ZǻibddlmZddlmZmZddlmZddlmZddl m Z m Z m Z GddeZ y) ) annotations)AnyOptional)Embedder)EmbeddingsGenerationError)RateLimitHandlerrate_limit_handlerasync_rate_limit_handlercZeZdZdZ d dfd ZeddZeddZxZ S)OllamaEmbeddingsz Ollama embeddings class. This class uses the ollama Python client to generate vector embeddings for text data. Args: model (str): The name of the Mistral AI text embedding model to use. Defaults to "mistral-embed". c  ddl}t| |||_|j di||_|jdi||_y#t$r tdwxYw)NrzuCould not import ollama python client. Please install it with `pip install "neo4j_graphrag[ollama]"`.) ollama ImportErrorsuper__init__modelClientclient AsyncClient async_client)selfrr kwargsr __class__s ^/opt/lhia/marcimex/agent/venv/lib/python3.12/site-packages/neo4j_graphrag/embeddings/ollama.pyrzOllamaEmbeddings.__init__&ss    +, #fmm-f- .F..88 R  s A A!c |jjd|j|d|}| |js t d|j}|d}t |t s t d|S)a Generate embeddings for a given query using an Ollama text embedding model. Args: text (str): The text to generate an embedding for. **kwargs (Any): Additional keyword arguments to pass to the Ollama client. rinputFailed to retrieve embeddings.r"Embedding is not a list of floats.r)rembedr embeddingsr isinstancelistrtextrembeddings_responser" embeddings r embed_queryzOllamaEmbeddings.embed_query8s0dkk// **    &.A.L.L+,LM M(33 qM )T*+,PQ QcK|jjd|j|d|d{}| |js t d|j}|d}t |t s t d|S7Kw)a Asynchronously generate embeddings for a given query using an Ollama text embedding model. Args: text (str): The text to generate an embedding for. **kwargs (Any): Additional keyword arguments to pass to the Ollama client. rNrrr r)rr!rr"rr#r$r%s rasync_embed_queryz"OllamaEmbeddings.async_embed_queryRs%9979 9  9$2r*r N) __future__rtypingrrneo4j_graphrag.embeddings.baserneo4j_graphrag.exceptionsrneo4j_graphrag.utils.rate_limitrr r r rr*rr:s+ # 3?MxMr*