id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
89803e6bdd65-5 | return prompt, tools
def _get_functions_prompt_and_tools(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
include_df_in_prompt: Optional[bool] = True,
number_of_head_rows: int = 5,
) -> Tuple[BasePromptTemplate, List[PythonAstREPL... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
89803e6bdd65-6 | )
[docs]def create_pandas_dataframe_agent(
llm: BaseLanguageModel,
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]] ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
89803e6bdd65-7 | agent = ZeroShotAgent(
llm_chain=llm_chain,
allowed_tools=tool_names,
callback_manager=callback_manager,
**kwargs,
)
elif agent_type == AgentType.OPENAI_FUNCTIONS:
_prompt, base_tools = _get_functions_prompt_and_tools(
df,
prefi... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
f43a57cdbe3f-0 | Source code for langchain.agents.agent_toolkits.ainetwork.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List, Literal, Optional
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.pydantic_v1 import root_validator
from langchain.tools import BaseTool
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/ainetwork/toolkit.html |
21cba0b77230-0 | Source code for langchain.agents.agent_toolkits.vectorstore.toolkit
"""Toolkit for interacting with a vector store."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.llms.openai import OpenAI
from langchain.pydantic_v1 import BaseModel, Field
from langchain.schema.la... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
21cba0b77230-1 | 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 |
c60bd728018b-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 |
c60bd728018b-1 | **kwargs: Additional named parameters to pass to the ZeroShotAgent.
Returns:
AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response
""" # noqa: E501
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tool... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
c60bd728018b-2 | prefix (str, optional): The prefix prompt for the router agent. If not provided uses default ROUTER_PREFIX.
verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ]
agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you wa... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
dd25c75f0836-0 | Source code for langchain.agents.agent_toolkits.csv.base
from io import IOBase
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.schema.language_model import BaseLanguageMode... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
bbbfcb13efd6-0 | Source code for langchain.agents.agent_toolkits.xorbits.base
"""Agent for working with xorbits objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.xorbits.prompt import (
NP_PREFIX,
NP_SUFFIX,
PD_PREFIX,
PD_SUFFIX... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/xorbits/base.html |
bbbfcb13efd6-1 | if not isinstance(data, (pd.DataFrame, np.ndarray)):
raise ValueError(
f"Expected Xorbits DataFrame or ndarray object, got {type(data)}"
)
if input_variables is None:
input_variables = ["data", "input", "agent_scratchpad"]
tools = [PythonAstREPLTool(locals={"data": data})]
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/xorbits/base.html |
bbbfcb13efd6-2 | 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/xorbits/base.html |
ff571b754253-0 | 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 |
ff571b754253-1 | 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 |
4f6fd7af4bb1-0 | Source code for langchain.agents.agent_toolkits.gmail.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.pydantic_v1 import Field
from langchain.tools import BaseTool
from langchain.tools.gmail.create_draft import... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/gmail/toolkit.html |
6cf144cba866-0 | Source code for langchain.agents.agent_toolkits.conversational_retrieval.openai_functions
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
AgentTokenBufferMemory,
)
from langchain.agents.openai_fun... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.html |
6cf144cba866-1 | steps or not. Intermediate steps refer to prior action/observation
pairs from previous questions. The benefit of remembering these is if
there is relevant information in there, the agent can use it to answer
follow up questions. The downside is it will take up more tokens.
me... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.html |
6cf144cba866-2 | tools=tools,
memory=memory,
verbose=verbose,
return_intermediate_steps=remember_intermediate_steps,
**kwargs
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.html |
5a3bcc98375b-0 | Source code for langchain.agents.agent_toolkits.conversational_retrieval.tool
from langchain.schema import BaseRetriever
from langchain.tools import Tool
[docs]def create_retriever_tool(
retriever: BaseRetriever, name: str, description: str
) -> Tool:
"""Create a tool to do retrieval of documents.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/conversational_retrieval/tool.html |
d9506bc49711-0 | 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 langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.pydantic_v1 import Extra, root_validator
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
d9506bc49711-1 | """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 |
005ced015924-0 | Source code for langchain.agents.agent_toolkits.json.toolkit
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, JsonListKeysTool, JsonSpec
[docs]class JsonToo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/toolkit.html |
a908231aa3ee-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 |
a908231aa3ee-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 |
89d161a7095c-0 | Source code for langchain.agents.agent_toolkits.spark_sql.toolkit
"""Toolkit for interacting with Spark SQL."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.pydantic_v1 import Field
from langchain.schema.language_model import BaseLanguageModel
from langchain.tools ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/toolkit.html |
43e07487a8ad-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 |
43e07487a8ad-1 | format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
callbacks=callbacks,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_c... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
06af9a24c527-0 | Source code for langchain.agents.agent_toolkits.office365.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.pydantic_v1 import Field
from langchain.tools import BaseTool
from langchain.tools.office365.create_draf... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/office365/toolkit.html |
886cb489a82e-0 | Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
from typing import Any, List, Optional, Sequence
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
886cb489a82e-1 | super()._validate_tools(tools)
if len(tools) != 2:
raise ValueError(f"Exactly two tools must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Lookup", "Search"}:
raise ValueError(
f"Tool names should be Lookup a... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
886cb489a82e-2 | raise ValueError("Cannot lookup without a successful search first")
if term.lower() != self.lookup_str:
self.lookup_str = term.lower()
self.lookup_index = 0
else:
self.lookup_index += 1
lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
886cb489a82e-3 | if tool_names != {"Play"}:
raise ValueError(f"Tool name should be Play, got {tool_names}")
[docs]class ReActChain(AgentExecutor):
"""[Deprecated] Chain that implements the ReAct paper."""
def __init__(self, llm: BaseLanguageModel, docstore: Docstore, **kwargs: Any):
"""Initialize with the LL... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
9849155c568a-0 | Source code for langchain.agents.react.output_parser
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish, OutputParserException
[docs]class ReActOutputParser(AgentOutputParser):
"""Output parser for the ReAct agent."""
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/output_parser.html |
77d6ee862c9b-0 | Source code for langchain.agents.openai_functions_agent.base
"""Module implements an agent that uses OpenAI's APIs function enabled API."""
from typing import Any, List, Optional, Sequence, Tuple, Union
from langchain.agents import BaseSingleActionAgent
from langchain.agents.format_scratchpad.openai_functions import (
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
77d6ee862c9b-1 | """
llm: BaseLanguageModel
tools: Sequence[BaseTool]
prompt: BasePromptTemplate
[docs] def get_allowed_tools(self) -> List[str]:
"""Get allowed tools."""
return [t.name for t in self.tools]
@root_validator
def validate_llm(cls, values: dict) -> dict:
if not isinstance(valu... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
77d6ee862c9b-2 | Returns:
Action specifying what tool to use.
"""
agent_scratchpad = format_to_openai_functions(intermediate_steps)
selected_inputs = {
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
}
full_inputs = dict(**selected_inputs, agen... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
77d6ee862c9b-3 | prompt = self.prompt.format_prompt(**full_inputs)
messages = prompt.to_messages()
predicted_message = await self.llm.apredict_messages(
messages, functions=self.functions, callbacks=callbacks
)
agent_decision = OpenAIFunctionsAgentOutputParser._parse_ai_message(
p... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
77d6ee862c9b-4 | ) -> BasePromptTemplate:
"""Create prompt for this agent.
Args:
system_message: Message to use as the system message that will be the
first in the prompt.
extra_prompt_messages: Prompt messages that will be placed between the
system message and the... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
77d6ee862c9b-5 | llm=llm,
prompt=prompt,
tools=tools,
callback_manager=callback_manager,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
6cbd7f6fe64e-0 | Source code for langchain.agents.openai_functions_agent.agent_token_buffer_memory
"""Memory used to save agent output AND intermediate steps."""
from typing import Any, Dict, List
from langchain.agents.format_scratchpad.openai_functions import (
format_to_openai_functions,
)
from langchain.memory.chat_memory import... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/agent_token_buffer_memory.html |
6cbd7f6fe64e-1 | human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
return {self.memory_key: final_buffer}
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> None:
"""Save context from this conversation to buffer. Pruned."""
input_str, output... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/agent_token_buffer_memory.html |
3e83e298d98c-0 | Source code for langchain.agents.openai_functions_multi_agent.base
"""Module implements an agent that uses OpenAI's APIs function enabled API."""
import json
from json import JSONDecodeError
from typing import Any, List, Optional, Sequence, Tuple, Union
from langchain.agents import BaseMultiActionAgent
from langchain.a... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_multi_agent/base.html |
3e83e298d98c-1 | f"Could not parse tool input: {function_call} because "
f"the `arguments` is not valid JSON."
)
final_tools: List[AgentAction] = []
for tool_schema in tools:
_tool_input = tool_schema["action"]
function_name = tool_schema["action_name"]
# H... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_multi_agent/base.html |
3e83e298d98c-2 | that supports using `functions`.
tools: The tools this agent has access to.
prompt: The prompt for this agent, should support agent_scratchpad as one
of the variables. For an easy way to construct this prompt, use
`OpenAIMultiFunctionsAgent.create_prompt(...)`
"""
llm: Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_multi_agent/base.html |
3e83e298d98c-3 | # to use.
"name": "tool_selection",
"description": "A list of actions to take.",
"parameters": {
"title": "tool_selection",
"description": "A list of actions to take.",
"type": "object",
"properties": {
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_multi_agent/base.html |
3e83e298d98c-4 | return [tool_selection]
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_multi_agent/base.html |
3e83e298d98c-5 | selected_inputs = {
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
}
full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
prompt = self.prompt.format_prompt(**full_inputs)
messages = prompt.to_messages()
predicted_mess... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_multi_agent/base.html |
3e83e298d98c-6 | cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
system_message: Optional[SystemMessage] = SystemMessage(
content="You are a helpful... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_multi_agent/base.html |
207e3ad38259-0 | Source code for langchain.agents.xml.base
from typing import Any, List, Tuple, Union
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.output_parsers.xml import XMLAgentOutputParser
from langchain.agents.xml.prompt import agent_instructions
from langchain.callbacks.base import Callbacks
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/xml/base.html |
207e3ad38259-1 | **kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
log = ""
for action, observation in intermediate_steps:
log += (
f"<tool>{action.tool}</tool><tool_input>{action.tool_input}"
f"</tool_input><observation>{observation}</observation>"
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/xml/base.html |
78e10745a914-0 | 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 langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
78e10745a914-1 | return AgentType.ZERO_SHOT_REACT_DESCRIPTION
@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] @cl... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
78e10745a914-2 | 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 |
78e10745a914-3 | f"a description must always be provided."
)
super()._validate_tools(tools)
[docs]class MRKLChain(AgentExecutor):
"""[Deprecated] Chain that implements the MRKL system."""
[docs] @classmethod
def from_chains(
cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
19083df0b3a3-0 | 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:"
MISS... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/output_parser.html |
19083df0b3a3-1 | # ensure if its a well formed SQL query we don't remove any trailing " chars
if tool_input.startswith("SELECT ") is False:
tool_input = tool_input.strip('"')
return AgentAction(action, tool_input, text)
elif includes_answer:
return AgentFinish(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/output_parser.html |
c53938827944-0 | Source code for langchain.agents.structured_chat.base
import re
from typing import Any, List, Optional, Sequence, Tuple
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.structured_chat.output_parser import (
StructuredChatOutputParserWithRetries,
)
from langchain.agents.structured_c... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
c53938827944-1 | if agent_scratchpad:
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 _valid... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
c53938827944-2 | format_instructions = format_instructions.format(tool_names=tool_names)
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 =... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
c53938827944-3 | )
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 |
59853fad3c14-0 | 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 langchain.agents.agent import AgentOutputParser
from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS
from langchain.output_par... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/output_parser.html |
59853fad3c14-1 | @property
def _type(self) -> str:
return "structured_chat"
[docs]class StructuredChatOutputParserWithRetries(AgentOutputParser):
"""Output parser with retries for the structured chat agent."""
base_parser: AgentOutputParser = Field(default_factory=StructuredChatOutputParser)
"""The base parser t... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/output_parser.html |
59853fad3c14-2 | else:
return cls()
@property
def _type(self) -> str:
return "structured_chat_with_retries" | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/output_parser.html |
0059ec368099-0 | Source code for langchain.agents.conversational_chat.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Tuple
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.conversational... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
0059ec368099-1 | @property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
0059ec368099-2 | ) -> List[BaseMessage]:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts: List[BaseMessage] = []
for action, observation in intermediate_steps:
thoughts.append(AIMessage(content=action.log))
human_message = HumanMessage(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
03d2cd4c6a04-0 | Source code for langchain.agents.conversational_chat.output_parser
from __future__ import annotations
from typing import Union
from langchain.agents import AgentOutputParser
from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS
from langchain.output_parsers.json import parse_json_markdown
from lan... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/output_parser.html |
03d2cd4c6a04-1 | # exception
raise OutputParserException(
f"Missing 'action' or 'action_input' in LLM output: {text}"
)
except Exception as e:
# If any other exception is raised during parsing, also raise an
# OutputParserException
raise Out... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/output_parser.html |
f787fa7a96d5-0 | Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.agent_types import AgentTy... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
f787fa7a96d5-1 | """Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
hum... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
f787fa7a96d5-2 | def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Option... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
eda171300981-0 | Source code for langchain.agents.conversational.output_parser
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
[docs]class ConvoOutpu... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/output_parser.html |
22af6a0a75d0-0 | Source code for langchain.agents.format_scratchpad.log_to_messages
from typing import List, Tuple
from langchain.schema.agent import AgentAction
from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
[docs]def format_log_to_messages(
intermediate_steps: List[Tuple[AgentAction, str]],
templat... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/format_scratchpad/log_to_messages.html |
115448d1d6ce-0 | Source code for langchain.agents.format_scratchpad.log
from typing import List, Tuple
from langchain.schema.agent import AgentAction
[docs]def format_log_to_str(
intermediate_steps: List[Tuple[AgentAction, str]],
observation_prefix: str = "Observation: ",
llm_prefix: str = "Thought: ",
) -> str:
"""Cons... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/format_scratchpad/log.html |
555c661f992d-0 | Source code for langchain.agents.format_scratchpad.xml
from typing import List, Tuple
from langchain.schema.agent import AgentAction
[docs]def format_xml(
intermediate_steps: List[Tuple[AgentAction, str]],
) -> str:
log = ""
for action, observation in intermediate_steps:
log += (
f"<tool... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/format_scratchpad/xml.html |
229d4ec12357-0 | Source code for langchain.agents.format_scratchpad.openai_functions
import json
from typing import List, Sequence, Tuple
from langchain.schema.agent import AgentAction, AgentActionMessageLog
from langchain.schema.messages import AIMessage, BaseMessage, FunctionMessage
def _convert_agent_action_to_messages(
agent_ac... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/format_scratchpad/openai_functions.html |
229d4ec12357-1 | ) -> List[BaseMessage]:
"""Format intermediate steps.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations
Returns:
list of messages to send to the LLM for the next prediction
"""
messages = []
for agent_action, observation in intermediate_steps:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/format_scratchpad/openai_functions.html |
f632580596a5-0 | Source code for langchain.agents.chat.base
from typing import Any, List, Optional, Sequence, Tuple
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.chat.output_parser import ChatOutputParser
from langchain.agents.chat.prompt import (
FORMAT_INSTRUCTIONS,
HUMAN_MESSAGE,
SYSTE... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/chat/base.html |
f632580596a5-1 | 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 _get_default_output_parser(cls, **kwarg... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/chat/base.html |
f632580596a5-2 | ]
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
return ChatPromptTemplate(input_variables=input_variables, messages=messages)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/chat/base.html |
b46b31890476-0 | Source code for langchain.agents.chat.output_parser
import json
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
FINAL_ANSWER_ACTION = "Final A... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/chat/output_parser.html |
b46b31890476-1 | @property
def _type(self) -> str:
return "chat" | https://api.python.langchain.com/en/latest/_modules/langchain/agents/chat/output_parser.html |
9580a4d39c6f-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 langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.self_ask_with_search.output_p... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
9580a4d39c6f-1 | super()._validate_tools(tools)
if len(tools) != 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 Interme... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
b8073225ed0d-0 | Source code for langchain.runnables.openai_functions
from operator import itemgetter
from typing import Any, Callable, List, Mapping, Optional, Union
from typing_extensions import TypedDict
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
from langchain.schema.output import ChatGeneration... | https://api.python.langchain.com/en/latest/_modules/langchain/runnables/openai_functions.html |
907574b0e75a-0 | Source code for langchain.evaluation.schema
"""Interfaces to be implemented by general evaluators."""
from __future__ import annotations
import asyncio
import logging
from abc import ABC, abstractmethod
from enum import Enum
from functools import partial
from typing import Any, Optional, Sequence, Tuple
from warnings i... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-1 | custom set of criteria, with a reference label."""
STRING_DISTANCE = "string_distance"
"""Compare predictions to a reference answer using string edit distances."""
EXACT_MATCH = "exact_match"
"""Compare predictions to a reference answer using exact matching."""
REGEX_MATCH = "regex_match"
"""Com... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-2 | """Warning to show when input is ignored."""
return f"Ignoring input in {self.__class__.__name__}, as it is not expected."
@property
def _skip_reference_warning(self) -> str:
"""Warning to show when reference is ignored."""
return (
f"Ignoring reference in {self.__class__.__n... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-3 | @property
def requires_reference(self) -> bool:
"""Whether this evaluator requires a reference label."""
return False
@abstractmethod
def _evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-4 | **kwargs: Additional keyword arguments, including callbacks, tags, etc.
Returns:
dict: The evaluation results containing the score or value.
It is recommended that the dictionary contain the following keys:
- score: the score of the evaluation, if applicable.
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-5 | *,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
**kwargs: Any,
) -> dict:
"""Asynchronously evaluate Chain or LLM output, based on optional input and label.
Args:
prediction (str): The LLM or chain prediction to evaluate.
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-6 | Returns:
dict: A dictionary containing the preference, scores, and/or other information.
""" # noqa: E501
async def _aevaluate_string_pairs(
self,
*,
prediction: str,
prediction_b: str,
reference: Optional[str] = None,
input: Optional[str] = None,... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-7 | prediction_b (str): The output string from the second model.
reference (Optional[str], optional): The expected output / reference string.
input (Optional[str], optional): The input string.
**kwargs: Additional keyword arguments, such as callbacks and optional reference strings.
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-8 | reference=reference,
input=input,
**kwargs,
)
[docs]class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
"""Interface for evaluating agent trajectories."""
@property
def requires_input(self) -> bool:
"""Whether this evaluator requires an input string."""
retur... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-9 | None,
partial(
self._evaluate_agent_trajectory,
prediction=prediction,
agent_trajectory=agent_trajectory,
reference=reference,
input=input,
**kwargs,
),
)
[docs] def evaluate_agent_trajecto... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
907574b0e75a-10 | input (str): The input to the agent.
reference (Optional[str]): The reference answer.
Returns:
dict: The evaluation result.
"""
self._check_evaluation_args(reference=reference, input=input)
return await self._aevaluate_agent_trajectory(
prediction=pred... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
a8412a8751d5-0 | Source code for langchain.evaluation.loading
"""Loading datasets and evaluators."""
from typing import Any, Dict, List, Optional, Sequence, Type, Union
from langchain.chains.base import Chain
from langchain.chat_models.openai import ChatOpenAI
from langchain.evaluation.agents.trajectory_eval_chain import TrajectoryEval... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/loading.html |
a8412a8751d5-1 | **Prerequisites**
.. code-block:: shell
pip install datasets
Examples
--------
.. code-block:: python
from langchain.evaluation import load_dataset
ds = load_dataset("llm-math")
""" # noqa: E501
try:
from datasets import load_dataset
except ImportError:
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/loading.html |
a8412a8751d5-2 | EvaluatorType.JSON_EQUALITY: JsonEqualityEvaluator,
EvaluatorType.REGEX_MATCH: RegexMatchStringEvaluator,
EvaluatorType.EXACT_MATCH: ExactMatchStringEvaluator,
}
[docs]def load_evaluator(
evaluator: EvaluatorType,
*,
llm: Optional[BaseLanguageModel] = None,
**kwargs: Any,
) -> Union[Chain, Strin... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/loading.html |
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