Oracle Database support

Add oracle 23ai database as the KV/vector/graph storage

examples/lightrag_oracle_demo.py

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+
+
+import sys, os
+print(os.getcwd())
+from pathlib import Path
+script_directory = Path(__file__).resolve().parent.parent
+sys.path.append(os.path.abspath(script_directory))
+
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import openai_complete_if_cache, openai_embedding
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+from datetime import datetime
+
+from lightrag.kg.oracle_impl import OracleDB
+
+
+WORKING_DIR = "./dickens"
+
+# We use OpenAI compatible API to call LLM on Oracle Cloud
+# More docs here https://github.com/jin38324/OCI_GenAI_access_gateway
+BASE_URL = "http://xxx.xxx.xxx.xxx:8088/v1/"
+APIKEY = "ocigenerativeai"
+CHATMODEL = "cohere.command-r-plus"
+EMBEDMODEL = "cohere.embed-multilingual-v3.0"
+
+
+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(
+        CHATMODEL,
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key=APIKEY,
+        base_url=BASE_URL,
+        **kwargs,
+    )
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await openai_embedding(
+        texts,
+        model=EMBEDMODEL,
+        api_key=APIKEY,
+        base_url=BASE_URL,
+    )
+
+
+async def get_embedding_dim():
+    test_text = ["This is a test sentence."]
+    embedding = await embedding_func(test_text)
+    embedding_dim = embedding.shape[1]
+    return embedding_dim
+
+
+async def main():
+    try:
+        # Detect embedding dimension
+        embedding_dimension = await get_embedding_dim()
+        print(f"Detected embedding dimension: {embedding_dimension}")
+
+        # Create Oracle DB connection
+        # The `config` parameter is the connection configuration of Oracle DB
+        # More docs here https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html
+        # We storage data in unified tables, so we need to set a `workspace` parameter to specify which docs we want to store and query
+        # Below is an example of how to connect to Oracle Autonomous Database on Oracle Cloud
+        oracle_db = OracleDB(config={
+            "user":"RAG",
+            "password":"xxxxxxxxx",
+            "dsn":"xxxxxxx_medium",
+            "config_dir":"dir/path/to/oracle/config",
+            "wallet_location":"dir/path/to/oracle/wallet",
+            "wallet_password":"xxxxxxxxx",
+            "workspace":"company"  # specify which docs we want to store and query
+            }
+            )
+        
+
+        # Check if Oracle DB tables exist, if not, tables will be created
+        await oracle_db.check_tables()
+
+
+        # Initialize LightRAG
+        # We use Oracle DB as the KV/vector/graph storage
+        rag = LightRAG(
+            enable_llm_cache=False,
+            working_dir=WORKING_DIR,
+            chunk_token_size=512,
+            llm_model_func=llm_model_func,
+            embedding_func=EmbeddingFunc(
+                embedding_dim=embedding_dimension,
+                max_token_size=512,
+                func=embedding_func,
+                ),            
+            graph_storage = "OracleGraphStorage",
+            kv_storage="OracleKVStorage", 
+            vector_storage="OracleVectorDBStorage"
+        )
+
+        # Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
+        rag.graph_storage_cls.db = oracle_db
+        rag.key_string_value_json_storage_cls.db = oracle_db
+        rag.vector_db_storage_cls.db = oracle_db
+
+        # Extract and Insert into LightRAG storage
+        with open("./dickens/demo.txt", "r", encoding="utf-8") as f:
+           await rag.ainsert(f.read())
+
+        # Perform search in different modes
+        modes = ["naive", "local", "global", "hybrid"]
+        for mode in modes:
+            print("="*20, mode, "="*20)
+            print(await rag.aquery("这个文章讲了什么?", param=QueryParam(mode=mode)))
+            print("-"*100, "\n")
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())