Update app.py

app.py

@@ -1,3 +1,4 @@
+"""
 import json
 import logging
 import os
@@ -94,4 +95,44 @@ def main():
     init_chromadb()
 
 if __name__ == '__main__':
-    main()
+    main()
+"""
+import chromadb
+from llama_index.vector_stores.chroma import ChromaVectorStore
+from llama_index.core import VectorStoreIndex, StorageContext, TextNode
+from llama_index.core.indices.vector_store.retrievers import VectorIndexAutoRetriever
+from llama_index.core.query_engine import RetrieverQueryEngine
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+# Initialize ChromaDB client and collection
+chroma_client = chromadb.HttpClient(host="localhost", port="8080", ssl=False)
+chroma_collection = chroma_client.get_or_create_collection("example_collection")
+
+# Define embedding function using HuggingFace
+embed_model = HuggingFaceEmbedding(model_name="all-MiniLM-L6-v2")
+
+# Initialize ChromaVectorStore with the collection
+vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+
+# Set up StorageContext and VectorStoreIndex
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+index = VectorStoreIndex(embed_model=embed_model, storage_context=storage_context)
+
+# Define and load documents with embeddings
+documents = [
+    {"text": "Your document text here", "embedding": [0.1, 0.2, 0.3]},
+    # Add more documents as needed
+]
+
+# Load documents into ChromaDB using VectorStoreIndex
+index.from_documents(documents=documents)
+
+# Initialize the AutoRetriever with VectorStoreIndex and VectorStoreInfo
+auto_retriever = VectorIndexAutoRetriever(index)
+
+# Set up the RetrieverQuery Engine with the AutoRetriever
+query_engine = RetrieverQueryEngine(auto_retriever)
+
+# Query documents using the RetrieverQuery Engine
+response = query_engine.query("Your query here")
+print(response)