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#import json
from flask import Flask,request
from dotenv import load_dotenv
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
from transformers import AutoTokenizer, pipeline
from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA
# Initializing flask app
app = Flask(__name__)
load_dotenv()
@app.route("/train/faq", methods=['GET','POST'])
def embeddings_faqs():
data = WebBaseLoader("https://rise.mmu.ac.uk/what-is-rise/").load()
# Create an instance of the RecursiveCharacterTextSplitter class with specific parameters.
# It splits text into chunks of 1000 characters each with a 150-character overlap.
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
# 'data' holds the text you want to split, split the text into documents using the text splitter.
docs = text_splitter.split_documents(data)
# Define the path to the pre-trained model you want to use
modelPath = "sentence-transformers/all-MiniLM-l6-v2"
# Create a dictionary with model configuration options, specifying to use the CPU for computations
model_kwargs = {'device':'cpu'}
# Create a dictionary with encoding options, specifically setting 'normalize_embeddings' to False
encode_kwargs = {'normalize_embeddings': False}
# Initialize an instance of HuggingFaceEmbeddings with the specified parameters
embeddings = HuggingFaceEmbeddings(
model_name=modelPath, # Provide the pre-trained model's path
model_kwargs=model_kwargs, # Pass the model configuration options
encode_kwargs=encode_kwargs # Pass the encoding options
)
# Create vectors
vectorstore = FAISS.from_documents(docs, embeddings)
# Persist the vectors locally on disk
vectorstore.save_local("_rise_faq_db");
return {"trained":"success"}
@app.route('/ask', methods=['GET','POST'])
def ask():
# Specify the model name you want to use
model_name = "Intel/dynamic_tinybert"
# Load the tokenizer associated with the specified model
tokenizer = AutoTokenizer.from_pretrained(model_name, padding=True, truncation=True, max_length=512)
# Define a question-answering pipeline using the model and tokenizer
question_answerer = pipeline(
"question-answering",
model=model_name,
tokenizer=tokenizer,
return_tensors='pt'
)
# Create an instance of the HuggingFacePipeline, which wraps the question-answering pipeline
# with additional model-specific arguments (temperature and max_length)
llm = HuggingFacePipeline(
pipeline=question_answerer,
model_kwargs={"temperature": 0.7, "max_length": 512},
)
# Define the path to the pre-trained model you want to use
modelPath = "sentence-transformers/all-MiniLM-l6-v2"
# Create a dictionary with model configuration options, specifying to use the CPU for computations
model_kwargs = {'device':'cpu'}
# Create a dictionary with encoding options, specifically setting 'normalize_embeddings' to False
encode_kwargs = {'normalize_embeddings': False}
# Initialize an instance of HuggingFaceEmbeddings with the specified parameters
embeddings = HuggingFaceEmbeddings(
model_name=modelPath, # Provide the pre-trained model's path
model_kwargs=model_kwargs, # Pass the model configuration options
encode_kwargs=encode_kwargs # Pass the encoding options
)
persisted_vectorstore = FAISS.load_local("_rise_faq_db", embeddings)
# Create a retriever object from the 'db' using the 'as_retriever' method.
# This retriever is likely used for retrieving data or documents from the database.
retriever = persisted_vectorstore.as_retriever()
docs = retriever.get_relevant_documents("What are the benefits?")
print(docs[0].page_content)
return "uip"
@app.route('/', methods=['GET','POST'])
def index():
return {"response":"just some junk response"} |