diff --git a/examples/llamaindex/README.md b/examples/llamaindex/README.md
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+# LocalAI Demonstration with Embeddings
+
+This demonstration shows you how to use embeddings with existing data in LocalAI. We are using the `llama_index` library to facilitate the embedding and querying processes. The `Weaviate` client is used as the embedding source.
+
+## Prerequisites
+
+Before proceeding, make sure you have the following installed:
+- Weaviate client
+- LocalAI and its dependencies
+- llama_index and its dependencies 
+
+## Getting Started
+
+1. Clone this repository:
+
+2. Navigate to the project directory:
+
+3. Run the example:
+
+`python main.py`
+
+```
+Downloading (…)lve/main/config.json: 100%|███████████████████████████| 684/684 [00:00<00:00, 6.01MB/s]
+Downloading model.safetensors: 100%|███████████████████████████████| 133M/133M [00:03<00:00, 39.5MB/s]
+Downloading (…)okenizer_config.json: 100%|███████████████████████████| 366/366 [00:00<00:00, 2.79MB/s]
+Downloading (…)solve/main/vocab.txt: 100%|█████████████████████████| 232k/232k [00:00<00:00, 6.00MB/s]
+Downloading (…)/main/tokenizer.json: 100%|█████████████████████████| 711k/711k [00:00<00:00, 18.8MB/s]
+Downloading (…)cial_tokens_map.json: 100%|███████████████████████████| 125/125 [00:00<00:00, 1.18MB/s]
+LocalAI is a community-driven project that aims to make AI accessible to everyone. It was created by Ettore Di Giacinto and is focused on providing various AI-related features such as text generation with GPTs, text to audio, audio to text, image generation, and more. The project is constantly growing and evolving, with a roadmap for future improvements. Anyone is welcome to contribute, provide feedback, and submit pull requests to help make LocalAI better.
+```
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diff --git a/examples/llamaindex/main.py b/examples/llamaindex/main.py
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+import os
+
+import weaviate
+
+from llama_index import ServiceContext, VectorStoreIndex, StorageContext
+from llama_index.llms import LocalAI
+from llama_index.vector_stores import WeaviateVectorStore
+from llama_index.storage.storage_context import StorageContext
+
+# Weaviate client setup
+client = weaviate.Client("http://weviate.default")
+
+# Weaviate vector store setup
+vector_store = WeaviateVectorStore(weaviate_client=client, index_name="AIChroma")
+
+# Storage context setup
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+# LocalAI setup
+llm = LocalAI(temperature=0, model_name="gpt-3.5-turbo", api_base="http://local-ai.default", api_key="stub")
+llm.globally_use_chat_completions = True;
+
+# Service context setup
+service_context = ServiceContext.from_defaults(llm=llm, embed_model="local")
+
+# Load index from stored vectors
+index = VectorStoreIndex.from_vector_store(
+    vector_store,
+    storage_context=storage_context,
+    service_context=service_context
+)
+
+# Query engine setup
+query_engine = index.as_query_engine(similarity_top_k=1, vector_store_query_mode="hybrid")
+
+# Query example
+response = query_engine.query("What is LocalAI?")
+print(response)
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