Merge branch 'jidodata-ykim/main'

examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py

@@ -53,7 +53,6 @@ async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwar
             prompt,
             system_prompt=system_prompt,
             history_messages=history_messages,
-            **kwargs,
         )
         return response
     except Exception as e:

examples/unofficial-sample/lightrag_llamaindex_litellm_opik_demo.py

@@ -0,0 +1,155 @@
+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
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from lightrag.kg.shared_storage import initialize_pipeline_status
+
+# Configure working directory
+WORKING_DIR = "./index_default"
+print(f"WORKING_DIR: {WORKING_DIR}")
+
+# Model configuration
+LLM_MODEL = os.environ.get("LLM_MODEL", "gemma-3-4b")
+print(f"LLM_MODEL: {LLM_MODEL}")
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "arctic-embed")
+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-4JdvGFKqSA3S0k_5p0xufw")
+
+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
+
+        chat_kwargs = {}
+        chat_kwargs["litellm_params"] = {
+            "metadata": {
+                "opik": {
+                    "project_name": "lightrag_llamaindex_litellm_opik_demo",
+                    "tags": ["lightrag", "litellm"],
+                }
+            }
+        }
+
+        response = await llama_index_complete_if_cache(
+            kwargs["llm_instance"],
+            prompt,
+            system_prompt=system_prompt,
+            history_messages=history_messages,
+            chat_kwargs=chat_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
+
+
+async def initialize_rag():
+    embedding_dimension = await get_embedding_dim()
+
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=llm_model_func,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=embedding_dimension,
+            max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
+            func=embedding_func,
+        ),
+    )
+
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
+
+    return rag
+
+
+def main():
+    # Initialize RAG instance
+    rag = asyncio.run(initialize_rag())
+
+    # 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")
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

lightrag/llm/llama_index_impl.py

@@ -95,7 +95,7 @@ async def llama_index_complete_if_cache(
     prompt: str,
     system_prompt: Optional[str] = None,
     history_messages: List[dict] = [],
-    **kwargs,
+    chat_kwargs={},
 ) -> str:
     """Complete the prompt using LlamaIndex."""
     try:
@@ -122,13 +122,9 @@ async def llama_index_complete_if_cache(
         # Add current prompt
         formatted_messages.append(ChatMessage(role=MessageRole.USER, content=prompt))
 
-        # Get LLM instance from kwargs
+        response: ChatResponse = await model.achat(
-        if "llm_instance" not in kwargs:
+            messages=formatted_messages, **chat_kwargs
-            raise ValueError("llm_instance must be provided in kwargs")
+        )
-        llm = kwargs["llm_instance"]
-
-        # Get response
-        response: ChatResponse = await llm.achat(messages=formatted_messages)
 
         # In newer versions, the response is in message.content
         content = response.message.content