id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
6d7697b1919a-3 | return [self.output_key]
def _moderate(self, text: str, results: dict) -> str:
if results["flagged"]:
error_str = "Text was found that violates OpenAI's content policy."
if self.error:
raise ValueError(error_str)
else:
return error_str
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
0a63d00c7259-0 | Source code for langchain.chains.transform
"""Chain that runs an arbitrary python function."""
from typing import Callable, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
[docs]class TransformChain(Chain):
"""Chain transform chain outp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/transform.html |
0a63d00c7259-1 | """Return output keys.
:meta private:
"""
return self.output_variables
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
return self.transform(inputs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/transform.html |
a79f4c5dbf02-0 | Source code for langchain.chains.sequential
"""Chain pipeline where the outputs of one step feed directly into next."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
a79f4c5dbf02-1 | """Return expected input keys to the chain.
:meta private:
"""
return self.input_variables
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return self.output_variables
@root_validator(pre=True)
def validate_ch... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
a79f4c5dbf02-2 | raise ValueError(
f"The the input key(s) {''.join(overlapping_keys)} are found "
f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
)
known_variables = set(input_variables + memory_keys)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
a79f4c5dbf02-3 | if values.get("return_all", False):
output_keys = known_variables.difference(input_variables)
else:
output_keys = chains[-1].output_keys
values["output_variables"] = output_keys
else:
missing_vars = set(values["output_variables"]).difference(kn... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
a79f4c5dbf02-4 | callbacks = _run_manager.get_child()
outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
known_values.update(outputs)
return {k: known_values[k] for k in self.output_variables}
async def _acall(
self,
inputs: Dict[str, Any],
run_manage... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
a79f4c5dbf02-5 | [docs]class SimpleSequentialChain(Chain):
"""Simple chain where the outputs of one step feed directly into next."""
chains: List[Chain]
strip_outputs: bool = False
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
class Config:
"""Configuration for... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
a79f4c5dbf02-6 | @root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that chains are all single input/output."""
for chain in values["chains"]:
if len(chain.input_keys) != 1:
raise ValueError(
"Chains used in SimplePipeline should all have one... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
a79f4c5dbf02-7 | ) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_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.chains):
_input = chain.run(_input, cal... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
a79f4c5dbf02-8 | ) -> 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/latest/_modules/langchain/chains/sequential.html |
7f1c4cd5d304-0 | Source code for langchain.chains.llm_requests
"""Chain that hits a URL and then uses an LLM to parse results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
7f1c4cd5d304-1 | llm_chain: LLMChain
requests_wrapper: TextRequestsWrapper = Field(
default_factory=lambda: TextRequestsWrapper(headers=DEFAULT_HEADERS),
exclude=True,
)
text_length: int = 8000
requests_key: str = "requests_result" #: :meta private:
input_key: str = "url" #: :meta private:
outp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
7f1c4cd5d304-2 | """Will always return text key.
:meta private:
"""
return [self.output_key]
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from bs4 import BeautifulSoup # noqa:... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
7f1c4cd5d304-3 | # 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}
url = inputs[self.input_key]
res = self.requests_wrapper.get(url)
# extract the text from the html
soup = BeautifulSoup(res, "html.parser")
other... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
2f8ec0204c6a-0 | Source code for langchain.chains.mapreduce
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra
from langchain.bas... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
2f8ec0204c6a-1 | [docs]class MapReduceChain(Chain):
"""Map-reduce chain."""
combine_documents_chain: BaseCombineDocumentsChain
"""Chain to use to combine documents."""
text_splitter: TextSplitter
"""Text splitter to use."""
input_key: str = "input_text" #: :meta private:
output_key: str = "output_text" #: ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
2f8ec0204c6a-2 | ) -> MapReduceChain:
"""Construct a map-reduce chain that uses the chain for map and reduce."""
llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks)
reduce_chain = StuffDocumentsChain(
llm_chain=llm_chain,
callbacks=callbacks,
**(reduce_chain_kwar... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
2f8ec0204c6a-3 | extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
2f8ec0204c6a-4 | docs = [Document(page_content=text) for text in texts]
_inputs: Dict[str, Any] = {
**inputs,
self.combine_documents_chain.input_key: docs,
}
outputs = self.combine_documents_chain.run(
_inputs, callbacks=_run_manager.get_child()
)
return {self.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
5cab961633cc-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 |
5cab961633cc-1 | from langchain.prompts import PromptTemplate
from langchain.schema import BaseRetriever
[docs]class MultiRetrievalQAChain(MultiRouteChain):
"""A multi-route chain that uses an LLM router chain to choose amongst retrieval
qa chains."""
router_chain: LLMRouterChain
"""Chain for deciding a destination chai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
5cab961633cc-2 | 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 |
5cab961633cc-3 | input_variables=["input"],
output_parser=RouterOutputParser(next_inputs_inner_key="query"),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
destination_chains = {}
for r_info in retriever_infos:
prompt = r_info.get("prompt")
retriever = r_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
5cab961633cc-4 | 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 |
4c58840b76e5-0 | 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 |
4c58840b76e5-1 | result = self(inputs, callbacks=callbacks)
return Route(result["destination"], result["next_inputs"])
[docs] async def aroute(
self, inputs: Dict[str, Any], callbacks: Callbacks = None
) -> Route:
result = await self.acall(inputs, callbacks=callbacks)
return Route(result["destinat... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
4c58840b76e5-2 | silent_errors: bool = False
"""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) -> Lis... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
4c58840b76e5-3 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
route = self.router_chain.route(inputs, callbacks=callbacks)
_run_manager.on_text(
str(route.destination) + ": " + str(route.next_inputs), ver... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
4c58840b76e5-4 | 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 |
4c58840b76e5-5 | return await self.default_chain.acall(
route.next_inputs, callbacks=callbacks
)
else:
raise ValueError(
f"Received invalid destination chain name '{route.destination}'"
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
b355a5bfed63-0 | 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 |
b355a5bfed63-1 | destination_chains: Mapping[str, LLMChain]
"""Map of name to candidate chains that inputs can be routed to."""
default_chain: LLMChain
"""Default chain to use when router doesn't map input to one of the destinations."""
@property
def output_keys(self) -> List[str]:
return ["text"]
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
b355a5bfed63-2 | 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 |
b355a5bfed63-3 | destination_chains=destination_chains,
default_chain=_default_chain,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
f1026661f747-0 | 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 |
f1026661f747-1 | @root_validator()
def validate_prompt(cls, values: dict) -> dict:
prompt = values["llm_chain"].prompt
if prompt.output_parser is None:
raise ValueError(
"LLMRouterChain requires base llm_chain prompt to have an output"
" parser that converts LLM text outpu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
f1026661f747-2 | 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/latest/_modules/langchain/chains/router/llm_router.html |
f1026661f747-3 | output = cast(
Dict[str, Any],
await self.llm_chain.apredict_and_parse(callbacks=callbacks, **inputs),
)
return output
[docs] @classmethod
def from_llm(
cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, **kwargs: Any
) -> LLMRouterChain:
"""Conve... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
f1026661f747-4 | 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/latest/_modules/langchain/chains/router/llm_router.html |
f1026661f747-5 | except Exception as e:
raise OutputParserException(
f"Parsing text\n{text}\n raised following error:\n{e}"
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
5ea6b7b6b3c7-0 | 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 |
5ea6b7b6b3c7-1 | objective: str
"""Objective that NatBot is tasked with completing."""
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
input_url_key: str = "url" #: :meta private:
input_browser_content_key: str = "browser_content" #: :meta private:
previous_command: str = "" #: ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
5ea6b7b6b3c7-2 | "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/latest/_modules/langchain/chains/natbot/base.html |
5ea6b7b6b3c7-3 | ) -> NatBotChain:
"""Load from LLM."""
llm_chain = LLMChain(llm=llm, prompt=PROMPT)
return cls(llm_chain=llm_chain, objective=objective, **kwargs)
@property
def input_keys(self) -> List[str]:
"""Expect url and browser content.
:meta private:
"""
return [se... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
5ea6b7b6b3c7-4 | ) -> 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 |
5ea6b7b6b3c7-5 | browser_content: Content of the page as currently displayed by the browser.
Returns:
Next browser command to run.
Example:
.. code-block:: python
browser_content = "...."
llm_command = natbot.run("www.google.com", browser_content)
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
04c0acd7fb70-0 | 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/latest/_modules/langchain/chains/graph_qa/base.html |
04c0acd7fb70-1 | qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[s... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
04c0acd7fb70-2 | ) -> 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/latest/_modules/langchain/chains/graph_qa/base.html |
04c0acd7fb70-3 | _run_manager.on_text("Entities Extracted:", end="\n", verbose=self.verbose)
_run_manager.on_text(
entity_string, color="green", end="\n", verbose=self.verbose
)
entities = get_entities(entity_string)
context = ""
for entity in entities:
triplets = self.gra... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
4a0ad313faf8-0 | 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/latest/_modules/langchain/chains/graph_qa/cypher.html |
4a0ad313faf8-1 | Returns:
Cypher code extracted from the text.
"""
# The pattern to find Cypher code enclosed in triple backticks
pattern = r"```(.*?)```"
# Find all matches in the input text
matches = re.findall(pattern, text, re.DOTALL)
return matches[0] if matches else text
[docs]class GraphCypherQACh... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
4a0ad313faf8-2 | return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the graph directly."""
@property
def input_keys(self) -> List[str]:
"""Return the input ke... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
4a0ad313faf8-3 | *,
qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
**kwargs: Any,
) -> GraphCypherQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
4a0ad313faf8-4 | _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
intermediate_steps: List = []
generated_cypher = self.cypher_generation_chain.run(
{"question": question, "schema": self.graph.ge... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
4a0ad313faf8-5 | # Retrieve and limit the number of results
context = self.graph.query(generated_cypher)[: self.top_k]
if self.return_direct:
final_result = context
else:
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
594fb952048a-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/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
594fb952048a-1 | ngql_generation_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
594fb952048a-2 | ngql_prompt: BasePromptTemplate = NGQL_GENERATION_PROMPT,
**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,... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
594fb952048a-3 | callbacks = _run_manager.get_child()
question = inputs[self.input_key]
generated_ngql = self.ngql_generation_chain.run(
{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
)
_run_manager.on_text("Generated nGQL:", end="\n", verbose=self.verbose)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
594fb952048a-4 | callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
52d7ba2221b8-0 | 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/latest/_modules/langchain/chains/graph_qa/kuzu.html |
52d7ba2221b8-1 | cypher_generation_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@proper... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
52d7ba2221b8-2 | 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/latest/_modules/langchain/chains/graph_qa/kuzu.html |
52d7ba2221b8-3 | callbacks = _run_manager.get_child()
question = inputs[self.input_key]
generated_cypher = self.cypher_generation_chain.run(
{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
)
_run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
52d7ba2221b8-4 | callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
3a6a17a2f1c0-0 | 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 |
3a6a17a2f1c0-1 | Example:
.. code-block:: python
from langchain import LLMBashChain, OpenAI
llm_bash = LLMBashChain.from_llm(OpenAI())
"""
llm_chain: LLMChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
input_key: str = "question" #: :meta private:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
3a6a17a2f1c0-2 | @root_validator(pre=True)
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."
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
3a6a17a2f1c0-3 | )
return values
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
return [self.output_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
3a6a17a2f1c0-4 | )
_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/latest/_modules/langchain/chains/llm_bash/base.html |
3a6a17a2f1c0-5 | @property
def _chain_type(self) -> str:
return "llm_bash_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate = PROMPT,
**kwargs: Any,
) -> LLMBashChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
556281f754c6-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-1 | from langchain.prompts import PromptTemplate
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores.base import VectorStore
class BaseRetrievalQA(Chain):
combine_documents_chain: BaseCombineDocumentsChain
"""Chain to use to combine the documents."""
input_key: str = "query" #: :me... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-2 | @property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@classmethod
def fr... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-3 | )
combine_documents_chain = StuffDocumentsChain(
llm_chain=llm_chain,
document_variable_name="context",
document_prompt=document_prompt,
)
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
@classmethod
def from_chain_type(
c... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-4 | @abstractmethod
def _get_docs(self, question: str) -> List[Document]:
"""Get documents to do question answering over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run get_relevant_text an... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-5 | question = inputs[self.input_key]
docs = self._get_docs(question)
answer = self.combine_documents_chain.run(
input_documents=docs, question=question, callbacks=_run_manager.get_child()
)
if self.return_source_documents:
return {self.output_key: answer, "source_doc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-6 | the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = indexqa({'query': 'This is my query'})
answer, docs = res['result'], res['source_documents']
"""
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-7 | Example:
.. code-block:: python
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.faiss import FAISS
from langchain.vectorstores.base import VectorStoreRetriever
retriever = VectorStoreRetriever(vectorstore=FAISS... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-8 | """Return the chain type."""
return "retrieval_qa"
[docs]class VectorDBQA(BaseRetrievalQA):
"""Chain for question-answering against a vector database."""
vectorstore: VectorStore = Field(exclude=True, alias="vectorstore")
"""Vector Database to connect to."""
k: int = 4
"""Number of documents... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-9 | "please use `from langchain.chains import RetrievalQA`"
)
return values
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
search_type = values["search_type"]
if search_type not in... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
556281f754c6-10 | )
else:
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 th... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
609e22990bae-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/latest/_modules/langchain/chains/llm_math/base.html |
609e22990bae-1 | .. code-block:: python
from langchain import LLMMathChain, OpenAI
llm_math = LLMMathChain.from_llm(OpenAI())
"""
llm_chain: LLMChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
prompt: BasePromptTemplate = PROMPT
"""[Deprecated] Prompt t... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
609e22990bae-2 | 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"] is not None:
pr... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
609e22990bae-3 | return [self.output_key]
def _evaluate_expression(self, expression: str) -> str:
try:
local_dict = {"pi": math.pi, "e": math.e}
output = str(
numexpr.evaluate(
expression.strip(),
global_dict={}, # restrict access to globals
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
609e22990bae-4 | ) -> 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/latest/_modules/langchain/chains/llm_math/base.html |
609e22990bae-5 | return {self.output_key: answer}
async def _aprocess_llm_result(
self,
llm_output: str,
run_manager: AsyncCallbackManagerForChainRun,
) -> Dict[str, str]:
await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
te... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
609e22990bae-6 | 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(
self,
inputs: Dict[str, str],
r... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
609e22990bae-7 | async def _acall(
self,
inputs: Dict[str, str],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
await _run_manager.on_text(inputs[self.input_key])
llm_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
609e22990bae-8 | 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/latest/_modules/langchain/chains/llm_math/base.html |
f954cd6df691-0 | 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/latest/_modules/langchain/chains/hyde/base.html |
f954cd6df691-1 | llm_chain: LLMChain
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Input keys for Hyde's LLM chain."""
return self.llm_chain.input_keys
@property
d... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
f954cd6df691-2 | [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 generation in result.generations[0]]
em... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
f954cd6df691-3 | def from_llm(
cls,
llm: BaseLanguageModel,
base_embeddings: Embeddings,
prompt_key: str,
**kwargs: Any,
) -> HypotheticalDocumentEmbedder:
"""Load and use LLMChain for a specific prompt key."""
prompt = PROMPT_MAP[prompt_key]
llm_chain = LLMChain(llm=l... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
1dda809fbc47-0 | 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/latest/_modules/langchain/chains/llm_checker/base.html |
1dda809fbc47-1 | create_draft_answer_prompt: PromptTemplate,
list_assertions_prompt: PromptTemplate,
check_assertions_prompt: PromptTemplate,
revised_answer_prompt: PromptTemplate,
) -> SequentialChain:
create_draft_answer_chain = LLMChain(
llm=llm,
prompt=create_draft_answer_prompt,
output_key="... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
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