a bg@sXddlZddlmZmZddlZddlmZddlmZe e Z GdddeeZ dS)N)ListOptional) Embeddings) BaseModelc@sreZdZUdZdZeed<dZee ed<ee e dddZ e ee e e d d d Z ee e dd d ZdS)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_timeout)textreturncCsz(tj|jdddid|i|jd}Wn,tjjyTtjd|jdYn0||}d|vrvtd |d}t |d krt d |S) 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?) requestspostrr exceptionsConnectionErrorraise_for_statusrKeyErrorsum ValueError)selfr responsecontentsrrv/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/embeddings/llamafile.py_embed0s6    zLlamafileEmbeddings._embed)textsr cCs"g}|D]}|||q|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 Zdoc_embeddingsr rrrembed_documents\s z#LlamafileEmbeddings.embed_documentscCs ||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 rrr embed_queryls zLlamafileEmbeddings.embed_query)__name__ __module__ __qualname____doc__rstr__annotations__rrintrfloatrr"r#rrrrr s  ,r) loggingtypingrrrZlangchain_core.embeddingsrZpydanticr getLoggerr$loggerrrrrrs