jddlZddlmZmZmZmZmZddlZddlm Z ddl m Z ddl m Z mZejeZe ddd Gd d e e Zy) N)AnyDictListMappingOptional) deprecated) Embeddings) BaseModel ConfigDictz0.3.1z1.0.0z!langchain_ollama.OllamaEmbeddings)sinceremovalalternative_importcveZdZUdZdZeed< 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< d Ze eed< ede ee!ffdZ"d Z#e eed< ede$ee!ffdZ%e&dd !Z'd"edeefd#Z(d"eedeeefd$Z)d%eedeeefd&Z*d'edeefd(Z+y ))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_progressheadersreturnc@|j|j|j|j|j|j |j |j|j|j|j|j|j|jd dS)z.Get the default parameters for calling Ollama.) rrrrrrrrrrr r!r")roptions)rrrrrrrrrrrr r!r"selfs o/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/embeddings/ollama.py_default_paramsz OllamaEmbeddings._default_paramsxsZZ MM $ 1 1 $ 1 1<<<<"oo!%!3!3"&"5"5#//   model_kwargsc:id|ji|jS)zGet the identifying parameters.r)rr+r(s r*_identifying_paramsz$OllamaEmbeddings._identifying_paramss$A7DJJ'@4+?+?@@r,forbid)extraprotected_namespacesinputc"ddi|jxsi} tj|jd||j|d|j }|jdk7r%td |jd |j |j}|d S#tj j$r}td|d}~wwxYw#tj j$r }td |d |jd}~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)r$jsonz$Error raised by inference endpoint: Nz)Error raised by inference API HTTP code: z, embeddingzError raised by inference API: z . Response: ) r$requestspostrrr+ exceptionsRequestException ValueError status_codetextr7JSONDecodeError)r)r4r$resets r*_process_emb_responsez&OllamaEmbeddings._process_emb_responses . ||!r   I--==/1#zzUSd>R>RSC ??c !??CHH.   A[> !""33 ICA3GH H I""22 1!M#((L  s/>B CC;C  CD.D  Dc|jr ddlm}||d}n|}|Dcgc]}|j |c}S#t$rtj d|}YAwxYwcc}w)Nr)tqdmr)desczgUnable to show progress bar because tqdm could not be imported. Please install with `pip install tqdm`.)r#rG ImportErrorloggerwarningrE)r)r4rGiter_r6s r*_embedzOllamaEmbeddings._embedsu    %U);<EAFGv**62GG >  HsAA& A#"A#textscl|Dcgc]}|j|}}|j|}|Scc}w)zEmbed documents using an Ollama deployed embedding model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. )rrM)r)rNr@instruction_pairs embeddingss r*embed_documentsz OllamaEmbeddings.embed_documentssFLQQ54 6 67v>5Q[[!23 Rs1r@cP|j|}|j|gd}|S)zEmbed a query using a Ollama deployed embedding model. Args: text: The text to embed. Returns: Embeddings for the text. r)rrM)r)r@instruction_pairr9s r* embed_queryzOllamaEmbeddings.embed_querys7#445dV<KK!1 23A6 r,),__name__ __module__ __qualname____doc__rstr__annotations__rrrrrintrfloatrrrrrrrrrr r!r"r#boolr$dictpropertyrrr+r-rr/r model_configrErMrRrUr1r,r*rr sP .-Hc,-E3(s(.&s&."Hhsm"B%)L(5/(5 %)L(5/(%"GXc]!$"GXc]!+!%J $N $(M8C='='+NHUO*,$(K%'=!%D(49 $&!E8E?!M E8C=0"E8E?!C M4J"GXd^"  c3h  *$(L(4.'" AWS#X%6AAH2FL"3"4;"HHDIH$tE{*;H T#Y 4U 3D   U r,r)loggingtypingrrrrrr:langchain_core._api.deprecationrlangchain_core.embeddingsr pydanticr r getLoggerrVrJrr1r,r*rhsZ5560*   8 $  : Sy*S  Sr,