Moved back to llm dir as per

https://github.com/HKUDS/LightRAG/pull/864#issuecomment-2669705946

- Created two new example scripts demonstrating LightRAG integration with LlamaIndex:
  - `lightrag_llamaindex_direct_demo.py`: Direct OpenAI integration
  - `lightrag_llamaindex_litellm_demo.py`: LiteLLM proxy integration
- Both examples showcase different search modes (naive, local, global, hybrid)
- Includes configuration for working directory, models, and API settings
- Demonstrates text insertion and querying using LightRAG with LlamaIndex
- removed wrapper directory and references to it

examples/lightrag_llamaindex_litellm_demo.py

@@ -0,0 +1,116 @@
+import os
+from lightrag import LightRAG, QueryParam
+from lightrag.llm.llama_index_impl import (
+    llama_index_complete_if_cache,
+    llama_index_embed,
+)
+from lightrag.utils import EmbeddingFunc
+from llama_index.llms.litellm import LiteLLM
+from llama_index.embeddings.litellm import LiteLLMEmbedding
+import asyncio
+
+# Configure working directory
+WORKING_DIR = "./index_default"
+print(f"WORKING_DIR: {WORKING_DIR}")
+
+# Model configuration
+LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4")
+print(f"LLM_MODEL: {LLM_MODEL}")
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
+print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
+EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
+print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
+
+# LiteLLM configuration
+LITELLM_URL = os.environ.get("LITELLM_URL", "http://localhost:4000")
+print(f"LITELLM_URL: {LITELLM_URL}")
+LITELLM_KEY = os.environ.get("LITELLM_KEY", "sk-1234")
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+
+# Initialize LLM function
+async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
+    try:
+        # Initialize LiteLLM if not in kwargs
+        if "llm_instance" not in kwargs:
+            llm_instance = LiteLLM(
+                model=f"openai/{LLM_MODEL}",  # Format: "provider/model_name"
+                api_base=LITELLM_URL,
+                api_key=LITELLM_KEY,
+                temperature=0.7,
+            )
+            kwargs["llm_instance"] = llm_instance
+
+        response = await llama_index_complete_if_cache(
+            kwargs["llm_instance"],
+            prompt,
+            system_prompt=system_prompt,
+            history_messages=history_messages,
+            **kwargs,
+        )
+        return response
+    except Exception as e:
+        print(f"LLM request failed: {str(e)}")
+        raise
+
+
+# Initialize embedding function
+async def embedding_func(texts):
+    try:
+        embed_model = LiteLLMEmbedding(
+            model_name=f"openai/{EMBEDDING_MODEL}",
+            api_base=LITELLM_URL,
+            api_key=LITELLM_KEY,
+        )
+        return await llama_index_embed(texts, embed_model=embed_model)
+    except Exception as e:
+        print(f"Embedding failed: {str(e)}")
+        raise
+
+
+# Get embedding dimension
+async def get_embedding_dim():
+    test_text = ["This is a test sentence."]
+    embedding = await embedding_func(test_text)
+    embedding_dim = embedding.shape[1]
+    print(f"embedding_dim={embedding_dim}")
+    return embedding_dim
+
+
+# Initialize RAG instance
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=llm_model_func,
+    embedding_func=EmbeddingFunc(
+        embedding_dim=asyncio.run(get_embedding_dim()),
+        max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
+        func=embedding_func,
+    ),
+)
+
+# Insert example text
+with open("./book.txt", "r", encoding="utf-8") as f:
+    rag.insert(f.read())
+
+# Test different query modes
+print("\nNaive Search:")
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+)
+
+print("\nLocal Search:")
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
+)
+
+print("\nGlobal Search:")
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
+)
+
+print("\nHybrid Search:")
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+)