昺h ddlZddlmZmZmZmZmZddlZddlm Z ddl m Z m Z ej eZGdde e ZdS)N)AnyDictListMappingOptional) Embeddings) BaseModelExtrac eZdZUdZdZeed< dZeed< dZeed< dZ eed < d Z e e ed < d Z e eed < d Ze eed < d Ze e ed< d Ze e ed< d Ze e ed< d Ze e ed< d Ze eed< d Ze eed< d Ze eeed< d Ze eed< d Ze e ed< d Ze eed< dZeed< edeeeffdZ d Z!e e"ed< ede#eeffdZ$GddZ%d edeefd!Z&d eedeeefd"Z'd#eedeeefd$Z(d%edeefd&Z)d S)'OllamaEmbeddingsaOllama locally runs large language models. To use, follow the instructions at https://ollama.ai/. Example: .. code-block:: python from langchain_community.embeddings import OllamaEmbeddings ollama_emb = OllamaEmbeddings( model="llama:7b", ) r1 = ollama_emb.embed_documents( [ "Alpha is the first letter of Greek alphabet", "Beta is the second letter of Greek alphabet", ] ) r2 = ollama_emb.embed_query( "What is the second letter of Greek alphabet" ) zhttp://localhost:11434base_urlllama2modelz passage: embed_instructionzquery: query_instructionNmirostat mirostat_eta mirostat_taunum_ctxnum_gpu num_thread repeat_last_nrepeat_penalty temperaturestoptfs_ztop_ktop_pF show_progressreturnc|j|j|j|j|j|j|j|j|j|j |j |j |j |j d dS)z.Get the default parameters for calling Ollama.) rrrrrrrrrrrrr)roptions)rrrrrrrrrrrrrrselfs m/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/langchain_community/embeddings/ollama.py_default_paramsz OllamaEmbeddings._default_paramslshZ M $ 1 $ 1<<"o!%!3"&"5#/    model_kwargsc&id|ji|jS)zGet the identifying parameters.r)rr&r#s r%_identifying_paramsz$OllamaEmbeddings._identifying_paramssA7DJ'@4+?@@r'c eZdZdZejZdS)OllamaEmbeddings.Configz'Configuration for this pydantic object.N)__name__ __module__ __qualname____doc__r forbidextrar'r%Configr,s55 r'r4inputcddi} tj|jd||j|d|j}n.#tjj$r}td|d}~wwxYw|jdkrtd |jd |j  | }|d S#tjj $r}td |d |j d}~wwxYw)zProcess a response from the API. Args: response: The response from the API. Returns: The response as a dictionary. z Content-Typezapplication/jsonz/api/embeddings)rprompt)headersjsonz$Error raised by inference endpoint: Nz)Error raised by inference API HTTP code: z, embeddingzError raised by inference API: z . Response: ) requestspostr rr& exceptionsRequestException ValueError status_codetextr9JSONDecodeError)r$r5r8resets r%_process_emb_responsez&OllamaEmbeddings._process_emb_responses6 .  I-=111#zUSSd>RSCC "3 I I IGAGGHH H I ?c ! !*???CHH.   A[> !"2   L!LL#(LL  s-.5A  AA B**C>CCcjrA ddlm}||d}n.#t$rtd|}YnwxYw|}fd|DS)Nr)tqdmr )desczgUnable to show progress bar because tqdm could not be imported. Please install with `pip install tqdm`.c:g|]}|Sr3)rG).0r7r$s r% z+OllamaEmbeddings._embed..s'GGGv**622GGGr')rrI ImportErrorloggerwarning)r$r5rIiter_s` r%_embedzOllamaEmbeddings._embeds    %%%%%%U);<<<   >  EGGGGGGGGs&AAtextscNfd|D}|}|S)zEmbed documents using an Ollama deployed embedding model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. c&g|] }j|Sr3)r)rLrBr$s r%rMz4OllamaEmbeddings.embed_documents..s'QQQ4 6>>>QQQr')rR)r$rSinstruction_pairs embeddingss` r%embed_documentsz OllamaEmbeddings.embed_documentss8RQQQ5QQQ[[!233 r'rBcT|j|}||gd}|S)zEmbed a query using a Ollama deployed embedding model. Args: text: The text to embed. Returns: Embeddings for the text. r)rrR)r$rBinstruction_pairr;s r% embed_queryzOllamaEmbeddings.embed_querys8#4rhs5555555555555500000077777777  8 $ $OOOOOy*OOOOOr'