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app.py
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app.py
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# from app import Flask, render_template, request, jsonify
from flask import Flask, render_template, request, jsonify
from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain_community.document_loaders import OnlinePDFLoader
from langchain_community.embeddings import OllamaEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.prompts import ChatPromptTemplate, PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_community.chat_models import ChatOllama
from langchain_core.runnables import RunnablePassthrough
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain.output_parsers import CommaSeparatedListOutputParser
app = Flask(__name__)
# Define your model setup code here
def model_load():
local_path = "books\Fundamentals-of-Psychological-Disorders.pdf"
loader = UnstructuredPDFLoader(file_path=local_path)
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=7500, chunk_overlap=100)
chunks = text_splitter.split_documents(data)
vector_db = Chroma.from_documents(
documents=chunks,
embedding=OllamaEmbeddings(model="nomic-embed-text",show_progress=True),
collection_name="local-rag"
)
local_model = "phi3"
llm = ChatOllama(model=local_model)
QUERY_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an AI language model assistant. Your task is to generate five
different versions of the given user question to retrieve relevant documents from
a vector database. By generating multiple perspectives on the user question, your
goal is to help the user overcome some of the limitations of the distance-based
similarity search. Provide these alternative questions separated by newlines.
Original question: {question}""",
)
retriever = MultiQueryRetriever.from_llm(
vector_db.as_retriever(),
llm,
prompt=QUERY_PROMPT
)
template = """Answer the question based ONLY on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
return chain,vector_db
# Define route for frontend
chain,vector_db = model_load()
@app.route("/model", methods=["GET"])
def home():
chain,vector_db = model_load()
return render_template("chat.html")
@app.route("/", methods=["GET"])
def chat():
return render_template("chat.html")
# Define route for handling queries
@app.route("/query", methods=["POST"])
def query_handler():
print(request.form)
query = str(request.form["query"])
output = chain.invoke(query)
return jsonify({"output": output})
@app.route("/rem", methods=["GET"])
def remove():
return vector_db.delete_collection()
if __name__ == "__main__":
app.run(debug=True)
#mkaing variable global