id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
4ba16e318613-0 | Source code for langchain.llms.openlm
from typing import Any, Dict
from pydantic import root_validator
from langchain.llms.openai import BaseOpenAI
[docs]class OpenLM(BaseOpenAI):
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openlm.html |
314e28a061a5-0 | Source code for langchain.llms.aviary
"""Wrapper around Aviary"""
import dataclasses
import os
from typing import Any, Dict, List, Mapping, Optional, Union, cast
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LL... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
314e28a061a5-1 | ) from e
result = sorted(
[k.lstrip("/").replace("--", "/") for k in result.keys() if "--" in k]
)
return result
[docs]def get_completions(
model: str,
prompt: str,
use_prompt_format: bool = True,
version: str = "",
) -> Dict[str, Union[str, float, int]]:
"""Get completions from ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
314e28a061a5-2 | os.environ["AVIARY_URL"] = "<URL>"
os.environ["AVIARY_TOKEN"] = "<TOKEN>"
light = Aviary(model='amazon/LightGPT')
output = light('How do you make fried rice?')
"""
model: str = "amazon/LightGPT"
aviary_url: Optional[str] = None
aviary_token: Optional[str] = None
#... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
314e28a061a5-3 | """Get the identifying parameters."""
return {
"model_name": self.model,
"aviary_url": self.aviary_url,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return f"aviary-{self.model.replace('/', '-')}"
def _call(
self,
p... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
ee85d9e5f640-0 | Source code for langchain.llms.deepinfra
"""Wrapper around DeepInfra APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils im... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
ee85d9e5f640-1 | return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
ee85d9e5f640-2 | if res.status_code != 200:
raise ValueError(
"Error raised by inference API HTTP code: %s, %s"
% (res.status_code, res.text)
)
try:
t = res.json()
text = t["results"][0]["generated_text"]
except requests.exceptions.JSONDecod... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
107f01b2a08c-0 | Source code for langchain.llms.writer
"""Wrapper around Writer APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import e... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
107f01b2a08c-1 | logprobs: bool = False
"""Whether to return log probabilities."""
n: Optional[int] = None
"""How many completions to generate."""
writer_api_key: Optional[str] = None
"""Writer API key."""
base_url: Optional[str] = None
"""Base url to use, if None decides based on model name."""
[docs] cl... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
107f01b2a08c-2 | """Get the identifying parameters."""
return {
**{"model_id": self.model_id, "writer_org_id": self.writer_org_id},
**self._default_params,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "writer"
def _call(
self,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
107f01b2a08c-3 | # are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
b31b5b132b18-0 | Source code for langchain.indexes.vectorstore
from typing import Any, List, Optional, Type
from pydantic import BaseModel, Extra, Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
from langchain.chains.retrieval_qa.base import ... | https://api.python.langchain.com/en/latest/_modules/langchain/indexes/vectorstore.html |
b31b5b132b18-1 | ) -> dict:
"""Query the vectorstore and get back sources."""
llm = llm or OpenAI(temperature=0)
chain = RetrievalQAWithSourcesChain.from_chain_type(
llm, retriever=self.vectorstore.as_retriever(), **kwargs
)
return chain({chain.question_key: question})
[docs]class Vec... | https://api.python.langchain.com/en/latest/_modules/langchain/indexes/vectorstore.html |
2eca2ebdb66e-0 | Source code for langchain.indexes.graph
"""Graph Index Creator."""
from typing import Optional, Type
from pydantic import BaseModel
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.graphs.networkx_graph import NetworkxEntityGraph, parse_triples
from langchai... | https://api.python.langchain.com/en/latest/_modules/langchain/indexes/graph.html |
b549446a09da-0 | Source code for langchain.experimental.autonomous_agents.autogpt.prompt
import time
from typing import Any, Callable, List
from pydantic import BaseModel
from langchain.experimental.autonomous_agents.autogpt.prompt_generator import get_prompt
from langchain.prompts.chat import (
BaseChatPromptTemplate,
)
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/prompt.html |
b549446a09da-1 | time_prompt = SystemMessage(
content=f"The current time and date is {time.strftime('%c')}"
)
used_tokens = self.token_counter(base_prompt.content) + self.token_counter(
time_prompt.content
)
memory: VectorStoreRetriever = kwargs["memory"]
previous_messages... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/prompt.html |
79f8174f5ecf-0 | Source code for langchain.experimental.autonomous_agents.autogpt.output_parser
import json
import re
from abc import abstractmethod
from typing import Dict, NamedTuple
from langchain.schema import BaseOutputParser
[docs]class AutoGPTAction(NamedTuple):
name: str
args: Dict
[docs]class BaseAutoGPTOutputParser(Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/output_parser.html |
79f8174f5ecf-1 | name=parsed["command"]["name"],
args=parsed["command"]["args"],
)
except (KeyError, TypeError):
# If the command is null or incomplete, return an erroneous tool
return AutoGPTAction(
name="ERROR", args={"error": f"Incomplete command args: {pars... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/output_parser.html |
7a4113b8cdd8-0 | Source code for langchain.experimental.autonomous_agents.autogpt.prompt_generator
import json
from typing import List
from langchain.tools.base import BaseTool
FINISH_NAME = "finish"
class PromptGenerator:
"""A class for generating custom prompt strings.
Does this based on constraints, commands, resources, and ... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/prompt_generator.html |
7a4113b8cdd8-1 | return output
def add_resource(self, resource: str) -> None:
"""
Add a resource to the resources list.
Args:
resource (str): The resource to be added.
"""
self.resources.append(resource)
def add_performance_evaluation(self, evaluation: str) -> None:
""... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/prompt_generator.html |
7a4113b8cdd8-2 | def generate_prompt_string(self) -> str:
"""Generate a prompt string.
Returns:
str: The generated prompt string.
"""
formatted_response_format = json.dumps(self.response_format, indent=4)
prompt_string = (
f"Constraints:\n{self._generate_numbered_list(self... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/prompt_generator.html |
7a4113b8cdd8-3 | )
prompt_generator.add_constraint("No user assistance")
prompt_generator.add_constraint(
'Exclusively use the commands listed in double quotes e.g. "command name"'
)
# Add commands to the PromptGenerator object
for tool in tools:
prompt_generator.add_tool(tool)
# Add resources to... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/prompt_generator.html |
1fe8740bb56b-0 | Source code for langchain.experimental.autonomous_agents.autogpt.memory
from typing import Any, Dict, List
from pydantic import Field
from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key
from langchain.vectorstores.base import VectorStoreRetriever
[docs]class AutoGPTMemory(BaseChatMemory):
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/memory.html |
41b46c68a4a4-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.task_creation
from langchain import LLMChain, PromptTemplate
from langchain.base_language import BaseLanguageModel
[docs]class TaskCreationChain(LLMChain):
"""Chain to generates tasks."""
[docs] @classmethod
def from_llm(cls, llm: BaseLanguage... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/task_creation.html |
61380d6c4155-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.task_prioritization
from langchain import LLMChain, PromptTemplate
from langchain.base_language import BaseLanguageModel
[docs]class TaskPrioritizationChain(LLMChain):
"""Chain to prioritize tasks."""
[docs] @classmethod
def from_llm(cls, llm:... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/task_prioritization.html |
55741eda3eff-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
"""BabyAGI agent."""
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerFo... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
55741eda3eff-1 | print(str(t["task_id"]) + ": " + t["task_name"])
[docs] def print_next_task(self, task: Dict) -> None:
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
print(str(task["task_id"]) + ": " + task["task_name"])
[docs] def print_task_result(self, result: str) -> None:
pri... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
55741eda3eff-2 | task_names = [t["task_name"] for t in list(self.task_list)]
next_task_id = int(this_task_id) + 1
response = self.task_prioritization_chain.run(
task_names=", ".join(task_names),
next_task_id=str(next_task_id),
objective=objective,
)
new_tasks = respons... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
55741eda3eff-3 | ) -> Dict[str, Any]:
"""Run the agent."""
objective = inputs["objective"]
first_task = inputs.get("first_task", "Make a todo list")
self.add_task({"task_id": 1, "task_name": first_task})
num_iters = 0
while True:
if self.task_list:
self.print_t... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
55741eda3eff-4 | )
break
return {}
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
verbose: bool = False,
task_execution_chain: Optional[Chain] = None,
**kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initiali... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
9f359bfc4520-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.task_execution
from langchain import LLMChain, PromptTemplate
from langchain.base_language import BaseLanguageModel
[docs]class TaskExecutionChain(LLMChain):
"""Chain to execute tasks."""
[docs] @classmethod
def from_llm(cls, llm: BaseLanguage... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/task_execution.html |
d8f9378f5859-0 | 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.base_language import BaseLanguageModel
from langchain.experimental.gen... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8f9378f5859-1 | arbitrary_types_allowed = True
# LLM-related methods
@staticmethod
def _parse_list(text: str) -> List[str]:
"""Parse a newline-separated string into a list of strings."""
lines = re.split(r"\n", text.strip())
return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
[docs]... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8f9378f5859-2 | entity_action = self._get_entity_action(observation, entity_name)
q1 = f"What is the relationship between {self.name} and {entity_name}"
q2 = f"{entity_name} is {entity_action}"
return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
def _generate_reaction(
self, observ... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8f9378f5859-3 | )
consumed_tokens = self.llm.get_num_tokens(
prompt.format(most_recent_memories="", **kwargs)
)
kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
return self.chain(prompt=prompt).run(**kwargs).strip()
def _clean_response(self, text: str) -> str:
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8f9378f5859-4 | if "SAY:" in result:
said_value = self._clean_response(result.split("SAY:")[-1])
return True, f"{self.name} said {said_value}"
else:
return False, result
[docs] def generate_dialogue_response(
self, observation: str, now: Optional[datetime] = None
) -> Tuple[bo... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8f9378f5859-5 | )
return True, f"{self.name} said {response_text}"
else:
return False, result
######################################################
# Agent stateful' summary methods. #
# Each dialog or response prompt includes a header #
# summarizing the agent's sel... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d8f9378f5859-6 | + f"\nInnate traits: {self.traits}"
+ f"\n{self.summary}"
)
[docs] def get_full_header(
self, force_refresh: bool = False, now: Optional[datetime] = None
) -> str:
"""Return a full header of the agent's status, summary, and current time."""
now = datetime.now() if now ... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
285ba5cee8c3-0 | Source code for langchain.experimental.generative_agents.memory
import logging
import re
from datetime import datetime
from typing import Any, Dict, List, Optional
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.prompts import PromptTemplate
from langchain.retrievers ... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
285ba5cee8c3-1 | # output keys
relevant_memories_key: str = "relevant_memories"
relevant_memories_simple_key: str = "relevant_memories_simple"
most_recent_memories_key: str = "most_recent_memories"
now_key: str = "now"
reflecting: bool = False
[docs] def chain(self, prompt: PromptTemplate) -> LLMChain:
re... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
285ba5cee8c3-2 | self, topic: str, now: Optional[datetime] = None
) -> List[str]:
"""Generate 'insights' on a topic of reflection, based on pertinent memories."""
prompt = PromptTemplate.from_template(
"Statements relevant to: '{topic}'\n"
"---\n"
"{related_statements}\n"
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
285ba5cee8c3-3 | insights = self._get_insights_on_topic(topic, now=now)
for insight in insights:
self.add_memory(insight, now=now)
new_insights.extend(insights)
return new_insights
def _score_memory_importance(self, memory_content: str) -> float:
"""Score the absolute importan... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
285ba5cee8c3-4 | + " acceptance), rate the likely poignancy of the"
+ " following piece of memory. Always answer with only a list of numbers."
+ " If just given one memory still respond in a list."
+ " Memories are separated by semi colans (;)"
+ "\Memories: {memory_content}"
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
285ba5cee8c3-5 | and not self.reflecting
):
self.reflecting = True
self.pause_to_reflect(now=now)
# Hack to clear the importance from reflection
self.aggregate_importance = 0.0
self.reflecting = False
return result
[docs] def add_memory(
self, memory... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
285ba5cee8c3-6 | else:
return self.memory_retriever.get_relevant_documents(observation)
[docs] def format_memories_detail(self, relevant_memories: List[Document]) -> str:
content = []
for mem in relevant_memories:
content.append(self._format_memory_detail(mem, prefix="- "))
return "\n"... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
285ba5cee8c3-7 | now = inputs.get(self.now_key)
if queries is not None:
relevant_memories = [
mem for query in queries for mem in self.fetch_memories(query, now=now)
]
return {
self.relevant_memories_key: self.format_memories_detail(
relevan... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
797fc32fc2c9-0 | Source code for langchain.experimental.llms.rellm_decoder
"""Experimental implementation of RELLM wrapped LLM."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, List, Optional, cast
from pydantic import Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/llms/rellm_decoder.html |
797fc32fc2c9-1 | from transformers import Text2TextGenerationPipeline
pipeline = cast(Text2TextGenerationPipeline, self.pipeline)
text = rellm.complete_re(
prompt,
self.regex,
tokenizer=pipeline.tokenizer,
model=pipeline.model,
max_new_tokens=self.max_new_token... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/llms/rellm_decoder.html |
11877270ce07-0 | Source code for langchain.experimental.llms.jsonformer_decoder
"""Experimental implementation of jsonformer wrapped LLM."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, List, Optional, cast
from pydantic import Field, root_validator
from langchain.callbacks.manager import Callba... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/llms/jsonformer_decoder.html |
11877270ce07-1 | model=pipeline.model,
tokenizer=pipeline.tokenizer,
json_schema=self.json_schema,
prompt=prompt,
max_number_tokens=self.max_new_tokens,
debug=self.debug,
)
text = model()
return json.dumps(text) | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/llms/jsonformer_decoder.html |
6b253142c513-0 | Source code for langchain.experimental.plan_and_execute.agent_executor
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.experimental.plan_and_execute.executors.base import Bas... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/plan_and_execute/agent_executor.html |
6b253142c513-1 | callbacks=run_manager.get_child() if run_manager else None,
)
if run_manager:
run_manager.on_text(
f"*****\n\nStep: {step.value}", verbose=self.verbose
)
run_manager.on_text(
f"\n\nResponse: {response.respons... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/plan_and_execute/agent_executor.html |
fe94faadb51e-0 | Source code for langchain.experimental.plan_and_execute.schema
from abc import abstractmethod
from typing import List, Tuple
from pydantic import BaseModel, Field
from langchain.schema import BaseOutputParser
[docs]class Step(BaseModel):
value: str
[docs]class Plan(BaseModel):
steps: List[Step]
[docs]class Step... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/plan_and_execute/schema.html |
dc68bc85faa4-0 | Source code for langchain.experimental.plan_and_execute.planners.base
from abc import abstractmethod
from typing import Any, List, Optional
from pydantic import BaseModel
from langchain.callbacks.manager import Callbacks
from langchain.chains.llm import LLMChain
from langchain.experimental.plan_and_execute.schema impor... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/plan_and_execute/planners/base.html |
5028c53af037-0 | Source code for langchain.experimental.plan_and_execute.planners.chat_planner
import re
from langchain.base_language import BaseLanguageModel
from langchain.chains import LLMChain
from langchain.experimental.plan_and_execute.planners.base import LLMPlanner
from langchain.experimental.plan_and_execute.schema import (
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/plan_and_execute/planners/chat_planner.html |
5028c53af037-1 | """
prompt_template = ChatPromptTemplate.from_messages(
[
SystemMessage(content=system_prompt),
HumanMessagePromptTemplate.from_template("{input}"),
]
)
llm_chain = LLMChain(llm=llm, prompt=prompt_template)
return LLMPlanner(
llm_chain=llm_chain,
o... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/plan_and_execute/planners/chat_planner.html |
3307acd638dd-0 | Source code for langchain.experimental.plan_and_execute.executors.agent_executor
from typing import List
from langchain.agents.agent import AgentExecutor
from langchain.agents.structured_chat.base import StructuredChatAgent
from langchain.base_language import BaseLanguageModel
from langchain.experimental.plan_and_execu... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/plan_and_execute/executors/agent_executor.html |
a201fe07733d-0 | Source code for langchain.experimental.plan_and_execute.executors.base
from abc import abstractmethod
from typing import Any
from pydantic import BaseModel
from langchain.callbacks.manager import Callbacks
from langchain.chains.base import Chain
from langchain.experimental.plan_and_execute.schema import StepResponse
[d... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/plan_and_execute/executors/base.html |
d4c36500784e-0 | Source code for langchain.tools.plugin
from __future__ import annotations
import json
from typing import Optional, Type
import requests
import yaml
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base impo... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
d4c36500784e-1 | plugin: AIPlugin
api_spec: str
args_schema: Type[AIPluginToolSchema] = AIPluginToolSchema
[docs] @classmethod
def from_plugin_url(cls, url: str) -> AIPluginTool:
plugin = AIPlugin.from_url(url)
description = (
f"Call this tool to get the OpenAPI spec (and usage guide) "
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
99c0d61aae16-0 | Source code for langchain.tools.ifttt
"""From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.
# Creating a webhook
- Go to https://ifttt.com/create
# Configuring the "If This"
- Click on the "If This" button in the IFTTT interface.
- Search for "Webhooks" in the search bar.
- Choose the first... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
99c0d61aae16-1 | - To get your webhook URL go to https://ifttt.com/maker_webhooks/settings
- Copy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.
"""
from typing import Optional
import requests
from langchain.callbacks.manager import (
AsyncCallbackMa... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
060a5f581749-0 | Source code for langchain.tools.convert_to_openai
from typing import TypedDict
from langchain.tools import BaseTool, StructuredTool
[docs]class FunctionDescription(TypedDict):
"""Representation of a callable function to the OpenAI API."""
name: str
"""The name of the function."""
description: str
""... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/convert_to_openai.html |
67c9cccc59c5-0 | Source code for langchain.tools.base
"""Base implementation for tools or skills."""
from __future__ import annotations
import warnings
from abc import ABC, abstractmethod
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, Optional, Tuple, Type, Union
from pydantic import (
BaseModel,
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-1 | typehint_mandate = """
class ChildTool(BaseTool):
...
args_schema: Type[BaseModel] = SchemaClass
..."""
raise SchemaAnnotationError(
f"Tool definition for {name} must include valid type annotations"
f" for argument 'args_schema' to behave as expected.\... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-2 | model_name: str,
func: Callable,
) -> Type[BaseModel]:
"""Create a pydantic schema from a function's signature.
Args:
model_name: Name to assign to the generated pydandic schema
func: Function to generate the schema from
Returns:
A pydantic model with the same arguments as the fu... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-3 | """Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
"""
args_schema: Optional[Type[BaseModel]] = None
"""Pydantic model class to validate and parse the tool's input arguments."""
return_direct: bool = False
"""Whether to re... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-4 | self,
tool_input: Union[str, Dict],
) -> Union[str, Dict[str, Any]]:
"""Convert tool input to pydantic model."""
input_args = self.args_schema
if isinstance(tool_input, str):
if input_args is not None:
key_ = next(iter(input_args.__fields__.keys()))
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-5 | to child implementations to enable tracing,
"""
def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
# For backwards compatibility, if run_input is a string,
# pass as a positional argument.
if isinstance(tool_input, str):
return (tool_input,... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-6 | )
except ToolException as e:
if not self.handle_tool_error:
run_manager.on_tool_error(e)
raise e
elif isinstance(self.handle_tool_error, bool):
if e.args:
observation = e.args[0]
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-7 | callbacks, self.callbacks, verbose=verbose_
)
new_arg_supported = signature(self._arun).parameters.get("run_manager")
run_manager = await callback_manager.on_tool_start(
{"name": self.name, "description": self.description},
tool_input if isinstance(tool_input, str) else s... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-8 | raise e
else:
await run_manager.on_tool_end(
str(observation), color=color, name=self.name, **kwargs
)
return observation
[docs] def __call__(self, tool_input: str, callbacks: Callbacks = None) -> str:
"""Make tool callable."""
return self.r... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-9 | def _run(
self,
*args: Any,
run_manager: Optional[CallbackManagerForToolRun] = None,
**kwargs: Any,
) -> Any:
"""Use the tool."""
new_argument_supported = signature(self.func).parameters.get("callbacks")
return (
self.func(
*args,
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-10 | def from_function(
cls,
func: Callable,
name: str, # We keep these required to support backwards compatibility
description: str,
return_direct: bool = False,
args_schema: Optional[Type[BaseModel]] = None,
**kwargs: Any,
) -> Tool:
"""Initialize tool f... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-11 | )
async def _arun(
self,
*args: Any,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
**kwargs: Any,
) -> str:
"""Use the tool asynchronously."""
if self.coroutine:
new_argument_supported = signature(self.coroutine).parameters.get(
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-12 | Returns:
The tool
Examples:
... code-block:: python
def add(a: int, b: int) -> int:
\"\"\"Add two numbers\"\"\"
return a + b
tool = StructuredTool.from_function(add)
tool.run(1, 2) # 3
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-13 | the function's signature. This also makes the resultant tool
accept a dictionary input to its `run()` function.
Requires:
- Function must be of type (str) -> str
- Function must have a docstring
Examples:
.. code-block:: python
@tool
def search_api(que... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
67c9cccc59c5-14 | elif len(args) == 1 and callable(args[0]):
# if the argument is a function, then we use the function name as the tool name
# Example usage: @tool
return _make_with_name(args[0].__name__)(args[0])
elif len(args) == 0:
# if there are no arguments, then we use the function name as the t... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
a694b319b5b6-0 | Source code for langchain.tools.zapier.tool
"""## Zapier Natural Language Actions API
\
Full docs here: https://nla.zapier.com/start/
**Zapier Natural Language Actions** gives you access to the 5k+ apps, 20k+ actions
on Zapier's platform through a natural language API interface.
NLA supports apps like Gmail, Salesforce... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
a694b319b5b6-1 | 2. Use LLMChain to generate a draft reply to (1)
3. Use NLA to send the draft reply (2) to someone in Slack via direct message
In code, below:
```python
import os
# get from https://platform.openai.com/
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")
# get from https://nla.zapier.com/docs/authen... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
a694b319b5b6-2 | agent = initialize_agent(
toolkit.get_tools(),
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
agent.run(("Summarize the last email I received regarding Silicon Valley Bank. "
"Send the summary to the #test-zapier channel in slack."))
```
"""
from typing import Any, Dict, Optional
f... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
a694b319b5b6-3 | name = ""
description = ""
[docs] @root_validator
def set_name_description(cls, values: Dict[str, Any]) -> Dict[str, Any]:
zapier_description = values["zapier_description"]
params_schema = values["params_schema"]
if "instructions" in params_schema:
del params_schema["instr... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
a694b319b5b6-4 | )
ZapierNLARunAction.__doc__ = (
ZapierNLAWrapper.run.__doc__ + ZapierNLARunAction.__doc__ # type: ignore
)
# other useful actions
[docs]class ZapierNLAListActions(BaseTool):
"""
Args:
None
"""
name = "ZapierNLA_list_actions"
description = BASE_ZAPIER_TOOL_PROMPT + (
"This tool ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
1d38c1a54394-0 | Source code for langchain.tools.bing_search.tool
"""Tool for the Bing search API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.bing_search import BingSearch... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
1d38c1a54394-1 | api_wrapper: BingSearchAPIWrapper
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.api_wrapper.results(query, self.num_results))
async def _arun(
self,
query: str,
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
ef1abdf64c5b-0 | Source code for langchain.tools.python.tool
"""A tool for running python code in a REPL."""
import ast
import re
import sys
from contextlib import redirect_stdout
from io import StringIO
from typing import Any, Dict, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import (
Async... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/python/tool.html |
ef1abdf64c5b-1 | sanitize_input: bool = True
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> Any:
"""Use the tool."""
if self.sanitize_input:
query = sanitize_input(query)
return self.python_repl.run(query)
async def _arun(... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/python/tool.html |
ef1abdf64c5b-2 | return values
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
try:
if self.sanitize_input:
query = sanitize_input(query)
tree = ast.parse(query)
module =... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/python/tool.html |
d87c200d13c6-0 | Source code for langchain.tools.arxiv.tool
"""Tool for the Arxiv API."""
from typing import Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.arxiv import A... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/arxiv/tool.html |
393603ad69f6-0 | Source code for langchain.tools.vectorstore.tool
"""Tools for interacting with vectorstores."""
import json
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
393603ad69f6-1 | def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
chain = RetrievalQA.from_chain_type(
self.llm, retriever=self.vectorstore.as_retriever()
)
return chain.run(query)
async def _aru... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
393603ad69f6-2 | self.llm, retriever=self.vectorstore.as_retriever()
)
return json.dumps(chain({chain.question_key: query}, return_only_outputs=True))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchr... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
e0983a4e3b8a-0 | Source code for langchain.tools.gmail.utils
"""Gmail tool utils."""
from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, List, Optional, Tuple
if TYPE_CHECKING:
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/gmail/utils.html |
e0983a4e3b8a-1 | """Import googleapiclient.discovery.build function.
Returns:
build_resource: googleapiclient.discovery.build function.
"""
try:
from googleapiclient.discovery import build
except ImportError:
raise ValueError(
"You need to install googleapiclient to use this toolkit. ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/gmail/utils.html |
e0983a4e3b8a-2 | creds.refresh(Request())
else:
# https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application # noqa
flow = InstalledAppFlow.from_client_secrets_file(
client_secrets_file, scopes
)
creds = flow.run_local... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/gmail/utils.html |
8e6c350e55b2-0 | Source code for langchain.tools.gmail.search
import base64
import email
from enum import Enum
from typing import Any, Dict, List, Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.gmail... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/gmail/search.html |
8e6c350e55b2-1 | name: str = "search_gmail"
description: str = (
"Use this tool to search for email messages or threads."
" The input must be a valid Gmail query."
" The output is a JSON list of the requested resource."
)
args_schema: Type[SearchArgsSchema] = SearchArgsSchema
def _parse_threads(s... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/gmail/search.html |
8e6c350e55b2-2 | body = clean_email_body(message_body)
results.append(
{
"id": message["id"],
"threadId": message_data["threadId"],
"snippet": message_data["snippet"],
"body": body,
"subject": subject,
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/gmail/search.html |
1a735b9a8632-0 | Source code for langchain.tools.gmail.get_thread
from typing import Dict, Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.gmail.base import GmailBaseTool
[docs]class GetThreadSchema(B... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/gmail/get_thread.html |
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