id stringlengths 14 16 | text stringlengths 31 3.14k | source stringlengths 58 124 |
|---|---|---|
dbd0f676cb8e-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/toolkit.html |
04f19de71931-0 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
04f19de71931-1 | )
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, tools... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
ab00510cba80-0 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ab00510cba80-1 | if input_variables is None:
input_variables = ["df", "input", "agent_scratchpad"]
tools = [PythonAstREPLTool(locals={"df": df})]
prompt = ZeroShotAgent.create_prompt(
tools, prefix=prefix, suffix=suffix, input_variables=input_variables
)
partial_prompt = prompt.partial(df=str(df.head()))... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
229637f1c83d-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
45bb4a65c165-0 | 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 ... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
45bb4a65c165-1 | tools = toolkit.get_tools()
prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
50b0d847c2bc-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
0ac24e6f4183-0 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
44507548f4b8-0 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
44507548f4b8-1 | http_operation_tools = []
for path in spec.paths:
for method in spec.get_methods_for_path(path):
endpoint_tool = NLATool.from_llm_and_method(
llm=llm,
path=path,
method=method,
spec=spec,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
44507548f4b8-2 | return cls.from_llm_and_spec(
llm=llm, spec=spec, requests=requests, verbose=verbose, **kwargs
)
[docs] @classmethod
def from_llm_and_ai_plugin(
cls,
llm: BaseLLM,
ai_plugin: AIPlugin,
requests: Optional[Requests] = None,
verbose: bool = False,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
44507548f4b8-3 | )
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
07ccfe43e15a-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
07ccfe43e15a-1 | return AgentType.SELF_ASK_WITH_SEARCH
@classmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Prompt does not depend on tools."""
return PROMPT
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
if len(tools) != 1:
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
07ccfe43e15a-2 | """
def __init__(
self,
llm: BaseLLM,
search_chain: Union[GoogleSerperAPIWrapper, SerpAPIWrapper],
**kwargs: Any,
):
"""Initialize with just an LLM and a search chain."""
search_tool = Tool(
name="Intermediate Answer", func=search_chain.run, descriptio... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
55d3fd94a115-0 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
55d3fd94a115-1 | return ConvoOutputParser()
@property
def _agent_type(self) -> str:
raise NotImplementedError
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to appe... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
55d3fd94a115-2 | messages = [
SystemMessagePromptTemplate.from_template(system_message),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template(final_prompt),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
return ChatPromptTem... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
55d3fd94a115-3 | ) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
_output_parser = output_parser or cls._get_default_output_parser()
prompt = cls.create_prompt(
tools,
system_message=system_message,
human_message=human_message,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
6113a633f02f-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
6113a633f02f-1 | return MRKLOutputParser()
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
return AgentType.ZERO_SHOT_REACT_DESCRIPTION
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@pr... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
6113a633f02f-2 | template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
return PromptTemplate(template=template, input_variables=input_variables)
[docs] @classmethod
def from_llm_and_tools(
cls,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
6113a633f02f-3 | llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
for tool in tools:
if tool.description is None:
raise ValueError(
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
6113a633f02f-4 | An initialized MRKL chain.
Example:
.. code-block:: python
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
c89182438f4c-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
c89182438f4c-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,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
c89182438f4c-2 | format_instructions = format_instructions.format(
tool_names=tool_names, ai_prefix=ai_prefix, human_prefix=human_prefix
)
template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "chat_history", "a... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
c89182438f4c-3 | )
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser(
ai_prefix=ai_prefix
)
return c... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
8b86c1147248-0 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
8b86c1147248-1 | return AgentType.REACT_DOCSTORE
@classmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Return default prompt."""
return WIKI_PROMPT
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
if len(tools) != 2:
raise Va... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
8b86c1147248-2 | def search(self, term: str) -> str:
"""Search for a term in the docstore, and if found save."""
result = self.docstore.search(term)
if isinstance(result, Document):
self.document = result
return self._summary
else:
self.document = None
retu... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
8b86c1147248-3 | return self.document.page_content.split("\n\n")
[docs]class ReActTextWorldAgent(ReActDocstoreAgent):
"""Agent for the ReAct TextWorld chain."""
[docs] @classmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Return default prompt."""
return TEXTWORLD_PROMPT
... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
8b86c1147248-4 | ),
Tool(
name="Lookup",
func=docstore_explorer.lookup,
description="Lookup a term in the docstore.",
),
]
agent = ReActDocstoreAgent.from_llm_and_tools(llm, tools)
super().__init__(agent=agent, tools=tools, **kwargs)
By Harr... | /content/https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
20083fdcd3aa-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.chains.base import Chain
from langchain.experimental.autonomous_agents.baby_agi.task_creation import (
TaskCreati... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
20083fdcd3aa-1 | def print_task_list(self) -> None:
print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m")
for t in self.task_list:
print(str(t["task_id"]) + ": " + t["task_name"])
def print_next_task(self, task: Dict) -> None:
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" ... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
20083fdcd3aa-2 | incomplete_tasks = ", ".join(task_names)
response = self.task_creation_chain.run(
result=result,
task_description=task_description,
incomplete_tasks=incomplete_tasks,
objective=objective,
)
new_tasks = response.split("\n")
return [
... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
20083fdcd3aa-3 | prioritized_task_list.append(
{"task_id": task_id, "task_name": task_name}
)
return prioritized_task_list
def _get_top_tasks(self, query: str, k: int) -> List[str]:
"""Get the top k tasks based on the query."""
results = self.vectorstore.similarity_search(... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
20083fdcd3aa-4 | self.print_next_task(task)
# Step 2: Execute the task
result = self.execute_task(objective, task["task_name"])
this_task_id = int(task["task_id"])
self.print_task_result(result)
# Step 3: Store the result in Pinecone
result_... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
20083fdcd3aa-5 | [docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
verbose: bool = False,
task_execution_chain: Optional[Chain] = None,
**kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initialize the BabyAGI Controller."""
task... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
d14dec42789f-0 | Source code for langchain.experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.au... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
d14dec42789f-1 | self.next_action_count = 0
self.chain = chain
self.output_parser = output_parser
self.tools = tools
self.feedback_tool = feedback_tool
@classmethod
def from_llm_and_tools(
cls,
ai_name: str,
ai_role: str,
memory: VectorStoreRetriever,
tools... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
d14dec42789f-2 | assistant_reply = self.chain.run(
goals=goals,
messages=self.full_message_history,
memory=self.memory,
user_input=user_input,
)
# Print Assistant thoughts
print(assistant_reply)
self.full_message_history.appe... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
d14dec42789f-3 | )
memory_to_add = (
f"Assistant Reply: {assistant_reply} " f"\nResult: {result} "
)
if self.feedback_tool is not None:
feedback = f"\n{self.feedback_tool.run('Input: ')}"
if feedback in {"q", "stop"}:
print("EXITING"... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
958615948f0a-0 | Source code for langchain.experimental.generative_agents.memory
import logging
import re
from typing import Any, Dict, List, Optional
from langchain import LLMChain
from langchain.prompts import PromptTemplate
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain.schema import BaseLanguageMod... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
958615948f0a-1 | add_memory_key: str = "add_memory"
# output keys
relevant_memories_key: str = "relevant_memories"
relevant_memories_simple_key: str = "relevant_memories_simple"
most_recent_memories_key: str = "most_recent_memories"
def chain(self, prompt: PromptTemplate) -> LLMChain:
return LLMChain(llm=sel... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
958615948f0a-2 | result = self.chain(prompt).run(observations=observation_str)
return self._parse_list(result)
def _get_insights_on_topic(self, topic: str) -> List[str]:
"""Generate 'insights' on a topic of reflection, based on pertinent memories."""
prompt = PromptTemplate.from_template(
"Statem... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
958615948f0a-3 | for insight in insights:
self.add_memory(insight)
new_insights.extend(insights)
return new_insights
def _score_memory_importance(self, memory_content: str) -> float:
"""Score the absolute importance of the given memory."""
prompt = PromptTemplate.from_template(
... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
958615948f0a-4 | )
result = self.memory_retriever.add_documents([document])
# After an agent has processed a certain amount of memories (as measured by
# aggregate importance), it is time to reflect on recent events to add
# more synthesized memories to the agent's memory stream.
if (
... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
958615948f0a-5 | return "; ".join([f"{mem.page_content}" for mem in relevant_memories])
def _get_memories_until_limit(self, consumed_tokens: int) -> str:
"""Reduce the number of tokens in the documents."""
result = []
for doc in self.memory_retriever.memory_stream[::-1]:
if consumed_tokens >= sel... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
958615948f0a-6 | relevant_memories
),
}
most_recent_memories_token = inputs.get(self.most_recent_memories_token_key)
if most_recent_memories_token is not None:
return {
self.most_recent_memories_key: self._get_memories_until_limit(
most_recent_m... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8ed14bbd3e4f-0 | Source code for langchain.experimental.generative_agents.generative_agent
import re
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain import LLMChain
from langchain.experimental.generative_agents.memory import GenerativeAgentMemory
fro... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
8ed14bbd3e4f-1 | """The last time the character's summary was regenerated."""
daily_summaries: List[str] = Field(default_factory=list) # : :meta private:
"""Summary of the events in the plan that the agent took."""
[docs] class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = T... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
8ed14bbd3e4f-2 | "What is the {entity} doing in the following observation? {observation}"
+ "\nThe {entity} is"
)
return (
self.chain(prompt).run(entity=entity_name, observation=observation).strip()
)
[docs] def summarize_related_memories(self, observation: str) -> str:
"""Summ... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
8ed14bbd3e4f-3 | + "\n{relevant_memories}"
+ "\nMost recent observations: {most_recent_memories}"
+ "\nObservation: {observation}"
+ "\n\n"
+ suffix
)
agent_summary_description = self.get_summary()
relevant_memories_str = self.summarize_related_memories(observation... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
8ed14bbd3e4f-4 | call_to_action_template = (
"Should {agent_name} react to the observation, and if so,"
+ " what would be an appropriate reaction? Respond in one line."
+ ' If the action is to engage in dialogue, write:\nSAY: "what to say"'
+ "\notherwise, write:\nREACT: {agent_name}'s re... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
8ed14bbd3e4f-5 | call_to_action_template = (
"What would {agent_name} say? To end the conversation, write:"
' GOODBYE: "what to say". Otherwise to continue the conversation,'
' write: SAY: "what to say next"\n\n'
)
full_result = self._generate_reaction(observation, call_to_action_temp... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
8ed14bbd3e4f-6 | # summarizing the agent's self-description. This is #
# updated periodically through probing its memories #
######################################################
def _compute_agent_summary(self) -> str:
""""""
prompt = PromptTemplate.from_template(
"How would you summarize {na... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
8ed14bbd3e4f-7 | + f"\n{self.summary}"
)
[docs] def get_full_header(self, force_refresh: bool = False) -> str:
"""Return a full header of the agent's status, summary, and current time."""
summary = self.get_summary(force_refresh=force_refresh)
current_time_str = datetime.now().strftime("%B %d, %Y, %I:... | /content/https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
644f17a2001f-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
644f17a2001f-1 | # Check if template_path exists in config.
if f"{var_name}_path" in config:
# If it does, make sure template variable doesn't also exist.
if var_name in config:
raise ValueError(
f"Both `{var_name}_path` and `{var_name}` cannot be provided."
)
# Pop th... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
644f17a2001f-2 | return config
def _load_output_parser(config: dict) -> dict:
"""Load output parser."""
if "output_parsers" in config:
if config["output_parsers"] is not None:
_config = config["output_parsers"]
output_parser_type = _config["_type"]
if output_parser_type == "regex_pars... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
644f17a2001f-3 | else:
config["example_prompt"] = load_prompt_from_config(config["example_prompt"])
# Load the examples.
config = _load_examples(config)
config = _load_output_parser(config)
return FewShotPromptTemplate(**config)
def _load_prompt(config: dict) -> PromptTemplate:
"""Load the prompt template fr... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
644f17a2001f-4 | if file_path.suffix == ".json":
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(
"promp... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
7bd17292d13d-0 | Source code for langchain.prompts.prompt
"""Prompt schema definition."""
from __future__ import annotations
from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Union
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
S... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
7bd17292d13d-1 | [docs] def format(self, **kwargs: Any) -> str:
"""Format the prompt with the inputs.
Args:
kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
7bd17292d13d-2 | Args:
examples: List of examples to use in the prompt.
suffix: String to go after the list of examples. Should generally
set up the user's input.
input_variables: A list of variable names the final prompt template
will expect.
example_separ... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
7bd17292d13d-3 | """Load a prompt template from a template."""
if "template_format" in kwargs and kwargs["template_format"] == "jinja2":
# Get the variables for the template
input_variables = _get_jinja2_variables_from_template(template)
else:
input_variables = {
v for... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
fc37709a0a24-0 | Source code for langchain.prompts.chat
"""Chat prompt template."""
from __future__ import annotations
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, List, Sequence, Tuple, Type, Union
from pydantic import BaseModel, Field
from langchain.memory.buffer import get_buffer_str... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
fc37709a0a24-1 | if not isinstance(value, list):
raise ValueError(
f"variable {self.variable_name} should be a list of base messages, "
f"got {value}"
)
for v in value:
if not isinstance(v, BaseMessage):
raise ValueError(
f"v... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
fc37709a0a24-2 | role: str
def format(self, **kwargs: Any) -> BaseMessage:
text = self.prompt.format(**kwargs)
return ChatMessage(
content=text, role=self.role, additional_kwargs=self.additional_kwargs
)
class HumanMessagePromptTemplate(BaseStringMessagePromptTemplate):
def format(self, **kwa... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
fc37709a0a24-3 | return get_buffer_string(self.messages)
def to_messages(self) -> List[BaseMessage]:
"""Return prompt as messages."""
return self.messages
[docs]class BaseChatPromptTemplate(BasePromptTemplate, ABC):
[docs] def format(self, **kwargs: Any) -> str:
return self.format_prompt(**kwargs).to_stri... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
fc37709a0a24-4 | @classmethod
def from_strings(
cls, string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]
) -> ChatPromptTemplate:
messages = [
role(content=PromptTemplate.from_template(template))
for role, template in string_messages
]
return cls.from_messag... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
fc37709a0a24-5 | k: v
for k, v in kwargs.items()
if k in message_template.input_variables
}
message = message_template.format_messages(**rel_params)
result.extend(message)
else:
raise ValueError(f"Unexpected input: {messa... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
4c76e7545486-0 | Source code for langchain.prompts.base
"""BasePrompt schema definition."""
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
import yaml
from pydantic import BaseModel, Extra, Field, roo... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
4c76e7545486-1 | error_message += f"Extra variables: {extra_variables}"
if error_message:
raise KeyError(error_message.strip())
def _get_jinja2_variables_from_template(template: str) -> Set[str]:
try:
from jinja2 import Environment, meta
except ImportError:
raise ImportError(
"jinja2 not ... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
4c76e7545486-2 | f" should be one of {valid_formats}"
)
try:
validator_func = DEFAULT_VALIDATOR_MAPPING[template_format]
validator_func(template, input_variables)
except KeyError as e:
raise ValueError(
"Invalid prompt schema; check for mismatched or missing input parameters. "
... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
4c76e7545486-3 | """Create Chat Messages."""
@root_validator()
def validate_variable_names(cls, values: Dict) -> Dict:
"""Validate variable names do not include restricted names."""
if "stop" in values["input_variables"]:
raise ValueError(
"Cannot have an input variable named 'stop', ... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
4c76e7545486-4 | # Get partial params:
partial_kwargs = {
k: v if isinstance(v, str) else v()
for k, v in self.partial_variables.items()
}
return {**partial_kwargs, **kwargs}
[docs] @abstractmethod
def format(self, **kwargs: Any) -> str:
"""Format the prompt with the inputs... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
4c76e7545486-5 | # Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
prompt_dict = self.... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
c38e2b0ffdb9-0 | Source code for langchain.prompts.few_shot_with_templates
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, StringPromptTemplate
from langchain.prompts.example_selec... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
c38e2b0ffdb9-1 | prefix: Optional[StringPromptTemplate] = None
"""A PromptTemplate to put before the examples."""
template_format: str = "f-string"
"""The format of the prompt template. Options are: 'f-string', 'jinja2'."""
validate_template: bool = True
"""Whether or not to try validating the template."""
@root... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
c38e2b0ffdb9-2 | expected_input_variables |= set(values["partial_variables"])
if values["prefix"] is not None:
expected_input_variables |= set(values["prefix"].input_variables)
missing_vars = expected_input_variables.difference(input_variables)
if missing_vars:
raise V... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
c38e2b0ffdb9-3 | # Format the examples.
example_strings = [
self.example_prompt.format(**example) for example in examples
]
# Create the overall prefix.
if self.prefix is None:
prefix = ""
else:
prefix_kwargs = {
k: v for k, v in kwargs.items() ... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
c38e2b0ffdb9-4 | if self.example_selector:
raise ValueError("Saving an example selector is not currently supported")
return super().dict(**kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
245354ce5d6e-0 | Source code for langchain.prompts.few_shot
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPromptTemplate,
check_valid_template,
)
from langcha... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
245354ce5d6e-1 | """A prompt template string to put before the examples."""
template_format: str = "f-string"
"""The format of the prompt template. Options are: 'f-string', 'jinja2'."""
validate_template: bool = True
"""Whether or not to try validating the template."""
@root_validator(pre=True)
def check_example... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
245354ce5d6e-2 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
def _get_examples(self, **kwargs: Any) -> List[dict]:
if self.examples is not None:
return self.examples
elif self.example_selector is not None:
... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
245354ce5d6e-3 | @property
def _prompt_type(self) -> str:
"""Return the prompt type key."""
return "few_shot"
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return a dictionary of the prompt."""
if self.example_selector:
raise ValueError("Saving an example selector is not currently s... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
719cdc323d38-0 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
719cdc323d38-1 | self.example_text_lengths.append(self.get_text_length(string_example))
@validator("example_text_lengths", always=True)
def calculate_example_text_lengths(cls, v: List[int], values: Dict) -> List[int]:
"""Calculate text lengths if they don't exist."""
# Check if text lengths were passed in
... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
719cdc323d38-2 | break
else:
examples.append(self.examples[i])
remaining_length = new_length
i += 1
return examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
b902582bc3dc-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
b902582bc3dc-1 | extra = Extra.forbid
arbitrary_types_allowed = True
[docs] def add_example(self, example: Dict[str, str]) -> str:
"""Add new example to vectorstore."""
if self.input_keys:
string_example = " ".join(
sorted_values({key: example[key] for key in self.input_keys})
... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
b902582bc3dc-2 | 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,
examples: List[dict],
embeddings: Embeddings,
vectorstore_cls: Type[VectorStore],
k: int = 4,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
b902582bc3dc-3 | vectorstore = vectorstore_cls.from_texts(
string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
)
return cls(vectorstore=vectorstore, k=k, input_keys=input_keys)
[docs]class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
"""ExampleSelector tha... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
b902582bc3dc-4 | 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,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
b902582bc3dc-5 | """
if input_keys:
string_examples = [
" ".join(sorted_values({k: eg[k] for k in input_keys}))
for eg in examples
]
else:
string_examples = [" ".join(sorted_values(eg)) for eg in examples]
vectorstore = vectorstore_cls.from_text... | /content/https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
ba6ac099f0c3-0 | Source code for langchain.docstore.in_memory
"""Simple in memory docstore in the form of a dict."""
from typing import Dict, Union
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
[docs]class InMemoryDocstore(Docstore, AddableMixin):
"""Simple in memory doc... | /content/https://python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html |
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