o Zh@sZddlmZmZmZddlmZddlmZmZm Z m Z ddl m Z GdddeeZ dS))AnyListOptional) Embeddings) BaseModel ConfigDictFieldmodel_validator)Selfc@seZdZUdZdZeed<eddZe ed<eddd Z e ed< ed d d Z e ed < ed d d Z e ed < ed dd Zeed< ed dd Zeed< ed dd Zeed< ed dd Zeed< eddd Zee ed< eddd Zee ed< eddd Zee ed< eddd Zeed< eddd Zee ed< edddZedddefddZd ee deeefd!d"Z d#e deefd$d%Z!dS)&LlamaCppEmbeddingsallama.cpp embedding models. To use, you should have the llama-cpp-python library installed, and provide the path to the Llama model as a named parameter to the constructor. Check out: https://github.com/abetlen/llama-cpp-python Example: .. code-block:: python from langchain_community.embeddings import LlamaCppEmbeddings llama = LlamaCppEmbeddings(model_path="/path/to/model.bin") Nclient)default model_pathin_ctx)aliasn_partsseedFf16_kv logits_all vocab_only use_mlock n_threadsn_batch n_gpu_layersTverbosedeviceZforbid)extraZprotected_namespacesafter)modereturnc sj}gd}fdd|D}jdurj|d<jsRzddlm}||fdd i|_WSty<td tyQ}z td |d |d}~wwS) z4Validate that llama-cpp-python library is installed.) rrrrrrrrrrrcsi|]}|t|qSr)getattr).0kselfr^/var/www/html/lang_env/lib/python3.10/site-packages/langchain_community/embeddings/llamacpp.py Vsz;LlamaCppEmbeddings.validate_environment..Nrr)Llama embeddingTzCould not import llama-cpp-python library. Please install the llama-cpp-python library to use this embedding model: pip install llama-cpp-pythonz&Could not load Llama model from path: z. Received error )rrr Z llama_cppr* ImportError Exception ValueError)r'rZmodel_param_namesZ model_paramsr*err&r(validate_environmentEs.    z'LlamaCppEmbeddings.validate_environmenttextsc Cs|j|}g}|dD]B}z)t|ddtr*|dD] }|ttt|qn |ttt|dWq ttfyN|ttt|dYq w|S)zEmbed a list of documents using the Llama model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. datar+r) r Zcreate_embedding isinstancelistappendmapfloat IndexError TypeError)r'r1Z embeddingsZfinal_embeddingsr/r2rrr(embed_documentsns   z"LlamaCppEmbeddings.embed_documentstextcCsH|j|}|rt|trt|dtrttt|dSttt|S)zEmbed a query using the Llama model. Args: text: The text to embed. Returns: Embeddings for the text. r)r Zembedr3r4r6r7)r'r;r+rrr( embed_querys zLlamaCppEmbeddings.embed_query)"__name__ __module__ __qualname____doc__r r__annotations__rrstrrintrrrboolrrrrrrrrrrZ model_configr r r0rr7r:r<rrrr(r sH  (r N)typingrrrZlangchain_core.embeddingsrZpydanticrrrr Ztyping_extensionsr r rrrr(s