id stringlengths 14 16 | text stringlengths 45 2.05k | source stringlengths 53 111 |
|---|---|---|
10b6eaed6327-7 | raise ValueError("Cannot stream results with multiple prompts.")
params["stream"] = True
response = _streaming_response_template()
async for stream_resp in await acompletion_with_retry(
self, prompt=_prompts, **params
):
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-8 | if len(prompts) != 1:
raise ValueError(
"max_tokens set to -1 not supported for multiple inputs."
)
params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
sub_prompts = [
prompts[i : i + self.batch_size]
for i in rang... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-9 | stop: Optional list of stop words to use when generating.
Returns:
A generator representing the stream of tokens from OpenAI.
Example:
.. code-block:: python
generator = openai.stream("Tell me a joke.")
for token in generator:
y... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-10 | if sys.version_info[1] <= 8:
return super().get_num_tokens(text)
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_num_tokens. "
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-11 | """
if modelname == "text-davinci-003":
return 4097
elif modelname == "text-curie-001":
return 2048
elif modelname == "text-babbage-001":
return 2048
elif modelname == "text-ada-001":
return 2048
elif modelname == "code-davinci-002"... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-12 | def _identifying_params(self) -> Mapping[str, Any]:
return {
**{"deployment_name": self.deployment_name},
**super()._identifying_params,
}
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"engine": self.deployment_name}, **super()._invocation_p... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-13 | extra = Extra.ignore
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwar... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-14 | "`from langchain.chat_models import ChatOpenAI`"
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
return self.model_kwargs
def _get_chat_params(
self, prompts: List[str], stop: Optional[Lis... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-15 | token,
verbose=self.verbose,
)
return LLMResult(
generations=[[Generation(text=response)]],
)
else:
full_response = completion_with_retry(self, messages=messages, **params)
llm_output = {
"token_u... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
10b6eaed6327-16 | }
return LLMResult(
generations=[
[Generation(text=full_response["choices"][0]["message"]["content"])]
],
llm_output=llm_output,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identify... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/openai.html |
683564f0070b-0 | Source code for langchain.llms.huggingface_endpoint
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.u... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
683564f0070b-1 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
683564f0070b-2 | 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 = hf("Tell me a joke.")
"""
_mod... | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
683564f0070b-3 | # stop tokens when making calls to huggingface_hub.
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
98fc6f80f498-0 | 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 Dict, List
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.chains import LLMChain
from langchain.chains.base import Chain
fro... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_requests.html |
98fc6f80f498-1 | """
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:
raise ValueError(... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_requests.html |
6630ecfa65ce-0 | Source code for langchain.chains.transform
"""Chain that runs an arbitrary python function."""
from typing import Callable, Dict, List
from pydantic import BaseModel
from langchain.chains.base import Chain
[docs]class TransformChain(Chain, BaseModel):
"""Chain transform chain output.
Example:
.. code-bl... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/transform.html |
c02e9bb60e02-0 | 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 Dict, List
from pydantic import BaseModel, Extra
from langchain.chains.base impo... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/mapreduce.html |
c02e9bb60e02-1 | )
return cls(
combine_documents_chain=combine_documents_chain, text_splitter=text_splitter
)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/mapreduce.html |
fddb43b3a42d-0 | Source code for langchain.chains.moderation
"""Pass input through a moderation endpoint."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.chains.base import Chain
from langchain.utils import get_from_dict_or_env
[docs]class OpenAIModerationChain(Chain, BaseMo... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/moderation.html |
fddb43b3a42d-1 | except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please it install it with `pip install openai`."
)
return values
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/moderation.html |
7df5b2e4bfc8-0 | Source code for langchain.chains.sequential
"""Chain pipeline where the outputs of one step feed directly into next."""
from typing import Dict, List
from pydantic import BaseModel, Extra, root_validator
from langchain.chains.base import Chain
from langchain.input import get_color_mapping
[docs]class SequentialChain(Ch... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sequential.html |
7df5b2e4bfc8-1 | 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)
for chain in chains:
missing_vars = set(chain.input_keys).difference(known_variables)
if mis... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sequential.html |
7df5b2e4bfc8-2 | chains: List[Chain]
strip_outputs: bool = False
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_ke... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sequential.html |
7df5b2e4bfc8-3 | if self.strip_outputs:
_input = _input.strip()
self.callback_manager.on_text(
_input, color=color_mapping[str(i)], end="\n", verbose=self.verbose
)
return {self.output_key: _input}
By Harrison Chase
© Copyright 2023, Harrison Chase.
Las... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sequential.html |
436b8db17b63-0 | 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 BaseModel, Extra
from langchain.chains.base import Chain
from langchain.input import get_colored_text... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
436b8db17b63-1 | def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
return self.apply([inputs])[0]
[docs] def generate(self, input_list: List[Dict[str, Any]]) -> LLMResult:
"""Generate LLM result from inputs."""
prompts, stop = self.prep_prompts(input_list)
return self.llm.generate_prompt(prom... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
436b8db17b63-2 | return prompts, stop
[docs] async def aprep_prompts(
self, input_list: List[Dict[str, Any]]
) -> Tuple[List[PromptValue], Optional[List[str]]]:
"""Prepare prompts from inputs."""
stop = None
if "stop" in input_list[0]:
stop = input_list[0]["stop"]
prompts = []
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
436b8db17b63-3 | response = await self.agenerate(input_list)
return self.create_outputs(response)
[docs] def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:
"""Create outputs from response."""
return [
# Get the text of the top generated string.
{self.output_key: gen... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
436b8db17b63-4 | return self.prompt.output_parser.parse(result)
else:
return result
[docs] def apply_and_parse(
self, input_list: List[Dict[str, Any]]
) -> Sequence[Union[str, List[str], Dict[str, str]]]:
"""Call apply and then parse the results."""
result = self.apply(input_list)
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
de8190b39392-0 | 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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-1 | 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 = config.pop("prompt")
pr... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-2 | 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_pat... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-3 | 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 ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-4 | if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
prompt = load_prompt(config.pop("prompt_path"))
return LLMBashChain(llm=llm, prompt=prompt, **config)
def _load_llm_checker_chain(config: dict, **kwar... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-5 | check_assertions_prompt_config
)
elif "check_assertions_prompt_path" in config:
check_assertions_prompt = load_prompt(
config.pop("check_assertions_prompt_path")
)
if "revised_answer_prompt" in config:
revised_answer_prompt_config = config.pop("revised_answer_prompt")... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-6 | config: dict, **kwargs: Any
) -> MapRerankDocumentsChain:
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:
rais... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-7 | initial_llm_chain_config = config.pop("initial_llm_chain")
initial_llm_chain = load_chain_from_config(initial_llm_chain_config)
elif "initial_llm_chain_path" in config:
initial_llm_chain = load_chain(config.pop("initial_llm_chain_path"))
else:
raise ValueError(
"One of `initi... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-8 | elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return QAWithSourcesC... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-9 | elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return VectorDBQAWith... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-10 | else:
raise ValueError(
"One of `api_request_chain` or `api_request_chain_path` must be present."
)
if "api_answer_chain" in config:
api_answer_chain_config = config.pop("api_answer_chain")
api_answer_chain = load_chain_from_config(api_answer_chain_config)
elif "api_a... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-11 | )
else:
return LLMRequestsChain(llm_chain=llm_chain, **config)
type_to_loader_dict = {
"api_chain": _load_api_chain,
"hyde_chain": _load_hyde_chain,
"llm_chain": _load_llm_chain,
"llm_bash_chain": _load_llm_bash_chain,
"llm_checker_chain": _load_llm_checker_chain,
"llm_math_chain": _... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
de8190b39392-12 | return chain_loader(config, **kwargs)
[docs]def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain:
"""Unified method for loading a chain from LangChainHub or local fs."""
if hub_result := try_load_from_hub(
path, _load_chain_from_file, "chains", {"json", "yaml"}, **kwargs
):
return ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
da6a572f7389-0 | Source code for langchain.chains.chat_vector_db.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic import BaseModel
from langchain.chains.base import Chain
from langchain.cha... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/chat_vector_db/base.html |
da6a572f7389-1 | """Input keys."""
return ["question", "chat_history"]
@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_d... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/chat_vector_db/base.html |
da6a572f7389-2 | )
else:
new_question = question
docs = self.vectorstore.similarity_search(
new_question, k=self.top_k_docs_for_context, **vectordbkwargs
)
new_inputs = inputs.copy()
new_inputs["question"] = new_question
new_inputs["chat_history"] = chat_history_st... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/chat_vector_db/base.html |
da6a572f7389-3 | else:
return {self.output_key: answer}
[docs] def save(self, file_path: Union[Path, str]) -> None:
if self.get_chat_history:
raise ValueError("Chain not savable when `get_chat_history` is not None.")
super().save(file_path)
By Harrison Chase
© Copyright 2023, Harris... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/chat_vector_db/base.html |
d6715cc5345d-0 | Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Dict, List
import numpy as np
from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.hyde.prompts import... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/hyde/base.html |
d6715cc5345d-1 | [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://langchain.readthedocs.io/en/latest/_modules/langchain/chains/hyde/base.html |
dc979763c6da-0 | Source code for langchain.chains.vector_db_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.chains.base import Chain
from langchain.chains.comb... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/vector_db_qa/base.html |
dc979763c6da-1 | """Extra search args."""
search_type: str = "similarity"
"""Search type to use over vectorstore. `similarity` or `mmr`."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[st... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/vector_db_qa/base.html |
dc979763c6da-2 | )
values["combine_documents_chain"] = combine_documents_chain
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_typ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/vector_db_qa/base.html |
dc979763c6da-3 | llm, chain_type=chain_type, **_chain_type_kwargs
)
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Run similarity search and llm on input query.
If chain has 'return_source_documents' as 'True', retur... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/vector_db_qa/base.html |
43c132fbf0bf-0 | Source code for langchain.chains.sql_database.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import BaseModel, Extra, Field
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sql... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
43c132fbf0bf-1 | @property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
if not self.return_int... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
43c132fbf0bf-2 | self.callback_manager.on_text(result, color="yellow", verbose=self.verbose)
# If return direct, we just set the final result equal to the sql query
if self.return_direct:
final_result = result
else:
self.callback_manager.on_text("\nAnswer:", verbose=self.verbose)
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
43c132fbf0bf-3 | """Load the necessary chains."""
sql_chain = SQLDatabaseChain(
llm=llm, database=database, prompt=query_prompt, **kwargs
)
decider_chain = LLMChain(
llm=llm, prompt=decider_prompt, output_key="table_names"
)
return cls(sql_chain=sql_chain, decider_chain=de... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
43c132fbf0bf-4 | str(table_names_to_use), color="yellow", verbose=self.verbose
)
new_inputs = {
self.sql_chain.input_key: inputs[self.input_key],
"table_names_to_use": table_names_to_use,
}
return self.sql_chain(new_inputs, return_only_outputs=True)
@property
def _chain_ty... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
b9bc8f75fdb9-0 | 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
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.llm i... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/pal/base.html |
b9bc8f75fdb9-1 | return [self.output_key]
else:
return [self.output_key, "intermediate_steps"]
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
code = llm_chain.predict(stop=[self.stop], **inputs)
self.callback_manager.on_te... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/pal/base.html |
b9bc8f75fdb9-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/pal/base.html |
f0ee9bd4392a-0 | Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.chains.base import Chain
fr... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f0ee9bd4392a-1 | combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Construct the chain from an LLM."""
llm_question_chain = LLMChain(llm=llm, prompt=question_prompt)
llm_combine_chain = LLMChain(llm=llm, prompt=combine_prompt)
combine_results_... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f0ee9bd4392a-2 | :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]
if self.return_source_documents:
_output_keys = _outp... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f0ee9bd4392a-3 | """Expect input key.
:meta private:
"""
return [self.input_docs_key, self.question_key]
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
return inputs.pop(self.input_docs_key)
@property
def _chain_type(self) -> str:
return "qa_with_sources_chain"
By Harr... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
ab6c21189b1b-0 | Source code for langchain.chains.qa_with_sources.vector_db
"""Question-answering with sources over a vector database."""
from typing import Any, Dict, List
from pydantic import BaseModel, Field
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import Bas... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
ab6c21189b1b-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://langchain.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
eb7c20c8eed4-0 | Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, PROMPT
from langchain.cha... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/graph_qa/base.html |
eb7c20c8eed4-1 | 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(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Extract entities, look up info and answer question... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/graph_qa/base.html |
902cd30202f4-0 | 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.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR
f... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/qa_generation/base.html |
902cd30202f4-1 | docs = self.text_splitter.create_documents([inputs[self.input_key]])
results = self.llm_chain.generate([{"text": d.page_content} for d in docs])
qa = [json.loads(res[0].text) for res in results.generations]
return {self.output_key: qa}
async def _acall(self, inputs: Dict[str, str]) -> Dict[s... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/qa_generation/base.html |
f9b06e1bf78d-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
from langchain.chains.base import Chain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
from langchain.chains.const... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
f9b06e1bf78d-1 | revision_prompt: BasePromptTemplate = REVISION_PROMPT,
**kwargs: Any,
) -> "ConstitutionalChain":
"""Create a chain from an LLM."""
critique_chain = LLMChain(llm=llm, prompt=critique_prompt)
revision_chain = LLMChain(llm=llm, prompt=revision_prompt)
return cls(
ch... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
f9b06e1bf78d-2 | critique_request=constitutional_principle.critique_request,
critique=critique,
revision_request=constitutional_principle.revision_request,
).strip()
response = revision
self.callback_manager.on_text(
text=f"Applying {constitutional_prin... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
20f053b0aa07-0 | Source code for langchain.chains.api.base
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field, root_validator
from langchain.chains.api.prompt import API_RESPONSE_PROMPT,... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/api/base.html |
20f053b0aa07-1 | )
return values
@root_validator(pre=True)
def validate_api_answer_prompt(cls, values: Dict) -> Dict:
"""Check that api answer prompt expects the right variables."""
input_vars = values["api_answer_chain"].prompt.input_variables
expected_vars = {"question", "api_docs", "api_url", ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/api/base.html |
20f053b0aa07-2 | """Load chain from just an LLM and the api docs."""
get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt)
requests_wrapper = RequestsWrapper(headers=headers)
get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
return cls(
api_request_chain=get_request_chai... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/api/base.html |
a617070f70f8-0 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from typing import Dict, List
from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_checker.prompt import (
CHECK_A... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_checker/base.html |
a617070f70f8-1 | @property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
return [self.output_ke... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_checker/base.html |
a617070f70f8-2 | return "llm_checker_chain"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_checker/base.html |
393cec7b0b0b-0 | Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from typing import Dict, List
from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_bash.prom... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_bash/base.html |
393cec7b0b0b-1 | bash_executor = BashProcess()
self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
t = llm_executor.predict(question=inputs[self.input_key])
self.callback_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
if t.startswith("```bash"):
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_bash/base.html |
326eebc0f2c1-0 | Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
from typing import Dict, List
from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_math.prompt import PROM... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_math/base.html |
326eebc0f2c1-1 | python_executor = PythonREPL()
self.callback_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
if t.startswith("```python"):
code = t[9:-4]
output = python_executor.run(code)
self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_math/base.html |
326eebc0f2c1-2 | def _chain_type(self) -> str:
return "llm_math_chain"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_math/base.html |
a9b7eebe2f3c-0 | Source code for langchain.chains.combine_documents.base
"""Base interface for chains combining documents."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain.chains.base import Chain
from langchain.docstore.document import Docum... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/combine_documents/base.html |
a9b7eebe2f3c-1 | docs = inputs[self.input_key]
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
output, extra_return_dict = self.combine_docs(docs, **other_keys)
extra_return_dict[self.output_key] = output
return extra_... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/combine_documents/base.html |
a9b7eebe2f3c-2 | docs = self.text_splitter.create_documents([document])
# 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}
other_keys[self.combine_docs_chain.input_key] = docs
return self.combine_docs_chain(other_keys, return_onl... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/combine_documents/base.html |
6686871f8983-0 | Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from pathlib import Path
from typing import Dict, List
from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.seque... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
6686871f8983-1 | revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT
are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
max_checks: int = 2
"""Maximum number of times to check the assertions. Default to doubl... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
6686871f8983-2 | output_key="revised_summary",
verbose=self.verbose,
),
LLMChain(
llm=self.llm,
output_key="all_true",
prompt=self.are_all_true_prompt,
verbose=self.verbose,
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
97610145a2ee-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 BaseModel, Extra, Field, root_validator
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memo... | https://langchain.readthedocs.io/en/latest/_modules/langchain/chains/conversation/base.html |
97610145a2ee-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://langchain.readthedocs.io/en/latest/_modules/langchain/chains/conversation/base.html |
72fe806493dd-0 | .rst
.pdf
Evaluation
Contents
The Problem
The Solution
The Examples
Other Examples
Evaluation#
This section of documentation covers how we approach and think about evaluation in LangChain.
Both evaluation of internal chains/agents, but also how we would recommend people building on top of LangChain approach evaluatio... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation.html |
72fe806493dd-1 | We intend this to be a collection of open source datasets for evaluating common chains and agents.
We have contributed five datasets of our own to start, but we highly intend this to be a community effort.
In order to contribute a dataset, you simply need to join the community and then you will be able to upload datase... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation.html |
72fe806493dd-2 | SQL Question Answering (Chinook): An notebook showing evaluation of a question-answering task over a SQL database (the Chinook database).
Agent Vectorstore: An notebook showing evaluation of an agent doing question answering while routing between two different vector databases.
Agent Search + Calculator: An notebook sh... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation.html |
6512e81bd3f4-0 | .ipynb
.pdf
Generate Examples
Generate Examples#
This notebook shows how to use LangChain to generate more examples similar to the ones you already have.
from langchain.llms.openai import OpenAI
from langchain.example_generator import generate_example
from langchain.prompts import PromptTemplate
# Use examples from ReA... | https://langchain.readthedocs.io/en/latest/use_cases/generate_examples.html |
6512e81bd3f4-1 | "answer": "Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of that area.\nAction 1: Search[Colorado orogeny]\nObservation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surro... | https://langchain.readthedocs.io/en/latest/use_cases/generate_examples.html |
6512e81bd3f4-2 | "answer": "Thought 1: The question simplifies to \"The Simpsons\" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.\nAction 1: Search[Milhouse]\nObservation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons vo... | https://langchain.readthedocs.io/en/latest/use_cases/generate_examples.html |
6512e81bd3f4-3 | "answer": "Thought 1: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups.\nAction 1: Search[Adam Clayton Powell]\nObservation 1 Could not find [Adam Clayton Powell]. Similar: [’Adam Clayton Powell III’, ’Seventh Avenue (Manhattan)’, ’Adam Clayton Powell ... | https://langchain.readthedocs.io/en/latest/use_cases/generate_examples.html |
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