id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
da64cddf264f-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 |
da64cddf264f-1 | initial_llm_chain: LLMChain
"""LLM chain to use on initial document."""
refine_llm_chain: LLMChain
"""LLM chain to use when refining."""
document_variable_name: str
"""The variable name in the initial_llm_chain to put the documents in.
If only one variable in the initial_llm_chain, this need not... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
da64cddf264f-2 | :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 = Extra.forbid
arbit... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
da64cddf264f-3 | if "document_variable_name" not in values:
llm_chain_variables = values["initial_llm_chain"].prompt.input_variables
if len(llm_chain_variables) == 1:
values["document_variable_name"] = llm_chain_variables[0]
else:
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
da64cddf264f-4 | [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 = self.initial_llm_chai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
da64cddf264f-5 | 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 = await self.initial_llm_chain.apredict(callbacks=callbacks... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
da64cddf264f-6 | 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 |
da64cddf264f-7 | }
inputs = {**base_inputs, **kwargs}
return inputs
@property
def _chain_type(self) -> str:
return "refine_documents_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
c610b41547ee-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 |
c610b41547ee-1 | llm_chain: LLMChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated]"""
prompt: BasePromptTemplate = MATH_PROMPT
"""[Deprecated]"""
stop: str = "\n\n"
get_answer_expr: str = "print(solution())"
python_globals: Optional[Dict[str, Any]] = None
python_locals: Optional[Dict[str, Any]]... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
c610b41547ee-2 | warnings.warn(
"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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
c610b41547ee-3 | if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, "intermediate_steps"]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manage... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
c610b41547ee-4 | 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 |
c610b41547ee-5 | return cls(
llm_chain=llm_chain,
stop="\n\n\n",
get_answer_expr="print(answer)",
**kwargs,
)
@property
def _chain_type(self) -> str:
return "pal_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
b4c6b2148c55-0 | 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 |
b4c6b2148c55-1 | from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseMessage, BaseRetriever, Document
from langchain.vectorstores.base import VectorStore
# Depending on the memory type and configuration, the chat history format may differ.
# This needs to be consolidated.
CHAT_TURN_TYPE = Union[Tuple[... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-2 | 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} "
)
return buffer
class BaseConversational... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-3 | extra = Extra.forbid
arbitrary_types_allowed = True
allow_population_by_field_name = True
@property
def input_keys(self) -> List[str]:
"""Input keys."""
return ["question", "chat_history"]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-4 | 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 = self.get_chat_history or _get_chat_history
chat_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-5 | answer = self.combine_docs_chain.run(
input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs
)
output: Dict[str, Any] = {self.output_key: answer}
if self.return_source_documents:
output["source_documents"] = docs
if self.return_generated_question:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-6 | 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 |
b4c6b2148c55-7 | if self.return_source_documents:
output["source_documents"] = docs
if self.return_generated_question:
output["generated_question"] = new_question
return output
def save(self, file_path: Union[Path, str]) -> None:
if self.get_chat_history:
raise ValueError(... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-8 | 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 |
b4c6b2148c55-9 | return self._reduce_tokens_below_limit(docs)
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
retriever: BaseRetriever,
condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
chain_type: str = "stuff",
verbose: bool = False,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-10 | 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,
callbacks=callbacks,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-11 | @property
def _chain_type(self) -> str:
return "chat-vector-db"
@root_validator()
def raise_deprecation(cls, values: Dict) -> Dict:
warnings.warn(
"`ChatVectorDBChain` is deprecated - "
"please use `from langchain.chains import ConversationalRetrievalChain`"
)... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b4c6b2148c55-12 | 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 |
b4c6b2148c55-13 | llm=llm, prompt=condense_question_prompt, callbacks=callbacks
)
return cls(
vectorstore=vectorstore,
combine_docs_chain=doc_chain,
question_generator=condense_question_chain,
callbacks=callbacks,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
3db8a3edbf92-0 | 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 |
3db8a3edbf92-1 | Example:
.. code-block:: python
from langchain import SQLDatabaseChain, OpenAI, SQLDatabase
db = SQLDatabase(...)
db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)
"""
llm_chain: LLMChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-2 | 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 |
3db8a3edbf92-3 | warnings.warn(
"Directly instantiating an SQLDatabaseChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the from_llm "
"class method."
)
if "llm_chain" not in values and values["llm"] is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-4 | """Return the singular output key.
: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[... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-5 | llm_inputs = {
"input": input_text,
"top_k": str(self.top_k),
"dialect": self.database.dialect,
"table_info": table_info,
"stop": ["\nSQLResult:"],
}
intermediate_steps: List = []
try:
intermediate_steps.append(llm_inputs) ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-6 | 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 |
3db8a3edbf92-7 | )
intermediate_steps.append(
{"sql_cmd": checked_sql_command}
) # input: sql exec
result = self.database.run(checked_sql_command)
intermediate_steps.append(str(result)) # output: sql exec
sql_cmd = checked_sql_command
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-8 | intermediate_steps.append(llm_inputs) # input: final answer
final_result = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
intermediate_steps.append(final_result) # output: final answer
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-9 | return "sql_database_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
db: SQLDatabase,
prompt: Optional[BasePromptTemplate] = None,
**kwargs: Any,
) -> SQLDatabaseChain:
prompt = prompt or SQL_PROMPTS.get(db.dialect, PROMPT)
llm_cha... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-10 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-11 | )
decider_chain = LLMChain(
llm=llm, prompt=decider_prompt, output_key="table_names"
)
return cls(sql_chain=sql_chain, decider_chain=decider_chain, **kwargs)
@property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-12 | 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_names = ", ".join(_table_names)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
3db8a3edbf92-13 | ]
_run_manager.on_text("Table names to use:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(table_names_to_use), color="yellow", verbose=self.verbose
)
new_inputs = {
self.sql_chain.input_key: inputs[self.input_key],
"table_names_to_use": t... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
9ef5013c7f4e-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 |
9ef5013c7f4e-1 | output_key: str = "questions"
k: Optional[int] = None
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[BasePromptTemplate] = None,
**kwargs: Any,
) -> QAGenerationChain:
_prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
chai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
9ef5013c7f4e-2 | 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 |
46e9cc63e03b-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 |
46e9cc63e03b-1 | return self.prompt.input_variables
def generate_tokens_and_log_probs(
self,
_input: Dict[str, Any],
*,
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Tuple[Sequence[str], Sequence[float]]:
llm_result = self.generate([_input], run_manager=run_manager)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
46e9cc63e03b-2 | )
)
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 |
46e9cc63e03b-3 | min_prob: float,
min_token_gap: int,
num_pad_tokens: int,
) -> List[str]:
_low_idx = np.where(np.exp(log_probs) < min_prob)[0]
low_idx = [i for i in _low_idx if re.search(r"\w", tokens[i])]
if len(low_idx) == 0:
return []
spans = [[low_idx[0], low_idx[0] + num_pad_tokens + 1]]
for i,... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
46e9cc63e03b-4 | 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_gap: int = 5
num_pad_tokens: in... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
46e9cc63e03b-5 | ) -> Tuple[str, bool]:
callbacks = _run_manager.get_child()
docs = []
for question in questions:
docs.extend(self.retriever.get_relevant_documents(question))
context = "\n\n".join(d.page_content for d in docs)
result = self.response_chain.predict(
user_inp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
46e9cc63e03b-6 | "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 = self.question_generator_chain.apply(
questi... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
46e9cc63e03b-7 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
user_input = inputs[self.input_keys[0]]
response = ""
for i in range(self.max_iter):
_run_manager.on_text(
f"Current Response: {response}", color="blue", end="\n"
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
46e9cc63e03b-8 | if not low_confidence_spans:
response = initial_response
final_response, finished = self.output_parser.parse(response)
if finished:
return {self.output_keys[0]: final_response}
continue
marginal, finished = self._do_retrieva... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
46e9cc63e03b-9 | response_llm = OpenAI(
max_tokens=max_generation_len, model_kwargs={"logprobs": 1}, temperature=0
)
response_chain = _OpenAIResponseChain(llm=response_llm)
return cls(
question_generator_chain=question_gen_chain,
response_chain=response_chain,
**kw... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
2c9c23868c54-0 | 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 |
2c9c23868c54-1 | )
REVISED_SUMMARY_PROMPT = PromptTemplate.from_file(
PROMPTS_DIR / "revise_summary.txt", ["checked_assertions", "summary"]
)
ARE_ALL_TRUE_PROMPT = PromptTemplate.from_file(
PROMPTS_DIR / "are_all_true_prompt.txt", ["checked_assertions"]
)
def _load_sequential_chain(
llm: BaseLanguageModel,
create_assert... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
2c9c23868c54-2 | ),
LLMChain(
llm=llm,
prompt=check_assertions_prompt,
output_key="checked_assertions",
verbose=verbose,
),
LLMChain(
llm=llm,
prompt=revised_summary_prompt,
output_key="rev... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
2c9c23868c54-3 | Example:
.. code-block:: python
from langchain import OpenAI, LLMSummarizationCheckerChain
llm = OpenAI(temperature=0.0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm)
"""
sequential_chain: SequentialChain
llm: Optional[BaseLanguageModel] = None
""... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
2c9c23868c54-4 | 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
arbitrary_types_allowed = True
@root_validator(pre... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
2c9c23868c54-5 | values["llm"],
values.get("create_assertions_prompt", CREATE_ASSERTIONS_PROMPT),
values.get("check_assertions_prompt", CHECK_ASSERTIONS_PROMPT),
values.get("revised_summary_prompt", REVISED_SUMMARY_PROMPT),
values.get("are_all_true_prompt",... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
2c9c23868c54-6 | 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 = inputs[self.input_key]
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
2c9c23868c54-7 | raise ValueError("No output from chain")
return {self.output_key: output["revised_summary"].strip()}
@property
def _chain_type(self) -> str:
return "llm_summarization_checker_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
create_assertions_pr... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
2c9c23868c54-8 | 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 |
34369806c54f-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 |
34369806c54f-1 | task_list: deque = Field(default_factory=deque)
task_creation_chain: Chain = Field(...)
task_prioritization_chain: Chain = Field(...)
execution_chain: Chain = Field(...)
task_id_counter: int = Field(1)
vectorstore: VectorStore = Field(init=False)
max_iterations: Optional[int] = None
[docs] cl... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
34369806c54f-2 | 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://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
34369806c54f-3 | ) -> List[Dict]:
"""Get the next task."""
task_names = [t["task_name"] for t in self.task_list]
incomplete_tasks = ", ".join(task_names)
response = self.task_creation_chain.run(
result=result,
task_description=task_description,
incomplete_tasks=incompl... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
34369806c54f-4 | 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://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
34369806c54f-5 | if not results:
return []
return [str(item.metadata["task"]) for item in results]
[docs] def execute_task(self, objective: str, task: str, k: int = 5) -> str:
"""Execute a task."""
context = self._get_top_tasks(query=objective, k=k)
return self.execution_chain.run(
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
34369806c54f-6 | num_iters = 0
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.execut... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
34369806c54f-7 | new_tasks = self.get_next_task(result, task["task_name"], objective)
for new_task in new_tasks:
self.task_id_counter += 1
new_task.update({"task_id": self.task_id_counter})
self.add_task(new_task)
self.task_list = deque(self.pri... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
34369806c54f-8 | verbose: bool = False,
task_execution_chain: Optional[Chain] = None,
**kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initialize the BabyAGI Controller."""
task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
task_prioritization_chain = TaskPrioritizationChain.f... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
14611743f34c-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 |
14611743f34c-1 | from langchain.vectorstores.base import VectorStoreRetriever
[docs]class AutoGPT:
"""Agent class for interacting with Auto-GPT."""
def __init__(
self,
ai_name: str,
memory: VectorStoreRetriever,
chain: LLMChain,
output_parser: BaseAutoGPTOutputParser,
tools: List[... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
14611743f34c-2 | @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 |
14611743f34c-3 | chain = LLMChain(llm=llm, prompt=prompt)
return cls(
ai_name,
memory,
chain,
output_parser or AutoGPTOutputParser(),
tools,
feedback_tool=human_feedback_tool,
chat_history_memory=chat_history_memory,
)
def run(self, ... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
14611743f34c-4 | 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 |
14611743f34c-5 | observation = (
f"Error: {str(e)}, {type(e).__name__}, args: {action.args}"
)
result = f"Command {tool.name} returned: {observation}"
elif action.name == "ERROR":
result = f"Error: {action.args}. "
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
14611743f34c-6 | 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)) | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
fb6508b1b953-0 | 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 |
fb6508b1b953-1 | current_plan: List[str] = []
"""The current plan of the agent."""
# A weight of 0.15 makes this less important than it
# would be otherwise, relative to salience and time
importance_weight: float = 0.15
"""How much weight to assign the memory importance."""
aggregate_importance: float = 0.0 # :... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-2 | 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 LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)
@staticm... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-3 | """Return the 3 most salient high-level questions about recent observations."""
prompt = PromptTemplate.from_template(
"{observations}\n\n"
"Given only the information above, what are the 3 most salient "
"high-level questions we can answer about the subjects in the statement... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-4 | ) -> 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"
"---\n"
"What 5 high-level novel insi... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-5 | [
self._format_memory_detail(memory, prefix=f"{i+1}. ")
for i, memory in enumerate(related_memories)
]
)
result = self.chain(prompt).run(
topic=topic, related_statements=related_statements
)
# TODO: Parse the connections between mem... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-6 | 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 importance of the given memory."""
prompt = PromptTemplate.from_tem... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-7 | if self.verbose:
logger.info(f"Importance score: {score}")
match = re.search(r"^\D*(\d+)", score)
if match:
return (float(match.group(1)) / 10) * self.importance_weight
else:
return 0.0
def _score_memories_importance(self, memory_content: str) -> List[floa... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-8 | + " If just given one memory still respond in a list."
+ " Memories are separated by semi colans (;)"
+ "\Memories: {memory_content}"
+ "\nRating: "
)
scores = self.chain(prompt).run(memory_content=memory_content).strip()
if self.verbose:
logger.in... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-9 | memory_list = memory_content.split(";")
documents = []
for i in range(len(memory_list)):
documents.append(
Document(
page_content=memory_list[i],
metadata={"importance": importance_scores[i]},
)
)
res... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-10 | self.reflecting = False
return result
[docs] def add_memory(
self, memory_content: str, now: Optional[datetime] = None
) -> List[str]:
"""Add an observation or memory to the agent's memory."""
importance_score = self._score_memory_importance(memory_content)
self.aggregate_... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-11 | 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 fetch_memories(
self, ob... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-12 | return "\n".join([f"{mem}" for mem in content])
def _format_memory_detail(self, memory: Document, prefix: str = "") -> str:
created_time = memory.metadata["created_at"].strftime("%B %d, %Y, %I:%M %p")
return f"{prefix}[{created_time}] {memory.page_content.strip()}"
def format_memories_simple(sel... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-13 | break
consumed_tokens += self.llm.get_num_tokens(doc.page_content)
if consumed_tokens < self.max_tokens_limit:
result.append(doc)
return self.format_memories_simple(result)
@property
def memory_variables(self) -> List[str]:
"""Input keys this memory class ... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-14 | 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://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
fb6508b1b953-15 | [docs] def clear(self) -> None:
"""Clear memory contents."""
# TODO | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
b446f531ca85-0 | 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 |
b446f531ca85-1 | memory: GenerativeAgentMemory
"""The memory object that combines relevance, recency, and 'importance'."""
llm: BaseLanguageModel
"""The underlying language model."""
verbose: bool = False
summary: str = "" #: :meta private:
"""Stateful self-summary generated via reflection on the character's me... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-2 | 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 |
b446f531ca85-3 | def _get_entity_action(self, observation: str, entity_name: str) -> str:
prompt = PromptTemplate.from_template(
"What is the {entity} doing in the following observation? {observation}"
+ "\nThe {entity} is"
)
return (
self.chain(prompt).run(entity=entity_name,... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
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