id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
c83fb8943a93-3 | """Return a dictionary of the prompt."""
if self.example_selector:
raise ValueError("Saving an example selector is not currently supported")
return super().dict(**kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
304b4a77e727-0 | Source code for langchain.prompts.prompt
"""Prompt schema definition."""
from __future__ import annotations
from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Union
from pydantic import root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPr... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
304b4a77e727-1 | """
kwargs = self._merge_partial_and_user_variables(**kwargs)
return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
[docs] @root_validator()
def template_is_valid(cls, values: Dict) -> Dict:
"""Check that template and input variables are consistent."""
if... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
304b4a77e727-2 | [docs] @classmethod
def from_file(
cls, template_file: Union[str, Path], input_variables: List[str], **kwargs: Any
) -> PromptTemplate:
"""Load a prompt from a file.
Args:
template_file: The path to the file containing the prompt template.
input_variables: A li... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
a72204898f7d-0 | Source code for langchain.prompts.chat
"""Chat prompt template."""
from __future__ import annotations
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, List, Sequence, Tuple, Type, TypeVar, Union
from pydantic import Field, root_validator
from langchain.load.serializable imp... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
a72204898f7d-1 | f"variable {self.variable_name} should be a list of base messages,"
f" got {value}"
)
return value
@property
def input_variables(self) -> List[str]:
"""Input variables for this prompt template."""
return [self.variable_name]
MessagePromptTemplateT = Ty... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
a72204898f7d-2 | [docs]class ChatMessagePromptTemplate(BaseStringMessagePromptTemplate):
role: str
[docs] def format(self, **kwargs: Any) -> BaseMessage:
text = self.prompt.format(**kwargs)
return ChatMessage(
content=text, role=self.role, additional_kwargs=self.additional_kwargs
)
[docs]class... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
a72204898f7d-3 | messages = self.format_messages(**kwargs)
return ChatPromptValue(messages=messages)
[docs] @abstractmethod
def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
"""Format kwargs into a list of messages."""
[docs]class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
input_variables: ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
a72204898f7d-4 | prompt=PromptTemplate.from_template(template), role=role
)
for role, template in string_messages
]
return cls.from_messages(messages)
[docs] @classmethod
def from_strings(
cls, string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]
) -> ChatPromptTe... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
a72204898f7d-5 | raise ValueError(f"Unexpected input: {message_template}")
return result
[docs] def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate:
raise NotImplementedError
@property
def _prompt_type(self) -> str:
return "chat"
[docs] def save(self, file_path: Union[P... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
cc1b08b49695-0 | Source code for langchain.prompts.few_shot
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPromptTemplate,
check_valid_template,
)
from langcha... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
cc1b08b49695-1 | def check_examples_and_selector(cls, values: Dict) -> Dict:
"""Check that one and only one of examples/example_selector are provided."""
examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise Val... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
cc1b08b49695-2 | .. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the examples to use.
examples = self._get_examples(**kwargs)
examples = [
{k: e[k] for k in self.example_prompt.input_variables} for e... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
c0b1b2afeb85-0 | Source code for langchain.prompts.base
"""BasePrompt schema definition."""
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
import yaml
from pydantic import Field, root_validator
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
c0b1b2afeb85-1 | if error_message:
raise KeyError(error_message.strip())
def _get_jinja2_variables_from_template(template: str) -> Set[str]:
try:
from jinja2 import Environment, meta
except ImportError:
raise ImportError(
"jinja2 not installed, which is needed to use the jinja2_formatter. "
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
c0b1b2afeb85-2 | """Return prompt as string."""
return self.text
[docs] def to_messages(self) -> List[BaseMessage]:
"""Return prompt as messages."""
return [HumanMessage(content=self.text)]
[docs]class BasePromptTemplate(Serializable, ABC):
"""Base class for all prompt templates, returning a prompt."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
c0b1b2afeb85-3 | values["partial_variables"]
)
if overall:
raise ValueError(
f"Found overlapping input and partial variables: {overall}"
)
return values
[docs] def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate:
"""Return a partial ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
c0b1b2afeb85-4 | """Save the prompt.
Args:
file_path: Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path="path/prompt.yaml")
"""
if self.partial_variables:
raise ValueError("Cannot save prompt with partial variables.")
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
5d7e39d806c6-0 | Source code for langchain.prompts.pipeline
from typing import Any, Dict, List, Tuple
from pydantic import root_validator
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.chat import BaseChatPromptTemplate
from langchain.schema import PromptValue
def _get_inputs(inputs: dict, input_variables:... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/pipeline.html |
5d7e39d806c6-1 | if isinstance(prompt, BaseChatPromptTemplate):
kwargs[k] = prompt.format_messages(**_inputs)
else:
kwargs[k] = prompt.format(**_inputs)
_inputs = _get_inputs(kwargs, self.final_prompt.input_variables)
return self.final_prompt.format_prompt(**_inputs)
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/pipeline.html |
5c8d714980e0-0 | Source code for langchain.prompts.loading
"""Load prompts from disk."""
import importlib
import json
import logging
from pathlib import Path
from typing import Union
import yaml
from langchain.output_parsers.regex import RegexParser
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot i... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
5c8d714980e0-1 | # Load the template.
if template_path.suffix == ".txt":
with open(template_path) as f:
template = f.read()
else:
raise ValueError
# Set the template variable to the extracted variable.
config[var_name] = template
return config
def _load_example... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
5c8d714980e0-2 | """Load the few shot prompt from the config."""
# Load the suffix and prefix templates.
config = _load_template("suffix", config)
config = _load_template("prefix", config)
# Load the example prompt.
if "example_prompt_path" in config:
if "example_prompt" in config:
raise ValueErr... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
5c8d714980e0-3 | file_path = Path(file)
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)
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
c0fc5376be58-0 | Source code for langchain.prompts.example_selector.length_based
"""Select examples based on length."""
import re
from typing import Callable, Dict, List
from pydantic import BaseModel, validator
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.prompts.prompt import PromptTemplate
d... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
c0fc5376be58-1 | get_text_length = values["get_text_length"]
string_examples = [example_prompt.format(**eg) for eg in values["examples"]]
return [get_text_length(eg) for eg in string_examples]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use base... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
14e99f5d5a83-0 | Source code for langchain.prompts.example_selector.semantic_similarity
"""Example selector that selects examples based on SemanticSimilarity."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
14e99f5d5a83-1 | return ids[0]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on semantic similarity."""
# Get the docs with the highest similarity.
if self.input_keys:
input_variables = {key: input_variables[key] for key in s... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
14e99f5d5a83-2 | instead of all variables.
vectorstore_cls_kwargs: optional kwargs containing url for vector store
Returns:
The ExampleSelector instantiated, backed by a vector store.
"""
if input_keys:
string_examples = [
" ".join(sorted_values({k: eg[k] for k... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
14e99f5d5a83-3 | examples = [dict(e.metadata) for e in example_docs]
# If example keys are provided, filter examples to those keys.
if self.example_keys:
examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
return examples
[docs] @classmethod
def from_examples(
cls,
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
14e99f5d5a83-4 | )
return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys) | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
0ec9d2a39c57-0 | Source code for langchain.prompts.example_selector.ngram_overlap
"""Select and order examples based on ngram overlap score (sentence_bleu score).
https://www.nltk.org/_modules/nltk/translate/bleu_score.html
https://aclanthology.org/P02-1040.pdf
"""
from typing import Dict, List
import numpy as np
from pydantic import B... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/ngram_overlap.html |
0ec9d2a39c57-1 | """
examples: List[dict]
"""A list of the examples that the prompt template expects."""
example_prompt: PromptTemplate
"""Prompt template used to format the examples."""
threshold: float = -1.0
"""Threshold at which algorithm stops. Set to -1.0 by default.
For negative threshold:
select_... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/ngram_overlap.html |
0ec9d2a39c57-2 | k = len(self.examples)
score = [0.0] * k
first_prompt_template_key = self.example_prompt.input_variables[0]
for i in range(k):
score[i] = ngram_overlap_score(
inputs, [self.examples[i][first_prompt_template_key]]
)
while True:
arg_max =... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/ngram_overlap.html |
cc3c11dd37d7-0 | Source code for langchain.prompts.example_selector.base
"""Interface for selecting examples to include in prompts."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List
[docs]class BaseExampleSelector(ABC):
"""Interface for selecting examples to include in prompts."""
[docs] @abstractmethod
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/base.html |
5faebf896115-0 | 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 |
5faebf896115-1 | 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 |
1f47301bead0-0 | 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 |
c021f8d124f1-0 | Source code for langchain.chains.prompt_selector
from abc import ABC, abstractmethod
from typing import Callable, List, Tuple
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.chat_models.base import BaseChatModel
from langchain.llms.base import BaseLLM
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/prompt_selector.html |
c021f8d124f1-1 | True if the language model is a BaseChatModel model, False otherwise.
"""
return isinstance(llm, BaseChatModel) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/prompt_selector.html |
48219f1e81fc-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 |
48219f1e81fc-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 |
48219f1e81fc-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 |
de4e1824e492-0 | 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 |
de4e1824e492-1 | 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 |
de4e1824e492-2 | 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 |
de4e1824e492-3 | :meta private:
"""
return [self.output_key]
[docs] @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(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
de4e1824e492-4 | run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
0abc961018fd-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 |
0abc961018fd-1 | """Will always return text key.
:meta private:
"""
return [self.output_key]
[docs] @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from bs4 import BeautifulSoup #... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
76db69dacd9a-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 |
76db69dacd9a-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 |
76db69dacd9a-2 | return self.llm.generate_prompt(
prompts,
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[AsyncCallbackManag... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
76db69dacd9a-3 | raise ValueError(
"If `stop` is present in any inputs, should be present in all."
)
prompts.append(prompt)
return prompts, stop
[docs] async def aprep_prompts(
self,
input_list: List[Dict[str, Any]],
run_manager: Optional[AsyncCallbackMa... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
76db69dacd9a-4 | dumpd(self),
{"input_list": input_list},
)
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)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
76db69dacd9a-5 | ]
if self.return_final_only:
result = [{self.output_key: r[self.output_key]} for r in result]
return result
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
response = a... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
76db69dacd9a-6 | """Call predict and then parse the results."""
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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
76db69dacd9a-7 | if self.prompt.output_parser is not None:
return [
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]], callb... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
d27c964b0b3c-0 | Source code for langchain.chains.base
"""Base interface that all chains should implement."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import Field, root_validator, validator
impor... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
d27c964b0b3c-1 | callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
"""Deprecated, use `callbacks` instead."""
verbose: bool = Field(default_factory=_get_verbosity)
"""Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
d27c964b0b3c-2 | return _get_verbosity()
else:
return verbose
@property
@abstractmethod
def input_keys(self) -> List[str]:
"""Input keys this chain expects."""
@property
@abstractmethod
def output_keys(self) -> List[str]:
"""Output keys this chain expects."""
def _validate... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
d27c964b0b3c-3 | include_run_info: bool = False,
) -> Dict[str, Any]:
"""Run the logic of this chain and add to output if desired.
Args:
inputs: Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs: boolean for whether to return only outpu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
d27c964b0b3c-4 | return final_outputs
[docs] async def acall(
self,
inputs: Union[Dict[str, Any], Any],
return_only_outputs: bool = False,
callbacks: Callbacks = None,
*,
tags: Optional[List[str]] = None,
include_run_info: bool = False,
) -> Dict[str, Any]:
"""Run t... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
d27c964b0b3c-5 | raise e
await run_manager.on_chain_end(outputs)
final_outputs: Dict[str, Any] = self.prep_outputs(
inputs, outputs, return_only_outputs
)
if include_run_info:
final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
return final_outputs
[docs] def pr... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
d27c964b0b3c-6 | )
inputs = {list(_input_keys)[0]: inputs}
if self.memory is not None:
external_context = self.memory.load_memory_variables(inputs)
inputs = dict(inputs, **external_context)
self._validate_inputs(inputs)
return inputs
[docs] def apply(
self, input_li... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
d27c964b0b3c-7 | if not kwargs and not args:
raise ValueError(
"`run` supported with either positional arguments or keyword arguments,"
" but none were provided."
)
raise ValueError(
f"`run` supported with either positional arguments or keyword arguments"
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
d27c964b0b3c-8 | raise ValueError("Saving of memory is not yet supported.")
_dict = super().dict()
_dict["_type"] = self._chain_type
return _dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the chain.
Args:
file_path: Path to file to save the chain to.
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
80e665de3a0c-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://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
80e665de3a0c-1 | def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain:
"""Load LLM chain from config dict."""
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 Val... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
80e665de3a0c-2 | return HypotheticalDocumentEmbedder(
llm_chain=llm_chain, base_embeddings=embeddings, **config
)
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(ll... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
80e665de3a0c-3 | llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "combine_document_chain" in config:
combine_docu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
80e665de3a0c-4 | 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 to support old configs
elif "llm" in config:
llm_config = config.pop("llm")
llm = load_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-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 |
80e665de3a0c-15 | file_path = Path(file)
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)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
479f19cafbcd-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
479f19cafbcd-1 | @property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
[docs] @classmethod
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
479f19cafbcd-2 | @abstractmethod
def _get_docs(
self,
question: str,
*,
run_manager: CallbackManagerForChainRun,
) -> List[Document]:
"""Get documents to do question answering over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManag... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
479f19cafbcd-3 | run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get documents to do question answering over."""
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run get_relevan... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
479f19cafbcd-4 | from langchain.chains import RetrievalQA
from langchain.faiss import FAISS
from langchain.vectorstores.base import VectorStoreRetriever
retriever = VectorStoreRetriever(vectorstore=FAISS(...))
retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever)
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
479f19cafbcd-5 | """Extra search args."""
[docs] @root_validator()
def raise_deprecation(cls, values: Dict) -> Dict:
warnings.warn(
"`VectorDBQA` is deprecated - "
"please use `from langchain.chains import RetrievalQA`"
)
return values
[docs] @root_validator()
def validate_s... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
8495c1986823-0 | 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 |
8495c1986823-1 | **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 |
946fbe3a3b53-0 | 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 |
946fbe3a3b53-1 | """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 |
946fbe3a3b53-2 | ) -> 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 |
22ea174d8cd9-0 | 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 |
22ea174d8cd9-1 | 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 |
22ea174d8cd9-2 | 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 |
94083b35038c-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 |
94083b35038c-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 |
60b11c06ae26-0 | 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 |
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