securing for production with env vars for creds

.gitignore

@@ -7,4 +7,5 @@ lightrag-dev/
 dist/
 env/
 local_neo4jWorkDir/
-neo4jWorkDir/
+neo4jWorkDir/
+ignore_this.txt

lightrag/kg/neo4j_impl.py

@@ -5,7 +5,6 @@ from dataclasses import dataclass
 from typing import Any, Union, cast
 import numpy as np
 import inspect
-# import package.common.utils as utils
 from lightrag.utils import load_json, logger, write_json
 from ..base import (
     BaseGraphStorage
@@ -22,27 +21,6 @@ from tenacity import (
 
 
 
-
-
-# @TODO: catch and retry "ERROR:neo4j.io:Failed to write data to connection ResolvedIPv4Address"
-#        during indexing.
-
-
-
-
-# Replace with your actual URI, username, and password
-#local
-URI = "neo4j://localhost:7687"
-USERNAME = "neo4j"
-PASSWORD = "password"
-
-#aura
-# URI = "neo4j+s://91fbae6c.databases.neo4j.io"
-# USERNAME = "neo4j"
-# PASSWORD = "KWKPXfXcClDbUlmDdGgIQhU5mL1N4E_2CJp2BDFbEbw"
-# Create a driver object
-
-
 @dataclass
 class GraphStorage(BaseGraphStorage):
     @staticmethod
@@ -51,6 +29,15 @@ class GraphStorage(BaseGraphStorage):
 
     def __post_init__(self):
         # self._graph = preloaded_graph or nx.Graph()
+        credetial_parts = ['URI', 'USERNAME','PASSWORD']
+        credentials_set = all(x in os.environ for x in credetial_parts  )
+        if credentials_set:
+            URI = os.environ["URI"]
+            USERNAME = os.environ["USERNAME"]
+            PASSWORD = os.environ["PASSWORD"]
+        else:
+            raise Exception (f"One or more Neo4J Credentials, {credetial_parts}, not found in the environment")
+
         self._driver = GraphDatabase.driver(URI, auth=(USERNAME, PASSWORD))
         self._node_embed_algorithms = {
             "node2vec": self._node2vec_embed,
@@ -65,7 +52,7 @@ class GraphStorage(BaseGraphStorage):
             query = f"MATCH (n:`{label}`) RETURN count(n) > 0 AS node_exists"  
             result = tx.run(query)  
             single_result = result.single()
-            logger.info(
+            logger.debug(
                     f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{single_result["node_exists"]}'
             )  
             
@@ -90,7 +77,7 @@ class GraphStorage(BaseGraphStorage):
             # if result.single() == None:
             #     print (f"this should not happen: ---- {label1}/{label2}   {query}")
 
-            logger.info(
+            logger.debug(
                     f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{single_result["edgeExists"]}'
             )  
             
@@ -111,7 +98,7 @@ class GraphStorage(BaseGraphStorage):
             result = session.run(query)
             for record in result:
                 result = record["n"]
-                logger.info(
+                logger.debug(
                     f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{result}'
                 )  
                 return result
@@ -133,7 +120,7 @@ class GraphStorage(BaseGraphStorage):
                 record = result.single()  
                 if record:
                     edge_count = record["totalEdgeCount"]  
-                    logger.info(
+                    logger.debug(
                         f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{edge_count}'
                     )  
                     return edge_count
@@ -154,7 +141,7 @@ class GraphStorage(BaseGraphStorage):
                 RETURN count(r) AS degree"""
             result = session.run(query)        
             record = result.single()
-            logger.info(
+            logger.debug(
                 f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{record["degree"]}'
             )       
             return record["degree"]
@@ -183,7 +170,7 @@ class GraphStorage(BaseGraphStorage):
             record = result.single()
             if record:
                 result = dict(record["edge_properties"])
-                logger.info(
+                logger.debug(
                     f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{result}'
                 )     
                 return result
@@ -254,7 +241,7 @@ class GraphStorage(BaseGraphStorage):
     #             if source_label and target_label:
     #                 connections.append((source_label, target_label))
 
-    #         logger.info(
+    #         logger.debug(
     #             f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{connections}'
     #         ) 
     #         return connections
@@ -308,7 +295,7 @@ class GraphStorage(BaseGraphStorage):
             result = tx.run(query, properties=properties)
             record = result.single()
             if record:
-                logger.info(
+                logger.debug(
                     f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{dict(record["n"])}'
                 )   
                 return dict(record["n"])
@@ -364,7 +351,7 @@ class GraphStorage(BaseGraphStorage):
             """
 
             result = tx.run(query, properties=edge_properties)
-            logger.info(
+            logger.debug(
                 f'{inspect.currentframe().f_code.co_name}:query:{query}:edge_properties:{edge_properties}'
             )               
             return result.single()
@@ -385,7 +372,7 @@ class GraphStorage(BaseGraphStorage):
         with self._driver.session()  as session:
             #Define the Cypher query
             options = self.global_config["node2vec_params"]
-            logger.info(f"building embeddings with options {options}")
+            logger.debug(f"building embeddings with options {options}")
             query = f"""CALL gds.node2vec.write('91fbae6c', {
                 options
                 })

lightrag/lightrag.py

@@ -28,6 +28,13 @@ from .storage import (
 from .kg.neo4j_impl import (
     GraphStorage as Neo4JStorage
 )
+#future KG integrations
+
+# from .kg.ArangoDB_impl import (
+#     GraphStorage as ArangoDBStorage
+# )
+
+
 
 from .utils import (
     EmbeddingFunc,
@@ -64,7 +71,11 @@ class LightRAG:
     )
 
     kg: str = field(default="NetworkXStorage")
-  
+
+    current_log_level = logger.level
+    log_level: str = field(default=current_log_level)
+
+
 
     # text chunking
     chunk_token_size: int = 1200
@@ -115,13 +126,14 @@ class LightRAG:
     def __post_init__(self):
         log_file = os.path.join(self.working_dir, "lightrag.log")
         set_logger(log_file)
+        logger.setLevel(self.log_level)
+
         logger.info(f"Logger initialized for working directory: {self.working_dir}")
 
         _print_config = ",\n  ".join([f"{k} = {v}" for k, v in asdict(self).items()])
         logger.debug(f"LightRAG init with param:\n  {_print_config}\n")
 
         #should move all storage setup here to leverage initial start params attached to self.
-        print (f"self.kg set to: {self.kg}")
         self.graph_storage_cls: Type[BaseGraphStorage] = self._get_storage_class()[self.kg]
 
         if not os.path.exists(self.working_dir):
@@ -176,7 +188,7 @@ class LightRAG:
         return {
             "Neo4JStorage": Neo4JStorage,
             "NetworkXStorage": NetworkXStorage,
-            # "new_kg_here": KGClass
+            # "ArangoDBStorage": ArangoDBStorage
         }
 
     def insert(self, string_or_strings):

lightrag/operate.py

@@ -71,7 +71,6 @@ async def _handle_entity_relation_summary(
     use_prompt = prompt_template.format(**context_base)
     logger.debug(f"Trigger summary: {entity_or_relation_name}")
     summary = await use_llm_func(use_prompt, max_tokens=summary_max_tokens)
-    print ("Summarized: {context_base} for entity relationship {}  ")
     return summary
 
 
@@ -79,7 +78,6 @@ async def _handle_single_entity_extraction(
     record_attributes: list[str],
     chunk_key: str,
 ):
-    print (f"_handle_single_entity_extraction {record_attributes}  chunk_key {chunk_key}")
     if len(record_attributes) < 4 or record_attributes[0] != '"entity"':
         return None
     # add this record as a node in the G
@@ -265,7 +263,6 @@ async def extract_entities(
 
     async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]):
         nonlocal already_processed, already_entities, already_relations
-        print (f"kw: processing a single chunk, {chunk_key_dp}")
         chunk_key = chunk_key_dp[0]
         chunk_dp = chunk_key_dp[1]
         content = chunk_dp["content"]
@@ -435,7 +432,6 @@ async def local_query(
             text_chunks_db,
             query_param,
         )
-    print (f"got the following context {context} based on prompt keywords {keywords}")
     if query_param.only_need_context:
         return context
     if context is None:
@@ -444,7 +440,6 @@ async def local_query(
     sys_prompt = sys_prompt_temp.format(
         context_data=context, response_type=query_param.response_type
     )
-    print (f"local query:{query} local sysprompt:{sys_prompt}")
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,
@@ -470,20 +465,16 @@ async def _build_local_query_context(
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
 ):
-    print ("kw1: ENTITIES VDB QUERY**********************************")
 
     results = await entities_vdb.query(query, top_k=query_param.top_k)
-    print (f"kw2: ENTITIES VDB QUERY, RESULTS {results}**********************************")
 
     if not len(results):
         return None
-    print ("kw3: using entities to get_nodes returned in above vdb query.  search results from embedding your query keywords")
     node_datas = await asyncio.gather(
         *[knowledge_graph_inst.get_node(r["entity_name"]) for r in results]
     )
     if not all([n is not None for n in node_datas]):
         logger.warning("Some nodes are missing, maybe the storage is damaged")
-    print ("kw4: getting node degrees next for the same entities/nodes")
     node_degrees = await asyncio.gather(
         *[knowledge_graph_inst.node_degree(r["entity_name"]) for r in results]
     )
@@ -729,7 +720,6 @@ async def _build_global_query_context(
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
 ):
-    print ("RELATIONSHIPS VDB QUERY**********************************")
     results = await relationships_vdb.query(keywords, top_k=query_param.top_k)
 
     if not len(results):
@@ -895,14 +885,12 @@ async def hybrid_query(
     query_param: QueryParam,
     global_config: dict,
 ) -> str:
-    print ("HYBRID QUERY *********")
     low_level_context = None
     high_level_context = None
     use_model_func = global_config["llm_model_func"]
 
     kw_prompt_temp = PROMPTS["keywords_extraction"]
     kw_prompt = kw_prompt_temp.format(query=query)
-    print ( f"kw:kw_prompt: {kw_prompt}")
 
     result = await use_model_func(kw_prompt)
     try:
@@ -911,8 +899,6 @@ async def hybrid_query(
         ll_keywords = keywords_data.get("low_level_keywords", [])
         hl_keywords = ", ".join(hl_keywords)
         ll_keywords = ", ".join(ll_keywords)
-        print (f"High level key words: {hl_keywords}")
-        print (f"Low level key words: {ll_keywords}")
     except json.JSONDecodeError:
         try:
             result = (
@@ -942,7 +928,6 @@ async def hybrid_query(
             query_param,
         )
 
-    print (f"low_level_context: {low_level_context}")
 
     if hl_keywords:
         high_level_context = await _build_global_query_context(
@@ -953,7 +938,6 @@ async def hybrid_query(
             text_chunks_db,
             query_param,
         )
-    print (f"high_level_context: {high_level_context}")
 
 
     context = combine_contexts(high_level_context, low_level_context)
@@ -971,7 +955,6 @@ async def hybrid_query(
         query,
         system_prompt=sys_prompt,
     )
-    print (f"kw: got system prompt: {sys_prompt}.  got response for that prompt: {response}")
     if len(response) > len(sys_prompt):
         response = (
             response.replace(sys_prompt, "")
@@ -1065,12 +1048,10 @@ async def naive_query(
 ):
     use_model_func = global_config["llm_model_func"]
     results = await chunks_vdb.query(query, top_k=query_param.top_k)
-    print (f"raw chunks from chunks_vdb.query {results}")
     if not len(results):
         return PROMPTS["fail_response"]
     chunks_ids = [r["id"] for r in results]
     chunks = await text_chunks_db.get_by_ids(chunks_ids)
-    print (f"raw chunks from text_chunks_db {chunks} retrieved by id using the above chunk ids from prev chunks_vdb ")
 
 
     maybe_trun_chunks = truncate_list_by_token_size(

testkg.py

@@ -16,12 +16,13 @@ if not os.path.exists(WORKING_DIR):
 rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=gpt_4o_mini_complete,  # Use gpt_4o_mini_complete LLM model
-    kg="Neo4JStorage"
+    kg="Neo4JStorage",
+    log_level="INFO"
     # llm_model_func=gpt_4o_complete  # Optionally, use a stronger model
 )
 
-with open("./book.txt") as f:
+# with open("./book.txt") as f:
-    rag.insert(f.read())
+#     rag.insert(f.read())
 
 # Perform naive search
 print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))