id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
4a77e11a2347-1 | def check_examples_and_selector(cls, values: Dict) -> Dict:
"""Check that one and only one of examples/example_selector are provided."""
examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise Val... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/few_shot.html |
4a77e11a2347-2 | .. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the examples to use.
examples = self._get_examples(**kwargs)
examples = [
{k: e[k] for k in self.example_prompt.input_variables} for e... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/few_shot.html |
b5db52c69dd5-0 | Source code for langchain.prompts.loading
"""Load prompts from disk."""
import importlib
import json
import logging
from pathlib import Path
from typing import Union
import yaml
from langchain.output_parsers.regex import RegexParser
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot i... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/loading.html |
b5db52c69dd5-1 | with open(template_path) as f:
template = f.read()
else:
raise ValueError
# Set the template variable to the extracted variable.
config[var_name] = template
return config
def _load_examples(config: dict) -> dict:
"""Load examples if necessary."""
if isinst... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/loading.html |
b5db52c69dd5-2 | config = _load_template("prefix", config)
# Load the example prompt.
if "example_prompt_path" in config:
if "example_prompt" in config:
raise ValueError(
"Only one of example_prompt and example_prompt_path should "
"be specified."
)
config[... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/loading.html |
b5db52c69dd5-3 | with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
elif file_path.suffix == ".py":
spec = importlib.util.spec_from_loader(
"prompt", loader=None, origin=str(file_path)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/loading.html |
0529f741d4a0-0 | Source code for langchain.prompts.example_selector.semantic_similarity
"""Example selector that selects examples based on SemanticSimilarity."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
fr... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/example_selector/semantic_similarity.html |
0529f741d4a0-1 | return ids[0]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on semantic similarity."""
# Get the docs with the highest similarity.
if self.input_keys:
input_variables = {key: input_variables[key] for key in s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/example_selector/semantic_similarity.html |
0529f741d4a0-2 | instead of all variables.
vectorstore_cls_kwargs: optional kwargs containing url for vector store
Returns:
The ExampleSelector instantiated, backed by a vector store.
"""
if input_keys:
string_examples = [
" ".join(sorted_values({k: eg[k] for k... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/example_selector/semantic_similarity.html |
0529f741d4a0-3 | examples = [dict(e.metadata) for e in example_docs]
# If example keys are provided, filter examples to those keys.
if self.example_keys:
examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
return examples
[docs] @classmethod
def from_examples(
cls,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/example_selector/semantic_similarity.html |
0529f741d4a0-4 | )
return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/example_selector/semantic_similarity.html |
b36c61ed348a-0 | Source code for langchain.prompts.example_selector.length_based
"""Select examples based on length."""
import re
from typing import Callable, Dict, List
from pydantic import BaseModel, validator
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.prompts.prompt import PromptTemplate
d... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/example_selector/length_based.html |
b36c61ed348a-1 | get_text_length = values["get_text_length"]
string_examples = [example_prompt.format(**eg) for eg in values["examples"]]
return [get_text_length(eg) for eg in string_examples]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use base... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/prompts/example_selector/length_based.html |
92162eda123b-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple
from mypy_extensions import Arg, KwArg
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base im... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-1 | from langchain.tools.shell.tool import ShellTool
from langchain.tools.sleep.tool import SleepTool
from langchain.tools.wikipedia.tool import WikipediaQueryRun
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.tools.openweathermap.tool import OpenWeatherMapQueryRun
from langchain.utiliti... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-2 | def _get_tools_requests_delete() -> BaseTool:
return RequestsDeleteTool(requests_wrapper=TextRequestsWrapper())
def _get_terminal() -> BaseTool:
return ShellTool()
def _get_sleep() -> BaseTool:
return SleepTool()
_BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
"python_repl": _get_python_repl,
"re... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-3 | return Tool(
name="Calculator",
description="Useful for when you need to answer questions about math.",
func=LLMMathChain.from_llm(llm=llm).run,
coroutine=LLMMathChain.from_llm(llm=llm).arun,
)
def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
chain = APIChain.from_llm... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-4 | func=chain.run,
)
def _get_tmdb_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
tmdb_bearer_token = kwargs["tmdb_bearer_token"]
chain = APIChain.from_llm_and_api_docs(
llm,
tmdb_docs.TMDB_DOCS,
headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
)
return Tool(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-5 | def _get_google_search(**kwargs: Any) -> BaseTool:
return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_wikipedia(**kwargs: Any) -> BaseTool:
return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))
def _get_arxiv(**kwargs: Any) -> BaseTool:
return ArxivQueryRun(api_wrapp... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-6 | )
def _get_searx_search(**kwargs: Any) -> BaseTool:
return SearxSearchRun(wrapper=SearxSearchWrapper(**kwargs))
def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
wrapper_kwargs = {k: v for k, v in kwargs.items() if k != "num_results"}
return SearxSearchResults(wrapper=SearxSearchWrapper(**wrapp... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-7 | ] = {
"news-api": (_get_news_api, ["news_api_key"]),
"tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
"podcast-api": (_get_podcast_api, ["listen_api_key"]),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
"wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-8 | "searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
"wikipedia": (_get_wikipedia, ["top_k_results", "lang"]),
"arxiv": (
_get_arxiv,
["top_k_results", "load_max_docs", "load_all_available_meta"],
),
"pupmed": (
_get_pupmed,
["top_k_results", "loa... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-9 | **kwargs: Any,
) -> BaseTool:
try:
from transformers import load_tool
except ImportError:
raise ValueError(
"HuggingFace tools require the libraries `transformers>=4.29.0`"
" and `huggingface_hub>=0.14.1` to be installed."
" Please install it with"
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-10 | callback_manager=kwargs.get("callback_manager"), callbacks=callbacks
)
for name in tool_names:
if name == "requests":
warnings.warn(
"tool name `requests` is deprecated - "
"please use `requests_all` or specify the requests method"
)
if nam... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
92162eda123b-11 | _get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
sub_kwargs = {k: kwargs[k] for k in extra_keys if k in kwargs}
tool = _get_tool_func(**sub_kwargs)
tools.append(tool)
else:
raise ValueError(f"Got unknown tool {name}")
if callbacks is not None:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
4177cc09dbb7-0 | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.base_language import BaseLanguageMod... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/initialize.html |
4177cc09dbb7-1 | "but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
agent_cls = AGENT_TO_CLASS[agent]
ag... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/initialize.html |
6deb311d0eb6-0 | Source code for langchain.agents.agent_types
from enum import Enum
[docs]class AgentType(str, Enum):
ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
REACT_DOCSTORE = "react-docstore"
SELF_ASK_WITH_SEARCH = "self-ask-with-search"
CONVERSATIONAL_REACT_DESCRIPTION = "conversational-react-descri... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_types.html |
42d3ed0f309f-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, Callable, Dict, List, Optional, Sequ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-1 | return None
[docs] @abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Ste... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-2 | # `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
[docs]... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-3 | directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
agent_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(agent_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(fil... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-4 | **kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-5 | Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path="path/agent.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-6 | return _dict
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has take... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-7 | }
[docs]class Agent(BaseSingleActionAgent):
"""Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called "agent_scratchpad" where the agent can put its
intermediary work.
"""
llm_chain: LLMCh... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-8 | return thoughts
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has t... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-9 | """Create the full inputs for the LLMChain from intermediate steps."""
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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-10 | """Create a prompt for this class."""
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
"""Validate that appropriate tools are passed in."""
pass
@classmethod
@abstractmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
"""G... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-11 | # `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 pass
thoughts = ""
for acti... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-12 | }
class ExceptionTool(BaseTool):
name = "_Exception"
description = "Exception tool"
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
return query
async def _arun(
self,
query: str,
run_manager: Opti... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-13 | tools = values["tools"]
allowed_tools = agent.get_allowed_tools()
if allowed_tools is not None:
if set(allowed_tools) != set([tool.name for tool in tools]):
raise ValueError(
f"Allowed tools ({allowed_tools}) different than "
f"provided... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-14 | :meta private:
"""
if self.return_intermediate_steps:
return self.agent.return_values + ["intermediate_steps"]
else:
return self.agent.return_values
[docs] def lookup_tool(self, name: str) -> BaseTool:
"""Lookup tool by name."""
return {tool.name: tool ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-15 | return final_output
def _take_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Union[Age... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-16 | if run_manager:
run_manager.on_agent_action(output, color="green")
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = ExceptionTool().run(
output.tool_input,
verbose=self.verbose,
color=None,
callba... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-17 | color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
result.append((agent_action, observation))
return result
async def _atake_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-18 | observation = self.handle_parsing_errors(e)
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, text)
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = await ExceptionT... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-19 | **tool_run_kwargs,
)
else:
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = await InvalidTool().arun(
agent_action.tool,
verbose=self.verbose,
color=None,
callb... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-20 | inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return self._return(
next_step_output, intermediate_steps, run_manager=run_manager
)
intermediate_s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-21 | time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
async with asyncio_timeout(self.max_execution_time):
try:
while self._should_continue(iterations, time_elapsed):
next_step_output = await self._... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
42d3ed0f309f-22 | self, next_step_output: Tuple[AgentAction, str]
) -> Optional[AgentFinish]:
"""Check if the tool is a returning tool."""
agent_action, observation = next_step_output
name_to_tool_map = {tool.name: tool for tool in self.tools}
# Invalid tools won't be in the map, so we return False.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
1a73d60d5beb-0 | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.tools import Tool
from langchain.agents.types impo... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/loading.html |
1a73d60d5beb-1 | if load_from_tools:
if llm is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
"then LLM must be provided"
)
if tools is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/loading.html |
1a73d60d5beb-2 | path, _load_agent_from_file, "agents", {"json", "yaml"}
):
return hub_result
else:
return _load_agent_from_file(path, **kwargs)
def _load_agent_from_file(
file: Union[str, Path], **kwargs: Any
) -> BaseSingleActionAgent:
"""Load agent from file."""
# Convert file to Path object.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/loading.html |
1a5741569bee-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/conversational_chat/base.html |
1a5741569bee-1 | return "Observation: "
@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__, too... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/conversational_chat/base.html |
1a5741569bee-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(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/conversational_chat/base.html |
1a5741569bee-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/conversational_chat/base.html |
a463d0998038-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/self_ask_with_search/base.html |
a463d0998038-1 | raise ValueError(f"Exactly one tool must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Intermediate Answer"}:
raise ValueError(
f"Tool name should be Intermediate Answer, got {tool_names}"
)
@property
def obs... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/self_ask_with_search/base.html |
462b51052103-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/conversational/base.html |
462b51052103-1 | [docs] @classmethod
def create_prompt(
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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/conversational/base.html |
462b51052103-2 | validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/conversational/base.html |
5273fc39a03f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/structured_chat/base.html |
5273fc39a03f-1 | return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _validate_tools(cls, tools: Sequence[Ba... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/structured_chat/base.html |
5273fc39a03f-2 | template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
_memory_prompts = memory_prompts or []
messages = [
SystemMessagePromptTemplate.from_template(template),
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/structured_chat/base.html |
5273fc39a03f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/structured_chat/base.html |
9d096162506b-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/react/base.html |
9d096162506b-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/react/base.html |
9d096162506b-2 | 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()]
if len(lookups) == 0:
return "No Results"
elif sel... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/react/base.html |
9d096162506b-3 | raise ValueError(f"Tool name should be Play, got {tool_names}")
[docs]class ReActChain(AgentExecutor):
"""Chain that implements the ReAct paper.
Example:
.. code-block:: python
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
"""
def __init__(self, llm... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/react/base.html |
3ebf079fb13f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
3ebf079fb13f-1 | @property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
3ebf079fb13f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
3ebf079fb13f-3 | Example:
.. code-block:: python
from langchain import OpenAI, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
prompt = PromptTemplate(...)
chains = [...]
mrkl = MRKLChain.from_chains(llm=llm, prompt=... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
3ebf079fb13f-4 | ]
mrkl = MRKLChain.from_chains(llm, chains)
"""
tools = [
Tool(
name=c.action_name,
func=c.action,
description=c.action_description,
)
for c in chains
]
agent = ZeroShotAgent.from_llm_and_... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
3d959cc6ff98-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/gmail/toolkit.html |
3cded0fcad33-0 | 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
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/json/base.html |
3cded0fcad33-1 | return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/json/base.html |
b0faf2fb14ef-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/json/toolkit.html |
7be52006ce9f-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.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
7be52006ce9f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
e0df8d006907-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/python/base.html |
e0df8d006907-1 | elif agent_type == AgentType.OPENAI_FUNCTIONS:
system_message = SystemMessage(content=prefix)
_prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_m... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/python/base.html |
cbc0a85fe8be-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
cbc0a85fe8be-1 | include_dfs_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad", "num_dfs"]
if include_dfs_head:
input_variables += ["dfs_head"]
if prefix is None:
prefix = MULTI_DF_PREFIX
df_locals = {}
for i, dataframe in enumerate(dfs):
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
cbc0a85fe8be-2 | include_df_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
if include_df_head:
input_variables += ["df_head"]
if prefix is None:
prefix = PREFIX
tools = [PythonAstREPLTool(locals={"df": df})]
prompt = ZeroShotAgent.create_promp... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
cbc0a85fe8be-3 | include_df_in_prompt=include_df_in_prompt,
)
else:
if not isinstance(df, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_single_prompt(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_v... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
cbc0a85fe8be-4 | suffix_to_use = suffix
if include_df_in_prompt:
dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
suffix_to_use = suffix_to_use.format(
dfs_head=dfs_head,
)
elif include_df_in_prompt:
dfs_head = "\n\n".join([d.head().to_markdown() for d... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
cbc0a85fe8be-5 | if include_df_in_prompt is not None and suffix is not None:
raise ValueError("If suffix is specified, include_df_in_prompt should not be.")
if isinstance(df, list):
for item in df:
if not isinstance(item, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
cbc0a85fe8be-6 | agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt, tools = _get_prompt_and_tools(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_prompt=include_df_in_prompt,
)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
cbc0a85fe8be-7 | **(agent_executor_kwargs or {}),
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
9aff6aa2b4d5-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/csv/base.html |
ba40d0587398-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
d21185ce5419-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
d21185ce5419-1 | llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
too... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
4fb6896e4cce-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 ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark_sql/toolkit.html |
6c0df05b4d27-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/azure_cognitive_services/toolkit.html |
d1465df74ebb-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
d1465df74ebb-1 | )
http_operation_tools.append(endpoint_tool)
return http_operation_tools
[docs] @classmethod
def from_llm_and_spec(
cls,
llm: BaseLanguageModel,
spec: OpenAPISpec,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
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