id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 115 |
|---|---|---|
310f6d8a7dec-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language i... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
310f6d8a7dec-1 | def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@classmethod
def from_llm(
... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
310f6d8a7dec-2 | @abstractmethod
def _get_docs(self, question: str) -> List[Document]:
"""Get documents to do question answering over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run get_relevant_text an... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
310f6d8a7dec-3 | the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = indexqa({'query': 'This is my query'})
answer, docs = res['result'], res['source_documents']
"""
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
310f6d8a7dec-4 | [docs]class VectorDBQA(BaseRetrievalQA):
"""Chain for question-answering against a vector database."""
vectorstore: VectorStore = Field(exclude=True, alias="vectorstore")
"""Vector Database to connect to."""
k: int = 4
"""Number of documents to query for."""
search_type: str = "similarity"
"... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
310f6d8a7dec-5 | raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_docs(self, question: str) -> List[Document]:
raise NotImplementedError("VectorDBQA does not support async")
@property
def _chain_type(self) -> str:
"""Return the chain type."""
ret... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
8a8f3492fddf-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.callbacks.manager impor... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
8a8f3492fddf-1 | verbose=verbose,
),
LLMChain(
llm=llm,
prompt=check_assertions_prompt,
output_key="checked_assertions",
verbose=verbose,
),
LLMChain(
llm=llm,
prompt=revised_summary_prompt,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
8a8f3492fddf-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://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
8a8f3492fddf-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://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
8a8f3492fddf-4 | create_assertions_prompt,
check_assertions_prompt,
revised_summary_prompt,
are_all_true_prompt,
verbose=verbose,
)
return cls(sequential_chain=chain, verbose=verbose, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last up... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
88cf6dc495cb-0 | Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from __future__ import annotations
import logging
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_lang... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
88cf6dc495cb-1 | def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMBashChain with an llm is deprecated. "
"Please instantiate with llm_chain or using the from_llm class method."
)
if "llm_chain" n... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
88cf6dc495cb-2 | )
_run_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
try:
parser = self.llm_chain.prompt.output_parser
command_list = parser.parse(t) # type: ignore[union-attr]
except OutputParserException as e:
_run_manager.on_chain_error(e, ... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
db51a0922359-0 | Source code for langchain.chains.conversation.base
"""Chain that carries on a conversation and calls an LLM."""
from typing import Dict, List
from pydantic import Extra, Field, root_validator
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer i... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
db51a0922359-1 | f"The input key {input_key} was also found in the memory keys "
f"({memory_keys}) - please provide keys that don't overlap."
)
prompt_variables = values["prompt"].input_variables
expected_keys = memory_keys + [input_key]
if set(expected_keys) != set(prompt_variables):... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
2142af9647f6-0 | 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://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
2142af9647f6-1 | """Whether or not to return the result of querying the SQL table directly."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" i... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
2142af9647f6-2 | _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
input_text = f"{inputs[self.input_key]}\nSQLQuery:"
_run_manager.on_text(input_text, verbose=self.verbose)
# If not present, then defaults to None which is all tables.
table_names_to_use = inputs.get("table_names... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
2142af9647f6-3 | )
_run_manager.on_text(final_result, color="green", verbose=self.verbose)
chain_result: Dict[str, Any] = {self.output_key: final_result}
if self.return_intermediate_steps:
chain_result["intermediate_steps"] = intermediate_steps
return chain_result
@property
def _c... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
2142af9647f6-4 | database: SQLDatabase,
query_prompt: BasePromptTemplate = PROMPT,
decider_prompt: BasePromptTemplate = DECIDER_PROMPT,
**kwargs: Any,
) -> SQLDatabaseSequentialChain:
"""Load the necessary chains."""
sql_chain = SQLDatabaseChain(
llm=llm, database=database, prompt... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
2142af9647f6-5 | callbacks=_run_manager.get_child(), **llm_inputs
)
_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: in... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
208c06356c47-0 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from ... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
208c06356c47-1 | )
chains = [
create_draft_answer_chain,
list_assertions_chain,
check_assertions_chain,
revised_answer_chain,
]
question_to_checked_assertions_chain = SequentialChain(
chains=chains,
input_variables=["question"],
output_variables=["revised_statement"],
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
208c06356c47-2 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMCheckerChain with an llm is deprecated. "
"Please instantiate with question_to_checked_assertions_chain "
"or using the from_llm class method."
)
if (
"que... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
208c06356c47-3 | question = inputs[self.input_key]
output = self.question_to_checked_assertions_chain(
{"question": question}, callbacks=_run_manager.get_child()
)
return {self.output_key: output["revised_statement"]}
@property
def _chain_type(self) -> str:
return "llm_checker_chain"
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
0ae00c05568d-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://python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
0ae00c05568d-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://python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
4404cecd9b68-0 | 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://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
4404cecd9b68-1 | "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://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
4404cecd9b68-2 | 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)
return cls(
llm_chain=llm_chain,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
64af2f7e6846-0 | 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://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
64af2f7e6846-1 | :meta private:
"""
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 prom... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
64af2f7e6846-2 | run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> 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 i... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
64af2f7e6846-3 | # Other keys are assumed to be needed for LLM prediction
other_keys: Dict = {k: v for k, v in inputs.items() if k != self.input_key}
other_keys[self.combine_docs_chain.input_key] = docs
return self.combine_docs_chain(
other_keys, return_only_outputs=True, callbacks=_run_manager.get_c... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
9421dba11f15-0 | Source code for langchain.chains.qa_with_sources.retrieval
"""Question-answering with sources over an index."""
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
9421dba11f15-1 | docs = self.retriever.get_relevant_documents(question)
return self._reduce_tokens_below_limit(docs)
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = await self.retriever.aget_relevant_documents(question)
return self._re... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
f2a867d0b1bd-0 | Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLan... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f2a867d0b1bd-1 | document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
question_prompt: BasePromptTemplate = QUESTION_PROMPT,
combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Construct the chain from an LLM."""
llm_question_chain = LLMChain(l... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f2a867d0b1bd-2 | def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.question_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
_output_keys = [self.answer_key, self.sources_answer_key]
... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f2a867d0b1bd-3 | if self.return_source_documents:
result["source_documents"] = docs
return result
@abstractmethod
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
"""Get docs to run questioning over."""
async def _acall(
self,
inputs: Dict[str, Any],
r... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f2a867d0b1bd-4 | return inputs.pop(self.input_docs_key)
@property
def _chain_type(self) -> str:
return "qa_with_sources_chain"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
dabcc8e1dd4d-0 | Source code for langchain.chains.qa_with_sources.vector_db
"""Question-answering with sources over a vector database."""
import warnings
from typing import Any, Dict, List
from pydantic import Field, root_validator
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_so... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
dabcc8e1dd4d-1 | num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = self.vectorstore.similarity_search(
question, k=self.k, **self.search_kwargs
)
... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
606ddd58277d-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://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
606ddd58277d-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://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
606ddd58277d-2 | ) -> 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_history_str = get_chat_history(inputs["chat_history"])
if chat_history_str:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
606ddd58277d-3 | new_question = await self.question_generator.arun(
question=question, chat_history=chat_history_str, callbacks=callbacks
)
else:
new_question = question
docs = await self._aget_docs(new_question, inputs)
new_inputs = inputs.copy()
new_inputs["quest... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
606ddd58277d-4 | while token_count > self.max_tokens_limit:
num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]:
docs = self.retriever.get_relevant_documents(question)
return self._re... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
606ddd58277d-5 | )
[docs]class ChatVectorDBChain(BaseConversationalRetrievalChain):
"""Chain for chatting with a vector database."""
vectorstore: VectorStore = Field(alias="vectorstore")
top_k_docs_for_context: int = 4
search_kwargs: dict = Field(default_factory=dict)
@property
def _chain_type(self) -> str:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
606ddd58277d-6 | combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
doc_chain = load_qa_chain(
llm,
chain_type=chain_type,
**combine_docs_chain_kwargs,
)
condense_question_chain = LLMChain(llm=llm, prompt=condense_question_prompt)
return cls(
vecto... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
3c06801c01b2-0 | Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chain... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
3c06801c01b2-1 | ) -> GraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
entity_chain = LLMChain(llm=llm, prompt=entity_prompt)
return cls(
qa_chain=qa_chain,
entity_extraction_chain=entity_chain,
**kwargs,
)
def _call(
... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
e466496c546d-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://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
e466496c546d-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://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
e466496c546d-2 | 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.question_key]
api_url = await se... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
e466496c546d-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://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
24c8414a684f-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://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
24c8414a684f-1 | :meta private:
"""
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, ... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
24c8414a684f-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://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
24c8414a684f-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://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
24c8414a684f-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://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
24c8414a684f-5 | api_operation=operation,
requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
5b2188942e99-0 | Source code for langchain.chains.constitutional_ai.base
"""Chain for applying constitutional principles to the outputs of another chain."""
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
5b2188942e99-1 | critique_chain: LLMChain
revision_chain: LLMChain
return_intermediate_steps: bool = False
[docs] @classmethod
def get_principles(
cls, names: Optional[List[str]] = None
) -> List[ConstitutionalPrinciple]:
if names is None:
return list(PRINCIPLES.values())
else:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
5b2188942e99-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(**inputs)
initial_response = response
input_prompt = self.chain.prompt.format(**inputs)
_run_manager.on_text(
text="Initial response: " + respons... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
5b2188942e99-3 | verbose=self.verbose,
color="green",
)
_run_manager.on_text(
text="Critique: " + critique + "\n\n",
verbose=self.verbose,
color="blue",
)
_run_manager.on_text(
text="Updated response: " + revi... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
853bd6bab442-0 | Source code for langchain.chat_models.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models import ChatOpenAI
from langchain.schema import Bas... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
853bd6bab442-1 | """Call ChatOpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(messages, stop, run_manager)
request_end_ti... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
853bd6bab442-2 | request_end_time = datetime.datetime.now().timestamp()
message_dicts, params = super()._create_message_dicts(messages, stop)
for i, generation in enumerate(generated_responses.generations):
response_dict, params = super()._create_message_dicts(
[generation.message], stop
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
43e4c2ce6c9e-0 | Source code for langchain.chat_models.google_palm
"""Wrapper around Google's PaLM Chat API."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
Ca... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
43e4c2ce6c9e-1 | raise ChatGooglePalmError(f"ChatResponse must have an author: {candidate}")
content = _truncate_at_stop_tokens(candidate.get("content", ""), stop)
if content is None:
raise ChatGooglePalmError(f"ChatResponse must have a content: {candidate}")
if author == "ai":
generation... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
43e4c2ce6c9e-2 | "Message examples must come before other messages."
)
_, next_input_message = remaining.pop(0)
if isinstance(next_input_message, AIMessage) and next_input_message.example:
examples.extend(
[
genai.types.MessageDict(
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
43e4c2ce6c9e-3 | To use you must have the google.generativeai Python package installed and
either:
1. The ``GOOGLE_API_KEY``` environment varaible set with your API key, or
2. Pass your API key using the google_api_key kwarg to the ChatGoogle
constructor.
Example:
.. code-block:: python
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
43e4c2ce6c9e-4 | )
try:
import google.generativeai as genai
genai.configure(api_key=google_api_key)
except ImportError:
raise ChatGooglePalmError(
"Could not import google.generativeai python package."
)
values["client"] = genai
if values["t... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
43e4c2ce6c9e-5 | ) -> ChatResult:
prompt = _messages_to_prompt_dict(messages)
response: genai.types.ChatResponse = await self.client.chat_async(
model=self.model_name,
prompt=prompt,
temperature=self.temperature,
top_p=self.top_p,
top_k=self.top_k,
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
8d4bdc91ae5b-0 | Source code for langchain.chat_models.openai
"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
from pydantic import Extra, Field, root_validator
from tenacity import (
before_sleep_log,
retry,
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-1 | | retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
async def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorat... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-2 | if "name" in message.additional_kwargs:
message_dict["name"] = message.additional_kwargs["name"]
return message_dict
[docs]class ChatOpenAI(BaseChatModel):
"""Wrapper around OpenAI Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment va... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-3 | max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.ignore
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from addit... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-4 | values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-5 | | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
[docs] def completion_with_retry(self, **kwargs: Any) -> Any:
"""Use tenacity to ... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-6 | role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content", "")
inner_completion += token
if run_manager:
run_manager.on_llm_new_token(token)
message = _convert_dict_to_message(
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-7 | messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages, stop)
if self.streaming:
inner_completion = ""
role = "ass... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-8 | raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install tiktoken`."
)
# create a GPT-3.5-Turbo encoder instance
enc = tiktoken.encoding_for_mode... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
8d4bdc91ae5b-9 | # Returning num tokens assuming gpt-4-0314.
model = "gpt-4-0314"
# Returns the number of tokens used by a list of messages.
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf1a4666859d-0 | Source code for langchain.chat_models.azure_openai
"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.utils import get_from_dict_or_env
logger = logging.get... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
bf1a4666859d-1 | openai_api_version: str = ""
openai_api_key: str = ""
openai_organization: str = ""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
openai_api_key = get_from_dict_or_env(
values,
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
bf1a4666859d-2 | "`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
1be066769a2d-0 | Source code for langchain.chat_models.anthropic
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.llms.anthropic import _... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
1be066769a2d-1 | elif isinstance(message, AIMessage):
message_text = f"{self.AI_PROMPT} {message.content}"
elif isinstance(message, SystemMessage):
message_text = f"{self.HUMAN_PROMPT} <admin>{message.content}</admin>"
else:
raise ValueError(f"Got unknown type {message}")
retu... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
1be066769a2d-2 | ) -> ChatResult:
prompt = self._convert_messages_to_prompt(messages)
params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""
stream_resp = self.client.completion_st... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
1be066769a2d-3 | completion = response["completion"]
message = AIMessage(content=completion)
return ChatResult(generations=[ChatGeneration(message=message)])
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
0c48a1efc436-0 | Source code for langchain.embeddings.cohere
"""Wrapper around Cohere embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(Base... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
0c48a1efc436-1 | raise ValueError(
"Could not import cohere python package. "
"Please install it with `pip install cohere`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Cohere's embedding endpoint.
Args:
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
5a8e6b714dc4-0 | Source code for langchain.embeddings.aleph_alpha
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
"""... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
5a8e6b714dc4-1 | """Attention control parameters only apply to those tokens that have
explicitly been set in the request."""
control_log_additive: Optional[bool] = True
"""Apply controls on prompt items by adding the log(control_factor)
to attention scores."""
@root_validator()
def validate_environment(cls, va... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
5a8e6b714dc4-2 | "representation": SemanticRepresentation.Document,
"compress_to_size": self.compress_to_size,
"normalize": self.normalize,
"contextual_control_threshold": self.contextual_control_threshold,
"control_log_additive": self.control_log_additive,
}
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
5a8e6b714dc4-3 | """The symmetric version of the Aleph Alpha's semantic embeddings.
The main difference is that here, both the documents and
queries are embedded with a SemanticRepresentation.Symmetric
Example:
.. code-block:: python
from aleph_alpha import AlephAlphaSymmetricSemanticEmbedding
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
5a8e6b714dc4-4 | """
document_embeddings = []
for text in texts:
document_embeddings.append(self._embed(text))
return document_embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to Aleph Alpha's asymmetric, query embedding endpoint
Args:
text... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
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