From 3cdc98f366db6f5307ed816dd496e624d23b6b8a Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 2 Sep 2025 00:26:04 +0800 Subject: [PATCH 01/13] Improve extraction parsing with better bracket handling and delimiter fixes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Standardize Chinese/English brackets • Fix incomplete tuple delimiters • Remove duplicate delimiter fix code • Support mixed bracket formats • Enhance record parsing robustness --- lightrag/operate.py | 63 +++++++++++++++++++++++++++++++++------------ 1 file changed, 47 insertions(+), 16 deletions(-) diff --git a/lightrag/operate.py b/lightrag/operate.py index 607143a5..1d0821c3 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -822,23 +822,39 @@ async def _parse_extraction_result( maybe_nodes = defaultdict(list) maybe_edges = defaultdict(list) - # Preventive fix: when tuple_delimiter is <|>, fix LLM output instability issues - if context_base["tuple_delimiter"] == "<|>": - # 1. Convert <||> to <|> - extraction_result = extraction_result.replace("<||>", "<|>") - # 2. Convert < | > to <|> - extraction_result = extraction_result.replace("< | >", "<|>") + # Standardize Chinese brackets around record_delimiter to English brackets + record_delimiter = context_base["record_delimiter"] + bracket_pattern = f"[）)](\\s*{re.escape(record_delimiter)}\\s*)[（(]" + extraction_result = re.sub(bracket_pattern, ")\\1(", extraction_result) # Parse the extraction result using the same logic as in extract_entities records = split_string_by_multi_markers( extraction_result, [context_base["record_delimiter"], context_base["completion_delimiter"]], ) + for record in records: - record = re.search(r"\((.*)\)", record, re.DOTALL) + # Remove outer brackets + record = record.strip() + if record.startswith("(") or record.startswith("（"): + record = record[1:] + if record.endswith(")") or record.endswith("）"): + record = record[:-1] + + record = record.strip() if record is None: continue - record = record.group(1) + + if context_base["tuple_delimiter"] == "<|>": + # fix entity<| with entity<|> + record = re.sub(r"^entity<\|(?!>)", r"entity<|>", record) + # fix relationship<| with relationship<|> + record = re.sub(r"^relationship<\|(?!>)", r"relationship<|>", record) + # fix <||> with <|> + record = record.replace("<||>", "<|>") + # fix < | > with <|> + record = record.replace("< | >", "<|>") + record_attributes = split_string_by_multi_markers( record, [context_base["tuple_delimiter"]] ) @@ -1736,12 +1752,10 @@ async def extract_entities( maybe_nodes = defaultdict(list) maybe_edges = defaultdict(list) - # Preventive fix: when tuple_delimiter is <|>, fix LLM output instability issues - if context_base["tuple_delimiter"] == "<|>": - # 1. Convert <||> to <|> - result = result.replace("<||>", "<|>") - # 2. Convert < | > to <|> - result = result.replace("< | >", "<|>") + # Standardize Chinese brackets around record_delimiter to English brackets + record_delimiter = context_base["record_delimiter"] + bracket_pattern = f"[）)](\\s*{re.escape(record_delimiter)}\\s*)[（(]" + result = re.sub(bracket_pattern, ")\\1(", result) records = split_string_by_multi_markers( result, @@ -1749,10 +1763,27 @@ async def extract_entities( ) for record in records: - record = re.search(r"\((.*)\)", record, re.DOTALL) + # Remove outer brackets (support English and Chinese brackets) + record = record.strip() + if record.startswith("(") or record.startswith("（"): + record = record[1:] + if record.endswith(")") or record.endswith("）"): + record = record[:-1] + + record = record.strip() if record is None: continue - record = record.group(1) + + if context_base["tuple_delimiter"] == "<|>": + # fix entity<| with entity<|> + record = re.sub(r"^entity<\|(?!>)", r"entity<|>", record) + # fix relationship<| with relationship<|> + record = re.sub(r"^relationship<\|(?!>)", r"relationship<|>", record) + # fix <||> with <|> + record = record.replace("<||>", "<|>") + # fix < | > with <|> + record = record.replace("< | >", "<|>") + record_attributes = split_string_by_multi_markers( record, [context_base["tuple_delimiter"]] ) From 3f8a9abe7ed19f11008f78ede3d367386d19a4ee Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 2 Sep 2025 01:22:29 +0800 Subject: [PATCH 02/13] Refactor extraction result processing to reduce code duplication MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Extract shared processing logic • Add delimiter pattern fixes • Improve bracket standardization --- lightrag/operate.py | 229 +++++++++++++++++++------------------------- 1 file changed, 101 insertions(+), 128 deletions(-) diff --git a/lightrag/operate.py b/lightrag/operate.py index 1d0821c3..ccf2550d 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -793,6 +793,87 @@ async def _get_cached_extraction_results( return sorted_cached_results +async def _process_extraction_result( + result: str, + chunk_key: str, + file_path: str = "unknown_source", + tuple_delimiter: str = "<|>", + record_delimiter: str = "##", + completion_delimiter: str = "<|COMPLETE|>", +) -> tuple[dict, dict]: + """Process a single extraction result (either initial or gleaning) + Args: + result (str): The extraction result to process + chunk_key (str): The chunk key for source tracking + file_path (str): The file path for citation + tuple_delimiter (str): Delimiter for tuple fields + record_delimiter (str): Delimiter for records + completion_delimiter (str): Delimiter for completion + Returns: + tuple: (nodes_dict, edges_dict) containing the extracted entities and relationships + """ + maybe_nodes = defaultdict(list) + maybe_edges = defaultdict(list) + + # Standardize Chinese brackets around record_delimiter to English brackets + bracket_pattern = f"[）)](\\s*{re.escape(record_delimiter)}\\s*)[（(]" + result = re.sub(bracket_pattern, ")\\1(", result) + + records = split_string_by_multi_markers( + result, + [record_delimiter, completion_delimiter], + ) + + for record in records: + # Remove outer brackets (support English and Chinese brackets) + record = record.strip() + if record.startswith("(") or record.startswith("（"): + record = record[1:] + if record.endswith(")") or record.endswith("）"): + record = record[:-1] + + record = record.strip() + if record is None: + continue + + if tuple_delimiter == "<|>": + # fix entity<| with entity<|> + record = re.sub(r"^entity<\|(?!>)", r"entity<|>", record) + # fix relationship<| with relationship<|> + record = re.sub(r"^relationship<\|(?!>)", r"relationship<|>", record) + # fix <||> with <|> + record = record.replace("<||>", "<|>") + # fix < | > with <|> + record = record.replace("< | >", "<|>") + # fix <<|>> with <|> + record = record.replace("<<|>>", "<|>") + # fix <|>> with <|> + record = record.replace("<|>>", "<|>") + # fix <<|> with <|> + record = record.replace("<<|>", "<|>") + + record_attributes = split_string_by_multi_markers(record, [tuple_delimiter]) + + # Try to parse as entity + entity_data = await _handle_single_entity_extraction( + record_attributes, chunk_key, file_path + ) + if entity_data is not None: + maybe_nodes[entity_data["entity_name"]].append(entity_data) + continue + + # Try to parse as relationship + relationship_data = await _handle_single_relationship_extraction( + record_attributes, chunk_key, file_path + ) + if relationship_data is not None: + maybe_edges[ + (relationship_data["src_id"], relationship_data["tgt_id"]) + ].append(relationship_data) + + return dict(maybe_nodes), dict(maybe_edges) + + async def _parse_extraction_result( text_chunks_storage: BaseKVStorage, extraction_result: str, chunk_id: str ) -> tuple[dict, dict]: @@ -814,69 +895,16 @@ async def _parse_extraction_result( if chunk_data else "unknown_source" ) - context_base = dict( + + # Call the shared processing function + return await _process_extraction_result( + extraction_result, + chunk_id, + file_path, tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"], completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], ) - maybe_nodes = defaultdict(list) - maybe_edges = defaultdict(list) - - # Standardize Chinese brackets around record_delimiter to English brackets - record_delimiter = context_base["record_delimiter"] - bracket_pattern = f"[）)](\\s*{re.escape(record_delimiter)}\\s*)[（(]" - extraction_result = re.sub(bracket_pattern, ")\\1(", extraction_result) - - # Parse the extraction result using the same logic as in extract_entities - records = split_string_by_multi_markers( - extraction_result, - [context_base["record_delimiter"], context_base["completion_delimiter"]], - ) - - for record in records: - # Remove outer brackets - record = record.strip() - if record.startswith("(") or record.startswith("（"): - record = record[1:] - if record.endswith(")") or record.endswith("）"): - record = record[:-1] - - record = record.strip() - if record is None: - continue - - if context_base["tuple_delimiter"] == "<|>": - # fix entity<| with entity<|> - record = re.sub(r"^entity<\|(?!>)", r"entity<|>", record) - # fix relationship<| with relationship<|> - record = re.sub(r"^relationship<\|(?!>)", r"relationship<|>", record) - # fix <||> with <|> - record = record.replace("<||>", "<|>") - # fix < | > with <|> - record = record.replace("< | >", "<|>") - - record_attributes = split_string_by_multi_markers( - record, [context_base["tuple_delimiter"]] - ) - - # Try to parse as entity - entity_data = await _handle_single_entity_extraction( - record_attributes, chunk_id, file_path - ) - if entity_data is not None: - maybe_nodes[entity_data["entity_name"]].append(entity_data) - continue - - # Try to parse as relationship - relationship_data = await _handle_single_relationship_extraction( - record_attributes, chunk_id, file_path - ) - if relationship_data is not None: - maybe_edges[ - (relationship_data["src_id"], relationship_data["tgt_id"]) - ].append(relationship_data) - - return dict(maybe_nodes), dict(maybe_edges) async def _rebuild_single_entity( @@ -1738,73 +1766,6 @@ async def extract_entities( processed_chunks = 0 total_chunks = len(ordered_chunks) - async def _process_extraction_result( - result: str, chunk_key: str, file_path: str = "unknown_source" - ): - """Process a single extraction result (either initial or gleaning) - Args: - result (str): The extraction result to process - chunk_key (str): The chunk key for source tracking - file_path (str): The file path for citation - Returns: - tuple: (nodes_dict, edges_dict) containing the extracted entities and relationships - """ - maybe_nodes = defaultdict(list) - maybe_edges = defaultdict(list) - - # Standardize Chinese brackets around record_delimiter to English brackets - record_delimiter = context_base["record_delimiter"] - bracket_pattern = f"[）)](\\s*{re.escape(record_delimiter)}\\s*)[（(]" - result = re.sub(bracket_pattern, ")\\1(", result) - - records = split_string_by_multi_markers( - result, - [context_base["record_delimiter"], context_base["completion_delimiter"]], - ) - - for record in records: - # Remove outer brackets (support English and Chinese brackets) - record = record.strip() - if record.startswith("(") or record.startswith("（"): - record = record[1:] - if record.endswith(")") or record.endswith("）"): - record = record[:-1] - - record = record.strip() - if record is None: - continue - - if context_base["tuple_delimiter"] == "<|>": - # fix entity<| with entity<|> - record = re.sub(r"^entity<\|(?!>)", r"entity<|>", record) - # fix relationship<| with relationship<|> - record = re.sub(r"^relationship<\|(?!>)", r"relationship<|>", record) - # fix <||> with <|> - record = record.replace("<||>", "<|>") - # fix < | > with <|> - record = record.replace("< | >", "<|>") - - record_attributes = split_string_by_multi_markers( - record, [context_base["tuple_delimiter"]] - ) - - if_entities = await _handle_single_entity_extraction( - record_attributes, chunk_key, file_path - ) - if if_entities is not None: - maybe_nodes[if_entities["entity_name"]].append(if_entities) - continue - - if_relation = await _handle_single_relationship_extraction( - record_attributes, chunk_key, file_path - ) - if if_relation is not None: - maybe_edges[(if_relation["src_id"], if_relation["tgt_id"])].append( - if_relation - ) - - return maybe_nodes, maybe_edges - async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]): """Process a single chunk Args: @@ -1842,7 +1803,12 @@ async def extract_entities( # Process initial extraction with file path maybe_nodes, maybe_edges = await _process_extraction_result( - final_result, chunk_key, file_path + final_result, + chunk_key, + file_path, + tuple_delimiter=context_base["tuple_delimiter"], + record_delimiter=context_base["record_delimiter"], + completion_delimiter=context_base["completion_delimiter"], ) # Process additional gleaning results @@ -1861,7 +1827,12 @@ async def extract_entities( # Process gleaning result separately with file path glean_nodes, glean_edges = await _process_extraction_result( - glean_result, chunk_key, file_path + glean_result, + chunk_key, + file_path, + tuple_delimiter=context_base["tuple_delimiter"], + record_delimiter=context_base["record_delimiter"], + completion_delimiter=context_base["completion_delimiter"], ) # Merge results - only add entities and edges with new names @@ -1869,11 +1840,13 @@ async def extract_entities( if ( entity_name not in maybe_nodes ): # Only accetp entities with new name in gleaning stage + maybe_nodes[entity_name] = [] # Explicitly create the list maybe_nodes[entity_name].extend(entities) for edge_key, edges in glean_edges.items(): if ( edge_key not in maybe_edges ): # Only accetp edges with new name in gleaning stage + maybe_edges[edge_key] = [] # Explicitly create the list maybe_edges[edge_key].extend(edges) if now_glean_index == entity_extract_max_gleaning - 1: From 29f0ecc88ca31c2aa18d3a06bcc3a33df5c947c4 Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 2 Sep 2025 02:14:14 +0800 Subject: [PATCH 03/13] Refactor entity extraction prompts and remove completion delimiter MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Update prompt structure and wording • Remove deprecated completion delimiter • Add quality guidelines section • Improve instruction clarity • Enhance continue extraction prompt --- lightrag/prompt.py | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/lightrag/prompt.py b/lightrag/prompt.py index 0bb22aa7..c71debe8 100644 --- a/lightrag/prompt.py +++ b/lightrag/prompt.py @@ -6,15 +6,16 @@ PROMPTS: dict[str, Any] = {} PROMPTS["DEFAULT_TUPLE_DELIMITER"] = "<|>" PROMPTS["DEFAULT_RECORD_DELIMITER"] = "##" + +# TODO: Deprecated, reserved for compatible with legacy LLM cache PROMPTS["DEFAULT_COMPLETION_DELIMITER"] = "<|COMPLETE|>" PROMPTS["DEFAULT_USER_PROMPT"] = "n/a" -PROMPTS["entity_extraction"] = """---Goal--- +PROMPTS["entity_extraction"] = """---Task--- Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities. -Use {language} as output language. ----Steps--- +---Instructions--- 1. Recognizing definitively conceptualized entities in text. For each identified entity, extract the following information: - entity_name: Name of the entity, use same language as input text. If English, capitalized the name - entity_type: One of the following types: [{entity_types}]. If the entity doesn't clearly fit any category, classify it as "Other". @@ -33,11 +34,9 @@ For each pair of related entities, extract the following information: 4. Format each relationship as: ("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) -5. Use `{tuple_delimiter}` as field delimiter. Use `{record_delimiter}` as the list delimiter. Ensure no spaces are added around the delimiters. +5. Use `{tuple_delimiter}` as field delimiter. Use `{record_delimiter}` as the entity or relation list delimiter. -6. When finished, output `{completion_delimiter}` - -7. Return identified entities and relationships in {language}. +6. Return identified entities and relationships in {language}. ---Quality Guidelines--- - Only extract entities that are clearly defined and meaningful in the context @@ -85,7 +84,6 @@ Output: (relationship{tuple_delimiter}Taylor{tuple_delimiter}Jordan{tuple_delimiter}conflict resolution, mutual respect{tuple_delimiter}Taylor and Jordan interact directly regarding the device, leading to a moment of mutual respect and an uneasy truce.){record_delimiter} (relationship{tuple_delimiter}Jordan{tuple_delimiter}Cruz{tuple_delimiter}ideological conflict, rebellion{tuple_delimiter}Jordan's commitment to discovery is in rebellion against Cruz's vision of control and order.){record_delimiter} (relationship{tuple_delimiter}Taylor{tuple_delimiter}The Device{tuple_delimiter}reverence, technological significance{tuple_delimiter}Taylor shows reverence towards the device, indicating its importance and potential impact.){record_delimiter} -{completion_delimiter} """, """------Example 2------ @@ -115,7 +113,6 @@ Output: (relationship{tuple_delimiter}Nexon Technologies{tuple_delimiter}Global Tech Index{tuple_delimiter}company impact, index movement{tuple_delimiter}Nexon Technologies' stock decline contributed to the overall drop in the Global Tech Index.){record_delimiter} (relationship{tuple_delimiter}Gold Futures{tuple_delimiter}Market Selloff{tuple_delimiter}market reaction, safe-haven investment{tuple_delimiter}Gold prices rose as investors sought safe-haven assets during the market selloff.){record_delimiter} (relationship{tuple_delimiter}Federal Reserve Policy Announcement{tuple_delimiter}Market Selloff{tuple_delimiter}interest rate impact, financial regulation{tuple_delimiter}Speculation over Federal Reserve policy changes contributed to market volatility and investor selloff.){record_delimiter} -{completion_delimiter} """, """------Example 3------ @@ -137,7 +134,6 @@ Output: (relationship{tuple_delimiter}Noah Carter{tuple_delimiter}100m Sprint Record{tuple_delimiter}athlete achievement, record-breaking{tuple_delimiter}Noah Carter set a new 100m sprint record at the championship.){record_delimiter} (relationship{tuple_delimiter}Noah Carter{tuple_delimiter}Carbon-Fiber Spikes{tuple_delimiter}athletic equipment, performance boost{tuple_delimiter}Noah Carter used carbon-fiber spikes to enhance performance during the race.){record_delimiter} (relationship{tuple_delimiter}Noah Carter{tuple_delimiter}World Athletics Championship{tuple_delimiter}athlete participation, competition{tuple_delimiter}Noah Carter is competing at the World Athletics Championship.){record_delimiter} -{completion_delimiter} """, """------Example 4------ @@ -146,7 +142,6 @@ Entity_types: [organization,person,equiment,product,technology,location,event,ca Text: ``` 在北京举行的人工智能大会上，腾讯公司的首席技术官张伟发布了最新的大语言模型"腾讯智言"，该模型在自然语言处理方面取得了重大突破。 - ``` Output: @@ -160,7 +155,6 @@ Output: (relationship{tuple_delimiter}张伟{tuple_delimiter}腾讯公司{tuple_delimiter}雇佣关系, 高管职位{tuple_delimiter}张伟担任腾讯公司的首席技术官。){record_delimiter} (relationship{tuple_delimiter}张伟{tuple_delimiter}腾讯智言{tuple_delimiter}产品发布, 技术展示{tuple_delimiter}张伟在大会上发布了腾讯智言大语言模型。){record_delimiter} (relationship{tuple_delimiter}腾讯智言{tuple_delimiter}自然语言处理技术{tuple_delimiter}技术应用, 突破创新{tuple_delimiter}腾讯智言在自然语言处理技术方面取得了重大突破。){record_delimiter} -{completion_delimiter} """, ] @@ -188,9 +182,10 @@ Description List: Output:""" PROMPTS["entity_continue_extraction"] = """ -MANY entities and relationships were missed in the last extraction. Please find only the missing entities and relationships from previous text. Do not include entities and relations that have been previously extracted. :\n +---Task--- +MANY entities and relationships were missed in the last extraction. Please find only the missing entities and relationships from previous text. ----Remember Steps--- +---Instructions--- 1. Recognizing definitively conceptualized entities in text. For each identified entity, extract the following information: - entity_name: Name of the entity, use same language as input text. If English, capitalized the name - entity_type: One of the following types: [{entity_types}]. If the entity doesn't clearly fit any category, classify it as "Other". @@ -209,11 +204,16 @@ For each pair of related entities, extract the following information: 4. Format each relationship as: ("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) -5. Use `{tuple_delimiter}` as field delimiter. Use `{record_delimiter}` as the list delimiter. Ensure no spaces are added around the delimiters. +5. Use `{tuple_delimiter}` as field delimiter. Use `{record_delimiter}` as the entity or relation list delimiter. -6. When finished, output `{completion_delimiter}` +6. Return identified entities and relationships in {language}. -7. Return identified entities and relationships in {language}. +---Quality Guidelines--- +- Only extract entities that are clearly defined and meaningful in the context +- Do not include entities and relations that have been previously extracted +- Avoid over-interpretation; stick to what is explicitly stated in the text +- Include specific numerical data in entity name when relevant +- Ensure entity names are consistent throughout the extraction ---Output--- Output: From 5b2deccbef2fde022d567862500d5689db8bd475 Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 2 Sep 2025 02:51:41 +0800 Subject: [PATCH 04/13] Improve text normalization and add entity type capitalization - Capitalize entity types with .title() - Add non-breaking space handling - Add narrow non-breaking space regex --- lightrag/operate.py | 3 +++ lightrag/utils.py | 38 +++++++++++++++++++++++--------------- 2 files changed, 26 insertions(+), 15 deletions(-) diff --git a/lightrag/operate.py b/lightrag/operate.py index ccf2550d..6c9a5538 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -347,6 +347,9 @@ async def _handle_single_entity_extraction( ) return None + # Captitalize first letter of entity_type + entity_type = entity_type.title() + # Process entity description with same cleaning pipeline entity_description = sanitize_and_normalize_extracted_text(record_attributes[3]) diff --git a/lightrag/utils.py b/lightrag/utils.py index c215d52b..f2d56282 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -1759,17 +1759,22 @@ def sanitize_and_normalize_extracted_text( def normalize_extracted_info(name: str, remove_inner_quotes=False) -> str: """Normalize entity/relation names and description with the following rules: - 1. Clean HTML tags (paragraph and line break tags) - 2. Convert Chinese symbols to English symbols - 3. Remove spaces between Chinese characters - 4. Remove spaces between Chinese characters and English letters/numbers - 5. Preserve spaces within English text and numbers - 6. Replace Chinese parentheses with English parentheses - 7. Replace Chinese dash with English dash - 8. Remove English quotation marks from the beginning and end of the text - 9. Remove English quotation marks in and around chinese - 10. Remove Chinese quotation marks - 11. Filter out short numeric-only text (length < 3 and only digits/dots) + - Clean HTML tags (paragraph and line break tags) + - Convert Chinese symbols to English symbols + - Remove spaces between Chinese characters + - Remove spaces between Chinese characters and English letters/numbers + - Preserve spaces within English text and numbers + - Replace Chinese parentheses with English parentheses + - Replace Chinese dash with English dash + - Remove English quotation marks from the beginning and end of the text + - Remove English quotation marks in and around chinese + - Remove Chinese quotation marks + - Filter out short numeric-only text (length < 3 and only digits/dots) + - remove_inner_quotes = True + remove Chinese quotes + remove English queotes in and around chinese + Convert non-breaking spaces to regular spaces + Convert narrow non-breaking spaces after non-digits to regular spaces Args: name: Entity name to normalize @@ -1778,11 +1783,10 @@ def normalize_extracted_info(name: str, remove_inner_quotes=False) -> str: Returns: Normalized entity name """ - # 1. Clean HTML tags - remove paragraph and line break tags + # Clean HTML tags - remove paragraph and line break tags name = re.sub(r"||

", "", name, flags=re.IGNORECASE) name = re.sub(r"||
", "", name, flags=re.IGNORECASE) - # 2. Convert Chinese symbols to English symbols # Chinese full-width letters to half-width (A-Z, a-z) name = name.translate( str.maketrans( @@ -1849,11 +1853,15 @@ def normalize_extracted_info(name: str, remove_inner_quotes=False) -> str: name = inner_content if remove_inner_quotes: - # remove Chinese quotes + # Remove Chinese quotes name = name.replace("“", "").replace("”", "").replace("‘", "").replace("’", "") - # remove English queotes in and around chinese + # Remove English queotes in and around chinese name = re.sub(r"['\"]+(?=[\u4e00-\u9fa5])", "", name) name = re.sub(r"(?<=[\u4e00-\u9fa5])['\"]+", "", name) + # Convert non-breaking space to regular space + name = name.replace("\u00a0", " ") + # Convert narrow non-breaking space to regular space when after non-digits + name = re.sub(r"(?<=[^\d])\u202F", " ", name) # Remove spaces from the beginning and end of the text name = name.strip() From 4db43c43f3de1cb4d10df94ba531ea591128bf0c Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 2 Sep 2025 03:01:59 +0800 Subject: [PATCH 05/13] Add product and other entity type translations - Add "product" translations - Add "other" translations - Update 5 locale files - Extend entity type coverage --- lightrag_webui/src/locales/ar.json | 4 +++- lightrag_webui/src/locales/en.json | 4 +++- lightrag_webui/src/locales/fr.json | 4 +++- lightrag_webui/src/locales/zh.json | 4 +++- lightrag_webui/src/locales/zh_TW.json | 4 +++- 5 files changed, 15 insertions(+), 5 deletions(-) diff --git a/lightrag_webui/src/locales/ar.json b/lightrag_webui/src/locales/ar.json index 41e98607..db50ab37 100644 --- a/lightrag_webui/src/locales/ar.json +++ b/lightrag_webui/src/locales/ar.json @@ -187,7 +187,9 @@ "unknown": "غير معروف", "object": "مصنوع", "group": "مجموعة", - "technology": "العلوم" + "technology": "العلوم", + "product": "منتج", + "other": "أخرى" }, "sideBar": { "settings": { diff --git a/lightrag_webui/src/locales/en.json b/lightrag_webui/src/locales/en.json index 58a57336..19006705 100644 --- a/lightrag_webui/src/locales/en.json +++ b/lightrag_webui/src/locales/en.json @@ -187,7 +187,9 @@ "unknown": "Unknown", "object": "Object", "group": "Group", - "technology": "Technology" + "technology": "Technology", + "product": "Product", + "other": "Other" }, "sideBar": { "settings": { diff --git a/lightrag_webui/src/locales/fr.json b/lightrag_webui/src/locales/fr.json index 59b8279b..f7327c70 100644 --- a/lightrag_webui/src/locales/fr.json +++ b/lightrag_webui/src/locales/fr.json @@ -187,7 +187,9 @@ "unknown": "Inconnu", "object": "Objet", "group": "Groupe", - "technology": "Technologie" + "technology": "Technologie", + "product": "Produit", + "other": "Autre" }, "sideBar": { "settings": { diff --git a/lightrag_webui/src/locales/zh.json b/lightrag_webui/src/locales/zh.json index abad2f63..a4d33509 100644 --- a/lightrag_webui/src/locales/zh.json +++ b/lightrag_webui/src/locales/zh.json @@ -187,7 +187,9 @@ "unknown": "未知", "object": "物品", "group": "群组", - "technology": "技术" + "technology": "技术", + "product": "产品", + "other": "其他" }, "sideBar": { "settings": { diff --git a/lightrag_webui/src/locales/zh_TW.json b/lightrag_webui/src/locales/zh_TW.json index 1f691ce6..b147b2cc 100644 --- a/lightrag_webui/src/locales/zh_TW.json +++ b/lightrag_webui/src/locales/zh_TW.json @@ -187,7 +187,9 @@ "unknown": "未知", "object": "物品", "group": "群組", - "technology": "技術" + "technology": "技術", + "product": "產品", + "other": "其他" }, "sideBar": { "settings": { From 4e37ff5f2f9584a3970f118f13f592c56b06c116 Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 2 Sep 2025 03:02:39 +0800 Subject: [PATCH 06/13] Bump API verstion to 0212 --- lightrag/api/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lightrag/api/__init__.py b/lightrag/api/__init__.py index d33fbb77..da0ff6dd 100644 --- a/lightrag/api/__init__.py +++ b/lightrag/api/__init__.py @@ -1 +1 @@ -__api_version__ = "0211" +__api_version__ = "0212" From 476b64c9d483081647bac59f336b37e078b04e16 Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 2 Sep 2025 03:03:19 +0800 Subject: [PATCH 07/13] Update webui assets --- .../assets/{index-CIdJpUuC.js => index-CjbmYv_Z.js} | 10 +++++----- lightrag/api/webui/index.html | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) rename lightrag/api/webui/assets/{index-CIdJpUuC.js => index-CjbmYv_Z.js} (91%) diff --git a/lightrag/api/webui/assets/index-CIdJpUuC.js b/lightrag/api/webui/assets/index-CjbmYv_Z.js similarity index 91% rename from lightrag/api/webui/assets/index-CIdJpUuC.js rename to lightrag/api/webui/assets/index-CjbmYv_Z.js index f8d9bf07..d31d07eb 100644 --- a/lightrag/api/webui/assets/index-CIdJpUuC.js +++ b/lightrag/api/webui/assets/index-CjbmYv_Z.js @@ -43,7 +43,7 @@ For more information, see https://radix-ui.com/primitives/docs/components/alert- {{error}}`,scanFailed:`Failed to scan documents {{error}}`,scanProgressFailed:`Failed to get scan progress {{error}}`},fileNameLabel:"File Name",showButton:"Show",hideButton:"Hide",showFileNameTooltip:"Show file name",hideFileNameTooltip:"Hide file name"},pipelineStatus:{title:"Pipeline Status",busy:"Pipeline Busy",requestPending:"Request Pending",jobName:"Job Name",startTime:"Start Time",progress:"Progress",unit:"batch",latestMessage:"Latest Message",historyMessages:"History Messages",errors:{fetchFailed:`Failed to get pipeline status -{{error}}`}}},Bp={dataIsTruncated:"Graph data is truncated to Max Nodes",statusDialog:{title:"LightRAG Server Settings",description:"View current system status and connection information"},legend:"Legend",nodeTypes:{person:"Person",category:"Category",geo:"Geographic",location:"Location",organization:"Organization",event:"Event",equipment:"Equipment",weapon:"Weapon",animal:"Animal",unknown:"Unknown",object:"Object",group:"Group",technology:"Technology"},sideBar:{settings:{settings:"Settings",healthCheck:"Health Check",showPropertyPanel:"Show Property Panel",showSearchBar:"Show Search Bar",showNodeLabel:"Show Node Label",nodeDraggable:"Node Draggable",showEdgeLabel:"Show Edge Label",hideUnselectedEdges:"Hide Unselected Edges",edgeEvents:"Edge Events",maxQueryDepth:"Max Query Depth",maxNodes:"Max Nodes",maxLayoutIterations:"Max Layout Iterations",resetToDefault:"Reset to default",edgeSizeRange:"Edge Size Range",depth:"D",max:"Max",degree:"Degree",apiKey:"API Key",enterYourAPIkey:"Enter your API key",save:"Save",refreshLayout:"Refresh Layout"},zoomControl:{zoomIn:"Zoom In",zoomOut:"Zoom Out",resetZoom:"Reset Zoom",rotateCamera:"Clockwise Rotate",rotateCameraCounterClockwise:"Counter-Clockwise Rotate"},layoutsControl:{startAnimation:"Continue layout animation",stopAnimation:"Stop layout animation",layoutGraph:"Layout Graph",layouts:{Circular:"Circular",Circlepack:"Circlepack",Random:"Random",Noverlaps:"Noverlaps","Force Directed":"Force Directed","Force Atlas":"Force Atlas"}},fullScreenControl:{fullScreen:"Full Screen",windowed:"Windowed"},legendControl:{toggleLegend:"Toggle Legend"}},statusIndicator:{connected:"Connected",disconnected:"Disconnected"},statusCard:{unavailable:"Status information unavailable",serverInfo:"Server Info",workingDirectory:"Working Directory",inputDirectory:"Input Directory",maxParallelInsert:"Concurrent Doc Processing",summarySettings:"Summary Settings",llmConfig:"LLM Configuration",llmBinding:"LLM Binding",llmBindingHost:"LLM Endpoint",llmModel:"LLM Model",embeddingConfig:"Embedding Configuration",embeddingBinding:"Embedding Binding",embeddingBindingHost:"Embedding Endpoint",embeddingModel:"Embedding Model",storageConfig:"Storage Configuration",kvStorage:"KV Storage",docStatusStorage:"Doc Status Storage",graphStorage:"Graph Storage",vectorStorage:"Vector Storage",workspace:"Workspace",maxGraphNodes:"Max Graph Nodes",rerankerConfig:"Reranker Configuration",rerankerBindingHost:"Reranker Endpoint",rerankerModel:"Reranker Model",lockStatus:"Lock Status",threshold:"Threshold"},propertiesView:{editProperty:"Edit {{property}}",editPropertyDescription:"Edit the property value in the text area below.",errors:{duplicateName:"Node name already exists",updateFailed:"Failed to update node",tryAgainLater:"Please try again later"},success:{entityUpdated:"Node updated successfully",relationUpdated:"Relation updated successfully"},node:{title:"Node",id:"ID",labels:"Labels",degree:"Degree",properties:"Properties",relationships:"Relations(within subgraph)",expandNode:"Expand Node",pruneNode:"Prune Node",deleteAllNodesError:"Refuse to delete all nodes in the graph",nodesRemoved:"{{count}} nodes removed, including orphan nodes",noNewNodes:"No expandable nodes found",propertyNames:{description:"Description",entity_id:"Name",entity_type:"Type",source_id:"SrcID",Neighbour:"Neigh",file_path:"Source",keywords:"Keys",weight:"Weight"}},edge:{title:"Relationship",id:"ID",type:"Type",source:"Source",target:"Target",properties:"Properties"}},search:{placeholder:"Search nodes...",message:"And {count} others"},graphLabels:{selectTooltip:"Select query label",noLabels:"No labels found",label:"Label",placeholder:"Search labels...",andOthers:"And {count} others",refreshTooltip:"Reload data(After file added)"},emptyGraph:"Empty(Try Reload Again)"},Gp={chatMessage:{copyTooltip:"Copy to clipboard",copyError:"Failed to copy text to clipboard"},retrieval:{startPrompt:"Start a retrieval by typing your query below",clear:"Clear",send:"Send",placeholder:"Enter your query (Support prefix: /)",error:"Error: Failed to get response",queryModeError:"Only supports the following query modes: {{modes}}",queryModePrefixInvalid:"Invalid query mode prefix. Use: / [space] your query"},querySettings:{parametersTitle:"Parameters",parametersDescription:"Configure your query parameters",queryMode:"Query Mode",queryModeTooltip:`Select the retrieval strategy: +{{error}}`}}},Bp={dataIsTruncated:"Graph data is truncated to Max Nodes",statusDialog:{title:"LightRAG Server Settings",description:"View current system status and connection information"},legend:"Legend",nodeTypes:{person:"Person",category:"Category",geo:"Geographic",location:"Location",organization:"Organization",event:"Event",equipment:"Equipment",weapon:"Weapon",animal:"Animal",unknown:"Unknown",object:"Object",group:"Group",technology:"Technology",product:"Product",other:"Other"},sideBar:{settings:{settings:"Settings",healthCheck:"Health Check",showPropertyPanel:"Show Property Panel",showSearchBar:"Show Search Bar",showNodeLabel:"Show Node Label",nodeDraggable:"Node Draggable",showEdgeLabel:"Show Edge Label",hideUnselectedEdges:"Hide Unselected Edges",edgeEvents:"Edge Events",maxQueryDepth:"Max Query Depth",maxNodes:"Max Nodes",maxLayoutIterations:"Max Layout Iterations",resetToDefault:"Reset to default",edgeSizeRange:"Edge Size Range",depth:"D",max:"Max",degree:"Degree",apiKey:"API Key",enterYourAPIkey:"Enter your API key",save:"Save",refreshLayout:"Refresh Layout"},zoomControl:{zoomIn:"Zoom In",zoomOut:"Zoom Out",resetZoom:"Reset Zoom",rotateCamera:"Clockwise Rotate",rotateCameraCounterClockwise:"Counter-Clockwise Rotate"},layoutsControl:{startAnimation:"Continue layout animation",stopAnimation:"Stop layout animation",layoutGraph:"Layout Graph",layouts:{Circular:"Circular",Circlepack:"Circlepack",Random:"Random",Noverlaps:"Noverlaps","Force Directed":"Force Directed","Force Atlas":"Force Atlas"}},fullScreenControl:{fullScreen:"Full Screen",windowed:"Windowed"},legendControl:{toggleLegend:"Toggle Legend"}},statusIndicator:{connected:"Connected",disconnected:"Disconnected"},statusCard:{unavailable:"Status information unavailable",serverInfo:"Server Info",workingDirectory:"Working Directory",inputDirectory:"Input Directory",maxParallelInsert:"Concurrent Doc Processing",summarySettings:"Summary Settings",llmConfig:"LLM Configuration",llmBinding:"LLM Binding",llmBindingHost:"LLM Endpoint",llmModel:"LLM Model",embeddingConfig:"Embedding Configuration",embeddingBinding:"Embedding Binding",embeddingBindingHost:"Embedding Endpoint",embeddingModel:"Embedding Model",storageConfig:"Storage Configuration",kvStorage:"KV Storage",docStatusStorage:"Doc Status Storage",graphStorage:"Graph Storage",vectorStorage:"Vector Storage",workspace:"Workspace",maxGraphNodes:"Max Graph Nodes",rerankerConfig:"Reranker Configuration",rerankerBindingHost:"Reranker Endpoint",rerankerModel:"Reranker Model",lockStatus:"Lock Status",threshold:"Threshold"},propertiesView:{editProperty:"Edit {{property}}",editPropertyDescription:"Edit the property value in the text area below.",errors:{duplicateName:"Node name already exists",updateFailed:"Failed to update node",tryAgainLater:"Please try again later"},success:{entityUpdated:"Node updated successfully",relationUpdated:"Relation updated successfully"},node:{title:"Node",id:"ID",labels:"Labels",degree:"Degree",properties:"Properties",relationships:"Relations(within subgraph)",expandNode:"Expand Node",pruneNode:"Prune Node",deleteAllNodesError:"Refuse to delete all nodes in the graph",nodesRemoved:"{{count}} nodes removed, including orphan nodes",noNewNodes:"No expandable nodes found",propertyNames:{description:"Description",entity_id:"Name",entity_type:"Type",source_id:"SrcID",Neighbour:"Neigh",file_path:"Source",keywords:"Keys",weight:"Weight"}},edge:{title:"Relationship",id:"ID",type:"Type",source:"Source",target:"Target",properties:"Properties"}},search:{placeholder:"Search nodes...",message:"And {count} others"},graphLabels:{selectTooltip:"Select query label",noLabels:"No labels found",label:"Label",placeholder:"Search labels...",andOthers:"And {count} others",refreshTooltip:"Reload data(After file added)"},emptyGraph:"Empty(Try Reload Again)"},Gp={chatMessage:{copyTooltip:"Copy to clipboard",copyError:"Failed to copy text to clipboard"},retrieval:{startPrompt:"Start a retrieval by typing your query below",clear:"Clear",send:"Send",placeholder:"Enter your query (Support prefix: /)",error:"Error: Failed to get response",queryModeError:"Only supports the following query modes: {{modes}}",queryModePrefixInvalid:"Invalid query mode prefix. Use: / [space] your query"},querySettings:{parametersTitle:"Parameters",parametersDescription:"Configure your query parameters",queryMode:"Query Mode",queryModeTooltip:`Select the retrieval strategy: • Naive: Basic search without advanced techniques • Local: Context-dependent information retrieval • Global: Utilizes global knowledge base @@ -67,7 +67,7 @@ For more information, see https://radix-ui.com/primitives/docs/components/alert- {{error}}`,scanFailed:`扫描文档失败 {{error}}`,scanProgressFailed:`获取扫描进度失败 {{error}}`},fileNameLabel:"文件名",showButton:"显示",hideButton:"隐藏",showFileNameTooltip:"显示文件名",hideFileNameTooltip:"隐藏文件名"},pipelineStatus:{title:"流水线状态",busy:"流水线忙碌",requestPending:"待处理请求",jobName:"作业名称",startTime:"开始时间",progress:"进度",unit:"批",latestMessage:"最新消息",historyMessages:"历史消息",errors:{fetchFailed:`获取流水线状态失败 -{{error}}`}}},Fp={dataIsTruncated:"图数据已截断至最大返回节点数",statusDialog:{title:"LightRAG 服务器设置",description:"查看当前系统状态和连接信息"},legend:"图例",nodeTypes:{person:"人物角色",category:"分类",geo:"地理名称",location:"位置",organization:"组织机构",event:"事件",equipment:"装备",weapon:"武器",animal:"动物",unknown:"未知",object:"物品",group:"群组",technology:"技术"},sideBar:{settings:{settings:"设置",healthCheck:"健康检查",showPropertyPanel:"显示属性面板",showSearchBar:"显示搜索栏",showNodeLabel:"显示节点标签",nodeDraggable:"节点可拖动",showEdgeLabel:"显示边标签",hideUnselectedEdges:"隐藏未选中的边",edgeEvents:"边事件",maxQueryDepth:"最大查询深度",maxNodes:"最大返回节点数",maxLayoutIterations:"最大布局迭代次数",resetToDefault:"重置为默认值",edgeSizeRange:"边粗细范围",depth:"深",max:"Max",degree:"邻边",apiKey:"API密钥",enterYourAPIkey:"输入您的API密钥",save:"保存",refreshLayout:"刷新布局"},zoomControl:{zoomIn:"放大",zoomOut:"缩小",resetZoom:"重置缩放",rotateCamera:"顺时针旋转图形",rotateCameraCounterClockwise:"逆时针旋转图形"},layoutsControl:{startAnimation:"继续布局动画",stopAnimation:"停止布局动画",layoutGraph:"图布局",layouts:{Circular:"环形",Circlepack:"圆形打包",Random:"随机",Noverlaps:"无重叠","Force Directed":"力导向","Force Atlas":"力地图"}},fullScreenControl:{fullScreen:"全屏",windowed:"窗口"},legendControl:{toggleLegend:"切换图例显示"}},statusIndicator:{connected:"已连接",disconnected:"未连接"},statusCard:{unavailable:"状态信息不可用",serverInfo:"服务器信息",workingDirectory:"工作目录",inputDirectory:"输入目录",maxParallelInsert:"并行处理文档",summarySettings:"摘要设置",llmConfig:"LLM配置",llmBinding:"LLM绑定",llmBindingHost:"LLM端点",llmModel:"LLM模型",embeddingConfig:"嵌入配置",embeddingBinding:"嵌入绑定",embeddingBindingHost:"嵌入端点",embeddingModel:"嵌入模型",storageConfig:"存储配置",kvStorage:"KV存储",docStatusStorage:"文档状态存储",graphStorage:"图存储",vectorStorage:"向量存储",workspace:"工作空间",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",threshold:"阈值"},propertiesView:{editProperty:"编辑{{property}}",editPropertyDescription:"在下方文本区域编辑属性值。",errors:{duplicateName:"节点名称已存在",updateFailed:"更新节点失败",tryAgainLater:"请稍后重试"},success:{entityUpdated:"节点更新成功",relationUpdated:"关系更新成功"},node:{title:"节点",id:"ID",labels:"标签",degree:"度数",properties:"属性",relationships:"关系(子图内)",expandNode:"扩展节点",pruneNode:"修剪节点",deleteAllNodesError:"拒绝删除图中的所有节点",nodesRemoved:"已删除 {{count}} 个节点，包括孤立节点",noNewNodes:"没有发现可以扩展的节点",propertyNames:{description:"描述",entity_id:"名称",entity_type:"类型",source_id:"信源ID",Neighbour:"邻接",file_path:"信源",keywords:"Keys",weight:"权重"}},edge:{title:"关系",id:"ID",type:"类型",source:"源节点",target:"目标节点",properties:"属性"}},search:{placeholder:"搜索节点...",message:"还有 {count} 个"},graphLabels:{selectTooltip:"选择查询标签",noLabels:"未找到标签",label:"标签",placeholder:"搜索标签...",andOthers:"还有 {count} 个",refreshTooltip:"重载图形数据(添加文件后需重载)"},emptyGraph:"无数据(请重载图形数据)"},Pp={chatMessage:{copyTooltip:"复制到剪贴板",copyError:"复制文本到剪贴板失败"},retrieval:{startPrompt:"输入查询开始检索",clear:"清空",send:"发送",placeholder:"输入查询内容 (支持模式前缀: /)",error:"错误：获取响应失败",queryModeError:"仅支持以下查询模式：{{modes}}",queryModePrefixInvalid:"无效的查询模式前缀。请使用：/<模式> [空格] 查询内容"},querySettings:{parametersTitle:"参数",parametersDescription:"配置查询参数",queryMode:"查询模式",queryModeTooltip:`选择检索策略： +{{error}}`}}},Fp={dataIsTruncated:"图数据已截断至最大返回节点数",statusDialog:{title:"LightRAG 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Atlas":"力地图"}},fullScreenControl:{fullScreen:"全屏",windowed:"窗口"},legendControl:{toggleLegend:"切换图例显示"}},statusIndicator:{connected:"已连接",disconnected:"未连接"},statusCard:{unavailable:"状态信息不可用",serverInfo:"服务器信息",workingDirectory:"工作目录",inputDirectory:"输入目录",maxParallelInsert:"并行处理文档",summarySettings:"摘要设置",llmConfig:"LLM配置",llmBinding:"LLM绑定",llmBindingHost:"LLM端点",llmModel:"LLM模型",embeddingConfig:"嵌入配置",embeddingBinding:"嵌入绑定",embeddingBindingHost:"嵌入端点",embeddingModel:"嵌入模型",storageConfig:"存储配置",kvStorage:"KV存储",docStatusStorage:"文档状态存储",graphStorage:"图存储",vectorStorage:"向量存储",workspace:"工作空间",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",threshold:"阈值"},propertiesView:{editProperty:"编辑{{property}}",editPropertyDescription:"在下方文本区域编辑属性值。",errors:{duplicateName:"节点名称已存在",updateFailed:"更新节点失败",tryAgainLater:"请稍后重试"},success:{entityUpdated:"节点更新成功",relationUpdated:"关系更新成功"},node:{title:"节点",id:"ID",labels:"标签",degree:"度数",properties:"属性",relationships:"关系(子图内)",expandNode:"扩展节点",pruneNode:"修剪节点",deleteAllNodesError:"拒绝删除图中的所有节点",nodesRemoved:"已删除 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entity types --- env.example | 2 +- lightrag/constants.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/env.example b/env.example index 4ab64aba..5d24c118 100644 --- a/env.example +++ b/env.example @@ -125,7 +125,7 @@ ENABLE_LLM_CACHE_FOR_EXTRACT=true SUMMARY_LANGUAGE=English ### Entity types that the LLM will attempt to recognize -# ENTITY_TYPES='["Organization", "Person", "Equiment", "Product", "Technology", "Location", "Event", "Category"]' +# ENTITY_TYPES='["Organization", "Person", "Equipment", "Product", "Technology", "Location", "Event", "Category"]' ### Chunk size for document splitting, 500~1500 is recommended # CHUNK_SIZE=1200 diff --git a/lightrag/constants.py b/lightrag/constants.py index 9accdc52..5cdba052 100644 --- a/lightrag/constants.py +++ b/lightrag/constants.py @@ -26,7 +26,7 @@ DEFAULT_SUMMARY_CONTEXT_SIZE = 12000 DEFAULT_ENTITY_TYPES = [ "Organization", "Person", - "Equiment", + "Equipment", "Product", "Technology", "Location", From c86f863fa445aedf9ab7216dddca5bf95517d4b7 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 3 Sep 2025 10:33:01 +0800 Subject: [PATCH 09/13] feat: optimize entity extraction for smaller LLMs Simplify entity relationship extraction process to improve compatibility and performance with smaller, less capable language models. Changes: - Remove iterative gleaning loop with LLM-based continuation decisions - Simplify to single gleaning pass when entity_extract_max_gleaning > 0 - Streamline entity extraction prompts with clearer instructions - Add explicit completion delimiter signals in all examples --- env.example | 10 ++-- lightrag/operate.py | 18 +------ lightrag/prompt.py | 116 +++++++++++++++++--------------------------- 3 files changed, 50 insertions(+), 94 deletions(-) diff --git a/env.example b/env.example index 5d24c118..ce875d3b 100644 --- a/env.example +++ b/env.example @@ -175,11 +175,9 @@ LLM_BINDING_API_KEY=your_api_key # LLM_BINDING=openai ### OpenAI Specific Parameters -### To mitigate endless output loops and prevent greedy decoding for Qwen3, set the temperature parameter to a value between 0.8 and 1.0 -# OPENAI_LLM_TEMPERATURE=1.0 -# OPENAI_LLM_REASONING_EFFORT=low -### If the presence penalty still can not stop the model from generates repetitive or unconstrained output -# OPENAI_LLM_MAX_COMPLETION_TOKENS=16384 +### To mitigate endless output loops and prevent greedy decoding for Qwen3, set the temperature and frequency penalty parameter to a highter value +# OPENAI_LLM_TEMPERATURE=1.2 +# OPENAI_FREQUENCY_PENALTY=1.5 ### OpenRouter Specific Parameters # OPENAI_LLM_EXTRA_BODY='{"reasoning": {"enabled": false}}' @@ -194,7 +192,7 @@ LLM_BINDING_API_KEY=your_api_key OLLAMA_LLM_NUM_CTX=32768 # OLLAMA_LLM_TEMPERATURE=1.0 ### Stop sequences for Ollama LLM -# OLLAMA_LLM_STOP='["", "Assistant:", "\n\n"]' +# OLLAMA_LLM_STOP='["", "<|EOT|>"]' ### use the following command to see all support options for Ollama LLM ### lightrag-server --llm-binding ollama --help diff --git a/lightrag/operate.py b/lightrag/operate.py index 6c9a5538..cbfbb858 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -1764,7 +1764,6 @@ async def extract_entities( ) continue_prompt = PROMPTS["entity_continue_extraction"].format(**context_base) - if_loop_prompt = PROMPTS["entity_if_loop_extraction"] processed_chunks = 0 total_chunks = len(ordered_chunks) @@ -1815,7 +1814,7 @@ async def extract_entities( ) # Process additional gleaning results - for now_glean_index in range(entity_extract_max_gleaning): + if entity_extract_max_gleaning > 0: glean_result = await use_llm_func_with_cache( continue_prompt, use_llm_func, @@ -1852,21 +1851,6 @@ async def extract_entities( maybe_edges[edge_key] = [] # Explicitly create the list maybe_edges[edge_key].extend(edges) - if now_glean_index == entity_extract_max_gleaning - 1: - break - - if_loop_result: str = await use_llm_func_with_cache( - if_loop_prompt, - use_llm_func, - llm_response_cache=llm_response_cache, - history_messages=history, - cache_type="extract", - cache_keys_collector=cache_keys_collector, - ) - if_loop_result = if_loop_result.strip().strip('"').strip("'").lower() - if if_loop_result != "yes": - break - # Batch update chunk's llm_cache_list with all collected cache keys if cache_keys_collector and text_chunks_storage: await update_chunk_cache_list( diff --git a/lightrag/prompt.py b/lightrag/prompt.py index c71debe8..0ee5ec20 100644 --- a/lightrag/prompt.py +++ b/lightrag/prompt.py @@ -6,37 +6,29 @@ PROMPTS: dict[str, Any] = {} PROMPTS["DEFAULT_TUPLE_DELIMITER"] = "<|>" PROMPTS["DEFAULT_RECORD_DELIMITER"] = "##" - -# TODO: Deprecated, reserved for compatible with legacy LLM cache PROMPTS["DEFAULT_COMPLETION_DELIMITER"] = "<|COMPLETE|>" PROMPTS["DEFAULT_USER_PROMPT"] = "n/a" PROMPTS["entity_extraction"] = """---Task--- -Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities. +Given a text document and a list of entity types, identify all entities of those types and all relationships among the identified entities. ---Instructions--- 1. Recognizing definitively conceptualized entities in text. For each identified entity, extract the following information: -- entity_name: Name of the entity, use same language as input text. If English, capitalized the name -- entity_type: One of the following types: [{entity_types}]. If the entity doesn't clearly fit any category, classify it as "Other". -- entity_description: Provide a comprehensive description of the entity's attributes and activities based on the information present in the input text. Do not add external knowledge. - -2. Format each entity as: -("entity"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) - + - entity_name: Name of the entity, use same language as input text. If English, capitalized the name + - entity_type: Categorize the entity using the provided `Entity_types` list. If a suitable category cannot be determined, classify it as "Other". + - entity_description: Provide a comprehensive description of the entity's attributes and activities based on the information present in the input text. Do not add external knowledge. +2. Format each entity as: ("entity"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) 3. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are directly and clearly related based on the text. Unsubstantiated relationships must be excluded from the output. For each pair of related entities, extract the following information: -- source_entity: name of the source entity, as identified in step 1 -- target_entity: name of the target entity, as identified in step 1 -- relationship_keywords: one or more high-level key words that summarize the overarching nature of the relationship, focusing on concepts or themes rather than specific details -- relationship_description: Explain the nature of the relationship between the source and target entities, providing a clear rationale for their connection - -4. Format each relationship as: -("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) - + - source_entity: name of the source entity, as identified in step 1 + - target_entity: name of the target entity, as identified in step 1 + - relationship_keywords: one or more high-level key words that summarize the overarching nature of the relationship, focusing on concepts or themes rather than specific details + - relationship_description: Explain the nature of the relationship between the source and target entities, providing a clear rationale for their connection +4. Format each relationship as: ("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) 5. Use `{tuple_delimiter}` as field delimiter. Use `{record_delimiter}` as the entity or relation list delimiter. - 6. Return identified entities and relationships in {language}. +7. Output `{completion_delimiter}` when all the entities and relationships are extracted. ---Quality Guidelines--- - Only extract entities that are clearly defined and meaningful in the context @@ -47,7 +39,7 @@ For each pair of related entities, extract the following information: ---Examples--- {examples} ----Real Data--- +---Input--- Entity_types: [{entity_types}] Text: ``` @@ -55,12 +47,12 @@ Text: ``` ---Output--- -Output: """ PROMPTS["entity_extraction_examples"] = [ - """------Example 1------ + """[Example 1] +---Input--- Entity_types: [organization,person,equiment,product,technology,location,event,category] Text: ``` @@ -73,7 +65,7 @@ The underlying dismissal earlier seemed to falter, replaced by a glimpse of relu It was a small transformation, barely perceptible, but one that Alex noted with an inward nod. They had all been brought here by different paths ``` -Output: +---Output--- (entity{tuple_delimiter}Alex{tuple_delimiter}person{tuple_delimiter}Alex is a character who experiences frustration and is observant of the dynamics among other characters.){record_delimiter} (entity{tuple_delimiter}Taylor{tuple_delimiter}person{tuple_delimiter}Taylor is portrayed with authoritarian certainty and shows a moment of reverence towards a device, indicating a change in perspective.){record_delimiter} (entity{tuple_delimiter}Jordan{tuple_delimiter}person{tuple_delimiter}Jordan shares a commitment to discovery and has a significant interaction with Taylor regarding a device.){record_delimiter} @@ -84,10 +76,12 @@ Output: (relationship{tuple_delimiter}Taylor{tuple_delimiter}Jordan{tuple_delimiter}conflict resolution, mutual respect{tuple_delimiter}Taylor and Jordan interact directly regarding the device, leading to a moment of mutual respect and an uneasy truce.){record_delimiter} (relationship{tuple_delimiter}Jordan{tuple_delimiter}Cruz{tuple_delimiter}ideological conflict, rebellion{tuple_delimiter}Jordan's commitment to discovery is in rebellion against Cruz's vision of control and order.){record_delimiter} (relationship{tuple_delimiter}Taylor{tuple_delimiter}The Device{tuple_delimiter}reverence, technological significance{tuple_delimiter}Taylor shows reverence towards the device, indicating its importance and potential impact.){record_delimiter} +{completion_delimiter} """, - """------Example 2------ + """[Example 2] +---Input--- Entity_types: [organization,person,equiment,product,technology,location,event,category] Text: ``` @@ -100,7 +94,7 @@ Meanwhile, commodity markets reflected a mixed sentiment. Gold futures rose by 1 Financial experts are closely watching the Federal Reserve's next move, as speculation grows over potential rate hikes. The upcoming policy announcement is expected to influence investor confidence and overall market stability. ``` -Output: +---Output--- (entity{tuple_delimiter}Global Tech Index{tuple_delimiter}category{tuple_delimiter}The Global Tech Index tracks the performance of major technology stocks and experienced a 3.4% decline today.){record_delimiter} (entity{tuple_delimiter}Nexon Technologies{tuple_delimiter}organization{tuple_delimiter}Nexon Technologies is a tech company that saw its stock decline by 7.8% after disappointing earnings.){record_delimiter} (entity{tuple_delimiter}Omega Energy{tuple_delimiter}organization{tuple_delimiter}Omega Energy is an energy company that gained 2.1% in stock value due to rising oil prices.){record_delimiter} @@ -113,17 +107,19 @@ Output: (relationship{tuple_delimiter}Nexon Technologies{tuple_delimiter}Global Tech Index{tuple_delimiter}company impact, index movement{tuple_delimiter}Nexon Technologies' stock decline contributed to the overall drop in the Global Tech Index.){record_delimiter} (relationship{tuple_delimiter}Gold Futures{tuple_delimiter}Market Selloff{tuple_delimiter}market reaction, safe-haven investment{tuple_delimiter}Gold prices rose as investors sought safe-haven assets during the market selloff.){record_delimiter} (relationship{tuple_delimiter}Federal Reserve Policy Announcement{tuple_delimiter}Market Selloff{tuple_delimiter}interest rate impact, financial regulation{tuple_delimiter}Speculation over Federal Reserve policy changes contributed to market volatility and investor selloff.){record_delimiter} +{completion_delimiter} """, - """------Example 3------ + """[Example 3] +---Input--- Entity_types: [organization,person,equiment,product,technology,location,event,category] Text: ``` At the World Athletics Championship in Tokyo, Noah Carter broke the 100m sprint record using cutting-edge carbon-fiber spikes. ``` -Output: +---Output--- (entity{tuple_delimiter}World Athletics Championship{tuple_delimiter}event{tuple_delimiter}The World Athletics Championship is a global sports competition featuring top athletes in track and field.){record_delimiter} (entity{tuple_delimiter}Tokyo{tuple_delimiter}location{tuple_delimiter}Tokyo is the host city of the World Athletics Championship.){record_delimiter} (entity{tuple_delimiter}Noah Carter{tuple_delimiter}person{tuple_delimiter}Noah Carter is a sprinter who set a new record in the 100m sprint at the World Athletics Championship.){record_delimiter} @@ -134,17 +130,19 @@ Output: (relationship{tuple_delimiter}Noah Carter{tuple_delimiter}100m Sprint Record{tuple_delimiter}athlete achievement, record-breaking{tuple_delimiter}Noah Carter set a new 100m sprint record at the championship.){record_delimiter} (relationship{tuple_delimiter}Noah Carter{tuple_delimiter}Carbon-Fiber Spikes{tuple_delimiter}athletic equipment, performance boost{tuple_delimiter}Noah Carter used carbon-fiber spikes to enhance performance during the race.){record_delimiter} (relationship{tuple_delimiter}Noah Carter{tuple_delimiter}World Athletics Championship{tuple_delimiter}athlete participation, competition{tuple_delimiter}Noah Carter is competing at the World Athletics Championship.){record_delimiter} +{completion_delimiter} """, - """------Example 4------ + """[Example 4] +---Input--- Entity_types: [organization,person,equiment,product,technology,location,event,category] Text: ``` 在北京举行的人工智能大会上，腾讯公司的首席技术官张伟发布了最新的大语言模型"腾讯智言"，该模型在自然语言处理方面取得了重大突破。 ``` -Output: +---Output--- (entity{tuple_delimiter}人工智能大会{tuple_delimiter}event{tuple_delimiter}人工智能大会是在北京举行的技术会议，专注于人工智能领域的最新发展。){record_delimiter} (entity{tuple_delimiter}北京{tuple_delimiter}location{tuple_delimiter}北京是人工智能大会的举办城市。){record_delimiter} (entity{tuple_delimiter}腾讯公司{tuple_delimiter}organization{tuple_delimiter}腾讯公司是参与人工智能大会的科技企业，发布了新的语言模型产品。){record_delimiter} @@ -155,6 +153,7 @@ Output: (relationship{tuple_delimiter}张伟{tuple_delimiter}腾讯公司{tuple_delimiter}雇佣关系, 高管职位{tuple_delimiter}张伟担任腾讯公司的首席技术官。){record_delimiter} (relationship{tuple_delimiter}张伟{tuple_delimiter}腾讯智言{tuple_delimiter}产品发布, 技术展示{tuple_delimiter}张伟在大会上发布了腾讯智言大语言模型。){record_delimiter} (relationship{tuple_delimiter}腾讯智言{tuple_delimiter}自然语言处理技术{tuple_delimiter}技术应用, 突破创新{tuple_delimiter}腾讯智言在自然语言处理技术方面取得了重大突破。){record_delimiter} +{completion_delimiter} """, ] @@ -179,54 +178,29 @@ Description List: {description_list} ---Output--- -Output:""" - -PROMPTS["entity_continue_extraction"] = """ ----Task--- -MANY entities and relationships were missed in the last extraction. Please find only the missing entities and relationships from previous text. - ----Instructions--- -1. Recognizing definitively conceptualized entities in text. For each identified entity, extract the following information: -- entity_name: Name of the entity, use same language as input text. If English, capitalized the name -- entity_type: One of the following types: [{entity_types}]. If the entity doesn't clearly fit any category, classify it as "Other". -- entity_description: Provide a comprehensive description of the entity's attributes and activities based on the information present in the input text. Do not add external knowledge. - -2. Format each entity as: -("entity"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) - -3. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are directly and clearly related based on the text. Unsubstantiated relationships must be excluded from the output. -For each pair of related entities, extract the following information: -- source_entity: name of the source entity, as identified in step 1 -- target_entity: name of the target entity, as identified in step 1 -- relationship_keywords: one or more high-level key words that summarize the overarching nature of the relationship, focusing on concepts or themes rather than specific details -- relationship_description: Explain the nature of the relationship between the source and target entities, providing a clear rationale for their connection - -4. Format each relationship as: -("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) - -5. Use `{tuple_delimiter}` as field delimiter. Use `{record_delimiter}` as the entity or relation list delimiter. - -6. Return identified entities and relationships in {language}. - ----Quality Guidelines--- -- Only extract entities that are clearly defined and meaningful in the context -- Do not include entities and relations that have been previously extracted -- Avoid over-interpretation; stick to what is explicitly stated in the text -- Include specific numerical data in entity name when relevant -- Ensure entity names are consistent throughout the extraction - ----Output--- -Output: """ +PROMPTS["entity_continue_extraction"] = """---Task--- +Identify any missed entities or relationships in the last extraction task. + +---Instructions--- +1. Output the entities and realtionships in the same format as previous extraction task. +2. Do not include entities and relations that have been previously extracted. +3. If the entity doesn't clearly fit in any of`Entity_types` provided, classify it as "Other". +4. Return identified entities and relationships in {language}. +5. Output `{completion_delimiter}` when all the entities and relationships are extracted. + +---Output--- +""" + +# TODO: Deprecated PROMPTS["entity_if_loop_extraction"] = """ ---Goal---' -It appears some entities may have still been missed. +Check if it appears some entities may have still been missed. Output "Yes" if so, otherwise "No". ---Output--- -Output: -""".strip() +Output:""" PROMPTS["fail_response"] = ( "Sorry, I'm not able to provide an answer to that question.[no-context]" @@ -270,7 +244,7 @@ Generate a concise response based on Knowledge Base and follow Response Rules, c - Additional user prompt: {user_prompt} ---Response--- -Output:""" +""" PROMPTS["keywords_extraction"] = """---Role--- You are an expert keyword extractor, specializing in analyzing user queries for a Retrieval-Augmented Generation (RAG) system. Your purpose is to identify both high-level and low-level keywords in the user's query that will be used for effective document retrieval. From 95c08cc7dcf1ce589c1c8f1b229fc335b6f97662 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 3 Sep 2025 12:35:52 +0800 Subject: [PATCH 10/13] Improve entity extraction prompt clarity by replacing pronouns with specific nouns --- lightrag/prompt.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lightrag/prompt.py b/lightrag/prompt.py index 0ee5ec20..76077af0 100644 --- a/lightrag/prompt.py +++ b/lightrag/prompt.py @@ -17,7 +17,7 @@ Given a text document and a list of entity types, identify all entities of those 1. Recognizing definitively conceptualized entities in text. For each identified entity, extract the following information: - entity_name: Name of the entity, use same language as input text. If English, capitalized the name - entity_type: Categorize the entity using the provided `Entity_types` list. If a suitable category cannot be determined, classify it as "Other". - - entity_description: Provide a comprehensive description of the entity's attributes and activities based on the information present in the input text. Do not add external knowledge. + - entity_description: Provide a comprehensive description of the entity's attributes and activities based on the information present in the input text. To ensure clarity and precision, all descriptions must replace pronouns and referential terms (e.g., "this document," "our company," "I," "you," "he/she") with the specific nouns they represent. 2. Format each entity as: ("entity"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) 3. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are directly and clearly related based on the text. Unsubstantiated relationships must be excluded from the output. For each pair of related entities, extract the following information: @@ -33,6 +33,7 @@ For each pair of related entities, extract the following information: ---Quality Guidelines--- - Only extract entities that are clearly defined and meaningful in the context - Avoid over-interpretation; stick to what is explicitly stated in the text +- For all output content, explicitly name the subject or object rather than using pronouns - Include specific numerical data in entity name when relevant - Ensure entity names are consistent throughout the extraction From 78abb397bfc67aaf208de7d76b2eaf41e639ba4f Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 3 Sep 2025 12:44:40 +0800 Subject: [PATCH 11/13] Reorder entity types and add Document type to extraction --- env.example | 2 +- lightrag/constants.py | 7 ++++--- lightrag/prompt.py | 8 ++++---- 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/env.example b/env.example index ce875d3b..749ddc3d 100644 --- a/env.example +++ b/env.example @@ -125,7 +125,7 @@ ENABLE_LLM_CACHE_FOR_EXTRACT=true SUMMARY_LANGUAGE=English ### Entity types that the LLM will attempt to recognize -# ENTITY_TYPES='["Organization", "Person", "Equipment", "Product", "Technology", "Location", "Event", "Category"]' +# ENTITY_TYPES='["Organization", "Person", "Location", "Event", "Technology", "Equipment", "Product", "Document", "Category"]' ### Chunk size for document splitting, 500~1500 is recommended # CHUNK_SIZE=1200 diff --git a/lightrag/constants.py b/lightrag/constants.py index 5cdba052..0b3962d0 100644 --- a/lightrag/constants.py +++ b/lightrag/constants.py @@ -26,11 +26,12 @@ DEFAULT_SUMMARY_CONTEXT_SIZE = 12000 DEFAULT_ENTITY_TYPES = [ "Organization", "Person", - "Equipment", - "Product", - "Technology", "Location", "Event", + "Technology", + "Equipment", + "Product", + "Document", "Category", ] diff --git a/lightrag/prompt.py b/lightrag/prompt.py index 76077af0..0d21375a 100644 --- a/lightrag/prompt.py +++ b/lightrag/prompt.py @@ -54,7 +54,7 @@ PROMPTS["entity_extraction_examples"] = [ """[Example 1] ---Input--- -Entity_types: [organization,person,equiment,product,technology,location,event,category] +Entity_types: [organization,person,location,event,technology,equiment,product,Document,category] Text: ``` while Alex clenched his jaw, the buzz of frustration dull against the backdrop of Taylor's authoritarian certainty. It was this competitive undercurrent that kept him alert, the sense that his and Jordan's shared commitment to discovery was an unspoken rebellion against Cruz's narrowing vision of control and order. @@ -83,7 +83,7 @@ It was a small transformation, barely perceptible, but one that Alex noted with """[Example 2] ---Input--- -Entity_types: [organization,person,equiment,product,technology,location,event,category] +Entity_types: [organization,person,location,event,technology,equiment,product,Document,category] Text: ``` Stock markets faced a sharp downturn today as tech giants saw significant declines, with the Global Tech Index dropping by 3.4% in midday trading. Analysts attribute the selloff to investor concerns over rising interest rates and regulatory uncertainty. @@ -114,7 +114,7 @@ Financial experts are closely watching the Federal Reserve's next move, as specu """[Example 3] ---Input--- -Entity_types: [organization,person,equiment,product,technology,location,event,category] +Entity_types: [organization,person,location,event,technology,equiment,product,Document,category] Text: ``` At the World Athletics Championship in Tokyo, Noah Carter broke the 100m sprint record using cutting-edge carbon-fiber spikes. @@ -137,7 +137,7 @@ At the World Athletics Championship in Tokyo, Noah Carter broke the 100m sprint """[Example 4] ---Input--- -Entity_types: [organization,person,equiment,product,technology,location,event,category] +Entity_types: [organization,person,location,event,technology,equiment,product,Document,category] Text: ``` 在北京举行的人工智能大会上，腾讯公司的首席技术官张伟发布了最新的大语言模型"腾讯智言"，该模型在自然语言处理方面取得了重大突破。 From 5a5d5e4a346a854b0f1f6685d3713d9df91269a7 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 3 Sep 2025 12:50:27 +0800 Subject: [PATCH 12/13] Add document translation key to all locale files --- lightrag_webui/src/locales/ar.json | 1 + lightrag_webui/src/locales/en.json | 1 + lightrag_webui/src/locales/fr.json | 1 + lightrag_webui/src/locales/zh.json | 1 + lightrag_webui/src/locales/zh_TW.json | 1 + 5 files changed, 5 insertions(+) diff --git a/lightrag_webui/src/locales/ar.json b/lightrag_webui/src/locales/ar.json index db50ab37..240ea053 100644 --- a/lightrag_webui/src/locales/ar.json +++ b/lightrag_webui/src/locales/ar.json @@ -189,6 +189,7 @@ "group": "مجموعة", "technology": "العلوم", "product": "منتج", + "document": "وثيقة", "other": "أخرى" }, "sideBar": { diff --git a/lightrag_webui/src/locales/en.json b/lightrag_webui/src/locales/en.json index 19006705..1c5cf6e9 100644 --- a/lightrag_webui/src/locales/en.json +++ b/lightrag_webui/src/locales/en.json @@ -189,6 +189,7 @@ "group": "Group", "technology": "Technology", "product": "Product", + "document": "Document", "other": "Other" }, "sideBar": { diff --git a/lightrag_webui/src/locales/fr.json b/lightrag_webui/src/locales/fr.json index f7327c70..a0629d17 100644 --- a/lightrag_webui/src/locales/fr.json +++ b/lightrag_webui/src/locales/fr.json @@ -189,6 +189,7 @@ "group": "Groupe", "technology": "Technologie", "product": "Produit", + "document": "Document", "other": "Autre" }, "sideBar": { diff --git a/lightrag_webui/src/locales/zh.json b/lightrag_webui/src/locales/zh.json index a4d33509..951090f7 100644 --- a/lightrag_webui/src/locales/zh.json +++ b/lightrag_webui/src/locales/zh.json @@ -189,6 +189,7 @@ "group": "群组", "technology": "技术", "product": "产品", + "document": "文档", "other": "其他" }, "sideBar": { diff --git a/lightrag_webui/src/locales/zh_TW.json b/lightrag_webui/src/locales/zh_TW.json index b147b2cc..df35434a 100644 --- a/lightrag_webui/src/locales/zh_TW.json +++ b/lightrag_webui/src/locales/zh_TW.json @@ -189,6 +189,7 @@ "group": "群組", "technology": "技術", "product": "產品", + "document": "文檔", "other": "其他" }, "sideBar": { From 0b07c022d668613e86a71891df3857095135f578 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 3 Sep 2025 12:51:08 +0800 Subject: [PATCH 13/13] Update webui assets and bump api version to 0213 --- lightrag/api/__init__.py | 2 +- .../assets/{index-CjbmYv_Z.js => index-BOxJ2b27.js} | 10 +++++----- lightrag/api/webui/index.html | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) rename lightrag/api/webui/assets/{index-CjbmYv_Z.js => index-BOxJ2b27.js} (91%) diff --git a/lightrag/api/__init__.py b/lightrag/api/__init__.py index da0ff6dd..7cc89012 100644 --- a/lightrag/api/__init__.py +++ b/lightrag/api/__init__.py @@ -1 +1 @@ -__api_version__ = "0212" +__api_version__ = "0213" diff --git a/lightrag/api/webui/assets/index-CjbmYv_Z.js b/lightrag/api/webui/assets/index-BOxJ2b27.js similarity index 91% rename from lightrag/api/webui/assets/index-CjbmYv_Z.js rename to lightrag/api/webui/assets/index-BOxJ2b27.js index d31d07eb..b97f9a10 100644 --- a/lightrag/api/webui/assets/index-CjbmYv_Z.js +++ b/lightrag/api/webui/assets/index-BOxJ2b27.js @@ -43,7 +43,7 @@ For more information, see https://radix-ui.com/primitives/docs/components/alert- {{error}}`,scanFailed:`Failed to scan documents {{error}}`,scanProgressFailed:`Failed to get scan progress {{error}}`},fileNameLabel:"File Name",showButton:"Show",hideButton:"Hide",showFileNameTooltip:"Show file name",hideFileNameTooltip:"Hide file name"},pipelineStatus:{title:"Pipeline Status",busy:"Pipeline Busy",requestPending:"Request Pending",jobName:"Job Name",startTime:"Start Time",progress:"Progress",unit:"batch",latestMessage:"Latest Message",historyMessages:"History Messages",errors:{fetchFailed:`Failed to get pipeline status -{{error}}`}}},Bp={dataIsTruncated:"Graph data is truncated to Max Nodes",statusDialog:{title:"LightRAG Server Settings",description:"View current system status and connection information"},legend:"Legend",nodeTypes:{person:"Person",category:"Category",geo:"Geographic",location:"Location",organization:"Organization",event:"Event",equipment:"Equipment",weapon:"Weapon",animal:"Animal",unknown:"Unknown",object:"Object",group:"Group",technology:"Technology",product:"Product",other:"Other"},sideBar:{settings:{settings:"Settings",healthCheck:"Health Check",showPropertyPanel:"Show Property Panel",showSearchBar:"Show Search Bar",showNodeLabel:"Show Node Label",nodeDraggable:"Node Draggable",showEdgeLabel:"Show Edge Label",hideUnselectedEdges:"Hide Unselected Edges",edgeEvents:"Edge Events",maxQueryDepth:"Max Query Depth",maxNodes:"Max Nodes",maxLayoutIterations:"Max Layout Iterations",resetToDefault:"Reset to default",edgeSizeRange:"Edge Size Range",depth:"D",max:"Max",degree:"Degree",apiKey:"API Key",enterYourAPIkey:"Enter your API key",save:"Save",refreshLayout:"Refresh Layout"},zoomControl:{zoomIn:"Zoom In",zoomOut:"Zoom Out",resetZoom:"Reset Zoom",rotateCamera:"Clockwise Rotate",rotateCameraCounterClockwise:"Counter-Clockwise Rotate"},layoutsControl:{startAnimation:"Continue layout animation",stopAnimation:"Stop layout animation",layoutGraph:"Layout Graph",layouts:{Circular:"Circular",Circlepack:"Circlepack",Random:"Random",Noverlaps:"Noverlaps","Force Directed":"Force Directed","Force Atlas":"Force Atlas"}},fullScreenControl:{fullScreen:"Full Screen",windowed:"Windowed"},legendControl:{toggleLegend:"Toggle Legend"}},statusIndicator:{connected:"Connected",disconnected:"Disconnected"},statusCard:{unavailable:"Status information unavailable",serverInfo:"Server Info",workingDirectory:"Working Directory",inputDirectory:"Input Directory",maxParallelInsert:"Concurrent Doc Processing",summarySettings:"Summary Settings",llmConfig:"LLM Configuration",llmBinding:"LLM Binding",llmBindingHost:"LLM Endpoint",llmModel:"LLM Model",embeddingConfig:"Embedding Configuration",embeddingBinding:"Embedding Binding",embeddingBindingHost:"Embedding Endpoint",embeddingModel:"Embedding Model",storageConfig:"Storage Configuration",kvStorage:"KV Storage",docStatusStorage:"Doc Status Storage",graphStorage:"Graph Storage",vectorStorage:"Vector Storage",workspace:"Workspace",maxGraphNodes:"Max Graph Nodes",rerankerConfig:"Reranker Configuration",rerankerBindingHost:"Reranker Endpoint",rerankerModel:"Reranker Model",lockStatus:"Lock Status",threshold:"Threshold"},propertiesView:{editProperty:"Edit {{property}}",editPropertyDescription:"Edit the property value in the text area below.",errors:{duplicateName:"Node name already exists",updateFailed:"Failed to update node",tryAgainLater:"Please try again later"},success:{entityUpdated:"Node updated successfully",relationUpdated:"Relation updated successfully"},node:{title:"Node",id:"ID",labels:"Labels",degree:"Degree",properties:"Properties",relationships:"Relations(within subgraph)",expandNode:"Expand Node",pruneNode:"Prune Node",deleteAllNodesError:"Refuse to delete all nodes in the graph",nodesRemoved:"{{count}} nodes removed, including orphan nodes",noNewNodes:"No expandable nodes found",propertyNames:{description:"Description",entity_id:"Name",entity_type:"Type",source_id:"SrcID",Neighbour:"Neigh",file_path:"Source",keywords:"Keys",weight:"Weight"}},edge:{title:"Relationship",id:"ID",type:"Type",source:"Source",target:"Target",properties:"Properties"}},search:{placeholder:"Search nodes...",message:"And {count} others"},graphLabels:{selectTooltip:"Select query label",noLabels:"No labels found",label:"Label",placeholder:"Search labels...",andOthers:"And {count} others",refreshTooltip:"Reload data(After file added)"},emptyGraph:"Empty(Try Reload Again)"},Gp={chatMessage:{copyTooltip:"Copy to clipboard",copyError:"Failed to copy text to clipboard"},retrieval:{startPrompt:"Start a retrieval by typing your query below",clear:"Clear",send:"Send",placeholder:"Enter your query (Support prefix: /)",error:"Error: Failed to get response",queryModeError:"Only supports the following query modes: {{modes}}",queryModePrefixInvalid:"Invalid query mode prefix. 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