id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
d409db8b99c8-2 | @root_validator(pre=True)
def validate_python_version(cls, values: Dict) -> Dict:
"""Validate valid python version."""
if sys.version_info < (3, 9):
raise ValueError(
"This tool relies on Python 3.9 or higher "
"(as it uses new functionality in the `ast` m... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tools/python/tool.html |
d409db8b99c8-3 | ) -> Any:
"""Use the tool asynchronously."""
loop = asyncio.get_running_loop()
result = await loop.run_in_executor(None, self._run, query)
return result | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tools/python/tool.html |
6e6146e4a537-0 | Source code for langchain_experimental.sql.vector_sql
"""Vector SQL Database Chain Retriever"""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.llm import LLMChain
from langchain.cha... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/vector_sql.html |
6e6146e4a537-1 | text = text.strip()
start = text.find("NeuralArray(")
_sql_str_compl = text
if start > 0:
_matched = text[text.find("NeuralArray(") + len("NeuralArray(") :]
end = _matched.find(")") + start + len("NeuralArray(") + 1
entity = _matched[: _matched.find(")")]
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/vector_sql.html |
6e6146e4a537-2 | result = db._execute(cmd, fetch="all")
return result
[docs]class VectorSQLDatabaseChain(SQLDatabaseChain):
"""Chain for interacting with Vector SQL Database.
Example:
.. code-block:: python
from langchain_experimental.sql import SQLDatabaseChain
from langchain.llms import Ope... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/vector_sql.html |
6e6146e4a537-3 | # If not present, then defaults to None which is all tables.
table_names_to_use = inputs.get("table_names_to_use")
table_info = self.database.get_table_info(table_names=table_names_to_use)
llm_inputs = {
"input": input_text,
"top_k": str(self.top_k),
"dialect"... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/vector_sql.html |
6e6146e4a537-4 | )
query_checker_inputs = {
"query": llm_out,
"dialect": self.database.dialect,
}
checked_llm_out = query_checker_chain.predict(
callbacks=_run_manager.get_child(), **query_checker_inputs
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/vector_sql.html |
6e6146e4a537-5 | final_result = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
intermediate_steps.append(final_result) # output: final answer
_run_manager.on_text(final_result, color="green", verbose=self.verbos... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/vector_sql.html |
04b337a3b4fc-0 | Source code for langchain_experimental.sql.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/base.html |
04b337a3b4fc-1 | """
llm_chain: LLMChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
database: SQLDatabase = Field(exclude=True)
"""SQL Database to connect to."""
prompt: Optional[BasePromptTemplate] = None
"""[Deprecated] Prompt to use to translate natural language to SQL.... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/base.html |
04b337a3b4fc-2 | "class method."
)
if "llm_chain" not in values and values["llm"] is not None:
database = values["database"]
prompt = values.get("prompt") or SQL_PROMPTS.get(
database.dialect, PROMPT
)
values["llm_chain"] = LLMCh... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/base.html |
04b337a3b4fc-3 | "dialect": self.database.dialect,
"table_info": table_info,
"stop": ["\nSQLResult:"],
}
if self.memory is not None:
for k in self.memory.memory_variables:
llm_inputs[k] = inputs[k]
intermediate_steps: List = []
try:
intermed... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/base.html |
04b337a3b4fc-4 | ) # output: sql generation (checker)
_run_manager.on_text(
checked_sql_command, color="green", verbose=self.verbose
)
intermediate_steps.append(
{"sql_cmd": checked_sql_command}
) # input: sql exec
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/base.html |
04b337a3b4fc-5 | # improvement of few shot prompt seeds
exc.intermediate_steps = intermediate_steps # type: ignore
raise exc
@property
def _chain_type(self) -> str:
return "sql_database_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
db: SQLDa... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/base.html |
04b337a3b4fc-6 | This is useful in cases where the number of tables in the database is large.
"""
decider_chain: LLMChain
sql_chain: SQLDatabaseChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
return_intermediate_steps: bool = False
[docs] @classmethod
def fr... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/base.html |
04b337a3b4fc-7 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_table_names = self.sql_chain.database.get_usable_table_names()
table_names = ", ".join(_table_names)
llm_inputs = {
"query": inputs[self.input_key],
"table_names": ta... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/sql/base.html |
6b30c1280f75-0 | Source code for langchain_experimental.tot.prompts
import json
from textwrap import dedent
from typing import List
from langchain.prompts import PromptTemplate
from langchain.schema import BaseOutputParser
from langchain_experimental.tot.thought import ThoughtValidity
COT_PROMPT = PromptTemplate(
template_format="j... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/prompts.html |
6b30c1280f75-1 | You are an intelligent agent that is generating thoughts in a tree of
thoughts setting.
The output should be a markdown code snippet formatted as a JSON list of
strings, including the leading and trailing "```json" and "```":
```json
[
"<thought-1>",
"<tho... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/prompts.html |
6b30c1280f75-2 | {problem_description}
THOUGHTS
{thoughts}
Evaluate the thoughts and respond with one word.
- Respond VALID if the last thought is a valid final solution to the
problem.
- Respond INVALID if the last thought is invalid.
- Respond INTERMEDIATE if the last t... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/prompts.html |
5341fd847aab-0 | Source code for langchain_experimental.tot.base
"""
This a Tree of Thought (ToT) chain based on the paper "Large Language Model
Guided Tree-of-Thought"
https://arxiv.org/pdf/2305.08291.pdf
The Tree of Thought (ToT) chain uses a tree structure to explore the space of
possible solutions to a problem.
"""
from __future__ ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/base.html |
5341fd847aab-1 | """The number of children to explore at each node"""
tot_memory: ToTDFSMemory = ToTDFSMemory()
tot_controller: ToTController = ToTController()
tot_strategy_class: Type[BaseThoughtGenerationStrategy] = ProposePromptStrategy
verbose_llm: bool = False
class Config:
"""Configuration for this pyd... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/base.html |
5341fd847aab-2 | ThoughtValidity.INVALID: "red",
}
text = indent(f"Thought: {thought.text}\n", prefix=" " * level)
run_manager.on_text(
text=text, color=colors[thought.validity], verbose=self.verbose
)
def _call(
self,
inputs: Dict[str, Any],
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/base.html |
5341fd847aab-3 | self.log_thought(thought, level, run_manager)
thoughts_path = self.tot_controller(self.tot_memory)
return {self.output_key: "No solution found"}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[st... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/base.html |
76a7b9e9f91c-0 | Source code for langchain_experimental.tot.controller
from typing import Tuple
from langchain_experimental.tot.memory import ToTDFSMemory
from langchain_experimental.tot.thought import ThoughtValidity
[docs]class ToTController:
"""
Tree of Thought (ToT) controller.
This is a version of a ToT controller, dub... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/controller.html |
76a7b9e9f91c-1 | ):
memory.pop(2)
return tuple(thought.text for thought in memory.current_path()) | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/controller.html |
be192eff2055-0 | Source code for langchain_experimental.tot.thought
from __future__ import annotations
from enum import Enum
from typing import Set
from langchain_experimental.pydantic_v1 import BaseModel, Field
[docs]class ThoughtValidity(Enum):
VALID_INTERMEDIATE = 0
VALID_FINAL = 1
INVALID = 2
[docs]class Thought(BaseMod... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/thought.html |
974715f31432-0 | Source code for langchain_experimental.tot.checker
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain_experimental.tot.thought import ThoughtValidity
[docs]class... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/checker.html |
80e6c3e1fd8d-0 | Source code for langchain_experimental.tot.memory
from __future__ import annotations
from typing import List, Optional
from langchain_experimental.tot.thought import Thought
[docs]class ToTDFSMemory:
"""
Memory for the Tree of Thought (ToT) chain. Implemented as a stack of
thoughts. This allows for a depth ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/memory.html |
80e6c3e1fd8d-1 | [docs] def current_path(self) -> List[Thought]:
"Return the thoughts path."
return self.stack[:] | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/memory.html |
c7920319a033-0 | Source code for langchain_experimental.tot.thought_generation
"""
We provide two strategies for generating thoughts in the Tree of Thoughts (ToT)
framework to avoid repetition:
These strategies ensure that the language model generates diverse and
non-repeating thoughts, which are crucial for problem-solving tasks that ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/thought_generation.html |
c7920319a033-1 | thoughts_path: Tuple[str, ...] = (),
**kwargs: Any,
) -> str:
response_text = self.predict_and_parse(
problem_description=problem_description, thoughts=thoughts_path, **kwargs
)
return response_text if isinstance(response_text, str) else ""
[docs]class ProposePromptStrate... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tot/thought_generation.html |
a73276e7323f-0 | Source code for langchain_experimental.tabular_synthetic_data.base
import asyncio
from typing import Any, Dict, List, Optional, Union
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.pydantic_v1 import BaseModel... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tabular_synthetic_data/base.html |
a73276e7323f-1 | llm = values.get("llm")
few_shot_template = values.get("template")
if not llm_chain: # If llm_chain is None or not present
if llm is None or few_shot_template is None:
raise ValueError(
"Both llm and few_shot_template must be provided if llm_chain is "
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tabular_synthetic_data/base.html |
a73276e7323f-2 | extra (str): Extra instructions for steerability in data generation.
Returns:
List[str]: List of generated synthetic data.
Usage Example:
>>> results = generator.generate(subject="climate change", runs=5,
extra="Focus on environmental impacts.")
"""
if... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tabular_synthetic_data/base.html |
a73276e7323f-3 | ) -> None:
if self.llm_chain is not None:
result = await self.llm_chain.arun(
subject=subject, extra=extra, *args, **kwargs
)
self.results.append(result)
await asyncio.gather(
*(run_chain(subject=subject, extra=extra) fo... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tabular_synthetic_data/base.html |
2dd96e6eb5e1-0 | Source code for langchain_experimental.tabular_synthetic_data.openai
from typing import Any, Dict, Optional, Type, Union
from langchain.chains.openai_functions import create_structured_output_chain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.pydantic_v1 impor... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tabular_synthetic_data/openai.html |
2dd96e6eb5e1-1 | `create_structured_output_chain`.
Returns: SyntheticDataGenerator: An instance of the data generator set up with
the constructed chain.
Usage:
To generate synthetic data with a structured output, first define your desired
output schema. Then, use this function to create a SyntheticDataGenera... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/tabular_synthetic_data/openai.html |
6df4d43f33ed-0 | Source code for langchain_experimental.cpal.constants
from enum import Enum
[docs]class Constant(Enum):
"""Enum for constants used in the CPAL."""
narrative_input = "narrative_input"
chain_answer = "chain_answer" # natural language answer
chain_data = "chain_data" # pydantic instance | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/constants.html |
c5b94b60a798-0 | Source code for langchain_experimental.cpal.base
"""
CPAL Chain and its subchains
"""
from __future__ import annotations
import json
from typing import Any, ClassVar, Dict, List, Optional, Type
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/base.html |
c5b94b60a798-1 | @classmethod
def parser(cls) -> PydanticOutputParser:
"""Parse LLM output into a pydantic object."""
if cls.pydantic_model is None:
raise NotImplementedError(
f"pydantic_model not implemented for {cls.__name__}"
)
return PydanticOutputParser(pydantic_o... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/base.html |
c5b94b60a798-2 | Constant.chain_answer.value: None,
}
[docs]class NarrativeChain(_BaseStoryElementChain):
"""Decompose the narrative into its story elements
- causal model
- query
- intervention
"""
pydantic_model: ClassVar[Type[pydantic.BaseModel]] = NarrativeModel
template: ClassVar[str] = narrativ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/base.html |
c5b94b60a798-3 | [docs]class CPALChain(_BaseStoryElementChain):
"""Causal program-aided language (CPAL) chain implementation.
*Security note*: The building blocks of this class include the implementation
of an AI technique that generates SQL code. If those SQL commands
are executed, it's critical to ensure they ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/base.html |
c5b94b60a798-4 | attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this chain.
"""
return cls(
llm=llm,
chain=LLMC... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/base.html |
c5b94b60a798-5 | self.query_chain = QueryChain.from_univariate_prompt(llm=self.llm)
# decompose narrative into three causal story elements
narrative = self.narrative_chain(inputs[Constant.narrative_input.value])[
Constant.chain_data.value
]
story = StoryModel(
causal_operations=se... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/base.html |
c5b94b60a798-6 | "query:\n"
f"{story.query.dict()}"
)
)
else:
query_result = float(story.query._result_table.values[0][-1])
if False:
"""TODO: add this back in when demanded by composable chains"""
reporting_chain = self.chai... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/base.html |
7bf625972c73-0 | Source code for langchain_experimental.cpal.models
from __future__ import annotations # allows pydantic model to reference itself
import re
from typing import Any, List, Optional, Union
from langchain.graphs.networkx_graph import NetworkxEntityGraph
from langchain_experimental.cpal.constants import Constant
from langc... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/models.html |
7bf625972c73-1 | entities: List[EntityModel] = Field(description="entities in the story")
# TODO: root validate each `entity.depends_on` using system's entity names
[docs]class EntitySettingModel(BaseModel):
"""
Initial conditions for an entity
{"name": "bud", "attribute": "pet_count", "value": 12}
"""
name: str... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/models.html |
7bf625972c73-2 | return v
[docs]class QueryModel(BaseModel):
"""translate a question about the story outcome into a programmatic expression"""
question: str = Field(alias=Constant.narrative_input.value) # input
expression: str # output, part of llm completion
llm_error_msg: str # output, part of llm completion
_r... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/models.html |
7bf625972c73-3 | for setting in values["intervention"].entity_settings:
if setting.name not in valid_names:
error_msg = f"""
Hypothetical question has an invalid entity name.
`{setting.name}` not in `{valid_names}`
"""
raise ValueError(e... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/models.html |
7bf625972c73-4 | def _forward_propagate(self) -> None:
try:
import pandas as pd
except ImportError as e:
raise ImportError(
"Unable to import pandas, please install with `pip install pandas`."
) from e
entity_scope = {
entity.name: entity for entity... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/models.html |
7bf625972c73-5 | "Unable to import duckdb, please install with `pip install duckdb`."
) from e
except Exception as e:
self.query._result_table = str(e)
else:
msg = "LLM maybe failed to translate question to SQL query."
raise ValueError(
{
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/cpal/models.html |
ae8c6f411c06-0 | Source code for langchain_experimental.retrievers.vector_sql_database
"""Vector SQL Database Chain Retriever"""
from typing import Any, Dict, List
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.schema import BaseRetriever, Document... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/retrievers/vector_sql_database.html |
61715d2d36ee-0 | Source code for langchain_experimental.autonomous_agents.baby_agi.task_prioritization
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.schema.language_model import BaseLanguageModel
[docs]class TaskPrioritizationChain(LLMChain):
"""Chain to prioritize tasks."""
[docs... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/task_prioritization.html |
d25f3ffdfc85-0 | 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 langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.schema.language_model impor... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/baby_agi.html |
d25f3ffdfc85-1 | # incompatible with definition in base class "BaseModel" [misc]
# Metaclass conflict: the metaclass of a derived class must be
# a (non-strict) subclass of the metaclasses of all its bases [misc]
# ```
#
# TODO: look into refactoring this class in a way that avoids the mypy type errors
[docs]class BabyAGI(Chain, ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/baby_agi.html |
d25f3ffdfc85-2 | print(str(task["task_id"]) + ": " + task["task_name"])
[docs] def print_task_result(self, result: str) -> None:
print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m")
print(result)
@property
def input_keys(self) -> List[str]:
return ["objective"]
@property
d... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/baby_agi.html |
d25f3ffdfc85-3 | objective=objective,
**kwargs,
)
new_tasks = response.split("\n")
prioritized_task_list = []
for task_string in new_tasks:
if not task_string.strip():
continue
task_parts = task_string.strip().split(".", 1)
if len(task_parts... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/baby_agi.html |
d25f3ffdfc85-4 | 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_task_list()
# Step 1: Pull the first task
task = self.task_list.pop... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/baby_agi.html |
d25f3ffdfc85-5 | print(
"\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m"
)
break
return {}
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
verbose: bool = False,
task_exec... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/baby_agi.html |
e39ee31c8aef-0 | Source code for langchain_experimental.autonomous_agents.baby_agi.task_creation
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.schema.language_model import BaseLanguageModel
[docs]class TaskCreationChain(LLMChain):
"""Chain generating tasks."""
[docs] @classmeth... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/task_creation.html |
b3eb4281dbd3-0 | Source code for langchain_experimental.autonomous_agents.baby_agi.task_execution
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.schema.language_model import BaseLanguageModel
[docs]class TaskExecutionChain(LLMChain):
"""Chain to execute tasks."""
[docs] @classme... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/baby_agi/task_execution.html |
e04189f9c0eb-0 | Source code for langchain_experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.memory import ChatMessageHistory
from langchain.schema import (
Ba... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/agent.html |
e04189f9c0eb-1 | self.chat_history_memory = chat_history_memory or ChatMessageHistory()
[docs] @classmethod
def from_llm_and_tools(
cls,
ai_name: str,
ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/agent.html |
e04189f9c0eb-2 | goals=goals,
messages=self.chat_history_memory.messages,
memory=self.memory,
user_input=user_input,
)
# Print Assistant thoughts
print(assistant_reply)
self.chat_history_memory.add_message(HumanMessage(content=user_input))
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/agent.html |
e04189f9c0eb-3 | if feedback in {"q", "stop"}:
print("EXITING")
return "EXITING"
memory_to_add += feedback
self.memory.add_documents([Document(page_content=memory_to_add)])
self.chat_history_memory.add_message(SystemMessage(content=result)) | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/agent.html |
3566e3bf02a5-0 | Source code for langchain_experimental.autonomous_agents.autogpt.prompt
import time
from typing import Any, Callable, List
from langchain.prompts.chat import (
BaseChatPromptTemplate,
)
from langchain.schema.messages import BaseMessage, HumanMessage, SystemMessage
from langchain.schema.vectorstore import VectorStor... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/prompt.html |
3566e3bf02a5-1 | # Metaclass conflict: the metaclass of a derived class must be
# a (non-strict) subclass of the metaclasses of all its bases [misc]
# ```
#
# TODO: look into refactoring this class in a way that avoids the mypy type errors
[docs]class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel): # type: ignore[misc]
"""Pro... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/prompt.html |
3566e3bf02a5-2 | 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 = kwargs["messages"]
relevant_do... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/prompt.html |
59d71ee41a46-0 | 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):
"""Action returned by AutoGPTOutputParser."""
name: str
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/output_parser.html |
59d71ee41a46-1 | args={"error": f"Could not parse invalid json: {text}"},
)
try:
return AutoGPTAction(
name=parsed["command"]["name"],
args=parsed["command"]["args"],
)
except (KeyError, TypeError):
# If the command is null or incomplete... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/output_parser.html |
cc09adcffe76-0 | 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"
[docs]class PromptGenerator:
"""A class for generating custom prompt strings.
Does this based on constraints, commands, resources... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/prompt_generator.html |
cc09adcffe76-1 | output = f"{tool.name}: {tool.description}"
output += f", args json schema: {json.dumps(tool.args)}"
return output
[docs] def add_resource(self, resource: str) -> None:
"""
Add a resource to the resources list.
Args:
resource (str): The resource to be added.
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/prompt_generator.html |
cc09adcffe76-2 | f"{finish_description}, args: {finish_args}"
)
return "\n".join(command_strings + [finish_string])
else:
return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
[docs] def generate_prompt_string(self) -> str:
"""Generate a prompt string.
Returns:... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/prompt_generator.html |
cc09adcffe76-3 | "so immediately save important information to files."
)
prompt_generator.add_constraint(
"If you are unsure how you previously did something "
"or want to recall past events, "
"thinking about similar events will help you remember."
)
prompt_generator.add_constraint("No user assi... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/prompt_generator.html |
7a1685d56c1b-0 | Source code for langchain_experimental.autonomous_agents.autogpt.memory
from typing import Any, Dict, List
from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key
from langchain.schema.vectorstore import VectorStoreRetriever
from langchain_experimental.pydantic_v1 import Field
[docs]class AutoGPTM... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/autogpt/memory.html |
a870991b6b13-0 | Source code for langchain_experimental.autonomous_agents.hugginggpt.task_executor
import copy
import uuid
from typing import Dict, List
import numpy as np
from langchain.tools.base import BaseTool
from langchain_experimental.autonomous_agents.hugginggpt.task_planner import Plan
[docs]class Task:
[docs] def __init__(... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_executor.html |
a870991b6b13-1 | self.result = filename
[docs] def completed(self) -> bool:
return self.status == "completed"
[docs] def failed(self) -> bool:
return self.status == "failed"
[docs] def pending(self) -> bool:
return self.status == "pending"
[docs] def run(self) -> str:
from diffusers.utils imp... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_executor.html |
a870991b6b13-2 | [docs] def pending(self) -> bool:
return any(task.pending() for task in self.tasks)
[docs] def check_dependency(self, task: Task) -> bool:
for dep_id in task.dep:
if dep_id == -1:
continue
dep_task = self.id_task_map[dep_id]
if dep_task.failed() ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_executor.html |
a870991b6b13-3 | result += f"result: {task.result}\n"
return result
def __repr__(self) -> str:
return self.__str__()
[docs] def describe(self) -> str:
return self.__str__() | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_executor.html |
9a25f0a8907a-0 | Source code for langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator
from typing import Any, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import Callbacks
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
[docs]c... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/repsonse_generator.html |
a16e61801e75-0 | Source code for langchain_experimental.autonomous_agents.hugginggpt.hugginggpt
from typing import List
from langchain.base_language import BaseLanguageModel
from langchain.tools.base import BaseTool
from langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator import (
load_response_generator,
)
from ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/hugginggpt.html |
10aaebf82672-0 | Source code for langchain_experimental.autonomous_agents.hugginggpt.task_planner
import json
import re
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import Callbacks
from langchain.chains import L... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_planner.html |
10aaebf82672-1 | },
{
"role": "assistant",
"content": '[ {{"task": "image_qa", "id": 0, "dep": [-1], "args": {{"image": "e1.jpg", "question": "How many sheep in the picture"}}}}, {{"task": "image_qa", "id": 1, "dep": [-1], "args": {{"image": "e2.jpg", "question": "How many sheep in the picture"}}}}, {{"task": "image... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_planner.html |
10aaebf82672-2 | ) -> LLMChain:
"""Get the response parser."""
system_template = """#1 Task Planning Stage: The AI assistant can parse user input to several tasks: [{{"task": task, "id": task_id, "dep": dependency_task_id, "args": {{"input name": text may contain <resource-dep_id>}}}}]. The special tag "dep_id" refer to... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_planner.html |
10aaebf82672-3 | )
# demo_messages.append(message)
prompt = ChatPromptTemplate.from_messages(
[system_message_prompt, *demo_messages, human_message_prompt]
)
return cls(prompt=prompt, llm=llm, verbose=verbose)
[docs]class Step:
[docs] def __init__(
self, task: str, id: int, dep... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_planner.html |
10aaebf82672-4 | if tool.name == v["task"]:
choose_tool = tool
break
if choose_tool:
steps.append(Step(v["task"], v["id"], v["dep"], v["args"], tool))
return Plan(steps=steps)
[docs]class TaskPlanner(BasePlanner):
llm_chain: LLMChain
output_parser: Plan... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/autonomous_agents/hugginggpt/task_planner.html |
23b16d52b33d-0 | Source code for langchain_experimental.plan_and_execute.schema
from abc import abstractmethod
from typing import List, Tuple
from langchain.schema import BaseOutputParser
from langchain_experimental.pydantic_v1 import BaseModel, Field
[docs]class Step(BaseModel):
"""Step."""
value: str
"""The value."""
[doc... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/schema.html |
1fad037fd3e3-0 | Source code for langchain_experimental.plan_and_execute.agent_executor
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain_experimental.plan_and_execute.execut... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/agent_executor.html |
1fad037fd3e3-1 | "previous_steps": self.step_container,
"current_step": step,
"objective": inputs[self.input_key],
}
new_inputs = {**_new_inputs, **inputs}
response = self.executor.step(
new_inputs,
callbacks=run_manager.get_child() if r... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/agent_executor.html |
1fad037fd3e3-2 | )
await run_manager.on_text(
f"\n\nResponse: {response.response}", verbose=self.verbose
)
self.step_container.add_step(step, response)
return {self.output_key: self.step_container.get_final_response()} | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/agent_executor.html |
6c1e8c955475-0 | Source code for langchain_experimental.plan_and_execute.executors.base
from abc import abstractmethod
from typing import Any
from langchain.callbacks.manager import Callbacks
from langchain.chains.base import Chain
from langchain_experimental.plan_and_execute.schema import StepResponse
from langchain_experimental.pydan... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/executors/base.html |
c86889b2234e-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.schema.language_model import BaseLanguageModel
from langchain.tools import BaseTo... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/executors/agent_executor.html |
14f4f5c4100d-0 | Source code for langchain_experimental.plan_and_execute.planners.chat_planner
import re
from langchain.chains import LLMChain
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.schema.language_model import BaseLanguageModel
from langchain.schema.messages import SystemMessage
fro... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/planners/chat_planner.html |
14f4f5c4100d-1 | Returns:
LLMPlanner
"""
prompt_template = ChatPromptTemplate.from_messages(
[
SystemMessage(content=system_prompt),
HumanMessagePromptTemplate.from_template("{input}"),
]
)
llm_chain = LLMChain(llm=llm, prompt=prompt_template)
return LLMPlanner(
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/planners/chat_planner.html |
1bd2f57609aa-0 | Source code for langchain_experimental.plan_and_execute.planners.base
from abc import abstractmethod
from typing import Any, List, Optional
from langchain.callbacks.manager import Callbacks
from langchain.chains.llm import LLMChain
from langchain_experimental.plan_and_execute.schema import Plan, PlanOutputParser
from l... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/planners/base.html |
1bd2f57609aa-1 | llm_response = await self.llm_chain.arun(
**inputs, stop=self.stop, callbacks=callbacks
)
return self.output_parser.parse(llm_response) | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/plan_and_execute/planners/base.html |
8fb97a68d0db-0 | Source code for langchain_experimental.agents.agent_toolkits.spark.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.callbacks.base import BaseCallbackManager... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/spark/base.html |
8fb97a68d0db-1 | ) -> AgentExecutor:
"""Construct a Spark agent from an LLM and dataframe."""
if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
raise ImportError("Spark is not installed. run `pip install pyspark`.")
if input_variables is None:
input_variables = ["df", "input", "agent_scra... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/spark/base.html |
f25fc56e5192-0 | Source code for langchain_experimental.agents.agent_toolkits.xorbits.base
"""Agent for working with xorbits objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.callbacks.base import BaseCallbackMana... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/xorbits/base.html |
f25fc56e5192-1 | if not isinstance(data, (pd.DataFrame, np.ndarray)):
raise ValueError(
f"Expected Xorbits DataFrame or ndarray object, got {type(data)}"
)
if input_variables is None:
input_variables = ["data", "input", "agent_scratchpad"]
tools = [PythonAstREPLTool(locals={"data": data})]
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/xorbits/base.html |
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