🗣️Multilingual RAG
Import the required libraries
from beyondllm import source,retrieve,embeddings,llms,generatorSetup API keys
import os
from getpass import getpass
os.environ['OPENAI_API_KEY'] = getpass("OpenAI API Key:")Load the Source Data
Here we will use a Website as the source data. Reference: https://www.christianitytoday.com/ct/2023/june-web-only/same-sex-attraction-not-threat-zh-hant.html
This article on Same-Sex attraction is not a threat - A Chinesse blog article.
data = source.fit(path="https://www.christianitytoday.com/ct/2023/june-web-only/same-sex-attraction-not-threat-zh-hant.html", dtype="url", chunk_size=512,chunk_overlap=0)Embedding model
We use intfloat/multilingual-e5-large, a Multilingual Embedding Model from HuggingFace.
embed_model = embeddings.HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")Auto retriever to retrieve documents
retriever = retrieve.auto_retriever(data,embed_model=embed_model,type="normal",top_k=4)Large Language Model
Define Custom System Prompt
Run Generator Model
Output
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