+MDB KG

examples/lightrag_openai_mongodb_graph_demo.py

@@ -0,0 +1,73 @@
+import os
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm.openai import gpt_4o_mini_complete, openai_embed
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+
+#########
+# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
+# import nest_asyncio
+# nest_asyncio.apply()
+#########
+WORKING_DIR = "./mongodb_test_dir"
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+
+os.environ["OPENAI_API_KEY"] = "sk-"
+os.environ["MONGO_URI"] = "mongodb://0.0.0.0:27017/?directConnection=true"
+os.environ["MONGO_DATABASE"] = "LightRAG"
+os.environ["MONGO_KG_COLLECTION"] = "MDB_KG"
+
+# Embedding Configuration and Functions
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
+EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await openai_embed(
+        texts,
+        model=EMBEDDING_MODEL,
+    )
+
+
+async def get_embedding_dimension():
+    test_text = ["This is a test sentence."]
+    embedding = await embedding_func(test_text)
+    return embedding.shape[1]
+
+
+async def create_embedding_function_instance():
+    # Get embedding dimension
+    embedding_dimension = await get_embedding_dimension()
+    # Create embedding function instance
+    return EmbeddingFunc(
+        embedding_dim=embedding_dimension,
+        max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
+        func=embedding_func,
+    )
+
+
+async def initialize_rag():
+    embedding_func_instance = await create_embedding_function_instance()
+
+    return LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=gpt_4o_mini_complete,
+        embedding_func=embedding_func_instance,
+        graph_storage="MongoGraphStorage",
+        log_level="DEBUG",
+    )
+
+
+# Run the initialization
+rag = asyncio.run(initialize_rag())
+
+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"))
+)