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fc338dcfa539-0 | langchain.evaluation.schema.PairwiseStringEvaluator¶
class langchain.evaluation.schema.PairwiseStringEvaluator(*args, **kwargs)[source]¶
Bases: Protocol
A protocol for comparing the output of two models.
Methods
__init__(*args, **kwargs)
aevaluate_string_pairs(prediction, prediction_b)
Evaluate the output string pairs.
evaluate_string_pairs(*, prediction, ...[, ...])
Evaluate the output string pairs.
async aevaluate_string_pairs(prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict[source]¶
Evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (str, optional) – The expected output / reference
string. Defaults to None.
input (str, optional) – The input string. Defaults to None.
**kwargs (Any) – Additional keyword arguments, such
as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/orother information.
Return type
dict
abstract evaluate_string_pairs(*, prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict[source]¶
Evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (str, optional) – The expected output / reference
string. Defaults to None.
input (str, optional) – The input string. Defaults to None.
**kwargs (Any) – Additional keyword arguments, such
as callbacks and optional reference strings.
Returns | [
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] | https://langchain.readthedocs.io/en/latest/evaluation/langchain.evaluation.schema.PairwiseStringEvaluator.html |
fc338dcfa539-1 | as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/orother information.
Return type
dict | [
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1de8ce28438d-0 | langchain.evaluation.run_evaluators.implementations.TrajectoryEvalOutputParser¶
class langchain.evaluation.run_evaluators.implementations.TrajectoryEvalOutputParser(*, eval_chain_output_key: str = 'text', evaluation_name: str = 'Agent Trajectory', evaluator_info: dict = None)[source]¶
Bases: RunEvaluatorOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param eval_chain_output_key: str = 'text'¶
param evaluation_name: str = 'Agent Trajectory'¶
The name assigned to the evaluation feedback.
param evaluator_info: dict [Optional]¶
Additional information to log as feedback metadata.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
parse(text: str) → EvaluationResult[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_chain_output(output: Dict[str, Any]) → EvaluationResult¶
Parse the output of a run.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output | [
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1de8ce28438d-1 | Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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b90e1c715d50-0 | langchain.evaluation.qa.generate_chain.QAGenerateChain¶
class langchain.evaluation.qa.generate_chain.QAGenerateChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, prompt: BasePromptTemplate, llm: BaseLanguageModel, output_key: str = 'text', output_parser: BaseLLMOutputParser = None, return_final_only: bool = True, llm_kwargs: dict = None)[source]¶
Bases: LLMChain
LLM Chain specifically for generating examples for question answering.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param llm: BaseLanguageModel [Required]¶
Language model to call.
param llm_kwargs: dict [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog. | [
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] | https://langchain.readthedocs.io/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
b90e1c715d50-1 | There are many different types of memory - please see memory docs
for the full catalog.
param output_key: str = 'text'¶
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False. | [
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] | https://langchain.readthedocs.io/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
b90e1c715d50-2 | chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info – Whether to include run info in the response. Defaults
to False.
async aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info – Whether to include run info in the response. Defaults
to False.
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶ | [
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] | https://langchain.readthedocs.io/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
b90e1c715d50-3 | Generate LLM result from inputs.
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async apredict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
async apredict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, str]]¶
Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
Run the chain as text in, text out or multiple variables, text out.
create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]¶
Create outputs from response. | [
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] | https://langchain.readthedocs.io/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
b90e1c715d50-4 | Create outputs from response.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of chain.
classmethod from_llm(llm: BaseLanguageModel, **kwargs: Any) → QAGenerateChain[source]¶
Load QA Generate Chain from LLM.
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, Any]]¶
Call predict and then parse the results.
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prep inputs.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prep outputs.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used. | [
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] | https://langchain.readthedocs.io/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
b90e1c715d50-5 | Raise deprecation warning if callback_manager is used.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
Run the chain as text in, text out or multiple variables, text out.
save(file_path: Union[Path, str]) → None¶
Save the chain.
Parameters
file_path – Path to file to save the chain to.
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
validator set_verbose » verbose¶
If verbose is None, set it.
This allows users to pass in None as verbose to access the global setting.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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2f6c11b44db6-0 | langchain.document_loaders.apify_dataset.ApifyDatasetLoader¶
class langchain.document_loaders.apify_dataset.ApifyDatasetLoader(dataset_id: str, dataset_mapping_function: Callable[[Dict], Document])[source]¶
Bases: BaseLoader, BaseModel
Logic for loading documents from Apify datasets.
Initialize the loader with an Apify dataset ID and a mapping function.
Parameters
dataset_id (str) – The ID of the dataset on the Apify platform.
dataset_mapping_function (Callable) – A function that takes a single
dictionary (an Apify dataset item) and converts it to an instance
of the Document class.
param apify_client: Any = None¶
param dataset_id: str [Required]¶
The ID of the dataset on the Apify platform.
param dataset_mapping_function: Callable[[Dict], langchain.schema.Document] [Required]¶
A custom function that takes a single dictionary (an Apify dataset item)
and converts it to an instance of the Document class.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
validator validate_environment » all fields[source]¶
Validate environment. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.apify_dataset.ApifyDatasetLoader.html |
bd4f48d00972-0 | langchain.document_loaders.parsers.pdf.PyMuPDFParser¶
class langchain.document_loaders.parsers.pdf.PyMuPDFParser(text_kwargs: Optional[Mapping[str, Any]] = None)[source]¶
Bases: BaseBlobParser
Parse PDFs with PyMuPDF.
Initialize the parser.
Parameters
text_kwargs – Keyword arguments to pass to fitz.Page.get_text().
Methods
__init__([text_kwargs])
Initialize the parser.
lazy_parse(blob)
Lazily parse the blob.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazily parse the blob.
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents | [
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422a4cef5ffd-0 | langchain.document_loaders.word_document.UnstructuredWordDocumentLoader¶
class langchain.document_loaders.word_document.UnstructuredWordDocumentLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load word documents.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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7e5447fa7c84-0 | langchain.document_loaders.airtable.AirtableLoader¶
class langchain.document_loaders.airtable.AirtableLoader(api_token: str, table_id: str, base_id: str)[source]¶
Bases: BaseLoader
Loader for Airtable tables.
Initialize with API token and the IDs for table and base
Methods
__init__(api_token, table_id, base_id)
Initialize with API token and the IDs for table and base
lazy_load()
Lazy load records from table.
load()
Load Table.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Lazy load records from table.
load() → List[Document][source]¶
Load Table.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.airtable.AirtableLoader.html |
e5c3a08cb4b9-0 | langchain.document_loaders.email.UnstructuredEmailLoader¶
class langchain.document_loaders.email.UnstructuredEmailLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load email files. Works with both
.eml and .msg files. You can process attachments in addition to the
e-mail message itself by passing process_attachments=True into the
constructor for the loader. By default, attachments will be processed
with the unstructured partition function. If you already know the document
types of the attachments, you can specify another partitioning function
with the attachment partitioner kwarg.
Example
from langchain.document_loaders import UnstructuredEmailLoader
loader = UnstructuredEmailLoader(“example_data/fake-email.eml”, mode=”elements”)
loader.load()
Example
from langchain.document_loaders import UnstructuredEmailLoader
loader = UnstructuredEmailLoader(“example_data/fake-email-attachment.eml”,
mode=”elements”,
process_attachments=True,
)
loader.load()
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.email.UnstructuredEmailLoader.html |
1144600fb018-0 | langchain.document_loaders.word_document.Docx2txtLoader¶
class langchain.document_loaders.word_document.Docx2txtLoader(file_path: str)[source]¶
Bases: BaseLoader, ABC
Loads a DOCX with docx2txt and chunks at character level.
Defaults to check for local file, but if the file is a web path, it will download it
to a temporary file, and use that, then clean up the temporary file after completion
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load given path as single page.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load given path as single page.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.word_document.Docx2txtLoader.html |
4172384b1c76-0 | langchain.document_loaders.text.TextLoader¶
class langchain.document_loaders.text.TextLoader(file_path: str, encoding: Optional[str] = None, autodetect_encoding: bool = False)[source]¶
Bases: BaseLoader
Load text files.
Parameters
file_path – Path to the file to load.
encoding – File encoding to use. If None, the file will be loaded
encoding. (with the default system) –
autodetect_encoding – Whether to try to autodetect the file encoding
if the specified encoding fails.
Initialize with file path.
Methods
__init__(file_path[, encoding, ...])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load from file path.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load from file path.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.text.TextLoader.html |
5a852fe16891-0 | langchain.document_loaders.notebook.concatenate_cells¶
langchain.document_loaders.notebook.concatenate_cells(cell: dict, include_outputs: bool, max_output_length: int, traceback: bool) → str[source]¶
Combine cells information in a readable format ready to be used. | [
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fe560c73ccaf-0 | langchain.document_loaders.imsdb.IMSDbLoader¶
class langchain.document_loaders.imsdb.IMSDbLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None, verify: Optional[bool] = True, proxies: Optional[dict] = None)[source]¶
Bases: WebBaseLoader
Loader that loads IMSDb webpages.
Initialize with webpage path.
Methods
__init__(web_path[, header_template, ...])
Initialize with webpage path.
aload()
Load text from the urls in web_path async into Documents.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Load webpage.
load_and_split([text_splitter])
Load documents and split into chunks.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
Attributes
bs_get_text_kwargs
kwargs for beatifulsoup4 get_text
default_parser
Default parser to use for BeautifulSoup.
raise_for_status
Raise an exception if http status code denotes an error.
requests_kwargs
kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path
aload() → List[Document]¶
Load text from the urls in web_path async into Documents.
async fetch_all(urls: List[str]) → Any¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Load webpage.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.imsdb.IMSDbLoader.html |
fe560c73ccaf-1 | Load documents and split into chunks.
scrape(parser: Optional[str] = None) → Any¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any]¶
Fetch all urls, then return soups for all results.
bs_get_text_kwargs: Dict[str, Any] = {}¶
kwargs for beatifulsoup4 get_text
default_parser: str = 'html.parser'¶
Default parser to use for BeautifulSoup.
raise_for_status: bool = False¶
Raise an exception if http status code denotes an error.
requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests
requests_per_second: int = 2¶
Max number of concurrent requests to make.
property web_path: str¶
web_paths: List[str]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.imsdb.IMSDbLoader.html |
5ba72f6a5689-0 | langchain.document_loaders.tomarkdown.ToMarkdownLoader¶
class langchain.document_loaders.tomarkdown.ToMarkdownLoader(url: str, api_key: str)[source]¶
Bases: BaseLoader
Loader that loads HTML to markdown using 2markdown.
Initialize with url and api key.
Methods
__init__(url, api_key)
Initialize with url and api key.
lazy_load()
Lazily load the file.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Lazily load the file.
load() → List[Document][source]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.tomarkdown.ToMarkdownLoader.html |
2cd2f875dbe0-0 | langchain.document_loaders.merge.MergedDataLoader¶
class langchain.document_loaders.merge.MergedDataLoader(loaders: List)[source]¶
Bases: BaseLoader
Merge documents from a list of loaders
Initialize with a list of loaders
Methods
__init__(loaders)
Initialize with a list of loaders
lazy_load()
Lazy load docs from each individual loader.
load()
Load docs.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Lazy load docs from each individual loader.
load() → List[Document][source]¶
Load docs.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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89547fce2f72-0 | langchain.document_loaders.dataframe.DataFrameLoader¶
class langchain.document_loaders.dataframe.DataFrameLoader(data_frame: Any, page_content_column: str = 'text')[source]¶
Bases: BaseLoader
Load Pandas DataFrames.
Initialize with dataframe object.
Methods
__init__(data_frame[, page_content_column])
Initialize with dataframe object.
lazy_load()
Lazy load records from dataframe.
load()
Load full dataframe.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Lazy load records from dataframe.
load() → List[Document][source]¶
Load full dataframe.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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f9487d535d19-0 | langchain.document_loaders.html.UnstructuredHTMLLoader¶
class langchain.document_loaders.html.UnstructuredHTMLLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load HTML files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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dd0294de40c9-0 | langchain.document_loaders.powerpoint.UnstructuredPowerPointLoader¶
class langchain.document_loaders.powerpoint.UnstructuredPowerPointLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load powerpoint files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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a2c4542cab98-0 | langchain.document_loaders.weather.WeatherDataLoader¶
class langchain.document_loaders.weather.WeatherDataLoader(client: OpenWeatherMapAPIWrapper, places: Sequence[str])[source]¶
Bases: BaseLoader
Weather Reader.
Reads the forecast & current weather of any location using OpenWeatherMap’s free
API. Checkout ‘https://openweathermap.org/appid’ for more on how to generate a free
OpenWeatherMap API.
Initialize with parameters.
Methods
__init__(client, places)
Initialize with parameters.
from_params(places, *[, openweathermap_api_key])
lazy_load()
Lazily load weather data for the given locations.
load()
Load weather data for the given locations.
load_and_split([text_splitter])
Load documents and split into chunks.
classmethod from_params(places: Sequence[str], *, openweathermap_api_key: Optional[str] = None) → WeatherDataLoader[source]¶
lazy_load() → Iterator[Document][source]¶
Lazily load weather data for the given locations.
load() → List[Document][source]¶
Load weather data for the given locations.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.weather.WeatherDataLoader.html |
bdc61e50aedb-0 | langchain.document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader¶
class langchain.document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader(conf: Any, bucket: str, prefix: str = '')[source]¶
Bases: BaseLoader
Loading logic for loading documents from Tencent Cloud COS.
Initialize with COS config, bucket and prefix.
:param conf(CosConfig): COS config.
:param bucket(str): COS bucket.
:param prefix(str): prefix.
Methods
__init__(conf, bucket[, prefix])
Initialize with COS config, bucket and prefix.
lazy_load()
Load documents.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Load documents.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader.html |
6909fb0e6043-0 | langchain.document_loaders.base.BaseLoader¶
class langchain.document_loaders.base.BaseLoader[source]¶
Bases: ABC
Interface for loading documents.
Implementations should implement the lazy-loading method using generators
to avoid loading all documents into memory at once.
The load method will remain as is for backwards compatibility, but its
implementation should be just list(self.lazy_load()).
Methods
__init__()
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
A lazy loader for document content.
abstract load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document][source]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.base.BaseLoader.html |
26a7cbc330c1-0 | langchain.document_loaders.stripe.StripeLoader¶
class langchain.document_loaders.stripe.StripeLoader(resource: str, access_token: Optional[str] = None)[source]¶
Bases: BaseLoader
Loader that fetches data from Stripe.
Methods
__init__(resource[, access_token])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.stripe.StripeLoader.html |
bffbebb0c5ec-0 | langchain.document_loaders.unstructured.satisfies_min_unstructured_version¶
langchain.document_loaders.unstructured.satisfies_min_unstructured_version(min_version: str) → bool[source]¶
Checks to see if the installed unstructured version exceeds the minimum version
for the feature in question. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.unstructured.satisfies_min_unstructured_version.html |
747425b6e779-0 | langchain.document_loaders.spreedly.SpreedlyLoader¶
class langchain.document_loaders.spreedly.SpreedlyLoader(access_token: str, resource: str)[source]¶
Bases: BaseLoader
Loader that fetches data from Spreedly API.
Methods
__init__(access_token, resource)
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.spreedly.SpreedlyLoader.html |
ea5ec522ac92-0 | langchain.document_loaders.embaas.EmbaasDocumentExtractionPayload¶
class langchain.document_loaders.embaas.EmbaasDocumentExtractionPayload[source]¶
Bases: EmbaasDocumentExtractionParameters
Payload for the Embaas document extraction API.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
bytes
The base64 encoded bytes of the document to extract text from.
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasDocumentExtractionPayload.html |
ea5ec522ac92-1 | Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k]
values() → an object providing a view on D's values¶
bytes: str¶
The base64 encoded bytes of the document to extract text from.
chunk_overlap: int¶
chunk_size: int¶
chunk_splitter: str¶
file_extension: str¶
file_name: str¶
instruction: str¶
mime_type: str¶
model: str¶
separators: List[str]¶
should_chunk: bool¶
should_embed: bool¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasDocumentExtractionPayload.html |
894e4258eb8a-0 | langchain.document_loaders.parsers.language.javascript.JavaScriptSegmenter¶
class langchain.document_loaders.parsers.language.javascript.JavaScriptSegmenter(code: str)[source]¶
Bases: CodeSegmenter
Methods
__init__(code)
extract_functions_classes()
is_valid()
simplify_code()
extract_functions_classes() → List[str][source]¶
is_valid() → bool[source]¶
simplify_code() → str[source]¶ | [
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748f66d0e9f8-0 | langchain.document_loaders.chatgpt.ChatGPTLoader¶
class langchain.document_loaders.chatgpt.ChatGPTLoader(log_file: str, num_logs: int = - 1)[source]¶
Bases: BaseLoader
Loader that loads conversations from exported ChatGPT data.
Methods
__init__(log_file[, num_logs])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.chatgpt.ChatGPTLoader.html |
1f6ba4bdc318-0 | langchain.document_loaders.srt.SRTLoader¶
class langchain.document_loaders.srt.SRTLoader(file_path: str)[source]¶
Bases: BaseLoader
Loader for .srt (subtitle) files.
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load using pysrt file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load using pysrt file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.srt.SRTLoader.html |
41bffce564a5-0 | langchain.document_loaders.onedrive.OneDriveLoader¶
class langchain.document_loaders.onedrive.OneDriveLoader(*, settings: _OneDriveSettings = None, drive_id: str, folder_path: Optional[str] = None, object_ids: Optional[List[str]] = None, auth_with_token: bool = False)[source]¶
Bases: BaseLoader, BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param auth_with_token: bool = False¶
param drive_id: str [Required]¶
param folder_path: Optional[str] = None¶
param object_ids: Optional[List[str]] = None¶
param settings: langchain.document_loaders.onedrive._OneDriveSettings [Optional]¶
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Loads all supported document files from the specified OneDrive drive a
nd returns a list of Document objects.
Returns
A list of Document objects
representing the loaded documents.
Return type
List[Document]
Raises
ValueError – If the specified drive ID
does not correspond to a drive in the OneDrive storage. –
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.onedrive.OneDriveLoader.html |
e9be925a4fd2-0 | langchain.document_loaders.odt.UnstructuredODTLoader¶
class langchain.document_loaders.odt.UnstructuredODTLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load open office ODT files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.odt.UnstructuredODTLoader.html |
f54ba093c978-0 | langchain.document_loaders.bilibili.BiliBiliLoader¶
class langchain.document_loaders.bilibili.BiliBiliLoader(video_urls: List[str])[source]¶
Bases: BaseLoader
Loader that loads bilibili transcripts.
Initialize with bilibili url.
Methods
__init__(video_urls)
Initialize with bilibili url.
lazy_load()
A lazy loader for document content.
load()
Load from bilibili url.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load from bilibili url.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.bilibili.BiliBiliLoader.html |
8e7e5c6aeaeb-0 | langchain.document_loaders.pdf.PyPDFium2Loader¶
class langchain.document_loaders.pdf.PyPDFium2Loader(file_path: str)[source]¶
Bases: BasePDFLoader
Loads a PDF with pypdfium2 and chunks at character level.
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
Lazy load given path as pages.
load()
Load given path as pages.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
source
lazy_load() → Iterator[Document][source]¶
Lazy load given path as pages.
load() → List[Document][source]¶
Load given path as pages.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
property source: str¶ | [
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422dd3df81c6-0 | langchain.document_loaders.open_city_data.OpenCityDataLoader¶
class langchain.document_loaders.open_city_data.OpenCityDataLoader(city_id: str, dataset_id: str, limit: int)[source]¶
Bases: BaseLoader
Loader that loads Open city data.
Initialize with dataset_id
Methods
__init__(city_id, dataset_id, limit)
Initialize with dataset_id
lazy_load()
Lazy load records.
load()
Load records.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Lazy load records.
load() → List[Document][source]¶
Load records.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.open_city_data.OpenCityDataLoader.html |
142ef4fb9441-0 | langchain.document_loaders.mhtml.MHTMLLoader¶
class langchain.document_loaders.mhtml.MHTMLLoader(file_path: str, open_encoding: Optional[str] = None, bs_kwargs: Optional[dict] = None, get_text_separator: str = '')[source]¶
Bases: BaseLoader
Loader that uses beautiful soup to parse HTML files.
Initialise with path, and optionally, file encoding to use, and any kwargs
to pass to the BeautifulSoup object.
Methods
__init__(file_path[, open_encoding, ...])
Initialise with path, and optionally, file encoding to use, and any kwargs to pass to the BeautifulSoup object.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.mhtml.MHTMLLoader.html |
5311ac63efc0-0 | langchain.document_loaders.html_bs.BSHTMLLoader¶
class langchain.document_loaders.html_bs.BSHTMLLoader(file_path: str, open_encoding: Optional[str] = None, bs_kwargs: Optional[dict] = None, get_text_separator: str = '')[source]¶
Bases: BaseLoader
Loader that uses beautiful soup to parse HTML files.
Initialise with path, and optionally, file encoding to use, and any kwargs
to pass to the BeautifulSoup object.
Methods
__init__(file_path[, open_encoding, ...])
Initialise with path, and optionally, file encoding to use, and any kwargs to pass to the BeautifulSoup object.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.html_bs.BSHTMLLoader.html |
fec6b6dae86a-0 | langchain.document_loaders.embaas.BaseEmbaasLoader¶
class langchain.document_loaders.embaas.BaseEmbaasLoader(*, embaas_api_key: Optional[str] = None, api_url: str = 'https://api.embaas.io/v1/document/extract-text/bytes/', params: EmbaasDocumentExtractionParameters = {})[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_url: str = 'https://api.embaas.io/v1/document/extract-text/bytes/'¶
The URL of the embaas document extraction API.
param embaas_api_key: Optional[str] = None¶
param params: langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters = {}¶
Additional parameters to pass to the embaas document extraction API.
validator validate_environment » all fields[source]¶
Validate that api key and python package exists in environment. | [
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ca7cb23e78af-0 | langchain.document_loaders.docugami.DocugamiLoader¶
class langchain.document_loaders.docugami.DocugamiLoader(*, api: str = 'https://api.docugami.com/v1preview1', access_token: Optional[str] = None, docset_id: Optional[str] = None, document_ids: Optional[Sequence[str]] = None, file_paths: Optional[Sequence[Union[Path, str]]] = None, min_chunk_size: int = 32)[source]¶
Bases: BaseLoader, BaseModel
Loader that loads processed docs from Docugami.
To use, you should have the lxml python package installed.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param access_token: Optional[str] = None¶
param api: str = 'https://api.docugami.com/v1preview1'¶
param docset_id: Optional[str] = None¶
param document_ids: Optional[Sequence[str]] = None¶
param file_paths: Optional[Sequence[Union[pathlib.Path, str]]] = None¶
param min_chunk_size: int = 32¶
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
validator validate_local_or_remote » all fields[source]¶
Validate that either local file paths are given, or remote API docset ID. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.docugami.DocugamiLoader.html |
f04ae5419594-0 | langchain.document_loaders.python.PythonLoader¶
class langchain.document_loaders.python.PythonLoader(file_path: str)[source]¶
Bases: TextLoader
Load Python files, respecting any non-default encoding if specified.
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load from file path.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load from file path.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.python.PythonLoader.html |
6c44149267ea-0 | langchain.document_loaders.discord.DiscordChatLoader¶
class langchain.document_loaders.discord.DiscordChatLoader(chat_log: pd.DataFrame, user_id_col: str = 'ID')[source]¶
Bases: BaseLoader
Load Discord chat logs.
Initialize with a Pandas DataFrame containing chat logs.
Methods
__init__(chat_log[, user_id_col])
Initialize with a Pandas DataFrame containing chat logs.
lazy_load()
A lazy loader for document content.
load()
Load all chat messages.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load all chat messages.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.discord.DiscordChatLoader.html |
aca5a05a5d7a-0 | langchain.document_loaders.parsers.registry.get_parser¶
langchain.document_loaders.parsers.registry.get_parser(parser_name: str) → BaseBlobParser[source]¶
Get a parser by parser name. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.parsers.registry.get_parser.html |
4b5df8bfae4a-0 | langchain.document_loaders.hugging_face_dataset.HuggingFaceDatasetLoader¶
class langchain.document_loaders.hugging_face_dataset.HuggingFaceDatasetLoader(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)[source]¶
Bases: BaseLoader
Loading logic for loading documents from the Hugging Face Hub.
Initialize the HuggingFaceDatasetLoader.
Parameters
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.
Methods
__init__(path[, page_content_column, name, ...])
Initialize the HuggingFaceDatasetLoader.
lazy_load()
Load documents lazily.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Load documents lazily.
load() → List[Document][source]¶
Load documents. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.hugging_face_dataset.HuggingFaceDatasetLoader.html |
4b5df8bfae4a-1 | load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.hugging_face_dataset.HuggingFaceDatasetLoader.html |
0c963431d430-0 | langchain.document_loaders.telegram.concatenate_rows¶
langchain.document_loaders.telegram.concatenate_rows(row: dict) → str[source]¶
Combine message information in a readable format ready to be used. | [
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fe610e4dcfab-0 | langchain.document_loaders.pdf.UnstructuredPDFLoader¶
class langchain.document_loaders.pdf.UnstructuredPDFLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load PDF files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.pdf.UnstructuredPDFLoader.html |
f1f3c3d5a243-0 | langchain.document_loaders.notebook.remove_newlines¶
langchain.document_loaders.notebook.remove_newlines(x: Any) → Any[source]¶
Remove recursively newlines, no matter the data structure they are stored in. | [
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353e341ff304-0 | langchain.document_loaders.json_loader.JSONLoader¶
class langchain.document_loaders.json_loader.JSONLoader(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)[source]¶
Bases: 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 = .[]
Initialize the JSONLoader.
Parameters
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
Methods
__init__(file_path, jq_schema[, ...])
Initialize the JSONLoader.
lazy_load()
A lazy loader for document content.
load()
Load and return documents from the JSON file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.json_loader.JSONLoader.html |
353e341ff304-1 | lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load and return documents from the JSON file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.json_loader.JSONLoader.html |
7dd09f02f384-0 | langchain.document_loaders.pdf.PDFPlumberLoader¶
class langchain.document_loaders.pdf.PDFPlumberLoader(file_path: str, text_kwargs: Optional[Mapping[str, Any]] = None)[source]¶
Bases: BasePDFLoader
Loader that uses pdfplumber to load PDF files.
Initialize with file path.
Methods
__init__(file_path[, text_kwargs])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
source
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
property source: str¶ | [
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8bcdbaf1e753-0 | langchain.document_loaders.sitemap.SitemapLoader¶
class langchain.document_loaders.sitemap.SitemapLoader(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)[source]¶
Bases: WebBaseLoader
Loader that fetches a sitemap and loads those URLs.
Initialize with webpage path and optional filter URLs.
Parameters
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
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
Methods
__init__(web_path[, filter_urls, ...])
Initialize with webpage path and optional filter URLs.
aload()
Load text from the urls in web_path async into Documents.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Load sitemap.
load_and_split([text_splitter])
Load documents and split into chunks.
parse_sitemap(soup)
Parse sitemap xml and load into a list of dicts.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.sitemap.SitemapLoader.html |
8bcdbaf1e753-1 | scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
Attributes
bs_get_text_kwargs
kwargs for beatifulsoup4 get_text
default_parser
Default parser to use for BeautifulSoup.
raise_for_status
Raise an exception if http status code denotes an error.
requests_kwargs
kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path
aload() → List[Document]¶
Load text from the urls in web_path async into Documents.
async fetch_all(urls: List[str]) → Any¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Load sitemap.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
parse_sitemap(soup: Any) → List[dict][source]¶
Parse sitemap xml and load into a list of dicts.
scrape(parser: Optional[str] = None) → Any¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any]¶
Fetch all urls, then return soups for all results.
bs_get_text_kwargs: Dict[str, Any] = {}¶
kwargs for beatifulsoup4 get_text
default_parser: str = 'html.parser'¶
Default parser to use for BeautifulSoup.
raise_for_status: bool = False¶
Raise an exception if http status code denotes an error.
requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests | [
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8bcdbaf1e753-2 | requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests
requests_per_second: int = 2¶
Max number of concurrent requests to make.
property web_path: str¶
web_paths: List[str]¶ | [
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470dc3fb05cd-0 | langchain.document_loaders.telegram.TelegramChatApiLoader¶
class langchain.document_loaders.telegram.TelegramChatApiLoader(chat_entity: Optional[EntityLike] = None, api_id: Optional[int] = None, api_hash: Optional[str] = None, username: Optional[str] = None, file_path: str = 'telegram_data.json')[source]¶
Bases: BaseLoader
Loader that loads Telegram chat json directory dump.
Initialize with API parameters.
Methods
__init__([chat_entity, api_id, api_hash, ...])
Initialize with API parameters.
fetch_data_from_telegram()
Fetch data from Telegram API and save it as a JSON file.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
async fetch_data_from_telegram() → None[source]¶
Fetch data from Telegram API and save it as a JSON file.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.telegram.TelegramChatApiLoader.html |
4c29a62d944e-0 | langchain.document_loaders.gcs_file.GCSFileLoader¶
class langchain.document_loaders.gcs_file.GCSFileLoader(project_name: str, bucket: str, blob: str)[source]¶
Bases: BaseLoader
Loading logic for loading documents from GCS.
Initialize with bucket and key name.
Methods
__init__(project_name, bucket, blob)
Initialize with bucket and key name.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.gcs_file.GCSFileLoader.html |
93fa25b112e6-0 | langchain.document_loaders.telegram.TelegramChatFileLoader¶
class langchain.document_loaders.telegram.TelegramChatFileLoader(path: str)[source]¶
Bases: BaseLoader
Loader that loads Telegram chat json directory dump.
Initialize with path.
Methods
__init__(path)
Initialize with path.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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efba139cf537-0 | langchain.document_loaders.duckdb_loader.DuckDBLoader¶
class langchain.document_loaders.duckdb_loader.DuckDBLoader(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)[source]¶
Bases: 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.
Methods
__init__(query[, database, read_only, ...])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.duckdb_loader.DuckDBLoader.html |
909e50246e85-0 | langchain.document_loaders.url_selenium.SeleniumURLLoader¶
class langchain.document_loaders.url_selenium.SeleniumURLLoader(urls: List[str], continue_on_failure: bool = True, browser: Literal['chrome', 'firefox'] = 'chrome', binary_location: Optional[str] = None, executable_path: Optional[str] = None, headless: bool = True, arguments: List[str] = [])[source]¶
Bases: BaseLoader
Loader that uses Selenium and to load a page and unstructured to load the html.
This is useful for loading pages that require javascript to render.
urls¶
List of URLs to load.
Type
List[str]
continue_on_failure¶
If True, continue loading other URLs on failure.
Type
bool
browser¶
The browser to use, either ‘chrome’ or ‘firefox’.
Type
str
binary_location¶
The location of the browser binary.
Type
Optional[str]
executable_path¶
The path to the browser executable.
Type
Optional[str]
headless¶
If True, the browser will run in headless mode.
Type
bool
arguments [List[str]]
List of arguments to pass to the browser.
Load a list of URLs using Selenium and unstructured.
Methods
__init__(urls[, continue_on_failure, ...])
Load a list of URLs using Selenium and unstructured.
lazy_load()
A lazy loader for document content.
load()
Load the specified URLs using Selenium and create Document instances.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load the specified URLs using Selenium and create Document instances.
Returns
A list of Document instances with loaded content.
Return type | [
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909e50246e85-1 | Returns
A list of Document instances with loaded content.
Return type
List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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f910a21440bc-0 | langchain.document_loaders.xml.UnstructuredXMLLoader¶
class langchain.document_loaders.xml.UnstructuredXMLLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load XML files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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a700a0d2d163-0 | langchain.document_loaders.twitter.TwitterTweetLoader¶
class langchain.document_loaders.twitter.TwitterTweetLoader(auth_handler: Union[OAuthHandler, OAuth2BearerHandler], twitter_users: Sequence[str], number_tweets: Optional[int] = 100)[source]¶
Bases: 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.
Methods
__init__(auth_handler, twitter_users[, ...])
from_bearer_token(oauth2_bearer_token, ...)
Create a TwitterTweetLoader from OAuth2 bearer token.
from_secrets(access_token, ...[, number_tweets])
Create a TwitterTweetLoader from access tokens and secrets.
lazy_load()
A lazy loader for document content.
load()
Load tweets.
load_and_split([text_splitter])
Load documents and split into chunks.
classmethod from_bearer_token(oauth2_bearer_token: str, twitter_users: Sequence[str], number_tweets: Optional[int] = 100) → TwitterTweetLoader[source]¶
Create a TwitterTweetLoader from OAuth2 bearer token.
classmethod from_secrets(access_token: str, access_token_secret: str, consumer_key: str, consumer_secret: str, twitter_users: Sequence[str], number_tweets: Optional[int] = 100) → TwitterTweetLoader[source]¶
Create a TwitterTweetLoader from access tokens and secrets.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load tweets.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.twitter.TwitterTweetLoader.html |
a700a0d2d163-1 | Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.twitter.TwitterTweetLoader.html |
663333befb3a-0 | langchain.document_loaders.blackboard.BlackboardLoader¶
class langchain.document_loaders.blackboard.BlackboardLoader(blackboard_course_url: str, bbrouter: str, load_all_recursively: bool = True, basic_auth: Optional[Tuple[str, str]] = None, cookies: Optional[dict] = None)[source]¶
Bases: WebBaseLoader
Loader that loads all documents from a Blackboard course.
This loader is not compatible with all Blackboard courses. It is only
compatible with courses that use the new Blackboard interface.
To use this loader, you must have the BbRouter cookie. You can get this
cookie by logging into the course and then copying the value of the
BbRouter cookie from the browser’s developer tools.
Example
from langchain.document_loaders import BlackboardLoader
loader = BlackboardLoader(
blackboard_course_url="https://blackboard.example.com/webapps/blackboard/execute/announcement?method=search&context=course_entry&course_id=_123456_1",
bbrouter="expires:12345...",
)
documents = loader.load()
Initialize with blackboard course url.
The BbRouter cookie is required for most blackboard courses.
Parameters
blackboard_course_url – Blackboard course url.
bbrouter – BbRouter cookie.
load_all_recursively – If True, load all documents recursively.
basic_auth – Basic auth credentials.
cookies – Cookies.
Raises
ValueError – If blackboard course url is invalid.
Methods
__init__(blackboard_course_url, bbrouter[, ...])
Initialize with blackboard course url.
aload()
Load text from the urls in web_path async into Documents.
check_bs4()
Check if BeautifulSoup4 is installed.
download(path)
Download a file from a url. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html |
663333befb3a-1 | Check if BeautifulSoup4 is installed.
download(path)
Download a file from a url.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
parse_filename(url)
Parse the filename from a url.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
Attributes
bs_get_text_kwargs
kwargs for beatifulsoup4 get_text
default_parser
Default parser to use for BeautifulSoup.
raise_for_status
Raise an exception if http status code denotes an error.
requests_kwargs
kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path
base_url
folder_path
load_all_recursively
aload() → List[Document]¶
Load text from the urls in web_path async into Documents.
check_bs4() → None[source]¶
Check if BeautifulSoup4 is installed.
Raises
ImportError – If BeautifulSoup4 is not installed.
download(path: str) → None[source]¶
Download a file from a url.
Parameters
path – Path to the file.
async fetch_all(urls: List[str]) → Any¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Load data into document objects.
Returns
List of documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html |
663333befb3a-2 | Load documents and split into chunks.
parse_filename(url: str) → str[source]¶
Parse the filename from a url.
Parameters
url – Url to parse the filename from.
Returns
The filename.
scrape(parser: Optional[str] = None) → Any¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any]¶
Fetch all urls, then return soups for all results.
base_url: str¶
bs_get_text_kwargs: Dict[str, Any] = {}¶
kwargs for beatifulsoup4 get_text
default_parser: str = 'html.parser'¶
Default parser to use for BeautifulSoup.
folder_path: str¶
load_all_recursively: bool¶
raise_for_status: bool = False¶
Raise an exception if http status code denotes an error.
requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests
requests_per_second: int = 2¶
Max number of concurrent requests to make.
property web_path: str¶
web_paths: List[str]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html |
5a054076a10e-0 | langchain.document_loaders.parsers.audio.OpenAIWhisperParser¶
class langchain.document_loaders.parsers.audio.OpenAIWhisperParser[source]¶
Bases: BaseBlobParser
Transcribe and parse audio files.
Audio transcription is with OpenAI Whisper model.
Methods
__init__()
lazy_parse(blob)
Lazily parse the blob.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazily parse the blob.
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents | [
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370fa7004562-0 | langchain.document_loaders.blockchain.BlockchainType¶
class langchain.document_loaders.blockchain.BlockchainType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: Enum
Enumerator of the supported blockchains.
Attributes
ETH_MAINNET
ETH_GOERLI
POLYGON_MAINNET
POLYGON_MUMBAI
ETH_GOERLI = 'eth-goerli'¶
ETH_MAINNET = 'eth-mainnet'¶
POLYGON_MAINNET = 'polygon-mainnet'¶
POLYGON_MUMBAI = 'polygon-mumbai'¶ | [
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13cc56c797b8-0 | langchain.document_loaders.github.BaseGitHubLoader¶
class langchain.document_loaders.github.BaseGitHubLoader(*, repo: str, access_token: str)[source]¶
Bases: BaseLoader, BaseModel, ABC
Load issues of a GitHub repository.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param access_token: str [Required]¶
Personal access token - see https://github.com/settings/tokens?type=beta
param repo: str [Required]¶
Name of repository
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
abstract load() → List[Document]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
validator validate_environment » all fields[source]¶
Validate that access token exists in environment.
property headers: Dict[str, str]¶ | [
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ae2cc879dfb9-0 | langchain.document_loaders.wikipedia.WikipediaLoader¶
class langchain.document_loaders.wikipedia.WikipediaLoader(query: str, lang: str = 'en', load_max_docs: Optional[int] = 100, load_all_available_meta: Optional[bool] = False, doc_content_chars_max: Optional[int] = 4000)[source]¶
Bases: BaseLoader
Loads a query result from www.wikipedia.org into a list of Documents.
The hard limit on the number of downloaded Documents is 300 for now.
Each wiki page represents one Document.
Initializes a new instance of the WikipediaLoader class.
Parameters
query (str) – The query string to search on Wikipedia.
lang (str, optional) – The language code for the Wikipedia language edition.
Defaults to “en”.
load_max_docs (int, optional) – The maximum number of documents to load.
Defaults to 100.
load_all_available_meta (bool, optional) – Indicates whether to load all
available metadata for each document. Defaults to False.
doc_content_chars_max (int, optional) – The maximum number of characters
for the document content. Defaults to 4000.
Methods
__init__(query[, lang, load_max_docs, ...])
Initializes a new instance of the WikipediaLoader class.
lazy_load()
A lazy loader for document content.
load()
Loads the query result from Wikipedia into a list of Documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Loads the query result from Wikipedia into a list of Documents.
Returns
A list of Document objects representing the loadedWikipedia pages.
Return type
List[Document] | [
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ae2cc879dfb9-1 | Return type
List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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18e5cb047896-0 | langchain.document_loaders.slack_directory.SlackDirectoryLoader¶
class langchain.document_loaders.slack_directory.SlackDirectoryLoader(zip_path: str, workspace_url: Optional[str] = None)[source]¶
Bases: BaseLoader
Loader for loading documents from a Slack directory dump.
Initialize the SlackDirectoryLoader.
Parameters
zip_path (str) – The path to the Slack directory dump zip file.
workspace_url (Optional[str]) – The Slack workspace URL.
Including the URL will turn
sources into links. Defaults to None.
Methods
__init__(zip_path[, workspace_url])
Initialize the SlackDirectoryLoader.
lazy_load()
A lazy loader for document content.
load()
Load and return documents from the Slack directory dump.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load and return documents from the Slack directory dump.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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b513e4c407e8-0 | langchain.document_loaders.fauna.FaunaLoader¶
class langchain.document_loaders.fauna.FaunaLoader(query: str, page_content_field: str, secret: str, metadata_fields: Optional[Sequence[str]] = None)[source]¶
Bases: BaseLoader
FaunaDB Loader.
query¶
The FQL query string to execute.
Type
str
page_content_field¶
The field that contains the content of each page.
Type
str
secret¶
The secret key for authenticating to FaunaDB.
Type
str
metadata_fields¶
Optional list of field names to include in metadata.
Type
Optional[Sequence[str]]
Methods
__init__(query, page_content_field, secret)
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.fauna.FaunaLoader.html |
a5a55125503d-0 | langchain.document_loaders.larksuite.LarkSuiteDocLoader¶
class langchain.document_loaders.larksuite.LarkSuiteDocLoader(domain: str, access_token: str, document_id: str)[source]¶
Bases: BaseLoader
Loader that loads LarkSuite (FeiShu) document.
Initialize with domain, access_token (tenant / user), and document_id.
Methods
__init__(domain, access_token, document_id)
Initialize with domain, access_token (tenant / user), and document_id.
lazy_load()
Lazy load LarkSuite (FeiShu) document.
load()
Load LarkSuite (FeiShu) document.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Lazy load LarkSuite (FeiShu) document.
load() → List[Document][source]¶
Load LarkSuite (FeiShu) document.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.larksuite.LarkSuiteDocLoader.html |
497c1c6c0eac-0 | langchain.document_loaders.unstructured.UnstructuredAPIFileLoader¶
class langchain.document_loaders.unstructured.UnstructuredAPIFileLoader(file_path: Union[str, List[str]] = '', mode: str = 'single', url: str = 'https://api.unstructured.io/general/v0/general', api_key: str = '', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses the unstructured web API to load files.
Initialize with file path.
Methods
__init__([file_path, mode, url, api_key])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.unstructured.UnstructuredAPIFileLoader.html |
9d1de6ab5fde-0 | langchain.document_loaders.joplin.JoplinLoader¶
class langchain.document_loaders.joplin.JoplinLoader(access_token: Optional[str] = None, port: int = 41184, host: str = 'localhost')[source]¶
Bases: BaseLoader
Loader that fetches notes from Joplin.
In order to use this loader, you need to have Joplin running with the
Web Clipper enabled (look for “Web Clipper” in the app settings).
To get the access token, you need to go to the Web Clipper options and
under “Advanced Options” you will find the access token.
You can find more information about the Web Clipper service here:
https://joplinapp.org/clipper/
Methods
__init__([access_token, port, host])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.joplin.JoplinLoader.html |
ce61d3c05c9e-0 | langchain.document_loaders.googledrive.GoogleDriveLoader¶
class langchain.document_loaders.googledrive.GoogleDriveLoader(*, service_account_key: Path = PosixPath('/home/docs/.credentials/keys.json'), credentials_path: Path = PosixPath('/home/docs/.credentials/credentials.json'), token_path: Path = PosixPath('/home/docs/.credentials/token.json'), folder_id: Optional[str] = None, document_ids: Optional[List[str]] = None, file_ids: Optional[List[str]] = None, recursive: bool = False, file_types: Optional[Sequence[str]] = None, load_trashed_files: bool = False, file_loader_cls: Any = None, file_loader_kwargs: Dict[str, Any] = {})[source]¶
Bases: BaseLoader, BaseModel
Loader that loads Google Docs from Google Drive.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param credentials_path: pathlib.Path = PosixPath('/home/docs/.credentials/credentials.json')¶
param document_ids: Optional[List[str]] = None¶
param file_ids: Optional[List[str]] = None¶
param file_loader_cls: Any = None¶
param file_loader_kwargs: Dict[str, Any] = {}¶
param file_types: Optional[Sequence[str]] = None¶
param folder_id: Optional[str] = None¶
param load_trashed_files: bool = False¶
param recursive: bool = False¶
param service_account_key: pathlib.Path = PosixPath('/home/docs/.credentials/keys.json')¶
param token_path: pathlib.Path = PosixPath('/home/docs/.credentials/token.json')¶
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents. | [
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ce61d3c05c9e-1 | load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
validator validate_credentials_path » credentials_path[source]¶
Validate that credentials_path exists.
validator validate_inputs » all fields[source]¶
Validate that either folder_id or document_ids is set, but not both. | [
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60508d622c6a-0 | langchain.document_loaders.airbyte_json.AirbyteJSONLoader¶
class langchain.document_loaders.airbyte_json.AirbyteJSONLoader(file_path: str)[source]¶
Bases: BaseLoader
Loader that loads local airbyte json files.
Initialize with file path. This should start with ‘/tmp/airbyte_local/’.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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8395314fedfd-0 | langchain.document_loaders.confluence.ContentFormat¶
class langchain.document_loaders.confluence.ContentFormat(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: str, Enum
Enumerator of the content formats of Confluence page.
Methods
get_content(page)
__init__(*args, **kwds)
capitalize()
Return a capitalized version of the string.
casefold()
Return a version of the string suitable for caseless comparisons.
center(width[, fillchar])
Return a centered string of length width.
count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring sub in string S[start:end].
encode([encoding, errors])
Encode the string using the codec registered for encoding.
endswith(suffix[, start[, end]])
Return True if S ends with the specified suffix, False otherwise.
expandtabs([tabsize])
Return a copy where all tab characters are expanded using spaces.
find(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
format(*args, **kwargs)
Return a formatted version of S, using substitutions from args and kwargs.
format_map(mapping)
Return a formatted version of S, using substitutions from mapping.
index(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
isalnum()
Return True if the string is an alpha-numeric string, False otherwise.
isalpha()
Return True if the string is an alphabetic string, False otherwise.
isascii()
Return True if all characters in the string are ASCII, False otherwise.
isdecimal() | [
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8395314fedfd-1 | Return True if all characters in the string are ASCII, False otherwise.
isdecimal()
Return True if the string is a decimal string, False otherwise.
isdigit()
Return True if the string is a digit string, False otherwise.
isidentifier()
Return True if the string is a valid Python identifier, False otherwise.
islower()
Return True if the string is a lowercase string, False otherwise.
isnumeric()
Return True if the string is a numeric string, False otherwise.
isprintable()
Return True if the string is printable, False otherwise.
isspace()
Return True if the string is a whitespace string, False otherwise.
istitle()
Return True if the string is a title-cased string, False otherwise.
isupper()
Return True if the string is an uppercase string, False otherwise.
join(iterable, /)
Concatenate any number of strings.
ljust(width[, fillchar])
Return a left-justified string of length width.
lower()
Return a copy of the string converted to lowercase.
lstrip([chars])
Return a copy of the string with leading whitespace removed.
maketrans
Return a translation table usable for str.translate().
partition(sep, /)
Partition the string into three parts using the given separator.
removeprefix(prefix, /)
Return a str with the given prefix string removed if present.
removesuffix(suffix, /)
Return a str with the given suffix string removed if present.
replace(old, new[, count])
Return a copy with all occurrences of substring old replaced by new.
rfind(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rindex(sub[, start[, end]]) | [
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8395314fedfd-2 | rindex(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rjust(width[, fillchar])
Return a right-justified string of length width.
rpartition(sep, /)
Partition the string into three parts using the given separator.
rsplit([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
rstrip([chars])
Return a copy of the string with trailing whitespace removed.
split([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
splitlines([keepends])
Return a list of the lines in the string, breaking at line boundaries.
startswith(prefix[, start[, end]])
Return True if S starts with the specified prefix, False otherwise.
strip([chars])
Return a copy of the string with leading and trailing whitespace removed.
swapcase()
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()
Return a version of the string where each word is titlecased.
translate(table, /)
Replace each character in the string using the given translation table.
upper()
Return a copy of the string converted to uppercase.
zfill(width, /)
Pad a numeric string with zeros on the left, to fill a field of the given width.
Attributes
STORAGE
VIEW
capitalize()¶
Return a capitalized version of the string.
More specifically, make the first character have upper case and the rest lower
case.
casefold()¶
Return a version of the string suitable for caseless comparisons.
center(width, fillchar=' ', /)¶
Return a centered string of length width.
Padding is done using the specified fill character (default is a space). | [
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8395314fedfd-3 | Padding is done using the specified fill character (default is a space).
count(sub[, start[, end]]) → int¶
Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are
interpreted as in slice notation.
encode(encoding='utf-8', errors='strict')¶
Encode the string using the codec registered for encoding.
encodingThe encoding in which to encode the string.
errorsThe error handling scheme to use for encoding errors.
The default is ‘strict’ meaning that encoding errors raise a
UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and
‘xmlcharrefreplace’ as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
endswith(suffix[, start[, end]]) → bool¶
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
expandtabs(tabsize=8)¶
Return a copy where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
find(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
format(*args, **kwargs) → str¶
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces (‘{’ and ‘}’).
format_map(mapping) → str¶
Return a formatted version of S, using substitutions from mapping.
The substitutions are identified by braces (‘{’ and ‘}’). | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.confluence.ContentFormat.html |
8395314fedfd-4 | The substitutions are identified by braces (‘{’ and ‘}’).
get_content(page: dict) → str[source]¶
index(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
isalnum()¶
Return True if the string is an alpha-numeric string, False otherwise.
A string is alpha-numeric if all characters in the string are alpha-numeric and
there is at least one character in the string.
isalpha()¶
Return True if the string is an alphabetic string, False otherwise.
A string is alphabetic if all characters in the string are alphabetic and there
is at least one character in the string.
isascii()¶
Return True if all characters in the string are ASCII, False otherwise.
ASCII characters have code points in the range U+0000-U+007F.
Empty string is ASCII too.
isdecimal()¶
Return True if the string is a decimal string, False otherwise.
A string is a decimal string if all characters in the string are decimal and
there is at least one character in the string.
isdigit()¶
Return True if the string is a digit string, False otherwise.
A string is a digit string if all characters in the string are digits and there
is at least one character in the string.
isidentifier()¶
Return True if the string is a valid Python identifier, False otherwise.
Call keyword.iskeyword(s) to test whether string s is a reserved identifier,
such as “def” or “class”.
islower()¶
Return True if the string is a lowercase string, False otherwise. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.confluence.ContentFormat.html |
8395314fedfd-5 | islower()¶
Return True if the string is a lowercase string, False otherwise.
A string is lowercase if all cased characters in the string are lowercase and
there is at least one cased character in the string.
isnumeric()¶
Return True if the string is a numeric string, False otherwise.
A string is numeric if all characters in the string are numeric and there is at
least one character in the string.
isprintable()¶
Return True if the string is printable, False otherwise.
A string is printable if all of its characters are considered printable in
repr() or if it is empty.
isspace()¶
Return True if the string is a whitespace string, False otherwise.
A string is whitespace if all characters in the string are whitespace and there
is at least one character in the string.
istitle()¶
Return True if the string is a title-cased string, False otherwise.
In a title-cased string, upper- and title-case characters may only
follow uncased characters and lowercase characters only cased ones.
isupper()¶
Return True if the string is an uppercase string, False otherwise.
A string is uppercase if all cased characters in the string are uppercase and
there is at least one cased character in the string.
join(iterable, /)¶
Concatenate any number of strings.
The string whose method is called is inserted in between each given string.
The result is returned as a new string.
Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’
ljust(width, fillchar=' ', /)¶
Return a left-justified string of length width.
Padding is done using the specified fill character (default is a space).
lower()¶
Return a copy of the string converted to lowercase. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.confluence.ContentFormat.html |
8395314fedfd-6 | lower()¶
Return a copy of the string converted to lowercase.
lstrip(chars=None, /)¶
Return a copy of the string with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
static maketrans()¶
Return a translation table usable for str.translate().
If there is only one argument, it must be a dictionary mapping Unicode
ordinals (integers) or characters to Unicode ordinals, strings or None.
Character keys will be then converted to ordinals.
If there are two arguments, they must be strings of equal length, and
in the resulting dictionary, each character in x will be mapped to the
character at the same position in y. If there is a third argument, it
must be a string, whose characters will be mapped to None in the result.
partition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string. If the separator is found,
returns a 3-tuple containing the part before the separator, the separator
itself, and the part after it.
If the separator is not found, returns a 3-tuple containing the original string
and two empty strings.
removeprefix(prefix, /)¶
Return a str with the given prefix string removed if present.
If the string starts with the prefix string, return string[len(prefix):].
Otherwise, return a copy of the original string.
removesuffix(suffix, /)¶
Return a str with the given suffix string removed if present.
If the string ends with the suffix string and that suffix is not empty,
return string[:-len(suffix)]. Otherwise, return a copy of the original
string.
replace(old, new, count=- 1, /)¶
Return a copy with all occurrences of substring old replaced by new. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.confluence.ContentFormat.html |
8395314fedfd-7 | Return a copy with all occurrences of substring old replaced by new.
countMaximum number of occurrences to replace.
-1 (the default value) means replace all occurrences.
If the optional argument count is given, only the first count occurrences are
replaced.
rfind(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
rindex(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
rjust(width, fillchar=' ', /)¶
Return a right-justified string of length width.
Padding is done using the specified fill character (default is a space).
rpartition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string, starting at the end. If
the separator is found, returns a 3-tuple containing the part before the
separator, the separator itself, and the part after it.
If the separator is not found, returns a 3-tuple containing two empty strings
and the original string.
rsplit(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left). | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.confluence.ContentFormat.html |
8395314fedfd-8 | empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Splitting starts at the end of the string and works to the front.
rstrip(chars=None, /)¶
Return a copy of the string with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
split(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Note, str.split() is mainly useful for data that has been intentionally
delimited. With natural text that includes punctuation, consider using
the regular expression module.
splitlines(keepends=False)¶
Return a list of the lines in the string, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends is given and
true.
startswith(prefix[, start[, end]]) → bool¶
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
strip(chars=None, /)¶
Return a copy of the string with leading and trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
swapcase()¶
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.confluence.ContentFormat.html |
8395314fedfd-9 | Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()¶
Return a version of the string where each word is titlecased.
More specifically, words start with uppercased characters and all remaining
cased characters have lower case.
translate(table, /)¶
Replace each character in the string using the given translation table.
tableTranslation table, which must be a mapping of Unicode ordinals to
Unicode ordinals, strings, or None.
The table must implement lookup/indexing via __getitem__, for instance a
dictionary or list. If this operation raises LookupError, the character is
left untouched. Characters mapped to None are deleted.
upper()¶
Return a copy of the string converted to uppercase.
zfill(width, /)¶
Pad a numeric string with zeros on the left, to fill a field of the given width.
The string is never truncated.
STORAGE = 'body.storage'¶
VIEW = 'body.view'¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.confluence.ContentFormat.html |
562c24b06bc3-0 | langchain.document_loaders.gutenberg.GutenbergLoader¶
class langchain.document_loaders.gutenberg.GutenbergLoader(file_path: str)[source]¶
Bases: BaseLoader
Loader that uses urllib to load .txt web files.
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.gutenberg.GutenbergLoader.html |
589a550c48be-0 | langchain.document_loaders.roam.RoamLoader¶
class langchain.document_loaders.roam.RoamLoader(path: str)[source]¶
Bases: BaseLoader
Loader that loads Roam files from disk.
Initialize with path.
Methods
__init__(path)
Initialize with path.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.roam.RoamLoader.html |
e82d10e30c05-0 | langchain.document_loaders.unstructured.UnstructuredFileLoader¶
class langchain.document_loaders.unstructured.UnstructuredFileLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredBaseLoader
Loader that uses unstructured to load files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.unstructured.UnstructuredFileLoader.html |
85abddb8c6a8-0 | langchain.document_loaders.bigquery.BigQueryLoader¶
class langchain.document_loaders.bigquery.BigQueryLoader(query: str, project: Optional[str] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None, credentials: Optional[Credentials] = None)[source]¶
Bases: BaseLoader
Loads a query result from BigQuery 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.
Initialize BigQuery document loader.
Parameters
query – The query to run in BigQuery.
project – Optional. The project to run the query in.
page_content_columns – Optional. The columns to write into the page_content
of the document.
metadata_columns – Optional. The columns to write into the metadata of the
document.
credentials – google.auth.credentials.Credentials, optional
override (Credentials for accessing Google APIs. Use this parameter to) – default credentials, such as to use Compute Engine
(google.auth.compute_engine.Credentials) or Service Account
(google.oauth2.service_account.Credentials) credentials directly.
Methods
__init__(query[, project, ...])
Initialize BigQuery document loader.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.bigquery.BigQueryLoader.html |
85abddb8c6a8-1 | Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.bigquery.BigQueryLoader.html |
8f922c9fb5b8-0 | langchain.document_loaders.modern_treasury.ModernTreasuryLoader¶
class langchain.document_loaders.modern_treasury.ModernTreasuryLoader(resource: str, organization_id: Optional[str] = None, api_key: Optional[str] = None)[source]¶
Bases: BaseLoader
Loader that fetches data from Modern Treasury.
Methods
__init__(resource[, organization_id, api_key])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.modern_treasury.ModernTreasuryLoader.html |
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