id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
867dcf7f7e10-5 | 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 |
f9ef63b30405-0 | 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 |
f9ef63b30405-1 | "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 |
f9ef63b30405-2 | 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 |
718f2b9b8b15-0 | 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 |
718f2b9b8b15-1 | 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 |
9c6693f6ae4c-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 |
9c6693f6ae4c-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 |
9c6693f6ae4c-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 |
9c6693f6ae4c-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 |
9c6693f6ae4c-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 25, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
0e1e3db7bc97-0 | 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 |
0e1e3db7bc97-1 | 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 |
aa7e7a67a60a-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.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from l... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
aa7e7a67a60a-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 |
e6702995ffe2-0 | Source code for langchain.chains.graph_qa.cypher
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
e6702995ffe2-1 | **kwargs: Any,
) -> GraphCypherQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
return cls(
qa_chain=qa_chain,
cypher_generation_chain=cypher_generation_chain,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
e6702995ffe2-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 25, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
85ac734147f3-0 | 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 |
85ac734147f3-1 | 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 |
fe17f6e40610-0 | 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 |
fe17f6e40610-1 | 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 |
fe17f6e40610-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_manager.get_child(),
)
initial_response = response
input_prompt = self.chain.prompt.format(**inputs)
... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
fe17f6e40610-3 | critiques_and_revisions.append((critique, revision))
_run_manager.on_text(
text=f"Applying {constitutional_principle.name}..." + "\n\n",
verbose=self.verbose,
color="green",
)
_run_manager.on_text(
text="Critique: " + cr... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
3d94d69ee5ec-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 |
3d94d69ee5ec-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 |
3d94d69ee5ec-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 |
3d94d69ee5ec-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 |
ffaf05c3f472-0 | 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 |
ffaf05c3f472-1 | :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 |
ffaf05c3f472-2 | 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 |
ffaf05c3f472-3 | 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 |
ffaf05c3f472-4 | # 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 |
ffaf05c3f472-5 | 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 |
12cab81bc9cb-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.base_language import BaseLanguageModel
from langchain.cal... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
12cab81bc9cb-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 |
12cab81bc9cb-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 |
12cab81bc9cb-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 |
a1ea72fdc235-0 | 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 |
a1ea72fdc235-1 | 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 |
a1ea72fdc235-2 | )
_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 |
7ae9d4b20a94-0 | Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
from __future__ import annotations
import math
import re
import warnings
from typing import Any, Dict, List, Optional
import numexpr
from pydantic import Extra, root_validator
from langchain.base_lan... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
7ae9d4b20a94-1 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMMathChain 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"]... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
7ae9d4b20a94-2 | ) -> Dict[str, str]:
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
if text_match:
expression = text_match.group(1)
output = self._evaluate_exp... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
7ae9d4b20a94-3 | elif llm_output.startswith("Answer:"):
answer = llm_output
elif "Answer:" in llm_output:
answer = "Answer: " + llm_output.split("Answer:")[-1]
else:
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
def _call(
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
7ae9d4b20a94-4 | [docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate = PROMPT,
**kwargs: Any,
) -> LLMMathChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs)
By Harrison Chase
© Copyright... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
8ba0164e295a-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 |
8ba0164e295a-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 |
8ba0164e295a-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 |
8ba0164e295a-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 |
26e97e52b241-0 | 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 |
26e97e52b241-1 | 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 |
26e97e52b241-2 | ) -> 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 |
26e97e52b241-3 | 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 |
26e97e52b241-4 | 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 |
26e97e52b241-5 | )
[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 |
26e97e52b241-6 | 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 |
42d4c9824f8c-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://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
42d4c9824f8c-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 |
42d4c9824f8c-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 |
42d4c9824f8c-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 |
42d4c9824f8c-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 |
3d4b4ac636f1-0 | 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://python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
3d4b4ac636f1-1 | )
)
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://python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
3d4b4ac636f1-2 | [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://python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
3d4b4ac636f1-3 | 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://python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
3d4b4ac636f1-4 | )
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://python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
39b5fd17d81f-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 |
39b5fd17d81f-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 |
a0e21e89c442-0 | .ipynb
.pdf
Model Comparison
Model Comparison#
Constructing your language model application will likely involved choosing between many different options of prompts, models, and even chains to use. When doing so, you will want to compare these different options on different inputs in an easy, flexible, and intuitive way... | https://python.langchain.com/en/latest/additional_resources/model_laboratory.html |
a0e21e89c442-1 | pink
prompt = PromptTemplate(template="What is the capital of {state}?", input_variables=["state"])
model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt)
model_lab_with_prompt.compare("New York")
Input:
New York
OpenAI
Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p'... | https://python.langchain.com/en/latest/additional_resources/model_laboratory.html |
a0e21e89c442-2 | names = [str(open_ai_llm), str(cohere_llm)]
model_lab = ModelLaboratory(chains, names=names)
model_lab.compare("What is the hometown of the reigning men's U.S. Open champion?")
Input:
What is the hometown of the reigning men's U.S. Open champion?
OpenAI
Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tok... | https://python.langchain.com/en/latest/additional_resources/model_laboratory.html |
a0e21e89c442-3 | So the final answer is:
Carlos Alcaraz
previous
Tracing
next
YouTube
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 25, 2023. | https://python.langchain.com/en/latest/additional_resources/model_laboratory.html |
e43e101f3bce-0 | .md
.pdf
Tracing
Contents
Tracing Walkthrough
Changing Sessions
Tracing#
By enabling tracing in your LangChain runs, you’ll be able to more effectively visualize, step through, and debug your chains and agents.
First, you should install tracing and set up your environment properly.
You can use either a locally hosted... | https://python.langchain.com/en/latest/additional_resources/tracing.html |
e43e101f3bce-1 | Changing Sessions#
To initially record traces to a session other than "default", you can set the LANGCHAIN_SESSION environment variable to the name of the session you want to record to:
import os
os.environ["LANGCHAIN_TRACING"] = "true"
os.environ["LANGCHAIN_SESSION"] = "my_session" # Make sure this session actually ex... | https://python.langchain.com/en/latest/additional_resources/tracing.html |
6b16ee3b662e-0 | .md
.pdf
YouTube
Contents
⛓️Official LangChain YouTube channel⛓️
Introduction to LangChain with Harrison Chase, creator of LangChain
Videos (sorted by views)
YouTube#
This is a collection of LangChain videos on YouTube.
⛓️Official LangChain YouTube channel⛓️#
Introduction to LangChain with Harrison Chase, creator of ... | https://python.langchain.com/en/latest/additional_resources/youtube.html |
6b16ee3b662e-1 | Run BabyAGI with Langchain Agents (with Python Code) by 1littlecoder
How to Use Langchain With Zapier | Write and Send Email with GPT-3 | OpenAI API Tutorial by StarMorph AI
Use Your Locally Stored Files To Get Response From GPT - OpenAI | Langchain | Python by Shweta Lodha
Langchain JS | How to Use GPT-3, GPT-4 to Ref... | https://python.langchain.com/en/latest/additional_resources/youtube.html |
6b16ee3b662e-2 | LangChain. Crear aplicaciones Python impulsadas por GPT by Jesús Conde
Easiest Way to Use GPT In Your Products | LangChain Basics Tutorial by Rachel Woods
BabyAGI + GPT-4 Langchain Agent with Internet Access by tylerwhatsgood
Learning LLM Agents. How does it actually work? LangChain, AutoGPT & OpenAI by Arnoldas Kemekl... | https://python.langchain.com/en/latest/additional_resources/youtube.html |
6b16ee3b662e-3 | ⛓️ QA over documents with Auto vector index selection with Langchain router chains by echohive
⛓️ Build your own custom LLM application with Bubble.io & Langchain (No Code & Beginner friendly) by No Code Blackbox
⛓️ Simple App to Question Your Docs: Leveraging Streamlit, Hugging Face Spaces, LangChain, and Claude! by C... | https://python.langchain.com/en/latest/additional_resources/youtube.html |
6b16ee3b662e-4 | ⛓️ LangChain In Action: Real-World Use Case With Step-by-Step Tutorial by Rabbitmetrics
⛓️ Summarizing and Querying Multiple Papers with LangChain by Automata Learning Lab
⛓️ Using Langchain (and Replit) through Tana, ask Google/Wikipedia/Wolfram Alpha to fill out a table by Stian Håklev
⛓️ Langchain PDF App (GUI) | Cr... | https://python.langchain.com/en/latest/additional_resources/youtube.html |
6b16ee3b662e-5 | Model Comparison
Contents
⛓️Official LangChain YouTube channel⛓️
Introduction to LangChain with Harrison Chase, creator of LangChain
Videos (sorted by views)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 25, 2023. | https://python.langchain.com/en/latest/additional_resources/youtube.html |
52c42fdb9e7f-0 | .ipynb
.pdf
WhyLabs Integration
WhyLabs Integration#
Enable observability to detect inputs and LLM issues faster, deliver continuous improvements, and avoid costly incidents.
%pip install langkit -q
Make sure to set the required API keys and config required to send telemetry to WhyLabs:
WhyLabs API Key: https://whylabs... | https://python.langchain.com/en/latest/integrations/whylabs_profiling.html |
52c42fdb9e7f-1 | result = llm.generate(["Hello, World!"])
print(result)
generations=[[Generation(text="\n\nMy name is John and I'm excited to learn more about programming.", generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 20, 'prompt_tokens': 4, 'completion_tokens': 16}, 'model... | https://python.langchain.com/en/latest/integrations/whylabs_profiling.html |
52c42fdb9e7f-2 | whylabs.close()
previous
Weaviate
next
Wolfram Alpha Wrapper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 25, 2023. | https://python.langchain.com/en/latest/integrations/whylabs_profiling.html |
cb9b92ac4261-0 | .ipynb
.pdf
Databricks
Contents
Installation and Setup
Connecting to Databricks
Syntax
Required Parameters
Optional Parameters
Examples
SQL Chain example
SQL Database Agent example
Databricks#
This notebook covers how to connect to the Databricks runtimes and Databricks SQL using the SQLDatabase wrapper of LangChain.... | https://python.langchain.com/en/latest/integrations/databricks.html |
cb9b92ac4261-1 | warehouse_id: The warehouse ID in the Databricks SQL.
cluster_id: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both ‘warehouse_id’ and ‘cluster_id’ are None, it uses the ID of the cluster the notebook is attached to.
engine_args: The arguments to be used when connecting Databricks.
... | https://python.langchain.com/en/latest/integrations/databricks.html |
cb9b92ac4261-2 | SQL Database Agent example#
This example demonstrates the use of the SQL Database Agent for answering questions over a Databricks database.
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent = create_sql_agent(
... | https://python.langchain.com/en/latest/integrations/databricks.html |
cb9b92ac4261-3 | 2016-02-17 17:13:57+00:00 2016-02-17 17:17:55+00:00 0.7 5.0 10103 10023
*/
Thought:The trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.
Action: query_checker_sql_db
Action Input: SELECT trip_distance, tpep_dropoff_datetime - t... | https://python.langchain.com/en/latest/integrations/databricks.html |
3d376c99e7d5-0 | .md
.pdf
AtlasDB
Contents
Installation and Setup
Wrappers
VectorStore
AtlasDB#
This page covers how to use Nomic’s Atlas ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.
Installation and Setup#
Install the Python package with pip install ... | https://python.langchain.com/en/latest/integrations/atlas.html |
7ad46bccf0c0-0 | .md
.pdf
PGVector
Contents
Installation
Setup
Wrappers
VectorStore
Usage
PGVector#
This page covers how to use the Postgres PGVector ecosystem within LangChain
It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.
Installation#
Install the Python package with pip inst... | https://python.langchain.com/en/latest/integrations/pgvector.html |
6148ff069fca-0 | .md
.pdf
Apify
Contents
Overview
Installation and Setup
Wrappers
Utility
Loader
Apify#
This page covers how to use Apify within LangChain.
Overview#
Apify is a cloud platform for web scraping and data extraction,
which provides an ecosystem of more than a thousand
ready-made apps called Actors for various scraping, c... | https://python.langchain.com/en/latest/integrations/apify.html |
21db1adc0854-0 | .md
.pdf
Vectara
Contents
Installation and Setup
VectorStore
Vectara#
What is Vectara?
Vectara Overview:
Vectara is developer-first API platform for building conversational search applications
To use Vectara - first sign up and create an account. Then create a corpus and an API key for indexing and searching.
You can... | https://python.langchain.com/en/latest/integrations/vectara.html |
21db1adc0854-1 | Contents
Installation and Setup
VectorStore
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 25, 2023. | https://python.langchain.com/en/latest/integrations/vectara.html |
e7eb8090d41d-0 | .md
.pdf
Zilliz
Contents
Installation and Setup
Wrappers
VectorStore
Zilliz#
This page covers how to use the Zilliz Cloud ecosystem within LangChain.
Zilliz uses the Milvus integration.
It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
Installation and Setup#
Instal... | https://python.langchain.com/en/latest/integrations/zilliz.html |
96fa8f3c57c5-0 | .md
.pdf
LanceDB
Contents
Installation and Setup
Wrappers
VectorStore
LanceDB#
This page covers how to use LanceDB within LangChain.
It is broken into two parts: installation and setup, and then references to specific LanceDB wrappers.
Installation and Setup#
Install the Python SDK with pip install lancedb
Wrappers#
... | https://python.langchain.com/en/latest/integrations/lancedb.html |
2861aecfe3fe-0 | .md
.pdf
Redis
Contents
Installation and Setup
Wrappers
Cache
Standard Cache
Semantic Cache
VectorStore
Retriever
Memory
Vector Store Retriever Memory
Chat Message History Memory
Redis#
This page covers how to use the Redis ecosystem within LangChain.
It is broken into two parts: installation and setup, and then refe... | https://python.langchain.com/en/latest/integrations/redis.html |
2861aecfe3fe-1 | To import this vectorstore:
from langchain.vectorstores import Redis
For a more detailed walkthrough of the Redis vectorstore wrapper, see this notebook.
Retriever#
The Redis vector store retriever wrapper generalizes the vectorstore class to perform low-latency document retrieval. To create the retriever, simply call ... | https://python.langchain.com/en/latest/integrations/redis.html |
7df7af853cec-0 | .md
.pdf
Google Search
Contents
Installation and Setup
Wrappers
Utility
Tool
Google Search#
This page covers how to use the Google Search API within LangChain.
It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper.
Installation and Setup#
Install requirements w... | https://python.langchain.com/en/latest/integrations/google_search.html |
923e106bf1a4-0 | .md
.pdf
Qdrant
Contents
Installation and Setup
Wrappers
VectorStore
Qdrant#
This page covers how to use the Qdrant ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
Installation and Setup#
Install the Python SDK with pip install qdrant-c... | https://python.langchain.com/en/latest/integrations/qdrant.html |
daf15316c53c-0 | .md
.pdf
PromptLayer
Contents
Installation and Setup
Wrappers
LLM
PromptLayer#
This page covers how to use PromptLayer within LangChain.
It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
Installation and Setup#
If you want to work with PromptLayer:
Install the ... | https://python.langchain.com/en/latest/integrations/promptlayer.html |
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