id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 115 |
|---|---|---|
205587642bcb-5 | Create Chat Messages.
langchain.prompts.load_prompt(path: Union[str, pathlib.Path]) → langchain.prompts.base.BasePromptTemplate[source]#
Unified method for loading a prompt from LangChainHub or local fs.
previous
Prompts
next
Example Selector
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last ... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
d0129f61b74e-0 | .rst
.pdf
Chat Models
Chat Models#
pydantic model langchain.chat_models.AzureChatOpenAI[source]#
Wrapper around Azure OpenAI Chat Completion API. To use this class you
must have a deployed model on Azure OpenAI. Use deployment_name in the
constructor to refer to the “Model deployment name” in the Azure portal.
In addit... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
d0129f61b74e-1 | field callback_manager: langchain.callbacks.base.BaseCallbackManager [Optional]#
field verbose: bool [Optional]#
Whether to print out response text.
pydantic model langchain.chat_models.ChatOpenAI[source]#
Wrapper around OpenAI Chat large language models.
To use, you should have the openai python package installed, and... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
d0129f61b74e-2 | get_num_tokens(text: str) → int[source]#
Calculate num tokens with tiktoken package.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int[source]#
Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: openai/openai-cookbook
main/examples/How_to_forma... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
d929561963a1-0 | Source code for langchain.document_transformers
"""Transform documents"""
from typing import Any, Callable, List, Sequence
import numpy as np
from pydantic import BaseModel, Field
from langchain.embeddings.base import Embeddings
from langchain.math_utils import cosine_similarity
from langchain.schema import BaseDocumen... | https://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
d929561963a1-1 | for first_idx, second_idx in redundant_stacked[redundant_sorted]:
if first_idx in included_idxs and second_idx in included_idxs:
# Default to dropping the second document of any highly similar pair.
included_idxs.remove(second_idx)
return list(sorted(included_idxs))
def _get_embeddin... | https://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
d929561963a1-2 | """Filter down documents."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
self.embeddings, stateful_documents
)
included_idxs = _filter_similar_embeddings(
embedded_documents, self.similarity_fn, s... | https://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
dff4e151e43f-0 | Source code for langchain.requests
"""Lightweight wrapper around requests library, with async support."""
from contextlib import asynccontextmanager
from typing import Any, AsyncGenerator, Dict, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra
class Requests(BaseModel):
"""Wrapper aroun... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
dff4e151e43f-1 | def delete(self, url: str, **kwargs: Any) -> requests.Response:
"""DELETE the URL and return the text."""
return requests.delete(url, headers=self.headers, **kwargs)
@asynccontextmanager
async def _arequest(
self, method: str, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.Clien... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
dff4e151e43f-2 | """PATCH the URL and return the text asynchronously."""
async with self._arequest("PATCH", url, **kwargs) as response:
yield response
@asynccontextmanager
async def aput(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
dff4e151e43f-3 | """POST to the URL and return the text."""
return self.requests.post(url, data, **kwargs).text
[docs] def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PATCH the URL and return the text."""
return self.requests.patch(url, data, **kwargs).text
[docs] def put(self, ur... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
dff4e151e43f-4 | """PUT the URL and return the text asynchronously."""
async with self.requests.aput(url, **kwargs) as response:
return await response.text()
[docs] async def adelete(self, url: str, **kwargs: Any) -> str:
"""DELETE the URL and return the text asynchronously."""
async with self.req... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
bb67b52880bc-0 | Source code for langchain.text_splitter
"""Functionality for splitting text."""
from __future__ import annotations
import copy
import logging
from abc import ABC, abstractmethod
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Iterable,
List,
Literal,
Optional,
Sequence,
... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-1 | for chunk in self.split_text(text):
new_doc = Document(
page_content=chunk, metadata=copy.deepcopy(_metadatas[i])
)
documents.append(new_doc)
return documents
[docs] def split_documents(self, documents: List[Document]) -> List[Document]:
... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-2 | docs.append(doc)
# Keep on popping if:
# - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
while total > self._chunk_overlap or (
total + _len + (separator_l... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-3 | [docs] @classmethod
def from_tiktoken_encoder(
cls,
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-4 | """Asynchronously transform a sequence of documents by splitting them."""
raise NotImplementedError
[docs]class CharacterTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at characters."""
def __init__(self, separator: str = "\n\n", **kwargs: Any):
"""Create a new TextSp... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-5 | enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
self._tokenizer = enc
self._allowed_special = allowed_special
self._disallowed_special = disallowed_special
[docs] def split_text(self, text: str) -> List[str]:
"""Split ... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-6 | # Get appropriate separator to use
separator = self._separators[-1]
for _s in self._separators:
if _s == "":
separator = _s
break
if _s in text:
separator = _s
break
# Now that we have the separator, split th... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-7 | [docs] def split_text(self, text: str) -> List[str]:
"""Split incoming text and return chunks."""
# First we naively split the large input into a bunch of smaller ones.
splits = self._tokenizer(text)
return self._merge_splits(splits, self._separator)
[docs]class SpacyTextSplitter(Text... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-8 | # Note the alternative syntax for headings (below) is not handled here
# Heading level 2
# ---------------
# End of code block
"```\n\n",
# Horizontal lines
"\n\n***\n\n",
"\n\n---\n\n",
"\n\n___\n\n",
# Note tha... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bb67b52880bc-9 | # Now split by the normal type of lines
" ",
"",
]
super().__init__(separators=separators, **kwargs)
[docs]class PythonCodeTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to split the text along Python syntax."""
def __init__(self, **kwargs: Any):
"""Ini... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
8b261a5b1918-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html |
053514873a8c-0 | Source code for langchain.docstore.wikipedia
"""Wrapper around wikipedia API."""
from typing import Union
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
[docs]class Wikipedia(Docstore):
"""Wrapper around wikipedia API."""
def __init__(self) -> None:
"""Chec... | https://python.langchain.com/en/latest/_modules/langchain/docstore/wikipedia.html |
856ac9ab2bad-0 | Source code for langchain.tools.base
"""Base implementation for tools or skills."""
from __future__ import annotations
from abc import ABC, abstractmethod
from inspect import signature
from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Type, Union
from pydantic import (
BaseModel,
Extra,
Fie... | https://python.langchain.com/en/latest/_modules/langchain/tools/base.html |
856ac9ab2bad-1 | # TODO: Use get_args / get_origin and fully
# specify valid annotations.
typehint_mandate = """
class ChildTool(BaseTool):
...
args_schema: Type[BaseModel] = SchemaClass
..."""
raise SchemaAnnotationError(
f"Tool definition for {name} must ... | https://python.langchain.com/en/latest/_modules/langchain/tools/base.html |
856ac9ab2bad-2 | """Create a pydantic schema from a function's signature."""
inferred_model = validate_arguments(func).model # type: ignore
# Pydantic adds placeholder virtual fields we need to strip
filtered_args = get_filtered_args(inferred_model, func)
return _create_subset_model(
f"{model_name}Schema", infe... | https://python.langchain.com/en/latest/_modules/langchain/tools/base.html |
856ac9ab2bad-3 | key_ = next(iter(input_args.__fields__.keys()))
input_args.validate({key_: tool_input})
else:
if input_args is not None:
input_args.validate(tool_input)
@validator("callback_manager", pre=True, always=True)
def set_callback_manager(
cls, callback_manag... | https://python.langchain.com/en/latest/_modules/langchain/tools/base.html |
856ac9ab2bad-4 | try:
tool_args, tool_kwargs = _to_args_and_kwargs(tool_input)
observation = self._run(*tool_args, **tool_kwargs)
except (Exception, KeyboardInterrupt) as e:
self.callback_manager.on_tool_error(e, verbose=verbose_)
raise e
self.callback_manager.on_tool_end(... | https://python.langchain.com/en/latest/_modules/langchain/tools/base.html |
856ac9ab2bad-5 | observation = await self._arun(*args, **kwargs)
except (Exception, KeyboardInterrupt) as e:
if self.callback_manager.is_async:
await self.callback_manager.on_tool_error(e, verbose=verbose_)
else:
self.callback_manager.on_tool_error(e, verbose=verbose_)
... | https://python.langchain.com/en/latest/_modules/langchain/tools/base.html |
0f797a5b6f30-0 | Source code for langchain.tools.plugin
from __future__ import annotations
import json
from typing import Optional
import requests
import yaml
from pydantic import BaseModel
from langchain.tools.base import BaseTool
class ApiConfig(BaseModel):
type: str
url: str
has_user_authentication: Optional[bool] = Fals... | https://python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
0f797a5b6f30-1 | ) + plugin.description_for_human
open_api_spec_str = requests.get(plugin.api.url).text
open_api_spec = marshal_spec(open_api_spec_str)
api_spec = (
f"Usage Guide: {plugin.description_for_model}\n\n"
f"OpenAPI Spec: {open_api_spec}"
)
return cls(
... | https://python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
db579423effe-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://python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
db579423effe-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.
"""
import requests
from langchain.tools.base import BaseTool
[docs]class IFTTTWebhook(BaseTool):
"""IFTT... | https://python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
65593f8098b2-0 | Source code for langchain.tools.ddg_search.tool
"""Tool for the DuckDuckGo search API."""
import warnings
from typing import Any
from pydantic import Field
from langchain.tools.base import BaseTool
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
[docs]class DuckDuckGoSearchRun(BaseTool):
... | https://python.langchain.com/en/latest/_modules/langchain/tools/ddg_search/tool.html |
65593f8098b2-1 | default_factory=DuckDuckGoSearchAPIWrapper
)
def _run(self, query: str) -> str:
"""Use the tool."""
return str(self.api_wrapper.results(query, self.num_results))
async def _arun(self, query: str) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("DuckDuckGo... | https://python.langchain.com/en/latest/_modules/langchain/tools/ddg_search/tool.html |
89e0c48e1fba-0 | Source code for langchain.tools.google_search.tool
"""Tool for the Google search API."""
from langchain.tools.base import BaseTool
from langchain.utilities.google_search import GoogleSearchAPIWrapper
[docs]class GoogleSearchRun(BaseTool):
"""Tool that adds the capability to query the Google search API."""
name ... | https://python.langchain.com/en/latest/_modules/langchain/tools/google_search/tool.html |
89e0c48e1fba-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/tools/google_search/tool.html |
e468defc393f-0 | Source code for langchain.tools.google_places.tool
"""Tool for the Google search API."""
from pydantic import Field
from langchain.tools.base import BaseTool
from langchain.utilities.google_places_api import GooglePlacesAPIWrapper
[docs]class GooglePlacesTool(BaseTool):
"""Tool that adds the capability to query the... | https://python.langchain.com/en/latest/_modules/langchain/tools/google_places/tool.html |
5208a5890a42-0 | Source code for langchain.tools.openapi.utils.api_models
"""Pydantic models for parsing an OpenAPI spec."""
import logging
from enum import Enum
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, Union
from openapi_schema_pydantic import MediaType, Parameter, Reference, RequestBody, Schema
from pydant... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-1 | + f"Valid values are {[loc.value for loc in SUPPORTED_LOCATIONS]}"
)
SCHEMA_TYPE = Union[str, Type, tuple, None, Enum]
class APIPropertyBase(BaseModel):
"""Base model for an API property."""
# The name of the parameter is required and is case sensitive.
# If "in" is "path", the "name" field must correspond ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-2 | type_ = schema.type
if not isinstance(type_, list):
return type_
else:
return tuple(type_)
@staticmethod
def _get_schema_type_for_enum(parameter: Parameter, schema: Schema) -> Enum:
"""Get the schema type when the parameter is an enum."""
param_name = f"{p... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-3 | schema_type = APIProperty._get_schema_type_for_enum(parameter, schema)
else:
# Directly use the primitive type
pass
else:
raise NotImplementedError(f"Unsupported type: {schema_type}")
return schema_type
@staticmethod
def _validate_location(... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-4 | location,
parameter.name,
)
cls._validate_content(parameter.content)
schema = cls._get_schema(parameter, spec)
schema_type = cls._get_schema_type(parameter, schema)
default_val = schema.default if schema is not None else None
return cls(
name=param... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-5 | cls.from_schema(
schema=prop_schema,
name=prop_name,
required=prop_name in required_props,
spec=spec,
references_used=references_used,
)
)
return schema.type, properties
@classmeth... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-6 | schema_type, properties = cls._process_object_schema(
schema, spec, references_used
)
elif schema_type == "array":
schema_type = cls._process_array_schema(schema, name, spec, references_used)
elif schema_type in PRIMITIVE_TYPES:
# Use the primitive typ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-7 | f"Could not resolve schema for media type: {media_type_obj}"
)
api_request_body_properties = []
required_properties = schema.required or []
if schema.type == "object" and schema.properties:
for prop_name, prop_schema in schema.properties.items():
if isinst... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-8 | operation_id: str = Field(alias="operation_id")
"""The unique identifier of the operation."""
description: Optional[str] = Field(alias="description")
"""The description of the operation."""
base_url: str = Field(alias="base_url")
"""The base URL of the operation."""
path: str = Field(alias="path... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-9 | def from_openapi_url(
cls,
spec_url: str,
path: str,
method: str,
) -> "APIOperation":
"""Create an APIOperation from an OpenAPI URL."""
spec = OpenAPISpec.from_url(spec_url)
return cls.from_openapi_spec(spec, path, method)
[docs] @classmethod
def from_... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-10 | # parsing specs that are < v3
return "any"
elif isinstance(type_, str):
return {
"str": "string",
"integer": "number",
"float": "number",
"date-time": "string",
}.get(type_, type_)
elif isinstance(type_, ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
5208a5890a42-11 | if self.request_body:
formatted_request_body_props = self._format_nested_properties(
self.request_body.properties
)
params.append(formatted_request_body_props)
for prop in self.properties:
prop_name = prop.name
prop_type = self.ts_type_... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
0197ffa057ff-0 | Source code for langchain.tools.openapi.utils.openapi_utils
"""Utility functions for parsing an OpenAPI spec."""
import copy
import json
import logging
import re
from enum import Enum
from pathlib import Path
from typing import Dict, List, Optional, Union
import requests
import yaml
from openapi_schema_pydantic import ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
0197ffa057ff-1 | return path_item
@property
def _components_strict(self) -> Components:
"""Get components or err."""
if self.components is None:
raise ValueError("No components found in spec. ")
return self.components
@property
def _parameters_strict(self) -> Dict[str, Union[Parameter... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
0197ffa057ff-2 | parameter = self._get_referenced_parameter(ref)
while isinstance(parameter, Reference):
parameter = self._get_referenced_parameter(parameter)
return parameter
[docs] def get_referenced_schema(self, ref: Reference) -> Schema:
"""Get a schema (or nested reference) or err."""
... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
0197ffa057ff-3 | """Alert if the spec is not supported."""
warning_message = (
" This may result in degraded performance."
+ " Convert your OpenAPI spec to 3.1.* spec"
+ " for better support."
)
swagger_version = obj.get("swagger")
openapi_version = obj.get("openapi")
... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
0197ffa057ff-4 | def from_spec_dict(cls, spec_dict: dict) -> "OpenAPISpec":
"""Get an OpenAPI spec from a dict."""
return cls.parse_obj(spec_dict)
[docs] @classmethod
def from_text(cls, text: str) -> "OpenAPISpec":
"""Get an OpenAPI spec from a text."""
try:
spec_dict = json.loads(text... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
0197ffa057ff-5 | if isinstance(operation, Operation):
results.append(method.value)
return results
[docs] def get_operation(self, path: str, method: str) -> Operation:
"""Get the operation object for a given path and HTTP method."""
path_item = self._get_path_strict(path)
operation_obj ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
0197ffa057ff-6 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
53249fa29fbc-0 | Source code for langchain.tools.bing_search.tool
"""Tool for the Bing search API."""
from langchain.tools.base import BaseTool
from langchain.utilities.bing_search import BingSearchAPIWrapper
[docs]class BingSearchRun(BaseTool):
"""Tool that adds the capability to query the Bing search API."""
name = "Bing Sear... | https://python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
53249fa29fbc-1 | raise NotImplementedError("BingSearchResults does not support async")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
bea98a672aac-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... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
bea98a672aac-1 | ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
) -> AutoGPT:
prompt = AutoGPTPrompt(
ai_name=ai_name,
ai_role=ai_r... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
bea98a672aac-2 | # Get command name and arguments
action = self.output_parser.parse(assistant_reply)
tools = {t.name: t for t in self.tools}
if action.name == FINISH_NAME:
return action.args["response"]
if action.name in tools:
tool = tools[action.name]
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
a3b8fa802362-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
a3b8fa802362-1 | 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"])
def print_task_result(self, result: str) -> None:
print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
a3b8fa802362-2 | task_names=", ".join(task_names),
next_task_id=str(next_task_id),
objective=objective,
)
new_tasks = response.split("\n")
prioritized_task_list = []
for task_string in new_tasks:
if not task_string.strip():
continue
task_par... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
a3b8fa802362-3 | while True:
if self.task_list:
self.print_task_list()
# Step 1: Pull the first task
task = self.task_list.popleft()
self.print_next_task(task)
# Step 2: Execute the task
result = self.execute_task(objective, task... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
a3b8fa802362-4 | **kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initialize the BabyAGI Controller."""
task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
task_prioritization_chain = TaskPrioritizationChain.from_llm(
llm, verbose=verbose
)
if task_execution_chain i... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
2d5c7e02689e-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... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2d5c7e02689e-1 | 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=self.llm, prompt=prompt, verbose=self.verbose)
@staticmethod
def _parse_list(text: str) -> List[str]:
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2d5c7e02689e-2 | + "What 5 high-level insights can you infer from the above statements?"
+ " (example format: insight (because of 1, 5, 3))"
)
related_memories = self.fetch_memories(topic)
related_statements = "\n".join(
[
f"{i+1}. {memory.page_content}"
fo... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2d5c7e02689e-3 | + "\nMemory: {memory_content}"
+ "\nRating: "
)
score = self.chain(prompt).run(memory_content=memory_content).strip()
if self.verbose:
logger.info(f"Importance score: {score}")
match = re.search(r"^\D*(\d+)", score)
if match:
return (float(scor... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2d5c7e02689e-4 | content = []
for mem in relevant_memories:
if mem.page_content in content_strs:
continue
content_strs.add(mem.page_content)
created_time = mem.metadata["created_at"].strftime("%B %d, %Y, %I:%M %p")
content.append(f"- {created_time}: {mem.page_conte... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
2d5c7e02689e-5 | relevant_memories
),
self.relevant_memories_simple_key: self.format_memories_simple(
relevant_memories
),
}
most_recent_memories_token = inputs.get(self.most_recent_memories_token_key)
if most_recent_memories_token is not No... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
d2c64b2780c5-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d2c64b2780c5-1 | 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]
de... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d2c64b2780c5-2 | 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, observation: st... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d2c64b2780c5-3 | return self.chain(prompt=prompt).run(**kwargs).strip()
def _clean_response(self, text: str) -> str:
return re.sub(f"^{self.name} ", "", text.strip()).strip()
[docs] def generate_reaction(self, observation: str) -> Tuple[bool, str]:
"""React to a given observation."""
call_to_action_templa... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d2c64b2780c5-4 | """React to a given observation."""
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._genera... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d2c64b2780c5-5 | "How would you summarize {name}'s core characteristics given the"
+ " following statements:\n"
+ "{relevant_memories}"
+ "Do not embellish."
+ "\n\nSummary: "
)
# The agent seeks to think about their core characteristics.
return (
self.... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
d2c64b2780c5-6 | )
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
0fc3bd52b651-0 | 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.callbacks.base import BaseCallbackMa... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
0fc3bd52b651-1 | "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... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
d2e9771c94c0-0 | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, Union
import yaml
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.agent_types import AgentType
from langchain.agents.ch... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
d2e9771c94c0-1 | if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
agent_cls = AGENT_TO_CLASS[config_type]
combined_config = {**config, **kwargs}
return agent_cls.from_llm_and_tools(llm, tools, **combined_config)
def load_agent_from_config(
config: dict,
llm... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
d2e9771c94c0-2 | config["llm_chain"] = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` and `llm_chain_path` should be specified.")
combined_config = {**config, **kwargs}
return agent_cls(**combined_config) # type: ignore
[docs]def load_agent(path: Union[str, Path], **kwargs: Any)... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
3f7702ce7bb6-0 | 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.callbacks.base import BaseCallbackManager
from langchain.chains.api imp... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
3f7702ce7bb6-1 | from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain.utilities.google_search import GoogleSearchAPIWrapper
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.searx_search import SearxSearchWrapper
from langchain.utilities.serpapi import S... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
3f7702ce7bb6-2 | "requests_patch": _get_tools_requests_patch,
"requests_put": _get_tools_requests_put,
"requests_delete": _get_tools_requests_delete,
"terminal": _get_terminal,
}
def _get_pal_math(llm: BaseLLM) -> BaseTool:
return Tool(
name="PAL-MATH",
description="A language model that is really good a... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
3f7702ce7bb6-3 | return Tool(
name="Open Meteo API",
description="Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
_LLM_TOOLS: Dict[str, Callable[[BaseLLM], BaseTool]] = {
"pal-math... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
3f7702ce7bb6-4 | )
return Tool(
name="TMDB API",
description="Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
def _get_podcast_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
listen_api... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
3f7702ce7bb6-5 | )
def _get_google_search_results_json(**kwargs: Any) -> BaseTool:
return GoogleSearchResults(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_serpapi(**kwargs: Any) -> BaseTool:
return Tool(
name="Search",
description="A search engine. Useful for when you need to answer questions about cur... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
3f7702ce7bb6-6 | "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_appid"]),
"google-search": (_get_google_search, ["google... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
3f7702ce7bb6-7 | callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> List[BaseTool]:
"""Load tools based on their name.
Args:
tool_names: name of tools to load.
llm: Optional language model, may be needed to initialize certain tools.
callback_manager: Optional callback manager... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
3f7702ce7bb6-8 | if missing_keys:
raise ValueError(
f"Tool {name} requires some parameters that were not "
f"provided: {missing_keys}"
)
sub_kwargs = {k: kwargs[k] for k in extra_keys}
tool = _get_llm_tool_func(llm=llm, **sub_kwargs)
... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
42b91575814d-0 | Source code for langchain.agents.tools
"""Interface for tools."""
from functools import partial
from inspect import signature
from typing import Any, Awaitable, Callable, Optional, Type, Union
from pydantic import BaseModel, validate_arguments, validator
from langchain.tools.base import (
BaseTool,
create_schem... | https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
42b91575814d-1 | # TODO: this is for backwards compatibility, remove in future
def __init__(
self, name: str, func: Callable[[str], str], description: str, **kwargs: Any
) -> None:
"""Initialize tool."""
super(Tool, self).__init__(
name=name, func=func, description=description, **kwargs
... | https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
42b91575814d-2 | - Function must have a docstring
Examples:
.. code-block:: python
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
... | https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
42b91575814d-3 | 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 tool name
# Example usage: @tool(return_direct=True)
def _partial(func: Callable[[str], str]) -> BaseTool:
return _make_with_name(func.__name__... | https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
84b9fdcea9ee-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... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_types.html |
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