Add huggingface model support

examples/insert.py

@@ -1,18 +0,0 @@
-import os
-import sys
-
-from lightrag import LightRAG
-
-# os.environ["OPENAI_API_KEY"] = ""
-
-WORKING_DIR = ""
-
-if not os.path.exists(WORKING_DIR):
-    os.mkdir(WORKING_DIR)
-
-rag = LightRAG(working_dir=WORKING_DIR)
-
-with open('./text.txt', 'r') as f:
-    text = f.read()
-
-rag.insert(text)

examples/lightrag_hf_demo.py

@@ -0,0 +1,36 @@
+import os
+import sys
+
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import hf_model_complete, hf_embedding
+from transformers import AutoModel,AutoTokenizer
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=hf_model_complete,  
+    llm_model_name='meta-llama/Llama-3.1-8B-Instruct',
+    embedding_func=hf_embedding,  
+    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") 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")))

examples/lightrag_openai_demo.py

@@ -0,0 +1,33 @@
+import os
+import sys
+
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
+from transformers import AutoModel,AutoTokenizer
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=gpt_4o_complete
+    # llm_model_func=gpt_4o_mini_complete
+)
+
+
+with open("./book.txt") 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")))

examples/query.py

@@ -1,16 +0,0 @@
-import os
-import sys
-
-from lightrag import LightRAG, QueryParam
-
-# os.environ["OPENAI_API_KEY"] = ""
-
-WORKING_DIR = ""
-
-rag = LightRAG(working_dir=WORKING_DIR)
-
-mode = 'global'
-query_param = QueryParam(mode=mode)
-
-result = rag.query("", param=query_param)
-print(result)