feat(lightrag): Implement mix search mode combining knowledge graph and vector retrieval

- Add 'mix' mode to QueryParam for hybrid search functionality
- Implement mix_kg_vector_query to combine knowledge graph and vector search results
- Update LightRAG class to handle 'mix' mode queries
- Enhance README with examples and explanations for the new mix search mode
- Introduce new prompt structure for generating responses based on combined search results

README.md

@@ -106,8 +106,21 @@ print(rag.query("What are the top themes in this story?", param=QueryParam(mode=
 
 # Perform hybrid search
 print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))
+
+# Perform mix search (Knowledge Graph + Vector Retrieval)
+# Mix mode combines knowledge graph and vector search:
+# - Uses both structured (KG) and unstructured (vector) information
+# - Provides comprehensive answers by analyzing relationships and context
+# - Supports image content through HTML img tags
+# - Allows control over retrieval depth via top_k parameter
+print(rag.query("What are the top themes in this story?", param=QueryParam(
+    mode="mix")))
+
 ```
 
+
+
+
 <details>
 <summary> Using Open AI-like APIs </summary>
 
@@ -262,7 +275,7 @@ In order to run this experiment on low RAM GPU you should select small model and
 
 ```python
 class QueryParam:
-    mode: Literal["local", "global", "hybrid", "naive"] = "global"
+    mode: Literal["local", "global", "hybrid", "naive", "mix"] = "global"
     only_need_context: bool = False
     response_type: str = "Multiple Paragraphs"
     # Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode.