feat: llamaindex example

examples/llamaindex_pipeline.py

@@ -0,0 +1,27 @@
+from typing import List, Union, Generator
+from schemas import OpenAIChatMessage
+
+import os
+
+# Set the OpenAI API key
+os.environ["OPENAI_API_KEY"] = "your_openai_api_key_here"
+
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+
+documents = SimpleDirectoryReader("./data").load_data()
+index = VectorStoreIndex.from_documents(documents)
+
+
+def get_response(
+    user_message: str, messages: List[OpenAIChatMessage]
+) -> Union[str, Generator]:
+    # This is where you can add your custom RAG pipeline.
+    # Typically, you would retrieve relevant information from your knowledge base and synthesize it to generate a response.
+
+    print(messages)
+    print(user_message)
+
+    query_engine = index.as_query_engine(streaming=True)
+    response = query_engine.query(user_message)
+
+    return response.response_gen