id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
72cd09940d58-4 | # TODO: Handle async
) -> "OpenAPIEndpointChain":
"""Create an OpenAPIEndpoint from a spec at the specified url."""
operation = APIOperation.from_openapi_url(spec_url, path, method)
return cls.from_api_operation(
operation,
requests=requests,
llm=llm,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
72cd09940d58-5 | requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
cf0a0fd0f692-0 | Source code for langchain.chains.api.openapi.requests_chain
"""request parser."""
import json
import re
from typing import Any
from langchain.base_language import BaseLanguageModel
from langchain.chains.api.openapi.prompts import REQUEST_TEMPLATE
from langchain.chains.llm import LLMChain
from langchain.prompts.prompt i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/requests_chain.html |
cf0a0fd0f692-1 | ) -> LLMChain:
"""Get the request parser."""
output_parser = APIRequesterOutputParser()
prompt = PromptTemplate(
template=REQUEST_TEMPLATE,
output_parser=output_parser,
partial_variables={"schema": typescript_definition},
input_variables=["instruct... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/requests_chain.html |
b611e4631285-0 | Source code for langchain.chains.api.openapi.response_chain
"""Response parser."""
import json
import re
from typing import Any
from langchain.base_language import BaseLanguageModel
from langchain.chains.api.openapi.prompts import RESPONSE_TEMPLATE
from langchain.chains.llm import LLMChain
from langchain.prompts.prompt... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/response_chain.html |
b611e4631285-1 | template=RESPONSE_TEMPLATE,
output_parser=output_parser,
input_variables=["response", "instructions"],
)
return cls(prompt=prompt, llm=llm, verbose=verbose, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/response_chain.html |
cd17347bc766-0 | Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic imp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
cd17347bc766-1 | elif isinstance(dialogue_turn, tuple):
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)}."
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
cd17347bc766-2 | question: str,
inputs: Dict[str, Any],
*,
run_manager: CallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
cd17347bc766-3 | output["generated_question"] = new_question
return output
@abstractmethod
async def _aget_docs(
self,
question: str,
inputs: Dict[str, Any],
*,
run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
async def _acall(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
cd17347bc766-4 | )
output: Dict[str, Any] = {self.output_key: answer}
if self.return_source_documents:
output["source_documents"] = docs
if self.return_generated_question:
output["generated_question"] = new_question
return output
[docs] def save(self, file_path: Union[Path, str... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
cd17347bc766-5 | """Get docs."""
docs = self.retriever.get_relevant_documents(
question, callbacks=run_manager.get_child()
)
return self._reduce_tokens_below_limit(docs)
async def _aget_docs(
self,
question: str,
inputs: Dict[str, Any],
*,
run_manager: Asyn... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
cd17347bc766-6 | verbose=verbose,
callbacks=callbacks,
)
return cls(
retriever=retriever,
combine_docs_chain=doc_chain,
question_generator=condense_question_chain,
callbacks=callbacks,
**kwargs,
)
[docs]class ChatVectorDBChain(BaseConversati... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
cd17347bc766-7 | ) -> List[Document]:
"""Get docs."""
raise NotImplementedError("ChatVectorDBChain does not support async")
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROM... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
67ab029c6ec9-0 | 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 |
67ab029c6ec9-1 | if "llm" in values:
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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
67ab029c6ec9-2 | output = {self.output_key: res.strip()}
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 = L... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
2dec832ca7c5-0 | Source code for langchain.chains.router.embedding_router
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.router.base import RouterChain
from langchai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/embedding_router.html |
2dec832ca7c5-1 | """Convenience constructor."""
documents = []
for name, descriptions in names_and_descriptions:
for description in descriptions:
documents.append(
Document(page_content=description, metadata={"name": name})
)
vectorstore = vectorsto... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/embedding_router.html |
4e56959913bf-0 | Source code for langchain.chains.router.multi_prompt
"""Use a single chain to route an input to one of multiple llm chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.base_language import BaseLanguageModel
from langchain.chains import ConversationChain
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
4e56959913bf-1 | router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
destinations=destinations_str
)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
f38444c62c6e-0 | Source code for langchain.chains.router.multi_retrieval_qa
"""Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.base_language import BaseLanguageModel
from langchain.chains import Conver... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
f38444c62c6e-1 | default_retriever: Optional[BaseRetriever] = None,
default_prompt: Optional[PromptTemplate] = None,
default_chain: Optional[Chain] = None,
**kwargs: Any,
) -> MultiRetrievalQAChain:
if default_prompt and not default_retriever:
raise ValueError(
"`default_r... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
f38444c62c6e-2 | prompt = PromptTemplate(
template=prompt_template, input_variables=["history", "query"]
)
_default_chain = ConversationChain(
llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result"
)
return cls(
router_chain=router_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
c4edbc7bd3cb-0 | Source code for langchain.chains.router.base
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from typing import Any, Dict, List, Mapping, NamedTuple, Optional
from pydantic import Extra
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
Callba... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
c4edbc7bd3cb-1 | silent_errors: bool = False
"""If True, use default_chain when an invalid destination name is provided.
Defaults to False."""
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
c4edbc7bd3cb-2 | self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
route = await self.router_chain.aroute(inputs, ca... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
e808add899a0-0 | Source code for langchain.chains.router.llm_router
"""Base classes for LLM-powered router chains."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type, cast
from pydantic import root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager impo... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
e808add899a0-1 | if not isinstance(outputs["next_inputs"], dict):
raise ValueError
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
e808add899a0-2 | next_inputs_inner_key: str = "input"
[docs] def parse(self, text: str) -> Dict[str, Any]:
try:
expected_keys = ["destination", "next_inputs"]
parsed = parse_and_check_json_markdown(text, expected_keys)
if not isinstance(parsed["destination"], str):
raise Va... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
0469f9a8fdb7-0 | Source code for langchain.chains.llm_bash.prompt
# flake8: noqa
from __future__ import annotations
import re
from typing import List
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseOutputParser, OutputParserException
_PROMPT_TEMPLATE = """If someone asks you to perform a task, your ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/prompt.html |
0469f9a8fdb7-1 | for match in pattern.finditer(t):
matched = match.group(1).strip()
if matched:
code_blocks.extend(
[line for line in matched.split("\n") if line.strip()]
)
return code_blocks
@property
def _type(self) -> str:
return "bas... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/prompt.html |
ec354e6e418c-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://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
ec354e6e418c-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://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
ec354e6e418c-2 | question=inputs[self.input_key], callbacks=_run_manager.get_child()
)
_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]
ex... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
cf98a0af4101-0 | Source code for langchain.chains.natbot.crawler
# flake8: noqa
import time
from sys import platform
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Optional,
Set,
Tuple,
TypedDict,
Union,
)
if TYPE_CHECKING:
from playwright.sync_api import Browser, CDPSession, ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-1 | self.page_element_buffer: Dict[int, ElementInViewPort]
self.client: CDPSession
def go_to_page(self, url: str) -> None:
self.page.goto(url=url if "://" in url else "http://" + url)
self.client = self.page.context.new_cdp_session(self.page)
self.page_element_buffer = {}
def scroll(... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-2 | self.page.keyboard.press("Enter")
def crawl(self) -> List[str]:
page = self.page
page_element_buffer = self.page_element_buffer
start = time.time()
page_state_as_text = []
device_pixel_ratio: float = page.evaluate("window.devicePixelRatio")
if platform == "darwin" and... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-3 | document: Dict[str, Any] = tree["documents"][0]
nodes: Dict[str, Any] = document["nodes"]
backend_node_id: Dict[int, int] = nodes["backendNodeId"]
attributes: Dict[int, Dict[int, Any]] = nodes["attributes"]
node_value: Dict[int, int] = nodes["nodeValue"]
parent: Dict[int, int] = ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-4 | if node_name == "img":
return "img"
if (
node_name == "button" or has_click_handler
): # found pages that needed this quirk
return "button"
else:
return "text"
def find_attributes(
attributes: Dict[i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-5 | elif (
is_parent_desc_anchor
): # reuse the parent's anchor_id (which could be much higher in the tree)
value = (True, anchor_id)
else:
value = (
False,
None,
) # not a descendant of an anch... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-6 | and elem_lower_bound >= win_upper_bound
)
if not partially_is_in_viewport:
continue
meta_data: List[str] = []
# inefficient to grab the same set of keys for kinds of objects, but it's fine for now
element_attributes = find_attributes(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-7 | element_node_value = None
if node_value[index] >= 0:
element_node_value = strings[node_value[index]]
if (
element_node_value == "|"
): # commonly used as a separator, does not add much context - lets save ourselves some token space
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-8 | element_node_value = element.get("node_value")
node_is_clickable = element.get("is_clickable")
node_meta_data: Optional[List[str]] = element.get("node_meta")
inner_text = f"{element_node_value} " if element_node_value else ""
meta = ""
if node_index in child_n... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
cf98a0af4101-9 | )
id_counter += 1
print("Parsing time: {:0.2f} seconds".format(time.time() - start))
return elements_of_interest | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
217de4f6fe1d-0 | Source code for langchain.chains.natbot.base
"""Implement an LLM driven browser."""
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.callbacks.manager import Cal... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
217de4f6fe1d-1 | "Directly instantiating an NatBotChain 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:
values["llm_chain"] = LLMChain(llm... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
217de4f6fe1d-2 | ) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
url = inputs[self.input_url_key]
browser_content = inputs[self.input_browser_content_key]
llm_cmd = self.llm_chain.predict(
objective=self.objective,
url=url[:100],
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
37c4da09001c-0 | Source code for langchain.chains.question_answering.__init__
"""Load question answering chains."""
from typing import Any, Mapping, Optional, Protocol
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.manager import Callbacks
from lan... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/question_answering/__init__.html |
37c4da09001c-1 | callbacks: Callbacks = None,
**kwargs: Any,
) -> MapRerankDocumentsChain:
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
verbose=verbose,
callback_manager=callback_manager,
callbacks=callbacks,
)
return MapRerankDocumentsChain(
llm_chain=llm_chain,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/question_answering/__init__.html |
37c4da09001c-2 | combine_prompt: Optional[BasePromptTemplate] = None,
combine_document_variable_name: str = "summaries",
map_reduce_document_variable_name: str = "context",
collapse_prompt: Optional[BasePromptTemplate] = None,
reduce_llm: Optional[BaseLanguageModel] = None,
collapse_llm: Optional[BaseLanguageModel] ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/question_answering/__init__.html |
37c4da09001c-3 | if collapse_llm is not None:
raise ValueError(
"collapse_llm provided, but collapse_prompt was not: please "
"provide one or stop providing collapse_llm."
)
else:
_collapse_llm = collapse_llm or llm
collapse_chain = StuffDocumentsChain(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/question_answering/__init__.html |
37c4da09001c-4 | )
_refine_prompt = refine_prompt or refine_prompts.REFINE_PROMPT_SELECTOR.get_prompt(
llm
)
initial_chain = LLMChain(
llm=llm,
prompt=_question_prompt,
verbose=verbose,
callback_manager=callback_manager,
callbacks=callbacks,
)
_refine_llm = refine_llm ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/question_answering/__init__.html |
37c4da09001c-5 | callback_manager: Callback manager to use for the chain.
Returns:
A chain to use for question answering.
"""
loader_mapping: Mapping[str, LoadingCallable] = {
"stuff": _load_stuff_chain,
"map_reduce": _load_map_reduce_chain,
"refine": _load_refine_chain,
"map_rerank":... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/question_answering/__init__.html |
6638c626e013-0 | 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 |
6638c626e013-1 | return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the SQL table directly."""
use_query_checker: bool = False
"""Whether or not the query checker too... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
6638c626e013-2 | """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 |
6638c626e013-3 | result = self.database.run(sql_cmd)
intermediate_steps.append(str(result)) # output: sql exec
else:
query_checker_prompt = self.query_checker_prompt or PromptTemplate(
template=QUERY_CHECKER, input_variables=["query", "dialect"]
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
6638c626e013-4 | llm_inputs["input"] = input_text
intermediate_steps.append(llm_inputs) # input: final answer
final_result = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
intermediate_steps.appe... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
6638c626e013-5 | 2. Based on those tables, call the normal SQL database chain.
This is useful in cases where the number of tables in the database is large.
"""
decider_chain: LLMChain
sql_chain: SQLDatabaseChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
return_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
6638c626e013-6 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_table_names = self.sql_chain.database.get_usable_table_names()
table_na... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
c73402a76270-0 | Source code for langchain.chains.flare.prompts
from typing import Tuple
from langchain.prompts import PromptTemplate
from langchain.schema import BaseOutputParser
[docs]class FinishedOutputParser(BaseOutputParser[Tuple[str, bool]]):
finished_value: str = "FINISHED"
[docs] def parse(self, text: str) -> Tuple[str,... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/prompts.html |
106956c91fd6-0 | 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 |
106956c91fd6-1 | )
)
def _extract_tokens_and_log_probs(
self, generations: List[Generation]
) -> Tuple[Sequence[str], Sequence[float]]:
tokens = []
log_probs = []
for gen in generations:
if gen.generation_info is None:
raise ValueError
tokens.extend(gen... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
106956c91fd6-2 | [docs]class FlareChain(Chain):
question_generator_chain: QuestionGeneratorChain
response_chain: _ResponseChain = Field(default_factory=_OpenAIResponseChain)
output_parser: FinishedOutputParser = Field(default_factory=FinishedOutputParser)
retriever: BaseRetriever
min_prob: float = 0.2
min_token_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
106956c91fd6-3 | question_gen_inputs = [
{
"user_input": user_input,
"current_response": initial_response,
"uncertain_span": span,
}
for span in low_confidence_spans
]
callbacks = _run_manager.get_child()
question_gen_outputs = s... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
106956c91fd6-4 | )
initial_response = response.strip() + " " + "".join(tokens)
if not low_confidence_spans:
response = initial_response
final_response, finished = self.output_parser.parse(response)
if finished:
return {self.output_keys[0]: final... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
9062a4f7a33f-0 | Source code for langchain.embeddings.jina
"""Wrapper around Jina embedding models."""
import os
from typing import Any, Dict, List, Optional
import requests
from pydantic import BaseModel, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class JinaEm... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/jina.html |
9062a4f7a33f-1 | headers={"Authorization": jina_auth_token},
)
if resp.status_code == 401:
raise ValueError(
"The given Jina auth token is invalid. "
"Please check your Jina auth token."
)
elif resp.status_code == 404:
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/jina.html |
9062a4f7a33f-2 | Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
from docarray import Document, DocumentArray
embedding = self._post(docs=DocumentArray([Document(text=text)])).embeddings[0]
return list(map(float, embedding)) | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/jina.html |
23ea29a6e38b-0 | Source code for langchain.embeddings.elasticsearch
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional
from langchain.utils import get_from_env
if TYPE_CHECKING:
from elasticsearch import Elasticsearch
from elasticsearch.client import MlClient
from langchain.embeddings.base impor... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
23ea29a6e38b-1 | es_user: Optional[str] = None,
es_password: Optional[str] = None,
input_field: str = "text_field",
) -> ElasticsearchEmbeddings:
"""Instantiate embeddings from Elasticsearch credentials.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
23ea29a6e38b-2 | from elasticsearch.client import MlClient
except ImportError:
raise ImportError(
"elasticsearch package not found, please install with 'pip install "
"elasticsearch'"
)
es_cloud_id = es_cloud_id or get_from_env("es_cloud_id", "ES_CLOUD_ID")
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
23ea29a6e38b-3 | Example:
.. code-block:: python
from elasticsearch import Elasticsearch
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
#... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
23ea29a6e38b-4 | list.
"""
response = self.client.infer_trained_model(
model_id=self.model_id, docs=[{self.input_field: text} for text in texts]
)
embeddings = [doc["predicted_value"] for doc in response["inference_results"]]
return embeddings
[docs] def embed_documents(self, texts... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f37bf8d3710-0 | Source code for langchain.embeddings.google_palm
"""Wrapper arround Google's PaLM Embeddings APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/google_palm.html |
4f37bf8d3710-1 | return embeddings.client.generate_embeddings(*args, **kwargs)
return _embed_with_retry(*args, **kwargs)
[docs]class GooglePalmEmbeddings(BaseModel, Embeddings):
client: Any
google_api_key: Optional[str]
model_name: str = "models/embedding-gecko-001"
"""Model name to use."""
[docs] @root_validator... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/google_palm.html |
6991ba944b41-0 | Source code for langchain.embeddings.dashscope
"""Wrapper around DashScope embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Optional,
)
from pydantic import BaseModel, Extra, root_validator
from requests.exceptions import HTTPErro... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
6991ba944b41-1 | elif resp.status_code in [400, 401]:
raise ValueError(
f"status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}"
)
else:
raise HTTPError(
f"HTTP error occurred: status_code: {resp.status_code} \n "
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
6991ba944b41-2 | max_retries: int = 5
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
import dashscope
"""Validate that api key and python package exists in environment."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
6991ba944b41-3 | Returns:
Embedding for the text.
"""
embedding = embed_with_retry(
self, input=text, text_type="query", model=self.model
)[0]["embedding"]
return embedding | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
5b4532fb538e-0 | Source code for langchain.embeddings.embaas
"""Wrapper around embaas embeddings API."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from typing_extensions import NotRequired, TypedDict
from langchain.embeddings.base import Embeddings
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
5b4532fb538e-1 | api_url: str = EMBAAS_API_URL
"""The URL for the embaas embeddings API."""
embaas_api_key: Optional[str] = None
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
5b4532fb538e-2 | return embeddings
def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings using the Embaas API."""
payload = self._generate_payload(texts)
try:
return self._handle_request(payload)
except requests.exceptions.RequestException as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
6e094b37f8b6-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
6e094b37f8b6-1 | """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."""
aleph_alpha_api_key: Optional[str] = None
"""API k... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
6e094b37f8b6-2 | document_embeddings = []
for text in texts:
document_params = {
"prompt": Prompt.from_text(text),
"representation": SemanticRepresentation.Document,
"compress_to_size": self.compress_to_size,
"normalize": self.normalize,
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
6e094b37f8b6-3 | request=symmetric_request, model=self.model
)
return symmetric_response.embedding
[docs]class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding):
"""The symmetric version of the Aleph Alpha's semantic embeddings.
The main difference is that here, both the documents and
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
6e094b37f8b6-4 | """Call out to Aleph Alpha's Document endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
document_embeddings = []
for text in texts:
document_embeddings.append(self._embed(text))
retur... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
e92bb8c22274-0 | Source code for langchain.embeddings.octoai_embeddings
"""Module providing a wrapper around OctoAI Compute Service embedding models."""
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/octoai_embeddings.html |
e92bb8c22274-1 | values["octoai_api_token"] = get_from_dict_or_env(
values, "octoai_api_token", "OCTOAI_API_TOKEN"
)
values["endpoint_url"] = get_from_dict_or_env(
values, "endpoint_url", "ENDPOINT_URL"
)
return values
@property
def _identifying_params(self) -> Mapping[str... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/octoai_embeddings.html |
e92bb8c22274-2 | return self._compute_embeddings(texts, self.embed_instruction)
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embedding using an OctoAI instruct model."""
text = text.replace("\n", " ")
return self._compute_embeddings([text], self.embed_instruction)[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/octoai_embeddings.html |
00d11116242b-0 | Source code for langchain.embeddings.self_hosted
"""Running custom embedding models on self-hosted remote hardware."""
from typing import Any, Callable, List
from pydantic import Extra
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
def _embed_documents(pipeline: Any, *arg... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
00d11116242b-1 | model_load_fn=get_pipeline,
hardware=gpu
model_reqs=["./", "torch", "transformers"],
)
Example passing in a pipeline path:
.. code-block:: python
from langchain.embeddings import SelfHostedHFEmbeddings
import runhouse as rh
from... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
00d11116242b-2 | if not isinstance(embeddings, list):
return embeddings.tolist()
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embe... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
b45d4b47150d-0 | Source code for langchain.embeddings.llamacpp
"""Wrapper around llama.cpp embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.embeddings.base import Embeddings
[docs]class LlamaCppEmbeddings(BaseModel, Embeddings):
"""Wrapper ... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
b45d4b47150d-1 | use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use. If None, the number
of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
b45d4b47150d-2 | raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception as e:
raise ValueError(
f... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
dfe36cf2d9a1-0 | Source code for langchain.embeddings.huggingface
"""Wrapper around HuggingFace embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_M... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
dfe36cf2d9a1-1 | """Key word arguments to pass when calling the `encode` method of the model."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
import sentence_transformers
except ImportError as exc:
raise ImportEr... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
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