Modernized LlamaIndex integration (#1613)

Updated LlamaIndex example

examples/llamaindex/README.md

@@ -1,25 +1,22 @@
 # 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.
+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.
-## Prerequisites
+The `Weaviate` client is used as the embedding source.
-
-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:
+1. Clone this repository and navigate to this directory
 
-2. Navigate to the project directory:
+    ```bash
+    git clone git@github.com:mudler/LocalAI.git
+    cd LocalAI/examples/llamaindex
+    ```
 
-3. Run the example:
+2. pip install LlamaIndex and Weviate's client: `pip install llama-index>=0.9.9 weviate-client`
+3. Run the example: `python main.py`
 
-`python main.py`
+```none
-
-```
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examples/llamaindex/main.py

@@ -1,37 +1,28 @@
-import os
-
 import weaviate
-
+from llama_index import ServiceContext, VectorStoreIndex
-from llama_index import ServiceContext, VectorStoreIndex, StorageContext
+from llama_index.llms import LOCALAI_DEFAULTS, OpenAILike
-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")
+vector_store = WeaviateVectorStore(
+    weaviate_client=weaviate.Client("http://weviate.default"), index_name="AIChroma"
+)
 
-# Storage context setup
+# LLM setup, served via LocalAI
-storage_context = StorageContext.from_defaults(vector_store=vector_store)
+llm = OpenAILike(temperature=0, model="gpt-3.5-turbo", **LOCALAI_DEFAULTS)
-
-# 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,
+    vector_store, service_context=service_context
-    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_engine = index.as_query_engine(
+    similarity_top_k=1, vector_store_query_mode="hybrid"
+)
 
 # Query example
 response = query_engine.query("What is LocalAI?")