id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
a87afe3f86a3-4 | ) -> Dict[str, Any]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.c... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/sequential.html |
f4a3b141c5ba-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://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/vector_db.html |
f4a3b141c5ba-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://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/vector_db.html |
0559a60dae25-0 | 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://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/retrieval.html |
0559a60dae25-1 | 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://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/retrieval.html |
bbe40450023c-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://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
bbe40450023c-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://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
bbe40450023c-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://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
bbe40450023c-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],... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
bbe40450023c-4 | return inputs.pop(self.input_docs_key)
@property
def _chain_type(self) -> str:
return "qa_with_sources_chain" | https://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
f685df588cf3-0 | 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/stable/_modules/langchain/chains/flare/base.html |
f685df588cf3-1 | )
)
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/stable/_modules/langchain/chains/flare/base.html |
f685df588cf3-2 | [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/stable/_modules/langchain/chains/flare/base.html |
f685df588cf3-3 | 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/stable/_modules/langchain/chains/flare/base.html |
f685df588cf3-4 | )
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/stable/_modules/langchain/chains/flare/base.html |
db066cfba1fa-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/stable/_modules/langchain/chains/natbot/base.html |
db066cfba1fa-1 | "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=values["llm"], prompt=PROMPT)
return values
[docs] @classmethod
def ... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/natbot/base.html |
db066cfba1fa-2 | _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],
previous_command=se... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/natbot/base.html |
f0e2edbdb169-0 | Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langchain.callback... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/hyde/base.html |
f0e2edbdb169-1 | return list(np.array(embeddings).mean(axis=0))
[docs] def embed_query(self, text: str) -> List[float]:
"""Generate a hypothetical document and embedded it."""
var_name = self.llm_chain.input_keys[0]
result = self.llm_chain.generate([{var_name: text}])
documents = [generation.text for ... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/hyde/base.html |
d976dcfea6f5-0 | 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/stable/_modules/langchain/chains/sql_database/base.html |
d976dcfea6f5-1 | 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/stable/_modules/langchain/chains/sql_database/base.html |
d976dcfea6f5-2 | :meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, INTERMEDIATE_STEPS_KEY]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/sql_database/base.html |
d976dcfea6f5-3 | 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/stable/_modules/langchain/chains/sql_database/base.html |
d976dcfea6f5-4 | 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/stable/_modules/langchain/chains/sql_database/base.html |
d976dcfea6f5-5 | 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/stable/_modules/langchain/chains/sql_database/base.html |
d976dcfea6f5-6 | 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/stable/_modules/langchain/chains/sql_database/base.html |
dc22c1494d95-0 | 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://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
dc22c1494d95-1 | 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://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
dc22c1494d95-2 | ) -> 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://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
dc22c1494d95-3 | 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://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
dc22c1494d95-4 | [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) | https://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
1f7326c5c97c-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/stable/_modules/langchain/chains/llm_bash/base.html |
1f7326c5c97c-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/stable/_modules/langchain/chains/llm_bash/base.html |
1f7326c5c97c-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://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_bash/base.html |
0b7c7896dccc-0 | Source code for langchain.chains.qa_generation.base
from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base i... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_generation/base.html |
0b7c7896dccc-1 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, List]:
docs = self.text_splitter.create_documents([inputs[self.input_key]])
results = self.llm_chain.generate(
[{"text": d.page_content} for d in docs... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/qa_generation/base.html |
4645b0937857-0 | 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/stable/_modules/langchain/chains/router/multi_prompt.html |
4645b0937857-1 | 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/stable/_modules/langchain/chains/router/multi_prompt.html |
98fd9bf7755f-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/stable/_modules/langchain/chains/router/multi_retrieval_qa.html |
98fd9bf7755f-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/stable/_modules/langchain/chains/router/multi_retrieval_qa.html |
98fd9bf7755f-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/stable/_modules/langchain/chains/router/multi_retrieval_qa.html |
829e915f87e7-0 | 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/stable/_modules/langchain/chains/router/base.html |
829e915f87e7-1 | """If True, use default_chain when an invalid destination name is provided.
Defaults to False."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Will be whate... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/router/base.html |
829e915f87e7-2 | 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, callbacks=callbacks)
_run_manager.on_tex... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/router/base.html |
e01f9c171275-0 | 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/stable/_modules/langchain/chains/router/llm_router.html |
e01f9c171275-1 | 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()
callbacks = _run_manager.get_child()
output = cast(... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/router/llm_router.html |
e01f9c171275-2 | 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 ValueError("Expected 'destination' to be a string... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/router/llm_router.html |
78ae21d88f2e-0 | Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic import Extra, Fiel... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
78ae21d88f2e-1 | human = "Human: " + dialogue_turn[0]
ai = "Assistant: " + dialogue_turn[1]
buffer += "\n" + "\n".join([human, ai])
else:
raise ValueError(
f"Unsupported chat history format: {type(dialogue_turn)}."
f" Full chat history: {chat_history} "
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
78ae21d88f2e-2 | """Get docs."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs["question"]
get_chat_history = sel... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
78ae21d88f2e-3 | question = inputs["question"]
get_chat_history = self.get_chat_history or _get_chat_history
chat_history_str = get_chat_history(inputs["chat_history"])
if chat_history_str:
callbacks = _run_manager.get_child()
new_question = await self.question_generator.arun(
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
78ae21d88f2e-4 | num_docs = len(docs)
if self.max_tokens_limit and isinstance(
self.combine_docs_chain, StuffDocumentsChain
):
tokens = [
self.combine_docs_chain.llm_chain.llm.get_num_tokens(doc.page_content)
for doc in docs
]
token_count = ... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
78ae21d88f2e-5 | chain_type=chain_type,
verbose=verbose,
callbacks=callbacks,
**combine_docs_chain_kwargs,
)
_llm = condense_question_llm or llm
condense_question_chain = LLMChain(
llm=_llm,
prompt=condense_question_prompt,
verbose=verbose,
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
78ae21d88f2e-6 | raise NotImplementedError("ChatVectorDBChain does not support async")
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
chain_type: str = "stuff",
combin... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
d582d47c1fde-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://api.python.langchain.com/en/stable/_modules/langchain/chains/conversation/base.html |
d582d47c1fde-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://api.python.langchain.com/en/stable/_modules/langchain/chains/conversation/base.html |
8ef577fce497-0 | Source code for langchain.chains.graph_qa.cypher
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForCha... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/cypher.html |
8ef577fce497-1 | """Number of results to return from the query"""
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 graph directly."""
@property
def input_ke... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/cypher.html |
8ef577fce497-2 | ) -> Dict[str, Any]:
"""Generate Cypher statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
intermediate_steps: List = []
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/cypher.html |
0c87c125473b-0 | Source code for langchain.chains.graph_qa.nebulagraph
"""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... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/nebulagraph.html |
0c87c125473b-1 | **kwargs: Any,
) -> NebulaGraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
ngql_generation_chain = LLMChain(llm=llm, prompt=ngql_prompt)
return cls(
qa_chain=qa_chain,
ngql_generation_chain=ngql_generation_chain,
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/nebulagraph.html |
08400cb551f5-0 | 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://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/base.html |
08400cb551f5-1 | ) -> 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://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/base.html |
97a24962dac8-0 | Source code for langchain.chains.graph_qa.kuzu
"""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://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/kuzu.html |
97a24962dac8-1 | cypher_prompt: BasePromptTemplate = KUZU_GENERATION_PROMPT,
**kwargs: Any,
) -> KuzuQAChain:
"""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,
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/kuzu.html |
97a24962dac8-2 | callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]} | https://api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/kuzu.html |
ce2f2a5d844d-0 | Source code for langchain.chains.openai_functions.qa_with_structure
from typing import Any, List, Optional, Type, Union
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/qa_with_structure.html |
ce2f2a5d844d-1 | Returns:
"""
if output_parser == "pydantic":
if not (isinstance(schema, type) and issubclass(schema, BaseModel)):
raise ValueError(
"Must provide a pydantic class for schema when output_parser is "
"'pydantic'."
)
_output_parser: BaseLLMOut... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/qa_with_structure.html |
ce2f2a5d844d-2 | output_parser=_output_parser,
)
return chain
[docs]def create_qa_with_sources_chain(llm: BaseLanguageModel, **kwargs: Any) -> LLMChain:
"""Create a question answering chain that returns an answer with sources.
Args:
llm: Language model to use for the chain.
**kwargs: Keyword arguments to... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/qa_with_structure.html |
95ccf04a3969-0 | Source code for langchain.chains.openai_functions.citation_fuzzy_match
from typing import Iterator, List
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
95ccf04a3969-1 | def get_spans(self, context: str) -> Iterator[str]:
for quote in self.substring_quote:
yield from self._get_span(quote, context)
class QuestionAnswer(BaseModel):
"""A question and its answer as a list of facts each one should have a source.
each sentence contains a body and a list of sources... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
95ccf04a3969-2 | content=(
"Tips: Make sure to cite your sources, "
"and use the exact words from the context."
)
),
]
prompt = ChatPromptTemplate(messages=messages)
chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_par... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
9452dc7151a4-0 | Source code for langchain.chains.openai_functions.tagging
from typing import Any
from langchain.base_language import BaseLanguageModel
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import _convert_schema, get_llm_kwargs
from langchain.outp... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/tagging.html |
9452dc7151a4-1 | pydantic_schema: Any, llm: BaseLanguageModel
) -> Chain:
"""Creates a chain that extracts information from a passage.
Args:
pydantic_schema: The pydantic schema of the entities to extract.
llm: The language model to use.
Returns:
Chain (LLMChain) that can be used to extract informati... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/tagging.html |
a268a55946bd-0 | Source code for langchain.chains.openai_functions.extraction
from typing import Any, List
from pydantic import BaseModel
from langchain.base_language import BaseLanguageModel
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import (
_conv... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/extraction.html |
a268a55946bd-1 | output_parser = JsonKeyOutputFunctionsParser(key_name="info")
llm_kwargs = get_llm_kwargs(function)
chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
)
return chain
[docs]def create_extraction_chain_pydantic(
pydantic_schema... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions/extraction.html |
cf99bf60b8d3-0 | 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://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_checker/base.html |
cf99bf60b8d3-1 | )
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://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_checker/base.html |
cf99bf60b8d3-2 | 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://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_checker/base.html |
cf99bf60b8d3-3 | 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"
[docs] @classmethod
def from_llm(
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/llm_checker/base.html |
2adcf12b2bd7-0 | Source code for langchain.chains.combine_documents.stuff
"""Chain that combines documents by stuffing into context."""
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callbacks
from langchain.chains.combine_documents.base impo... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/stuff.html |
2adcf12b2bd7-1 | if "document_variable_name" not in values:
if len(llm_chain_variables) == 1:
values["document_variable_name"] = llm_chain_variables[0]
else:
raise ValueError(
"document_variable_name must be provided if there are "
"multiple... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/stuff.html |
2adcf12b2bd7-2 | """Stuff all documents into one prompt and pass to LLM."""
inputs = self._get_inputs(docs, **kwargs)
# Call predict on the LLM.
return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
[docs] async def acombine_docs(
self, docs: List[Document], callbacks: Callbacks = None, **k... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/stuff.html |
3f85c4465be5-0 | Source code for langchain.chains.combine_documents.refine
"""Combining documents by doing a first pass and then refining on more documents."""
from __future__ import annotations
from typing import Any, Dict, List, Tuple
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callbacks
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/refine.html |
3f85c4465be5-1 | """Expect input key.
:meta private:
"""
_output_keys = super().output_keys
if self.return_intermediate_steps:
_output_keys = _output_keys + ["intermediate_steps"]
return _output_keys
class Config:
"""Configuration for this pydantic object."""
extra... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/refine.html |
3f85c4465be5-2 | )
return values
[docs] def combine_docs(
self, docs: List[Document], callbacks: Callbacks = None, **kwargs: Any
) -> Tuple[str, dict]:
"""Combine by mapping first chain over all, then stuffing into final chain."""
inputs = self._construct_initial_inputs(docs, **kwargs)
res... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/refine.html |
3f85c4465be5-3 | if self.return_intermediate_steps:
extra_return_dict = {"intermediate_steps": refine_steps}
else:
extra_return_dict = {}
return res, extra_return_dict
def _construct_refine_inputs(self, doc: Document, res: str) -> Dict[str, Any]:
return {
self.document_var... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/refine.html |
cf653bd16733-0 | Source code for langchain.chains.combine_documents.map_rerank
"""Combining documents by mapping a chain over them first, then reranking results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
from pydantic import Extra, root_validator
from langchain.call... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/map_rerank.html |
cf653bd16733-1 | _output_keys += self.metadata_keys
return _output_keys
@root_validator()
def validate_llm_output(cls, values: Dict) -> Dict:
"""Validate that the combine chain outputs a dictionary."""
output_parser = values["llm_chain"].prompt.output_parser
if not isinstance(output_parser, Regex... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/map_rerank.html |
cf653bd16733-2 | else:
llm_chain_variables = values["llm_chain"].prompt.input_variables
if values["document_variable_name"] not in llm_chain_variables:
raise ValueError(
f"document_variable_name {values['document_variable_name']} was "
f"not found in llm_ch... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/map_rerank.html |
cf653bd16733-3 | def _process_results(
self,
docs: List[Document],
results: Sequence[Union[str, List[str], Dict[str, str]]],
) -> Tuple[str, dict]:
typed_results = cast(List[dict], results)
sorted_res = sorted(
zip(typed_results, docs), key=lambda x: -int(x[0][self.rank_key])
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/map_rerank.html |
e9e8f3f237f8-0 | Source code for langchain.chains.combine_documents.base
"""Base interface for chains combining documents."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManag... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/base.html |
e9e8f3f237f8-1 | """
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
"""Return the prompt length given the doc... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/base.html |
e9e8f3f237f8-2 | ) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
output, extra_return_... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/base.html |
e9e8f3f237f8-3 | other_keys[self.combine_docs_chain.input_key] = docs
return self.combine_docs_chain(
other_keys, return_only_outputs=True, callbacks=_run_manager.get_child()
) | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/base.html |
bbcbfffaf1e7-0 | Source code for langchain.chains.combine_documents.map_reduce
"""Combining documents by mapping a chain over them first, then combining results."""
from __future__ import annotations
from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple
from pydantic import Extra, root_validator
from langchain.callbac... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/map_reduce.html |
bbcbfffaf1e7-1 | return new_result_doc_list
def _collapse_docs(
docs: List[Document],
combine_document_func: CombineDocsProtocol,
**kwargs: Any,
) -> Document:
result = combine_document_func(docs, **kwargs)
combined_metadata = {k: str(v) for k, v in docs[0].metadata.items()}
for doc in docs[1:]:
for k, v... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/map_reduce.html |
bbcbfffaf1e7-2 | _output_keys = _output_keys + ["intermediate_steps"]
return _output_keys
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def get_return_intermediate_steps(cls, values: Dict) -> Dict:
... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/map_reduce.html |
bbcbfffaf1e7-3 | return self.combine_document_chain
[docs] def combine_docs(
self,
docs: List[Document],
token_max: int = 3000,
callbacks: Callbacks = None,
**kwargs: Any,
) -> Tuple[str, dict]:
"""Combine documents in a map reduce manner.
Combine by mapping first chain ove... | https://api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/map_reduce.html |
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