Flask App Serving Mistral + Llama Index + Ollama + ChromaDB -RAG

Flask App Serving Mistral + Llama Index + Ollama + ChromaDB

Import modules

import chromadb
from flask import Flask, request, jsonify
from flask_cors import CORS, cross_origin
from llama_index import VectorStoreIndex, ServiceContext
from llama_index.llms import Ollama
from llama_index.vector_stores.chroma import ChromaVectorStore

Create Chroma DB client and access the existing vector store

client = chromadb.PersistentClient(path="./chroma_db_data")
chroma_collection = client.get_collection(name="tweets")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)

Initialize Ollama and ServiceContext

llm = Ollama(model="mistral")
service_context = ServiceContext.from_defaults(llm=llm, embed_model="local")

Create VectorStoreIndex with a similarity threshold of 20

index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context, similarity_top_k=20)

Set up Flask server

app = Flask(name)
cors = CORS(app)
app.config['CORS_HEADERS'] = 'Content-Type'

@app.route('/')
def hello_world():
return 'Hello, World!'

@app.route('/chat', methods=['GET', 'POST'])
@cross_origin()
def chat():
query = request.args.get('query') if request.method == 'GET' else request.form.get('query')
if query is not None:
query_engine = index.as_query_engine()
response = query_engine.query(query)
return jsonify({"response": str(response)})
else:
return jsonify({"error": "query field is missing"}), 400

if name == 'main':
app.run()