Merge remote-tracking branch 'origin/main'

README.md

@@ -596,11 +596,7 @@ if __name__ == "__main__":
 | **enable\_llm\_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
 | **addon\_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese"}`: sets example limit and output language | `example_number: all examples, language: English` |
 | **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
-| **embedding\_cache\_config** | `dict` | Configuration for question-answer caching. Contains two parameters:
-- `enabled`: Boolean value to enable/disable caching functionality. When enabled, questions and answers will be cached.
-- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.
-
-Default: `{"enabled": False, "similarity_threshold": 0.95}` | `{"enabled": False, "similarity_threshold": 0.95}` |
+| **embedding\_cache\_config** | `dict` | Configuration for question-answer caching. Contains three parameters:<br>- `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers.<br>- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.<br>- `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |
 
 ## API Server Implementation
 

examples/graph_visual_with_html.py

@@ -11,9 +11,16 @@
 # Convert NetworkX graph to Pyvis network
 net.from_nx(G)
 
-# Add colors to nodes
+# Add colors and title to nodes
 for node in net.nodes:
     node["color"] = "#{:06x}".format(random.randint(0, 0xFFFFFF))
+    if "description" in node:
+        node["title"] = node["description"]
+
+# Add title to edges
+for edge in net.edges:
+    if "description" in edge:
+        edge["title"] = edge["description"]
 
 # Save and display the network
 net.show("knowledge_graph.html")

examples/lightrag_jinaai_demo.py

@@ -0,0 +1,114 @@
+import numpy as np
+from lightrag import LightRAG, QueryParam
+from lightrag.utils import EmbeddingFunc
+from lightrag.llm import jina_embedding, openai_complete_if_cache
+import os
+import asyncio
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await jina_embedding(texts, api_key="YourJinaAPIKey")
+
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    return await openai_complete_if_cache(
+        "solar-mini",
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key=os.getenv("UPSTAGE_API_KEY"),
+        base_url="https://api.upstage.ai/v1/solar",
+        **kwargs,
+    )
+
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=llm_model_func,
+    embedding_func=EmbeddingFunc(
+        embedding_dim=1024, max_token_size=8192, func=embedding_func
+    ),
+)
+
+
+async def lightraginsert(file_path, semaphore):
+    async with semaphore:
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+        except UnicodeDecodeError:
+            # If UTF-8 decoding fails, try other encodings
+            with open(file_path, "r", encoding="gbk") as f:
+                content = f.read()
+        await rag.ainsert(content)
+
+
+async def process_files(directory, concurrency_limit):
+    semaphore = asyncio.Semaphore(concurrency_limit)
+    tasks = []
+    for root, dirs, files in os.walk(directory):
+        for f in files:
+            file_path = os.path.join(root, f)
+            if f.startswith("."):
+                continue
+            tasks.append(lightraginsert(file_path, semaphore))
+    await asyncio.gather(*tasks)
+
+
+async def main():
+    try:
+        rag = LightRAG(
+            working_dir=WORKING_DIR,
+            llm_model_func=llm_model_func,
+            embedding_func=EmbeddingFunc(
+                embedding_dim=1024,
+                max_token_size=8192,
+                func=embedding_func,
+            ),
+        )
+
+        asyncio.run(process_files(WORKING_DIR, concurrency_limit=4))
+
+        # Perform naive search
+        print(
+            await rag.aquery(
+                "What are the top themes in this story?", param=QueryParam(mode="naive")
+            )
+        )
+
+        # Perform local search
+        print(
+            await rag.aquery(
+                "What are the top themes in this story?", param=QueryParam(mode="local")
+            )
+        )
+
+        # Perform global search
+        print(
+            await rag.aquery(
+                "What are the top themes in this story?",
+                param=QueryParam(mode="global"),
+            )
+        )
+
+        # Perform hybrid search
+        print(
+            await rag.aquery(
+                "What are the top themes in this story?",
+                param=QueryParam(mode="hybrid"),
+            )
+        )
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

lightrag/lightrag.py

@@ -87,7 +87,11 @@ class LightRAG:
     )
     # Default not to use embedding cache
     embedding_cache_config: dict = field(
-        default_factory=lambda: {"enabled": False, "similarity_threshold": 0.95}
+        default_factory=lambda: {
+            "enabled": False,
+            "similarity_threshold": 0.95,
+            "use_llm_check": False,
+        }
     )
     kv_storage: str = field(default="JsonKVStorage")
     vector_storage: str = field(default="NanoVectorDBStorage")
@@ -174,7 +178,6 @@ def __post_init__(self):
             if self.enable_llm_cache
             else None
         )
-
         self.embedding_func = limit_async_func_call(self.embedding_func_max_async)(
             self.embedding_func
         )
@@ -481,6 +484,7 @@ async def aquery(self, query: str, param: QueryParam = QueryParam()):
                 self.text_chunks,
                 param,
                 asdict(self),
+                hashing_kv=self.llm_response_cache,
             )
         elif param.mode == "naive":
             response = await naive_query(
@@ -489,6 +493,7 @@ async def aquery(self, query: str, param: QueryParam = QueryParam()):
                 self.text_chunks,
                 param,
                 asdict(self),
+                hashing_kv=self.llm_response_cache,
             )
         else:
             raise ValueError(f"Unknown mode {param.mode}")