j3ddlmZddlZddlZddlmZmZmZmZm Z ddl Z ddl m Z ddlmZddlmZddlmZej(eZGdd eZy) ) annotationsN)AnyIterableListOptionalTuple)Document) Embeddings) VectorStore)maximal_marginal_relevancec eZdZdZdddddddd ddZedd Z d dd Z d dd Z d ddZ d ddZ d ddZ e ddd ddddgddd f ddZ d d dZy)!Dingoax`Dingo` vector store. To use, you should have the ``dingodb`` python package installed. Example: .. code-block:: python from langchain_community.vectorstores import Dingo from langchain_community.embeddings.openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() dingo = Dingo(embeddings, "text") Niroot123123F)client index_name dimensionhostuserpasswordself_idc ddl} ||ndg}||} n | j|||} ||_| |_|^|| j vrL|j| j vr,| dur| j||dn| j|| ||_ ||_ y#t$r tdwxYw#t$r} td| d} ~ wwxYw) zInitialize with Dingo client.rNzSCould not import dingo python package. Please install it with `pip install dingodb.172.20.31.10:13000Dingo failed to connect: TFrauto_idr) dingodb ImportErrorDingoDB ValueError _text_key_client get_indexupper create_index _index_name _embedding) self embeddingtext_keyrrrrrrrr dingo_clientes p/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/vectorstores/dingo.py__init__zDingo.__init__ s  't.B-C  !L B&tXtD "#   ","8"8"::  ",*@*@*BB$)))U*))* )J%#E ?   B # R$    s D !-Dc b|jj|}|j|||||S)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. )r(rir)r)rWrTr}rvrUrIr*s r.max_marginal_relevance_searchz#Dingo.max_marginal_relevance_searchs62OO//6 ;; q';   r3rc  ddl}||}n |j| | | }| \| j ddurI|||j vr||j |j vr\|j||dnG|E||j vr3|j |j vr|j|| |xs8|Dcgc],}ttjjdd .c}}g}t|}|j|}t|D]$\}}|r||ni}|||<|j|&t!dt#t|| D]1}|| z}|j%||||||||||}|r(t'd ||||| S#t$r tdwxYw#t$r}td|d}~wwxYwcc}w) a=Construct Dingo wrapper from raw documents. This is a user friendly interface that: 1. Embeds documents. 2. Adds the documents to a provided Dingo index This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Dingo from langchain_community.embeddings import OpenAIEmbeddings import dingodb sss embeddings = OpenAIEmbeddings() dingo = Dingo.from_texts( texts, embeddings, index_name="langchain-demo" ) rNzTCould not import dingo python package. Please install it with `pip install dingodb`.rrTFrrr7r8)rr)rrr r!rkr$r%r&r9r:r;r<r=r>r?r@rArBrCrD)clsrEr*rFrGr+rrrrrrrHrIrr,r-rJrKrLrMr4rNrOrPs r. from_textszDingo.from_textss)N    !L B&tXtD   &**Y"74"?&l&<&<&>>$$&l.D.D.FF)))U* &l&<&<&>>$$&l.D.D.FF))* )J@%@%Qc$**,**+CR0%@U **51 'GAt'0y|bH!%HX   ! !( +( q#d5k*J7AJA"--N1Q/!c!AhG 122 89h| SSe @   B #rsE" 77-03M   8 $mEKmEr3