id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 54 121 |
|---|---|---|
b0635f6496b6-2 | values["vectorstore"]
)
return values
[docs] def get_relevant_documents(
self, query: str, callbacks: Callbacks = None
) -> List[Document]:
"""Get documents relevant for a query.
Args:
query: string to find relevant documents for
Returns:
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
b0635f6496b6-3 | if structured_query_translator is None:
structured_query_translator = _get_builtin_translator(vectorstore)
chain_kwargs = chain_kwargs or {}
if "allowed_comparators" not in chain_kwargs:
chain_kwargs[
"allowed_comparators"
] = structured_query_translat... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
6632a8be1a98-0 | 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 |
6632a8be1a98-1 | 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 |
5e20c793ea8c-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 |
5e20c793ea8c-1 | 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_documents = get_stateful_documents(documents)
embedded_documents = _get_embed... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
7cec6d49b566-0 | 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 |
7cec6d49b566-1 | 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 |
2d403d3f5892-0 | 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 |
2d403d3f5892-1 | [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 |
2d403d3f5892-2 | _get_input = get_input if get_input is not None else default_get_input
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 |
94154df0fdeb-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 |
94154df0fdeb-1 | 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 |
85e271f02d53-0 | 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 |
85e271f02d53-1 | )
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 |
9aa9e029c385-0 | 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 |
9aa9e029c385-1 | ) from e
@property
def _type(self) -> str:
return "datetime" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
6fd7632133a0-0 | 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 |
a2f0ed6d50bd-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 |
e63d6ac646f7-0 | 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 |
0a3dec6de5ed-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 |
735750354b8d-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 |
735750354b8d-1 | texts = text.split("\n\n")
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 |
15436f06af05-0 | 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 |
0cd2294524a6-0 | 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 |
0cd2294524a6-1 | @property
def _type(self) -> str:
return "pydantic" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
1041e86f03f8-0 | 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 |
1041e86f03f8-1 | 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 OutputParserException:
new_completio... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
1041e86f03f8-2 | ) -> 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) -> T:
try:
parsed_completion = self.parser.parse(completion)
except Outp... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
6e4f3642087d-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 |
a3cfd29bd272-0 | 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 |
cd09695e78d9-0 | 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 |
cd09695e78d9-1 | 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 |
cd09695e78d9-2 | .. 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 |
518a5b6960e3-0 | 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 |
518a5b6960e3-1 | 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 |
518a5b6960e3-2 | """Return prompt as string."""
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."""
inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
518a5b6960e3-3 | f"Found overlapping input and partial variables: {overall}"
)
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_variabl... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
518a5b6960e3-4 | """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 |
81c458169787-0 | 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 |
81c458169787-1 | # 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 |
81c458169787-2 | """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 |
81c458169787-3 | 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 |
917a9ff2fc9a-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 |
917a9ff2fc9a-1 | """
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 |
917a9ff2fc9a-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 |
f0a6c87ce3bd-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 |
f0a6c87ce3bd-1 | f" got {value}"
)
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 Bas... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
f0a6c87ce3bd-2 | 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 |
f0a6c87ce3bd-3 | """Format kwargs into a list of messages."""
[docs]class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
input_variables: List[str]
messages: List[Union[BaseMessagePromptTemplate, BaseMessage]]
@root_validator(pre=True)
def validate_input_variables(cls, values: dict) -> dict:
messages = values[... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
f0a6c87ce3bd-4 | [docs] @classmethod
def from_strings(
cls, string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]
) -> ChatPromptTemplate:
messages = [
role(prompt=PromptTemplate.from_template(template))
for role, template in string_messages
]
return cls.fr... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
f0a6c87ce3bd-5 | 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 |
1ea66dd00bf0-0 | 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 |
1ea66dd00bf0-1 | 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 |
71945812d122-0 | 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 |
71945812d122-1 | 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_selector' should be provided"
)
if examples is None and example_selecto... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
71945812d122-2 | Args:
kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the example... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
71945812d122-3 | """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 |
db935087fcd6-0 | 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 |
db935087fcd6-1 | """
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 |
db935087fcd6-2 | 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 |
87d7a6f9ef90-0 | 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 |
87d7a6f9ef90-1 | 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 |
87d7a6f9ef90-2 | 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 |
87d7a6f9ef90-3 | 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 |
87d7a6f9ef90-4 | )
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 |
5e65121cb205-0 | 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 |
5e65121cb205-1 | 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 |
9880df7d8d1f-0 | Source code for langchain.chat_models.azure_openai
"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict, Mapping
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.schema import ChatResult
from langchain.utils... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
9880df7d8d1f-1 | openai_api_base: str = ""
openai_api_version: str = ""
openai_api_key: str = ""
openai_organization: str = ""
openai_proxy: str = ""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
9880df7d8d1f-2 | except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
3dba71088f66-0 | Source code for langchain.chat_models.fake
"""Fake ChatModel for testing purposes."""
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.chat_models.base import SimpleChatModel
from langchain.schema import BaseMessage
[docs]class FakeListChatM... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/fake.html |
aaab389ab84b-0 | Source code for langchain.chat_models.openai
"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Mapping,
Optional,
Tuple,
Union,
)
from pydantic import Field, root_validator
from tenac... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-1 | return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-2 | elif role == "system":
return SystemMessage(content=_dict["content"])
elif role == "function":
return FunctionMessage(content=_dict["content"], name=_dict["name"])
else:
return ChatMessage(content=_dict["content"], role=role)
def _convert_message_to_dict(message: BaseMessage) -> dict:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-3 | Example:
.. code-block:: python
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
"""
@property
def lc_secrets(self) -> Dict[str, str]:
return {"openai_api_key": "OPENAI_API_KEY"}
@property
def lc_serializable(self) -... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-4 | max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default,... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-5 | )
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead t... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-6 | "due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when strea... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-7 | ),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
[docs] def completion_with_retry(self, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = self._create_retry_decorator()
@retry_decorator
def _completion_with_retry(... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-8 | role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content") or ""
inner_completion += token
_function_call = stream_resp["choices"][0]["delta"].get("function_call")
if _function_call:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-9 | gen = ChatGeneration(message=message)
generations.append(gen)
llm_output = {"token_usage": response["usage"], "model_name": self.model_name}
return ChatResult(generations=generations, llm_output=llm_output)
async def _agenerate(
self,
messages: List[BaseMessage],
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-10 | return ChatResult(generations=[ChatGeneration(message=message)])
else:
response = await acompletion_with_retry(
self, messages=message_dicts, **params
)
return self._create_chat_result(response)
@property
def _identifying_params(self) -> Mapping[str, A... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-11 | # gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
elif model == "gpt-4":
# gpt-4 may change over time.
# Returning num tokens assuming gpt-4-0314.
model = "gpt... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
aaab389ab84b-12 | return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
if model.startswith("gpt-3.5-turbo"):
# every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_message = 4
# if there's a name, the role is omitted
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
46fd8f075b37-0 | Source code for langchain.chat_models.anthropic
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.llms.anthropic import _AnthropicCommon
from langch... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
46fd8f075b37-1 | message_text = f"{self.AI_PROMPT} {message.content}"
elif isinstance(message, SystemMessage):
message_text = f"{self.HUMAN_PROMPT} <admin>{message.content}</admin>"
else:
raise ValueError(f"Got unknown type {message}")
return message_text
def _convert_messages_to_text... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
46fd8f075b37-2 | run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
prompt = self._convert_messages_to_prompt(messages)
params: Dict[str, Any] = {"prompt": prompt, **self._default_params, **kwargs}
if stop:
params["stop_sequences"] = stop
if se... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
46fd8f075b37-3 | delta,
)
else:
response = await self.client.acompletion(**params)
completion = response["completion"]
message = AIMessage(content=completion)
return ChatResult(generations=[ChatGeneration(message=message)])
[docs] def get_num_tokens(self, text: str)... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
021e83cffc34-0 | Source code for langchain.chat_models.google_palm
"""Wrapper around Google's PaLM Chat API."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
before_sleep_log,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
021e83cffc34-1 | if not response.candidates:
raise ChatGooglePalmError("ChatResponse must have at least one candidate.")
generations: List[ChatGeneration] = []
for candidate in response.candidates:
author = candidate.get("author")
if author is None:
raise ChatGooglePalmError(f"ChatResponse mu... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
021e83cffc34-2 | if isinstance(input_message, SystemMessage):
if index != 0:
raise ChatGooglePalmError("System message must be first input message.")
context = input_message.content
elif isinstance(input_message, HumanMessage) and input_message.example:
if messages:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
021e83cffc34-3 | "Messages without an explicit role not supported by PaLM API."
)
return genai.types.MessagePromptDict(
context=context,
examples=examples,
messages=messages,
)
def _create_retry_decorator() -> Callable[[Any], Any]:
"""Returns a tenacity retry decorator, preconfigured to h... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
021e83cffc34-4 | async def _achat_with_retry(**kwargs: Any) -> Any:
# Use OpenAI's async api https://github.com/openai/openai-python#async-api
return await llm.client.chat_async(**kwargs)
return await _achat_with_retry(**kwargs)
[docs]class ChatGooglePalm(BaseChatModel, BaseModel):
"""Wrapper around Google's PaL... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
021e83cffc34-5 | not return the full n completions if duplicates are generated."""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate api key, python package exists, temperature, top_p, and top_k."""
google_api_key = get_from_dict_or_env(
values, "google_api_key", "GOO... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
021e83cffc34-6 | self,
model=self.model_name,
prompt=prompt,
temperature=self.temperature,
top_p=self.top_p,
top_k=self.top_k,
candidate_count=self.n,
**kwargs,
)
return _response_to_result(response, stop)
async def _agenerate(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
fe905092523d-0 | Source code for langchain.chat_models.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models import ChatOpenAI
from langchain.sch... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
fe905092523d-1 | **kwargs: Any
) -> ChatResult:
"""Call ChatOpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(messages... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
fe905092523d-2 | request_start_time = datetime.datetime.now().timestamp()
generated_responses = await super()._agenerate(messages, stop, run_manager)
request_end_time = datetime.datetime.now().timestamp()
message_dicts, params = super()._create_message_dicts(messages, stop)
for i, generation in enumerate... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
fd3dd65946fd-0 | Source code for langchain.chat_models.vertexai
"""Wrapper around Google VertexAI chat-based models."""
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManage... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
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