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langchain.document_loaders.trello.TrelloLoader¶
class langchain.document_loaders.trello.TrelloLoader(client: TrelloClient, board_name: str, *, include_card_name: bool = True, include_comments: bool = True, include_checklist: bool = True, card_filter: Literal['closed', 'open', 'all'] = 'all', extra_metadata: Tuple[str, ...] = ('due_date', 'labels', 'list', 'closed'))[source]¶
Load cards from a Trello board.
Initialize Trello loader.
Parameters
client – Trello API client.
board_name – The name of the Trello board.
include_card_name – Whether to include the name of the card in the document.
include_comments – Whether to include the comments on the card in the
document.
include_checklist – Whether to include the checklist on the card in the
document.
card_filter – Filter on card status. Valid values are “closed”, “open”,
“all”.
extra_metadata – List of additional metadata fields to include as document
metadata.Valid values are “due_date”, “labels”, “list”, “closed”.
Methods
__init__(client, board_name, *[, ...])
Initialize Trello loader.
from_credentials(board_name, *[, api_key, token])
Convenience constructor that builds TrelloClient init param for you.
lazy_load()
A lazy loader for Documents.
load()
Loads all cards from the specified Trello board.
load_and_split([text_splitter])
Load Documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.trello.TrelloLoader.html
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load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(client: TrelloClient, board_name: str, *, include_card_name: bool = True, include_comments: bool = True, include_checklist: bool = True, card_filter: Literal['closed', 'open', 'all'] = 'all', extra_metadata: Tuple[str, ...] = ('due_date', 'labels', 'list', 'closed'))[source]¶
Initialize Trello loader.
Parameters
client – Trello API client.
board_name – The name of the Trello board.
include_card_name – Whether to include the name of the card in the document.
include_comments – Whether to include the comments on the card in the
document.
include_checklist – Whether to include the checklist on the card in the
document.
card_filter – Filter on card status. Valid values are “closed”, “open”,
“all”.
extra_metadata – List of additional metadata fields to include as document
metadata.Valid values are “due_date”, “labels”, “list”, “closed”.
classmethod from_credentials(board_name: str, *, api_key: Optional[str] = None, token: Optional[str] = None, **kwargs: Any) → TrelloLoader[source]¶
Convenience constructor that builds TrelloClient init param for you.
Parameters
board_name – The name of the Trello board.
api_key – Trello API key. Can also be specified as environment variable
TRELLO_API_KEY.
token – Trello token. Can also be specified as environment variable
TRELLO_TOKEN.
include_card_name – Whether to include the name of the card in the document.
include_comments – Whether to include the comments on the card in the
document.
include_checklist – Whether to include the checklist on the card in the
document.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.trello.TrelloLoader.html
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include_checklist – Whether to include the checklist on the card in the
document.
card_filter – Filter on card status. Valid values are “closed”, “open”,
“all”.
extra_metadata – List of additional metadata fields to include as document
metadata.Valid values are “due_date”, “labels”, “list”, “closed”.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Loads all cards from the specified Trello board.
You can filter the cards, metadata and text included by using the optional
parameters.
Returns:A list of documents, one for each card in the board.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using TrelloLoader¶
Trello
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.trello.TrelloLoader.html
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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.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.telegram.concatenate_rows.html
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langchain.document_loaders.concurrent.ConcurrentLoader¶
class langchain.document_loaders.concurrent.ConcurrentLoader(blob_loader: BlobLoader, blob_parser: BaseBlobParser, num_workers: int = 4)[source]¶
Load and pars Documents concurrently.
A generic document loader.
Parameters
blob_loader – A blob loader which knows how to yield blobs
blob_parser – A blob parser which knows how to parse blobs into documents
Methods
__init__(blob_loader, blob_parser[, num_workers])
A generic document loader.
from_filesystem(path, *[, glob, exclude, ...])
Create a concurrent generic document loader using a filesystem blob loader.
lazy_load()
Load documents lazily with concurrent parsing.
load()
Load all documents.
load_and_split([text_splitter])
Load all documents and split them into sentences.
__init__(blob_loader: BlobLoader, blob_parser: BaseBlobParser, num_workers: int = 4) → None[source]¶
A generic document loader.
Parameters
blob_loader – A blob loader which knows how to yield blobs
blob_parser – A blob parser which knows how to parse blobs into documents
classmethod from_filesystem(path: Union[str, Path], *, glob: str = '**/[!.]*', exclude: Sequence[str] = (), suffixes: Optional[Sequence[str]] = None, show_progress: bool = False, parser: Union[Literal['default'], BaseBlobParser] = 'default', num_workers: int = 4) → ConcurrentLoader[source]¶
Create a concurrent generic document loader using a
filesystem blob loader.
Parameters
path – The path to the directory to load documents from.
glob – The glob pattern to use to find documents.
suffixes – The suffixes to use to filter documents. If None, all files
matching the glob will be loaded.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.concurrent.ConcurrentLoader.html
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matching the glob will be loaded.
exclude – A list of patterns to exclude from the loader.
show_progress – Whether to show a progress bar or not (requires tqdm).
Proxies to the file system loader.
parser – A blob parser which knows how to parse blobs into documents
num_workers – Max number of concurrent workers to use.
lazy_load() → Iterator[Document][source]¶
Load documents lazily with concurrent parsing.
load() → List[Document]¶
Load all documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load all documents and split them into sentences.
Examples using ConcurrentLoader¶
Concurrent Loader
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.concurrent.ConcurrentLoader.html
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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.
Parameters
cell – A dictionary
include_outputs – Whether to include the outputs of the cell.
max_output_length – Maximum length of the output to be displayed.
traceback – Whether to return a traceback of the error.
Returns
A string with the cell information.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.notebook.concatenate_cells.html
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langchain.document_loaders.base_o365.fetch_mime_types¶
langchain.document_loaders.base_o365.fetch_mime_types(file_types: Sequence[_FileType]) → Dict[str, str][source]¶
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.base_o365.fetch_mime_types.html
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langchain.document_loaders.geodataframe.GeoDataFrameLoader¶
class langchain.document_loaders.geodataframe.GeoDataFrameLoader(data_frame: Any, page_content_column: str = 'geometry')[source]¶
Load geopandas Dataframe.
Initialize with geopandas Dataframe.
Parameters
data_frame – geopandas DataFrame object.
page_content_column – Name of the column containing the page content.
Defaults to “geometry”.
Methods
__init__(data_frame[, page_content_column])
Initialize with geopandas Dataframe.
lazy_load()
Lazy load records from dataframe.
load()
Load full dataframe.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(data_frame: Any, page_content_column: str = 'geometry')[source]¶
Initialize with geopandas Dataframe.
Parameters
data_frame – geopandas DataFrame object.
page_content_column – Name of the column containing the page content.
Defaults to “geometry”.
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. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using GeoDataFrameLoader¶
Geopandas
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.geodataframe.GeoDataFrameLoader.html
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langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader¶
class langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader(path: Union[str, Path], *, glob: str = '**/[!.]*', exclude: Sequence[str] = (), suffixes: Optional[Sequence[str]] = None, show_progress: bool = False)[source]¶
Load blobs in the local file system.
Example:
from langchain.document_loaders.blob_loaders import FileSystemBlobLoader
loader = FileSystemBlobLoader("/path/to/directory")
for blob in loader.yield_blobs():
print(blob)
Initialize with a path to directory and how to glob over it.
Parameters
path – Path to directory to load from
glob – Glob pattern relative to the specified path
by default set to pick up all non-hidden files
exclude – patterns to exclude from results, use glob syntax
suffixes – Provide to keep only files with these suffixes
Useful when wanting to keep files with different suffixes
Suffixes must include the dot, e.g. “.txt”
show_progress – If true, will show a progress bar as the files are loaded.
This forces an iteration through all matching files
to count them prior to loading them.
Examples
# Recursively load all text files in a directory.
loader = FileSystemBlobLoader("/path/to/directory", glob="**/*.txt")
# Recursively load all non-hidden files in a directory.
loader = FileSystemBlobLoader("/path/to/directory", glob="**/[!.]*")
# Load all files in a directory without recursion.
loader = FileSystemBlobLoader("/path/to/directory", glob="*")
# Recursively load all files in a directory, except for py or pyc files.
loader = FileSystemBlobLoader(
"/path/to/directory",
glob="**/*.txt",
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader.html
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"/path/to/directory",
glob="**/*.txt",
exclude=["**/*.py", "**/*.pyc"]
)
Methods
__init__(path, *[, glob, exclude, suffixes, ...])
Initialize with a path to directory and how to glob over it.
count_matching_files()
Count files that match the pattern without loading them.
yield_blobs()
Yield blobs that match the requested pattern.
__init__(path: Union[str, Path], *, glob: str = '**/[!.]*', exclude: Sequence[str] = (), suffixes: Optional[Sequence[str]] = None, show_progress: bool = False) → None[source]¶
Initialize with a path to directory and how to glob over it.
Parameters
path – Path to directory to load from
glob – Glob pattern relative to the specified path
by default set to pick up all non-hidden files
exclude – patterns to exclude from results, use glob syntax
suffixes – Provide to keep only files with these suffixes
Useful when wanting to keep files with different suffixes
Suffixes must include the dot, e.g. “.txt”
show_progress – If true, will show a progress bar as the files are loaded.
This forces an iteration through all matching files
to count them prior to loading them.
Examples
# Recursively load all text files in a directory.
loader = FileSystemBlobLoader("/path/to/directory", glob="**/*.txt")
# Recursively load all non-hidden files in a directory.
loader = FileSystemBlobLoader("/path/to/directory", glob="**/[!.]*")
# Load all files in a directory without recursion.
loader = FileSystemBlobLoader("/path/to/directory", glob="*")
# Recursively load all files in a directory, except for py or pyc files.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader.html
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# Recursively load all files in a directory, except for py or pyc files.
loader = FileSystemBlobLoader(
"/path/to/directory",
glob="**/*.txt",
exclude=["**/*.py", "**/*.pyc"]
)
count_matching_files() → int[source]¶
Count files that match the pattern without loading them.
yield_blobs() → Iterable[Blob][source]¶
Yield blobs that match the requested pattern.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader.html
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langchain.document_loaders.figma.FigmaFileLoader¶
class langchain.document_loaders.figma.FigmaFileLoader(access_token: str, ids: str, key: str)[source]¶
Load Figma file.
Initialize with access token, ids, and key.
Parameters
access_token – The access token for the Figma REST API.
ids – The ids of the Figma file.
key – The key for the Figma file
Methods
__init__(access_token, ids, key)
Initialize with access token, ids, and key.
lazy_load()
A lazy loader for Documents.
load()
Load file
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(access_token: str, ids: str, key: str)[source]¶
Initialize with access token, ids, and key.
Parameters
access_token – The access token for the Figma REST API.
ids – The ids of the Figma file.
key – The key for the Figma file
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load file
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using FigmaFileLoader¶
Figma
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.figma.FigmaFileLoader.html
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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, continue_on_failure: bool = False)[source]¶
Load 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.
continue_on_failure – whether to continue loading the sitemap if an error
occurs loading a url, emitting a warning instead of raising an
exception. Setting this to True makes the loader more robust, but also
may result in missing data. Default: False
Raises
ValueError – If blackboard course url is invalid.
Attributes
web_path
Methods
__init__(blackboard_course_url, bbrouter[, ...])
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html
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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 an 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 an 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.
__init__(blackboard_course_url: str, bbrouter: str, load_all_recursively: bool = True, basic_auth: Optional[Tuple[str, str]] = None, cookies: Optional[dict] = None, continue_on_failure: bool = False)[source]¶
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.
continue_on_failure – whether to continue loading the sitemap if an error
occurs loading a url, emitting a warning instead of raising an
exception. Setting this to True makes the loader more robust, but also
may result in missing data. Default: False
Raises
ValueError – If blackboard course url is invalid.
aload() → List[Document]¶
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html
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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 an 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. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
parse_filename(url: str) → str[source]¶
Parse the filename from an 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.
Examples using BlackboardLoader¶
Blackboard
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html
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langchain.document_loaders.async_html.AsyncHtmlLoader¶
class langchain.document_loaders.async_html.AsyncHtmlLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None, verify_ssl: Optional[bool] = True, proxies: Optional[dict] = None, requests_per_second: int = 2, requests_kwargs: Optional[Dict[str, Any]] = None, raise_for_status: bool = False)[source]¶
Load HTML asynchronously.
Initialize with a webpage path.
Methods
__init__(web_path[, header_template, ...])
Initialize with a webpage path.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Load text from the url(s) in web_path.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(web_path: Union[str, List[str]], header_template: Optional[dict] = None, verify_ssl: Optional[bool] = True, proxies: Optional[dict] = None, requests_per_second: int = 2, requests_kwargs: Optional[Dict[str, Any]] = None, raise_for_status: bool = False)[source]¶
Initialize with a webpage path.
async fetch_all(urls: List[str]) → Any[source]¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document][source]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Load text from the url(s) in web_path.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.async_html.AsyncHtmlLoader.html
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Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using AsyncHtmlLoader¶
html2text
AsyncHtmlLoader
Set env var OPENAI_API_KEY or load from a .env file:
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.async_html.AsyncHtmlLoader.html
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langchain.document_loaders.airtable.AirtableLoader¶
class langchain.document_loaders.airtable.AirtableLoader(api_token: str, table_id: str, base_id: str)[source]¶
Load the Airtable tables.
Initialize with API token and the IDs for table and base
Attributes
api_token
Airtable API token.
table_id
Airtable table ID.
base_id
Airtable base ID.
Methods
__init__(api_token, table_id, base_id)
Initialize with API token and the IDs for table and base
lazy_load()
Lazy load Documents from table.
load()
Load Documents from table.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(api_token: str, table_id: str, base_id: str)[source]¶
Initialize with API token and the IDs for table and base
lazy_load() → Iterator[Document][source]¶
Lazy load Documents from table.
load() → List[Document][source]¶
Load Documents from table.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using AirtableLoader¶
Airtable
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.airtable.AirtableLoader.html
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langchain.document_loaders.telegram.TelegramChatFileLoader¶
class langchain.document_loaders.telegram.TelegramChatFileLoader(path: str)[source]¶
Load from Telegram chat dump.
Initialize with a path.
Methods
__init__(path)
Initialize with a path.
lazy_load()
A lazy loader for Documents.
load()
Load documents.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(path: str)[source]¶
Initialize with a path.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using TelegramChatFileLoader¶
Telegram
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.telegram.TelegramChatFileLoader.html
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langchain.document_loaders.unstructured.UnstructuredFileIOLoader¶
class langchain.document_loaders.unstructured.UnstructuredFileIOLoader(file: Union[IO, Sequence[IO]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Load files using Unstructured.
The file loader
uses the unstructured partition function and will automatically detect the file
type. You can run the loader in one of two modes: “single” and “elements”.
If you use “single” mode, the document will be returned as a single
langchain Document object. If you use “elements” mode, the unstructured
library will split the document into elements such as Title and NarrativeText.
You can pass in additional unstructured kwargs after mode to apply
different unstructured settings.
Examples
from langchain.document_loaders import UnstructuredFileIOLoader
with open(“example.pdf”, “rb”) as f:
loader = UnstructuredFileIOLoader(f, mode=”elements”, strategy=”fast”,
)
docs = loader.load()
References
https://unstructured-io.github.io/unstructured/bricks.html#partition
Initialize with file path.
Methods
__init__(file[, mode])
Initialize with file path.
lazy_load()
A lazy loader for Documents.
load()
Load file.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(file: Union[IO, Sequence[IO]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Initialize with file path.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.unstructured.UnstructuredFileIOLoader.html
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Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using UnstructuredFileIOLoader¶
Google Drive
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.unstructured.UnstructuredFileIOLoader.html
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langchain.document_loaders.github.BaseGitHubLoader¶
class langchain.document_loaders.github.BaseGitHubLoader[source]¶
Bases: BaseLoader, BaseModel, ABC
Load GitHub repository Issues.
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
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.github.BaseGitHubLoader.html
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
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. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.github.BaseGitHubLoader.html
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Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
property headers: Dict[str, str]¶
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.github.BaseGitHubLoader.html
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langchain.document_loaders.pdf.OnlinePDFLoader¶
class langchain.document_loaders.pdf.OnlinePDFLoader(file_path: str, *, headers: Optional[Dict] = None)[source]¶
Load online PDF.
Initialize with a file path.
Parameters
file_path – Either a local, S3 or web path to a PDF file.
headers – Headers to use for GET request to download a file from a web path.
Attributes
source
Methods
__init__(file_path, *[, headers])
Initialize with a file path.
lazy_load()
A lazy loader for Documents.
load()
Load documents.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(file_path: str, *, headers: Optional[Dict] = None)¶
Initialize with a file path.
Parameters
file_path – Either a local, S3 or web path to a PDF file.
headers – Headers to use for GET request to download a file from a web path.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.pdf.OnlinePDFLoader.html
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langchain.document_loaders.open_city_data.OpenCityDataLoader¶
class langchain.document_loaders.open_city_data.OpenCityDataLoader(city_id: str, dataset_id: str, limit: int)[source]¶
Load from Open City.
Initialize with dataset_id.
Example: https://dev.socrata.com/foundry/data.sfgov.org/vw6y-z8j6
e.g., city_id = data.sfgov.org
e.g., dataset_id = vw6y-z8j6
Parameters
city_id – The Open City city identifier.
dataset_id – The Open City dataset identifier.
limit – The maximum number of documents to load.
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.
__init__(city_id: str, dataset_id: str, limit: int)[source]¶
Initialize with dataset_id.
Example: https://dev.socrata.com/foundry/data.sfgov.org/vw6y-z8j6
e.g., city_id = data.sfgov.org
e.g., dataset_id = vw6y-z8j6
Parameters
city_id – The Open City city identifier.
dataset_id – The Open City dataset identifier.
limit – The maximum number of documents to load.
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. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.open_city_data.OpenCityDataLoader.html
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Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using OpenCityDataLoader¶
Geopandas
Open City Data
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.open_city_data.OpenCityDataLoader.html
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langchain.document_loaders.xml.UnstructuredXMLLoader¶
class langchain.document_loaders.xml.UnstructuredXMLLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Load XML file using Unstructured.
You can run the loader in one of two modes: “single” and “elements”.
If you use “single” mode, the document will be returned as a single
langchain Document object. If you use “elements” mode, the unstructured
library will split the document into elements such as Title and NarrativeText.
You can pass in additional unstructured kwargs after mode to apply
different unstructured settings.
Examples
from langchain.document_loaders import UnstructuredXMLLoader
loader = UnstructuredXMLLoader(“example.xml”, mode=”elements”, strategy=”fast”,
)
docs = loader.load()
References
https://unstructured-io.github.io/unstructured/bricks.html#partition-xml
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for Documents.
load()
Load file.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Initialize with file path.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using UnstructuredXMLLoader¶
XML
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.xml.UnstructuredXMLLoader.html
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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]¶
Load from Modern Treasury.
Parameters
resource – The Modern Treasury resource to load.
organization_id – The Modern Treasury organization ID. It can also be
specified via the environment variable
“MODERN_TREASURY_ORGANIZATION_ID”.
api_key – The Modern Treasury API key. It can also be specified via
the environment variable “MODERN_TREASURY_API_KEY”.
Methods
__init__(resource[, organization_id, api_key])
param resource
The Modern Treasury resource to load.
lazy_load()
A lazy loader for Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(resource: str, organization_id: Optional[str] = None, api_key: Optional[str] = None) → None[source]¶
Parameters
resource – The Modern Treasury resource to load.
organization_id – The Modern Treasury organization ID. It can also be
specified via the environment variable
“MODERN_TREASURY_ORGANIZATION_ID”.
api_key – The Modern Treasury API key. It can also be specified via
the environment variable “MODERN_TREASURY_API_KEY”.
lazy_load() → Iterator[Document]¶
A lazy loader for 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. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.modern_treasury.ModernTreasuryLoader.html
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Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using ModernTreasuryLoader¶
Modern Treasury
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.modern_treasury.ModernTreasuryLoader.html
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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]¶
Load from the Google Cloud Platform BigQuery.
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
Credentials for accessing Google APIs. Use this parameter to override
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 Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(query: str, project: Optional[str] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None, credentials: Optional[Credentials] = None)[source]¶
Initialize BigQuery document loader.
Parameters
query – The query to run in BigQuery.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.bigquery.BigQueryLoader.html
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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
Credentials for accessing Google APIs. Use this parameter to override
default credentials, such as to use Compute Engine
(google.auth.compute_engine.Credentials) or Service Account
(google.oauth2.service_account.Credentials) credentials directly.
lazy_load() → Iterator[Document]¶
A lazy loader for 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. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using BigQueryLoader¶
Google BigQuery
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.bigquery.BigQueryLoader.html
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langchain.document_loaders.airbyte.AirbyteTypeformLoader¶
class langchain.document_loaders.airbyte.AirbyteTypeformLoader(config: Mapping[str, Any], stream_name: str, record_handler: Optional[Callable[[Any, Optional[str]], Document]] = None, state: Optional[Any] = None)[source]¶
Load from Typeform using an Airbyte source connector.
Initializes the loader.
Parameters
config – The config to pass to the source connector.
stream_name – The name of the stream to load.
record_handler – A function that takes in a record and an optional id and
returns a Document. If None, the record will be used as the document.
Defaults to None.
state – The state to pass to the source connector. Defaults to None.
Attributes
last_state
Methods
__init__(config, stream_name[, ...])
Initializes the loader.
lazy_load()
A lazy loader for Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(config: Mapping[str, Any], stream_name: str, record_handler: Optional[Callable[[Any, Optional[str]], Document]] = None, state: Optional[Any] = None) → None[source]¶
Initializes the loader.
Parameters
config – The config to pass to the source connector.
stream_name – The name of the stream to load.
record_handler – A function that takes in a record and an optional id and
returns a Document. If None, the record will be used as the document.
Defaults to None.
state – The state to pass to the source connector. Defaults to None.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document]¶
Load data into Document objects.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.airbyte.AirbyteTypeformLoader.html
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load() → List[Document]¶
Load data into Document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using AirbyteTypeformLoader¶
Airbyte Typeform
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.airbyte.AirbyteTypeformLoader.html
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langchain.document_loaders.youtube.GoogleApiClient¶
class langchain.document_loaders.youtube.GoogleApiClient(credentials_path: Path = PosixPath('/home/docs/.credentials/credentials.json'), service_account_path: Path = PosixPath('/home/docs/.credentials/credentials.json'), token_path: Path = PosixPath('/home/docs/.credentials/token.json'))[source]¶
Generic Google API Client.
To use, you should have the google_auth_oauthlib,youtube_transcript_api,google
python package installed.
As the google api expects credentials you need to set up a google account and
register your Service. “https://developers.google.com/docs/api/quickstart/python”
Example
from langchain.document_loaders import GoogleApiClient
google_api_client = GoogleApiClient(
service_account_path=Path("path_to_your_sec_file.json")
)
Attributes
credentials_path
service_account_path
token_path
Methods
__init__([credentials_path, ...])
validate_channel_or_videoIds_is_set(values)
Validate that either folder_id or document_ids is set, but not both.
__init__(credentials_path: Path = PosixPath('/home/docs/.credentials/credentials.json'), service_account_path: Path = PosixPath('/home/docs/.credentials/credentials.json'), token_path: Path = PosixPath('/home/docs/.credentials/token.json')) → None¶
classmethod validate_channel_or_videoIds_is_set(values: Dict[str, Any]) → Dict[str, Any][source]¶
Validate that either folder_id or document_ids is set, but not both.
Examples using GoogleApiClient¶
YouTube transcripts
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.youtube.GoogleApiClient.html
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langchain.document_loaders.pdf.MathpixPDFLoader¶
class langchain.document_loaders.pdf.MathpixPDFLoader(file_path: str, processed_file_format: str = 'md', max_wait_time_seconds: int = 500, should_clean_pdf: bool = False, **kwargs: Any)[source]¶
Load PDF files using Mathpix service.
Initialize with a file path.
Parameters
file_path – a file for loading.
processed_file_format – a format of the processed file. Default is “md”.
max_wait_time_seconds – a maximum time to wait for the response from
the server. Default is 500.
should_clean_pdf – a flag to clean the PDF file. Default is False.
**kwargs – additional keyword arguments.
Attributes
data
source
url
Methods
__init__(file_path[, processed_file_format, ...])
Initialize with a file path.
clean_pdf(contents)
Clean the PDF file.
get_processed_pdf(pdf_id)
lazy_load()
A lazy loader for Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
send_pdf()
wait_for_processing(pdf_id)
Wait for processing to complete.
__init__(file_path: str, processed_file_format: str = 'md', max_wait_time_seconds: int = 500, should_clean_pdf: bool = False, **kwargs: Any) → None[source]¶
Initialize with a file path.
Parameters
file_path – a file for loading.
processed_file_format – a format of the processed file. Default is “md”.
max_wait_time_seconds – a maximum time to wait for the response from
the server. Default is 500.
should_clean_pdf – a flag to clean the PDF file. Default is False.
**kwargs – additional keyword arguments.
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.pdf.MathpixPDFLoader.html
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**kwargs – additional keyword arguments.
clean_pdf(contents: str) → str[source]¶
Clean the PDF file.
Parameters
contents – a PDF file contents.
Returns:
get_processed_pdf(pdf_id: str) → str[source]¶
lazy_load() → Iterator[Document]¶
A lazy loader for 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. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
send_pdf() → str[source]¶
wait_for_processing(pdf_id: str) → None[source]¶
Wait for processing to complete.
Parameters
pdf_id – a PDF id.
Returns: None
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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.pdf.MathpixPDFLoader.html
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langchain.document_loaders.parsers.pdf.PDFMinerParser¶
class langchain.document_loaders.parsers.pdf.PDFMinerParser[source]¶
Parse PDF using PDFMiner.
Methods
__init__()
lazy_parse(blob)
Lazily parse the blob.
parse(blob)
Eagerly parse the blob into a document or documents.
__init__()¶
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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https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.parsers.pdf.PDFMinerParser.html
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langchain.document_loaders.airbyte.AirbyteSalesforceLoader¶
class langchain.document_loaders.airbyte.AirbyteSalesforceLoader(config: Mapping[str, Any], stream_name: str, record_handler: Optional[Callable[[Any, Optional[str]], Document]] = None, state: Optional[Any] = None)[source]¶
Load from Salesforce using an Airbyte source connector.
Initializes the loader.
Parameters
config – The config to pass to the source connector.
stream_name – The name of the stream to load.
record_handler – A function that takes in a record and an optional id and
returns a Document. If None, the record will be used as the document.
Defaults to None.
state – The state to pass to the source connector. Defaults to None.
Attributes
last_state
Methods
__init__(config, stream_name[, ...])
Initializes the loader.
lazy_load()
A lazy loader for Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(config: Mapping[str, Any], stream_name: str, record_handler: Optional[Callable[[Any, Optional[str]], Document]] = None, state: Optional[Any] = None) → None[source]¶
Initializes the loader.
Parameters
config – The config to pass to the source connector.
stream_name – The name of the stream to load.
record_handler – A function that takes in a record and an optional id and
returns a Document. If None, the record will be used as the document.
Defaults to None.
state – The state to pass to the source connector. Defaults to None.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document]¶
Load data into Document objects.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.airbyte.AirbyteSalesforceLoader.html
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load() → List[Document]¶
Load data into Document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using AirbyteSalesforceLoader¶
Airbyte Salesforce
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.airbyte.AirbyteSalesforceLoader.html
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langchain.smith.evaluation.string_run_evaluator.StringExampleMapper¶
class langchain.smith.evaluation.string_run_evaluator.StringExampleMapper[source]¶
Bases: Serializable
Map an example, or row in the dataset, to the inputs of an evaluation.
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 reference_key: Optional[str] = None¶
__call__(example: Example) → Dict[str, str][source]¶
Maps the Run and Example to a dictionary.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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The unique identifier is a list of strings that describes the path
to the object.
map(example: Example) → Dict[str, str][source]¶
Maps the Example, or dataset row to a dictionary.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
serialize_chat_messages(messages: List[Dict]) → str[source]¶
Extract the input messages from the run.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_keys: List[str]¶
The keys to extract from the run.
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langchain.smith.evaluation.runner_utils.InputFormatError¶
class langchain.smith.evaluation.runner_utils.InputFormatError[source]¶
Raised when the input format is invalid.
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langchain.smith.evaluation.config.RunEvalConfig¶
class langchain.smith.evaluation.config.RunEvalConfig[source]¶
Bases: BaseModel
Configuration for a run evaluation.
Parameters
evaluators (List[Union[EvaluatorType, EvalConfig]]) – Configurations for which evaluators to apply to the dataset run.
Each can be the string of an EvaluatorType, such
as EvaluatorType.QA, the evaluator type string (“qa”), or a configuration for a
given evaluator (e.g., RunEvalConfig.QA).
custom_evaluators (Optional[List[Union[RunEvaluator, StringEvaluator]]]) – Custom evaluators to apply to the dataset run.
reference_key (Optional[str]) – The key in the dataset run to use as the reference string.
If not provided, it will be inferred automatically.
prediction_key (Optional[str]) – The key from the traced run’s outputs dictionary to use to
represent the prediction. If not provided, it will be inferred
automatically.
input_key (Optional[str]) – The key from the traced run’s inputs dictionary to use to represent the
input. If not provided, it will be inferred automatically.
eval_llm (Optional[BaseLanguageModel]) – The language model to pass to any evaluators that use a language model.
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 custom_evaluators: Optional[List[Union[langsmith.evaluation.evaluator.RunEvaluator, langchain.evaluation.schema.StringEvaluator]]] = None¶
Custom evaluators to apply to the dataset run.
param eval_llm: Optional[langchain.schema.language_model.BaseLanguageModel] = None¶
The language model to pass to any evaluators that require one.
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The language model to pass to any evaluators that require one.
param evaluators: List[Union[langchain.evaluation.schema.EvaluatorType, str, langchain.smith.evaluation.config.EvalConfig]] [Optional]¶
Configurations for which evaluators to apply to the dataset run.
Each can be the string of an
EvaluatorType, such
as EvaluatorType.QA, the evaluator type string (“qa”), or a configuration for a
given evaluator
(e.g.,
RunEvalConfig.QA).
param input_key: Optional[str] = None¶
The key from the traced run’s inputs dictionary to use to represent the
input. If not provided, it will be inferred automatically.
param prediction_key: Optional[str] = None¶
The key from the traced run’s outputs dictionary to use to
represent the prediction. If not provided, it will be inferred
automatically.
param reference_key: Optional[str] = None¶
The key in the dataset run to use as the reference string.
If not provided, we will attempt to infer automatically.
class CoTQA[source]¶
Bases: EvalConfig
Configuration for a context-based QA evaluator.
Parameters
prompt (Optional[BasePromptTemplate]) – The prompt template to use for generating the question.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.
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 evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.CONTEXT_QA¶
param llm: Optional[langchain.schema.language_model.BaseLanguageModel] = None¶
param prompt: Optional[langchain.schema.prompt_template.BasePromptTemplate] = None¶
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param prompt: Optional[langchain.schema.prompt_template.BasePromptTemplate] = None¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
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Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class ContextQA[source]¶
Bases: EvalConfig
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class ContextQA[source]¶
Bases: EvalConfig
Configuration for a context-based QA evaluator.
Parameters
prompt (Optional[BasePromptTemplate]) – The prompt template to use for generating the question.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.
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 evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.CONTEXT_QA¶
param llm: Optional[langchain.schema.language_model.BaseLanguageModel] = None¶
param prompt: Optional[langchain.schema.prompt_template.BasePromptTemplate] = None¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
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classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class Criteria[source]¶
Bases: EvalConfig
Configuration for a reference-free criteria evaluator.
Parameters
criteria (Optional[CRITERIA_TYPE]) – The criteria to evaluate.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.
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 criteria: Optional[Union[Mapping[str, str], langchain.evaluation.criteria.eval_chain.Criteria, langchain.chains.constitutional_ai.models.ConstitutionalPrinciple]] = None¶
param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.CRITERIA¶
param llm: Optional[langchain.schema.language_model.BaseLanguageModel] = None¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
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Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
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The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class EmbeddingDistance[source]¶
Bases: EvalConfig
Configuration for an embedding distance evaluator.
Parameters
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Bases: EvalConfig
Configuration for an embedding distance evaluator.
Parameters
embeddings (Optional[Embeddings]) – The embeddings to use for computing the distance.
distance_metric (Optional[EmbeddingDistanceEnum]) – The distance metric to use for computing the distance.
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 distance_metric: Optional[langchain.evaluation.embedding_distance.base.EmbeddingDistance] = None¶
param embeddings: Optional[langchain.schema.embeddings.Embeddings] = None¶
param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.EMBEDDING_DISTANCE¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
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classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class ExactMatch[source]¶
Bases: EvalConfig
Configuration for an exact match string evaluator.
Parameters
ignore_case (bool) – Whether to ignore case when comparing strings.
ignore_punctuation (bool) – Whether to ignore punctuation when comparing strings.
ignore_numbers (bool) – Whether to ignore numbers when comparing strings.
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 evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.STRING_DISTANCE¶
param ignore_case: bool = False¶
param ignore_numbers: bool = False¶
param ignore_punctuation: bool = False¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
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Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
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The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class JsonEqualityEvaluator[source]¶
Bases: EvalConfig
Configuration for a json equality evaluator.
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Bases: EvalConfig
Configuration for a json equality evaluator.
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 evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.JSON_EQUALITY¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
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Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
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classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class JsonValidity[source]¶
Bases: EvalConfig
Configuration for a json validity evaluator.
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 evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.JSON_VALIDITY¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
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classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class LabeledCriteria[source]¶
Bases: EvalConfig
Configuration for a labeled (with references) criteria evaluator.
Parameters
criteria (Optional[CRITERIA_TYPE]) – The criteria to evaluate.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.
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 criteria: Optional[Union[Mapping[str, str], langchain.evaluation.criteria.eval_chain.Criteria, langchain.chains.constitutional_ai.models.ConstitutionalPrinciple]] = None¶
param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.LABELED_CRITERIA¶
param llm: Optional[langchain.schema.language_model.BaseLanguageModel] = None¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
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Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
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The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class QA[source]¶
Bases: EvalConfig
Configuration for a QA evaluator.
Parameters
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Bases: EvalConfig
Configuration for a QA evaluator.
Parameters
prompt (Optional[BasePromptTemplate]) – The prompt template to use for generating the question.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.
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 evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.QA¶
param llm: Optional[langchain.schema.language_model.BaseLanguageModel] = None¶
param prompt: Optional[langchain.schema.prompt_template.BasePromptTemplate] = None¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
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classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class RegexMatch[source]¶
Bases: EvalConfig
Configuration for a regex match string evaluator.
Parameters
flags (int) – The flags to pass to the regex. Example: re.IGNORECASE.
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 evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.REGEX_MATCH¶
param flags: int = 0¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
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Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
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The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
class StringDistance[source]¶
Bases: EvalConfig
Configuration for a string distance evaluator.
Parameters
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Bases: EvalConfig
Configuration for a string distance evaluator.
Parameters
distance (Optional[StringDistanceEnum]) – The string distance metric to use.
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 distance: Optional[langchain.evaluation.string_distance.base.StringDistance] = None¶
The string distance metric to use.
damerau_levenshtein: The Damerau-Levenshtein distance.
levenshtein: The Levenshtein distance.
jaro: The Jaro distance.
jaro_winkler: The Jaro-Winkler distance.
param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.STRING_DISTANCE¶
param normalize_score: bool = True¶
Whether to normalize the distance to between 0 and 1.
Applies only to the Levenshtein and Damerau-Levenshtein distances.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
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exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
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classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
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exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
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classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
Examples using RunEvalConfig¶
LangSmith Walkthrough
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langchain.smith.evaluation.string_run_evaluator.ToolStringRunMapper¶
class langchain.smith.evaluation.string_run_evaluator.ToolStringRunMapper[source]¶
Bases: StringRunMapper
Map an input to the tool.
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.
__call__(run: Run) → Dict[str, str]¶
Maps the Run to a dictionary.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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The unique identifier is a list of strings that describes the path
to the object.
map(run: Run) → Dict[str, str][source]¶
Maps the Run to a dictionary.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_keys: List[str]¶
The keys to extract from the run.
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langchain.smith.evaluation.config.EvalConfig¶
class langchain.smith.evaluation.config.EvalConfig[source]¶
Bases: BaseModel
Configuration for a given run evaluator.
Parameters
evaluator_type (EvaluatorType) – The type of evaluator to use.
get_kwargs()[source]¶
Get the keyword arguments for the evaluator configuration.
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 evaluator_type: langchain.evaluation.schema.EvaluatorType [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_kwargs() → Dict[str, Any][source]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
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classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
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langchain.smith.evaluation.runner_utils.run_on_dataset¶
langchain.smith.evaluation.runner_utils.run_on_dataset(client: Optional[Client], dataset_name: str, llm_or_chain_factory: Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel, Callable[[dict], Any], Runnable, Chain], *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, project_metadata: Optional[Dict[str, Any]] = None, verbose: bool = False, tags: Optional[List[str]] = None, **kwargs: Any) → Dict[str, Any][source]¶
Run the Chain or language model on a dataset and store traces
to the specified project name.
Parameters
dataset_name – Name of the dataset to run the chain on.
llm_or_chain_factory – Language model or Chain constructor to run
over the dataset. The Chain constructor is used to permit
independent calls on each example without carrying over state.
evaluation – Configuration for evaluators to run on the
results of the chain
concurrency_level – The number of async tasks to run concurrently.
project_name – Name of the project to store the traces in.
Defaults to {dataset_name}-{chain class name}-{datetime}.
project_metadata – Optional metadata to add to the project.
Useful for storing information the test variant.
(prompt version, model version, etc.)
client – LangSmith client to use to access the dataset and to
log feedback and run traces.
verbose – Whether to print progress.
tags – Tags to add to each run in the project.
Returns
A dictionary containing the run’s project name and the resulting model outputs.
For the (usually faster) async version of this function, see arun_on_dataset().
Examples
from langsmith import Client
from langchain.chat_models import ChatOpenAI
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Examples
from langsmith import Client
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.smith import smith_eval.RunEvalConfig, run_on_dataset
# Chains may have memory. Passing in a constructor function lets the
# evaluation framework avoid cross-contamination between runs.
def construct_chain():
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(
llm,
"What's the answer to {your_input_key}"
)
return chain
# Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
evaluation_config = smith_eval.RunEvalConfig(
evaluators=[
"qa", # "Correctness" against a reference answer
"embedding_distance",
smith_eval.RunEvalConfig.Criteria("helpfulness"),
smith_eval.RunEvalConfig.Criteria({
"fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
}),
]
)
client = Client()
run_on_dataset(
client,
"<my_dataset_name>",
construct_chain,
evaluation=evaluation_config,
)
You can also create custom evaluators by subclassing the
StringEvaluator
or LangSmith’s RunEvaluator classes.
from typing import Optional
from langchain.evaluation import StringEvaluator
class MyStringEvaluator(StringEvaluator):
@property
def requires_input(self) -> bool:
return False
@property
def requires_reference(self) -> bool:
return True
@property
def evaluation_name(self) -> str:
return "exact_match"
def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict:
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return {"score": prediction == reference}
evaluation_config = smith_eval.RunEvalConfig(
custom_evaluators = [MyStringEvaluator()],
)
run_on_dataset(
client,
"<my_dataset_name>",
construct_chain,
evaluation=evaluation_config,
)
Examples using run_on_dataset¶
LangSmith Walkthrough
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langchain.smith.evaluation.runner_utils.TestResult¶
class langchain.smith.evaluation.runner_utils.TestResult[source]¶
A dictionary of the results of a single test run.
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.
get_aggregate_feedback([quantiles])
Return quantiles for the feedback scores.
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.
to_dataframe()
Convert the results to a dataframe.
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()
__init__(*args, **kwargs)¶
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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Return the value for key if key is in the dictionary, else default.
get_aggregate_feedback(quantiles: Optional[Sequence[float]] = None) → pd.DataFrame[source]¶
Return quantiles for the feedback scores.
This method calculates and prints the quantiles for the feedback scores
across all feedback keys.
Returns
A DataFrame containing the quantiles for each feedback key.
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.
to_dataframe() → pd.DataFrame[source]¶
Convert the results to a dataframe.
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¶
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langchain.smith.evaluation.string_run_evaluator.StringRunEvaluatorChain¶
class langchain.smith.evaluation.string_run_evaluator.StringRunEvaluatorChain[source]¶
Bases: Chain, RunEvaluator
Evaluate Run and optional examples.
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 example_mapper: Optional[StringExampleMapper] = None¶
Maps the Example (dataset row) to a dictionary
with a ‘reference’ string.
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.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata 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 name: str [Required]¶
The name of the evaluation metric.
param run_mapper: StringRunMapper [Required]¶
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The name of the evaluation metric.
param run_mapper: StringRunMapper [Required]¶
Maps the Run to a dictionary with ‘input’ and ‘prediction’ strings.
param string_evaluator: StringEvaluator [Required]¶
The evaluation chain.
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, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – 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. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
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these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
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, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – 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. These will be called in
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callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async aevaluate_run(run: Run, example: Optional[Example] = None) → EvaluationResult[source]¶
Evaluate an example.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
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with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
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Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
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Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
evaluate_run(run: Run, example: Optional[Example] = None) → EvaluationResult[source]¶
Evaluate an example.
classmethod from_orm(obj: Any) → Model¶
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Evaluate an example.
classmethod from_orm(obj: Any) → Model¶
classmethod from_run_and_data_type(evaluator: StringEvaluator, run_type: str, data_type: DataType, input_key: Optional[str] = None, prediction_key: Optional[str] = None, reference_key: Optional[str] = None, tags: Optional[List[str]] = None) → StringRunEvaluatorChain[source]¶
Create a StringRunEvaluatorChain from an evaluator and the run and dataset types.
This method provides an easy way to instantiate a StringRunEvaluatorChain, by
taking an evaluator and information about the type of run and the data.
The method supports LLM and chain runs.
Parameters
evaluator (StringEvaluator) – The string evaluator to use.
run_type (str) – The type of run being evaluated.
Supported types are LLM and Chain.
data_type (DataType) – The type of dataset used in the run.
input_key (str, optional) – The key used to map the input from the run.
prediction_key (str, optional) – The key used to map the prediction from the run.
reference_key (str, optional) – The key used to map the reference from the dataset.
tags (List[str], optional) – List of tags to attach to the evaluation chain.
Returns
The instantiated evaluation chain.
Return type
StringRunEvaluatorChain
Raises
ValueError – If the run type is not supported, or if the evaluator requires a
reference from the dataset but the reference key is not provided.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
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namespace is [“langchain”, “llms”, “openai”]
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
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prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the final outputs.
Returns
A dict of the final chain outputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
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these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
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to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Type[langchain.schema.runnable.utils.Input]¶
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
property input_keys: List[str]¶
Keys expected to be in the chain input.
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
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List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_keys: List[str]¶
Keys expected to be in the chain output.
property output_schema: Type[pydantic.main.BaseModel]¶
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