From 4e425b1b5993e817dca7b2d0b8a302b123c4028d Mon Sep 17 00:00:00 2001 From: zrguo <49157727+LarFii@users.noreply.github.com> Date: Mon, 14 Jul 2025 16:29:00 +0800 Subject: [PATCH] Revert "update from main" This reverts commit 1d0376d6a926ef60d641af4406dacf5b8bbb430f. --- README-zh.md | 2 +- README.md | 2 +- env.example | 8 +- lightrag/operate.py | 421 +++++++++++------- lightrag_webui/src/api/lightrag.ts | 16 + .../components/retrieval/QuerySettings.tsx | 38 +- lightrag_webui/src/locales/ar.json | 10 +- lightrag_webui/src/locales/en.json | 10 +- lightrag_webui/src/locales/fr.json | 10 +- lightrag_webui/src/locales/zh.json | 10 +- lightrag_webui/src/locales/zh_TW.json | 10 +- 11 files changed, 332 insertions(+), 205 deletions(-) diff --git a/README-zh.md b/README-zh.md index 02d7295c..d0b3ec70 100644 --- a/README-zh.md +++ b/README-zh.md @@ -250,7 +250,7 @@ if __name__ == "__main__": | **embedding_func_max_async** | `int` | 最大并发异步嵌入进程数 | `16` | | **llm_model_func** | `callable` | LLM生成的函数 | `gpt_4o_mini_complete` | | **llm_model_name** | `str` | 用于生成的LLM模型名称 | `meta-llama/Llama-3.2-1B-Instruct` | -| **llm_model_max_token_size** | `int` | LLM生成的最大令牌大小（影响实体关系摘要） | `32768`（默认值由环境变量MAX_TOKENS更改） | +| **llm_model_max_token_size** | `int` | 生成实体关系摘要时送给LLM的最大令牌数 | `32000`（默认值由环境变量MAX_TOKENS更改） | | **llm_model_max_async** | `int` | 最大并发异步LLM进程数 | `4`（默认值由环境变量MAX_ASYNC更改） | | **llm_model_kwargs** | `dict` | LLM生成的附加参数 | | | **vector_db_storage_cls_kwargs** | `dict` | 向量数据库的附加参数，如设置节点和关系检索的阈值 | cosine_better_than_threshold: 0.2（默认值由环境变量COSINE_THRESHOLD更改） | diff --git a/README.md b/README.md index a04eb1d7..5be7389d 100644 --- a/README.md +++ b/README.md @@ -257,7 +257,7 @@ A full list of LightRAG init parameters: | **embedding_func_max_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` | | **llm_model_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` | | **llm_model_name** | `str` | LLM model name for generation | `meta-llama/Llama-3.2-1B-Instruct` | -| **llm_model_max_token_size** | `int` | Maximum token size for LLM generation (affects entity relation summaries) | `32768`（default value changed by env var MAX_TOKENS) | +| **llm_model_max_token_size** | `int` | Maximum tokens send to LLM to generate entity relation summaries | `32000`（default value changed by env var MAX_TOKENS) | | **llm_model_max_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `4`（default value changed by env var MAX_ASYNC) | | **llm_model_kwargs** | `dict` | Additional parameters for LLM generation | | | **vector_db_storage_cls_kwargs** | `dict` | Additional parameters for vector database, like setting the threshold for nodes and relations retrieval | cosine_better_than_threshold: 0.2（default value changed by env var COSINE_THRESHOLD) | diff --git a/env.example b/env.example index ec5d0bad..50b68c1f 100644 --- a/env.example +++ b/env.example @@ -46,7 +46,6 @@ OLLAMA_EMULATING_MODEL_TAG=latest ### Chunk size for document splitting, 500~1500 is recommended # CHUNK_SIZE=1200 # CHUNK_OVERLAP_SIZE=100 -# MAX_TOKEN_SUMMARY=500 ### RAG Query Configuration # HISTORY_TURNS=3 @@ -94,8 +93,7 @@ TEMPERATURE=0 ### Max concurrency requests of LLM MAX_ASYNC=4 ### MAX_TOKENS: max tokens send to LLM for entity relation summaries (less than context size of the model) -### MAX_TOKENS: set as num_ctx option for Ollama by API Server -MAX_TOKENS=32768 +MAX_TOKENS=32000 ### LLM Binding type: openai, ollama, lollms, azure_openai LLM_BINDING=openai LLM_MODEL=gpt-4o @@ -104,6 +102,8 @@ LLM_BINDING_API_KEY=your_api_key ### Optional for Azure # AZURE_OPENAI_API_VERSION=2024-08-01-preview # AZURE_OPENAI_DEPLOYMENT=gpt-4o +### set as num_ctx option for Ollama LLM +# OLLAMA_NUM_CTX=32768 ### Embedding Configuration ### Embedding Binding type: openai, ollama, lollms, azure_openai @@ -116,7 +116,7 @@ EMBEDDING_BINDING_HOST=http://localhost:11434 ### Num of chunks send to Embedding in single request # EMBEDDING_BATCH_NUM=10 ### Max concurrency requests for Embedding -# EMBEDDING_FUNC_MAX_ASYNC=16 +# EMBEDDING_FUNC_MAX_ASYNC=8 ### Maximum tokens sent to Embedding for each chunk (no longer in use?) # MAX_EMBED_TOKENS=8192 ### Optional for Azure diff --git a/lightrag/operate.py b/lightrag/operate.py index 668d42a9..3d009363 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -37,6 +37,7 @@ from .base import ( ) from .prompt import PROMPTS from .constants import GRAPH_FIELD_SEP +from .kg.shared_storage import get_storage_keyed_lock import time from dotenv import load_dotenv @@ -117,7 +118,7 @@ async def _handle_entity_relation_summary( tokenizer: Tokenizer = global_config["tokenizer"] llm_max_tokens = global_config["llm_model_max_token_size"] - summary_max_tokens = global_config["summary_to_max_tokens"] + # summary_max_tokens = global_config["summary_to_max_tokens"] language = global_config["addon_params"].get( "language", PROMPTS["DEFAULT_LANGUAGE"] @@ -144,7 +145,7 @@ async def _handle_entity_relation_summary( use_prompt, use_llm_func, llm_response_cache=llm_response_cache, - max_tokens=summary_max_tokens, + # max_tokens=summary_max_tokens, cache_type="extract", ) return summary @@ -274,20 +275,26 @@ async def _rebuild_knowledge_from_chunks( pipeline_status: dict | None = None, pipeline_status_lock=None, ) -> None: - """Rebuild entity and relationship descriptions from cached extraction results + """Rebuild entity and relationship descriptions from cached extraction results with parallel processing This method uses cached LLM extraction results instead of calling LLM again, - following the same approach as the insert process. + following the same approach as the insert process. Now with parallel processing + controlled by llm_model_max_async and using get_storage_keyed_lock for data consistency. Args: entities_to_rebuild: Dict mapping entity_name -> set of remaining chunk_ids relationships_to_rebuild: Dict mapping (src, tgt) -> set of remaining chunk_ids - text_chunks_data: Pre-loaded chunk data dict {chunk_id: chunk_data} + knowledge_graph_inst: Knowledge graph storage + entities_vdb: Entity vector database + relationships_vdb: Relationship vector database + text_chunks_storage: Text chunks storage + llm_response_cache: LLM response cache + global_config: Global configuration containing llm_model_max_async + pipeline_status: Pipeline status dictionary + pipeline_status_lock: Lock for pipeline status """ if not entities_to_rebuild and not relationships_to_rebuild: return - rebuilt_entities_count = 0 - rebuilt_relationships_count = 0 # Get all referenced chunk IDs all_referenced_chunk_ids = set() @@ -296,7 +303,7 @@ async def _rebuild_knowledge_from_chunks( for chunk_ids in relationships_to_rebuild.values(): all_referenced_chunk_ids.update(chunk_ids) - status_message = f"Rebuilding knowledge from {len(all_referenced_chunk_ids)} cached chunk extractions" + status_message = f"Rebuilding knowledge from {len(all_referenced_chunk_ids)} cached chunk extractions (parallel processing)" logger.info(status_message) if pipeline_status is not None and pipeline_status_lock is not None: async with pipeline_status_lock: @@ -366,66 +373,116 @@ async def _rebuild_knowledge_from_chunks( pipeline_status["history_messages"].append(status_message) continue - # Rebuild entities + # Get max async tasks limit from global_config for semaphore control + graph_max_async = global_config.get("llm_model_max_async", 4) * 2 + semaphore = asyncio.Semaphore(graph_max_async) + + # Counters for tracking progress + rebuilt_entities_count = 0 + rebuilt_relationships_count = 0 + failed_entities_count = 0 + failed_relationships_count = 0 + + async def _locked_rebuild_entity(entity_name, chunk_ids): + nonlocal rebuilt_entities_count, failed_entities_count + async with semaphore: + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [entity_name], namespace=namespace, enable_logging=False + ): + try: + await _rebuild_single_entity( + knowledge_graph_inst=knowledge_graph_inst, + entities_vdb=entities_vdb, + entity_name=entity_name, + chunk_ids=chunk_ids, + chunk_entities=chunk_entities, + llm_response_cache=llm_response_cache, + global_config=global_config, + ) + rebuilt_entities_count += 1 + status_message = ( + f"Rebuilt entity: {entity_name} from {len(chunk_ids)} chunks" + ) + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + except Exception as e: + failed_entities_count += 1 + status_message = f"Failed to rebuild entity {entity_name}: {e}" + logger.info(status_message) # Per requirement, change to info + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + async def _locked_rebuild_relationship(src, tgt, chunk_ids): + nonlocal rebuilt_relationships_count, failed_relationships_count + async with semaphore: + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + f"{src}-{tgt}", namespace=namespace, enable_logging=False + ): + try: + await _rebuild_single_relationship( + knowledge_graph_inst=knowledge_graph_inst, + relationships_vdb=relationships_vdb, + src=src, + tgt=tgt, + chunk_ids=chunk_ids, + chunk_relationships=chunk_relationships, + llm_response_cache=llm_response_cache, + global_config=global_config, + ) + rebuilt_relationships_count += 1 + status_message = f"Rebuilt relationship: {src}->{tgt} from {len(chunk_ids)} chunks" + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + except Exception as e: + failed_relationships_count += 1 + status_message = f"Failed to rebuild relationship {src}->{tgt}: {e}" + logger.info(status_message) # Per requirement, change to info + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + # Create tasks for parallel processing + tasks = [] + + # Add entity rebuilding tasks for entity_name, chunk_ids in entities_to_rebuild.items(): - try: - await _rebuild_single_entity( - knowledge_graph_inst=knowledge_graph_inst, - entities_vdb=entities_vdb, - entity_name=entity_name, - chunk_ids=chunk_ids, - chunk_entities=chunk_entities, - llm_response_cache=llm_response_cache, - global_config=global_config, - ) - rebuilt_entities_count += 1 - status_message = ( - f"Rebuilt entity: {entity_name} from {len(chunk_ids)} chunks" - ) - logger.info(status_message) - if pipeline_status is not None and pipeline_status_lock is not None: - async with pipeline_status_lock: - pipeline_status["latest_message"] = status_message - pipeline_status["history_messages"].append(status_message) - except Exception as e: - status_message = f"Failed to rebuild entity {entity_name}: {e}" - logger.info(status_message) # Per requirement, change to info - if pipeline_status is not None and pipeline_status_lock is not None: - async with pipeline_status_lock: - pipeline_status["latest_message"] = status_message - pipeline_status["history_messages"].append(status_message) + task = asyncio.create_task(_locked_rebuild_entity(entity_name, chunk_ids)) + tasks.append(task) - # Rebuild relationships + # Add relationship rebuilding tasks for (src, tgt), chunk_ids in relationships_to_rebuild.items(): - try: - await _rebuild_single_relationship( - knowledge_graph_inst=knowledge_graph_inst, - relationships_vdb=relationships_vdb, - src=src, - tgt=tgt, - chunk_ids=chunk_ids, - chunk_relationships=chunk_relationships, - llm_response_cache=llm_response_cache, - global_config=global_config, - ) - rebuilt_relationships_count += 1 - status_message = ( - f"Rebuilt relationship: {src}->{tgt} from {len(chunk_ids)} chunks" - ) - logger.info(status_message) - if pipeline_status is not None and pipeline_status_lock is not None: - async with pipeline_status_lock: - pipeline_status["latest_message"] = status_message - pipeline_status["history_messages"].append(status_message) - except Exception as e: - status_message = f"Failed to rebuild relationship {src}->{tgt}: {e}" - logger.info(status_message) - if pipeline_status is not None and pipeline_status_lock is not None: - async with pipeline_status_lock: - pipeline_status["latest_message"] = status_message - pipeline_status["history_messages"].append(status_message) + task = asyncio.create_task(_locked_rebuild_relationship(src, tgt, chunk_ids)) + tasks.append(task) + + # Log parallel processing start + status_message = f"Starting parallel rebuild of {len(entities_to_rebuild)} entities and {len(relationships_to_rebuild)} relationships (async: {graph_max_async})" + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + # Execute all tasks in parallel with semaphore control + await asyncio.gather(*tasks) + + # Final status report + status_message = f"KG rebuild completed: {rebuilt_entities_count} entities and {rebuilt_relationships_count} relationships rebuilt successfully." + if failed_entities_count > 0 or failed_relationships_count > 0: + status_message += f" Failed: {failed_entities_count} entities, {failed_relationships_count} relationships." - status_message = f"KG rebuild completed: {rebuilt_entities_count} entities and {rebuilt_relationships_count} relationships." logger.info(status_message) if pipeline_status is not None and pipeline_status_lock is not None: async with pipeline_status_lock: @@ -630,7 +687,10 @@ async def _rebuild_single_entity( # Helper function to generate final description with optional LLM summary async def _generate_final_description(combined_description: str) -> str: - if len(combined_description) > global_config["summary_to_max_tokens"]: + force_llm_summary_on_merge = global_config["force_llm_summary_on_merge"] + num_fragment = combined_description.count(GRAPH_FIELD_SEP) + 1 + + if num_fragment >= force_llm_summary_on_merge: return await _handle_entity_relation_summary( entity_name, combined_description, @@ -725,7 +785,11 @@ async def _rebuild_single_relationship( llm_response_cache: BaseKVStorage, global_config: dict[str, str], ) -> None: - """Rebuild a single relationship from cached extraction results""" + """Rebuild a single relationship from cached extraction results + + Note: This function assumes the caller has already acquired the appropriate + keyed lock for the relationship pair to ensure thread safety. + """ # Get current relationship data current_relationship = await knowledge_graph_inst.get_edge(src, tgt) @@ -781,8 +845,11 @@ async def _rebuild_single_relationship( # ) weight = sum(weights) if weights else current_relationship.get("weight", 1.0) - # Use summary if description is too long - if len(combined_description) > global_config["summary_to_max_tokens"]: + # Use summary if description has too many fragments + force_llm_summary_on_merge = global_config["force_llm_summary_on_merge"] + num_fragment = combined_description.count(GRAPH_FIELD_SEP) + 1 + + if num_fragment >= force_llm_summary_on_merge: final_description = await _handle_entity_relation_summary( f"{src}-{tgt}", combined_description, @@ -1015,28 +1082,23 @@ async def _merge_edges_then_upsert( ) for need_insert_id in [src_id, tgt_id]: - if not (await knowledge_graph_inst.has_node(need_insert_id)): - # # Discard this edge if the node does not exist - # if need_insert_id == src_id: - # logger.warning( - # f"Discard edge: {src_id} - {tgt_id} | Source node missing" - # ) - # else: - # logger.warning( - # f"Discard edge: {src_id} - {tgt_id} | Target node missing" - # ) - # return None - await knowledge_graph_inst.upsert_node( - need_insert_id, - node_data={ - "entity_id": need_insert_id, - "source_id": source_id, - "description": description, - "entity_type": "UNKNOWN", - "file_path": file_path, - "created_at": int(time.time()), - }, - ) + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [need_insert_id], namespace=namespace, enable_logging=False + ): + if not (await knowledge_graph_inst.has_node(need_insert_id)): + await knowledge_graph_inst.upsert_node( + need_insert_id, + node_data={ + "entity_id": need_insert_id, + "source_id": source_id, + "description": description, + "entity_type": "UNKNOWN", + "file_path": file_path, + "created_at": int(time.time()), + }, + ) force_llm_summary_on_merge = global_config["force_llm_summary_on_merge"] @@ -1118,8 +1180,6 @@ async def merge_nodes_and_edges( pipeline_status_lock: Lock for pipeline status llm_response_cache: LLM response cache """ - # Get lock manager from shared storage - from .kg.shared_storage import get_graph_db_lock # Collect all nodes and edges from all chunks all_nodes = defaultdict(list) @@ -1136,94 +1196,109 @@ async def merge_nodes_and_edges( all_edges[sorted_edge_key].extend(edges) # Centralized processing of all nodes and edges - entities_data = [] - relationships_data = [] + total_entities_count = len(all_nodes) + total_relations_count = len(all_edges) # Merge nodes and edges - # Use graph database lock to ensure atomic merges and updates - graph_db_lock = get_graph_db_lock(enable_logging=False) - async with graph_db_lock: - async with pipeline_status_lock: - log_message = ( - f"Merging stage {current_file_number}/{total_files}: {file_path}" - ) - logger.info(log_message) - pipeline_status["latest_message"] = log_message - pipeline_status["history_messages"].append(log_message) + log_message = f"Merging stage {current_file_number}/{total_files}: {file_path}" + logger.info(log_message) + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) - # Process and update all entities at once - for entity_name, entities in all_nodes.items(): - entity_data = await _merge_nodes_then_upsert( - entity_name, - entities, - knowledge_graph_inst, - global_config, - pipeline_status, - pipeline_status_lock, - llm_response_cache, - ) - entities_data.append(entity_data) + # Get max async tasks limit from global_config for semaphore control + graph_max_async = global_config.get("llm_model_max_async", 4) * 2 + semaphore = asyncio.Semaphore(graph_max_async) - # Process and update all relationships at once - for edge_key, edges in all_edges.items(): - edge_data = await _merge_edges_then_upsert( - edge_key[0], - edge_key[1], - edges, - knowledge_graph_inst, - global_config, - pipeline_status, - pipeline_status_lock, - llm_response_cache, - ) - if edge_data is not None: - relationships_data.append(edge_data) + # Process and update all entities and relationships in parallel + log_message = f"Processing: {total_entities_count} entities and {total_relations_count} relations (async: {graph_max_async})" + logger.info(log_message) + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) - # Update total counts - total_entities_count = len(entities_data) - total_relations_count = len(relationships_data) + async def _locked_process_entity_name(entity_name, entities): + async with semaphore: + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [entity_name], namespace=namespace, enable_logging=False + ): + entity_data = await _merge_nodes_then_upsert( + entity_name, + entities, + knowledge_graph_inst, + global_config, + pipeline_status, + pipeline_status_lock, + llm_response_cache, + ) + if entity_vdb is not None: + data_for_vdb = { + compute_mdhash_id(entity_data["entity_name"], prefix="ent-"): { + "entity_name": entity_data["entity_name"], + "entity_type": entity_data["entity_type"], + "content": f"{entity_data['entity_name']}\n{entity_data['description']}", + "source_id": entity_data["source_id"], + "file_path": entity_data.get("file_path", "unknown_source"), + } + } + await entity_vdb.upsert(data_for_vdb) + return entity_data - log_message = f"Updating {total_entities_count} entities {current_file_number}/{total_files}: {file_path}" - logger.info(log_message) - if pipeline_status is not None: - async with pipeline_status_lock: - pipeline_status["latest_message"] = log_message - pipeline_status["history_messages"].append(log_message) + async def _locked_process_edges(edge_key, edges): + async with semaphore: + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + f"{edge_key[0]}-{edge_key[1]}", + namespace=namespace, + enable_logging=False, + ): + edge_data = await _merge_edges_then_upsert( + edge_key[0], + edge_key[1], + edges, + knowledge_graph_inst, + global_config, + pipeline_status, + pipeline_status_lock, + llm_response_cache, + ) + if edge_data is None: + return None - # Update vector databases with all collected data - if entity_vdb is not None and entities_data: - data_for_vdb = { - compute_mdhash_id(dp["entity_name"], prefix="ent-"): { - "entity_name": dp["entity_name"], - "entity_type": dp["entity_type"], - "content": f"{dp['entity_name']}\n{dp['description']}", - "source_id": dp["source_id"], - "file_path": dp.get("file_path", "unknown_source"), - } - for dp in entities_data - } - await entity_vdb.upsert(data_for_vdb) + if relationships_vdb is not None: + data_for_vdb = { + compute_mdhash_id( + edge_data["src_id"] + edge_data["tgt_id"], prefix="rel-" + ): { + "src_id": edge_data["src_id"], + "tgt_id": edge_data["tgt_id"], + "keywords": edge_data["keywords"], + "content": f"{edge_data['src_id']}\t{edge_data['tgt_id']}\n{edge_data['keywords']}\n{edge_data['description']}", + "source_id": edge_data["source_id"], + "file_path": edge_data.get("file_path", "unknown_source"), + } + } + await relationships_vdb.upsert(data_for_vdb) + return edge_data - log_message = f"Updating {total_relations_count} relations {current_file_number}/{total_files}: {file_path}" - logger.info(log_message) - if pipeline_status is not None: - async with pipeline_status_lock: - pipeline_status["latest_message"] = log_message - pipeline_status["history_messages"].append(log_message) + # Create a single task queue for both entities and edges + tasks = [] - if relationships_vdb is not None and relationships_data: - data_for_vdb = { - compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): { - "src_id": dp["src_id"], - "tgt_id": dp["tgt_id"], - "keywords": dp["keywords"], - "content": f"{dp['src_id']}\t{dp['tgt_id']}\n{dp['keywords']}\n{dp['description']}", - "source_id": dp["source_id"], - "file_path": dp.get("file_path", "unknown_source"), - } - for dp in relationships_data - } - await relationships_vdb.upsert(data_for_vdb) + # Add entity processing tasks + for entity_name, entities in all_nodes.items(): + tasks.append( + asyncio.create_task(_locked_process_entity_name(entity_name, entities)) + ) + + # Add edge processing tasks + for edge_key, edges in all_edges.items(): + tasks.append(asyncio.create_task(_locked_process_edges(edge_key, edges))) + + # Execute all tasks in parallel with semaphore control + await asyncio.gather(*tasks) async def extract_entities( @@ -1433,8 +1508,8 @@ async def extract_entities( return maybe_nodes, maybe_edges # Get max async tasks limit from global_config - llm_model_max_async = global_config.get("llm_model_max_async", 4) - semaphore = asyncio.Semaphore(llm_model_max_async) + chunk_max_async = global_config.get("llm_model_max_async", 4) + semaphore = asyncio.Semaphore(chunk_max_async) async def _process_with_semaphore(chunk): async with semaphore: diff --git a/lightrag_webui/src/api/lightrag.ts b/lightrag_webui/src/api/lightrag.ts index 77601ec7..1ecde87b 100644 --- a/lightrag_webui/src/api/lightrag.ts +++ b/lightrag_webui/src/api/lightrag.ts @@ -42,12 +42,28 @@ export type LightragStatus = { vector_storage: string workspace?: string max_graph_nodes?: string + enable_rerank?: boolean + rerank_model?: string | null + rerank_binding_host?: string | null } update_status?: Record core_version?: string api_version?: string auth_mode?: 'enabled' | 'disabled' pipeline_busy: boolean + keyed_locks?: { + process_id: number + cleanup_performed: { + mp_cleaned: number + async_cleaned: number + } + current_status: { + total_mp_locks: number + pending_mp_cleanup: number + total_async_locks: number + pending_async_cleanup: number + } + } webui_title?: string webui_description?: string } diff --git a/lightrag_webui/src/components/retrieval/QuerySettings.tsx b/lightrag_webui/src/components/retrieval/QuerySettings.tsx index b21f5b11..f1514a4b 100644 --- a/lightrag_webui/src/components/retrieval/QuerySettings.tsx +++ b/lightrag_webui/src/components/retrieval/QuerySettings.tsx @@ -2,7 +2,6 @@ import { useCallback } from 'react' import { QueryMode, QueryRequest } from '@/api/lightrag' // Removed unused import for Text component import Checkbox from '@/components/ui/Checkbox' -import NumberInput from '@/components/ui/NumberInput' import Input from '@/components/ui/Input' import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/Card' import { @@ -121,11 +120,20 @@ export default function QuerySettings() {
{/* Removed sr-only label */} - handleChange('top_k', v)} + type="number" + value={querySettings.top_k ?? ''} + onChange={(e) => { + const value = e.target.value + handleChange('top_k', value === '' ? '' : parseInt(value) || 0) + }} + onBlur={(e) => { + const value = e.target.value + if (value === '' || isNaN(parseInt(value))) { + handleChange('top_k', 1) + } + }} min={1} placeholder={t('retrievePanel.querySettings.topKPlaceholder')} /> @@ -278,15 +286,23 @@ export default function QuerySettings() {
{/* Removed sr-only label */} - handleChange('history_turns', v)} + type="number" + value={querySettings.history_turns ?? ''} + onChange={(e) => { + const value = e.target.value + handleChange('history_turns', value === '' ? '' : parseInt(value) || 0) + }} + onBlur={(e) => { + const value = e.target.value + if (value === '' || isNaN(parseInt(value))) { + handleChange('history_turns', 0) + } + }} min={0} placeholder={t('retrievePanel.querySettings.historyTurnsPlaceholder')} + className="h-9" />
diff --git a/lightrag_webui/src/locales/ar.json b/lightrag_webui/src/locales/ar.json index 44a2d65d..deca4edc 100644 --- a/lightrag_webui/src/locales/ar.json +++ b/lightrag_webui/src/locales/ar.json @@ -252,12 +252,12 @@ "inputDirectory": "دليل الإدخال", "llmConfig": "تكوين نموذج اللغة الكبير", "llmBinding": "ربط نموذج اللغة الكبير", - "llmBindingHost": "مضيف ربط نموذج اللغة الكبير", + "llmBindingHost": "نقطة نهاية نموذج اللغة الكبير", "llmModel": "نموذج اللغة الكبير", "maxTokens": "أقصى عدد من الرموز", "embeddingConfig": "تكوين التضمين", "embeddingBinding": "ربط التضمين", - "embeddingBindingHost": "مضيف ربط التضمين", + "embeddingBindingHost": "نقطة نهاية التضمين", "embeddingModel": "نموذج التضمين", "storageConfig": "تكوين التخزين", "kvStorage": "تخزين المفتاح-القيمة", @@ -265,7 +265,11 @@ "graphStorage": "تخزين الرسم البياني", "vectorStorage": "تخزين المتجهات", "workspace": "مساحة العمل", - "maxGraphNodes": "الحد الأقصى لعقد الرسم البياني" + "maxGraphNodes": "الحد الأقصى لعقد الرسم البياني", + "rerankerConfig": "تكوين إعادة الترتيب", + "rerankerBindingHost": "نقطة نهاية إعادة الترتيب", + "rerankerModel": "نموذج إعادة الترتيب", + "lockStatus": "حالة القفل" }, "propertiesView": { "editProperty": "تعديل {{property}}", diff --git a/lightrag_webui/src/locales/en.json b/lightrag_webui/src/locales/en.json index 219e1fcc..4a55ab40 100644 --- a/lightrag_webui/src/locales/en.json +++ b/lightrag_webui/src/locales/en.json @@ -252,12 +252,12 @@ "inputDirectory": "Input Directory", "llmConfig": "LLM Configuration", "llmBinding": "LLM Binding", - "llmBindingHost": "LLM Binding Host", + "llmBindingHost": "LLM Endpoint", "llmModel": "LLM Model", "maxTokens": "Max Tokens", "embeddingConfig": "Embedding Configuration", "embeddingBinding": "Embedding Binding", - "embeddingBindingHost": "Embedding Binding Host", + "embeddingBindingHost": "Embedding Endpoint", "embeddingModel": "Embedding Model", "storageConfig": "Storage Configuration", "kvStorage": "KV Storage", @@ -265,7 +265,11 @@ "graphStorage": "Graph Storage", "vectorStorage": "Vector Storage", "workspace": "Workspace", - "maxGraphNodes": "Max Graph Nodes" + "maxGraphNodes": "Max Graph Nodes", + "rerankerConfig": "Reranker Configuration", + "rerankerBindingHost": "Reranker Endpoint", + "rerankerModel": "Reranker Model", + "lockStatus": "Lock Status" }, "propertiesView": { "editProperty": "Edit {{property}}", diff --git a/lightrag_webui/src/locales/fr.json b/lightrag_webui/src/locales/fr.json index 75ca6732..1395fa75 100644 --- a/lightrag_webui/src/locales/fr.json +++ b/lightrag_webui/src/locales/fr.json @@ -252,12 +252,12 @@ "inputDirectory": "Répertoire d'entrée", "llmConfig": "Configuration du modèle de langage", "llmBinding": "Liaison du modèle de langage", - "llmBindingHost": "Hôte de liaison du modèle de langage", + "llmBindingHost": "Point de terminaison LLM", "llmModel": "Modèle de langage", "maxTokens": "Nombre maximum de jetons", "embeddingConfig": "Configuration d'incorporation", "embeddingBinding": "Liaison d'incorporation", - "embeddingBindingHost": "Hôte de liaison d'incorporation", + "embeddingBindingHost": "Point de terminaison d'incorporation", "embeddingModel": "Modèle d'incorporation", "storageConfig": "Configuration de stockage", "kvStorage": "Stockage clé-valeur", @@ -265,7 +265,11 @@ "graphStorage": "Stockage du graphe", "vectorStorage": "Stockage vectoriel", "workspace": "Espace de travail", - "maxGraphNodes": "Nombre maximum de nœuds du graphe" + "maxGraphNodes": "Nombre maximum de nœuds du graphe", + "rerankerConfig": "Configuration du reclassement", + "rerankerBindingHost": "Point de terminaison de reclassement", + "rerankerModel": "Modèle de reclassement", + "lockStatus": "État des verrous" }, "propertiesView": { "editProperty": "Modifier {{property}}", diff --git a/lightrag_webui/src/locales/zh.json b/lightrag_webui/src/locales/zh.json index 4482317f..430336c3 100644 --- a/lightrag_webui/src/locales/zh.json +++ b/lightrag_webui/src/locales/zh.json @@ -252,12 +252,12 @@ "inputDirectory": "输入目录", "llmConfig": "LLM配置", "llmBinding": "LLM绑定", - "llmBindingHost": "LLM绑定主机", + "llmBindingHost": "LLM端点", "llmModel": "LLM模型", "maxTokens": "最大令牌数", "embeddingConfig": "嵌入配置", "embeddingBinding": "嵌入绑定", - "embeddingBindingHost": "嵌入绑定主机", + "embeddingBindingHost": "嵌入端点", "embeddingModel": "嵌入模型", "storageConfig": "存储配置", "kvStorage": "KV存储", @@ -265,7 +265,11 @@ "graphStorage": "图存储", "vectorStorage": "向量存储", "workspace": "工作空间", - "maxGraphNodes": "最大图节点数" + "maxGraphNodes": "最大图节点数", + "rerankerConfig": "重排序配置", + "rerankerBindingHost": "重排序端点", + "rerankerModel": "重排序模型", + "lockStatus": "锁状态" }, "propertiesView": { "editProperty": "编辑{{property}}", diff --git a/lightrag_webui/src/locales/zh_TW.json b/lightrag_webui/src/locales/zh_TW.json index cf571e62..acb85ce6 100644 --- a/lightrag_webui/src/locales/zh_TW.json +++ b/lightrag_webui/src/locales/zh_TW.json @@ -252,12 +252,12 @@ "inputDirectory": "輸入目錄", "llmConfig": "LLM 設定", "llmBinding": "LLM 綁定", - "llmBindingHost": "LLM 綁定主機", + "llmBindingHost": "LLM 端點", "llmModel": "LLM 模型", "maxTokens": "最大權杖數", "embeddingConfig": "嵌入設定", "embeddingBinding": "嵌入綁定", - "embeddingBindingHost": "嵌入綁定主機", + "embeddingBindingHost": "嵌入端點", "embeddingModel": "嵌入模型", "storageConfig": "儲存設定", "kvStorage": "KV 儲存", @@ -265,7 +265,11 @@ "graphStorage": "圖形儲存", "vectorStorage": "向量儲存", "workspace": "工作空間", - "maxGraphNodes": "最大圖形節點數" + "maxGraphNodes": "最大圖形節點數", + "rerankerConfig": "重排序設定", + "rerankerBindingHost": "重排序端點", + "rerankerModel": "重排序模型", + "lockStatus": "鎖定狀態" }, "propertiesView": { "editProperty": "編輯{{property}}",