id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 54 121 |
|---|---|---|
1040804c62b5-1 | pydantic_schema: Any, llm: BaseLanguageModel
) -> Chain:
"""Creates a chain that extracts information from a passage.
Args:
pydantic_schema: The pydantic schema of the entities to extract.
llm: The language model to use.
Returns:
Chain (LLMChain) that can be used to extract informati... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
a0934a367be4-0 | Source code for langchain.chains.api.base
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.ca... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
a0934a367be4-1 | if set(input_vars) != expected_vars:
raise ValueError(
f"Input variables should be {expected_vars}, got {input_vars}"
)
return values
@root_validator(pre=True)
def validate_api_answer_prompt(cls, values: Dict) -> Dict:
"""Check that api answer prompt expec... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
a0934a367be4-2 | return {self.output_key: answer}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
question = inputs[self.que... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
a0934a367be4-3 | requests_wrapper = TextRequestsWrapper(headers=headers)
get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
return cls(
api_request_chain=get_request_chain,
api_answer_chain=get_answer_chain,
requests_wrapper=requests_wrapper,
api_docs=api_doc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
f6a1398e90f8-0 | Source code for langchain.chains.api.openapi.chain
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
import json
from typing import Any, Dict, List, NamedTuple, Optional, cast
from pydantic import BaseModel, Field
from requests import Response
from la... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
f6a1398e90f8-1 | """
return [self.instructions_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, "intermediate_steps"]
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
f6a1398e90f8-2 | path = self._construct_path(args)
body_params = self._extract_body_params(args)
query_params = self._extract_query_params(args)
return {
"url": path,
"data": body_params,
"params": query_params,
}
def _get_output(self, output: str, intermediate_ste... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
f6a1398e90f8-3 | method = getattr(self.requests, self.api_operation.method.value)
api_response: Response = method(**request_args)
if api_response.status_code != 200:
method_str = str(self.api_operation.method.value)
response_text = (
f"{api_response.status_code... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
f6a1398e90f8-4 | # TODO: Handle async
) -> "OpenAPIEndpointChain":
"""Create an OpenAPIEndpoint from a spec at the specified url."""
operation = APIOperation.from_openapi_url(spec_url, path, method)
return cls.from_api_operation(
operation,
requests=requests,
llm=llm,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
f6a1398e90f8-5 | requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
1ebd10c5982b-0 | Source code for langchain.chains.combine_documents.base
"""Base interface for chains combining documents."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManag... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
1ebd10c5982b-1 | """
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
"""Return the prompt length given the doc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
1ebd10c5982b-2 | ) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
output, extra_return_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
1ebd10c5982b-3 | other_keys[self.combine_docs_chain.input_key] = docs
return self.combine_docs_chain(
other_keys, return_only_outputs=True, callbacks=_run_manager.get_child()
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
0d7bc7e19e0a-0 | Source code for langchain.chains.combine_documents.stuff
"""Chain that combines documents by stuffing into context."""
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callbacks
from langchain.chains.combine_documents.base impo... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
0d7bc7e19e0a-1 | if "document_variable_name" not in values:
if len(llm_chain_variables) == 1:
values["document_variable_name"] = llm_chain_variables[0]
else:
raise ValueError(
"document_variable_name must be provided if there are "
"multiple... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
0d7bc7e19e0a-2 | """Stuff all documents into one prompt and pass to LLM."""
inputs = self._get_inputs(docs, **kwargs)
# Call predict on the LLM.
return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
[docs] async def acombine_docs(
self, docs: List[Document], callbacks: Callbacks = None, **k... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
e81db1ed4ae5-0 | Source code for langchain.chains.combine_documents.map_reduce
"""Combining documents by mapping a chain over them first, then combining results."""
from __future__ import annotations
from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple
from pydantic import Extra, root_validator
from langchain.callbac... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
e81db1ed4ae5-1 | return new_result_doc_list
def _collapse_docs(
docs: List[Document],
combine_document_func: CombineDocsProtocol,
**kwargs: Any,
) -> Document:
result = combine_document_func(docs, **kwargs)
combined_metadata = {k: str(v) for k, v in docs[0].metadata.items()}
for doc in docs[1:]:
for k, v... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
e81db1ed4ae5-2 | _output_keys = _output_keys + ["intermediate_steps"]
return _output_keys
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def get_return_intermediate_steps(cls, values: Dict) -> Dict:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
e81db1ed4ae5-3 | return self.combine_document_chain
[docs] def combine_docs(
self,
docs: List[Document],
token_max: int = 3000,
callbacks: Callbacks = None,
**kwargs: Any,
) -> Tuple[str, dict]:
"""Combine documents in a map reduce manner.
Combine by mapping first chain ove... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
e81db1ed4ae5-4 | self,
results: List[Dict],
docs: List[Document],
token_max: int = 3000,
callbacks: Callbacks = None,
**kwargs: Any,
) -> Tuple[List[Document], dict]:
question_result_key = self.llm_chain.output_key
result_docs = [
Document(page_content=r[question_r... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
e81db1ed4ae5-5 | docs: List[Document],
token_max: int = 3000,
callbacks: Callbacks = None,
**kwargs: Any,
) -> Tuple[str, dict]:
result_docs, extra_return_dict = self._process_results_common(
results, docs, token_max, callbacks=callbacks, **kwargs
)
output = self.combine_d... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
d5e6c9dd5e49-0 | Source code for langchain.chains.combine_documents.map_rerank
"""Combining documents by mapping a chain over them first, then reranking results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
from pydantic import Extra, root_validator
from langchain.call... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
d5e6c9dd5e49-1 | _output_keys += self.metadata_keys
return _output_keys
@root_validator()
def validate_llm_output(cls, values: Dict) -> Dict:
"""Validate that the combine chain outputs a dictionary."""
output_parser = values["llm_chain"].prompt.output_parser
if not isinstance(output_parser, Regex... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
d5e6c9dd5e49-2 | else:
llm_chain_variables = values["llm_chain"].prompt.input_variables
if values["document_variable_name"] not in llm_chain_variables:
raise ValueError(
f"document_variable_name {values['document_variable_name']} was "
f"not found in llm_ch... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
d5e6c9dd5e49-3 | def _process_results(
self,
docs: List[Document],
results: Sequence[Union[str, List[str], Dict[str, str]]],
) -> Tuple[str, dict]:
typed_results = cast(List[dict], results)
sorted_res = sorted(
zip(typed_results, docs), key=lambda x: -int(x[0][self.rank_key])
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
07104dfa9808-0 | Source code for langchain.chains.combine_documents.refine
"""Combining documents by doing a first pass and then refining on more documents."""
from __future__ import annotations
from typing import Any, Dict, List, Tuple
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callbacks
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
07104dfa9808-1 | """Expect input key.
:meta private:
"""
_output_keys = super().output_keys
if self.return_intermediate_steps:
_output_keys = _output_keys + ["intermediate_steps"]
return _output_keys
class Config:
"""Configuration for this pydantic object."""
extra... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
07104dfa9808-2 | )
return values
[docs] def combine_docs(
self, docs: List[Document], callbacks: Callbacks = None, **kwargs: Any
) -> Tuple[str, dict]:
"""Combine by mapping first chain over all, then stuffing into final chain."""
inputs = self._construct_initial_inputs(docs, **kwargs)
res... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
07104dfa9808-3 | if self.return_intermediate_steps:
extra_return_dict = {"intermediate_steps": refine_steps}
else:
extra_return_dict = {}
return res, extra_return_dict
def _construct_refine_inputs(self, doc: Document, res: str) -> Dict[str, Any]:
return {
self.document_var... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
ec29731682d8-0 | Source code for langchain.chains.pal.base
"""Implements Program-Aided Language Models.
As in https://arxiv.org/pdf/2211.10435.pdf.
"""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLangua... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
ec29731682d8-1 | "Directly instantiating an PALChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the one of "
"the class method constructors from_math_prompt, "
"from_colored_object_prompt."
)
if "llm_chain" not in values and v... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
ec29731682d8-2 | if self.return_intermediate_steps:
output["intermediate_steps"] = code
return output
[docs] @classmethod
def from_math_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PALChain:
"""Load PAL from math prompt."""
llm_chain = LLMChain(llm=llm, prompt=MATH_PROMPT)
ret... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
c596050b2c94-0 | Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic import Extra, Fiel... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
c596050b2c94-1 | human = "Human: " + dialogue_turn[0]
ai = "Assistant: " + dialogue_turn[1]
buffer += "\n" + "\n".join([human, ai])
else:
raise ValueError(
f"Unsupported chat history format: {type(dialogue_turn)}."
f" Full chat history: {chat_history} "
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
c596050b2c94-2 | """Get docs."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs["question"]
get_chat_history = sel... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
c596050b2c94-3 | question = inputs["question"]
get_chat_history = self.get_chat_history or _get_chat_history
chat_history_str = get_chat_history(inputs["chat_history"])
if chat_history_str:
callbacks = _run_manager.get_child()
new_question = await self.question_generator.arun(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
c596050b2c94-4 | num_docs = len(docs)
if self.max_tokens_limit and isinstance(
self.combine_docs_chain, StuffDocumentsChain
):
tokens = [
self.combine_docs_chain.llm_chain.llm.get_num_tokens(doc.page_content)
for doc in docs
]
token_count = ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
c596050b2c94-5 | chain_type=chain_type,
verbose=verbose,
callbacks=callbacks,
**combine_docs_chain_kwargs,
)
_llm = condense_question_llm or llm
condense_question_chain = LLMChain(
llm=_llm,
prompt=condense_question_prompt,
verbose=verbose,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
c596050b2c94-6 | raise NotImplementedError("ChatVectorDBChain does not support async")
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
chain_type: str = "stuff",
combin... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
38c9150a2e43-0 | Source code for langchain.chains.sql_database.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbac... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
38c9150a2e43-1 | return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the SQL table directly."""
use_query_checker: bool = False
"""Whether or not the query checker too... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
38c9150a2e43-2 | :meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, INTERMEDIATE_STEPS_KEY]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
38c9150a2e43-3 | result = self.database.run(sql_cmd)
intermediate_steps.append(str(result)) # output: sql exec
else:
query_checker_prompt = self.query_checker_prompt or PromptTemplate(
template=QUERY_CHECKER, input_variables=["query", "dialect"]
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
38c9150a2e43-4 | llm_inputs["input"] = input_text
intermediate_steps.append(llm_inputs) # input: final answer
final_result = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
intermediate_steps.appe... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
38c9150a2e43-5 | 2. Based on those tables, call the normal SQL database chain.
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_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
38c9150a2e43-6 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_table_names = self.sql_chain.database.get_usable_table_names()
table_na... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
049f6d4f43a8-0 | Source code for langchain.chains.qa_generation.base
from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
049f6d4f43a8-1 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, List]:
docs = self.text_splitter.create_documents([inputs[self.input_key]])
results = self.llm_chain.generate(
[{"text": d.page_content} for d in docs... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
78014d9e345b-0 | Source code for langchain.chains.flare.base
from __future__ import annotations
import re
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Tuple
import numpy as np
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager impor... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
78014d9e345b-1 | )
)
def _extract_tokens_and_log_probs(
self, generations: List[Generation]
) -> Tuple[Sequence[str], Sequence[float]]:
tokens = []
log_probs = []
for gen in generations:
if gen.generation_info is None:
raise ValueError
tokens.extend(gen... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
78014d9e345b-2 | [docs]class FlareChain(Chain):
question_generator_chain: QuestionGeneratorChain
response_chain: _ResponseChain = Field(default_factory=_OpenAIResponseChain)
output_parser: FinishedOutputParser = Field(default_factory=FinishedOutputParser)
retriever: BaseRetriever
min_prob: float = 0.2
min_token_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
78014d9e345b-3 | question_gen_inputs = [
{
"user_input": user_input,
"current_response": initial_response,
"uncertain_span": span,
}
for span in low_confidence_spans
]
callbacks = _run_manager.get_child()
question_gen_outputs = s... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
78014d9e345b-4 | )
initial_response = response.strip() + " " + "".join(tokens)
if not low_confidence_spans:
response = initial_response
final_response, finished = self.output_parser.parse(response)
if finished:
return {self.output_keys[0]: final... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
d7b7a789c4af-0 | Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from __future__ import annotations
import warnings
from pathlib import Path
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
d7b7a789c4af-1 | verbose=verbose,
),
LLMChain(
llm=llm,
prompt=check_assertions_prompt,
output_key="checked_assertions",
verbose=verbose,
),
LLMChain(
llm=llm,
prompt=revised_summary_prompt,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
d7b7a789c4af-2 | input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
max_checks: int = 2
"""Maximum number of times to check the assertions. Default to double-checking."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitr... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
d7b7a789c4af-3 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
all_true = False
count = 0
output = None
original_input ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
d7b7a789c4af-4 | create_assertions_prompt,
check_assertions_prompt,
revised_summary_prompt,
are_all_true_prompt,
verbose=verbose,
)
return cls(sequential_chain=chain, verbose=verbose, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
4ec3fb483d2b-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 |
4ec3fb483d2b-1 | print(str(t["task_id"]) + ": " + t["task_name"])
def print_next_task(self, task: Dict) -> None:
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
print(str(task["task_id"]) + ": " + task["task_name"])
def print_task_result(self, result: str) -> None:
print("\033[93m... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
4ec3fb483d2b-2 | 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 = response.split("\n")
prioritized_task_list = []
for task_st... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
4ec3fb483d2b-3 | """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_task_list()
# ... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
4ec3fb483d2b-4 | 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":
"""Initialize the BabyAGI Controller."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
43cfbc207f33-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
43cfbc207f33-1 | @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,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
chat_histor... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
43cfbc207f33-2 | user_input=user_input,
)
# Print Assistant thoughts
print(assistant_reply)
self.chat_history_memory.add_message(HumanMessage(content=user_input))
self.chat_history_memory.add_message(AIMessage(content=assistant_reply))
# Get command name and argume... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
43cfbc207f33-3 | 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)) | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
8fddb7ad3a73-0 | 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 |
8fddb7ad3a73-1 | # 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
def chain(self, prompt: PromptTemplate) -> LLMChain:
return L... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8fddb7ad3a73-2 | 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 |
8fddb7ad3a73-3 | 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 |
8fddb7ad3a73-4 | + " 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 |
8fddb7ad3a73-5 | 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 |
8fddb7ad3a73-6 | else:
return self.memory_retriever.get_relevant_documents(observation)
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".join(... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
8fddb7ad3a73-7 | 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 |
09975923a7eb-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 |
09975923a7eb-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://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
09975923a7eb-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 |
09975923a7eb-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 |
09975923a7eb-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 |
09975923a7eb-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 |
09975923a7eb-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 |
79eac95dbfb0-0 | Source code for langchain.llms.anyscale
"""Wrapper around Anyscale"""
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 enf... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
79eac95dbfb0-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
anyscale_service_url = get_from_dict_or_env(
values, "anyscale_service_url", "ANYSCALE_SERVICE_URL"
)
anyscale_service_route = get_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
79eac95dbfb0-2 | def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Anyscale Service endpoint.
Args:
prompt: The prompt to pass into the model.
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
16d833370d2f-0 | Source code for langchain.llms.bedrock
import json
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
class LLMInputOutp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
16d833370d2f-1 | else:
return response_body.get("results")[0].get("outputText")
[docs]class Bedrock(LLM):
"""LLM provider to invoke Bedrock models.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/crede... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
16d833370d2f-2 | equivalent to the modelId property in the list-foundation-models api"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
16d833370d2f-3 | """Return type of llm."""
return "amazon_bedrock"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Bedrock service model.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
c4697f5c0bf4-0 | Source code for langchain.llms.self_hosted
"""Run model inference on self-hosted remote hardware."""
import importlib.util
import logging
import pickle
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llm... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
c4697f5c0bf4-1 | )
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer ass... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
c4697f5c0bf4-2 | llm = SelfHostedPipeline(
model_load_fn=load_pipeline,
hardware=gpu,
model_reqs=model_reqs, inference_fn=inference_fn
)
Example for <2GB model (can be serialized and sent directly to the server):
.. code-block:: python
from langchain.ll... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
c4697f5c0bf4-3 | load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
model_reqs: List[str] = ["./", "torch"]
"""Requirements to install on hardware to inference the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
c4697f5c0bf4-4 | if not isinstance(pipeline, str):
logger.warning(
"Serializing pipeline to send to remote hardware. "
"Note, it can be quite slow"
"to serialize and send large models with each execution. "
"Consider sending the pipeline"
"to th... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
368561eaf35f-0 | Source code for langchain.llms.aleph_alpha
"""Wrapper around Aleph Alpha APIs."""
from typing import Any, Dict, List, Optional, Sequence
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforc... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
368561eaf35f-1 | """Total probability mass of tokens to consider at each step."""
presence_penalty: float = 0.0
"""Penalizes repeated tokens."""
frequency_penalty: float = 0.0
"""Penalizes repeated tokens according to frequency."""
repetition_penalties_include_prompt: Optional[bool] = False
"""Flag deciding whet... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
368561eaf35f-2 | echo: bool = False
"""Echo the prompt in the completion."""
use_multiplicative_frequency_penalty: bool = False
sequence_penalty: float = 0.0
sequence_penalty_min_length: int = 2
use_multiplicative_sequence_penalty: bool = False
completion_bias_inclusion: Optional[Sequence[str]] = None
comple... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
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