codekingpro/portable-devtools
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j5 r/ SQrg)an Utilities for working with embedding functions and LangChain's Embeddings interface.5 6This module provides tools to wrap arbitrary embedding functions (both sync and async)7into LangChain's Embeddings interface. This enables using custom embedding functions8with LangChain-compatible tools while maintaining support for both synchronous and9asynchronous operations.10� )� annotationsN)� Awaitable�Callable�Sequence)�Any)�11Embeddingsc �$ � U c [ S5 e[ U [ 5 ( a> [ 5 nUc) SSKJnJn U" S5 nSU S3n[ SU S U S1235 eU" U 5 $ [ U [ 5 ( a U $ [ U 5 $ ! U a Sn NFf = f) a, Ensure that an embedding function conforms to LangChain's Embeddings interface.13 14This function wraps arbitrary embedding functions to make them compatible with15LangChain's Embeddings interface. It handles both synchronous and asynchronous16functions.17 18Args:19 embed: Either an existing Embeddings instance, or a function that converts20 text to embeddings. If the function is async, it will be used for both21 sync and async operations.22 23Returns:24 An Embeddings instance that wraps the provided function(s).25 26??? example "Examples"27 28 Wrap a synchronous embedding function:29 30 ```python31 def my_embed_fn(texts):32 return [[0.1, 0.2] for _ in texts]33 34 embeddings = ensure_embeddings(my_embed_fn)35 result = embeddings.embed_query("hello") # Returns [0.1, 0.2]36 ```37 38 Wrap an asynchronous embedding function:39 40 ```python41 async def my_async_fn(texts):42 return [[0.1, 0.2] for _ in texts]43 44 embeddings = ensure_embeddings(my_async_fn)45 result = await embeddings.aembed_query("hello") # Returns [0.1, 0.2]46 ```47 48 Initialize embeddings using a provider string:49 50 ```python51 # Requires langchain>=0.3.9 and langgraph-checkpoint>=2.0.1152 embeddings = ensure_embeddings("openai:text-embedding-3-small")53 result = embeddings.embed_query("hello")54 ```55zembed must be providedr )�PackageNotFoundError�version� langchainzFound langchain version z, butzlangchain is not installed;z'Could not load embeddings from string 'z'. aF loading embeddings by provider:identifier string requires langchain>=0.3.9 as well as the provider-specific package. Install LangChain with: pip install 'langchain>=0.3.9' and the provider-specific package (e.g., 'langchain-openai>=0.3.0'). Alternatively, specify 'embed' as a compatible Embeddings object or python function.) �56ValueError�57isinstance�str�_get_init_embeddings�importlib.metadatar58 r r �EmbeddingsLambda)�embed�init_embeddingsr59 r �60lc_version� version_infos �`D:\code\apps\devtools\python\user_packages\Python313\site-packages\langgraph/store/base/embed.py�ensure_embeddingsr " s� � �^
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=�s �B �B�Bc �f ^ � \ rS rSrSr S S jrS62S jrS S jrS63U 4S jjrS U 4S jjr Sr64U =r $ ) r �m a� Wrapper to convert embedding functions into LangChain's Embeddings interface.65 66This class allows arbitrary embedding functions to be used with LangChain-compatible67tools. It supports both synchronous and asynchronous operations, and can handle:681. A synchronous function for sync operations (async operations will use sync function)692. An async function for both sync/async operations (sync operations will raise an error)70 71The embedding functions should convert text into fixed-dimensional vectors that72capture the semantic meaning of the text.73 74Args:75 func: Function that converts text to embeddings. Can be sync or async.76 If async, it will be used for async operations, but sync operations77 will raise an error. If sync, it will be used for both sync and async operations.78 79??? example "Examples"80 81 With a sync function:82 83 ```python84 def my_embed_fn(texts):85 # Return 2D embeddings for each text86 return [[0.1, 0.2] for _ in texts]87 88 embeddings = EmbeddingsLambda(my_embed_fn)89 result = embeddings.embed_query("hello") # Returns [0.1, 0.2]90 await embeddings.aembed_query("hello") # Also returns [0.1, 0.2]91 ```92 93 With an async function:94 95 ```python96 async def my_async_fn(texts):97 return [[0.1, 0.2] for _ in texts]98 99 embeddings = EmbeddingsLambda(my_async_fn)100 await embeddings.aembed_query("hello") # Returns [0.1, 0.2]101 # Note: embed_query() would raise an error102 ```103c �Z � Uc [ S5 e[ U5 ( a Xl g Xl g )Nzfunc must be provided)r
�_is_async_callable�afunc�func)�selfr s r �__init__�EmbeddingsLambda.__init__� s* � � �<� � 4�5� 5� �d� #� #� �J� �I� c �H � [ U SS5 nUc [ S5 eU" U5 $ )a Embed a list of texts into vectors.104 105Args:106 texts: list of texts to convert to embeddings.107 108Returns:109 list of embeddings, one per input text. Each embedding is a list of floats.110 111Raises:112 ValueError: If the instance was initialized with only an async function.113r Nz�EmbeddingsLambda was initialized with an async function but no sync function. Use aembed_documents for async operation or provide a sync function.)�getattrr
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� �E�{�r" c �, � U R U/5 S $ )z�Embed a single piece of text.114 115Args:116 text: Text to convert to an embedding.117 118Returns:119 Embedding vector as a list of floats.120 121Note:122 This is equivalent to calling embed_documents with a single text123 and taking the first result.124r )r& )r �texts r � embed_query� EmbeddingsLambda.embed_query� s � � �#�#�T�F�+�A�.�.r" c � � ># � [ U SS5 nUc [ TU ] U5 I Sh v�N $ U" U5 I Sh v�N $ N N7f)a! Asynchronously embed a list of texts into vectors.125 126Args:127 texts: list of texts to convert to embeddings.128 129Returns:130 list of embeddings, one per input text. Each embedding is a list of floats.131 132Note:133 If no async function was provided, this falls back to the sync implementation.134r N)r$ �super�aembed_documents)r r% r � __class__s �r r. �!EmbeddingsLambda.aembed_documents� sC �� � � ��g�t�,�� �=� ��1�%�8�8� 8��5�\�!�!� 9�!�s �#?�;�?�=�?�?c � � ># � [ U SS5 nUc [ TU ] U5 I Sh v�N $ U" U/5 I Sh v�N S $ N N 7f)a Asynchronously embed a single piece of text.135 136Args:137 text: Text to convert to an embedding.138 139Returns:140 Embedding vector as a list of floats.141 142Note:143 This is equivalent to calling aembed_documents with a single text144 and taking the first result.145r Nr )r$ r- � aembed_query)r r) r r/ s �r r2 � EmbeddingsLambda.aembed_query� sJ �� � � ��g�t�,�� �=� ��-�d�3�3� 3��T�F�m�#�Q�'�'� 4�#�s �#A�?�A�A�A�A)r r )r z EmbeddingsFunc | AEmbeddingsFunc�return�None)r% � list[str]r4 zlist[list[float]])r) r r4 z list[float]) �__name__�146__module__� __qualname__�__firstlineno__�__doc__r r& r* r. r2 �__static_attributes__�
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