修改llm为deepseek-chat

examples/lightrag_yangdx.py

@@ -2,34 +2,46 @@ import asyncio
 import os
 import inspect
 import logging
+from dotenv import load_dotenv
 from lightrag import LightRAG, QueryParam
-from lightrag.llm import ollama_model_complete, ollama_embedding
+from lightrag.llm import openai_complete_if_cache, ollama_embedding
 from lightrag.utils import EmbeddingFunc
 
-WORKING_DIR = "./dickens"
+load_dotenv()
+
+WORKING_DIR = "./examples/input"
 
 logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
 
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+) -> str:
+    return await openai_complete_if_cache(
+        "deepseek-chat",
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key=os.getenv("DEEPSEEK_API_KEY"),
+        base_url=os.getenv("DEEPSEEK__ENDPOINT"),
+        **kwargs,
+    )
+
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
 rag = LightRAG(
     working_dir=WORKING_DIR,
-    llm_model_func=ollama_model_complete,
+    llm_model_func=llm_model_func,
-    llm_model_name="gemma2:2b",
-    llm_model_max_async=4,
-    llm_model_max_token_size=32768,
-    llm_model_kwargs={"host": "http://localhost:11434", "options": {"num_ctx": 32768}},
     embedding_func=EmbeddingFunc(
         embedding_dim=768,
         max_token_size=8192,
         func=lambda texts: ollama_embedding(
-            texts, embed_model="nomic-embed-text", host="http://localhost:11434"
+            texts, embed_model="nomic-embed-text", host="http://m4.lan.znipower.com:11434"
         ),
     ),
 )
 
-with open("./book.txt", "r", encoding="utf-8") as f:
+with open("./input/book.txt", "r", encoding="utf-8") as f:
     rag.insert(f.read())
 
 # Perform naive search