id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
87e1e92e7d92-1 | """
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 values["validate_template"]:
all_inputs = values["input_variables"] + list(values["partial_variables"])
check_valid_template(
values["template"], values["template_format"], all_inputs
)
return values
[docs] @classmethod
def from_examples(
cls,
examples: List[str],
suffix: str,
input_variables: List[str],
example_separator: str = "\n\n",
prefix: str = "",
**kwargs: Any,
) -> 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 examples. Should generally
set up the user's input.
input_variables: A list of variable names the final prompt template
will expect.
example_separator: The separator to use in between examples. Defaults
to two new line characters.
prefix: String that should go before any examples. Generally includes
examples. Default to an empty string.
Returns:
The final prompt generated.
"""
template = example_separator.join([prefix, *examples, suffix])
return cls(input_variables=input_variables, template=template, **kwargs)
[docs] @classmethod
def from_file( | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
87e1e92e7d92-2 | [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 list of variable names the final prompt template
will expect.
Returns:
The prompt loaded from the file.
"""
with open(str(template_file), "r") as f:
template = f.read()
return cls(input_variables=input_variables, template=template, **kwargs)
[docs] @classmethod
def from_template(cls, template: str, **kwargs: Any) -> PromptTemplate:
"""Load a prompt template from a template."""
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 None
}
if "partial_variables" in kwargs:
partial_variables = kwargs["partial_variables"]
input_variables = {
var for var in input_variables if var not in partial_variables
}
return cls(
input_variables=list(sorted(input_variables)), template=template, **kwargs
)
# For backwards compatibility.
Prompt = PromptTemplate
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
d200e87046bd-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_selector.base import BaseExampleSelector
from langchain.prompts.prompt import PromptTemplate
[docs]class FewShotPromptWithTemplates(StringPromptTemplate):
"""Prompt template that contains few shot examples."""
examples: Optional[List[dict]] = None
"""Examples to format into the prompt.
Either this or example_selector should be provided."""
example_selector: Optional[BaseExampleSelector] = None
"""ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided."""
example_prompt: PromptTemplate
"""PromptTemplate used to format an individual example."""
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: Optional[StringPromptTemplate] = None
"""A PromptTemplate to put before the examples."""
template_format: str = "f-string"
"""The format of the prompt template. Options are: 'f-string', 'jinja2'."""
validate_template: bool = True
"""Whether or not to try validating the template."""
@root_validator(pre=True)
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) | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
d200e87046bd-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_selector is None:
raise ValueError(
"One of 'examples' and 'example_selector' should be provided"
)
return values
@root_validator()
def template_is_valid(cls, values: Dict) -> Dict:
"""Check that prefix, suffix and input variables are consistent."""
if values["validate_template"]:
input_variables = values["input_variables"]
expected_input_variables = set(values["suffix"].input_variables)
expected_input_variables |= set(values["partial_variables"])
if values["prefix"] is not None:
expected_input_variables |= set(values["prefix"].input_variables)
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
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_selector is not None:
return self.example_selector.select_examples(kwargs)
else:
raise ValueError
[docs] def format(self, **kwargs: Any) -> str:
"""Format the prompt with the inputs.
Args:
kwargs: Any arguments to be passed to the prompt template. | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
d200e87046bd-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 examples to use.
examples = self._get_examples(**kwargs)
# Format the examples.
example_strings = [
self.example_prompt.format(**example) for example in examples
]
# Create the overall prefix.
if self.prefix is None:
prefix = ""
else:
prefix_kwargs = {
k: v for k, v in kwargs.items() if k in self.prefix.input_variables
}
for k in prefix_kwargs.keys():
kwargs.pop(k)
prefix = self.prefix.format(**prefix_kwargs)
# Create the overall suffix
suffix_kwargs = {
k: v for k, v in kwargs.items() if k in self.suffix.input_variables
}
for k in suffix_kwargs.keys():
kwargs.pop(k)
suffix = self.suffix.format(
**suffix_kwargs,
)
pieces = [prefix, *example_strings, suffix]
template = self.example_separator.join([piece for piece in pieces if piece])
# Format the template with the input variables.
return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs)
@property
def _prompt_type(self) -> str:
"""Return the prompt type key."""
return "few_shot_with_templates"
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return a dictionary of the prompt."""
if self.example_selector: | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
d200e87046bd-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)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
db9e167b75a3-0 | 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 import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.utilities.loading import try_load_from_hub
URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/prompts/"
logger = logging.getLogger(__name__)
def load_prompt_from_config(config: dict) -> BasePromptTemplate:
"""Load prompt from Config Dict."""
if "_type" not in config:
logger.warning("No `_type` key found, defaulting to `prompt`.")
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 template from disk if applicable."""
# Check if template_path exists in config.
if f"{var_name}_path" in config:
# If it does, make sure template variable doesn't also exist.
if var_name in config:
raise ValueError(
f"Both `{var_name}_path` and `{var_name}` cannot be provided."
)
# Pop the template path from the config.
template_path = Path(config.pop(f"{var_name}_path"))
# Load the template.
if template_path.suffix == ".txt":
with open(template_path) as f: | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
db9e167b75a3-1 | 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_examples(config: dict) -> dict:
"""Load examples if necessary."""
if isinstance(config["examples"], list):
pass
elif isinstance(config["examples"], str):
with open(config["examples"]) as f:
if config["examples"].endswith(".json"):
examples = json.load(f)
elif config["examples"].endswith((".yaml", ".yml")):
examples = yaml.safe_load(f)
else:
raise ValueError(
"Invalid file format. Only json or yaml formats are supported."
)
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.pop("output_parser")
output_parser_type = _config.pop("_type")
if output_parser_type == "regex_parser":
output_parser = RegexParser(**_config)
else:
raise ValueError(f"Unsupported output parser {output_parser_type}")
config["output_parser"] = output_parser
return config
def _load_few_shot_prompt(config: dict) -> FewShotPromptTemplate:
"""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. | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
db9e167b75a3-2 | config = _load_template("prefix", config)
# Load the example prompt.
if "example_prompt_path" in config:
if "example_prompt" in config:
raise ValueError(
"Only one of example_prompt and example_prompt_path should "
"be specified."
)
config["example_prompt"] = load_prompt(config.pop("example_prompt_path"))
else:
config["example_prompt"] = load_prompt_from_config(config["example_prompt"])
# Load the examples.
config = _load_examples(config)
config = _load_output_parser(config)
return FewShotPromptTemplate(**config)
def _load_prompt(config: dict) -> PromptTemplate:
"""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 a prompt from LangChainHub or local fs."""
if hub_result := try_load_from_hub(
path, _load_prompt_from_file, "prompts", {"py", "json", "yaml"}
):
return hub_result
else:
return _load_prompt_from_file(path)
def _load_prompt_from_file(file: Union[str, Path]) -> BasePromptTemplate:
"""Load prompt from file."""
# Convert file to Path object.
if isinstance(file, str):
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) | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
db9e167b75a3-3 | 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 == ".py":
spec = importlib.util.spec_from_loader(
"prompt", loader=None, origin=str(file_path)
)
if spec is None:
raise ValueError("could not load spec")
helper = importlib.util.module_from_spec(spec)
with open(file_path, "rb") as f:
exec(f.read(), helper.__dict__)
if not isinstance(helper.PROMPT, BasePromptTemplate):
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,
"few_shot": _load_few_shot_prompt,
# "few_shot_with_templates": _load_few_shot_with_templates_prompt,
}
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
ef47f5d358e6-0 | 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 BaseModel, Extra, Field, root_validator
from langchain.formatting import formatter
from langchain.schema import BaseMessage, BaseOutputParser, HumanMessage, PromptValue
def jinja2_formatter(template: str, **kwargs: Any) -> str:
"""Format a template using jinja2."""
try:
from jinja2 import Template
except ImportError:
raise ImportError(
"jinja2 not installed, which is needed to use the jinja2_formatter. "
"Please install it with `pip install jinja2`."
)
return Template(template).render(**kwargs)
def validate_jinja2(template: str, input_variables: List[str]) -> None:
input_variables_set = set(input_variables)
valid_variables = _get_jinja2_variables_from_template(template)
missing_variables = valid_variables - input_variables_set
extra_variables = input_variables_set - valid_variables
error_message = ""
if missing_variables:
error_message += f"Missing variables: {missing_variables} "
if extra_variables:
error_message += f"Extra variables: {extra_variables}"
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://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
ef47f5d358e6-1 | "Please install it with `pip install jinja2`."
)
env = Environment()
ast = env.parse(template)
variables = meta.find_undeclared_variables(ast)
return variables
DEFAULT_FORMATTER_MAPPING: Dict[str, Callable] = {
"f-string": formatter.format,
"jinja2": jinja2_formatter,
}
DEFAULT_VALIDATOR_MAPPING: Dict[str, Callable] = {
"f-string": formatter.validate_input_variables,
"jinja2": validate_jinja2,
}
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 template format. Got `{template_format}`;"
f" should be one of {valid_formats}"
)
try:
validator_func = DEFAULT_VALIDATOR_MAPPING[template_format]
validator_func(template, input_variables)
except KeyError as e:
raise ValueError(
"Invalid prompt schema; check for mismatched or missing input parameters. "
+ str(e)
)
class StringPromptValue(PromptValue):
text: str
def to_string(self) -> str:
"""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(BaseModel, ABC):
"""Base class for all prompt templates, returning a prompt."""
input_variables: List[str]
"""A list of the names of the variables the prompt template expects.""" | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
ef47f5d358e6-2 | """A list of the names of the variables the prompt template expects."""
output_parser: Optional[BaseOutputParser] = None
"""How to parse the output of calling an LLM on this formatted prompt."""
partial_variables: Mapping[str, Union[str, Callable[[], str]]] = Field(
default_factory=dict
)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
[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"]:
raise ValueError(
"Cannot have an input variable named 'stop', as it is used internally,"
" please rename."
)
if "stop" in values["partial_variables"]:
raise ValueError(
"Cannot have an partial variable named 'stop', as it is used "
"internally, please rename."
)
overall = set(values["input_variables"]).intersection(
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 of the prompt template."""
prompt_dict = self.__dict__.copy()
prompt_dict["input_variables"] = list(
set(self.input_variables).difference(kwargs)
)
prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs} | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
ef47f5d358e6-3 | prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs}
return type(self)(**prompt_dict)
def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]:
# Get partial params:
partial_kwargs = {
k: v if isinstance(v, str) else v()
for k, v in self.partial_variables.items()
}
return {**partial_kwargs, **kwargs}
[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.format(variable1="foo")
"""
@property
def _prompt_type(self) -> str:
"""Return the prompt type key."""
raise NotImplementedError
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of prompt."""
prompt_dict = super().dict(**kwargs)
prompt_dict["_type"] = self._prompt_type
return prompt_dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""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.")
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
ef47f5d358e6-4 | save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
prompt_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(prompt_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(file_path, "w") as f:
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) -> PromptValue:
"""Create Chat Messages."""
return StringPromptValue(text=self.format(**kwargs))
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
659a15f43f79-0 | 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
def _get_length_based(text: str) -> int:
return len(re.split("\n| ", text))
[docs]class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
"""Select examples based on length."""
examples: List[dict]
"""A list of the examples that the prompt template expects."""
example_prompt: PromptTemplate
"""Prompt template used to format the examples."""
get_text_length: Callable[[str], int] = _get_length_based
"""Function to measure prompt length. Defaults to word count."""
max_length: int = 2048
"""Max length for the prompt, beyond which examples are cut."""
example_text_lengths: List[int] = [] #: :meta private:
[docs] def add_example(self, example: Dict[str, str]) -> None:
"""Add new example to list."""
self.examples.append(example)
string_example = self.example_prompt.format(**example)
self.example_text_lengths.append(self.get_text_length(string_example))
@validator("example_text_lengths", always=True)
def calculate_example_text_lengths(cls, v: List[int], values: Dict) -> List[int]:
"""Calculate text lengths if they don't exist."""
# Check if text lengths were passed in
if v:
return v
# If they were not, calculate them
example_prompt = values["example_prompt"]
get_text_length = values["get_text_length"] | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
659a15f43f79-1 | 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 based on the input lengths."""
inputs = " ".join(input_variables.values())
remaining_length = self.max_length - self.get_text_length(inputs)
i = 0
examples = []
while remaining_length > 0 and i < len(self.examples):
new_length = remaining_length - self.example_text_lengths[i]
if new_length < 0:
break
else:
examples.append(self.examples[i])
remaining_length = new_length
i += 1
return examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
271c36f9f9be-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
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.vectorstores.base import VectorStore
def sorted_values(values: Dict[str, str]) -> List[Any]:
"""Return a list of values in dict sorted by key."""
return [values[val] for val in sorted(values)]
[docs]class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
"""Example selector that selects examples based on SemanticSimilarity."""
vectorstore: VectorStore
"""VectorStore than contains information about examples."""
k: int = 4
"""Number of examples to select."""
example_keys: Optional[List[str]] = None
"""Optional keys to filter examples to."""
input_keys: Optional[List[str]] = None
"""Optional keys to filter input to. If provided, the search is based on
the input variables instead of all variables."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
[docs] def add_example(self, example: Dict[str, str]) -> str:
"""Add new example to vectorstore."""
if self.input_keys:
string_example = " ".join(
sorted_values({key: example[key] for key in self.input_keys})
)
else:
string_example = " ".join(sorted_values(example))
ids = self.vectorstore.add_texts([string_example], metadatas=[example])
return ids[0] | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
271c36f9f9be-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 self.input_keys}
query = " ".join(sorted_values(input_variables))
example_docs = self.vectorstore.similarity_search(query, k=self.k)
# Get the examples from the metadata.
# This assumes that examples are stored in metadata.
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,
examples: List[dict],
embeddings: Embeddings,
vectorstore_cls: Type[VectorStore],
k: int = 4,
input_keys: Optional[List[str]] = None,
**vectorstore_cls_kwargs: Any,
) -> SemanticSimilarityExampleSelector:
"""Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Args:
examples: List of examples to use in the prompt.
embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls: A vector store DB interface class, e.g. FAISS.
k: Number of examples to select
input_keys: If provided, the search is based on the input variables
instead of all variables. | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
271c36f9f9be-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 in input_keys}))
for eg in examples
]
else:
string_examples = [" ".join(sorted_values(eg)) for eg in examples]
vectorstore = vectorstore_cls.from_texts(
string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
)
return cls(vectorstore=vectorstore, k=k, input_keys=input_keys)
[docs]class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
"""ExampleSelector that selects examples based on Max Marginal Relevance.
This was shown to improve performance in this paper:
https://arxiv.org/pdf/2211.13892.pdf
"""
fetch_k: int = 20
"""Number of examples to fetch to rerank."""
[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 self.input_keys}
query = " ".join(sorted_values(input_variables))
example_docs = self.vectorstore.max_marginal_relevance_search(
query, k=self.k, fetch_k=self.fetch_k
)
# Get the examples from the metadata.
# This assumes that examples are stored in metadata.
examples = [dict(e.metadata) for e in example_docs] | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
271c36f9f9be-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,
examples: List[dict],
embeddings: Embeddings,
vectorstore_cls: Type[VectorStore],
k: int = 4,
input_keys: Optional[List[str]] = None,
fetch_k: int = 20,
**vectorstore_cls_kwargs: Any,
) -> MaxMarginalRelevanceExampleSelector:
"""Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Args:
examples: List of examples to use in the prompt.
embeddings: An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls: A vector store DB interface class, e.g. FAISS.
k: Number of examples to select
input_keys: If provided, the search is based on the input variables
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 in input_keys}))
for eg in examples
]
else:
string_examples = [" ".join(sorted_values(eg)) for eg in examples]
vectorstore = vectorstore_cls.from_texts(
string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
) | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
271c36f9f9be-4 | )
return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
83cfb2d54b94-0 | 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]
default_output_key: Optional[str] = None
@property
def _type(self) -> str:
"""Return the type key."""
return "regex_parser"
[docs] def parse(self, text: str) -> Dict[str, str]:
"""Parse the output of an LLM call."""
match = re.search(self.regex, text)
if match:
return {key: match.group(i + 1) for i, key in enumerate(self.output_keys)}
else:
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
}
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/regex.html |
96edd75a55b6-0 | 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
from langchain.schema import BaseOutputParser, OutputParserException
T = TypeVar("T")
[docs]class OutputFixingParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors."""
parser: BaseOutputParser[T]
retry_chain: LLMChain
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_FIX_PROMPT,
) -> 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_completion = self.retry_chain.run(
instructions=self.parser.get_format_instructions(),
completion=completion,
error=repr(e),
)
parsed_completion = self.parser.parse(new_completion)
return parsed_completion
[docs] def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "output_fixing"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html |
021b59e9067b-0 | Source code for langchain.output_parsers.rail_parser
from __future__ import annotations
from typing import Any, Dict
from langchain.schema import BaseOutputParser
[docs]class GuardrailsOutputParser(BaseOutputParser):
guard: Any
@property
def _type(self) -> str:
return "guardrails"
[docs] @classmethod
def from_rail(cls, rail_file: str, num_reasks: int = 1) -> GuardrailsOutputParser:
try:
from guardrails import Guard
except ImportError:
raise ValueError(
"guardrails-ai package not installed. "
"Install it by running `pip install guardrails-ai`."
)
return cls(guard=Guard.from_rail(rail_file, num_reasks=num_reasks))
[docs] @classmethod
def from_rail_string(
cls, rail_str: str, num_reasks: int = 1
) -> GuardrailsOutputParser:
try:
from guardrails import Guard
except ImportError:
raise ValueError(
"guardrails-ai package not installed. "
"Install it by running `pip install guardrails-ai`."
)
return cls(guard=Guard.from_rail_string(rail_str, num_reasks=num_reasks))
[docs] def get_format_instructions(self) -> str:
return self.guard.raw_prompt.format_instructions
[docs] def parse(self, text: str) -> Dict:
return self.guard.parse(text)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
14981bb5d4eb-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 langchain.schema import (
BaseOutputParser,
OutputParserException,
PromptValue,
)
NAIVE_COMPLETION_RETRY = """Prompt:
{prompt}
Completion:
{completion}
Above, the Completion did not satisfy the constraints given in the Prompt.
Please try again:"""
NAIVE_COMPLETION_RETRY_WITH_ERROR = """Prompt:
{prompt}
Completion:
{completion}
Above, the Completion did not satisfy the constraints given in the Prompt.
Details: {error}
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.
Does this by passing the original prompt and the completion to another
LLM, and telling it the completion did not satisfy criteria in the prompt.
"""
parser: BaseOutputParser[T]
retry_chain: LLMChain
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_RETRY_PROMPT,
) -> RetryOutputParser[T]:
chain = LLMChain(llm=llm, prompt=prompt) | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
14981bb5d4eb-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_completion = self.retry_chain.run(
prompt=prompt_value.to_string(), completion=completion
)
parsed_completion = self.parser.parse(new_completion)
return parsed_completion
[docs] def parse(self, completion: str) -> T:
raise NotImplementedError(
"This OutputParser can only be called by the `parse_with_prompt` method."
)
[docs] def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@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 the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error that was raised back to the
LLM, which in theory should give it more information on how to fix it.
"""
parser: BaseOutputParser[T]
retry_chain: LLMChain
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_RETRY_WITH_ERROR_PROMPT,
) -> RetryWithErrorOutputParser[T]: | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
14981bb5d4eb-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 OutputParserException as e:
new_completion = self.retry_chain.run(
prompt=prompt_value.to_string(), completion=completion, error=repr(e)
)
parsed_completion = self.parser.parse(new_completion)
return parsed_completion
[docs] def parse(self, completion: str) -> T:
raise NotImplementedError(
"This OutputParser can only be called by the `parse_with_prompt` method."
)
[docs] def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "retry_with_error"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
837cd02ebe52-0 | 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']*)\.?" # : :meta private:
output_key_to_format: Dict[str, str]
no_update_value: Optional[str] = None
@property
def _type(self) -> str:
"""Return the type key."""
return "regex_dict_parser"
[docs] def parse(self, text: str) -> Dict[str, str]:
"""Parse the output of an LLM call."""
result = {}
for output_key, expected_format in self.output_key_to_format.items():
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} on text {text}"
)
elif len(matches) > 1:
raise ValueError(
f"Multiple matches found for output key: {output_key} with \
expected format {expected_format} on text {text}"
)
elif (
self.no_update_value is not None and matches[0] == self.no_update_value
):
continue
else:
result[output_key] = matches[0]
return result
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html |
ea3d0d96c8ce-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("T", bound=BaseModel)
[docs]class PydanticOutputParser(BaseOutputParser[T]):
pydantic_object: Type[T]
[docs] def parse(self, text: str) -> T:
try:
# Greedy search for 1st json candidate.
match = re.search(
r"\{.*\}", text.strip(), re.MULTILINE | re.IGNORECASE | re.DOTALL
)
json_str = ""
if match:
json_str = match.group()
json_object = json.loads(json_str, strict=False)
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(self) -> str:
schema = self.pydantic_object.schema()
# Remove extraneous fields.
reduced_schema = schema
if "title" in reduced_schema:
del reduced_schema["title"]
if "type" in reduced_schema:
del reduced_schema["type"]
# Ensure json in context is well-formed with double quotes.
schema_str = json.dumps(reduced_schema)
return PYDANTIC_FORMAT_INSTRUCTIONS.format(schema=schema_str)
@property
def _type(self) -> str: | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
ea3d0d96c8ce-1 | @property
def _type(self) -> str:
return "pydantic"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
d4a2a9b5119d-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(self) -> str:
return "list"
[docs] @abstractmethod
def parse(self, text: str) -> List[str]:
"""Parse the output of an LLM call."""
[docs]class CommaSeparatedListOutputParser(ListOutputParser):
"""Parse out comma separated lists."""
[docs] def get_format_instructions(self) -> str:
return (
"Your response should be a list of comma separated values, "
"eg: `foo, bar, baz`"
)
[docs] def parse(self, text: str) -> List[str]:
"""Parse the output of an LLM call."""
return text.strip().split(", ")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html |
3dcbcde21ecd-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 langchain.schema import BaseOutputParser
line_template = '\t"{name}": {type} // {description}'
[docs]class ResponseSchema(BaseModel):
name: str
description: str
def _get_sub_string(schema: ResponseSchema) -> str:
return line_template.format(
name=schema.name, description=schema.description, type="string"
)
[docs]class StructuredOutputParser(BaseOutputParser):
response_schemas: List[ResponseSchema]
[docs] @classmethod
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_schemas]
)
return STRUCTURED_FORMAT_INSTRUCTIONS.format(format=schema_str)
[docs] def parse(self, text: str) -> Any:
expected_keys = [rs.name for rs in self.response_schemas]
return parse_and_check_json_markdown(text, expected_keys)
@property
def _type(self) -> str:
return "structured"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html |
c41df8cdef7e-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_date: datetime = datetime(1, 1, 1),
end_date: datetime = datetime.now() + timedelta(days=3650),
) -> List[str]:
"""
Generates n random datetime strings conforming to the
given pattern within the specified date range.
Pattern should be a string containing the desired format codes.
start_date and end_date should be datetime objects representing
the start and end of the date range.
"""
examples = []
delta = end_date - start_date
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 = "%Y-%m-%dT%H:%M:%S.%fZ"
[docs] def get_format_instructions(self) -> str:
examples = comma_list(_generate_random_datetime_strings(self.format))
return f"""Write a datetime string that matches the
following pattern: "{self.format}". Examples: {examples}"""
[docs] def parse(self, response: str) -> datetime:
try:
return datetime.strptime(response.strip(), self.format)
except ValueError as e:
raise OutputParserException(
f"Could not parse datetime string: {response}"
) from e
@property | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
c41df8cdef7e-1 | ) from e
@property
def _type(self) -> str:
return "datetime"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
3f111b6292be-0 | Source code for langchain.document_loaders.notebook
"""Loader that loads .ipynb notebook files."""
import json
from pathlib import Path
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_cells(
cell: dict, include_outputs: bool, max_output_length: int, traceback: bool
) -> str:
"""Combine cells information in a readable format ready to be used."""
cell_type = cell["cell_type"]
source = cell["source"]
output = cell["outputs"]
if include_outputs and cell_type == "code" and output:
if "ename" in output[0].keys():
error_name = output[0]["ename"]
error_value = output[0]["evalue"]
if traceback:
traceback = output[0]["traceback"]
return (
f"'{cell_type}' cell: '{source}'\n, gives error '{error_name}',"
f" with description '{error_value}'\n"
f"and traceback '{traceback}'\n\n"
)
else:
return (
f"'{cell_type}' cell: '{source}'\n, gives error '{error_name}',"
f"with description '{error_value}'\n\n"
)
elif output[0]["output_type"] == "stream":
output = output[0]["text"]
min_output = min(max_output_length, len(output))
return (
f"'{cell_type}' cell: '{source}'\n with "
f"output: '{output[:min_output]}'\n\n"
)
else:
return f"'{cell_type}' cell: '{source}'\n\n" | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
3f111b6292be-1 | return f"'{cell_type}' cell: '{source}'\n\n"
return ""
def remove_newlines(x: Any) -> Any:
"""Remove recursively newlines, no matter the data structure they are stored in."""
import pandas as pd
if isinstance(x, str):
return x.replace("\n", "")
elif isinstance(x, list):
return [remove_newlines(elem) for elem in x]
elif isinstance(x, pd.DataFrame):
return x.applymap(remove_newlines)
else:
return x
[docs]class NotebookLoader(BaseLoader):
"""Loader that loads .ipynb notebook files."""
def __init__(
self,
path: str,
include_outputs: bool = False,
max_output_length: int = 10,
remove_newline: bool = False,
traceback: bool = False,
):
"""Initialize with path."""
self.file_path = path
self.include_outputs = include_outputs
self.max_output_length = max_output_length
self.remove_newline = remove_newline
self.traceback = traceback
[docs] def load(
self,
) -> List[Document]:
"""Load documents."""
try:
import pandas as pd
except ImportError:
raise ImportError(
"pandas is needed for Notebook Loader, "
"please install with `pip install pandas`"
)
p = Path(self.file_path)
with open(p, encoding="utf8") as f:
d = json.load(f)
data = pd.json_normalize(d["cells"])
filtered_data = data[["cell_type", "source", "outputs"]]
if self.remove_newline: | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
3f111b6292be-2 | if self.remove_newline:
filtered_data = filtered_data.applymap(remove_newlines)
text = filtered_data.apply(
lambda x: concatenate_cells(
x, self.include_outputs, self.max_output_length, self.traceback
),
axis=1,
).str.cat(sep=" ")
metadata = {"source": str(p)}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
cdda8055d124-0 | Source code for langchain.document_loaders.duckdb_loader
from typing import Dict, List, Optional, cast
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class DuckDBLoader(BaseLoader):
"""Loads a query result from DuckDB into a list of documents.
Each document represents one row of the result. The `page_content_columns`
are written into the `page_content` of the document. The `metadata_columns`
are written into the `metadata` of the document. By default, all columns
are written into the `page_content` and none into the `metadata`.
"""
def __init__(
self,
query: str,
database: str = ":memory:",
read_only: bool = False,
config: Optional[Dict[str, str]] = None,
page_content_columns: Optional[List[str]] = None,
metadata_columns: Optional[List[str]] = None,
):
self.query = query
self.database = database
self.read_only = read_only
self.config = config or {}
self.page_content_columns = page_content_columns
self.metadata_columns = metadata_columns
[docs] def load(self) -> List[Document]:
try:
import duckdb
except ImportError:
raise ImportError(
"Could not import duckdb python package. "
"Please install it with `pip install duckdb`."
)
docs = []
with duckdb.connect(
database=self.database, read_only=self.read_only, config=self.config
) as con:
query_result = con.execute(self.query)
results = query_result.fetchall()
description = cast(list, query_result.description) | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/duckdb_loader.html |
cdda8055d124-1 | results = query_result.fetchall()
description = cast(list, query_result.description)
field_names = [c[0] for c in description]
if self.page_content_columns is None:
page_content_columns = field_names
else:
page_content_columns = self.page_content_columns
if self.metadata_columns is None:
metadata_columns = []
else:
metadata_columns = self.metadata_columns
for result in results:
page_content = "\n".join(
f"{column}: {result[field_names.index(column)]}"
for column in page_content_columns
)
metadata = {
column: result[field_names.index(column)]
for column in metadata_columns
}
doc = Document(page_content=page_content, metadata=metadata)
docs.append(doc)
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/duckdb_loader.html |
93b2acd9c32a-0 | Source code for langchain.document_loaders.gitbook
"""Loader that loads GitBook."""
from typing import Any, List, Optional
from urllib.parse import urljoin, urlparse
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class GitbookLoader(WebBaseLoader):
"""Load GitBook data.
1. load from either a single page, or
2. load all (relative) paths in the navbar.
"""
def __init__(
self,
web_page: str,
load_all_paths: bool = False,
base_url: Optional[str] = None,
content_selector: str = "main",
):
"""Initialize with web page and whether to load all paths.
Args:
web_page: The web page to load or the starting point from where
relative paths are discovered.
load_all_paths: If set to True, all relative paths in the navbar
are loaded instead of only `web_page`.
base_url: If `load_all_paths` is True, the relative paths are
appended to this base url. Defaults to `web_page` if not set.
"""
self.base_url = base_url or web_page
if self.base_url.endswith("/"):
self.base_url = self.base_url[:-1]
if load_all_paths:
# set web_path to the sitemap if we want to crawl all paths
web_paths = f"{self.base_url}/sitemap.xml"
else:
web_paths = web_page
super().__init__(web_paths)
self.load_all_paths = load_all_paths
self.content_selector = content_selector
[docs] def load(self) -> List[Document]: | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/gitbook.html |
93b2acd9c32a-1 | [docs] def load(self) -> List[Document]:
"""Fetch text from one single GitBook page."""
if self.load_all_paths:
soup_info = self.scrape()
relative_paths = self._get_paths(soup_info)
documents = []
for path in relative_paths:
url = urljoin(self.base_url, path)
print(f"Fetching text from {url}")
soup_info = self._scrape(url)
documents.append(self._get_document(soup_info, url))
return [d for d in documents if d]
else:
soup_info = self.scrape()
documents = [self._get_document(soup_info, self.web_path)]
return [d for d in documents if d]
def _get_document(
self, soup: Any, custom_url: Optional[str] = None
) -> Optional[Document]:
"""Fetch content from page and return Document."""
page_content_raw = soup.find(self.content_selector)
if not page_content_raw:
return None
content = page_content_raw.get_text(separator="\n").strip()
title_if_exists = page_content_raw.find("h1")
title = title_if_exists.text if title_if_exists else ""
metadata = {"source": custom_url or self.web_path, "title": title}
return Document(page_content=content, metadata=metadata)
def _get_paths(self, soup: Any) -> List[str]:
"""Fetch all relative paths in the navbar."""
return [urlparse(loc.text).path for loc in soup.find_all("loc")]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/gitbook.html |
b33a94e51cbb-0 | Source code for langchain.document_loaders.bilibili
import json
import re
import warnings
from typing import List, Tuple
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class BiliBiliLoader(BaseLoader):
"""Loader that loads bilibili transcripts."""
def __init__(self, video_urls: List[str]):
"""Initialize with bilibili url."""
self.video_urls = video_urls
[docs] def load(self) -> List[Document]:
"""Load from bilibili url."""
results = []
for url in self.video_urls:
transcript, video_info = self._get_bilibili_subs_and_info(url)
doc = Document(page_content=transcript, metadata=video_info)
results.append(doc)
return results
def _get_bilibili_subs_and_info(self, url: str) -> Tuple[str, dict]:
try:
from bilibili_api import sync, video
except ImportError:
raise ValueError(
"requests package not found, please install it with "
"`pip install bilibili-api-python`"
)
bvid = re.search(r"BV\w+", url)
if bvid is not None:
v = video.Video(bvid=bvid.group())
else:
aid = re.search(r"av[0-9]+", url)
if aid is not None:
try:
v = video.Video(aid=int(aid.group()[2:]))
except AttributeError:
raise ValueError(f"{url} is not bilibili url.")
else:
raise ValueError(f"{url} is not bilibili url.")
video_info = sync(v.get_info()) | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/bilibili.html |
b33a94e51cbb-1 | video_info = sync(v.get_info())
video_info.update({"url": url})
# Get subtitle url
subtitle = video_info.pop("subtitle")
sub_list = subtitle["list"]
if sub_list:
sub_url = sub_list[0]["subtitle_url"]
result = requests.get(sub_url)
raw_sub_titles = json.loads(result.content)["body"]
raw_transcript = " ".join([c["content"] for c in raw_sub_titles])
raw_transcript_with_meta_info = (
f"Video Title: {video_info['title']},"
f"description: {video_info['desc']}\n\n"
f"Transcript: {raw_transcript}"
)
return raw_transcript_with_meta_info, video_info
else:
raw_transcript = ""
warnings.warn(
f"""
No subtitles found for video: {url}.
Return Empty transcript.
"""
)
return raw_transcript, video_info
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/bilibili.html |
77aafcf8ea89-0 | Source code for langchain.document_loaders.hn
"""Loader that loads HN."""
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class HNLoader(WebBaseLoader):
"""Load Hacker News data from either main page results or the comments page."""
[docs] def load(self) -> List[Document]:
"""Get important HN webpage information.
Components are:
- title
- content
- source url,
- time of post
- author of the post
- number of comments
- rank of the post
"""
soup_info = self.scrape()
if "item" in self.web_path:
return self.load_comments(soup_info)
else:
return self.load_results(soup_info)
[docs] def load_comments(self, soup_info: Any) -> List[Document]:
"""Load comments from a HN post."""
comments = soup_info.select("tr[class='athing comtr']")
title = soup_info.select_one("tr[id='pagespace']").get("title")
return [
Document(
page_content=comment.text.strip(),
metadata={"source": self.web_path, "title": title},
)
for comment in comments
]
[docs] def load_results(self, soup: Any) -> List[Document]:
"""Load items from an HN page."""
items = soup.select("tr[class='athing']")
documents = []
for lineItem in items:
ranking = lineItem.select_one("span[class='rank']").text
link = lineItem.find("span", {"class": "titleline"}).find("a").get("href") | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hn.html |
77aafcf8ea89-1 | title = lineItem.find("span", {"class": "titleline"}).text.strip()
metadata = {
"source": self.web_path,
"title": title,
"link": link,
"ranking": ranking,
}
documents.append(
Document(
page_content=title, link=link, ranking=ranking, metadata=metadata
)
)
return documents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hn.html |
8d80ecf25ac5-0 | Source code for langchain.document_loaders.twitter
"""Twitter document loader."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import tweepy
from tweepy import OAuth2BearerHandler, OAuthHandler
def _dependable_tweepy_import() -> tweepy:
try:
import tweepy
except ImportError:
raise ImportError(
"tweepy package not found, please install it with `pip install tweepy`"
)
return tweepy
[docs]class TwitterTweetLoader(BaseLoader):
"""Twitter tweets loader.
Read tweets of user twitter handle.
First you need to go to
`https://developer.twitter.com/en/docs/twitter-api
/getting-started/getting-access-to-the-twitter-api`
to get your token. And create a v2 version of the app.
"""
def __init__(
self,
auth_handler: Union[OAuthHandler, OAuth2BearerHandler],
twitter_users: Sequence[str],
number_tweets: Optional[int] = 100,
):
self.auth = auth_handler
self.twitter_users = twitter_users
self.number_tweets = number_tweets
[docs] def load(self) -> List[Document]:
"""Load tweets."""
tweepy = _dependable_tweepy_import()
api = tweepy.API(self.auth, parser=tweepy.parsers.JSONParser())
results: List[Document] = []
for username in self.twitter_users:
tweets = api.user_timeline(screen_name=username, count=self.number_tweets)
user = api.get_user(screen_name=username) | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/twitter.html |
8d80ecf25ac5-1 | user = api.get_user(screen_name=username)
docs = self._format_tweets(tweets, user)
results.extend(docs)
return results
def _format_tweets(
self, tweets: List[Dict[str, Any]], user_info: dict
) -> Iterable[Document]:
"""Format tweets into a string."""
for tweet in tweets:
metadata = {
"created_at": tweet["created_at"],
"user_info": user_info,
}
yield Document(
page_content=tweet["text"],
metadata=metadata,
)
[docs] @classmethod
def from_bearer_token(
cls,
oauth2_bearer_token: str,
twitter_users: Sequence[str],
number_tweets: Optional[int] = 100,
) -> TwitterTweetLoader:
"""Create a TwitterTweetLoader from OAuth2 bearer token."""
tweepy = _dependable_tweepy_import()
auth = tweepy.OAuth2BearerHandler(oauth2_bearer_token)
return cls(
auth_handler=auth,
twitter_users=twitter_users,
number_tweets=number_tweets,
)
[docs] @classmethod
def from_secrets(
cls,
access_token: str,
access_token_secret: str,
consumer_key: str,
consumer_secret: str,
twitter_users: Sequence[str],
number_tweets: Optional[int] = 100,
) -> TwitterTweetLoader:
"""Create a TwitterTweetLoader from access tokens and secrets."""
tweepy = _dependable_tweepy_import()
auth = tweepy.OAuthHandler(
access_token=access_token,
access_token_secret=access_token_secret, | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/twitter.html |
8d80ecf25ac5-2 | access_token=access_token,
access_token_secret=access_token_secret,
consumer_key=consumer_key,
consumer_secret=consumer_secret,
)
return cls(
auth_handler=auth,
twitter_users=twitter_users,
number_tweets=number_tweets,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/twitter.html |
5386a81d4538-0 | Source code for langchain.document_loaders.notiondb
"""Notion DB loader for langchain"""
from typing import Any, Dict, List, Optional
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
NOTION_BASE_URL = "https://api.notion.com/v1"
DATABASE_URL = NOTION_BASE_URL + "/databases/{database_id}/query"
PAGE_URL = NOTION_BASE_URL + "/pages/{page_id}"
BLOCK_URL = NOTION_BASE_URL + "/blocks/{block_id}/children"
[docs]class NotionDBLoader(BaseLoader):
"""Notion DB Loader.
Reads content from pages within a Noton Database.
Args:
integration_token (str): Notion integration token.
database_id (str): Notion database id.
request_timeout_sec (int): Timeout for Notion requests in seconds.
"""
def __init__(
self,
integration_token: str,
database_id: str,
request_timeout_sec: Optional[int] = 10,
) -> None:
"""Initialize with parameters."""
if not integration_token:
raise ValueError("integration_token must be provided")
if not database_id:
raise ValueError("database_id must be provided")
self.token = integration_token
self.database_id = database_id
self.headers = {
"Authorization": "Bearer " + self.token,
"Content-Type": "application/json",
"Notion-Version": "2022-06-28",
}
self.request_timeout_sec = request_timeout_sec
[docs] def load(self) -> List[Document]:
"""Load documents from the Notion database.
Returns:
List[Document]: List of documents.
""" | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notiondb.html |
5386a81d4538-1 | Returns:
List[Document]: List of documents.
"""
page_ids = self._retrieve_page_ids()
return list(self.load_page(page_id) for page_id in page_ids)
def _retrieve_page_ids(
self, query_dict: Dict[str, Any] = {"page_size": 100}
) -> List[str]:
"""Get all the pages from a Notion database."""
pages: List[Dict[str, Any]] = []
while True:
data = self._request(
DATABASE_URL.format(database_id=self.database_id),
method="POST",
query_dict=query_dict,
)
pages.extend(data.get("results"))
if not data.get("has_more"):
break
query_dict["start_cursor"] = data.get("next_cursor")
page_ids = [page["id"] for page in pages]
return page_ids
[docs] def load_page(self, page_id: str) -> Document:
"""Read a page."""
data = self._request(PAGE_URL.format(page_id=page_id))
# load properties as metadata
metadata: Dict[str, Any] = {}
for prop_name, prop_data in data["properties"].items():
prop_type = prop_data["type"]
if prop_type == "rich_text":
value = (
prop_data["rich_text"][0]["plain_text"]
if prop_data["rich_text"]
else None
)
elif prop_type == "title":
value = (
prop_data["title"][0]["plain_text"] if prop_data["title"] else None
)
elif prop_type == "multi_select":
value = (
[item["name"] for item in prop_data["multi_select"]] | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notiondb.html |
5386a81d4538-2 | [item["name"] for item in prop_data["multi_select"]]
if prop_data["multi_select"]
else []
)
elif prop_type == "url":
value = prop_data["url"]
else:
value = None
metadata[prop_name.lower()] = value
metadata["id"] = page_id
return Document(page_content=self._load_blocks(page_id), metadata=metadata)
def _load_blocks(self, block_id: str, num_tabs: int = 0) -> str:
"""Read a block and its children."""
result_lines_arr: List[str] = []
cur_block_id: str = block_id
while cur_block_id:
data = self._request(BLOCK_URL.format(block_id=cur_block_id))
for result in data["results"]:
result_obj = result[result["type"]]
if "rich_text" not in result_obj:
continue
cur_result_text_arr: List[str] = []
for rich_text in result_obj["rich_text"]:
if "text" in rich_text:
cur_result_text_arr.append(
"\t" * num_tabs + rich_text["text"]["content"]
)
if result["has_children"]:
children_text = self._load_blocks(
result["id"], num_tabs=num_tabs + 1
)
cur_result_text_arr.append(children_text)
result_lines_arr.append("\n".join(cur_result_text_arr))
cur_block_id = data.get("next_cursor")
return "\n".join(result_lines_arr)
def _request(
self, url: str, method: str = "GET", query_dict: Dict[str, Any] = {}
) -> Any:
res = requests.request(
method, | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notiondb.html |
5386a81d4538-3 | ) -> Any:
res = requests.request(
method,
url,
headers=self.headers,
json=query_dict,
timeout=self.request_timeout_sec,
)
res.raise_for_status()
return res.json()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notiondb.html |
aaa00843d773-0 | Source code for langchain.document_loaders.airbyte_json
"""Loader that loads local airbyte json files."""
import json
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utils import stringify_dict
[docs]class AirbyteJSONLoader(BaseLoader):
"""Loader that loads local airbyte json files."""
def __init__(self, file_path: str):
"""Initialize with file path. This should start with '/tmp/airbyte_local/'."""
self.file_path = file_path
[docs] def load(self) -> List[Document]:
"""Load file."""
text = ""
for line in open(self.file_path, "r"):
data = json.loads(line)["_airbyte_data"]
text += stringify_dict(data)
metadata = {"source": self.file_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/airbyte_json.html |
d0111970411e-0 | Source code for langchain.document_loaders.imsdb
"""Loader that loads IMSDb."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class IMSDbLoader(WebBaseLoader):
"""Loader that loads IMSDb webpages."""
[docs] def load(self) -> List[Document]:
"""Load webpage."""
soup = self.scrape()
text = soup.select_one("td[class='scrtext']").text
metadata = {"source": self.web_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/imsdb.html |
64c4c702fd90-0 | Source code for langchain.document_loaders.powerpoint
"""Loader that loads powerpoint files."""
import os
from typing import List
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class UnstructuredPowerPointLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load powerpoint files."""
def _get_elements(self) -> List:
from unstructured.__version__ import __version__ as __unstructured_version__
from unstructured.file_utils.filetype import FileType, detect_filetype
unstructured_version = tuple(
[int(x) for x in __unstructured_version__.split(".")]
)
# NOTE(MthwRobinson) - magic will raise an import error if the libmagic
# system dependency isn't installed. If it's not installed, we'll just
# check the file extension
try:
import magic # noqa: F401
is_ppt = detect_filetype(self.file_path) == FileType.PPT
except ImportError:
_, extension = os.path.splitext(str(self.file_path))
is_ppt = extension == ".ppt"
if is_ppt and unstructured_version < (0, 4, 11):
raise ValueError(
f"You are on unstructured version {__unstructured_version__}. "
"Partitioning .ppt files is only supported in unstructured>=0.4.11. "
"Please upgrade the unstructured package and try again."
)
if is_ppt:
from unstructured.partition.ppt import partition_ppt
return partition_ppt(filename=self.file_path, **self.unstructured_kwargs)
else:
from unstructured.partition.pptx import partition_pptx
return partition_pptx(filename=self.file_path, **self.unstructured_kwargs) | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/powerpoint.html |
64c4c702fd90-1 | return partition_pptx(filename=self.file_path, **self.unstructured_kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/powerpoint.html |
dcac1bf37121-0 | Source code for langchain.document_loaders.image_captions
"""
Loader that loads image captions
By default, the loader utilizes the pre-trained BLIP image captioning model.
https://huggingface.co/Salesforce/blip-image-captioning-base
"""
from typing import Any, List, Tuple, Union
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ImageCaptionLoader(BaseLoader):
"""Loader that loads the captions of an image"""
def __init__(
self,
path_images: Union[str, List[str]],
blip_processor: str = "Salesforce/blip-image-captioning-base",
blip_model: str = "Salesforce/blip-image-captioning-base",
):
"""
Initialize with a list of image paths
"""
if isinstance(path_images, str):
self.image_paths = [path_images]
else:
self.image_paths = path_images
self.blip_processor = blip_processor
self.blip_model = blip_model
[docs] def load(self) -> List[Document]:
"""
Load from a list of image files
"""
try:
from transformers import BlipForConditionalGeneration, BlipProcessor
except ImportError:
raise ImportError(
"`transformers` package not found, please install with "
"`pip install transformers`."
)
processor = BlipProcessor.from_pretrained(self.blip_processor)
model = BlipForConditionalGeneration.from_pretrained(self.blip_model)
results = []
for path_image in self.image_paths:
caption, metadata = self._get_captions_and_metadata(
model=model, processor=processor, path_image=path_image
) | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/image_captions.html |
dcac1bf37121-1 | model=model, processor=processor, path_image=path_image
)
doc = Document(page_content=caption, metadata=metadata)
results.append(doc)
return results
def _get_captions_and_metadata(
self, model: Any, processor: Any, path_image: str
) -> Tuple[str, dict]:
"""
Helper function for getting the captions and metadata of an image
"""
try:
from PIL import Image
except ImportError:
raise ImportError(
"`PIL` package not found, please install with `pip install pillow`"
)
try:
if path_image.startswith("http://") or path_image.startswith("https://"):
image = Image.open(requests.get(path_image, stream=True).raw).convert(
"RGB"
)
else:
image = Image.open(path_image).convert("RGB")
except Exception:
raise ValueError(f"Could not get image data for {path_image}")
inputs = processor(image, "an image of", return_tensors="pt")
output = model.generate(**inputs)
caption: str = processor.decode(output[0])
metadata: dict = {"image_path": path_image}
return caption, metadata
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/image_captions.html |
cef1e811a8b4-0 | Source code for langchain.document_loaders.obsidian
"""Loader that loads Obsidian directory dump."""
import re
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ObsidianLoader(BaseLoader):
"""Loader that loads Obsidian files from disk."""
FRONT_MATTER_REGEX = re.compile(r"^---\n(.*?)\n---\n", re.MULTILINE | re.DOTALL)
def __init__(
self, path: str, encoding: str = "UTF-8", collect_metadata: bool = True
):
"""Initialize with path."""
self.file_path = path
self.encoding = encoding
self.collect_metadata = collect_metadata
def _parse_front_matter(self, content: str) -> dict:
"""Parse front matter metadata from the content and return it as a dict."""
if not self.collect_metadata:
return {}
match = self.FRONT_MATTER_REGEX.search(content)
front_matter = {}
if match:
lines = match.group(1).split("\n")
for line in lines:
if ":" in line:
key, value = line.split(":", 1)
front_matter[key.strip()] = value.strip()
else:
# Skip lines without a colon
continue
return front_matter
def _remove_front_matter(self, content: str) -> str:
"""Remove front matter metadata from the given content."""
if not self.collect_metadata:
return content
return self.FRONT_MATTER_REGEX.sub("", content)
[docs] def load(self) -> List[Document]:
"""Load documents."""
ps = list(Path(self.file_path).glob("**/*.md")) | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/obsidian.html |
cef1e811a8b4-1 | ps = list(Path(self.file_path).glob("**/*.md"))
docs = []
for p in ps:
with open(p, encoding=self.encoding) as f:
text = f.read()
front_matter = self._parse_front_matter(text)
text = self._remove_front_matter(text)
metadata = {
"source": str(p.name),
"path": str(p),
"created": p.stat().st_ctime,
"last_modified": p.stat().st_mtime,
"last_accessed": p.stat().st_atime,
**front_matter,
}
docs.append(Document(page_content=text, metadata=metadata))
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/obsidian.html |
b58308569745-0 | Source code for langchain.document_loaders.notion
"""Loader that loads Notion directory dump."""
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class NotionDirectoryLoader(BaseLoader):
"""Loader that loads Notion directory dump."""
def __init__(self, path: str):
"""Initialize with path."""
self.file_path = path
[docs] def load(self) -> List[Document]:
"""Load documents."""
ps = list(Path(self.file_path).glob("**/*.md"))
docs = []
for p in ps:
with open(p) as f:
text = f.read()
metadata = {"source": str(p)}
docs.append(Document(page_content=text, metadata=metadata))
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notion.html |
f5936365ad6f-0 | Source code for langchain.document_loaders.facebook_chat
"""Loader that loads Facebook chat json dump."""
import datetime
import json
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_rows(row: dict) -> str:
"""Combine message information in a readable format ready to be used."""
sender = row["sender_name"]
text = row["content"]
date = datetime.datetime.fromtimestamp(row["timestamp_ms"] / 1000).strftime(
"%Y-%m-%d %H:%M:%S"
)
return f"{sender} on {date}: {text}\n\n"
[docs]class FacebookChatLoader(BaseLoader):
"""Loader that loads Facebook messages json directory dump."""
def __init__(self, path: str):
"""Initialize with path."""
self.file_path = path
[docs] def load(self) -> List[Document]:
"""Load documents."""
p = Path(self.file_path)
with open(p, encoding="utf8") as f:
d = json.load(f)
text = "".join(
concatenate_rows(message)
for message in d["messages"]
if message.get("content") and isinstance(message["content"], str)
)
metadata = {"source": str(p)}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/facebook_chat.html |
645b69a40b37-0 | Source code for langchain.document_loaders.sitemap
"""Loader that fetches a sitemap and loads those URLs."""
import itertools
import re
from typing import Any, Callable, Generator, Iterable, List, Optional
from langchain.document_loaders.web_base import WebBaseLoader
from langchain.schema import Document
def _default_parsing_function(content: Any) -> str:
return str(content.get_text())
def _default_meta_function(meta: dict, _content: Any) -> dict:
return {"source": meta["loc"], **meta}
def _batch_block(iterable: Iterable, size: int) -> Generator[List[dict], None, None]:
it = iter(iterable)
while item := list(itertools.islice(it, size)):
yield item
[docs]class SitemapLoader(WebBaseLoader):
"""Loader that fetches a sitemap and loads those URLs."""
def __init__(
self,
web_path: str,
filter_urls: Optional[List[str]] = None,
parsing_function: Optional[Callable] = None,
blocksize: Optional[int] = None,
blocknum: int = 0,
meta_function: Optional[Callable] = None,
is_local: bool = False,
):
"""Initialize with webpage path and optional filter URLs.
Args:
web_path: url of the sitemap. can also be a local path
filter_urls: list of strings or regexes that will be applied to filter the
urls that are parsed and loaded
parsing_function: Function to parse bs4.Soup output
blocksize: number of sitemap locations per block
blocknum: the number of the block that should be loaded - zero indexed
meta_function: Function to parse bs4.Soup output for metadata | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/sitemap.html |
645b69a40b37-1 | meta_function: Function to parse bs4.Soup output for metadata
remember when setting this method to also copy metadata["loc"]
to metadata["source"] if you are using this field
is_local: whether the sitemap is a local file
"""
if blocksize is not None and blocksize < 1:
raise ValueError("Sitemap blocksize should be at least 1")
if blocknum < 0:
raise ValueError("Sitemap blocknum can not be lower then 0")
try:
import lxml # noqa:F401
except ImportError:
raise ImportError(
"lxml package not found, please install it with " "`pip install lxml`"
)
super().__init__(web_path)
self.filter_urls = filter_urls
self.parsing_function = parsing_function or _default_parsing_function
self.meta_function = meta_function or _default_meta_function
self.blocksize = blocksize
self.blocknum = blocknum
self.is_local = is_local
[docs] def parse_sitemap(self, soup: Any) -> List[dict]:
"""Parse sitemap xml and load into a list of dicts."""
els = []
for url in soup.find_all("url"):
loc = url.find("loc")
if not loc:
continue
if self.filter_urls and not any(
re.match(r, loc.text) for r in self.filter_urls
):
continue
els.append(
{
tag: prop.text
for tag in ["loc", "lastmod", "changefreq", "priority"]
if (prop := url.find(tag))
}
)
for sitemap in soup.find_all("sitemap"): | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/sitemap.html |
645b69a40b37-2 | }
)
for sitemap in soup.find_all("sitemap"):
loc = sitemap.find("loc")
if not loc:
continue
soup_child = self.scrape_all([loc.text], "xml")[0]
els.extend(self.parse_sitemap(soup_child))
return els
[docs] def load(self) -> List[Document]:
"""Load sitemap."""
if self.is_local:
try:
import bs4
except ImportError:
raise ImportError(
"beautifulsoup4 package not found, please install it"
" with `pip install beautifulsoup4`"
)
fp = open(self.web_path)
soup = bs4.BeautifulSoup(fp, "xml")
else:
soup = self.scrape("xml")
els = self.parse_sitemap(soup)
if self.blocksize is not None:
elblocks = list(_batch_block(els, self.blocksize))
blockcount = len(elblocks)
if blockcount - 1 < self.blocknum:
raise ValueError(
"Selected sitemap does not contain enough blocks for given blocknum"
)
else:
els = elblocks[self.blocknum]
results = self.scrape_all([el["loc"].strip() for el in els if "loc" in el])
return [
Document(
page_content=self.parsing_function(results[i]),
metadata=self.meta_function(els[i], results[i]),
)
for i in range(len(results))
]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/sitemap.html |
87fb606992a2-0 | Source code for langchain.document_loaders.image
"""Loader that loads image files."""
from typing import List
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class UnstructuredImageLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load image files, such as PNGs and JPGs."""
def _get_elements(self) -> List:
from unstructured.partition.image import partition_image
return partition_image(filename=self.file_path, **self.unstructured_kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/image.html |
fe134a50b75b-0 | Source code for langchain.document_loaders.modern_treasury
"""Loader that fetches data from Modern Treasury"""
import json
import urllib.request
from base64 import b64encode
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utils import get_from_env, stringify_value
MODERN_TREASURY_ENDPOINTS = {
"payment_orders": "https://app.moderntreasury.com/api/payment_orders",
"expected_payments": "https://app.moderntreasury.com/api/expected_payments",
"returns": "https://app.moderntreasury.com/api/returns",
"incoming_payment_details": "https://app.moderntreasury.com/api/\
incoming_payment_details",
"counterparties": "https://app.moderntreasury.com/api/counterparties",
"internal_accounts": "https://app.moderntreasury.com/api/internal_accounts",
"external_accounts": "https://app.moderntreasury.com/api/external_accounts",
"transactions": "https://app.moderntreasury.com/api/transactions",
"ledgers": "https://app.moderntreasury.com/api/ledgers",
"ledger_accounts": "https://app.moderntreasury.com/api/ledger_accounts",
"ledger_transactions": "https://app.moderntreasury.com/api/ledger_transactions",
"events": "https://app.moderntreasury.com/api/events",
"invoices": "https://app.moderntreasury.com/api/invoices",
}
[docs]class ModernTreasuryLoader(BaseLoader):
def __init__(
self,
resource: str,
organization_id: Optional[str] = None, | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/modern_treasury.html |
fe134a50b75b-1 | resource: str,
organization_id: Optional[str] = None,
api_key: Optional[str] = None,
) -> None:
self.resource = resource
organization_id = organization_id or get_from_env(
"organization_id", "MODERN_TREASURY_ORGANIZATION_ID"
)
api_key = api_key or get_from_env("api_key", "MODERN_TREASURY_API_KEY")
credentials = f"{organization_id}:{api_key}".encode("utf-8")
basic_auth_token = b64encode(credentials).decode("utf-8")
self.headers = {"Authorization": f"Basic {basic_auth_token}"}
def _make_request(self, url: str) -> List[Document]:
request = urllib.request.Request(url, headers=self.headers)
with urllib.request.urlopen(request) as response:
json_data = json.loads(response.read().decode())
text = stringify_value(json_data)
metadata = {"source": url}
return [Document(page_content=text, metadata=metadata)]
def _get_resource(self) -> List[Document]:
endpoint = MODERN_TREASURY_ENDPOINTS.get(self.resource)
if endpoint is None:
return []
return self._make_request(endpoint)
[docs] def load(self) -> List[Document]:
return self._get_resource()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/modern_treasury.html |
9a285a16332c-0 | Source code for langchain.document_loaders.hugging_face_dataset
"""Loader that loads HuggingFace datasets."""
from typing import Iterator, List, Mapping, Optional, Sequence, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class HuggingFaceDatasetLoader(BaseLoader):
"""Loading logic for loading documents from the Hugging Face Hub."""
def __init__(
self,
path: str,
page_content_column: str = "text",
name: Optional[str] = None,
data_dir: Optional[str] = None,
data_files: Optional[
Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]
] = None,
cache_dir: Optional[str] = None,
keep_in_memory: Optional[bool] = None,
save_infos: bool = False,
use_auth_token: Optional[Union[bool, str]] = None,
num_proc: Optional[int] = None,
):
"""Initialize the HuggingFaceDatasetLoader.
Args:
path: Path or name of the dataset.
page_content_column: Page content column name.
name: Name of the dataset configuration.
data_dir: Data directory of the dataset configuration.
data_files: Path(s) to source data file(s).
cache_dir: Directory to read/write data.
keep_in_memory: Whether to copy the dataset in-memory.
save_infos: Save the dataset information (checksums/size/splits/...).
use_auth_token: Bearer token for remote files on the Datasets Hub.
num_proc: Number of processes.
"""
self.path = path
self.page_content_column = page_content_column
self.name = name | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hugging_face_dataset.html |
9a285a16332c-1 | self.page_content_column = page_content_column
self.name = name
self.data_dir = data_dir
self.data_files = data_files
self.cache_dir = cache_dir
self.keep_in_memory = keep_in_memory
self.save_infos = save_infos
self.use_auth_token = use_auth_token
self.num_proc = num_proc
[docs] def lazy_load(
self,
) -> Iterator[Document]:
"""Load documents lazily."""
try:
from datasets import load_dataset
except ImportError:
raise ImportError(
"Could not import datasets python package. "
"Please install it with `pip install datasets`."
)
dataset = load_dataset(
path=self.path,
name=self.name,
data_dir=self.data_dir,
data_files=self.data_files,
cache_dir=self.cache_dir,
keep_in_memory=self.keep_in_memory,
save_infos=self.save_infos,
use_auth_token=self.use_auth_token,
num_proc=self.num_proc,
)
yield from (
Document(
page_content=row.pop(self.page_content_column),
metadata=row,
)
for key in dataset.keys()
for row in dataset[key]
)
[docs] def load(self) -> List[Document]:
"""Load documents."""
return list(self.lazy_load())
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hugging_face_dataset.html |
93547699c3d2-0 | Source code for langchain.document_loaders.s3_file
"""Loading logic for loading documents from an s3 file."""
import os
import tempfile
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class S3FileLoader(BaseLoader):
"""Loading logic for loading documents from s3."""
def __init__(self, bucket: str, key: str):
"""Initialize with bucket and key name."""
self.bucket = bucket
self.key = key
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
import boto3
except ImportError:
raise ImportError(
"Could not import `boto3` python package. "
"Please install it with `pip install boto3`."
)
s3 = boto3.client("s3")
with tempfile.TemporaryDirectory() as temp_dir:
file_path = f"{temp_dir}/{self.key}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
s3.download_file(self.bucket, self.key, file_path)
loader = UnstructuredFileLoader(file_path)
return loader.load()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/s3_file.html |
5bd361fcc80d-0 | Source code for langchain.document_loaders.gutenberg
"""Loader that loads .txt web files."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class GutenbergLoader(BaseLoader):
"""Loader that uses urllib to load .txt web files."""
def __init__(self, file_path: str):
"""Initialize with file path."""
if not file_path.startswith("https://www.gutenberg.org"):
raise ValueError("file path must start with 'https://www.gutenberg.org'")
if not file_path.endswith(".txt"):
raise ValueError("file path must end with '.txt'")
self.file_path = file_path
[docs] def load(self) -> List[Document]:
"""Load file."""
from urllib.request import urlopen
elements = urlopen(self.file_path)
text = "\n\n".join([str(el.decode("utf-8-sig")) for el in elements])
metadata = {"source": self.file_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/gutenberg.html |
5869e9898fa0-0 | Source code for langchain.document_loaders.unstructured
"""Loader that uses unstructured to load files."""
import collections
from abc import ABC, abstractmethod
from typing import IO, Any, List, Sequence, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def satisfies_min_unstructured_version(min_version: str) -> bool:
"""Checks to see if the installed unstructured version exceeds the minimum version
for the feature in question."""
from unstructured.__version__ import __version__ as __unstructured_version__
min_version_tuple = tuple([int(x) for x in min_version.split(".")])
# NOTE(MthwRobinson) - enables the loader to work when you're using pre-release
# versions of unstructured like 0.4.17-dev1
_unstructured_version = __unstructured_version__.split("-")[0]
unstructured_version_tuple = tuple(
[int(x) for x in _unstructured_version.split(".")]
)
return unstructured_version_tuple >= min_version_tuple
def validate_unstructured_version(min_unstructured_version: str) -> None:
"""Raises an error if the unstructured version does not exceed the
specified minimum."""
if not satisfies_min_unstructured_version(min_unstructured_version):
raise ValueError(
f"unstructured>={min_unstructured_version} is required in this loader."
)
class UnstructuredBaseLoader(BaseLoader, ABC):
"""Loader that uses unstructured to load files."""
def __init__(self, mode: str = "single", **unstructured_kwargs: Any):
"""Initialize with file path."""
try:
import unstructured # noqa:F401
except ImportError:
raise ValueError( | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
5869e9898fa0-1 | import unstructured # noqa:F401
except ImportError:
raise ValueError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
_valid_modes = {"single", "elements"}
if mode not in _valid_modes:
raise ValueError(
f"Got {mode} for `mode`, but should be one of `{_valid_modes}`"
)
self.mode = mode
if not satisfies_min_unstructured_version("0.5.4"):
if "strategy" in unstructured_kwargs:
unstructured_kwargs.pop("strategy")
self.unstructured_kwargs = unstructured_kwargs
@abstractmethod
def _get_elements(self) -> List:
"""Get elements."""
@abstractmethod
def _get_metadata(self) -> dict:
"""Get metadata."""
def load(self) -> List[Document]:
"""Load file."""
elements = self._get_elements()
if self.mode == "elements":
docs: List[Document] = list()
for element in elements:
metadata = self._get_metadata()
# NOTE(MthwRobinson) - the attribute check is for backward compatibility
# with unstructured<0.4.9. The metadata attributed was added in 0.4.9.
if hasattr(element, "metadata"):
metadata.update(element.metadata.to_dict())
if hasattr(element, "category"):
metadata["category"] = element.category
docs.append(Document(page_content=str(element), metadata=metadata))
elif self.mode == "single":
metadata = self._get_metadata()
text = "\n\n".join([str(el) for el in elements])
docs = [Document(page_content=text, metadata=metadata)] | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
5869e9898fa0-2 | docs = [Document(page_content=text, metadata=metadata)]
else:
raise ValueError(f"mode of {self.mode} not supported.")
return docs
[docs]class UnstructuredFileLoader(UnstructuredBaseLoader):
"""Loader that uses unstructured to load files."""
def __init__(
self,
file_path: Union[str, List[str]],
mode: str = "single",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
self.file_path = file_path
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.auto import partition
return partition(filename=self.file_path, **self.unstructured_kwargs)
def _get_metadata(self) -> dict:
return {"source": self.file_path}
def get_elements_from_api(
file_path: Union[str, List[str], None] = None,
file: Union[IO, Sequence[IO], None] = None,
api_url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
) -> List:
"""Retrieves a list of elements from the Unstructured API."""
if isinstance(file, collections.abc.Sequence) or isinstance(file_path, list):
from unstructured.partition.api import partition_multiple_via_api
_doc_elements = partition_multiple_via_api(
filenames=file_path,
files=file,
api_key=api_key,
api_url=api_url,
**unstructured_kwargs,
)
elements = []
for _elements in _doc_elements:
elements.extend(_elements)
return elements
else: | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
5869e9898fa0-3 | elements.extend(_elements)
return elements
else:
from unstructured.partition.api import partition_via_api
return partition_via_api(
filename=file_path,
file=file,
api_key=api_key,
api_url=api_url,
**unstructured_kwargs,
)
[docs]class UnstructuredAPIFileLoader(UnstructuredFileLoader):
"""Loader that uses the unstructured web API to load files."""
def __init__(
self,
file_path: Union[str, List[str]] = "",
mode: str = "single",
url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
if isinstance(file_path, str):
validate_unstructured_version(min_unstructured_version="0.6.2")
else:
validate_unstructured_version(min_unstructured_version="0.6.3")
self.url = url
self.api_key = api_key
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_metadata(self) -> dict:
return {"source": self.file_path}
def _get_elements(self) -> List:
return get_elements_from_api(
file_path=self.file_path,
api_key=self.api_key,
api_url=self.url,
**self.unstructured_kwargs,
)
[docs]class UnstructuredFileIOLoader(UnstructuredBaseLoader):
"""Loader that uses unstructured to load file IO objects."""
def __init__(
self,
file: Union[IO, Sequence[IO]],
mode: str = "single", | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
5869e9898fa0-4 | mode: str = "single",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
self.file = file
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.auto import partition
return partition(file=self.file, **self.unstructured_kwargs)
def _get_metadata(self) -> dict:
return {}
[docs]class UnstructuredAPIFileIOLoader(UnstructuredFileIOLoader):
"""Loader that uses the unstructured web API to load file IO objects."""
def __init__(
self,
file: Union[IO, Sequence[IO]],
mode: str = "single",
url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
if isinstance(file, collections.abc.Sequence):
validate_unstructured_version(min_unstructured_version="0.6.3")
if file:
validate_unstructured_version(min_unstructured_version="0.6.2")
self.url = url
self.api_key = api_key
super().__init__(file=file, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
return get_elements_from_api(
file=self.file,
api_key=self.api_key,
api_url=self.url,
**self.unstructured_kwargs,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
1c7332c837f6-0 | Source code for langchain.document_loaders.json_loader
"""Loader that loads data from JSON."""
import json
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class JSONLoader(BaseLoader):
"""Loads a JSON file and references a jq schema provided to load the text into
documents.
Example:
[{"text": ...}, {"text": ...}, {"text": ...}] -> schema = .[].text
{"key": [{"text": ...}, {"text": ...}, {"text": ...}]} -> schema = .key[].text
["", "", ""] -> schema = .[]
"""
def __init__(
self,
file_path: Union[str, Path],
jq_schema: str,
content_key: Optional[str] = None,
metadata_func: Optional[Callable[[Dict, Dict], Dict]] = None,
text_content: bool = True,
):
"""Initialize the JSONLoader.
Args:
file_path (Union[str, Path]): The path to the JSON file.
jq_schema (str): The jq schema to use to extract the data or text from
the JSON.
content_key (str): The key to use to extract the content from the JSON if
the jq_schema results to a list of objects (dict).
metadata_func (Callable[Dict, Dict]): A function that takes in the JSON
object extracted by the jq_schema and the default metadata and returns
a dict of the updated metadata.
text_content (bool): Boolean flag to indicates whether the content is in
string format, default to True
"""
try:
import jq # noqa:F401 | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/json_loader.html |
1c7332c837f6-1 | """
try:
import jq # noqa:F401
except ImportError:
raise ImportError(
"jq package not found, please install it with `pip install jq`"
)
self.file_path = Path(file_path).resolve()
self._jq_schema = jq.compile(jq_schema)
self._content_key = content_key
self._metadata_func = metadata_func
self._text_content = text_content
[docs] def load(self) -> List[Document]:
"""Load and return documents from the JSON file."""
data = self._jq_schema.input(json.loads(self.file_path.read_text()))
# Perform some validation
# This is not a perfect validation, but it should catch most cases
# and prevent the user from getting a cryptic error later on.
if self._content_key is not None:
self._validate_content_key(data)
docs = []
for i, sample in enumerate(data, 1):
metadata = dict(
source=str(self.file_path),
seq_num=i,
)
text = self._get_text(sample=sample, metadata=metadata)
docs.append(Document(page_content=text, metadata=metadata))
return docs
def _get_text(self, sample: Any, metadata: dict) -> str:
"""Convert sample to string format"""
if self._content_key is not None:
content = sample.get(self._content_key)
if self._metadata_func is not None:
# We pass in the metadata dict to the metadata_func
# so that the user can customize the default metadata
# based on the content of the JSON object.
metadata = self._metadata_func(sample, metadata)
else:
content = sample | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/json_loader.html |
1c7332c837f6-2 | else:
content = sample
if self._text_content and not isinstance(content, str):
raise ValueError(
f"Expected page_content is string, got {type(content)} instead. \
Set `text_content=False` if the desired input for \
`page_content` is not a string"
)
# In case the text is None, set it to an empty string
elif isinstance(content, str):
return content
elif isinstance(content, dict):
return json.dumps(content) if content else ""
else:
return str(content) if content is not None else ""
def _validate_content_key(self, data: Any) -> None:
"""Check if content key is valid"""
sample = data.first()
if not isinstance(sample, dict):
raise ValueError(
f"Expected the jq schema to result in a list of objects (dict), \
so sample must be a dict but got `{type(sample)}`"
)
if sample.get(self._content_key) is None:
raise ValueError(
f"Expected the jq schema to result in a list of objects (dict) \
with the key `{self._content_key}`"
)
if self._metadata_func is not None:
sample_metadata = self._metadata_func(sample, {})
if not isinstance(sample_metadata, dict):
raise ValueError(
f"Expected the metadata_func to return a dict but got \
`{type(sample_metadata)}`"
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/json_loader.html |
e683630d4006-0 | Source code for langchain.document_loaders.diffbot
"""Loader that uses Diffbot to load webpages in text format."""
import logging
from typing import Any, List
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class DiffbotLoader(BaseLoader):
"""Loader that loads Diffbot file json."""
def __init__(
self, api_token: str, urls: List[str], continue_on_failure: bool = True
):
"""Initialize with API token, ids, and key."""
self.api_token = api_token
self.urls = urls
self.continue_on_failure = continue_on_failure
def _diffbot_api_url(self, diffbot_api: str) -> str:
return f"https://api.diffbot.com/v3/{diffbot_api}"
def _get_diffbot_data(self, url: str) -> Any:
"""Get Diffbot file from Diffbot REST API."""
# TODO: Add support for other Diffbot APIs
diffbot_url = self._diffbot_api_url("article")
params = {
"token": self.api_token,
"url": url,
}
response = requests.get(diffbot_url, params=params, timeout=10)
# TODO: handle non-ok errors
return response.json() if response.ok else {}
[docs] def load(self) -> List[Document]:
"""Extract text from Diffbot on all the URLs and return Document instances"""
docs: List[Document] = list()
for url in self.urls:
try:
data = self._get_diffbot_data(url)
text = data["objects"][0]["text"] if "objects" in data else "" | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/diffbot.html |
e683630d4006-1 | text = data["objects"][0]["text"] if "objects" in data else ""
metadata = {"source": url}
docs.append(Document(page_content=text, metadata=metadata))
except Exception as e:
if self.continue_on_failure:
logger.error(f"Error fetching or processing {url}, exception: {e}")
else:
raise e
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/diffbot.html |
029a707940fd-0 | Source code for langchain.document_loaders.azlyrics
"""Loader that loads AZLyrics."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class AZLyricsLoader(WebBaseLoader):
"""Loader that loads AZLyrics webpages."""
[docs] def load(self) -> List[Document]:
"""Load webpage."""
soup = self.scrape()
title = soup.title.text
lyrics = soup.find_all("div", {"class": ""})[2].text
text = title + lyrics
metadata = {"source": self.web_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/azlyrics.html |
2a0679319302-0 | Source code for langchain.document_loaders.gcs_file
"""Loading logic for loading documents from a GCS file."""
import os
import tempfile
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class GCSFileLoader(BaseLoader):
"""Loading logic for loading documents from GCS."""
def __init__(self, project_name: str, bucket: str, blob: str):
"""Initialize with bucket and key name."""
self.bucket = bucket
self.blob = blob
self.project_name = project_name
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
from google.cloud import storage
except ImportError:
raise ValueError(
"Could not import google-cloud-storage python package. "
"Please install it with `pip install google-cloud-storage`."
)
# Initialise a client
storage_client = storage.Client(self.project_name)
# Create a bucket object for our bucket
bucket = storage_client.get_bucket(self.bucket)
# Create a blob object from the filepath
blob = bucket.blob(self.blob)
with tempfile.TemporaryDirectory() as temp_dir:
file_path = f"{temp_dir}/{self.blob}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
# Download the file to a destination
blob.download_to_filename(file_path)
loader = UnstructuredFileLoader(file_path)
return loader.load()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/gcs_file.html |
3e5b0ec025a8-0 | Source code for langchain.document_loaders.dataframe
"""Load from Dataframe object"""
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class DataFrameLoader(BaseLoader):
"""Load Pandas DataFrames."""
def __init__(self, data_frame: Any, page_content_column: str = "text"):
"""Initialize with dataframe object."""
import pandas as pd
if not isinstance(data_frame, pd.DataFrame):
raise ValueError(
f"Expected data_frame to be a pd.DataFrame, got {type(data_frame)}"
)
self.data_frame = data_frame
self.page_content_column = page_content_column
[docs] def load(self) -> List[Document]:
"""Load from the dataframe."""
result = []
# For very large dataframes, this needs to yield instead of building a list
# but that would require chaging return type to a generator for BaseLoader
# and all its subclasses, which is a bigger refactor. Marking as future TODO.
# This change will allow us to extend this to Spark and Dask dataframes.
for _, row in self.data_frame.iterrows():
text = row[self.page_content_column]
metadata = row.to_dict()
metadata.pop(self.page_content_column)
result.append(Document(page_content=text, metadata=metadata))
return result
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/dataframe.html |
8c2b0fbfaf37-0 | Source code for langchain.document_loaders.mediawikidump
"""Load Data from a MediaWiki dump xml."""
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class MWDumpLoader(BaseLoader):
"""
Load MediaWiki dump from XML file
Example:
.. code-block:: python
from langchain.document_loaders import MWDumpLoader
loader = MWDumpLoader(
file_path="myWiki.xml",
encoding="utf8"
)
docs = loader.load()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=0
)
texts = text_splitter.split_documents(docs)
:param file_path: XML local file path
:type file_path: str
:param encoding: Charset encoding, defaults to "utf8"
:type encoding: str, optional
"""
def __init__(self, file_path: str, encoding: Optional[str] = "utf8"):
"""Initialize with file path."""
self.file_path = file_path
self.encoding = encoding
[docs] def load(self) -> List[Document]:
"""Load from file path."""
import mwparserfromhell
import mwxml
dump = mwxml.Dump.from_file(open(self.file_path, encoding=self.encoding))
docs = []
for page in dump.pages:
for revision in page:
code = mwparserfromhell.parse(revision.text)
text = code.strip_code(
normalize=True, collapse=True, keep_template_params=False
)
metadata = {"source": page.title} | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/mediawikidump.html |
8c2b0fbfaf37-1 | )
metadata = {"source": page.title}
docs.append(Document(page_content=text, metadata=metadata))
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/mediawikidump.html |
416691caa476-0 | Source code for langchain.document_loaders.tomarkdown
"""Loader that loads HTML to markdown using 2markdown."""
from __future__ import annotations
from typing import Iterator, List
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ToMarkdownLoader(BaseLoader):
"""Loader that loads HTML to markdown using 2markdown."""
def __init__(self, url: str, api_key: str):
"""Initialize with url and api key."""
self.url = url
self.api_key = api_key
[docs] def lazy_load(
self,
) -> Iterator[Document]:
"""Lazily load the file."""
response = requests.post(
"https://2markdown.com/api/2md",
headers={"X-Api-Key": self.api_key},
json={"url": self.url},
)
text = response.json()["article"]
metadata = {"source": self.url}
yield Document(page_content=text, metadata=metadata)
[docs] def load(self) -> List[Document]:
"""Load file."""
return list(self.lazy_load())
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/tomarkdown.html |
0079adb05425-0 | Source code for langchain.document_loaders.web_base
"""Web base loader class."""
import asyncio
import logging
import warnings
from typing import Any, Dict, List, Optional, Union
import aiohttp
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
default_header_template = {
"User-Agent": "",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*"
";q=0.8",
"Accept-Language": "en-US,en;q=0.5",
"Referer": "https://www.google.com/",
"DNT": "1",
"Connection": "keep-alive",
"Upgrade-Insecure-Requests": "1",
}
def _build_metadata(soup: Any, url: str) -> dict:
"""Build metadata from BeautifulSoup output."""
metadata = {"source": url}
if title := soup.find("title"):
metadata["title"] = title.get_text()
if description := soup.find("meta", attrs={"name": "description"}):
metadata["description"] = description.get("content", None)
if html := soup.find("html"):
metadata["language"] = html.get("lang", None)
return metadata
[docs]class WebBaseLoader(BaseLoader):
"""Loader that uses urllib and beautiful soup to load webpages."""
web_paths: List[str]
requests_per_second: int = 2
"""Max number of concurrent requests to make."""
default_parser: str = "html.parser"
"""Default parser to use for BeautifulSoup."""
requests_kwargs: Dict[str, Any] = {}
"""kwargs for requests"""
def __init__( | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/web_base.html |
0079adb05425-1 | """kwargs for requests"""
def __init__(
self, web_path: Union[str, List[str]], header_template: Optional[dict] = None
):
"""Initialize with webpage path."""
# TODO: Deprecate web_path in favor of web_paths, and remove this
# left like this because there are a number of loaders that expect single
# urls
if isinstance(web_path, str):
self.web_paths = [web_path]
elif isinstance(web_path, List):
self.web_paths = web_path
self.session = requests.Session()
try:
import bs4 # noqa:F401
except ImportError:
raise ValueError(
"bs4 package not found, please install it with " "`pip install bs4`"
)
headers = header_template or default_header_template
if not headers.get("User-Agent"):
try:
from fake_useragent import UserAgent
headers["User-Agent"] = UserAgent().random
except ImportError:
logger.info(
"fake_useragent not found, using default user agent."
"To get a realistic header for requests, "
"`pip install fake_useragent`."
)
self.session.headers = dict(headers)
@property
def web_path(self) -> str:
if len(self.web_paths) > 1:
raise ValueError("Multiple webpaths found.")
return self.web_paths[0]
async def _fetch(
self, url: str, retries: int = 3, cooldown: int = 2, backoff: float = 1.5
) -> str:
async with aiohttp.ClientSession() as session:
for i in range(retries):
try: | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/web_base.html |
0079adb05425-2 | for i in range(retries):
try:
async with session.get(
url, headers=self.session.headers
) as response:
return await response.text()
except aiohttp.ClientConnectionError as e:
if i == retries - 1:
raise
else:
logger.warning(
f"Error fetching {url} with attempt "
f"{i + 1}/{retries}: {e}. Retrying..."
)
await asyncio.sleep(cooldown * backoff**i)
raise ValueError("retry count exceeded")
async def _fetch_with_rate_limit(
self, url: str, semaphore: asyncio.Semaphore
) -> str:
async with semaphore:
return await self._fetch(url)
[docs] async def fetch_all(self, urls: List[str]) -> Any:
"""Fetch all urls concurrently with rate limiting."""
semaphore = asyncio.Semaphore(self.requests_per_second)
tasks = []
for url in urls:
task = asyncio.ensure_future(self._fetch_with_rate_limit(url, semaphore))
tasks.append(task)
try:
from tqdm.asyncio import tqdm_asyncio
return await tqdm_asyncio.gather(
*tasks, desc="Fetching pages", ascii=True, mininterval=1
)
except ImportError:
warnings.warn("For better logging of progress, `pip install tqdm`")
return await asyncio.gather(*tasks)
@staticmethod
def _check_parser(parser: str) -> None:
"""Check that parser is valid for bs4."""
valid_parsers = ["html.parser", "lxml", "xml", "lxml-xml", "html5lib"]
if parser not in valid_parsers:
raise ValueError( | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/web_base.html |
0079adb05425-3 | if parser not in valid_parsers:
raise ValueError(
"`parser` must be one of " + ", ".join(valid_parsers) + "."
)
[docs] def scrape_all(self, urls: List[str], parser: Union[str, None] = None) -> List[Any]:
"""Fetch all urls, then return soups for all results."""
from bs4 import BeautifulSoup
results = asyncio.run(self.fetch_all(urls))
final_results = []
for i, result in enumerate(results):
url = urls[i]
if parser is None:
if url.endswith(".xml"):
parser = "xml"
else:
parser = self.default_parser
self._check_parser(parser)
final_results.append(BeautifulSoup(result, parser))
return final_results
def _scrape(self, url: str, parser: Union[str, None] = None) -> Any:
from bs4 import BeautifulSoup
if parser is None:
if url.endswith(".xml"):
parser = "xml"
else:
parser = self.default_parser
self._check_parser(parser)
html_doc = self.session.get(url, **self.requests_kwargs)
html_doc.encoding = html_doc.apparent_encoding
return BeautifulSoup(html_doc.text, parser)
[docs] def scrape(self, parser: Union[str, None] = None) -> Any:
"""Scrape data from webpage and return it in BeautifulSoup format."""
if parser is None:
parser = self.default_parser
return self._scrape(self.web_path, parser)
[docs] def load(self) -> List[Document]:
"""Load text from the url(s) in web_path."""
docs = []
for path in self.web_paths: | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/web_base.html |
0079adb05425-4 | docs = []
for path in self.web_paths:
soup = self._scrape(path)
text = soup.get_text()
metadata = _build_metadata(soup, path)
docs.append(Document(page_content=text, metadata=metadata))
return docs
[docs] def aload(self) -> List[Document]:
"""Load text from the urls in web_path async into Documents."""
results = self.scrape_all(self.web_paths)
docs = []
for i in range(len(results)):
soup = results[i]
text = soup.get_text()
metadata = _build_metadata(soup, self.web_paths[i])
docs.append(Document(page_content=text, metadata=metadata))
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/web_base.html |
7eed0c96734d-0 | Source code for langchain.document_loaders.youtube
"""Loader that loads YouTube transcript."""
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional
from urllib.parse import parse_qs, urlparse
from pydantic import root_validator
from pydantic.dataclasses import dataclass
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
SCOPES = ["https://www.googleapis.com/auth/youtube.readonly"]
[docs]@dataclass
class GoogleApiClient:
"""A Generic Google Api Client.
To use, you should have the ``google_auth_oauthlib,youtube_transcript_api,google``
python package installed.
As the google api expects credentials you need to set up a google account and
register your Service. "https://developers.google.com/docs/api/quickstart/python"
Example:
.. code-block:: python
from langchain.document_loaders import GoogleApiClient
google_api_client = GoogleApiClient(
service_account_path=Path("path_to_your_sec_file.json")
)
"""
credentials_path: Path = Path.home() / ".credentials" / "credentials.json"
service_account_path: Path = Path.home() / ".credentials" / "credentials.json"
token_path: Path = Path.home() / ".credentials" / "token.json"
def __post_init__(self) -> None:
self.creds = self._load_credentials()
[docs] @root_validator
def validate_channel_or_videoIds_is_set(
cls, values: Dict[str, Any]
) -> Dict[str, Any]:
"""Validate that either folder_id or document_ids is set, but not both.""" | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
7eed0c96734d-1 | """Validate that either folder_id or document_ids is set, but not both."""
if not values.get("credentials_path") and not values.get(
"service_account_path"
):
raise ValueError("Must specify either channel_name or video_ids")
return values
def _load_credentials(self) -> Any:
"""Load credentials."""
# Adapted from https://developers.google.com/drive/api/v3/quickstart/python
try:
from google.auth.transport.requests import Request
from google.oauth2 import service_account
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from youtube_transcript_api import YouTubeTranscriptApi # noqa: F401
except ImportError:
raise ImportError(
"You must run"
"`pip install --upgrade "
"google-api-python-client google-auth-httplib2 "
"google-auth-oauthlib "
"youtube-transcript-api` "
"to use the Google Drive loader"
)
creds = None
if self.service_account_path.exists():
return service_account.Credentials.from_service_account_file(
str(self.service_account_path)
)
if self.token_path.exists():
creds = Credentials.from_authorized_user_file(str(self.token_path), SCOPES)
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(
str(self.credentials_path), SCOPES
)
creds = flow.run_local_server(port=0)
with open(self.token_path, "w") as token:
token.write(creds.to_json())
return creds | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
7eed0c96734d-2 | token.write(creds.to_json())
return creds
ALLOWED_SCHEMAS = {"http", "https"}
ALLOWED_NETLOCK = {
"youtu.be",
"m.youtube.com",
"youtube.com",
"www.youtube.com",
"www.youtube-nocookie.com",
"vid.plus",
}
def _parse_video_id(url: str) -> Optional[str]:
"""Parse a youtube url and return the video id if valid, otherwise None."""
parsed_url = urlparse(url)
if parsed_url.scheme not in ALLOWED_SCHEMAS:
return None
if parsed_url.netloc not in ALLOWED_NETLOCK:
return None
path = parsed_url.path
if path.endswith("/watch"):
query = parsed_url.query
parsed_query = parse_qs(query)
if "v" in parsed_query:
ids = parsed_query["v"]
video_id = ids if isinstance(ids, str) else ids[0]
else:
return None
else:
path = parsed_url.path.lstrip("/")
video_id = path.split("/")[-1]
if len(video_id) != 11: # Video IDs are 11 characters long
return None
return video_id
[docs]class YoutubeLoader(BaseLoader):
"""Loader that loads Youtube transcripts."""
def __init__(
self,
video_id: str,
add_video_info: bool = False,
language: str = "en",
continue_on_failure: bool = False,
):
"""Initialize with YouTube video ID."""
self.video_id = video_id
self.add_video_info = add_video_info
self.language = language
self.continue_on_failure = continue_on_failure | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
7eed0c96734d-3 | self.language = language
self.continue_on_failure = continue_on_failure
[docs] @staticmethod
def extract_video_id(youtube_url: str) -> str:
"""Extract video id from common YT urls."""
video_id = _parse_video_id(youtube_url)
if not video_id:
raise ValueError(
f"Could not determine the video ID for the URL {youtube_url}"
)
return video_id
[docs] @classmethod
def from_youtube_url(cls, youtube_url: str, **kwargs: Any) -> YoutubeLoader:
"""Given youtube URL, load video."""
video_id = cls.extract_video_id(youtube_url)
return cls(video_id, **kwargs)
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
from youtube_transcript_api import (
NoTranscriptFound,
TranscriptsDisabled,
YouTubeTranscriptApi,
)
except ImportError:
raise ImportError(
"Could not import youtube_transcript_api python package. "
"Please install it with `pip install youtube-transcript-api`."
)
metadata = {"source": self.video_id}
if self.add_video_info:
# Get more video meta info
# Such as title, description, thumbnail url, publish_date
video_info = self._get_video_info()
metadata.update(video_info)
try:
transcript_list = YouTubeTranscriptApi.list_transcripts(self.video_id)
except TranscriptsDisabled:
return []
try:
transcript = transcript_list.find_transcript([self.language])
except NoTranscriptFound:
en_transcript = transcript_list.find_transcript(["en"]) | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
7eed0c96734d-4 | en_transcript = transcript_list.find_transcript(["en"])
transcript = en_transcript.translate(self.language)
transcript_pieces = transcript.fetch()
transcript = " ".join([t["text"].strip(" ") for t in transcript_pieces])
return [Document(page_content=transcript, metadata=metadata)]
def _get_video_info(self) -> dict:
"""Get important video information.
Components are:
- title
- description
- thumbnail url,
- publish_date
- channel_author
- and more.
"""
try:
from pytube import YouTube
except ImportError:
raise ImportError(
"Could not import pytube python package. "
"Please install it with `pip install pytube`."
)
yt = YouTube(f"https://www.youtube.com/watch?v={self.video_id}")
video_info = {
"title": yt.title,
"description": yt.description,
"view_count": yt.views,
"thumbnail_url": yt.thumbnail_url,
"publish_date": yt.publish_date,
"length": yt.length,
"author": yt.author,
}
return video_info
[docs]@dataclass
class GoogleApiYoutubeLoader(BaseLoader):
"""Loader that loads all Videos from a Channel
To use, you should have the ``googleapiclient,youtube_transcript_api``
python package installed.
As the service needs a google_api_client, you first have to initialize
the GoogleApiClient.
Additionally you have to either provide a channel name or a list of videoids
"https://developers.google.com/docs/api/quickstart/python"
Example:
.. code-block:: python
from langchain.document_loaders import GoogleApiClient | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
7eed0c96734d-5 | .. code-block:: python
from langchain.document_loaders import GoogleApiClient
from langchain.document_loaders import GoogleApiYoutubeLoader
google_api_client = GoogleApiClient(
service_account_path=Path("path_to_your_sec_file.json")
)
loader = GoogleApiYoutubeLoader(
google_api_client=google_api_client,
channel_name = "CodeAesthetic"
)
load.load()
"""
google_api_client: GoogleApiClient
channel_name: Optional[str] = None
video_ids: Optional[List[str]] = None
add_video_info: bool = True
captions_language: str = "en"
continue_on_failure: bool = False
def __post_init__(self) -> None:
self.youtube_client = self._build_youtube_client(self.google_api_client.creds)
def _build_youtube_client(self, creds: Any) -> Any:
try:
from googleapiclient.discovery import build
from youtube_transcript_api import YouTubeTranscriptApi # noqa: F401
except ImportError:
raise ImportError(
"You must run"
"`pip install --upgrade "
"google-api-python-client google-auth-httplib2 "
"google-auth-oauthlib "
"youtube-transcript-api` "
"to use the Google Drive loader"
)
return build("youtube", "v3", credentials=creds)
[docs] @root_validator
def validate_channel_or_videoIds_is_set(
cls, values: Dict[str, Any]
) -> Dict[str, Any]:
"""Validate that either folder_id or document_ids is set, but not both."""
if not values.get("channel_name") and not values.get("video_ids"): | https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
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