jxddlZddlmZmZddlZddlmZddlmZeje Z GddeeZ y)N)ListOptional) Embeddings) BaseModelceZdZUdZdZeed< dZee ed< dede e fdZ d e ede e e fd Z dede e fd Zy) LlamafileEmbeddingsaLlamafile lets you distribute and run large language models with a single file. To get started, see: https://github.com/Mozilla-Ocho/llamafile To use this class, you will need to first: 1. Download a llamafile. 2. Make the downloaded file executable: `chmod +x path/to/model.llamafile` 3. Start the llamafile in server mode with embeddings enabled: `./path/to/model.llamafile --server --nobrowser --embedding` Example: .. code-block:: python from langchain_community.embeddings import LlamafileEmbeddings embedder = LlamafileEmbeddings() doc_embeddings = embedder.embed_documents( [ "Alpha is the first letter of the Greek alphabet", "Beta is the second letter of the Greek alphabet", ] ) query_embedding = embedder.embed_query( "What is the second letter of the Greek alphabet" ) zhttp://localhost:8080base_urlNrequest_timeouttextreturnc tj|jdddid|i|j}|j |j}d|vr td |d}t|d k(r td |S#tjj $r.tjj d|jdwxYw) Nz /embeddingz Content-Typezapplication/jsoncontent)urlheadersjsontimeoutzTCould not connect to Llamafile server. Please make sure that a server is running at . embeddingzPUnexpected output from /embedding endpoint, output dict missing 'embedding' key.gz^Embedding sums to 0, did you start the llamafile server with the `--embedding` option enabled?) requestspostr r exceptionsConnectionErrorraise_for_statusrKeyErrorsum ValueError)selfr responsecontentsrs r/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/embeddings/llamafile.py_embedzLlamafileEmbeddings._embed0s }}}}oZ0"$6t,, H" !!#==? h &+  [)  y>S 4  =""22 %%55//3}}oQ@  s 4BA CtextscXg}|D]"}|j|j|$|S)a6Embed documents using a llamafile server running at `self.base_url`. llamafile server should be started in a separate process before invoking this method. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. )appendr!)rr"doc_embeddingsr s r embed_documentsz#LlamafileEmbeddings.embed_documents\s0D  ! !$++d"3 4c$|j|S)aEmbed a query using a llamafile server running at `self.base_url`. llamafile server should be started in a separate process before invoking this method. Args: text: The text to embed. Returns: Embeddings for the text. )r!)rr s r embed_queryzLlamafileEmbeddings.embed_queryls{{4  r')__name__ __module__ __qualname____doc__r str__annotations__r rintrfloatr!r&r)r'r rr sv<,Hc+;%)OXc])%*3*4;*XT#Y4U 3D ! !U !r'r) loggingtypingrrrlangchain_core.embeddingsrpydanticr getLoggerr*loggerrr2r'r r9s6!0   8 $l!)Zl!r'