Centralize query parameters into LightRAG class

This commit refactors query parameter management by consolidating settings like `top_k`, token limits, and thresholds into the `LightRAG` class, and consistently sourcing parameters from a single location.

env.example

@@ -62,6 +62,8 @@ ENABLE_LLM_CACHE=true
 # MAX_RELATION_TOKENS=10000
 ### control the maximum tokens send to LLM (include entities, raltions and chunks)
 # MAX_TOTAL_TOKENS=32000
+### maxumium related chunks grab from single entity or relations
+# RELATED_CHUNK_NUMBER=5
 
 ### Reranker configuration (Set ENABLE_RERANK to true in reranking model is configed)
 ENABLE_RERANK=False

lightrag/api/config.py

@@ -14,6 +14,11 @@ from lightrag.constants import (
     DEFAULT_TOP_K,
     DEFAULT_CHUNK_TOP_K,
     DEFAULT_HISTORY_TURNS,
+    DEFAULT_MAX_ENTITY_TOKENS,
+    DEFAULT_MAX_RELATION_TOKENS,
+    DEFAULT_MAX_TOTAL_TOKENS,
+    DEFAULT_COSINE_THRESHOLD,
+    DEFAULT_RELATED_CHUNK_NUMBER,
 )
 
 # use the .env that is inside the current folder
@@ -154,33 +159,6 @@ def parse_args() -> argparse.Namespace:
         help="Path to SSL private key file (required if --ssl is enabled)",
     )
 
-    parser.add_argument(
-        "--history-turns",
-        type=int,
-        default=get_env_value("HISTORY_TURNS", DEFAULT_HISTORY_TURNS, int),
-        help="Number of conversation history turns to include (default: from env or 3)",
-    )
-
-    # Search parameters
-    parser.add_argument(
-        "--top-k",
-        type=int,
-        default=get_env_value("TOP_K", DEFAULT_TOP_K, int),
-        help="Number of most similar results to return (default: from env or 60)",
-    )
-    parser.add_argument(
-        "--chunk-top-k",
-        type=int,
-        default=get_env_value("CHUNK_TOP_K", DEFAULT_CHUNK_TOP_K, int),
-        help="Number of text chunks to retrieve initially from vector search and keep after reranking (default: from env or 5)",
-    )
-    parser.add_argument(
-        "--cosine-threshold",
-        type=float,
-        default=get_env_value("COSINE_THRESHOLD", 0.2, float),
-        help="Cosine similarity threshold (default: from env or 0.4)",
-    )
-
     # Ollama model name
     parser.add_argument(
         "--simulated-model-name",
@@ -312,6 +290,26 @@ def parse_args() -> argparse.Namespace:
     args.rerank_binding_host = get_env_value("RERANK_BINDING_HOST", None)
     args.rerank_binding_api_key = get_env_value("RERANK_BINDING_API_KEY", None)
 
+    # Query configuration
+    args.history_turns = get_env_value("HISTORY_TURNS", DEFAULT_HISTORY_TURNS, int)
+    args.top_k = get_env_value("TOP_K", DEFAULT_TOP_K, int)
+    args.chunk_top_k = get_env_value("CHUNK_TOP_K", DEFAULT_CHUNK_TOP_K, int)
+    args.max_entity_tokens = get_env_value(
+        "MAX_ENTITY_TOKENS", DEFAULT_MAX_ENTITY_TOKENS, int
+    )
+    args.max_relation_tokens = get_env_value(
+        "MAX_RELATION_TOKENS", DEFAULT_MAX_RELATION_TOKENS, int
+    )
+    args.max_total_tokens = get_env_value(
+        "MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS, int
+    )
+    args.cosine_threshold = get_env_value(
+        "COSINE_THRESHOLD", DEFAULT_COSINE_THRESHOLD, float
+    )
+    args.related_chunk_number = get_env_value(
+        "RELATED_CHUNK_NUMBER", DEFAULT_RELATED_CHUNK_NUMBER, int
+    )
+
     ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name
 
     return args

lightrag/constants.py

@@ -20,6 +20,8 @@ DEFAULT_MAX_RELATION_TOKENS = 10000
 DEFAULT_MAX_TOTAL_TOKENS = 32000
 DEFAULT_HISTORY_TURNS = 3
 DEFAULT_ENABLE_RERANK = True
+DEFAULT_COSINE_THRESHOLD = 0.2
+DEFAULT_RELATED_CHUNK_NUMBER = 5
 
 # Separator for graph fields
 GRAPH_FIELD_SEP = "<SEP>"
@@ -28,6 +30,3 @@ GRAPH_FIELD_SEP = "<SEP>"
 DEFAULT_LOG_MAX_BYTES = 10485760  # Default 10MB
 DEFAULT_LOG_BACKUP_COUNT = 5  # Default 5 backups
 DEFAULT_LOG_FILENAME = "lightrag.log"  # Default log filename
-
-# Related Chunk Number for Single Entity or Relation
-DEFAULT_RELATED_CHUNK_NUMBER = 5

lightrag/lightrag.py

@@ -24,6 +24,13 @@ from typing import (
 from lightrag.constants import (
     DEFAULT_MAX_GLEANING,
     DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE,
+    DEFAULT_TOP_K,
+    DEFAULT_CHUNK_TOP_K,
+    DEFAULT_MAX_ENTITY_TOKENS,
+    DEFAULT_MAX_RELATION_TOKENS,
+    DEFAULT_MAX_TOTAL_TOKENS,
+    DEFAULT_COSINE_THRESHOLD,
+    DEFAULT_RELATED_CHUNK_NUMBER,
 )
 from lightrag.utils import get_env_value
 
@@ -125,6 +132,42 @@ class LightRAG:
     log_level: int | None = field(default=None)
     log_file_path: str | None = field(default=None)
 
+    # Query parameters
+    # ---
+
+    top_k: int = field(default=get_env_value("TOP_K", DEFAULT_TOP_K, int))
+    """Number of entities/relations to retrieve for each query."""
+
+    chunk_top_k: int = field(
+        default=get_env_value("CHUNK_TOP_K", DEFAULT_CHUNK_TOP_K, int)
+    )
+    """Maximum number of chunks in context."""
+
+    max_entity_tokens: int = field(
+        default=get_env_value("MAX_ENTITY_TOKENS", DEFAULT_MAX_ENTITY_TOKENS, int)
+    )
+    """Maximum number of tokens for entity in context."""
+
+    max_relation_tokens: int = field(
+        default=get_env_value("MAX_RELATION_TOKENS", DEFAULT_MAX_RELATION_TOKENS, int)
+    )
+    """Maximum number of tokens for relation in context."""
+
+    max_total_tokens: int = field(
+        default=get_env_value("MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS, int)
+    )
+    """Maximum total tokens in context (including system prompt, entities, relations and chunks)."""
+
+    cosine_threshold: int = field(
+        default=get_env_value("COSINE_THRESHOLD", DEFAULT_COSINE_THRESHOLD, int)
+    )
+    """Cosine threshold of vector DB retrieval for entities, relations and chunks."""
+
+    related_chunk_number: int = field(
+        default=get_env_value("RELATED_CHUNK_NUMBER", DEFAULT_RELATED_CHUNK_NUMBER, int)
+    )
+    """Number of related chunks to grab from single entity or relation."""
+
     # Entity extraction
     # ---
 

lightrag/operate.py

@@ -1908,7 +1908,6 @@ async def _build_query_context(
             ll_keywords,
             knowledge_graph_inst,
             entities_vdb,
-            text_chunks_db,
             query_param,
         )
         original_node_datas = node_datas
@@ -1924,7 +1923,6 @@ async def _build_query_context(
             hl_keywords,
             knowledge_graph_inst,
             relationships_vdb,
-            text_chunks_db,
             query_param,
         )
         original_edge_datas = edge_datas
@@ -1935,14 +1933,12 @@ async def _build_query_context(
             ll_keywords,
             knowledge_graph_inst,
             entities_vdb,
-            text_chunks_db,
             query_param,
         )
         hl_data = await _get_edge_data(
             hl_keywords,
             knowledge_graph_inst,
             relationships_vdb,
-            text_chunks_db,
             query_param,
         )
 
@@ -1985,23 +1981,17 @@ async def _build_query_context(
         max_entity_tokens = getattr(
             query_param,
             "max_entity_tokens",
-            text_chunks_db.global_config.get(
+            text_chunks_db.global_config.get("max_entity_tokens", DEFAULT_MAX_ENTITY_TOKENS),
-                "MAX_ENTITY_TOKENS", DEFAULT_MAX_ENTITY_TOKENS
-            ),
         )
         max_relation_tokens = getattr(
             query_param,
             "max_relation_tokens",
-            text_chunks_db.global_config.get(
+            text_chunks_db.global_config.get("max_relation_tokens", DEFAULT_MAX_RELATION_TOKENS),
-                "MAX_RELATION_TOKENS", DEFAULT_MAX_RELATION_TOKENS
-            ),
         )
         max_total_tokens = getattr(
             query_param,
             "max_total_tokens",
-            text_chunks_db.global_config.get(
+            text_chunks_db.global_config.get("max_total_tokens", DEFAULT_MAX_TOTAL_TOKENS),
-                "MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS
-            ),
         )
 
         # Truncate entities based on complete JSON serialization
@@ -2095,7 +2085,6 @@ async def _build_query_context(
                 final_edge_datas,
                 query_param,
                 text_chunks_db,
-                knowledge_graph_inst,
             )
         )
 
@@ -2255,7 +2244,6 @@ async def _get_node_data(
     query: str,
     knowledge_graph_inst: BaseGraphStorage,
     entities_vdb: BaseVectorStorage,
-    text_chunks_db: BaseKVStorage,
     query_param: QueryParam,
 ):
     # get similar entities
@@ -2362,7 +2350,7 @@ async def _find_most_related_text_unit_from_entities(
 
     text_units = [
         split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])[
-            :DEFAULT_RELATED_CHUNK_NUMBER
+            : text_chunks_db.global_config.get("related_chunk_number", DEFAULT_RELATED_CHUNK_NUMBER)
         ]
         for dp in node_datas
         if dp["source_id"] is not None
@@ -2519,7 +2507,6 @@ async def _get_edge_data(
     keywords,
     knowledge_graph_inst: BaseGraphStorage,
     relationships_vdb: BaseVectorStorage,
-    text_chunks_db: BaseKVStorage,
     query_param: QueryParam,
 ):
     logger.info(
@@ -2668,13 +2655,12 @@ async def _find_related_text_unit_from_relationships(
     edge_datas: list[dict],
     query_param: QueryParam,
     text_chunks_db: BaseKVStorage,
-    knowledge_graph_inst: BaseGraphStorage,
 ):
     logger.debug(f"Searching text chunks for {len(edge_datas)} relationships")
 
     text_units = [
         split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])[
-            :DEFAULT_RELATED_CHUNK_NUMBER
+            : text_chunks_db.global_config.get("related_chunk_number", DEFAULT_RELATED_CHUNK_NUMBER)
         ]
         for dp in edge_datas
         if dp["source_id"] is not None
@@ -2761,7 +2747,7 @@ async def naive_query(
     max_total_tokens = getattr(
         query_param,
         "max_total_tokens",
-        global_config.get("MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS),
+        global_config.get("max_total_tokens", DEFAULT_MAX_TOTAL_TOKENS),
     )
 
     # Calculate conversation history tokens