fix examples

examples/lightrag_api_oracle_demo.py

@@ -48,6 +48,14 @@ print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+os.environ["ORACLE_USER"] = ""
+os.environ["ORACLE_PASSWORD"] = ""
+os.environ["ORACLE_DSN"] = ""
+os.environ["ORACLE_CONFIG_DIR"] = "path_to_config_dir"
+os.environ["ORACLE_WALLET_LOCATION"] = "path_to_wallet_location"
+os.environ["ORACLE_WALLET_PASSWORD"] = "wallet_password"
+os.environ["ORACLE_WORKSPACE"] = "company"
+
 
 async def llm_model_func(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
@@ -89,20 +97,6 @@ async def init():
     # 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": "",
-            "password": "",
-            "dsn": "",
-            "config_dir": "path_to_config_dir",
-            "wallet_location": "path_to_wallet_location",
-            "wallet_password": "wallet_password",
-            "workspace": "company",
-        }  # specify which docs you 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
     # You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt
@@ -121,11 +115,6 @@ async def init():
         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
-
     return rag
 
 

examples/lightrag_oracle_demo.py

@@ -26,6 +26,14 @@ MAX_TOKENS = 4000
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+os.environ["ORACLE_USER"] = "username"
+os.environ["ORACLE_PASSWORD"] = "xxxxxxxxx"
+os.environ["ORACLE_DSN"] = "xxxxxxx_medium"
+os.environ["ORACLE_CONFIG_DIR"] = "path_to_config_dir"
+os.environ["ORACLE_WALLET_LOCATION"] = "path_to_wallet_location"
+os.environ["ORACLE_WALLET_PASSWORD"] = "wallet_password"
+os.environ["ORACLE_WORKSPACE"] = "company"
+
 
 async def llm_model_func(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
@@ -63,26 +71,6 @@ async def main():
         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": "username",
-                "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 you 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
         # You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt
@@ -112,26 +100,6 @@ async def main():
             },
         )
 
-        # Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
-
-        for storage in [
-            rag.vector_db_storage_cls,
-            rag.graph_storage_cls,
-            rag.doc_status,
-            rag.full_docs,
-            rag.text_chunks,
-            rag.llm_response_cache,
-            rag.key_string_value_json_storage_cls,
-            rag.chunks_vdb,
-            rag.relationships_vdb,
-            rag.entities_vdb,
-            rag.graph_storage_cls,
-            rag.chunk_entity_relation_graph,
-            rag.llm_response_cache,
-        ]:
-            # set client
-            storage.db = oracle_db
-
         # Extract and Insert into LightRAG storage
         with open(WORKING_DIR + "/docs.txt", "r", encoding="utf-8") as f:
             all_text = f.read()

examples/lightrag_tidb_demo.py

@@ -17,11 +17,11 @@ APIKEY = ""
 CHATMODEL = ""
 EMBEDMODEL = ""
 
-TIDB_HOST = ""
+os.environ["TIDB_HOST"] = ""
-TIDB_PORT = ""
+os.environ["TIDB_PORT"] = ""
-TIDB_USER = ""
+os.environ["TIDB_USER"] = ""
-TIDB_PASSWORD = ""
+os.environ["TIDB_PASSWORD"] = ""
-TIDB_DATABASE = "lightrag"
+os.environ["TIDB_DATABASE"] = "lightrag"
 
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
@@ -62,21 +62,6 @@ async def main():
         embedding_dimension = await get_embedding_dim()
         print(f"Detected embedding dimension: {embedding_dimension}")
 
-        # Create TiDB DB connection
-        tidb = TiDB(
-            config={
-                "host": TIDB_HOST,
-                "port": TIDB_PORT,
-                "user": TIDB_USER,
-                "password": TIDB_PASSWORD,
-                "database": TIDB_DATABASE,
-                "workspace": "company",  # specify which docs you want to store and query
-            }
-        )
-
-        # Check if TiDB DB tables exist, if not, tables will be created
-        await tidb.check_tables()
-
         # Initialize LightRAG
         # We use TiDB DB as the KV/vector
         # You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt
@@ -95,15 +80,6 @@ async def main():
             graph_storage="TiDBGraphStorage",
         )
 
-        if rag.llm_response_cache:
-            rag.llm_response_cache.db = tidb
-        rag.full_docs.db = tidb
-        rag.text_chunks.db = tidb
-        rag.entities_vdb.db = tidb
-        rag.relationships_vdb.db = tidb
-        rag.chunks_vdb.db = tidb
-        rag.chunk_entity_relation_graph.db = tidb
-
         # Extract and Insert into LightRAG storage
         with open("./dickens/demo.txt", "r", encoding="utf-8") as f:
             await rag.ainsert(f.read())

examples/lightrag_zhipu_postgres_demo.py

@@ -22,22 +22,14 @@ if not os.path.exists(WORKING_DIR):
 # AGE
 os.environ["AGE_GRAPH_NAME"] = "dickens"
 
-postgres_db = PostgreSQLDB(
+os.environ["POSTGRES_HOST"] = "localhost"
-    config={
+os.environ["POSTGRES_PORT"] = "15432"
-        "host": "localhost",
+os.environ["POSTGRES_USER"] = "rag"
-        "port": 15432,
+os.environ["POSTGRES_PASSWORD"] = "rag"
-        "user": "rag",
+os.environ["POSTGRES_DATABASE"] = "rag"
-        "password": "rag",
-        "database": "rag",
-    }
-)
 
 
 async def main():
-    await postgres_db.initdb()
-    # Check if PostgreSQL DB tables exist, if not, tables will be created
-    await postgres_db.check_tables()
-
     rag = LightRAG(
         working_dir=WORKING_DIR,
         llm_model_func=zhipu_complete,
@@ -57,17 +49,7 @@ async def main():
         graph_storage="PGGraphStorage",
         vector_storage="PGVectorStorage",
     )
-    # Set the KV/vector/graph storage's `db` property, so all operation will use same connection pool
+
-    rag.doc_status.db = postgres_db
-    rag.full_docs.db = postgres_db
-    rag.text_chunks.db = postgres_db
-    rag.llm_response_cache.db = postgres_db
-    rag.key_string_value_json_storage_cls.db = postgres_db
-    rag.chunks_vdb.db = postgres_db
-    rag.relationships_vdb.db = postgres_db
-    rag.entities_vdb.db = postgres_db
-    rag.graph_storage_cls.db = postgres_db
-    rag.chunk_entity_relation_graph.db = postgres_db
     # add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c
     rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func