id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 54 121 |
|---|---|---|
87c035800629-5 | if "create_draft_answer_prompt" in config:
create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt")
create_draft_answer_prompt = load_prompt_from_config(
create_draft_answer_prompt_config
)
elif "create_draft_answer_prompt_path" in config:
create_draft... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-6 | revised_answer_prompt=revised_answer_prompt,
**config,
)
def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain:
llm_chain = None
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_cha... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-7 | if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_config` must be... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-8 | prompt = load_prompt(config.pop("prompt_path"))
else:
raise ValueError("One of `prompt` or `prompt_path` must be present.")
if llm_chain:
return PALChain(llm_chain=llm_chain, prompt=prompt, **config)
else:
return PALChain(llm=llm, prompt=prompt, **config)
def _load_refine_documents_c... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-9 | document_prompt = load_prompt(config.pop("document_prompt_path"))
return RefineDocumentsChain(
initial_llm_chain=initial_llm_chain,
refine_llm_chain=refine_llm_chain,
document_prompt=document_prompt,
**config,
)
def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWi... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-10 | prompt = load_prompt_from_config(prompt_config)
else:
prompt = None
return SQLDatabaseChain.from_llm(llm, database, prompt=prompt, **config)
def _load_vector_db_qa_with_sources_chain(
config: dict, **kwargs: Any
) -> VectorDBQAWithSourcesChain:
if "vectorstore" in kwargs:
vectorstore = k... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-11 | else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return RetrievalQA(
combine_documents_chain=combine_documents_chain,
retriever=retriever,
**config,
)
def _load_vector_db_qa(config: ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-12 | if "qa_chain" in config:
qa_chain_config = config.pop("qa_chain")
qa_chain = load_chain_from_config(qa_chain_config)
else:
raise ValueError("`qa_chain` must be present.")
return GraphCypherQAChain(
graph=graph,
cypher_generation_chain=cypher_generation_chain,
qa_c... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-13 | requests_wrapper=requests_wrapper,
**config,
)
def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-14 | "map_rerank_documents_chain": _load_map_rerank_documents_chain,
"refine_documents_chain": _load_refine_documents_chain,
"sql_database_chain": _load_sql_database_chain,
"vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain,
"vector_db_qa": _load_vector_db_qa,
"retrieval_qa": _load_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
87c035800629-15 | else:
file_path = file
# Load from either json or yaml.
if file_path.suffix == ".json":
with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
else:
raise ValueE... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
a750a2cef08a-0 | Source code for langchain.chains.llm
"""Chain that just formats a prompt and calls an LLM."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from pydantic import Extra, Field
from langchain.base_language import BaseLanguageModel
from langchain.cal... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
a750a2cef08a-1 | """Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise."""
return_final_only: bool = True
"""Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation."""
llm_kwa... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
a750a2cef08a-2 | stop,
callbacks=run_manager.get_child() if run_manager else None,
**self.llm_kwargs,
)
[docs] async def agenerate(
self,
input_list: List[Dict[str, Any]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> LLMResult:
"""Generate LLM... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
a750a2cef08a-3 | )
prompts.append(prompt)
return prompts, stop
[docs] async def aprep_prompts(
self,
input_list: List[Dict[str, Any]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Tuple[List[PromptValue], Optional[List[str]]]:
"""Prepare prompts from inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
a750a2cef08a-4 | try:
response = self.generate(input_list, run_manager=run_manager)
except (KeyboardInterrupt, Exception) as e:
run_manager.on_chain_error(e)
raise e
outputs = self.create_outputs(response)
run_manager.on_chain_end({"outputs": outputs})
return outputs
[... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
a750a2cef08a-5 | return result
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
response = await self.agenerate([inputs], run_manager=run_manager)
return self.create_outputs(response)[0]
[docs] def predi... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
a750a2cef08a-6 | warnings.warn(
"The predict_and_parse method is deprecated, "
"instead pass an output parser directly to LLMChain."
)
result = self.predict(callbacks=callbacks, **kwargs)
if self.prompt.output_parser is not None:
return self.prompt.output_parser.parse(result)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
a750a2cef08a-7 | self.prompt.output_parser.parse(res[self.output_key])
for res in generation
]
else:
return generation
[docs] async def aapply_and_parse(
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
) -> Sequence[Union[str, List[str], Dict[str, str]]]... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
682a16784371-0 | Source code for langchain.chains.moderation
"""Pass input through a moderation endpoint."""
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.utils import get_from_dic... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
682a16784371-1 | values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
values["client"] = ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
88af0da816bd-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 |
c40598880724-0 | 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 |
c40598880724-1 | overlapping_keys = set(input_variables) & set(memory_keys)
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."
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
c40598880724-2 | for i, chain in enumerate(self.chains):
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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
c40598880724-3 | """
return [self.output_key]
@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 u... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
c40598880724-4 | ) -> 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/latest/_modules/langchain/chains/sequential.html |
1b5463676e5e-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 |
1b5463676e5e-1 | """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 |
8a832ff0b3a4-0 | 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 |
8a832ff0b3a4-1 | **kwargs: Any,
) -> 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,
*... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
8a832ff0b3a4-2 | texts = self.text_splitter.split_text(doc_text)
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=... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
fb8f4dad48c0-0 | 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 |
fb8f4dad48c0-1 | 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 |
fb8f4dad48c0-2 | 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 |
c8be2718eed5-0 | 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 |
c8be2718eed5-1 | """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/latest/_modules/langchain/chains/router/base.html |
c8be2718eed5-2 | 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/latest/_modules/langchain/chains/router/base.html |
8f34344aff0b-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 |
8f34344aff0b-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 |
106ec7c634f3-0 | 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 |
106ec7c634f3-1 | 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/latest/_modules/langchain/chains/router/llm_router.html |
106ec7c634f3-2 | 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/latest/_modules/langchain/chains/router/llm_router.html |
cfa93e891e5b-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 |
cfa93e891e5b-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/latest/_modules/langchain/chains/natbot/base.html |
cfa93e891e5b-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/latest/_modules/langchain/chains/natbot/base.html |
d56991f75c9e-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 |
d56991f75c9e-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/latest/_modules/langchain/chains/graph_qa/base.html |
88ba74da2c25-0 | 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 |
88ba74da2c25-1 | """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/latest/_modules/langchain/chains/graph_qa/cypher.html |
88ba74da2c25-2 | ) -> 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/latest/_modules/langchain/chains/graph_qa/cypher.html |
5e1b2d864262-0 | 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 |
5e1b2d864262-1 | **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/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
6430c2eebd76-0 | 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 |
6430c2eebd76-1 | 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/latest/_modules/langchain/chains/graph_qa/kuzu.html |
6430c2eebd76-2 | 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 |
cba4b5754a59-0 | 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 |
cba4b5754a59-1 | 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 |
cba4b5754a59-2 | )
_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/latest/_modules/langchain/chains/llm_bash/base.html |
365129e9c75d-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 |
365129e9c75d-1 | def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@classmethod
def from_llm(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
365129e9c75d-2 | 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 and llm on input query... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
365129e9c75d-3 | the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = indexqa({'query': 'This is my query'})
answer, docs = res['result'], res['source_documents']
"""
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
365129e9c75d-4 | def _chain_type(self) -> str:
"""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."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
365129e9c75d-5 | question, k=self.k, **self.search_kwargs
)
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
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
22d30a360c1d-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 |
22d30a360c1d-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/latest/_modules/langchain/chains/llm_math/base.html |
22d30a360c1d-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/latest/_modules/langchain/chains/llm_math/base.html |
22d30a360c1d-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/latest/_modules/langchain/chains/llm_math/base.html |
22d30a360c1d-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/latest/_modules/langchain/chains/llm_math/base.html |
659a191b9cdb-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 |
659a191b9cdb-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/latest/_modules/langchain/chains/hyde/base.html |
75ae6523907f-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 |
75ae6523907f-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/latest/_modules/langchain/chains/llm_checker/base.html |
75ae6523907f-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/latest/_modules/langchain/chains/llm_checker/base.html |
75ae6523907f-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/latest/_modules/langchain/chains/llm_checker/base.html |
b6ad8fd2bfee-0 | Source code for langchain.chains.constitutional_ai.base
"""Chain for applying constitutional principles to the outputs of another chain."""
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
b6ad8fd2bfee-1 | critique_chain: LLMChain
revision_chain: LLMChain
return_intermediate_steps: bool = False
[docs] @classmethod
def get_principles(
cls, names: Optional[List[str]] = None
) -> List[ConstitutionalPrinciple]:
if names is None:
return list(PRINCIPLES.values())
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
b6ad8fd2bfee-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_manager.get_child("original"),
)
initial_response = response
input_prompt = self.chain.prompt.format(**inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
b6ad8fd2bfee-3 | _run_manager.on_text(
text=f"Applying {constitutional_principle.name}..." + "\n\n",
verbose=self.verbose,
color="green",
)
_run_manager.on_text(
text="Critique: " + critique + "\n\n",
verbose=self.verbose,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
6e145a168885-0 | 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/latest/_modules/langchain/chains/conversation/base.html |
6e145a168885-1 | 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/latest/_modules/langchain/chains/conversation/base.html |
1194f1d1ec81-0 | 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/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
1194f1d1ec81-1 | 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/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
a70bdcbec8bc-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/latest/_modules/langchain/chains/qa_with_sources/base.html |
a70bdcbec8bc-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/latest/_modules/langchain/chains/qa_with_sources/base.html |
a70bdcbec8bc-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/latest/_modules/langchain/chains/qa_with_sources/base.html |
a70bdcbec8bc-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/latest/_modules/langchain/chains/qa_with_sources/base.html |
a70bdcbec8bc-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/latest/_modules/langchain/chains/qa_with_sources/base.html |
2b7475a403f3-0 | 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/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
2b7475a403f3-1 | 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/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
06a5c6601dd4-0 | 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/latest/_modules/langchain/chains/openai_functions/extraction.html |
06a5c6601dd4-1 | 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/latest/_modules/langchain/chains/openai_functions/extraction.html |
fb8c7913912e-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/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
fb8c7913912e-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/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
fb8c7913912e-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/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
a7a0d25e47a9-0 | 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/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
a7a0d25e47a9-1 | 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/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
a7a0d25e47a9-2 | 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/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
1040804c62b5-0 | 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/latest/_modules/langchain/chains/openai_functions/tagging.html |
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