Initialize RAG

app.py

@@ -0,0 +1,122 @@
+import torch
+import gradio as gr
+
+from textwrap import fill
+from IPython.display import Markdown, display
+
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
+    )
+
+from langchain import PromptTemplate
+from langchain import HuggingFacePipeline
+
+from langchain.vectorstores import Chroma
+from langchain.schema import AIMessage, HumanMessage
+from langchain.memory import ConversationBufferMemory
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import UnstructuredMarkdownLoader, UnstructuredURLLoader
+from langchain.chains import LLMChain, SimpleSequentialChain, RetrievalQA, ConversationalRetrievalChain
+
+from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline
+
+import warnings
+warnings.filterwarnings('ignore')
+
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.3"
+EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME, torch_dtype=torch.float16,
+    trust_remote_code=True,
+    device_map="auto",
+    quantization_config=quantization_config
+)
+
+generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
+generation_config.max_new_tokens = 1024
+generation_config.temperature = 0.0001
+generation_config.top_p = 0.95
+generation_config.do_sample = True
+generation_config.repetition_penalty = 1.15
+
+llm = HuggingFacePipeline(pipeline=pipeline)
+embeddings = HuggingFaceEmbeddings(model_name = EMBEDDING_MODEL)
+
+urls = [
+    "https://www.boe.es/diario_boe/txt.php?id=BOE-A-2024-9523"
+]
+
+
+loader = UnstructuredURLLoader(urls=urls)
+documents = loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+texts_chunks = text_splitter.split_documents(documents)
+
+db = Chroma.from_documents(texts_chunks, embeddings, persist_directory="db")
+
+template = """Act as an lawyer assistant manager expert. Use the following information to answer the question at the end.
+'You must always answer in Spanish' If you do not know the answer reply with 'I am sorry, I dont have enough information'.
+Chat History
+{chat_history}
+Follow Up Input: {question}
+Standalone question:
+"""
+
+CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(template)
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+llm_chain = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    retriever=db.as_retriever(search_kwargs={"k": 2}),
+    memory=memory,
+    condense_question_prompt=CUSTOM_QUESTION_PROMPT,
+)
+
+def querying(query, history):
+  memory = ConversationBufferMemory(memory_key="chat_history", return_messages=False)
+
+  qa_chain = ConversationalRetrievalChain.from_llm(
+      llm=llm,
+      retriever=db.as_retriever(search_kwargs={"k": 2}),
+      memory=memory,
+      condense_question_prompt=CUSTOM_QUESTION_PROMPT,
+  )
+
+  result = qa_chain({"question": query})
+  return result["answer"].strip()
+
+iface = gr.ChatInterface(
+    fn = querying,
+    chatbot=gr.Chatbot(height=600),
+    textbox=gr.Textbox(placeholder="Cuantos segmentos hay y en que consisten?", container=False, scale=7),
+    title="LawyerBot",
+    theme="soft",
+    examples=["¿Cuantos segmentos hay?",
+              "¿Que importe del bono digital corresponde a cada uno de los 5 segmentos?",
+              "¿Cuál es el importe de la ayuda para el segmento III en canto a dispositivo hardware?",
+              "Si tengo una microempresa de 2 empleado, ¿qué importe del bono digital me corresponde?",
+              "¿Qué nuevos segmentos de beneficiarios se han introducido?"],
+    cache_examples=True,
+    retry_btn="Repetir",
+    undo_btn="Deshacer",
+    clear_btn="Borrar",
+    submit_btn="Enviar"
+)
+
+iface.launch(share=True)