path:
if not isinstance(item, str):
raise ValueError(f"Expected str, got {type(path)}")
df.append(pd.read_csv(item, **_kwargs))
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
4442e01abcc7-0 | 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 |
4442e01abcc7-1 | prefix: str = OPENAPI_PREFIX,
suffix: str = OPENAPI_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
verbose: bool = False... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
4442e01abcc7-2 | 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 |
1b044097dbb5-0 | 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 |
1b044097dbb5-1 | """Toolkit for making requests."""
requests_wrapper: TextRequestsWrapper
def get_tools(self) -> List[BaseTool]:
"""Return a list of tools."""
return [
RequestsGetTool(requests_wrapper=self.requests_wrapper),
RequestsPostTool(requests_wrapper=self.requests_wrapper),
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
1b044097dbb5-2 | 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 |
1b19b546931e-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 |
1b19b546931e-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/jira/toolkit.html |
f1a6e76ed575-0 | 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 |
f1a6e76ed575-1 | ]
# TODO: Remove check once azure-ai-vision supports MacOS.
if sys.platform.startswith("linux") or sys.platform.startswith("win"):
tools.append(AzureCogsImageAnalysisTool())
return tools | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/azure_cognitive_services/toolkit.html |
4147cce1b552-0 | 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 |
4147cce1b552-1 | suffix: str = SQL_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
top_k: int = 10,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
verbose: bool = False,
agent_execu... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
4147cce1b552-2 | 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/spark_sql/base.html |
c6f1f0dc0499-0 | 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 |
c6f1f0dc0499-1 | arbitrary_types_allowed = True
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return [
QuerySparkSQLTool(db=self.db),
InfoSparkSQLTool(db=self.db),
ListSparkSQLTool(db=self.db),
QueryCheckerTool(db=self.db, llm=self.ll... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/toolkit.html |
58f31290022d-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 |
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