id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
bb2b8267c137-2 | echo: bool = False
"""Echo the prompt in the completion."""
use_multiplicative_frequency_penalty: bool = False
sequence_penalty: float = 0.0
sequence_penalty_min_length: int = 2
use_multiplicative_sequence_penalty: bool = False
completion_bias_inclusion: Optional[Sequence[str]] = None
comple... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/aleph_alpha.html |
bb2b8267c137-3 | """Validate that api key and python package exists in environment."""
aleph_alpha_api_key = get_from_dict_or_env(
values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
)
try:
import aleph_alpha_client
values["client"] = aleph_alpha_client.Client(token=aleph_alp... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/aleph_alpha.html |
bb2b8267c137-4 | "minimum_tokens": self.minimum_tokens,
"echo": self.echo,
"use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501
"sequence_penalty": self.sequence_penalty,
"sequence_penalty_min_length": self.sequence_penalty_min_length,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/aleph_alpha.html |
bb2b8267c137-5 | Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = alpeh_alpha("Tell me a joke.")
"""
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/aleph_alpha.html |
d3d0f089a85f-0 | Source code for langchain.llms.cerebriumai
"""Wrapper around CerebriumAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/cerebriumai.html |
d3d0f089a85f-1 | all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/cerebriumai.html |
d3d0f089a85f-2 | """Call to CerebriumAI endpoint."""
try:
from cerebrium import model_api_request
except ImportError:
raise ValueError(
"Could not import cerebrium python package. "
"Please install it with `pip install cerebrium`."
)
params = se... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/cerebriumai.html |
4e5163eae481-0 | Source code for langchain.llms.human
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
def _display_prompt(prompt: str) -> None:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/human.html |
4e5163eae481-1 | """Returns the type of LLM."""
return "human-input"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""
Displays the prompt to the user and returns the... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/human.html |
74169f2c21e9-0 | Source code for langchain.llms.google_palm
"""Wrapper arround Google's PaLM Text APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/google_palm.html |
74169f2c21e9-1 | ),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def generate_with_retry(llm: GooglePalm, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator()
@retry_decorator
def _generate_with_retry(**kwargs: Any) -> Any:
r... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/google_palm.html |
74169f2c21e9-2 | Must be positive."""
max_output_tokens: Optional[int] = None
"""Maximum number of tokens to include in a candidate. Must be greater than zero.
If unset, will default to 64."""
n: int = 1
"""Number of chat completions to generate for each prompt. Note that the API may
not return the full n ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/google_palm.html |
74169f2c21e9-3 | return values
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations = []
for prompt in prompts:
completion = generate_with_r... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/google_palm.html |
2a98289ee570-0 | Source code for langchain.llms.huggingface_text_gen_inference
"""Wrapper around Huggingface text generation inference API."""
from functools import partial
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
2a98289ee570-1 | inference_server_url = "http://localhost:8010/",
max_new_tokens = 512,
top_k = 10,
top_p = 0.95,
typical_p = 0.95,
temperature = 0.01,
repetition_penalty = 1.03,
)
print(llm("What is Deep Learning?"))... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
2a98289ee570-2 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that python package exists in environment."""
try:
import text_generation
values["client"] = text_generation.Client(
values["inference_server_url"], timeout=values["timeout"]
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
2a98289ee570-3 | else:
text_callback = None
if run_manager:
text_callback = partial(
run_manager.on_llm_new_token, verbose=self.verbose
)
params = {
"stop_sequences": stop,
"max_new_tokens": self.max_new_tokens,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
563eb63e1fc6-0 | Source code for langchain.llms.huggingface_pipeline
"""Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from la... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
563eb63e1fc6-1 | """
pipeline: Any #: :meta private:
model_id: str = DEFAULT_MODEL_ID
"""Model name to use."""
model_kwargs: Optional[dict] = None
"""Key word arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
"""Key word arguments passed to the pipeline."""
class Config:
"... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
563eb63e1fc6-2 | else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
563eb63e1fc6-3 | )
return cls(
pipeline=pipeline,
model_id=model_id,
model_kwargs=_model_kwargs,
pipeline_kwargs=_pipeline_kwargs,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
563eb63e1fc6-4 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
42208d9a35b2-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/transform.html |
34530c35284d-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/mapreduce.html |
34530c35284d-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,
*... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/mapreduce.html |
34530c35284d-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=... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/mapreduce.html |
61f2067b6b2c-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,
)... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sequential.html |
61f2067b6b2c-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."
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sequential.html |
61f2067b6b2c-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sequential.html |
61f2067b6b2c-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sequential.html |
61f2067b6b2c-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sequential.html |
03bd5206a62f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_requests.html |
03bd5206a62f-1 | :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: F401
except ImportError:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_requests.html |
d9b7f3a2ee01-0 | Source code for langchain.chains.llm
"""Chain that just formats a prompt and calls an LLM."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm.html |
d9b7f3a2ee01-1 | """Will be whatever keys the prompt expects.
:meta private:
"""
return self.prompt.input_variables
@property
def output_keys(self) -> List[str]:
"""Will always return text key.
:meta private:
"""
return [self.output_key]
def _call(
self,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm.html |
d9b7f3a2ee01-2 | self,
input_list: List[Dict[str, Any]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Tuple[List[PromptValue], Optional[List[str]]]:
"""Prepare prompts from inputs."""
stop = None
if "stop" in input_list[0]:
stop = input_list[0]["stop"]
pr... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm.html |
d9b7f3a2ee01-3 | _colored_text = get_colored_text(prompt.to_string(), "green")
_text = "Prompt after formatting:\n" + _colored_text
if run_manager:
await run_manager.on_text(_text, end="\n", verbose=self.verbose)
if "stop" in inputs and inputs["stop"] != stop:
raise Va... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm.html |
d9b7f3a2ee01-4 | {"input_list": input_list},
)
try:
response = await self.agenerate(input_list, run_manager=run_manager)
except (KeyboardInterrupt, Exception) as e:
await run_manager.on_chain_error(e)
raise e
outputs = self.create_outputs(response)
await run_ma... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm.html |
d9b7f3a2ee01-5 | """Format prompt with kwargs and pass to LLM.
Args:
callbacks: Callbacks to pass to LLMChain
**kwargs: Keys to pass to prompt template.
Returns:
Completion from LLM.
Example:
.. code-block:: python
completion = llm.predict(adjective... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm.html |
d9b7f3a2ee01-6 | def _parse_result(
self, result: List[Dict[str, str]]
) -> Sequence[Union[str, List[str], Dict[str, str]]]:
if self.prompt.output_parser is not None:
return [
self.prompt.output_parser.parse(res[self.output_key]) for res in result
]
else:
r... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm.html |
0782652d9687-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/moderation.html |
0782652d9687-1 | values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
values["client"] = ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/moderation.html |
88d3b7cf2280-0 | Source code for langchain.chains.openai_functions
import json
from functools import partial
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
Callback... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions.html |
88d3b7cf2280-1 | return func_call["arguments"]
def _parse_tag(inputs: dict) -> dict:
args = _get_function_arguments(inputs)
return {"output": json.loads(args)}
def _parse_tag_pydantic(inputs: dict, pydantic_schema: Any) -> dict:
args = _get_function_arguments(inputs)
args = pydantic_schema.parse_raw(args)
return {"o... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions.html |
88d3b7cf2280-2 | callbacks = _run_manager.get_child()
predicted_message = self.llm.predict_messages(
messages, functions=self.functions, callbacks=callbacks, **self.kwargs
)
return {"output": predicted_message}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: O... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions.html |
88d3b7cf2280-3 | },
"required": ["info"],
},
}
]
def _get_tagging_functions(schema: dict) -> List[dict]:
return [
{
"name": EXTRACTION_NAME,
"description": "Extracts the relevant information from the passage.",
"parameters": _convert_schema(schema),... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions.html |
88d3b7cf2280-4 | chain = OpenAIFunctionsChain(
llm=llm, prompt=prompt, functions=functions, kwargs=EXTRACTION_KWARGS
)
pydantic_parsing_chain = TransformChain(
transform=partial(_parse_entities_pydantic, pydantic_schema=PydanticSchema),
input_variables=["input"],
output_variables=["output"],
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions.html |
88d3b7cf2280-5 | )
pydantic_parsing_chain = TransformChain(
transform=partial(_parse_tag_pydantic, pydantic_schema=pydantic_schema),
input_variables=["input"],
output_variables=["output"],
)
return SimpleSequentialChain(chains=[chain, pydantic_parsing_chain])
By Harrison Chase
© Copyright ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/openai_functions.html |
56a7ec5e72cf-0 | Source code for langchain.chains.loading
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocume... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-1 | if "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm` or `llm_path` must be present.")
if "prompt" in config:
prompt_config = con... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-2 | )
def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain:
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_p... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-3 | if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "combine_document_chain" in config:
combine_document_chain_config = config.pop("combine_document_chain")
combine_document_chain = load_chain_from_config(combine_document_chain_config)
elif ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-4 | # llm attribute is deprecated in favor of llm_chain, here to support old configs
elif "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
# llm_path attribute is deprecated in favor of llm_chain_path,
# its to support old configs
elif "llm_path" in conf... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-5 | create_draft_answer_prompt_config
)
elif "create_draft_answer_prompt_path" in config:
create_draft_answer_prompt = load_prompt(
config.pop("create_draft_answer_prompt_path")
)
if "list_assertions_prompt" in config:
list_assertions_prompt_config = config.pop("list_asse... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-6 | 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_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
# llm attribute is deprecated in favor of llm_chain, here t... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-7 | 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 present.")
return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-8 | if llm_chain:
return PALChain(llm_chain=llm_chain, prompt=prompt, **config)
else:
return PALChain(llm=llm, prompt=prompt, **config)
def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
if "initial_llm_chain" in config:
initial_llm_chain_config = config.p... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-9 | refine_llm_chain=refine_llm_chain,
document_prompt=document_prompt,
**config,
)
def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain:
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_documents_chain")
combi... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-10 | config: dict, **kwargs: Any
) -> VectorDBQAWithSourcesChain:
if "vectorstore" in kwargs:
vectorstore = kwargs.pop("vectorstore")
else:
raise ValueError("`vectorstore` must be present.")
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_docum... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-11 | retriever=retriever,
**config,
)
def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
if "vectorstore" in kwargs:
vectorstore = kwargs.pop("vectorstore")
else:
raise ValueError("`vectorstore` must be present.")
if "combine_documents_chain" in config:
combine... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-12 | elif "api_answer_chain_path" in config:
api_answer_chain = load_chain(config.pop("api_answer_chain_path"))
else:
raise ValueError(
"One of `api_answer_chain` or `api_answer_chain_path` must be present."
)
if "requests_wrapper" in kwargs:
requests_wrapper = kwargs.pop(... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-13 | "llm_bash_chain": _load_llm_bash_chain,
"llm_checker_chain": _load_llm_checker_chain,
"llm_math_chain": _load_llm_math_chain,
"llm_requests_chain": _load_llm_requests_chain,
"pal_chain": _load_pal_chain,
"qa_with_sources_chain": _load_qa_with_sources_chain,
"stuff_documents_chain": _load_stuff_d... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
56a7ec5e72cf-14 | ):
return hub_result
else:
return _load_chain_from_file(path, **kwargs)
def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain:
"""Load chain from file."""
# Convert file to Path object.
if isinstance(file, str):
file_path = Path(file)
else:
file_pa... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/loading.html |
209dafa2fa0a-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/constitutional_ai/base.html |
209dafa2fa0a-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:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/constitutional_ai/base.html |
209dafa2fa0a-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/constitutional_ai/base.html |
209dafa2fa0a-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,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/constitutional_ai/base.html |
b777d01dc2e3-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/base.html |
b777d01dc2e3-1 | ) -> 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(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/base.html |
681c8f58002b-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/cypher.html |
681c8f58002b-1 | """Whether or not to return the result of querying the graph directly."""
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/cypher.html |
681c8f58002b-2 | generated_cypher = self.cypher_generation_chain.run(
{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
)
# Extract Cypher code if it is wrapped in backticks
generated_cypher = extract_cypher(generated_cypher)
_run_manager.on_text("Generated Cypher:"... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/cypher.html |
4d9d8870a777-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/nebulagraph.html |
4d9d8870a777-1 | **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,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/nebulagraph.html |
4d9d8870a777-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/graph_qa/nebulagraph.html |
28dda867c41f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
28dda867c41f-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} "
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
28dda867c41f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
28dda867c41f-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(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
28dda867c41f-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 = ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
28dda867c41f-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,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
28dda867c41f-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html |
dbcf35fa34c1-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_bash/base.html |
dbcf35fa34c1-1 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_bash/base.html |
dbcf35fa34c1-2 | )
_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, ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_bash/base.html |
ede3f62a4d55-0 | Source code for langchain.chains.pal.base
"""Implements Program-Aided Language Models.
As in https://arxiv.org/pdf/2211.10435.pdf.
"""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLangua... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/pal/base.html |
ede3f62a4d55-1 | "Directly instantiating an PALChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the one of "
"the class method constructors from_math_prompt, "
"from_colored_object_prompt."
)
if "llm_chain" not in values and v... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/pal/base.html |
ede3f62a4d55-2 | if self.return_intermediate_steps:
output["intermediate_steps"] = code
return output
[docs] @classmethod
def from_math_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PALChain:
"""Load PAL from math prompt."""
llm_chain = LLMChain(llm=llm, prompt=MATH_PROMPT)
ret... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/pal/base.html |
b0d36af50343-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_generation/base.html |
b0d36af50343-1 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_generation/base.html |
2fe7a8cae991-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/flare/base.html |
2fe7a8cae991-1 | )
)
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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/flare/base.html |
2fe7a8cae991-2 | [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_... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/flare/base.html |
2fe7a8cae991-3 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/flare/base.html |
2fe7a8cae991-4 | )
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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/flare/base.html |
5efa9f4a47c4-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_checker/base.html |
5efa9f4a47c4-1 | )
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"],
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_checker/base.html |
5efa9f4a47c4-2 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_checker/base.html |
5efa9f4a47c4-3 | 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(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_checker/base.html |
d791178e0d57-0 | Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from __future__ import annotations
import warnings
from pathlib import Path
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import Ba... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_summarization_checker/base.html |
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