id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 115 |
|---|---|---|
36b1ff7cc39c-0 | Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tupl... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-1 | along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
[docs] @abstractmethod
async def aplan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[AgentAction, AgentFinish]:
"""Given... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-2 | raise NotImplementedError
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
raise NotImplementedError
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
_dict["_type"] = str... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-3 | def return_values(self) -> List[str]:
"""Return values of the agent."""
return ["output"]
[docs] def get_allowed_tools(self) -> Optional[List[str]]:
return None
[docs] @abstractmethod
def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Unio... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-4 | return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
r... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-5 | [docs] def tool_run_logging_kwargs(self) -> Dict:
return {}
[docs]class AgentOutputParser(BaseOutputParser):
[docs] @abstractmethod
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Parse text into agent action/finish."""
[docs]class LLMSingleActionAgent(BaseSingleActionAgent):... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-6 | """
output = await self.llm_chain.arun(
intermediate_steps=intermediate_steps, stop=self.stop, **kwargs
)
return self.output_parser.parse(output)
[docs] def tool_run_logging_kwargs(self) -> Dict:
return {
"llm_prefix": "",
"observation_prefix": "" i... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-7 | thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
return thoughts
[docs] def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-8 | thoughts = self._construct_scratchpad(intermediate_steps)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
return full_inputs
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-9 | def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
"""Validate that appropriate tools are passed in."""
for tool in tools:
if not tool.is_single_input:
raise ValueError(
f"{cls.__name__} does not support multi-input tool {tool.name}."
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-10 | if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
elif early_stopping_method == "generate":
# Generate does one final forward pa... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-11 | "observation_prefix": self.observation_prefix,
}
[docs]class AgentExecutor(Chain):
"""Consists of an agent using tools."""
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]
tools: Sequence[BaseTool]
return_intermediate_steps: bool = False
max_iterations: Optional[int] = 15
max_ex... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-12 | tools = values["tools"]
if isinstance(agent, BaseMultiActionAgent):
for tool in tools:
if tool.return_direct:
raise ValueError(
"Tools that have `return_direct=True` are not allowed "
"in multi-action agents"
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-13 | and time_elapsed >= self.max_execution_time
):
return False
return True
def _return(self, output: AgentFinish, intermediate_steps: list) -> Dict[str, Any]:
self.callback_manager.on_agent_finish(
output, color="green", verbose=self.verbose
)
final_outpu... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-14 | if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions = [output]
else:
actions = output
result = []
for agent_action in actions:
self.callback_manager.on_agent_actio... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-15 | Override this to take control of how the agent makes and acts on choices.
"""
# Call the LLM to see what to do.
output = await self.agent.aplan(intermediate_steps, **inputs)
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-16 | verbose=self.verbose,
color=None,
**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... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-17 | if tool_return is not None:
return self._return(tool_return, intermediate_steps)
iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
36b1ff7cc39c-18 | tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return await self._areturn(tool_return, intermediate_steps)
iterations += 1
time_elapsed = time.time() - start_time
output = self.... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
7004914154be-0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAgent
f... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
7004914154be-1 | llm_chain = LLMChain(
llm=llm,
prompt=partial_prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(
llm_chain=llm_chain,
allowed_tools=tool_names,
callback_manager=callback_manager,
**kwargs,
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
fb158cdc3f27-0 | Source code for langchain.agents.agent_toolkits.python.base
"""Python agent."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.python.prompt import PREFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.callbacks.base import Bas... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
29785d591406-0 | Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, 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 import... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
29785d591406-1 | prefix: str = ROUTER_PREFIX,
verbose: bool = False,
**kwargs: Any,
) -> AgentExecutor:
"""Construct a vectorstore router agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=pr... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
ae9a277f9ba2-0 | 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.llms.base import BaseLLM
from langchain.llms.openai import Open... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
ae9a277f9ba2-1 | 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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
6ce2d41639f7-0 | 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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
21467b5b38f3-0 | 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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
4de99e96daff-0 | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, 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.opena... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
4de99e96daff-1 | 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,
tools=toolkit.get_tools(),
verbose=verbose... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
b62ffd4f27a5-0 | 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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
b62ffd4f27a5-1 | 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: BaseLLM,
json_spec: JsonSpe... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
baa8fdf888a1-0 | Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, 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
from l... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
baa8fdf888a1-1 | )
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
f311444d7c53-0 | 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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/toolkit.html |
f849c5c3b512-0 | 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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
f849c5c3b512-1 | **kwargs,
)
http_operation_tools.append(endpoint_tool)
return http_operation_tools
[docs] @classmethod
def from_llm_and_spec(
cls,
llm: BaseLLM,
spec: OpenAPISpec,
requests: Optional[Requests] = None,
verbose: bool = False,
*... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
f849c5c3b512-2 | 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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
ff428d75340e-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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
ff428d75340e-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://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
224942409bba-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.callbacks.base import BaseCallbackManager
from langchain.chains.... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
224942409bba-1 | prompt=PromptTemplate(
template=QUESTION_TO_QUERY,
input_variables=["tool_input", "tables", "schemas", "examples"],
),
)
return [
QueryPowerBITool(powerbi=self.powerbi),
InfoPowerBITool(powerbi=self.powerbi),
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
1c4dbdc97f2c-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_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
POWERBI_CHAT_SUFFIX,
)
from langchain.agents.agent_too... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
1c4dbdc97f2c-1 | 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,
tools=tools,
system_message=prefix.format(top_k=top_... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
32fe6191a4aa-0 | Source code for langchain.agents.agent_toolkits.csv.base
"""Agent for working with csvs."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.llms.base import BaseLLM
[docs]def create_csv... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
544b1c2cfb97-0 | Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
544b1c2cfb97-1 | 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,
tools=tools,
verbose=verbose,
max_... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
310e71048cf1-0 | 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.llms.base import BaseLLM
from langchain.sql_database import SQLDatabase
from langc... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
29da75771775-0 | 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 pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types impo... | https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
29da75771775-1 | tool_names = {tool.name for tool in tools}
if tool_names != {"Lookup", "Search"}:
raise ValueError(
f"Tool names should be Lookup and Search, got {tool_names}"
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
... | https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
29da75771775-2 | self.lookup_index = 0
else:
self.lookup_index += 1
lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
if len(lookups) == 0:
return "No Results"
elif self.lookup_index >= len(lookups):
return "No More Results"
else:
... | https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
29da75771775-3 | Example:
.. code-block:: python
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
"""
def __init__(self, llm: BaseLLM, docstore: Docstore, **kwargs: Any):
"""Initialize with the LLM and a docstore."""
docstore_explorer = DocstoreExplorer(docstor... | https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
f156bdf39847-0 | 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 pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
f156bdf39847-1 | cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str]] = None,
) -> PromptTemplate:
"""Create ... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
f156bdf39847-2 | callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variabl... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
fb3713b7d3d3-0 | 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://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
fb3713b7d3d3-1 | """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,
tools: Sequence[BaseTool],
prefix: str = PREFI... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
fb3713b7d3d3-2 | callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
**kwargs: Any,
) -> Agen... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
fb3713b7d3d3-3 | from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
prompt = PromptTemplate(...)
chains = [...]
mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)
"""
[docs] @classmethod
def from_chains(
cls, llm: BaseLanguageModel, chains:... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
fb3713b7d3d3-4 | Tool(
name=c.action_name,
func=c.action,
description=c.action_description,
)
for c in chains
]
agent = ZeroShotAgent.from_llm_and_tools(llm, tools)
return cls(agent=agent, tools=tools, **kwargs)
By Harrison Chase
... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
1c56036ddf8a-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 pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from lang... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
1c56036ddf8a-1 | return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
system_message: str = PREFIX,
human_message: str = SUFFIX,
input_variables: Optional[List[str]] = None,
output_parser: Optional[BaseOutputParser] = None,
) -> BasePromptTem... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
1c56036ddf8a-2 | content=TEMPLATE_TOOL_RESPONSE.format(observation=observation)
)
thoughts.append(human_message)
return thoughts
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallba... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
74955c00b0f2-0 | 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://python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
74955c00b0f2-1 | if tool_names != {"Intermediate Answer"}:
raise ValueError(
f"Tool name should be Intermediate Answer, got {tool_names}"
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Intermediate answer: "
@prope... | https://python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
5a17ae8bc495-0 | Source code for langchain.chat_models.openai
"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple
from pydantic import Extra, Field, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-1 | ),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
async def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwar... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-2 | message_dict["name"] = message.additional_kwargs["name"]
return message_dict
[docs]class ChatOpenAI(BaseChatModel):
"""Wrapper around OpenAI Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-3 | """Configuration for this pydantic object."""
extra = Extra.ignore
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fie... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-4 | "due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when strea... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-5 | ),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
[docs] def completion_with_retry(self, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = self._create_retry_decorator()
@retry_decorator
def _completion_with_retry(... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-6 | token,
verbose=self.verbose,
)
message = _convert_dict_to_message(
{"content": inner_completion, "role": role}
)
return ChatResult(generations=[ChatGeneration(message=message)])
response = self.completion_with_retry(messages... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-7 | inner_completion = ""
role = "assistant"
params["stream"] = True
async for stream_resp in await acompletion_with_retry(
self, messages=message_dicts, **params
):
role = stream_resp["choices"][0]["delta"].get("role", role)
to... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-8 | "This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install tiktoken`."
)
# create a GPT-3.5-Turbo encoder instance
enc = tiktoken.encoding_for_model(self.model_name)
# encode the text using the GPT-3.5-Turbo encoder
tokenized_... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
5a17ae8bc495-9 | model = "gpt-4-0314"
# Returns the number of tokens used by a list of messages.
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
17d7bc399703-0 | Source code for langchain.chat_models.azure_openai
"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.utils import get_from_dict_or_env
logger = logging.get... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
17d7bc399703-1 | openai_api_version: str = ""
openai_api_key: str = ""
openai_organization: str = ""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
openai_api_key = get_from_dict_or_env(
values,
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
17d7bc399703-2 | "`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
f6218f03fa84-0 | Source code for langchain.chat_models.anthropic
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.chat_models.base import BaseChatModel
from langchain.llms.anthropic import _AnthropicCommon
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatMe... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
f6218f03fa84-1 | elif isinstance(message, SystemMessage):
message_text = f"{self.HUMAN_PROMPT} <admin>{message.content}</admin>"
else:
raise ValueError(f"Got unknown type {message}")
return message_text
def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:
"""Format... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
f6218f03fa84-2 | if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""
stream_resp = self.client.completion_stream(**params)
for data in stream_resp:
delta = data["completion"][len(completion) :]
completion = data["completion"]... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
f6218f03fa84-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
be66bd9efb42-0 | Source code for langchain.chat_models.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import List, Optional
from langchain.chat_models import ChatOpenAI
from langchain.schema import BaseMessage, ChatResult
[docs]class PromptLayerChatOpenAI(ChatOpenAI):
"""Wrapper around OpenAI Chat large l... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
be66bd9efb42-1 | request_end_time = datetime.datetime.now().timestamp()
message_dicts, params = super()._create_message_dicts(messages, stop)
for i, generation in enumerate(generated_responses.generations):
response_dict, params = super()._create_message_dicts(
[generation.message], stop
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
be66bd9efb42-2 | "langchain",
message_dicts,
params,
self.pl_tags,
response_dict,
request_start_time,
request_end_time,
get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_p... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
ddb056478fe8-0 | Source code for langchain.memory.token_buffer
from typing import Any, Dict, List
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
[docs]class ConversationTokenBufferMemory(BaseChatMemory):
"""Buffer for storing conversation memory... | https://python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
ddb056478fe8-1 | if curr_buffer_length > self.max_token_limit:
pruned_memory = []
while curr_buffer_length > self.max_token_limit:
pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
By Harrison Chase
© Copyright 2023, ... | https://python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
5618a4f44415-0 | Source code for langchain.memory.buffer_window
from typing import Any, Dict, List
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_pr... | https://python.langchain.com/en/latest/_modules/langchain/memory/buffer_window.html |
b223df550825-0 | Source code for langchain.memory.buffer
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import get_buffer_string
[docs]class ConversationBuff... | https://python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
b223df550825-1 | @root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that return messages is not True."""
if values.get("return_messages", False):
raise ValueError(
"return_messages must be False for ConversationStringBufferMemory"
)
return va... | https://python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
deaa1d1860c5-0 | Source code for langchain.memory.summary_buffer
from typing import Any, Dict, List
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.summary import SummarizerMixin
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationSummaryB... | https://python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
deaa1d1860c5-1 | if expected_keys != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input variables. The prompt expects "
f"{prompt_variables}, but it should have {expected_keys}."
)
return values
[docs] def save_context(self, inputs: Dict[str, Any], ou... | https://python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
472328cfba6a-0 | Source code for langchain.memory.vectorstore
"""Class for a VectorStore-backed memory object."""
from typing import Any, Dict, List, Optional, Union
from pydantic import Field
from langchain.memory.chat_memory import BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import Documen... | https://python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
472328cfba6a-1 | docs = self.retriever.get_relevant_documents(query)
result: Union[List[Document], str]
if not self.return_docs:
result = "\n".join([doc.page_content for doc in docs])
else:
result = docs
return {self.memory_key: result}
def _form_documents(
self, input... | https://python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
f8df88c66196-0 | Source code for langchain.memory.kg
from typing import Any, Dict, List, Type, Union
from pydantic import Field
from langchain.chains.llm import LLMChain
from langchain.graphs import NetworkxEntityGraph
from langchain.graphs.networkx_graph import KnowledgeTriple, get_entities, parse_triples
from langchain.memory.chat_me... | https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
f8df88c66196-1 | summary_strings = []
for entity in entities:
knowledge = self.kg.get_entity_knowledge(entity)
if knowledge:
summary = f"On {entity}: {'. '.join(knowledge)}."
summary_strings.append(summary)
context: Union[str, List]
if not summary_strings:
... | https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
f8df88c66196-2 | human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
output = chain.predict(
history=buffer_string,
input=input_string,
)
return get_entities(output)
def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
"""Get the cu... | https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
f8df88c66196-3 | """Clear memory contents."""
super().clear()
self.kg.clear()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
1fc04cfdfe2e-0 | Source code for langchain.memory.summary
from typing import Any, Dict, List, Type
from pydantic import BaseModel, root_validator
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import SUMMARY_PROMPT
from langchain.prompts.base import BasePro... | https://python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
1fc04cfdfe2e-1 | """Return history buffer."""
if self.return_messages:
buffer: Any = [self.summary_message_cls(content=self.buffer)]
else:
buffer = self.buffer
return {self.memory_key: buffer}
@root_validator()
def validate_prompt_input_variables(cls, values: Dict) -> Dict:
... | https://python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
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