support lmdeploy backend

examples/lightrag_lmdeploy_demo.py

@@ -0,0 +1,74 @@
+import os
+
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import lmdeploy_model_if_cache, hf_embedding
+from lightrag.utils import EmbeddingFunc
+from transformers import AutoModel, AutoTokenizer
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+async def lmdeploy_model_complete(
+    prompt=None, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    model_name = kwargs["hashing_kv"].global_config["llm_model_name"] 
+    return await lmdeploy_model_if_cache(
+        model_name,
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        ## please specify chat_template if your local path does not follow original HF file name,
+        ## or model_name is a pytorch model on huggingface.co,
+        ## you can refer to https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/model.py
+        ## for a list of chat_template available in lmdeploy.
+        chat_template = "llama3",
+        # model_format ='awq', # if you are using awq quantization model.
+        # quant_policy=8, # if you want to use online kv cache, 4=kv int4, 8=kv int8.
+        **kwargs,
+    )
+
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=lmdeploy_model_complete,
+    llm_model_name="meta-llama/Llama-3.1-8B-Instruct", # please use definite path for local model
+    embedding_func=EmbeddingFunc(
+        embedding_dim=384,
+        max_token_size=5000,
+        func=lambda texts: hf_embedding(
+            texts,
+            tokenizer=AutoTokenizer.from_pretrained(
+                "sentence-transformers/all-MiniLM-L6-v2"
+            ),
+            embed_model=AutoModel.from_pretrained(
+                "sentence-transformers/all-MiniLM-L6-v2"
+            ),
+        ),
+    ),
+)
+
+
+with open("./book.txt", "r", encoding="utf-8") as f:
+    rag.insert(f.read())
+
+# Perform naive search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+)
+
+# Perform local search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
+)
+
+# Perform global search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
+)
+
+# Perform hybrid search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+)