id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
5c7ef22eeb66-6 | chain_kwargs = chain_kwargs or {}
if "allowed_comparators" not in chain_kwargs:
chain_kwargs[
"allowed_comparators"
] = structured_query_translator.allowed_comparators
if "allowed_operators" not in chain_kwargs:
chain_kwargs[
"allowed_o... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
39b0e1026974-0 | Source code for langchain.retrievers.document_compressors.base
"""Interface for retrieved document compressors."""
from abc import ABC, abstractmethod
from typing import List, Sequence, Union
from pydantic import BaseModel
from langchain.schema import BaseDocumentTransformer, Document
class BaseDocumentCompressor(BaseM... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
39b0e1026974-1 | transformers: List[Union[BaseDocumentTransformer, BaseDocumentCompressor]]
"""List of document filters that are chained together and run in sequence."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def compress_documents(
self, docu... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
39b0e1026974-2 | self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress retrieved documents given the query context."""
for _transformer in self.transformers:
if isinstance(_transformer, BaseDocumentCompressor):
documents = await _transformer.acompress_docume... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
65b1fa70d250-0 | Source code for langchain.retrievers.document_compressors.embeddings_filter
"""Document compressor that uses embeddings to drop documents unrelated to the query."""
from typing import Callable, Dict, Optional, Sequence
import numpy as np
from pydantic import root_validator
from langchain.document_transformers import (
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
65b1fa70d250-1 | two matrices (List[List[float]]) and return a matrix of scores where higher values
indicate greater similarity."""
k: Optional[int] = 20
"""The number of relevant documents to return. Can be set to None, in which case
`similarity_threshold` must be specified. Defaults to 20."""
similarity_threshold:... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
65b1fa70d250-2 | raise ValueError("Must specify one of `k` or `similarity_threshold`.")
return values
[docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter documents based on similarity of their embeddings to the query."""
stateful_docume... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
65b1fa70d250-3 | similar_enough = np.where(
similarity[included_idxs] > self.similarity_threshold
)
included_idxs = included_idxs[similar_enough]
return [stateful_documents[i] for i in included_idxs]
[docs] async def acompress_documents(
self, documents: Sequence[Document], que... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
7353d1ed55ae-0 | Source code for langchain.retrievers.document_compressors.chain_filter
"""Filter that uses an LLM to drop documents that aren't relevant to the query."""
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import BasePromptTemplate, LLMChain, PromptTemplate
from langchain.base_language import Base... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
7353d1ed55ae-1 | """Return the compression chain input."""
return {"question": query, "context": doc.page_content}
[docs]class LLMChainFilter(BaseDocumentCompressor):
"""Filter that drops documents that aren't relevant to the query."""
llm_chain: LLMChain
"""LLM wrapper to use for filtering documents.
The chain pro... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
7353d1ed55ae-2 | include_doc = self.llm_chain.predict_and_parse(**_input)
if include_doc:
filtered_docs.append(doc)
return filtered_docs
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter down documents."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
70687afcf17a-0 | Source code for langchain.retrievers.document_compressors.chain_extract
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
import asyncio
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import LLMChain, PromptTemplate
from... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
70687afcf17a-1 | no_output_str: str = "NO_OUTPUT"
def parse(self, text: str) -> str:
cleaned_text = text.strip()
if cleaned_text == self.no_output_str:
return ""
return cleaned_text
def _get_default_chain_prompt() -> PromptTemplate:
output_parser = NoOutputParser()
template = prompt_templ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
70687afcf17a-2 | [docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress page content of raw documents."""
compressed_docs = []
for doc in documents:
_input = self.get_input(query, doc)
output = self.llm_chain.pred... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
70687afcf17a-3 | ]
)
compressed_docs = []
for i, doc in enumerate(documents):
if len(outputs[i]) == 0:
continue
compressed_docs.append(
Document(page_content=outputs[i], metadata=doc.metadata)
)
return compressed_docs
[docs] @classmet... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
70687afcf17a-4 | llm_chain = LLMChain(llm=llm, prompt=_prompt, **(llm_chain_kwargs or {}))
return cls(llm_chain=llm_chain, get_input=_get_input) | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
41ce04b19fc0-0 | Source code for langchain.retrievers.document_compressors.cohere_rerank
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, Sequence
from pydantic import Extra, root_validator
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from langchain.schema import Document
f... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
41ce04b19fc0-1 | extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
cohere_api_key = get_from_dict_or_env(
values, "cohere_api_key", "COHERE_AP... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
41ce04b19fc0-2 | return []
doc_list = list(documents)
_docs = [d.page_content for d in doc_list]
results = self.client.rerank(
model=self.model, query=query, documents=_docs, top_n=self.top_n
)
final_results = []
for r in results:
doc = doc_list[r.index]
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
1b1d53016eff-0 | Source code for langchain.output_parsers.rail_parser
from __future__ import annotations
from typing import Any, Callable, Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class GuardrailsOutputParser(BaseOutputParser):
guard: Any
api: Optional[Callable]
args: Any
kwargs: Any
@prope... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
1b1d53016eff-1 | "Install it by running `pip install guardrails-ai`."
)
return cls(
guard=Guard.from_rail(rail_file, num_reasks=num_reasks),
api=api,
args=args,
kwargs=kwargs,
)
[docs] @classmethod
def from_rail_string(
cls,
rail_str: str... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
1b1d53016eff-2 | )
return cls(
guard=Guard.from_rail_string(rail_str, num_reasks=num_reasks),
api=api,
args=args,
kwargs=kwargs,
)
[docs] def get_format_instructions(self) -> str:
return self.guard.raw_prompt.format_instructions
[docs] def parse(self, text: s... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
558bc2bc64fa-0 | Source code for langchain.output_parsers.datetime
import random
from datetime import datetime, timedelta
from typing import List
from langchain.schema import BaseOutputParser, OutputParserException
from langchain.utils import comma_list
def _generate_random_datetime_strings(
pattern: str,
n: int = 3,
start_... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
558bc2bc64fa-1 | for i in range(n):
random_delta = random.uniform(0, delta.total_seconds())
dt = start_date + timedelta(seconds=random_delta)
date_string = dt.strftime(pattern)
examples.append(date_string)
return examples
[docs]class DatetimeOutputParser(BaseOutputParser[datetime]):
format: str =... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
558bc2bc64fa-2 | raise OutputParserException(
f"Could not parse datetime string: {response}"
) from e
@property
def _type(self) -> str:
return "datetime" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
8338ab2e6207-0 | Source code for langchain.output_parsers.structured
from __future__ import annotations
from typing import Any, List
from pydantic import BaseModel
from langchain.output_parsers.format_instructions import STRUCTURED_FORMAT_INSTRUCTIONS
from langchain.output_parsers.json import parse_and_check_json_markdown
from langchai... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html |
8338ab2e6207-1 | def from_response_schemas(
cls, response_schemas: List[ResponseSchema]
) -> StructuredOutputParser:
return cls(response_schemas=response_schemas)
[docs] def get_format_instructions(self) -> str:
schema_str = "\n".join(
[_get_sub_string(schema) for schema in self.response_schem... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html |
ede89fe15bda-0 | Source code for langchain.output_parsers.regex_dict
from __future__ import annotations
import re
from typing import Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexDictParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex_pattern: str = r"{}:\s?([^.'\n'... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html |
ede89fe15bda-1 | specific_regex = self.regex_pattern.format(re.escape(expected_format))
matches = re.findall(specific_regex, text)
if not matches:
raise ValueError(
f"No match found for output key: {output_key} with expected format \
{expected_format} o... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html |
5a624ab3d738-0 | Source code for langchain.output_parsers.list
from __future__ import annotations
from abc import abstractmethod
from typing import List
from langchain.schema import BaseOutputParser
[docs]class ListOutputParser(BaseOutputParser):
"""Class to parse the output of an LLM call to a list."""
@property
def _type(... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html |
5a624ab3d738-1 | )
[docs] def parse(self, text: str) -> List[str]:
"""Parse the output of an LLM call."""
return text.strip().split(", ") | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html |
b2513dc2410c-0 | Source code for langchain.output_parsers.boolean
from langchain.schema import BaseOutputParser
[docs]class BooleanOutputParser(BaseOutputParser[bool]):
true_val: str = "YES"
false_val: str = "NO"
[docs] def parse(self, text: str) -> bool:
"""Parse the output of an LLM call to a boolean.
Args:... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/boolean.html |
b2513dc2410c-1 | @property
def _type(self) -> str:
"""Snake-case string identifier for output parser type."""
return "boolean_output_parser" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/boolean.html |
0d9f9446f26e-0 | Source code for langchain.output_parsers.combining
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import root_validator
from langchain.schema import BaseOutputParser
[docs]class CombiningOutputParser(BaseOutputParser):
"""Class to combine multiple output parsers into one."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/combining.html |
0d9f9446f26e-1 | return values
@property
def _type(self) -> str:
"""Return the type key."""
return "combining"
[docs] def get_format_instructions(self) -> str:
"""Instructions on how the LLM output should be formatted."""
initial = f"For your first output: {self.parsers[0].get_format_instructi... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/combining.html |
0d9f9446f26e-2 | output = dict()
for txt, parser in zip(texts, self.parsers):
output.update(parser.parse(txt.strip()))
return output | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/combining.html |
84ffdab9b705-0 | Source code for langchain.output_parsers.regex
from __future__ import annotations
import re
from typing import Dict, List, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex: str
output_keys: List[str]
... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex.html |
84ffdab9b705-1 | if self.default_output_key is None:
raise ValueError(f"Could not parse output: {text}")
else:
return {
key: text if key == self.default_output_key else ""
for key in self.output_keys
} | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex.html |
3dcab953ed5a-0 | Source code for langchain.output_parsers.pydantic
import json
import re
from typing import Type, TypeVar
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS
from langchain.schema import BaseOutputParser, OutputParserException
T = TypeVar(... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
3dcab953ed5a-1 | return self.pydantic_object.parse_obj(json_object)
except (json.JSONDecodeError, ValidationError) as e:
name = self.pydantic_object.__name__
msg = f"Failed to parse {name} from completion {text}. Got: {e}"
raise OutputParserException(msg)
[docs] def get_format_instructions... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
3dcab953ed5a-2 | def _type(self) -> str:
return "pydantic" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
b7637d662cde-0 | Source code for langchain.output_parsers.retry
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
b7637d662cde-1 | Please try again:"""
NAIVE_RETRY_PROMPT = PromptTemplate.from_template(NAIVE_COMPLETION_RETRY)
NAIVE_RETRY_WITH_ERROR_PROMPT = PromptTemplate.from_template(
NAIVE_COMPLETION_RETRY_WITH_ERROR
)
T = TypeVar("T")
[docs]class RetryOutputParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors.... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
b7637d662cde-2 | ) -> RetryOutputParser[T]:
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserE... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
b7637d662cde-3 | @property
def _type(self) -> str:
return "retry"
[docs]class RetryWithErrorOutputParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt, the completion, AND the error
that was raised to another language model and telling it that t... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
b7637d662cde-4 | parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_RETRY_WITH_ERROR_PROMPT,
) -> RetryWithErrorOutputParser[T]:
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue)... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
b7637d662cde-5 | )
[docs] def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "retry_with_error" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
6c00e70ac549-0 | Source code for langchain.output_parsers.enum
from enum import Enum
from typing import Any, Dict, List, Type
from pydantic import root_validator
from langchain.schema import BaseOutputParser, OutputParserException
[docs]class EnumOutputParser(BaseOutputParser):
enum: Type[Enum]
@root_validator()
def raise_d... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/enum.html |
6c00e70ac549-1 | raise OutputParserException(
f"Response '{response}' is not one of the "
f"expected values: {self._valid_values}"
)
[docs] def get_format_instructions(self) -> str:
return f"Select one of the following options: {', '.join(self._valid_values)}" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/enum.html |
f59e2848e3fa-0 | Source code for langchain.output_parsers.fix
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
from langchain.prompts.base import BasePromptTemplate
f... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html |
f59e2848e3fa-1 | ) -> OutputFixingParser[T]:
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse(self, completion: str) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException as e:
new_complet... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html |
49c5b8b064fb-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 |
49c5b8b064fb-1 | Either this or examples should be provided."""
example_prompt: PromptTemplate
"""PromptTemplate used to format an individual example."""
suffix: str
"""A prompt template string to put after the examples."""
input_variables: List[str]
"""A list of the names of the variables the prompt template ex... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
49c5b8b064fb-2 | """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 ValueError(
"Only one of 'examples' and 'example_select... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
49c5b8b064fb-3 | )
return values
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
def _get_examples(self, **kwargs: Any) -> List[dict]:
if self.examples is not None:
return self.examples
elif self.example_s... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
49c5b8b064fb-4 | # Get the examples to use.
examples = self._get_examples(**kwargs)
examples = [
{k: e[k] for k in self.example_prompt.input_variables} for e in examples
]
# Format the examples.
example_strings = [
self.example_prompt.format(**example) for example in examp... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
49c5b8b064fb-5 | """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.html |
9bc4754a03aa-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 |
9bc4754a03aa-1 | )
return Template(template).render(**kwargs)
def validate_jinja2(template: str, input_variables: List[str]) -> None:
"""
Validate that the input variables are valid for the template.
Raise an exception if missing or extra variables are found.
Args:
template: The template string.
inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9bc4754a03aa-2 | 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. "
"Please install... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9bc4754a03aa-3 | }
def check_valid_template(
template: str, template_format: str, input_variables: List[str]
) -> None:
"""Check that template string is valid."""
if template_format not in DEFAULT_FORMATTER_MAPPING:
valid_formats = list(DEFAULT_FORMATTER_MAPPING)
raise ValueError(
f"Invalid templ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9bc4754a03aa-4 | return self.text
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."""
input_variables: List[str]
"""A list of... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9bc4754a03aa-5 | [docs] @abstractmethod
def format_prompt(self, **kwargs: Any) -> PromptValue:
"""Create Chat Messages."""
@root_validator()
def validate_variable_names(cls, values: Dict) -> Dict:
"""Validate variable names do not include restricted names."""
if "stop" in values["input_variables"]... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9bc4754a03aa-6 | )
return values
[docs] def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate:
"""Return a partial of the prompt template."""
prompt_dict = self.__dict__.copy()
prompt_dict["input_variables"] = list(
set(self.input_variables).difference(kwargs)
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9bc4754a03aa-7 | [docs] @abstractmethod
def format(self, **kwargs: Any) -> str:
"""Format the prompt with the inputs.
Args:
kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.for... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9bc4754a03aa-8 | 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.")
# Convert file to Path... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9bc4754a03aa-9 | yaml.dump(prompt_dict, f, default_flow_style=False)
else:
raise ValueError(f"{save_path} must be json or yaml")
[docs]class StringPromptTemplate(BasePromptTemplate, ABC):
"""String prompt should expose the format method, returning a prompt."""
[docs] def format_prompt(self, **kwargs: Any) -> ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
cae2ff0f47ed-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 |
cae2ff0f47ed-1 | config_type = config.pop("_type", "prompt")
if config_type not in type_to_loader_dict:
raise ValueError(f"Loading {config_type} prompt not supported")
prompt_loader = type_to_loader_dict[config_type]
return prompt_loader(config)
def _load_template(var_name: str, config: dict) -> dict:
"""Load te... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
cae2ff0f47ed-2 | # 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 |
cae2ff0f47ed-3 | )
config["examples"] = examples
else:
raise ValueError("Invalid examples format. Only list or string are supported.")
return config
def _load_output_parser(config: dict) -> dict:
"""Load output parser."""
if "output_parser" in config and config["output_parser"]:
_config = config.... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
cae2ff0f47ed-4 | """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 |
cae2ff0f47ed-5 | """Load the prompt template from config."""
# Load the template from disk if necessary.
config = _load_template("template", config)
config = _load_output_parser(config)
return PromptTemplate(**config)
[docs]def load_prompt(path: Union[str, Path]) -> BasePromptTemplate:
"""Unified method for loading ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
cae2ff0f47ed-6 | 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)
elif file_path.suffix == "... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
cae2ff0f47ed-7 | raise ValueError("Did not get object of type BasePromptTemplate.")
return helper.PROMPT
else:
raise ValueError(f"Got unsupported file type {file_path.suffix}")
# Load the prompt from the config now.
return load_prompt_from_config(config)
type_to_loader_dict = {
"prompt": _load_prompt,
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
5082bdb3c4cf-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 |
5082bdb3c4cf-1 | input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
template: str
"""The prompt template."""
template_format: str = "f-string"
"""The format of the prompt template. Options are: 'f-string', 'jinja2'."""
validate_template: bool = True
"""Whether ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
5082bdb3c4cf-2 | """
kwargs = self._merge_partial_and_user_variables(**kwargs)
return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
@root_validator()
def template_is_valid(cls, values: Dict) -> Dict:
"""Check that template and input variables are consistent."""
if value... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
5082bdb3c4cf-3 | ) -> PromptTemplate:
"""Take examples in list format with prefix and suffix to create a prompt.
Intended to be used as a way to dynamically create a prompt from examples.
Args:
examples: List of examples to use in the prompt.
suffix: String to go after the list of example... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
5082bdb3c4cf-4 | [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 |
5082bdb3c4cf-5 | if "template_format" in kwargs and kwargs["template_format"] == "jinja2":
# Get the variables for the template
input_variables = _get_jinja2_variables_from_template(template)
else:
input_variables = {
v for _, v, _, _ in Formatter().parse(template) if v is not... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
5dcd2a22ed2e-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 |
5dcd2a22ed2e-1 | """To messages."""
@property
@abstractmethod
def input_variables(self) -> List[str]:
"""Input variables for this prompt template."""
[docs]class MessagesPlaceholder(BaseMessagePromptTemplate):
"""Prompt template that assumes variable is already list of messages."""
variable_name: str
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-2 | )
return value
@property
def input_variables(self) -> List[str]:
"""Input variables for this prompt template."""
return [self.variable_name]
MessagePromptTemplateT = TypeVar(
"MessagePromptTemplateT", bound="BaseStringMessagePromptTemplate"
)
class BaseStringMessagePromptTemplate(Bas... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-3 | def from_template_file(
cls: Type[MessagePromptTemplateT],
template_file: Union[str, Path],
input_variables: List[str],
**kwargs: Any,
) -> MessagePromptTemplateT:
prompt = PromptTemplate.from_file(template_file, input_variables)
return cls(prompt=prompt, **kwargs)
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-4 | text = self.prompt.format(**kwargs)
return ChatMessage(
content=text, role=self.role, additional_kwargs=self.additional_kwargs
)
[docs]class HumanMessagePromptTemplate(BaseStringMessagePromptTemplate):
[docs] def format(self, **kwargs: Any) -> BaseMessage:
text = self.prompt.forma... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-5 | text = self.prompt.format(**kwargs)
return SystemMessage(content=text, additional_kwargs=self.additional_kwargs)
class ChatPromptValue(PromptValue):
messages: List[BaseMessage]
def to_string(self) -> str:
"""Return prompt as string."""
return get_buffer_string(self.messages)
def to_m... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-6 | [docs] @abstractmethod
def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
"""Format kwargs into a list of messages."""
[docs]class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
input_variables: List[str]
messages: List[Union[BaseMessagePromptTemplate, BaseMessage]]
@root_valida... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-7 | raise ValueError(
"Got mismatched input_variables. "
f"Expected: {input_vars}. "
f"Got: {values['input_variables']}"
)
else:
values["input_variables"] = list(input_vars)
return values
[docs] @classmethod
def f... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-8 | for role, template in string_messages
]
return cls.from_messages(messages)
[docs] @classmethod
def from_strings(
cls, string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]
) -> ChatPromptTemplate:
messages = [
role(prompt=PromptTemplate.from_template(t... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-9 | [docs] def format(self, **kwargs: Any) -> str:
return self.format_prompt(**kwargs).to_string()
[docs] def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
kwargs = self._merge_partial_and_user_variables(**kwargs)
result = []
for message_template in self.messages:
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
5dcd2a22ed2e-10 | 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[Path, str]) -> None:
raise NotImplementedError | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
2eefeb7612e9-0 | 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 |
2eefeb7612e9-1 | to future prompt templates as a variable with
the same name as `name`
"""
final_prompt: BasePromptTemplate
pipeline_prompts: List[Tuple[str, BasePromptTemplate]]
@root_validator(pre=True)
def get_input_variables(cls, values: Dict) -> Dict:
"""Get input variables."""
creat... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/pipeline.html |
2eefeb7612e9-2 | 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] def format(self, **kwargs: Any) -> str:
return self.fo... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/pipeline.html |
a49b5cd9c1f6-0 | Source code for langchain.prompts.few_shot_with_templates
"""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
from langchain.prompts.example_selec... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
a49b5cd9c1f6-1 | suffix: StringPromptTemplate
"""A PromptTemplate to put after the examples."""
input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
example_separator: str = "\n\n"
"""String separator used to join the prefix, the examples, and suffix."""
prefix: Opti... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
a49b5cd9c1f6-2 | example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_selector' should be provided"
)
if examples is None and example_selector is None:
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
a49b5cd9c1f6-3 | missing_vars = expected_input_variables.difference(input_variables)
if missing_vars:
raise ValueError(
f"Got input_variables={input_variables}, but based on "
f"prefix/suffix expected {expected_input_variables}"
)
return values
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
a49b5cd9c1f6-4 | Returns:
A formatted string.
Example:
.. 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)
# Format the exampl... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.