From d137ba584338ba232602d0d8c3b259ccd64c0f28 Mon Sep 17 00:00:00 2001 From: Tong Da Date: Sun, 9 Nov 2025 14:52:42 +0800 Subject: [PATCH 01/37] support async chunking func to improve processing performance when a heavy `chunking_func` is passed in by user --- lightrag/lightrag.py | 28 ++++++++++++++++++++-------- 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index acf157da..d56687c2 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -1,5 +1,6 @@ from __future__ import annotations +from inspect import iscoroutinefunction import traceback import asyncio import configparser @@ -1757,14 +1758,8 @@ class LightRAG: content = content_data["content"] # Generate chunks from document - chunks: dict[str, Any] = { - compute_mdhash_id(dp["content"], prefix="chunk-"): { - **dp, - "full_doc_id": doc_id, - "file_path": file_path, # Add file path to each chunk - "llm_cache_list": [], # Initialize empty LLM cache list for each chunk - } - for dp in self.chunking_func( + if iscoroutinefunction(self.chunking_func): + chunks = await self.chunking_func( self.tokenizer, content, split_by_character, @@ -1772,6 +1767,23 @@ class LightRAG: self.chunk_overlap_token_size, self.chunk_token_size, ) + else: + chunks = self.chunking_func( + self.tokenizer, + content, + split_by_character, + split_by_character_only, + self.chunk_overlap_token_size, + self.chunk_token_size, + ) + chunks: dict[str, Any] = { + compute_mdhash_id(dp["content"], prefix="chunk-"): { + **dp, + "full_doc_id": doc_id, + "file_path": file_path, # Add file path to each chunk + "llm_cache_list": [], # Initialize empty LLM cache list for each chunk + } + for dp in chunks } if not chunks: From 245df75d9c4cf0f69b5c85c9b96d7c506fdae779 Mon Sep 17 00:00:00 2001 From: Tong Da Date: Mon, 10 Nov 2025 20:49:50 +0800 Subject: [PATCH 02/37] easier version: detect chunking_func result is coroutine or not --- lightrag/lightrag.py | 30 +++++++++++------------------- 1 file changed, 11 insertions(+), 19 deletions(-) diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index d56687c2..af953fdf 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -1,6 +1,5 @@ from __future__ import annotations -from inspect import iscoroutinefunction import traceback import asyncio import configparser @@ -1758,24 +1757,17 @@ class LightRAG: content = content_data["content"] # Generate chunks from document - if iscoroutinefunction(self.chunking_func): - chunks = await self.chunking_func( - self.tokenizer, - content, - split_by_character, - split_by_character_only, - self.chunk_overlap_token_size, - self.chunk_token_size, - ) - else: - chunks = self.chunking_func( - self.tokenizer, - content, - split_by_character, - split_by_character_only, - self.chunk_overlap_token_size, - self.chunk_token_size, - ) + chunks = self.chunking_func( + self.tokenizer, + content, + split_by_character, + split_by_character_only, + self.chunk_overlap_token_size, + self.chunk_token_size, + ) + # 判断chunks是否是异步异步函数的返回 + if asyncio.iscoroutine(chunks): + chunks = await chunks chunks: dict[str, Any] = { compute_mdhash_id(dp["content"], prefix="chunk-"): { **dp, From fdcb4d0b6d6bc66580868f3b74f6dbfe41ffa9a4 Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 11 Nov 2025 01:38:09 +0800 Subject: [PATCH 03/37] Replace PyPDF2 with pypdf for PDF processing - Update import from PyPDF2 to pypdf - Change dependency to pypdf>=6.1.0 - Update all requirements files - Remove PyPDF2 from lock file - Use modern pypdf library --- lightrag/api/routers/document_routes.py | 6 +++--- pyproject.toml | 2 +- requirements-offline-docs.txt | 2 +- requirements-offline.txt | 2 +- uv.lock | 15 +++------------ 5 files changed, 9 insertions(+), 18 deletions(-) diff --git a/lightrag/api/routers/document_routes.py b/lightrag/api/routers/document_routes.py index 3e479a53..f86bcea8 100644 --- a/lightrag/api/routers/document_routes.py +++ b/lightrag/api/routers/document_routes.py @@ -1081,11 +1081,11 @@ async def pipeline_enqueue_file( result = converter.convert(file_path) content = result.document.export_to_markdown() else: - if not pm.is_installed("pypdf2"): # type: ignore - pm.install("pypdf2") + if not pm.is_installed("pypdf"): # type: ignore + pm.install("pypdf") if not pm.is_installed("pycryptodome"): # type: ignore pm.install("pycryptodome") - from PyPDF2 import PdfReader # type: ignore + from pypdf import PdfReader # type: ignore from io import BytesIO pdf_file = BytesIO(file) diff --git a/pyproject.toml b/pyproject.toml index 29f7c58e..81e44aff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,7 +86,7 @@ offline-docs = [ # Document processing dependencies "openpyxl>=3.0.0,<4.0.0", "pycryptodome>=3.0.0,<4.0.0", - "pypdf2>=3.0.0", + "pypdf>=6.1.0", "python-docx>=0.8.11,<2.0.0", "python-pptx>=0.6.21,<2.0.0", ] diff --git a/requirements-offline-docs.txt b/requirements-offline-docs.txt index 14d782fd..12f02080 100644 --- a/requirements-offline-docs.txt +++ b/requirements-offline-docs.txt @@ -10,6 +10,6 @@ # Document processing dependencies (with version constraints matching pyproject.toml) openpyxl>=3.0.0,<4.0.0 pycryptodome>=3.0.0,<4.0.0 -pypdf2>=3.0.0 +pypdf>=6.1.0 python-docx>=0.8.11,<2.0.0 python-pptx>=0.6.21,<2.0.0 diff --git a/requirements-offline.txt b/requirements-offline.txt index 0582eaca..50848093 100644 --- a/requirements-offline.txt +++ b/requirements-offline.txt @@ -24,7 +24,7 @@ openpyxl>=3.0.0,<4.0.0 pycryptodome>=3.0.0,<4.0.0 pymilvus>=2.6.2,<3.0.0 pymongo>=4.0.0,<5.0.0 -pypdf2>=3.0.0 +pypdf>=6.1.0 python-docx>=0.8.11,<2.0.0 python-pptx>=0.6.21,<2.0.0 qdrant-client>=1.11.0,<2.0.0 diff --git a/uv.lock b/uv.lock index 63fa0a78..b942632f 100644 --- a/uv.lock +++ b/uv.lock @@ -1981,7 +1981,7 @@ offline = [ { name = "pycryptodome" }, { name = "pymilvus" }, { name = "pymongo" }, - { name = "pypdf2" }, + { name = "pypdf" }, { name = "python-docx" }, { name = "python-pptx" }, { name = "qdrant-client" }, @@ -1992,7 +1992,7 @@ offline = [ offline-docs = [ { name = "openpyxl" }, { name = "pycryptodome" }, - { name = "pypdf2" }, + { name = "pypdf" }, { name = "python-docx" }, { name = "python-pptx" }, ] @@ -2071,7 +2071,7 @@ requires-dist = [ { name = "pyjwt", marker = "extra == 'api'", specifier = ">=2.8.0,<3.0.0" }, { name = "pymilvus", marker = "extra == 'offline-storage'", specifier = ">=2.6.2,<3.0.0" }, { name = "pymongo", marker = "extra == 'offline-storage'", specifier = ">=4.0.0,<5.0.0" }, - { name = "pypdf2", marker = "extra == 'offline-docs'", specifier = ">=3.0.0" }, + { name = "pypdf", marker = "extra == 'offline-docs'", specifier = ">=6.1.0" }, { name = "pypinyin" }, { name = "pypinyin", marker = "extra == 'api'" }, { name = "pytest", marker = "extra == 'evaluation'", specifier = ">=8.4.2" }, @@ -3977,15 +3977,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fa/ed/494fd0cc1190a7c335e6958eeaee6f373a281869830255c2ed4785dac135/pypdf-6.1.3-py3-none-any.whl", hash = "sha256:eb049195e46f014fc155f566fa20e09d70d4646a9891164ac25fa0cbcfcdbcb5", size = 323863, upload-time = "2025-10-22T16:13:44.174Z" }, ] -[[package]] -name = "pypdf2" -version = "3.0.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/9f/bb/18dc3062d37db6c491392007dfd1a7f524bb95886eb956569ac38a23a784/PyPDF2-3.0.1.tar.gz", hash = "sha256:a74408f69ba6271f71b9352ef4ed03dc53a31aa404d29b5d31f53bfecfee1440", size = 227419, upload-time = "2022-12-31T10:36:13.13Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8e/5e/c86a5643653825d3c913719e788e41386bee415c2b87b4f955432f2de6b2/pypdf2-3.0.1-py3-none-any.whl", hash = "sha256:d16e4205cfee272fbdc0568b68d82be796540b1537508cef59388f839c191928", size = 232572, upload-time = "2022-12-31T10:36:10.327Z" }, -] - [[package]] name = "pypinyin" version = "0.55.0" From 1ffb5338128a32e2760280cbdd54e3329853a52e Mon Sep 17 00:00:00 2001 From: yangdx Date: Tue, 11 Nov 2025 12:02:37 +0800 Subject: [PATCH 04/37] Update env.example --- env.example | 39 +++++++++++++++++++++++---------------- 1 file changed, 23 insertions(+), 16 deletions(-) diff --git a/env.example b/env.example index 534bd22a..4590ceee 100644 --- a/env.example +++ b/env.example @@ -172,6 +172,8 @@ MAX_PARALLEL_INSERT=2 ### LLM Configuration ### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock, gemini ### LLM_BINDING_HOST: host only for Ollama, endpoint for other LLM service +### If LightRAG deployed in Docker: +### uses host.docker.internal instead of localhost in LLM_BINDING_HOST ########################################################################### ### LLM request timeout setting for all llm (0 means no timeout for Ollma) # LLM_TIMEOUT=180 @@ -181,7 +183,7 @@ LLM_MODEL=gpt-4o LLM_BINDING_HOST=https://api.openai.com/v1 LLM_BINDING_API_KEY=your_api_key -### Optional for Azure +### Env vars for Azure openai # AZURE_OPENAI_API_VERSION=2024-08-01-preview # AZURE_OPENAI_DEPLOYMENT=gpt-4o @@ -196,22 +198,16 @@ LLM_BINDING_API_KEY=your_api_key # LLM_MODEL=gemini-flash-latest # LLM_BINDING_API_KEY=your_gemini_api_key # LLM_BINDING_HOST=https://generativelanguage.googleapis.com -GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}' + +### use the following command to see all support options for OpenAI, azure_openai or OpenRouter +### lightrag-server --llm-binding gemini --help +### Gemini Specific Parameters # GEMINI_LLM_MAX_OUTPUT_TOKENS=9000 # GEMINI_LLM_TEMPERATURE=0.7 - -### OpenAI Compatible API Specific Parameters -### Increased temperature values may mitigate infinite inference loops in certain LLM, such as Qwen3-30B. -# OPENAI_LLM_TEMPERATURE=0.9 -### Set the max_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s) -### Typically, max_tokens does not include prompt content, though some models, such as Gemini Models, are exceptions -### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider -# OPENAI_LLM_MAX_TOKENS=9000 -### For OpenAI o1-mini or newer modles -OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 - -#### OpenAI's new API utilizes max_completion_tokens instead of max_tokens -# OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 +### Enable Thinking +# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": -1, "include_thoughts": true}' +### Disable Thinking +# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}' ### use the following command to see all support options for OpenAI, azure_openai or OpenRouter ### lightrag-server --llm-binding openai --help @@ -222,8 +218,17 @@ OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 ### Qwen3 Specific Parameters deploy by vLLM # OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}' +### OpenAI Compatible API Specific Parameters +### Increased temperature values may mitigate infinite inference loops in certain LLM, such as Qwen3-30B. +# OPENAI_LLM_TEMPERATURE=0.9 +### Set the max_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s) +### Typically, max_tokens does not include prompt content +### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider +# OPENAI_LLM_MAX_TOKENS=9000 +### For OpenAI o1-mini or newer modles utilizes max_completion_tokens instead of max_tokens +OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 + ### use the following command to see all support options for Ollama LLM -### If LightRAG deployed in Docker uses host.docker.internal instead of localhost in LLM_BINDING_HOST ### lightrag-server --llm-binding ollama --help ### Ollama Server Specific Parameters ### OLLAMA_LLM_NUM_CTX must be provided, and should at least larger than MAX_TOTAL_TOKENS + 2000 @@ -240,6 +245,8 @@ OLLAMA_LLM_NUM_CTX=32768 ### Embedding Configuration (Should not be changed after the first file processed) ### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, aws_bedrock ### EMBEDDING_BINDING_HOST: host only for Ollama, endpoint for other Embedding service +### If LightRAG deployed in Docker: +### uses host.docker.internal instead of localhost in EMBEDDING_BINDING_HOST ####################################################################################### # EMBEDDING_TIMEOUT=30 From d1f4b6e515a7cd63555b93af3b93c685d60bb299 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 12 Nov 2025 00:11:13 +0800 Subject: [PATCH 05/37] Add data sanitization to JSON writing to prevent UTF-8 encoding errors MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Add _sanitize_json_data helper function • Recursively clean strings in data • Sanitize before JSON serialization • Prevent encoding-related crashes • Use existing sanitize_text_for_encoding --- lightrag/utils.py | 23 ++++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/lightrag/utils.py b/lightrag/utils.py index 460ede3c..064e4804 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -927,9 +927,30 @@ def load_json(file_name): return json.load(f) +def _sanitize_json_data(data: Any) -> Any: + """Recursively sanitize all string values in data structure for safe UTF-8 encoding + + Args: + data: Data to sanitize (dict, list, str, or other types) + + Returns: + Sanitized data with all strings cleaned of problematic characters + """ + if isinstance(data, dict): + return {k: _sanitize_json_data(v) for k, v in data.items()} + elif isinstance(data, list): + return [_sanitize_json_data(item) for item in data] + elif isinstance(data, str): + return sanitize_text_for_encoding(data, replacement_char="") + else: + return data + + def write_json(json_obj, file_name): + # Sanitize data before writing to prevent UTF-8 encoding errors + sanitized_obj = _sanitize_json_data(json_obj) with open(file_name, "w", encoding="utf-8") as f: - json.dump(json_obj, f, indent=2, ensure_ascii=False) + json.dump(sanitized_obj, f, indent=2, ensure_ascii=False) class TokenizerInterface(Protocol): From 6918a88f927bdf123826b1a5f0dbfd64bf6b39f8 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 12 Nov 2025 00:38:47 +0800 Subject: [PATCH 06/37] Add specialized JSON string sanitizer to prevent UTF-8 encoding errors MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Remove surrogate characters (U+D800-DFFF) • Filter Unicode non-characters • Direct char-by-char filtering --- lightrag/utils.py | 33 ++++++++++++++++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) diff --git a/lightrag/utils.py b/lightrag/utils.py index 064e4804..7232a91c 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -927,6 +927,37 @@ def load_json(file_name): return json.load(f) +def _sanitize_string_for_json(text: str) -> str: + """Remove characters that cannot be encoded in UTF-8 for JSON serialization. + + This is a simpler sanitizer specifically for JSON that directly removes + problematic characters without attempting to encode first. + + Args: + text: String to sanitize + + Returns: + Sanitized string safe for UTF-8 encoding in JSON + """ + if not text: + return text + + # Directly filter out problematic characters without pre-validation + sanitized = "" + for char in text: + code_point = ord(char) + # Skip surrogate characters (U+D800 to U+DFFF) - main cause of encoding errors + if 0xD800 <= code_point <= 0xDFFF: + continue + # Skip other non-characters in Unicode + elif code_point == 0xFFFE or code_point == 0xFFFF: + continue + else: + sanitized += char + + return sanitized + + def _sanitize_json_data(data: Any) -> Any: """Recursively sanitize all string values in data structure for safe UTF-8 encoding @@ -941,7 +972,7 @@ def _sanitize_json_data(data: Any) -> Any: elif isinstance(data, list): return [_sanitize_json_data(item) for item in data] elif isinstance(data, str): - return sanitize_text_for_encoding(data, replacement_char="") + return _sanitize_string_for_json(data) else: return data From f28a0c25b178d3347f9f72bf9e63242cefc1ea53 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 12 Nov 2025 00:50:18 +0800 Subject: [PATCH 07/37] Improve JSON data sanitization to handle tuples and dict keys - Sanitize dictionary keys - Preserve tuple types - Handle nested structures better --- lightrag/utils.py | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/lightrag/utils.py b/lightrag/utils.py index 7232a91c..4bfd20f2 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -961,19 +961,34 @@ def _sanitize_string_for_json(text: str) -> str: def _sanitize_json_data(data: Any) -> Any: """Recursively sanitize all string values in data structure for safe UTF-8 encoding + Handles all JSON-serializable types including: + - Dictionary keys and values + - Lists and tuples (preserves type) + - Nested structures + - Strings at any level + Args: - data: Data to sanitize (dict, list, str, or other types) + data: Data to sanitize (dict, list, tuple, str, or other types) Returns: Sanitized data with all strings cleaned of problematic characters """ if isinstance(data, dict): - return {k: _sanitize_json_data(v) for k, v in data.items()} - elif isinstance(data, list): - return [_sanitize_json_data(item) for item in data] + # Sanitize both keys and values + return { + _sanitize_string_for_json(k) + if isinstance(k, str) + else k: _sanitize_json_data(v) + for k, v in data.items() + } + elif isinstance(data, (list, tuple)): + # Handle both lists and tuples, preserve original type + sanitized = [_sanitize_json_data(item) for item in data] + return type(data)(sanitized) elif isinstance(data, str): return _sanitize_string_for_json(data) else: + # Numbers, booleans, None, etc. - return as-is return data From 8c07c91833041f0c98b29189a5f4b9259aad9ec2 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 12 Nov 2025 12:19:30 +0800 Subject: [PATCH 08/37] Remove deprecated response_type parameter from query settings - Bump API version to 0254 - Remove response format UI controls - Hard-code response_type in query params - Add migration for version 19 - Clean up settings store structure --- lightrag/api/__init__.py | 2 +- .../components/retrieval/QuerySettings.tsx | 41 ------------------- .../src/features/RetrievalTesting.tsx | 1 + lightrag_webui/src/stores/settings.ts | 9 +++- 4 files changed, 9 insertions(+), 44 deletions(-) diff --git a/lightrag/api/__init__.py b/lightrag/api/__init__.py index d9cb8673..8fdc9fae 100644 --- a/lightrag/api/__init__.py +++ b/lightrag/api/__init__.py @@ -1 +1 @@ -__api_version__ = "0253" +__api_version__ = "0254" diff --git a/lightrag_webui/src/components/retrieval/QuerySettings.tsx b/lightrag_webui/src/components/retrieval/QuerySettings.tsx index 4ffebbb1..0b0096c0 100644 --- a/lightrag_webui/src/components/retrieval/QuerySettings.tsx +++ b/lightrag_webui/src/components/retrieval/QuerySettings.tsx @@ -40,7 +40,6 @@ export default function QuerySettings() { // Default values for reset functionality const defaultValues = useMemo(() => ({ mode: 'mix' as QueryMode, - response_type: 'Multiple Paragraphs', top_k: 40, chunk_top_k: 20, max_entity_tokens: 6000, @@ -153,46 +152,6 @@ export default function QuerySettings() { - {/* Response Format */} - <> - - - - - - -

{t('retrievePanel.querySettings.responseFormatTooltip')}

- - - -
- - handleReset('response_type')} - title="Reset to default (Multiple Paragraphs)" - /> -
- - {/* Top K */} <> diff --git a/lightrag_webui/src/features/RetrievalTesting.tsx b/lightrag_webui/src/features/RetrievalTesting.tsx index 3fe8ec13..d7a475f6 100644 --- a/lightrag_webui/src/features/RetrievalTesting.tsx +++ b/lightrag_webui/src/features/RetrievalTesting.tsx @@ -357,6 +357,7 @@ export default function RetrievalTesting() { const queryParams = { ...state.querySettings, query: actualQuery, + response_type: 'Multiple Paragraphs', conversation_history: effectiveHistoryTurns > 0 ? prevMessages .filter((m) => m.isError !== true) diff --git a/lightrag_webui/src/stores/settings.ts b/lightrag_webui/src/stores/settings.ts index 966a2504..ac300af8 100644 --- a/lightrag_webui/src/stores/settings.ts +++ b/lightrag_webui/src/stores/settings.ts @@ -123,7 +123,6 @@ const useSettingsStoreBase = create()( querySettings: { mode: 'global', - response_type: 'Multiple Paragraphs', top_k: 40, chunk_top_k: 20, max_entity_tokens: 6000, @@ -239,7 +238,7 @@ const useSettingsStoreBase = create()( { name: 'settings-storage', storage: createJSONStorage(() => localStorage), - version: 18, + version: 19, migrate: (state: any, version: number) => { if (version < 2) { state.showEdgeLabel = false @@ -336,6 +335,12 @@ const useSettingsStoreBase = create()( // Add userPromptHistory field for older versions state.userPromptHistory = [] } + if (version < 19) { + // Remove deprecated response_type parameter + if (state.querySettings) { + delete state.querySettings.response_type + } + } return state } } From 777c987371d5183aee17d1daf172ca192e4fc9d1 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 12 Nov 2025 13:48:56 +0800 Subject: [PATCH 09/37] Optimize JSON write with fast/slow path to reduce memory usage - Fast path for clean data (no sanitization) - Slow path sanitizes during encoding - Reload shared memory after sanitization - Custom encoder avoids deep copies - Comprehensive test coverage --- lightrag/kg/json_doc_status_impl.py | 15 +- lightrag/kg/json_kv_impl.py | 15 +- lightrag/utils.py | 99 ++++++++++- tests/test_write_json_optimization.py | 244 ++++++++++++++++++++++++++ 4 files changed, 368 insertions(+), 5 deletions(-) create mode 100644 tests/test_write_json_optimization.py diff --git a/lightrag/kg/json_doc_status_impl.py b/lightrag/kg/json_doc_status_impl.py index 014499f2..3a36f58c 100644 --- a/lightrag/kg/json_doc_status_impl.py +++ b/lightrag/kg/json_doc_status_impl.py @@ -161,7 +161,20 @@ class JsonDocStatusStorage(DocStatusStorage): logger.debug( f"[{self.workspace}] Process {os.getpid()} doc status writting {len(data_dict)} records to {self.namespace}" ) - write_json(data_dict, self._file_name) + + # Write JSON and check if sanitization was applied + needs_reload = write_json(data_dict, self._file_name) + + # If data was sanitized, reload cleaned data to update shared memory + if needs_reload: + logger.info( + f"[{self.workspace}] Reloading sanitized data into shared memory for {self.namespace}" + ) + cleaned_data = load_json(self._file_name) + if cleaned_data: + self._data.clear() + self._data.update(cleaned_data) + await clear_all_update_flags(self.final_namespace) async def upsert(self, data: dict[str, dict[str, Any]]) -> None: diff --git a/lightrag/kg/json_kv_impl.py b/lightrag/kg/json_kv_impl.py index fd016b14..b3d9a34f 100644 --- a/lightrag/kg/json_kv_impl.py +++ b/lightrag/kg/json_kv_impl.py @@ -81,7 +81,20 @@ class JsonKVStorage(BaseKVStorage): logger.debug( f"[{self.workspace}] Process {os.getpid()} KV writting {data_count} records to {self.namespace}" ) - write_json(data_dict, self._file_name) + + # Write JSON and check if sanitization was applied + needs_reload = write_json(data_dict, self._file_name) + + # If data was sanitized, reload cleaned data to update shared memory + if needs_reload: + logger.info( + f"[{self.workspace}] Reloading sanitized data into shared memory for {self.namespace}" + ) + cleaned_data = load_json(self._file_name) + if cleaned_data: + self._data.clear() + self._data.update(cleaned_data) + await clear_all_update_flags(self.final_namespace) async def get_by_id(self, id: str) -> dict[str, Any] | None: diff --git a/lightrag/utils.py b/lightrag/utils.py index 4bfd20f2..da27926c 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -961,6 +961,10 @@ def _sanitize_string_for_json(text: str) -> str: def _sanitize_json_data(data: Any) -> Any: """Recursively sanitize all string values in data structure for safe UTF-8 encoding + DEPRECATED: This function creates a deep copy of the data which can be memory-intensive. + For new code, prefer using write_json with SanitizingJSONEncoder which sanitizes during + serialization without creating copies. + Handles all JSON-serializable types including: - Dictionary keys and values - Lists and tuples (preserves type) @@ -992,11 +996,100 @@ def _sanitize_json_data(data: Any) -> Any: return data +class SanitizingJSONEncoder(json.JSONEncoder): + """ + Custom JSON encoder that sanitizes data during serialization. + + This encoder cleans strings during the encoding process without creating + a full copy of the data structure, making it memory-efficient for large datasets. + """ + + def encode(self, o): + """Override encode method to handle simple string cases""" + if isinstance(o, str): + return json.encoder.encode_basestring(_sanitize_string_for_json(o)) + return super().encode(o) + + def iterencode(self, o, _one_shot=False): + """ + Override iterencode to sanitize strings during serialization. + This is the core method that handles complex nested structures. + """ + # Preprocess: sanitize all strings in the object + sanitized = self._sanitize_for_encoding(o) + + # Call parent's iterencode with sanitized data + for chunk in super().iterencode(sanitized, _one_shot): + yield chunk + + def _sanitize_for_encoding(self, obj): + """ + Recursively sanitize strings in an object. + Creates new objects only when necessary to avoid deep copies. + + Args: + obj: Object to sanitize + + Returns: + Sanitized object with cleaned strings + """ + if isinstance(obj, str): + return _sanitize_string_for_json(obj) + + elif isinstance(obj, dict): + # Create new dict with sanitized keys and values + new_dict = {} + for k, v in obj.items(): + clean_k = _sanitize_string_for_json(k) if isinstance(k, str) else k + clean_v = self._sanitize_for_encoding(v) + new_dict[clean_k] = clean_v + return new_dict + + elif isinstance(obj, (list, tuple)): + # Sanitize list/tuple elements + cleaned = [self._sanitize_for_encoding(item) for item in obj] + return type(obj)(cleaned) if isinstance(obj, tuple) else cleaned + + else: + # Numbers, booleans, None, etc. remain unchanged + return obj + + def write_json(json_obj, file_name): - # Sanitize data before writing to prevent UTF-8 encoding errors - sanitized_obj = _sanitize_json_data(json_obj) + """ + Write JSON data to file with optimized sanitization strategy. + + This function uses a two-stage approach: + 1. Fast path: Try direct serialization (works for clean data ~99% of time) + 2. Slow path: Use custom encoder that sanitizes during serialization + + The custom encoder approach avoids creating a deep copy of the data, + making it memory-efficient. When sanitization occurs, the caller should + reload the cleaned data from the file to update shared memory. + + Args: + json_obj: Object to serialize (may be a shallow copy from shared memory) + file_name: Output file path + + Returns: + bool: True if sanitization was applied (caller should reload data), + False if direct write succeeded (no reload needed) + """ + try: + # Strategy 1: Fast path - try direct serialization + with open(file_name, "w", encoding="utf-8") as f: + json.dump(json_obj, f, indent=2, ensure_ascii=False) + return False # No sanitization needed, no reload required + + except (UnicodeEncodeError, UnicodeDecodeError) as e: + logger.debug(f"Direct JSON write failed, using sanitizing encoder: {e}") + + # Strategy 2: Use custom encoder (sanitizes during serialization, zero memory copy) with open(file_name, "w", encoding="utf-8") as f: - json.dump(sanitized_obj, f, indent=2, ensure_ascii=False) + json.dump(json_obj, f, indent=2, ensure_ascii=False, cls=SanitizingJSONEncoder) + + logger.info(f"JSON sanitization applied during write: {file_name}") + return True # Sanitization applied, reload recommended class TokenizerInterface(Protocol): diff --git a/tests/test_write_json_optimization.py b/tests/test_write_json_optimization.py new file mode 100644 index 00000000..ea555c50 --- /dev/null +++ b/tests/test_write_json_optimization.py @@ -0,0 +1,244 @@ +""" +Test suite for write_json optimization + +This test verifies: +1. Fast path works for clean data (no sanitization) +2. Slow path applies sanitization for dirty data +3. Sanitization is done during encoding (memory-efficient) +4. Reloading updates shared memory with cleaned data +""" + +import os +import json +import tempfile +from lightrag.utils import write_json, load_json, SanitizingJSONEncoder + + +class TestWriteJsonOptimization: + """Test write_json optimization with two-stage approach""" + + def test_fast_path_clean_data(self): + """Test that clean data takes the fast path without sanitization""" + clean_data = { + "name": "John Doe", + "age": 30, + "items": ["apple", "banana", "cherry"], + "nested": {"key": "value", "number": 42}, + } + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + # Write clean data - should return False (no sanitization) + needs_reload = write_json(clean_data, temp_file) + assert not needs_reload, "Clean data should not require sanitization" + + # Verify data was written correctly + loaded_data = load_json(temp_file) + assert loaded_data == clean_data, "Loaded data should match original" + finally: + os.unlink(temp_file) + + def test_slow_path_dirty_data(self): + """Test that dirty data triggers sanitization""" + # Create data with surrogate characters (U+D800 to U+DFFF) + dirty_string = "Hello\ud800World" # Contains surrogate character + dirty_data = {"text": dirty_string, "number": 123} + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + # Write dirty data - should return True (sanitization applied) + needs_reload = write_json(dirty_data, temp_file) + assert needs_reload, "Dirty data should trigger sanitization" + + # Verify data was written and sanitized + loaded_data = load_json(temp_file) + assert loaded_data is not None, "Data should be written" + assert loaded_data["number"] == 123, "Clean fields should remain unchanged" + # Surrogate character should be removed + assert ( + "\ud800" not in loaded_data["text"] + ), "Surrogate character should be removed" + finally: + os.unlink(temp_file) + + def test_sanitizing_encoder_removes_surrogates(self): + """Test that SanitizingJSONEncoder removes surrogate characters""" + data_with_surrogates = { + "text": "Hello\ud800\udc00World", # Contains surrogate pair + "clean": "Clean text", + "nested": {"dirty_key\ud801": "value", "clean_key": "clean\ud802value"}, + } + + # Encode using custom encoder + encoded = json.dumps( + data_with_surrogates, cls=SanitizingJSONEncoder, ensure_ascii=False + ) + + # Verify no surrogate characters in output + assert "\ud800" not in encoded, "Surrogate U+D800 should be removed" + assert "\udc00" not in encoded, "Surrogate U+DC00 should be removed" + assert "\ud801" not in encoded, "Surrogate U+D801 should be removed" + assert "\ud802" not in encoded, "Surrogate U+D802 should be removed" + + # Verify clean parts remain + assert "Clean text" in encoded, "Clean text should remain" + assert "clean_key" in encoded, "Clean keys should remain" + + def test_nested_structure_sanitization(self): + """Test sanitization of deeply nested structures""" + nested_data = { + "level1": { + "level2": { + "level3": {"dirty": "text\ud800here", "clean": "normal text"}, + "list": ["item1", "item\ud801dirty", "item3"], + } + } + } + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + needs_reload = write_json(nested_data, temp_file) + assert needs_reload, "Nested dirty data should trigger sanitization" + + # Verify nested structure is preserved + loaded_data = load_json(temp_file) + assert "level1" in loaded_data + assert "level2" in loaded_data["level1"] + assert "level3" in loaded_data["level1"]["level2"] + + # Verify surrogates are removed + dirty_text = loaded_data["level1"]["level2"]["level3"]["dirty"] + assert "\ud800" not in dirty_text, "Nested surrogate should be removed" + + # Verify list items are sanitized + list_items = loaded_data["level1"]["level2"]["list"] + assert ( + "\ud801" not in list_items[1] + ), "List item surrogates should be removed" + finally: + os.unlink(temp_file) + + def test_unicode_non_characters_removed(self): + """Test that Unicode non-characters (U+FFFE, U+FFFF) don't cause encoding errors + + Note: U+FFFE and U+FFFF are valid UTF-8 characters (though discouraged), + so they don't trigger sanitization. They only get removed when explicitly + using the SanitizingJSONEncoder. + """ + data_with_nonchars = {"text1": "Hello\ufffeWorld", "text2": "Test\uffffString"} + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + # These characters are valid UTF-8, so they take the fast path + needs_reload = write_json(data_with_nonchars, temp_file) + assert not needs_reload, "U+FFFE/U+FFFF are valid UTF-8 characters" + + loaded_data = load_json(temp_file) + # They're written as-is in the fast path + assert loaded_data == data_with_nonchars + finally: + os.unlink(temp_file) + + def test_mixed_clean_dirty_data(self): + """Test data with both clean and dirty fields""" + mixed_data = { + "clean_field": "This is perfectly fine", + "dirty_field": "This has\ud800issues", + "number": 42, + "boolean": True, + "null_value": None, + "clean_list": [1, 2, 3], + "dirty_list": ["clean", "dirty\ud801item"], + } + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + needs_reload = write_json(mixed_data, temp_file) + assert ( + needs_reload + ), "Mixed data with dirty fields should trigger sanitization" + + loaded_data = load_json(temp_file) + + # Clean fields should remain unchanged + assert loaded_data["clean_field"] == "This is perfectly fine" + assert loaded_data["number"] == 42 + assert loaded_data["boolean"] + assert loaded_data["null_value"] is None + assert loaded_data["clean_list"] == [1, 2, 3] + + # Dirty fields should be sanitized + assert "\ud800" not in loaded_data["dirty_field"] + assert "\ud801" not in loaded_data["dirty_list"][1] + finally: + os.unlink(temp_file) + + def test_empty_and_none_strings(self): + """Test handling of empty and None values""" + data = { + "empty": "", + "none": None, + "zero": 0, + "false": False, + "empty_list": [], + "empty_dict": {}, + } + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + needs_reload = write_json(data, temp_file) + assert ( + not needs_reload + ), "Clean empty values should not trigger sanitization" + + loaded_data = load_json(temp_file) + assert loaded_data == data, "Empty/None values should be preserved" + finally: + os.unlink(temp_file) + + +if __name__ == "__main__": + # Run tests + test = TestWriteJsonOptimization() + + print("Running test_fast_path_clean_data...") + test.test_fast_path_clean_data() + print("✓ Passed") + + print("Running test_slow_path_dirty_data...") + test.test_slow_path_dirty_data() + print("✓ Passed") + + print("Running test_sanitizing_encoder_removes_surrogates...") + test.test_sanitizing_encoder_removes_surrogates() + print("✓ Passed") + + print("Running test_nested_structure_sanitization...") + test.test_nested_structure_sanitization() + print("✓ Passed") + + print("Running test_unicode_non_characters_removed...") + test.test_unicode_non_characters_removed() + print("✓ Passed") + + print("Running test_mixed_clean_dirty_data...") + test.test_mixed_clean_dirty_data() + print("✓ Passed") + + print("Running test_empty_and_none_strings...") + test.test_empty_and_none_strings() + print("✓ Passed") + + print("\n✅ All tests passed!") From 6de4123f74e4309e7a0689b4aa8d140b361858f5 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 12 Nov 2025 15:42:07 +0800 Subject: [PATCH 10/37] Optimize JSON string sanitization with precompiled regex and zero-copy - Precompile regex pattern at module level - Zero-copy path for clean strings - Use C-level regex for performance - Remove deprecated _sanitize_json_data - Fast detection for common case --- lightrag/utils.py | 65 ++++++++--------------------------------------- 1 file changed, 11 insertions(+), 54 deletions(-) diff --git a/lightrag/utils.py b/lightrag/utils.py index da27926c..b78b7523 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -56,6 +56,9 @@ if not logger.handlers: # Set httpx logging level to WARNING logging.getLogger("httpx").setLevel(logging.WARNING) +# Precompile regex pattern for JSON sanitization (module-level, compiled once) +_SURROGATE_PATTERN = re.compile(r"[\uD800-\uDFFF\uFFFE\uFFFF]") + # Global import for pypinyin with startup-time logging try: import pypinyin @@ -930,70 +933,24 @@ def load_json(file_name): def _sanitize_string_for_json(text: str) -> str: """Remove characters that cannot be encoded in UTF-8 for JSON serialization. - This is a simpler sanitizer specifically for JSON that directly removes - problematic characters without attempting to encode first. + Uses regex for optimal performance with zero-copy optimization for clean strings. + Fast detection path for clean strings (99% of cases) with efficient removal for dirty strings. Args: text: String to sanitize Returns: - Sanitized string safe for UTF-8 encoding in JSON + Original string if clean (zero-copy), sanitized string if dirty """ if not text: return text - # Directly filter out problematic characters without pre-validation - sanitized = "" - for char in text: - code_point = ord(char) - # Skip surrogate characters (U+D800 to U+DFFF) - main cause of encoding errors - if 0xD800 <= code_point <= 0xDFFF: - continue - # Skip other non-characters in Unicode - elif code_point == 0xFFFE or code_point == 0xFFFF: - continue - else: - sanitized += char + # Fast path: Check if sanitization is needed using C-level regex search + if not _SURROGATE_PATTERN.search(text): + return text # Zero-copy for clean strings - most common case - return sanitized - - -def _sanitize_json_data(data: Any) -> Any: - """Recursively sanitize all string values in data structure for safe UTF-8 encoding - - DEPRECATED: This function creates a deep copy of the data which can be memory-intensive. - For new code, prefer using write_json with SanitizingJSONEncoder which sanitizes during - serialization without creating copies. - - Handles all JSON-serializable types including: - - Dictionary keys and values - - Lists and tuples (preserves type) - - Nested structures - - Strings at any level - - Args: - data: Data to sanitize (dict, list, tuple, str, or other types) - - Returns: - Sanitized data with all strings cleaned of problematic characters - """ - if isinstance(data, dict): - # Sanitize both keys and values - return { - _sanitize_string_for_json(k) - if isinstance(k, str) - else k: _sanitize_json_data(v) - for k, v in data.items() - } - elif isinstance(data, (list, tuple)): - # Handle both lists and tuples, preserve original type - sanitized = [_sanitize_json_data(item) for item in data] - return type(data)(sanitized) - elif isinstance(data, str): - return _sanitize_string_for_json(data) - else: - # Numbers, booleans, None, etc. - return as-is - return data + # Slow path: Remove problematic characters using C-level regex substitution + return _SURROGATE_PATTERN.sub("", text) class SanitizingJSONEncoder(json.JSONEncoder): From dcf1d286813217168953346ed1e77e70165f9195 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 12 Nov 2025 16:16:28 +0800 Subject: [PATCH 11/37] Fix migration to reload sanitized data and prevent memory corruption MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Reload cleaned data after sanitization • Update shared memory with clean data • Add specific surrogate char tests • Test migration sanitization flow • Prevent dirty data in memory --- lightrag/kg/json_kv_impl.py | 15 ++++- tests/test_write_json_optimization.py | 90 +++++++++++++++++++++++++++ 2 files changed, 102 insertions(+), 3 deletions(-) diff --git a/lightrag/kg/json_kv_impl.py b/lightrag/kg/json_kv_impl.py index b3d9a34f..3f99dd4d 100644 --- a/lightrag/kg/json_kv_impl.py +++ b/lightrag/kg/json_kv_impl.py @@ -237,7 +237,7 @@ class JsonKVStorage(BaseKVStorage): data: Original data dictionary that may contain legacy structure Returns: - Migrated data dictionary with flattened cache keys + Migrated data dictionary with flattened cache keys (sanitized if needed) """ from lightrag.utils import generate_cache_key @@ -274,8 +274,17 @@ class JsonKVStorage(BaseKVStorage): logger.info( f"[{self.workspace}] Migrated {migration_count} legacy cache entries to flattened structure" ) - # Persist migrated data immediately - write_json(migrated_data, self._file_name) + # Persist migrated data immediately and check if sanitization was applied + needs_reload = write_json(migrated_data, self._file_name) + + # If data was sanitized during write, reload cleaned data + if needs_reload: + logger.info( + f"[{self.workspace}] Reloading sanitized migration data for {self.namespace}" + ) + cleaned_data = load_json(self._file_name) + if cleaned_data: + return cleaned_data # Return cleaned data to update shared memory return migrated_data diff --git a/tests/test_write_json_optimization.py b/tests/test_write_json_optimization.py index ea555c50..9c4105b9 100644 --- a/tests/test_write_json_optimization.py +++ b/tests/test_write_json_optimization.py @@ -208,6 +208,88 @@ class TestWriteJsonOptimization: finally: os.unlink(temp_file) + def test_specific_surrogate_udc9a(self): + """Test specific surrogate character \\udc9a mentioned in the issue""" + # Test the exact surrogate character from the error message: + # UnicodeEncodeError: 'utf-8' codec can't encode character '\\udc9a' + data_with_udc9a = { + "text": "Some text with surrogate\udc9acharacter", + "position": 201, # As mentioned in the error + "clean_field": "Normal text", + } + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + # Write data - should trigger sanitization + needs_reload = write_json(data_with_udc9a, temp_file) + assert needs_reload, "Data with \\udc9a should trigger sanitization" + + # Verify surrogate was removed + loaded_data = load_json(temp_file) + assert loaded_data is not None + assert "\udc9a" not in loaded_data["text"], "\\udc9a should be removed" + assert ( + loaded_data["clean_field"] == "Normal text" + ), "Clean fields should remain" + finally: + os.unlink(temp_file) + + def test_migration_with_surrogate_sanitization(self): + """Test that migration process handles surrogate characters correctly + + This test simulates the scenario where legacy cache contains surrogate + characters and ensures they are cleaned during migration. + """ + # Simulate legacy cache data with surrogate characters + legacy_data_with_surrogates = { + "cache_entry_1": { + "return": "Result with\ud800surrogate", + "cache_type": "extract", + "original_prompt": "Some\udc9aprompt", + }, + "cache_entry_2": { + "return": "Clean result", + "cache_type": "query", + "original_prompt": "Clean prompt", + }, + } + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + # First write the dirty data directly (simulating legacy cache file) + # Use custom encoder to force write even with surrogates + with open(temp_file, "w", encoding="utf-8") as f: + json.dump( + legacy_data_with_surrogates, + f, + cls=SanitizingJSONEncoder, + ensure_ascii=False, + ) + + # Load and verify surrogates were cleaned during initial write + loaded_data = load_json(temp_file) + assert loaded_data is not None + + # The data should be sanitized + assert ( + "\ud800" not in loaded_data["cache_entry_1"]["return"] + ), "Surrogate in return should be removed" + assert ( + "\udc9a" not in loaded_data["cache_entry_1"]["original_prompt"] + ), "Surrogate in prompt should be removed" + + # Clean data should remain unchanged + assert ( + loaded_data["cache_entry_2"]["return"] == "Clean result" + ), "Clean data should remain" + + finally: + os.unlink(temp_file) + if __name__ == "__main__": # Run tests @@ -241,4 +323,12 @@ if __name__ == "__main__": test.test_empty_and_none_strings() print("✓ Passed") + print("Running test_specific_surrogate_udc9a...") + test.test_specific_surrogate_udc9a() + print("✓ Passed") + + print("Running test_migration_with_surrogate_sanitization...") + test.test_migration_with_surrogate_sanitization() + print("✓ Passed") + print("\n✅ All tests passed!") From 70cc2419f2e8707aaf4b2c273d650373d6e3f6a0 Mon Sep 17 00:00:00 2001 From: yangdx Date: Wed, 12 Nov 2025 16:40:57 +0800 Subject: [PATCH 12/37] Fix empty dict handling after JSON sanitization MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Replace truthy checks with `is not None` • Handle empty dict edge case properly • Prevent data reload failures • Add comprehensive test coverage • Fix JsonKVStorage and DocStatusStorage --- lightrag/kg/json_doc_status_impl.py | 2 +- lightrag/kg/json_kv_impl.py | 4 +- tests/test_write_json_optimization.py | 53 +++++++++++++++++++++++++++ 3 files changed, 56 insertions(+), 3 deletions(-) diff --git a/lightrag/kg/json_doc_status_impl.py b/lightrag/kg/json_doc_status_impl.py index 3a36f58c..bf6e7b17 100644 --- a/lightrag/kg/json_doc_status_impl.py +++ b/lightrag/kg/json_doc_status_impl.py @@ -171,7 +171,7 @@ class JsonDocStatusStorage(DocStatusStorage): f"[{self.workspace}] Reloading sanitized data into shared memory for {self.namespace}" ) cleaned_data = load_json(self._file_name) - if cleaned_data: + if cleaned_data is not None: self._data.clear() self._data.update(cleaned_data) diff --git a/lightrag/kg/json_kv_impl.py b/lightrag/kg/json_kv_impl.py index 3f99dd4d..f9adb20f 100644 --- a/lightrag/kg/json_kv_impl.py +++ b/lightrag/kg/json_kv_impl.py @@ -91,7 +91,7 @@ class JsonKVStorage(BaseKVStorage): f"[{self.workspace}] Reloading sanitized data into shared memory for {self.namespace}" ) cleaned_data = load_json(self._file_name) - if cleaned_data: + if cleaned_data is not None: self._data.clear() self._data.update(cleaned_data) @@ -283,7 +283,7 @@ class JsonKVStorage(BaseKVStorage): f"[{self.workspace}] Reloading sanitized migration data for {self.namespace}" ) cleaned_data = load_json(self._file_name) - if cleaned_data: + if cleaned_data is not None: return cleaned_data # Return cleaned data to update shared memory return migrated_data diff --git a/tests/test_write_json_optimization.py b/tests/test_write_json_optimization.py index 9c4105b9..0a92904f 100644 --- a/tests/test_write_json_optimization.py +++ b/tests/test_write_json_optimization.py @@ -290,6 +290,55 @@ class TestWriteJsonOptimization: finally: os.unlink(temp_file) + def test_empty_values_after_sanitization(self): + """Test that data with empty values after sanitization is properly handled + + Critical edge case: When sanitization results in data with empty string values, + we must use 'if cleaned_data is not None' instead of 'if cleaned_data' to ensure + proper reload, since truthy check on dict depends on content, not just existence. + """ + # Create data where ALL values are only surrogate characters + all_dirty_data = { + "key1": "\ud800\udc00\ud801", + "key2": "\ud802\ud803", + } + + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as f: + temp_file = f.name + + try: + # Write dirty data - should trigger sanitization + needs_reload = write_json(all_dirty_data, temp_file) + assert needs_reload, "All-dirty data should trigger sanitization" + + # Load the sanitized data + cleaned_data = load_json(temp_file) + + # Critical assertions for the edge case + assert cleaned_data is not None, "Cleaned data should not be None" + # Sanitization removes surrogates but preserves keys with empty values + assert cleaned_data == { + "key1": "", + "key2": "", + }, "Surrogates should be removed, keys preserved" + # This dict is truthy because it has keys (even with empty values) + assert cleaned_data, "Dict with keys is truthy" + + # Test the actual edge case: empty dict + empty_data = {} + needs_reload2 = write_json(empty_data, temp_file) + assert not needs_reload2, "Empty dict is clean" + + reloaded_empty = load_json(temp_file) + assert reloaded_empty is not None, "Empty dict should not be None" + assert reloaded_empty == {}, "Empty dict should remain empty" + assert ( + not reloaded_empty + ), "Empty dict evaluates to False (the critical check)" + + finally: + os.unlink(temp_file) + if __name__ == "__main__": # Run tests @@ -331,4 +380,8 @@ if __name__ == "__main__": test.test_migration_with_surrogate_sanitization() print("✓ Passed") + print("Running test_empty_values_after_sanitization...") + test.test_empty_values_after_sanitization() + print("✓ Passed") + print("\n✅ All tests passed!") From f7432a260e0fd543dccbfb9606a8db7d316fed50 Mon Sep 17 00:00:00 2001 From: Louis Lacombe Date: Wed, 12 Nov 2025 16:11:05 +0000 Subject: [PATCH 13/37] Add support for environment variable fallback for API key and default host for cloud models --- lightrag/llm/ollama.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/lightrag/llm/ollama.py b/lightrag/llm/ollama.py index b013496e..28d91075 100644 --- a/lightrag/llm/ollama.py +++ b/lightrag/llm/ollama.py @@ -1,4 +1,5 @@ from collections.abc import AsyncIterator +import os import pipmaster as pm @@ -53,6 +54,9 @@ async def _ollama_model_if_cache( timeout = None kwargs.pop("hashing_kv", None) api_key = kwargs.pop("api_key", None) + # fallback to environment variable when not provided explicitly + if not api_key: + api_key = os.getenv("OLLAMA_API_KEY") headers = { "Content-Type": "application/json", "User-Agent": f"LightRAG/{__api_version__}", @@ -60,6 +64,16 @@ async def _ollama_model_if_cache( if api_key: headers["Authorization"] = f"Bearer {api_key}" + # If this is a cloud model (names include '-cloud' or ':cloud'), default + # the host to Ollama cloud when no explicit host was provided. + try: + model_name_str = str(model) if model is not None else "" + except Exception: + model_name_str = "" + + if host is None and ("-cloud" in model_name_str or ":cloud" in model_name_str): + host = "https://ollama.com" + ollama_client = ollama.AsyncClient(host=host, timeout=timeout, headers=headers) try: @@ -144,6 +158,8 @@ async def ollama_model_complete( async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray: api_key = kwargs.pop("api_key", None) + if not api_key: + api_key = os.getenv("OLLAMA_API_KEY") headers = { "Content-Type": "application/json", "User-Agent": f"LightRAG/{__api_version__}", @@ -154,6 +170,15 @@ async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray: host = kwargs.pop("host", None) timeout = kwargs.pop("timeout", None) + # If embed_model targets Ollama cloud, default host when not provided + try: + embed_model_name = str(embed_model) if embed_model is not None else "" + except Exception: + embed_model_name = "" + + if host is None and ("-cloud" in embed_model_name or ":cloud" in embed_model_name): + host = "https://ollama.com" + ollama_client = ollama.AsyncClient(host=host, timeout=timeout, headers=headers) try: options = kwargs.pop("options", {}) From 343d30727ae6951881b819bbf055cbb9d6f93866 Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 11:40:56 +0800 Subject: [PATCH 14/37] Update env.example --- env.example | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/env.example b/env.example index 4590ceee..a95ff9bf 100644 --- a/env.example +++ b/env.example @@ -29,7 +29,7 @@ WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System" # OLLAMA_EMULATING_MODEL_NAME=lightrag OLLAMA_EMULATING_MODEL_TAG=latest -### Max nodes return from graph retrieval in webui +### Max nodes for graph retrieval (Ensure WebUI local settings are also updated, which is limited to this value) # MAX_GRAPH_NODES=1000 ### Logging level From 940bec0b31e6743d269ca02167c82d0b28854893 Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 12:37:15 +0800 Subject: [PATCH 15/37] Support async chunking functions in LightRAG processing pipeline - Add Awaitable and Union type imports - Update chunking_func type annotation - Handle coroutine results with await - Add return type validation - Update docstring for async support --- lightrag/lightrag.py | 31 +++++++++++++++++++++++-------- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index af953fdf..8bec154f 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -12,6 +12,7 @@ from functools import partial from typing import ( Any, AsyncIterator, + Awaitable, Callable, Iterator, cast, @@ -20,6 +21,7 @@ from typing import ( Optional, List, Dict, + Union, ) from lightrag.prompt import PROMPTS from lightrag.exceptions import PipelineCancelledException @@ -243,11 +245,13 @@ class LightRAG: int, int, ], - List[Dict[str, Any]], + Union[List[Dict[str, Any]], Awaitable[List[Dict[str, Any]]]], ] = field(default_factory=lambda: chunking_by_token_size) """ Custom chunking function for splitting text into chunks before processing. + The function can be either synchronous or asynchronous. + The function should take the following parameters: - `tokenizer`: A Tokenizer instance to use for tokenization. @@ -257,7 +261,8 @@ class LightRAG: - `chunk_token_size`: The maximum number of tokens per chunk. - `chunk_overlap_token_size`: The number of overlapping tokens between consecutive chunks. - The function should return a list of dictionaries, where each dictionary contains the following keys: + The function should return a list of dictionaries (or an awaitable that resolves to a list), + where each dictionary contains the following keys: - `tokens`: The number of tokens in the chunk. - `content`: The text content of the chunk. @@ -1756,8 +1761,8 @@ class LightRAG: ) content = content_data["content"] - # Generate chunks from document - chunks = self.chunking_func( + # Call chunking function, supporting both sync and async implementations + chunking_result = self.chunking_func( self.tokenizer, content, split_by_character, @@ -1765,9 +1770,19 @@ class LightRAG: self.chunk_overlap_token_size, self.chunk_token_size, ) - # 判断chunks是否是异步异步函数的返回 - if asyncio.iscoroutine(chunks): - chunks = await chunks + + # If result is a coroutine, await to get actual result + if asyncio.iscoroutine(chunking_result): + chunking_result = await chunking_result + + # Validate return type + if not isinstance(chunking_result, (list, tuple)): + raise TypeError( + f"chunking_func must return a list or tuple of dicts, " + f"got {type(chunking_result)}" + ) + + # Build chunks dictionary chunks: dict[str, Any] = { compute_mdhash_id(dp["content"], prefix="chunk-"): { **dp, @@ -1775,7 +1790,7 @@ class LightRAG: "file_path": file_path, # Add file path to each chunk "llm_cache_list": [], # Initialize empty LLM cache list for each chunk } - for dp in chunks + for dp in chunking_result } if not chunks: From c230d1a28d1ffb8de43010d49e945c1dabaee257 Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 12:56:01 +0800 Subject: [PATCH 16/37] Replace asyncio.iscoroutine with inspect.isawaitable for better detection --- lightrag/lightrag.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index 8bec154f..e540e3b7 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -3,6 +3,7 @@ from __future__ import annotations import traceback import asyncio import configparser +import inspect import os import time import warnings @@ -1771,8 +1772,8 @@ class LightRAG: self.chunk_token_size, ) - # If result is a coroutine, await to get actual result - if asyncio.iscoroutine(chunking_result): + # If result is awaitable, await to get actual result + if inspect.isawaitable(chunking_result): chunking_result = await chunking_result # Validate return type From 4b31942e2a970f77f2ac0d2053c3ab969d4f7372 Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 13:34:09 +0800 Subject: [PATCH 17/37] refactor: move document deps to api group, remove dynamic imports - Merge offline-docs into api extras - Remove pipmaster dynamic installs - Add async document processing - Pre-check docling availability - Update offline deployment docs --- docs/OfflineDeployment.md | 34 +-- lightrag/api/routers/document_routes.py | 323 +++++++++++++----------- pyproject.toml | 24 +- requirements-offline-docs.txt | 15 -- 4 files changed, 205 insertions(+), 191 deletions(-) delete mode 100644 requirements-offline-docs.txt diff --git a/docs/OfflineDeployment.md b/docs/OfflineDeployment.md index 5307da6f..e186dda0 100644 --- a/docs/OfflineDeployment.md +++ b/docs/OfflineDeployment.md @@ -23,10 +23,11 @@ LightRAG uses dynamic package installation (`pipmaster`) for optional features b LightRAG dynamically installs packages for: -- **Document Processing**: `docling`, `pypdf2`, `python-docx`, `python-pptx`, `openpyxl` - **Storage Backends**: `redis`, `neo4j`, `pymilvus`, `pymongo`, `asyncpg`, `qdrant-client` - **LLM Providers**: `openai`, `anthropic`, `ollama`, `zhipuai`, `aioboto3`, `voyageai`, `llama-index`, `lmdeploy`, `transformers`, `torch` -- Tiktoken Models**: BPE encoding models downloaded from OpenAI CDN +- **Tiktoken Models**: BPE encoding models downloaded from OpenAI CDN + +**Note**: Document processing dependencies (`pypdf`, `python-docx`, `python-pptx`, `openpyxl`) are now pre-installed with the `api` extras group and no longer require dynamic installation. ## Quick Start @@ -75,32 +76,31 @@ LightRAG provides flexible dependency groups for different use cases: | Group | Description | Use Case | |-------|-------------|----------| -| `offline-docs` | Document processing | PDF, DOCX, PPTX, XLSX files | +| `api` | API server + document processing | FastAPI server with PDF, DOCX, PPTX, XLSX support | | `offline-storage` | Storage backends | Redis, Neo4j, MongoDB, PostgreSQL, etc. | | `offline-llm` | LLM providers | OpenAI, Anthropic, Ollama, etc. | -| `offline` | All of the above | Complete offline deployment | +| `offline` | Complete offline package | API + Storage + LLM (all features) | + +**Note**: Document processing (PDF, DOCX, PPTX, XLSX) is included in the `api` extras group. The previous `offline-docs` group has been merged into `api` for better integration. > Software packages requiring `transformers`, `torch`, or `cuda` will not be included in the offline dependency group. ### Installation Examples ```bash -# Install only document processing dependencies -pip install lightrag-hku[offline-docs] +# Install API with document processing +pip install lightrag-hku[api] -# Install document processing and storage backends -pip install lightrag-hku[offline-docs,offline-storage] +# Install API and storage backends +pip install lightrag-hku[api,offline-storage] -# Install all offline dependencies +# Install all offline dependencies (recommended for offline deployment) pip install lightrag-hku[offline] ``` ### Using Individual Requirements Files ```bash -# Document processing only -pip install -r requirements-offline-docs.txt - # Storage backends only pip install -r requirements-offline-storage.txt @@ -244,8 +244,8 @@ ls -la ~/.tiktoken_cache/ **Solution**: ```bash # Pre-install the specific package you need -# For document processing: -pip install lightrag-hku[offline-docs] +# For API with document processing: +pip install lightrag-hku[api] # For storage backends: pip install lightrag-hku[offline-storage] @@ -297,9 +297,9 @@ mkdir -p ~/my_tiktoken_cache 5. **Minimal Installation**: Only install what you need: ```bash - # If you only process PDFs with OpenAI - pip install lightrag-hku[offline-docs] - # Then manually add: pip install openai + # If you only need API with document processing + pip install lightrag-hku[api] + # Then manually add specific LLM: pip install openai ``` ## Additional Resources diff --git a/lightrag/api/routers/document_routes.py b/lightrag/api/routers/document_routes.py index f86bcea8..3f87517d 100644 --- a/lightrag/api/routers/document_routes.py +++ b/lightrag/api/routers/document_routes.py @@ -7,10 +7,10 @@ from lightrag.utils import logger, get_pinyin_sort_key import aiofiles import shutil import traceback -import pipmaster as pm from datetime import datetime, timezone from pathlib import Path from typing import Dict, List, Optional, Any, Literal +from io import BytesIO from fastapi import ( APIRouter, BackgroundTasks, @@ -27,6 +27,20 @@ from lightrag.utils import generate_track_id from lightrag.api.utils_api import get_combined_auth_dependency from ..config import global_args +# Check docling availability at module load time +DOCLING_AVAILABLE = False +try: + import docling # noqa: F401 # type: ignore[import-not-found] + + DOCLING_AVAILABLE = True +except ImportError: + if global_args.document_loading_engine == "DOCLING": + logger.warning( + "DOCLING engine requested but 'docling' package not installed. " + "Falling back to standard document processing. " + "To use DOCLING, install with: pip install lightrag-hku[api,docling]" + ) + # Function to format datetime to ISO format string with timezone information def format_datetime(dt: Any) -> Optional[str]: @@ -879,7 +893,6 @@ def get_unique_filename_in_enqueued(target_dir: Path, original_name: str) -> str Returns: str: Unique filename (may have numeric suffix added) """ - from pathlib import Path import time original_path = Path(original_name) @@ -902,6 +915,122 @@ def get_unique_filename_in_enqueued(target_dir: Path, original_name: str) -> str return f"{base_name}_{timestamp}{extension}" +# Document processing helper functions (synchronous) +# These functions run in thread pool via asyncio.to_thread() to avoid blocking the event loop + + +def _convert_with_docling(file_path: Path) -> str: + """Convert document using docling (synchronous). + + Args: + file_path: Path to the document file + + Returns: + str: Extracted markdown content + """ + from docling.document_converter import DocumentConverter # type: ignore + + converter = DocumentConverter() + result = converter.convert(file_path) + return result.document.export_to_markdown() + + +def _extract_pdf_pypdf(file_bytes: bytes, password: str = None) -> str: + """Extract PDF content using pypdf (synchronous). + + Args: + file_bytes: PDF file content as bytes + password: Optional password for encrypted PDFs + + Returns: + str: Extracted text content + + Raises: + Exception: If PDF is encrypted and password is incorrect or missing + """ + from pypdf import PdfReader # type: ignore + + pdf_file = BytesIO(file_bytes) + reader = PdfReader(pdf_file) + + # Check if PDF is encrypted + if reader.is_encrypted: + if not password: + raise Exception("PDF is encrypted but no password provided") + + decrypt_result = reader.decrypt(password) + if decrypt_result == 0: + raise Exception("Incorrect PDF password") + + # Extract text from all pages + content = "" + for page in reader.pages: + content += page.extract_text() + "\n" + + return content + + +def _extract_docx(file_bytes: bytes) -> str: + """Extract DOCX content (synchronous). + + Args: + file_bytes: DOCX file content as bytes + + Returns: + str: Extracted text content + """ + from docx import Document # type: ignore + + docx_file = BytesIO(file_bytes) + doc = Document(docx_file) + return "\n".join([paragraph.text for paragraph in doc.paragraphs]) + + +def _extract_pptx(file_bytes: bytes) -> str: + """Extract PPTX content (synchronous). + + Args: + file_bytes: PPTX file content as bytes + + Returns: + str: Extracted text content + """ + from pptx import Presentation # type: ignore + + pptx_file = BytesIO(file_bytes) + prs = Presentation(pptx_file) + content = "" + for slide in prs.slides: + for shape in slide.shapes: + if hasattr(shape, "text"): + content += shape.text + "\n" + return content + + +def _extract_xlsx(file_bytes: bytes) -> str: + """Extract XLSX content (synchronous). + + Args: + file_bytes: XLSX file content as bytes + + Returns: + str: Extracted text content + """ + from openpyxl import load_workbook # type: ignore + + xlsx_file = BytesIO(file_bytes) + wb = load_workbook(xlsx_file) + content = "" + for sheet in wb: + content += f"Sheet: {sheet.title}\n" + for row in sheet.iter_rows(values_only=True): + content += ( + "\t".join(str(cell) if cell is not None else "" for cell in row) + "\n" + ) + content += "\n" + return content + + async def pipeline_enqueue_file( rag: LightRAG, file_path: Path, track_id: str = None ) -> tuple[bool, str]: @@ -1072,87 +1201,21 @@ async def pipeline_enqueue_file( case ".pdf": try: - if global_args.document_loading_engine == "DOCLING": - if not pm.is_installed("docling"): # type: ignore - pm.install("docling") - from docling.document_converter import DocumentConverter # type: ignore - - converter = DocumentConverter() - result = converter.convert(file_path) - content = result.document.export_to_markdown() + # Try DOCLING first if configured and available + if ( + global_args.document_loading_engine == "DOCLING" + and DOCLING_AVAILABLE + ): + content = await asyncio.to_thread( + _convert_with_docling, file_path + ) else: - if not pm.is_installed("pypdf"): # type: ignore - pm.install("pypdf") - if not pm.is_installed("pycryptodome"): # type: ignore - pm.install("pycryptodome") - from pypdf import PdfReader # type: ignore - from io import BytesIO - - pdf_file = BytesIO(file) - reader = PdfReader(pdf_file) - - # Check if PDF is encrypted - if reader.is_encrypted: - pdf_password = global_args.pdf_decrypt_password - if not pdf_password: - # PDF is encrypted but no password provided - error_files = [ - { - "file_path": str(file_path.name), - "error_description": "[File Extraction]PDF is encrypted but no password provided", - "original_error": "Please set PDF_DECRYPT_PASSWORD environment variable to decrypt this PDF file", - "file_size": file_size, - } - ] - await rag.apipeline_enqueue_error_documents( - error_files, track_id - ) - logger.error( - f"[File Extraction]PDF is encrypted but no password provided: {file_path.name}" - ) - return False, track_id - - # Try to decrypt with password - try: - decrypt_result = reader.decrypt(pdf_password) - if decrypt_result == 0: - # Password is incorrect - error_files = [ - { - "file_path": str(file_path.name), - "error_description": "[File Extraction]Failed to decrypt PDF - incorrect password", - "original_error": "The provided PDF_DECRYPT_PASSWORD is incorrect for this file", - "file_size": file_size, - } - ] - await rag.apipeline_enqueue_error_documents( - error_files, track_id - ) - logger.error( - f"[File Extraction]Incorrect PDF password: {file_path.name}" - ) - return False, track_id - except Exception as decrypt_error: - # Decryption process error - error_files = [ - { - "file_path": str(file_path.name), - "error_description": "[File Extraction]PDF decryption failed", - "original_error": f"Error during PDF decryption: {str(decrypt_error)}", - "file_size": file_size, - } - ] - await rag.apipeline_enqueue_error_documents( - error_files, track_id - ) - logger.error( - f"[File Extraction]PDF decryption error for {file_path.name}: {str(decrypt_error)}" - ) - return False, track_id - - # Extract text from PDF (encrypted PDFs are now decrypted, unencrypted PDFs proceed directly) - for page in reader.pages: - content += page.extract_text() + "\n" + # Use pypdf (non-blocking via to_thread) + content = await asyncio.to_thread( + _extract_pdf_pypdf, + file, + global_args.pdf_decrypt_password, + ) except Exception as e: error_files = [ { @@ -1172,28 +1235,17 @@ async def pipeline_enqueue_file( case ".docx": try: - if global_args.document_loading_engine == "DOCLING": - if not pm.is_installed("docling"): # type: ignore - pm.install("docling") - from docling.document_converter import DocumentConverter # type: ignore - - converter = DocumentConverter() - result = converter.convert(file_path) - content = result.document.export_to_markdown() - else: - if not pm.is_installed("python-docx"): # type: ignore - try: - pm.install("python-docx") - except Exception: - pm.install("docx") - from docx import Document # type: ignore - from io import BytesIO - - docx_file = BytesIO(file) - doc = Document(docx_file) - content = "\n".join( - [paragraph.text for paragraph in doc.paragraphs] + # Try DOCLING first if configured and available + if ( + global_args.document_loading_engine == "DOCLING" + and DOCLING_AVAILABLE + ): + content = await asyncio.to_thread( + _convert_with_docling, file_path ) + else: + # Use python-docx (non-blocking via to_thread) + content = await asyncio.to_thread(_extract_docx, file) except Exception as e: error_files = [ { @@ -1213,26 +1265,17 @@ async def pipeline_enqueue_file( case ".pptx": try: - if global_args.document_loading_engine == "DOCLING": - if not pm.is_installed("docling"): # type: ignore - pm.install("docling") - from docling.document_converter import DocumentConverter # type: ignore - - converter = DocumentConverter() - result = converter.convert(file_path) - content = result.document.export_to_markdown() + # Try DOCLING first if configured and available + if ( + global_args.document_loading_engine == "DOCLING" + and DOCLING_AVAILABLE + ): + content = await asyncio.to_thread( + _convert_with_docling, file_path + ) else: - if not pm.is_installed("python-pptx"): # type: ignore - pm.install("pptx") - from pptx import Presentation # type: ignore - from io import BytesIO - - pptx_file = BytesIO(file) - prs = Presentation(pptx_file) - for slide in prs.slides: - for shape in slide.shapes: - if hasattr(shape, "text"): - content += shape.text + "\n" + # Use python-pptx (non-blocking via to_thread) + content = await asyncio.to_thread(_extract_pptx, file) except Exception as e: error_files = [ { @@ -1252,33 +1295,17 @@ async def pipeline_enqueue_file( case ".xlsx": try: - if global_args.document_loading_engine == "DOCLING": - if not pm.is_installed("docling"): # type: ignore - pm.install("docling") - from docling.document_converter import DocumentConverter # type: ignore - - converter = DocumentConverter() - result = converter.convert(file_path) - content = result.document.export_to_markdown() + # Try DOCLING first if configured and available + if ( + global_args.document_loading_engine == "DOCLING" + and DOCLING_AVAILABLE + ): + content = await asyncio.to_thread( + _convert_with_docling, file_path + ) else: - if not pm.is_installed("openpyxl"): # type: ignore - pm.install("openpyxl") - from openpyxl import load_workbook # type: ignore - from io import BytesIO - - xlsx_file = BytesIO(file) - wb = load_workbook(xlsx_file) - for sheet in wb: - content += f"Sheet: {sheet.title}\n" - for row in sheet.iter_rows(values_only=True): - content += ( - "\t".join( - str(cell) if cell is not None else "" - for cell in row - ) - + "\n" - ) - content += "\n" + # Use openpyxl (non-blocking via to_thread) + content = await asyncio.to_thread(_extract_xlsx, file) except Exception as e: error_files = [ { diff --git a/pyproject.toml b/pyproject.toml index 81e44aff..53378de0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -79,18 +79,20 @@ api = [ "python-multipart", "pytz", "uvicorn", + # Document processing dependencies (required for API document upload functionality) + "openpyxl>=3.0.0,<4.0.0", # XLSX processing + "pycryptodome>=3.0.0,<4.0.0", # PDF encryption support + "pypdf>=6.1.0", # PDF processing + "python-docx>=0.8.11,<2.0.0", # DOCX processing + "python-pptx>=0.6.21,<2.0.0", # PPTX processing +] + +# Advanced document processing engine (optional) +docling = [ + "docling>=2.0.0,<3.0.0", ] # Offline deployment dependencies (layered design for flexibility) -offline-docs = [ - # Document processing dependencies - "openpyxl>=3.0.0,<4.0.0", - "pycryptodome>=3.0.0,<4.0.0", - "pypdf>=6.1.0", - "python-docx>=0.8.11,<2.0.0", - "python-pptx>=0.6.21,<2.0.0", -] - offline-storage = [ # Storage backend dependencies "redis>=5.0.0,<8.0.0", @@ -115,8 +117,8 @@ offline-llm = [ ] offline = [ - # Complete offline package (includes all offline dependencies) - "lightrag-hku[offline-docs,offline-storage,offline-llm]", + # Complete offline package (includes api for document processing, plus storage and LLM) + "lightrag-hku[api,offline-storage,offline-llm]", ] evaluation = [ diff --git a/requirements-offline-docs.txt b/requirements-offline-docs.txt deleted file mode 100644 index 12f02080..00000000 --- a/requirements-offline-docs.txt +++ /dev/null @@ -1,15 +0,0 @@ -# LightRAG Offline Dependencies - Document Processing -# Install with: pip install -r requirements-offline-docs.txt -# For offline installation: -# pip download -r requirements-offline-docs.txt -d ./packages -# pip install --no-index --find-links=./packages -r requirements-offline-docs.txt -# -# Recommended: Use pip install lightrag-hku[offline-docs] for the same effect -# Or use constraints: pip install --constraint constraints-offline.txt -r requirements-offline-docs.txt - -# Document processing dependencies (with version constraints matching pyproject.toml) -openpyxl>=3.0.0,<4.0.0 -pycryptodome>=3.0.0,<4.0.0 -pypdf>=6.1.0 -python-docx>=0.8.11,<2.0.0 -python-pptx>=0.6.21,<2.0.0 From 746c069ab08653be8bd2e2c5b98b1c78628d2736 Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 15:28:05 +0800 Subject: [PATCH 18/37] Implement lazy configuration initialization for API server MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Add lazy config initialization • Maintain backward compatibility • Support programmatic usage • Add gunicorn dependency • Explicit config in entry points --- lightrag/api/config.py | 81 ++++++++++++++++++++++++++++++- lightrag/api/lightrag_server.py | 6 +++ lightrag/api/run_with_gunicorn.py | 5 ++ pyproject.toml | 1 + 4 files changed, 92 insertions(+), 1 deletion(-) diff --git a/lightrag/api/config.py b/lightrag/api/config.py index 1f46d147..e41684d0 100644 --- a/lightrag/api/config.py +++ b/lightrag/api/config.py @@ -449,4 +449,83 @@ def update_uvicorn_mode_config(): ) -global_args = parse_args() +# Global configuration with lazy initialization +_global_args = None +_initialized = False + + +def initialize_config(args=None, force=False): + """Initialize global configuration + + This function allows explicit initialization of the configuration, + which is useful for programmatic usage, testing, or embedding LightRAG + in other applications. + + Args: + args: Pre-parsed argparse.Namespace or None to parse from sys.argv + force: Force re-initialization even if already initialized + + Returns: + argparse.Namespace: The configured arguments + + Example: + # Use parsed command line arguments (default) + initialize_config() + + # Use custom configuration programmatically + custom_args = argparse.Namespace( + host='localhost', + port=8080, + working_dir='./custom_rag', + # ... other config + ) + initialize_config(custom_args) + """ + global _global_args, _initialized + + if _initialized and not force: + return _global_args + + _global_args = args if args is not None else parse_args() + _initialized = True + return _global_args + + +def get_config(): + """Get global configuration, auto-initializing if needed + + Returns: + argparse.Namespace: The configured arguments + """ + if not _initialized: + initialize_config() + return _global_args + + +class _GlobalArgsProxy: + """Proxy object that auto-initializes configuration on first access + + This maintains backward compatibility with existing code while + allowing programmatic control over initialization timing. + """ + + def __getattr__(self, name): + if not _initialized: + initialize_config() + return getattr(_global_args, name) + + def __setattr__(self, name, value): + if not _initialized: + initialize_config() + setattr(_global_args, name, value) + + def __repr__(self): + if not _initialized: + return "" + return repr(_global_args) + + +# Create proxy instance for backward compatibility +# Existing code like `from config import global_args` continues to work +# The proxy will auto-initialize on first attribute access +global_args = _GlobalArgsProxy() diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py index ded70d67..e5a86b3e 100644 --- a/lightrag/api/lightrag_server.py +++ b/lightrag/api/lightrag_server.py @@ -1214,6 +1214,12 @@ def check_and_install_dependencies(): def main(): + # Explicitly initialize configuration for clarity + # (The proxy will auto-initialize anyway, but this makes intent clear) + from .config import initialize_config + + initialize_config() + # Check if running under Gunicorn if "GUNICORN_CMD_ARGS" in os.environ: # If started with Gunicorn, return directly as Gunicorn will call get_application diff --git a/lightrag/api/run_with_gunicorn.py b/lightrag/api/run_with_gunicorn.py index f2d4d859..999211aa 100644 --- a/lightrag/api/run_with_gunicorn.py +++ b/lightrag/api/run_with_gunicorn.py @@ -34,6 +34,11 @@ def check_and_install_dependencies(): def main(): + # Explicitly initialize configuration for Gunicorn mode + from lightrag.api.config import initialize_config + + initialize_config() + # Set Gunicorn mode flag for lifespan cleanup detection os.environ["LIGHTRAG_GUNICORN_MODE"] = "1" diff --git a/pyproject.toml b/pyproject.toml index 53378de0..1c0ea12b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -79,6 +79,7 @@ api = [ "python-multipart", "pytz", "uvicorn", + "gunicorn", # Document processing dependencies (required for API document upload functionality) "openpyxl>=3.0.0,<4.0.0", # XLSX processing "pycryptodome>=3.0.0,<4.0.0", # PDF encryption support From e6588f91195475ce2b329a08a4e3f599f584064a Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 15:31:51 +0800 Subject: [PATCH 19/37] Update uv.lock --- uv.lock | 1940 ++++++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 1784 insertions(+), 156 deletions(-) diff --git a/uv.lock b/uv.lock index 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"2025-10-29T06:57:37.598Z" }, +] + [[package]] name = "voyageai" version = "0.3.5" From 89e63aa49b9f9c07809656d412d67271a68cc79f Mon Sep 17 00:00:00 2001 From: Sleeep <91170291+sleeepyin@users.noreply.github.com> Date: Thu, 13 Nov 2025 15:52:14 +0800 Subject: [PATCH 20/37] Update edge keywords extraction in graph visualization MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 构建neo4j时候 关键字的取值默认为d7 应该为修改后的d9 --- examples/graph_visual_with_neo4j.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/graph_visual_with_neo4j.py b/examples/graph_visual_with_neo4j.py index 1cd2e7a3..b1fc9438 100644 --- a/examples/graph_visual_with_neo4j.py +++ b/examples/graph_visual_with_neo4j.py @@ -53,8 +53,8 @@ def xml_to_json(xml_file): "description": edge.find("./data[@key='d6']", namespace).text if edge.find("./data[@key='d6']", namespace) is not None else "", - "keywords": edge.find("./data[@key='d7']", namespace).text - if edge.find("./data[@key='d7']", namespace) is not None + "keywords": edge.find("./data[@key='d9']", namespace).text + if edge.find("./data[@key='d9']", namespace) is not None else "", "source_id": edge.find("./data[@key='d8']", namespace).text if edge.find("./data[@key='d8']", namespace) is not None From a24d8181c29e77de427dea9ae516b7420eda06b7 Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 18:58:09 +0800 Subject: [PATCH 21/37] Improve docling integration with macOS compatibility and CLI flag - Add --docling CLI flag for easier setup - Add numpy version constraints - Exclude docling on macOS (fork-safety) --- lightrag/api/config.py | 17 +- lightrag/api/routers/document_routes.py | 65 +- pyproject.toml | 8 +- uv.lock | 1465 +++++++++++++---------- 4 files changed, 874 insertions(+), 681 deletions(-) diff --git a/lightrag/api/config.py b/lightrag/api/config.py index e41684d0..95ab9f70 100644 --- a/lightrag/api/config.py +++ b/lightrag/api/config.py @@ -258,6 +258,14 @@ def parse_args() -> argparse.Namespace: help=f"Rerank binding type (default: from env or {DEFAULT_RERANK_BINDING})", ) + # Document loading engine configuration + parser.add_argument( + "--docling", + action="store_true", + default=False, + help="Enable DOCLING document loading engine (default: from env or DEFAULT)", + ) + # Conditionally add binding options defined in binding_options module # This will add command line arguments for all binding options (e.g., --ollama-embedding-num_ctx) # and corresponding environment variables (e.g., OLLAMA_EMBEDDING_NUM_CTX) @@ -371,8 +379,13 @@ def parse_args() -> argparse.Namespace: ) args.enable_llm_cache = get_env_value("ENABLE_LLM_CACHE", True, bool) - # Select Document loading tool (DOCLING, DEFAULT) - args.document_loading_engine = get_env_value("DOCUMENT_LOADING_ENGINE", "DEFAULT") + # Set document_loading_engine from --docling flag + if args.docling: + args.document_loading_engine = "DOCLING" + else: + args.document_loading_engine = get_env_value( + "DOCUMENT_LOADING_ENGINE", "DEFAULT" + ) # PDF decryption password args.pdf_decrypt_password = get_env_value("PDF_DECRYPT_PASSWORD", None) diff --git a/lightrag/api/routers/document_routes.py b/lightrag/api/routers/document_routes.py index 3f87517d..a59f10c7 100644 --- a/lightrag/api/routers/document_routes.py +++ b/lightrag/api/routers/document_routes.py @@ -3,6 +3,7 @@ This module contains all document-related routes for the LightRAG API. """ import asyncio +from functools import lru_cache from lightrag.utils import logger, get_pinyin_sort_key import aiofiles import shutil @@ -27,19 +28,23 @@ from lightrag.utils import generate_track_id from lightrag.api.utils_api import get_combined_auth_dependency from ..config import global_args -# Check docling availability at module load time -DOCLING_AVAILABLE = False -try: - import docling # noqa: F401 # type: ignore[import-not-found] - DOCLING_AVAILABLE = True -except ImportError: - if global_args.document_loading_engine == "DOCLING": - logger.warning( - "DOCLING engine requested but 'docling' package not installed. " - "Falling back to standard document processing. " - "To use DOCLING, install with: pip install lightrag-hku[api,docling]" - ) +@lru_cache(maxsize=1) +def _is_docling_available() -> bool: + """Check if docling is available (cached check). + + This function uses lru_cache to avoid repeated import attempts. + The result is cached after the first call. + + Returns: + bool: True if docling is available, False otherwise + """ + try: + import docling # noqa: F401 # type: ignore[import-not-found] + + return True + except ImportError: + return False # Function to format datetime to ISO format string with timezone information @@ -1204,12 +1209,19 @@ async def pipeline_enqueue_file( # Try DOCLING first if configured and available if ( global_args.document_loading_engine == "DOCLING" - and DOCLING_AVAILABLE + and _is_docling_available() ): content = await asyncio.to_thread( _convert_with_docling, file_path ) else: + if ( + global_args.document_loading_engine == "DOCLING" + and not _is_docling_available() + ): + logger.warning( + f"DOCLING engine configured but not available for {file_path.name}. Falling back to pypdf." + ) # Use pypdf (non-blocking via to_thread) content = await asyncio.to_thread( _extract_pdf_pypdf, @@ -1238,12 +1250,19 @@ async def pipeline_enqueue_file( # Try DOCLING first if configured and available if ( global_args.document_loading_engine == "DOCLING" - and DOCLING_AVAILABLE + and _is_docling_available() ): content = await asyncio.to_thread( _convert_with_docling, file_path ) else: + if ( + global_args.document_loading_engine == "DOCLING" + and not _is_docling_available() + ): + logger.warning( + f"DOCLING engine configured but not available for {file_path.name}. Falling back to python-docx." + ) # Use python-docx (non-blocking via to_thread) content = await asyncio.to_thread(_extract_docx, file) except Exception as e: @@ -1268,12 +1287,19 @@ async def pipeline_enqueue_file( # Try DOCLING first if configured and available if ( global_args.document_loading_engine == "DOCLING" - and DOCLING_AVAILABLE + and _is_docling_available() ): content = await asyncio.to_thread( _convert_with_docling, file_path ) else: + if ( + global_args.document_loading_engine == "DOCLING" + and not _is_docling_available() + ): + logger.warning( + f"DOCLING engine configured but not available for {file_path.name}. Falling back to python-pptx." + ) # Use python-pptx (non-blocking via to_thread) content = await asyncio.to_thread(_extract_pptx, file) except Exception as e: @@ -1298,12 +1324,19 @@ async def pipeline_enqueue_file( # Try DOCLING first if configured and available if ( global_args.document_loading_engine == "DOCLING" - and DOCLING_AVAILABLE + and _is_docling_available() ): content = await asyncio.to_thread( _convert_with_docling, file_path ) else: + if ( + global_args.document_loading_engine == "DOCLING" + and not _is_docling_available() + ): + logger.warning( + f"DOCLING engine configured but not available for {file_path.name}. Falling back to openpyxl." + ) # Use openpyxl (non-blocking via to_thread) content = await asyncio.to_thread(_extract_xlsx, file) except Exception as e: diff --git a/pyproject.toml b/pyproject.toml index 1c0ea12b..3c7450f4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,7 @@ dependencies = [ "json_repair", "nano-vectordb", "networkx", - "numpy", + "numpy>=1.24.0,<2.0.0", "pandas>=2.0.0,<2.4.0", "pipmaster", "pydantic", @@ -50,7 +50,7 @@ api = [ "json_repair", "nano-vectordb", "networkx", - "numpy", + "numpy>=1.24.0,<2.0.0", "openai>=1.0.0,<3.0.0", "pandas>=2.0.0,<2.4.0", "pipmaster", @@ -90,7 +90,9 @@ api = [ # Advanced document processing engine (optional) docling = [ - "docling>=2.0.0,<3.0.0", + # On macOS, pytorch and frameworks use Objective-C are not fork-safe, + # and not compatible to gunicorn multi-worker mode + "docling>=2.0.0,<3.0.0; sys_platform != 'darwin'", ] # Offline deployment dependencies (layered design for flexibility) diff --git a/uv.lock b/uv.lock 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+ "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", +] sdist = { url = "https://files.pythonhosted.org/packages/fd/aa/3e0508d5a5dd96529cdc5a97011299056e14c6505b678fd58938792794b1/zstandard-0.25.0.tar.gz", hash = "sha256:7713e1179d162cf5c7906da876ec2ccb9c3a9dcbdffef0cc7f70c3667a205f0b", size = 711513, upload-time = "2025-09-14T22:15:54.002Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/56/7a/28efd1d371f1acd037ac64ed1c5e2b41514a6cc937dd6ab6a13ab9f0702f/zstandard-0.25.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e59fdc271772f6686e01e1b3b74537259800f57e24280be3f29c8a0deb1904dd", size = 795256, upload-time = "2025-09-14T22:15:56.415Z" }, From 844537e378471b95a42705de8542edc82e7d304f Mon Sep 17 00:00:00 2001 From: LacombeLouis Date: Thu, 13 Nov 2025 12:17:51 +0100 Subject: [PATCH 22/37] Add a better regex --- lightrag/llm/ollama.py | 38 ++++++++++++++++++++------------------ 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/lightrag/llm/ollama.py b/lightrag/llm/ollama.py index 28d91075..9c2d17ee 100644 --- a/lightrag/llm/ollama.py +++ b/lightrag/llm/ollama.py @@ -1,5 +1,6 @@ from collections.abc import AsyncIterator import os +import re import pipmaster as pm @@ -23,10 +24,26 @@ from lightrag.exceptions import ( from lightrag.api import __api_version__ import numpy as np -from typing import Union +from typing import Optional, Union from lightrag.utils import logger +_OLLAMA_CLOUD_HOST = "https://ollama.com" +_CLOUD_MODEL_SUFFIX_PATTERN = re.compile(r"(?:-cloud|:cloud)$") + + +def _coerce_host_for_cloud_model(host: Optional[str], model: object) -> Optional[str]: + if host: + return host + try: + model_name_str = str(model) if model is not None else "" + except Exception: + model_name_str = "" + if _CLOUD_MODEL_SUFFIX_PATTERN.search(model_name_str): + return _OLLAMA_CLOUD_HOST + return host + + @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10), @@ -64,15 +81,7 @@ async def _ollama_model_if_cache( if api_key: headers["Authorization"] = f"Bearer {api_key}" - # If this is a cloud model (names include '-cloud' or ':cloud'), default - # the host to Ollama cloud when no explicit host was provided. - try: - model_name_str = str(model) if model is not None else "" - except Exception: - model_name_str = "" - - if host is None and ("-cloud" in model_name_str or ":cloud" in model_name_str): - host = "https://ollama.com" + host = _coerce_host_for_cloud_model(host, model) ollama_client = ollama.AsyncClient(host=host, timeout=timeout, headers=headers) @@ -170,14 +179,7 @@ async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray: host = kwargs.pop("host", None) timeout = kwargs.pop("timeout", None) - # If embed_model targets Ollama cloud, default host when not provided - try: - embed_model_name = str(embed_model) if embed_model is not None else "" - except Exception: - embed_model_name = "" - - if host is None and ("-cloud" in embed_model_name or ":cloud" in embed_model_name): - host = "https://ollama.com" + host = _coerce_host_for_cloud_model(host, embed_model) ollama_client = ollama.AsyncClient(host=host, timeout=timeout, headers=headers) try: From cc031a3db9473447e2f8970057a7ca5bc0661a15 Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 19:18:04 +0800 Subject: [PATCH 23/37] Add macOS compatibility check for DOCLING with multi-worker Gunicorn --- lightrag/api/run_with_gunicorn.py | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/lightrag/api/run_with_gunicorn.py b/lightrag/api/run_with_gunicorn.py index 999211aa..0de2ac36 100644 --- a/lightrag/api/run_with_gunicorn.py +++ b/lightrag/api/run_with_gunicorn.py @@ -5,6 +5,7 @@ Start LightRAG server with Gunicorn import os import sys +import platform import pipmaster as pm from lightrag.api.utils_api import display_splash_screen, check_env_file from lightrag.api.config import global_args @@ -46,6 +47,35 @@ def main(): if not check_env_file(): sys.exit(1) + # Check DOCLING compatibility with Gunicorn multi-worker mode on macOS + if ( + platform.system() == "Darwin" + and global_args.document_loading_engine == "DOCLING" + and global_args.workers > 1 + ): + print("\n" + "=" * 80) + print("❌ ERROR: Incompatible configuration detected!") + print("=" * 80) + print( + "\nDOCLING engine with Gunicorn multi-worker mode is not supported on macOS" + ) + print("\nReason:") + print(" PyTorch (required by DOCLING) has known compatibility issues with") + print(" fork-based multiprocessing on macOS, which can cause crashes or") + print(" unexpected behavior when using Gunicorn with multiple workers.") + print("\nCurrent configuration:") + print(" - Operating System: macOS (Darwin)") + print(f" - Document Engine: {global_args.document_loading_engine}") + print(f" - Workers: {global_args.workers}") + print("\nPossible solutions:") + print(" 1. Use single worker mode:") + print(" --workers 1") + print("\n 2. Change document loading engine in .env:") + print(" DOCUMENT_LOADING_ENGINE=DEFAULT") + print("\n 3. Deploy on Linux where multi-worker mode is fully supported") + print("=" * 80 + "\n") + sys.exit(1) + # Check and install dependencies check_and_install_dependencies() From 77ad906d3a4a6913f6d3f5e1d613f61ba27c0628 Mon Sep 17 00:00:00 2001 From: yangdx Date: Thu, 13 Nov 2025 20:41:44 +0800 Subject: [PATCH 24/37] Improve error handling and logging in cloud model detection --- lightrag/llm/ollama.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/lightrag/llm/ollama.py b/lightrag/llm/ollama.py index 9c2d17ee..670351bc 100644 --- a/lightrag/llm/ollama.py +++ b/lightrag/llm/ollama.py @@ -37,9 +37,13 @@ def _coerce_host_for_cloud_model(host: Optional[str], model: object) -> Optional return host try: model_name_str = str(model) if model is not None else "" - except Exception: + except (TypeError, ValueError, AttributeError) as e: + logger.warning(f"Failed to convert model to string: {e}, using empty string") model_name_str = "" if _CLOUD_MODEL_SUFFIX_PATTERN.search(model_name_str): + logger.debug( + f"Detected cloud model '{model_name_str}', using Ollama Cloud host" + ) return _OLLAMA_CLOUD_HOST return host From 1ccef2b93200e9f446ae50427f7842f3e9564fee Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 10:39:04 +0800 Subject: [PATCH 25/37] Fix null reference errors in graph database error handling - Initialize result vars to None - Add null checks before consume calls - Prevent crashes in except blocks - Apply fix to both Neo4J and Memgraph --- lightrag/kg/memgraph_impl.py | 70 +++++++++++++++++++++++++----------- lightrag/kg/neo4j_impl.py | 18 +++++++--- 2 files changed, 63 insertions(+), 25 deletions(-) diff --git a/lightrag/kg/memgraph_impl.py b/lightrag/kg/memgraph_impl.py index d81c2ebd..c9a96064 100644 --- a/lightrag/kg/memgraph_impl.py +++ b/lightrag/kg/memgraph_impl.py @@ -133,6 +133,7 @@ class MemgraphStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + result = None try: workspace_label = self._get_workspace_label() query = f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN count(n) > 0 AS node_exists" @@ -146,7 +147,10 @@ class MemgraphStorage(BaseGraphStorage): logger.error( f"[{self.workspace}] Error checking node existence for {node_id}: {str(e)}" ) - await result.consume() # Ensure the result is consumed even on error + if result is not None: + await ( + result.consume() + ) # Ensure the result is consumed even on error raise async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: @@ -170,6 +174,7 @@ class MemgraphStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + result = None try: workspace_label = self._get_workspace_label() query = ( @@ -190,7 +195,10 @@ class MemgraphStorage(BaseGraphStorage): logger.error( f"[{self.workspace}] Error checking edge existence between {source_node_id} and {target_node_id}: {str(e)}" ) - await result.consume() # Ensure the result is consumed even on error + if result is not None: + await ( + result.consume() + ) # Ensure the result is consumed even on error raise async def get_node(self, node_id: str) -> dict[str, str] | None: @@ -312,6 +320,7 @@ class MemgraphStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + result = None try: workspace_label = self._get_workspace_label() query = f""" @@ -328,7 +337,10 @@ class MemgraphStorage(BaseGraphStorage): return labels except Exception as e: logger.error(f"[{self.workspace}] Error getting all labels: {str(e)}") - await result.consume() # Ensure the result is consumed even on error + if result is not None: + await ( + result.consume() + ) # Ensure the result is consumed even on error raise async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None: @@ -352,6 +364,7 @@ class MemgraphStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + results = None try: workspace_label = self._get_workspace_label() query = f"""MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) @@ -389,7 +402,10 @@ class MemgraphStorage(BaseGraphStorage): logger.error( f"[{self.workspace}] Error getting edges for node {source_node_id}: {str(e)}" ) - await results.consume() # Ensure results are consumed even on error + if results is not None: + await ( + results.consume() + ) # Ensure results are consumed even on error raise except Exception as e: logger.error( @@ -419,6 +435,7 @@ class MemgraphStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + result = None try: workspace_label = self._get_workspace_label() query = f""" @@ -451,7 +468,10 @@ class MemgraphStorage(BaseGraphStorage): logger.error( f"[{self.workspace}] Error getting edge between {source_node_id} and {target_node_id}: {str(e)}" ) - await result.consume() # Ensure the result is consumed even on error + if result is not None: + await ( + result.consume() + ) # Ensure the result is consumed even on error raise async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: @@ -1030,11 +1050,12 @@ class MemgraphStorage(BaseGraphStorage): "Memgraph driver is not initialized. Call 'await initialize()' first." ) - try: - workspace_label = self._get_workspace_label() - async with self._driver.session( - database=self._DATABASE, default_access_mode="READ" - ) as session: + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: query = f""" MATCH (n:`{workspace_label}`) WHERE n.entity_id IS NOT NULL @@ -1054,9 +1075,13 @@ class MemgraphStorage(BaseGraphStorage): f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})" ) return labels - except Exception as e: - logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}") - return [] + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting popular labels: {str(e)}" + ) + if result is not None: + await result.consume() + return [] async def search_labels(self, query: str, limit: int = 50) -> list[str]: """Search labels with fuzzy matching @@ -1078,11 +1103,12 @@ class MemgraphStorage(BaseGraphStorage): if not query_lower: return [] - try: - workspace_label = self._get_workspace_label() - async with self._driver.session( - database=self._DATABASE, default_access_mode="READ" - ) as session: + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: cypher_query = f""" MATCH (n:`{workspace_label}`) WHERE n.entity_id IS NOT NULL @@ -1109,6 +1135,8 @@ class MemgraphStorage(BaseGraphStorage): f"[{self.workspace}] Search query '{query}' returned {len(labels)} results (limit: {limit})" ) return labels - except Exception as e: - logger.error(f"[{self.workspace}] Error searching labels: {str(e)}") - return [] + except Exception as e: + logger.error(f"[{self.workspace}] Error searching labels: {str(e)}") + if result is not None: + await result.consume() + return [] diff --git a/lightrag/kg/neo4j_impl.py b/lightrag/kg/neo4j_impl.py index 76fa11f2..31df4623 100644 --- a/lightrag/kg/neo4j_impl.py +++ b/lightrag/kg/neo4j_impl.py @@ -371,6 +371,7 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + result = None try: query = f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN count(n) > 0 AS node_exists" result = await session.run(query, entity_id=node_id) @@ -381,7 +382,8 @@ class Neo4JStorage(BaseGraphStorage): logger.error( f"[{self.workspace}] Error checking node existence for {node_id}: {str(e)}" ) - await result.consume() # Ensure results are consumed even on error + if result is not None: + await result.consume() # Ensure results are consumed even on error raise async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: @@ -403,6 +405,7 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + result = None try: query = ( f"MATCH (a:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(b:`{workspace_label}` {{entity_id: $target_entity_id}}) " @@ -420,7 +423,8 @@ class Neo4JStorage(BaseGraphStorage): logger.error( f"[{self.workspace}] Error checking edge existence between {source_node_id} and {target_node_id}: {str(e)}" ) - await result.consume() # Ensure results are consumed even on error + if result is not None: + await result.consume() # Ensure results are consumed even on error raise async def get_node(self, node_id: str) -> dict[str, str] | None: @@ -799,6 +803,7 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + results = None try: workspace_label = self._get_workspace_label() query = f"""MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) @@ -836,7 +841,10 @@ class Neo4JStorage(BaseGraphStorage): logger.error( f"[{self.workspace}] Error getting edges for node {source_node_id}: {str(e)}" ) - await results.consume() # Ensure results are consumed even on error + if results is not None: + await ( + results.consume() + ) # Ensure results are consumed even on error raise except Exception as e: logger.error( @@ -1592,6 +1600,7 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: + result = None try: query = f""" MATCH (n:`{workspace_label}`) @@ -1616,7 +1625,8 @@ class Neo4JStorage(BaseGraphStorage): logger.error( f"[{self.workspace}] Error getting popular labels: {str(e)}" ) - await result.consume() + if result is not None: + await result.consume() raise async def search_labels(self, query: str, limit: int = 50) -> list[str]: From 4401f86f0795a6060a4142537900c0d375c1894e Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 11:01:26 +0800 Subject: [PATCH 26/37] Refactor exception handling in MemgraphStorage label methods --- lightrag/kg/memgraph_impl.py | 46 +++++++++++++++++------------------- 1 file changed, 22 insertions(+), 24 deletions(-) diff --git a/lightrag/kg/memgraph_impl.py b/lightrag/kg/memgraph_impl.py index c9a96064..e82aceec 100644 --- a/lightrag/kg/memgraph_impl.py +++ b/lightrag/kg/memgraph_impl.py @@ -1050,12 +1050,12 @@ class MemgraphStorage(BaseGraphStorage): "Memgraph driver is not initialized. Call 'await initialize()' first." ) - workspace_label = self._get_workspace_label() - async with self._driver.session( - database=self._DATABASE, default_access_mode="READ" - ) as session: - result = None - try: + result = None + try: + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: query = f""" MATCH (n:`{workspace_label}`) WHERE n.entity_id IS NOT NULL @@ -1075,13 +1075,11 @@ class MemgraphStorage(BaseGraphStorage): f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})" ) return labels - except Exception as e: - logger.error( - f"[{self.workspace}] Error getting popular labels: {str(e)}" - ) - if result is not None: - await result.consume() - return [] + except Exception as e: + logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}") + if result is not None: + await result.consume() + return [] async def search_labels(self, query: str, limit: int = 50) -> list[str]: """Search labels with fuzzy matching @@ -1103,12 +1101,12 @@ class MemgraphStorage(BaseGraphStorage): if not query_lower: return [] - workspace_label = self._get_workspace_label() - async with self._driver.session( - database=self._DATABASE, default_access_mode="READ" - ) as session: - result = None - try: + result = None + try: + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: cypher_query = f""" MATCH (n:`{workspace_label}`) WHERE n.entity_id IS NOT NULL @@ -1135,8 +1133,8 @@ class MemgraphStorage(BaseGraphStorage): f"[{self.workspace}] Search query '{query}' returned {len(labels)} results (limit: {limit})" ) return labels - except Exception as e: - logger.error(f"[{self.workspace}] Error searching labels: {str(e)}") - if result is not None: - await result.consume() - return [] + except Exception as e: + logger.error(f"[{self.workspace}] Error searching labels: {str(e)}") + if result is not None: + await result.consume() + return [] From 05852e1ab20007e746800961191de5f030cd46ad Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 18:41:43 +0800 Subject: [PATCH 27/37] Add max_token_size parameter to embedding function decorators - Add max_token_size=8192 to all embed funcs - Move siliconcloud to deprecated folder - Import wrap_embedding_func_with_attrs - Update EmbeddingFunc docstring - Fix langfuse import type annotation --- lightrag/llm/bedrock.py | 3 ++- lightrag/llm/{ => deprecated}/siliconcloud.py | 0 lightrag/llm/gemini.py | 2 +- lightrag/llm/hf.py | 2 ++ lightrag/llm/jina.py | 2 +- lightrag/llm/llama_index_impl.py | 2 +- lightrag/llm/lollms.py | 5 +++++ lightrag/llm/nvidia_openai.py | 2 +- lightrag/llm/ollama.py | 6 +++++- lightrag/llm/openai.py | 4 ++-- lightrag/utils.py | 2 +- 11 files changed, 21 insertions(+), 9 deletions(-) rename lightrag/llm/{ => deprecated}/siliconcloud.py (100%) diff --git a/lightrag/llm/bedrock.py b/lightrag/llm/bedrock.py index 16737341..ccfbb4f7 100644 --- a/lightrag/llm/bedrock.py +++ b/lightrag/llm/bedrock.py @@ -16,6 +16,7 @@ from tenacity import ( ) import sys +from lightrag.utils import wrap_embedding_func_with_attrs if sys.version_info < (3, 9): from typing import AsyncIterator @@ -253,7 +254,7 @@ async def bedrock_complete( return result -# @wrap_embedding_func_with_attrs(embedding_dim=1024) +@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) # @retry( # stop=stop_after_attempt(3), # wait=wait_exponential(multiplier=1, min=4, max=10), diff --git a/lightrag/llm/siliconcloud.py b/lightrag/llm/deprecated/siliconcloud.py similarity index 100% rename from lightrag/llm/siliconcloud.py rename to lightrag/llm/deprecated/siliconcloud.py diff --git a/lightrag/llm/gemini.py b/lightrag/llm/gemini.py index 983d6b9f..37ce7206 100644 --- a/lightrag/llm/gemini.py +++ b/lightrag/llm/gemini.py @@ -453,7 +453,7 @@ async def gemini_model_complete( ) -@wrap_embedding_func_with_attrs(embedding_dim=1536) +@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=2048) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=60), diff --git a/lightrag/llm/hf.py b/lightrag/llm/hf.py index c33b1c7f..447f95c3 100644 --- a/lightrag/llm/hf.py +++ b/lightrag/llm/hf.py @@ -26,6 +26,7 @@ from lightrag.exceptions import ( ) import torch import numpy as np +from lightrag.utils import wrap_embedding_func_with_attrs os.environ["TOKENIZERS_PARALLELISM"] = "false" @@ -141,6 +142,7 @@ async def hf_model_complete( return result +@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) async def hf_embed(texts: list[str], tokenizer, embed_model) -> np.ndarray: # Detect the appropriate device if torch.cuda.is_available(): diff --git a/lightrag/llm/jina.py b/lightrag/llm/jina.py index 70de5995..f61faadd 100644 --- a/lightrag/llm/jina.py +++ b/lightrag/llm/jina.py @@ -58,7 +58,7 @@ async def fetch_data(url, headers, data): return data_list -@wrap_embedding_func_with_attrs(embedding_dim=2048) +@wrap_embedding_func_with_attrs(embedding_dim=2048, max_token_size=8192) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=60), diff --git a/lightrag/llm/llama_index_impl.py b/lightrag/llm/llama_index_impl.py index 38ec7cd1..c44e6c7a 100644 --- a/lightrag/llm/llama_index_impl.py +++ b/lightrag/llm/llama_index_impl.py @@ -174,7 +174,7 @@ async def llama_index_complete( return result -@wrap_embedding_func_with_attrs(embedding_dim=1536) +@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=60), diff --git a/lightrag/llm/lollms.py b/lightrag/llm/lollms.py index 9274dbfc..2f2a1dbf 100644 --- a/lightrag/llm/lollms.py +++ b/lightrag/llm/lollms.py @@ -26,6 +26,10 @@ from lightrag.exceptions import ( from typing import Union, List import numpy as np +from lightrag.utils import ( + wrap_embedding_func_with_attrs, +) + @retry( stop=stop_after_attempt(3), @@ -134,6 +138,7 @@ async def lollms_model_complete( ) +@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) async def lollms_embed( texts: List[str], embed_model=None, base_url="http://localhost:9600", **kwargs ) -> np.ndarray: diff --git a/lightrag/llm/nvidia_openai.py b/lightrag/llm/nvidia_openai.py index 1cbab380..1ebaf3a6 100644 --- a/lightrag/llm/nvidia_openai.py +++ b/lightrag/llm/nvidia_openai.py @@ -33,7 +33,7 @@ from lightrag.utils import ( import numpy as np -@wrap_embedding_func_with_attrs(embedding_dim=2048) +@wrap_embedding_func_with_attrs(embedding_dim=2048, max_token_size=8192) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=60), diff --git a/lightrag/llm/ollama.py b/lightrag/llm/ollama.py index 670351bc..e35dc293 100644 --- a/lightrag/llm/ollama.py +++ b/lightrag/llm/ollama.py @@ -25,7 +25,10 @@ from lightrag.api import __api_version__ import numpy as np from typing import Optional, Union -from lightrag.utils import logger +from lightrag.utils import ( + wrap_embedding_func_with_attrs, + logger, +) _OLLAMA_CLOUD_HOST = "https://ollama.com" @@ -169,6 +172,7 @@ async def ollama_model_complete( ) +@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray: api_key = kwargs.pop("api_key", None) if not api_key: diff --git a/lightrag/llm/openai.py b/lightrag/llm/openai.py index a2bbfa23..8c984e51 100644 --- a/lightrag/llm/openai.py +++ b/lightrag/llm/openai.py @@ -47,7 +47,7 @@ try: # Only enable Langfuse if both keys are configured if langfuse_public_key and langfuse_secret_key: - from langfuse.openai import AsyncOpenAI + from langfuse.openai import AsyncOpenAI # type: ignore[import-untyped] LANGFUSE_ENABLED = True logger.info("Langfuse observability enabled for OpenAI client") @@ -604,7 +604,7 @@ async def nvidia_openai_complete( return result -@wrap_embedding_func_with_attrs(embedding_dim=1536) +@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=60), diff --git a/lightrag/utils.py b/lightrag/utils.py index b78b7523..d653c1e3 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -355,7 +355,7 @@ class TaskState: class EmbeddingFunc: embedding_dim: int func: callable - max_token_size: int | None = None # deprecated keep it for compatible only + max_token_size: int | None = None # Token limit for the embedding model send_dimensions: bool = ( False # Control whether to send embedding_dim to the function ) From 680e36c6ebe6a2302914956845541f77c3ea855a Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 18:51:41 +0800 Subject: [PATCH 28/37] Improve Bedrock error handling with retry logic and custom exceptions MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Add specific exception types • Implement proper retry mechanism • Better error classification • Enhanced logging and validation • Enable embedding retry decorator --- lightrag/llm/bedrock.py | 256 ++++++++++++++++++++++++++++++++-------- 1 file changed, 205 insertions(+), 51 deletions(-) diff --git a/lightrag/llm/bedrock.py b/lightrag/llm/bedrock.py index ccfbb4f7..f6871422 100644 --- a/lightrag/llm/bedrock.py +++ b/lightrag/llm/bedrock.py @@ -1,6 +1,7 @@ import copy import os import json +import logging import pipmaster as pm # Pipmaster for dynamic library install @@ -24,21 +25,121 @@ else: from collections.abc import AsyncIterator from typing import Union +# Import botocore exceptions for proper exception handling +try: + from botocore.exceptions import ( + ClientError, + ConnectionError as BotocoreConnectionError, + ReadTimeoutError, + ) +except ImportError: + # If botocore is not installed, define placeholders + ClientError = Exception + BotocoreConnectionError = Exception + ReadTimeoutError = Exception + class BedrockError(Exception): """Generic error for issues related to Amazon Bedrock""" +class BedrockRateLimitError(BedrockError): + """Error for rate limiting and throttling issues""" + + +class BedrockConnectionError(BedrockError): + """Error for network and connection issues""" + + +class BedrockTimeoutError(BedrockError): + """Error for timeout issues""" + + def _set_env_if_present(key: str, value): """Set environment variable only if a non-empty value is provided.""" if value is not None and value != "": os.environ[key] = value +def _handle_bedrock_exception(e: Exception, operation: str = "Bedrock API") -> None: + """Convert AWS Bedrock exceptions to appropriate custom exceptions. + + Args: + e: The exception to handle + operation: Description of the operation for error messages + + Raises: + BedrockRateLimitError: For rate limiting and throttling issues (retryable) + BedrockConnectionError: For network and server issues (retryable) + BedrockTimeoutError: For timeout issues (retryable) + BedrockError: For validation and other non-retryable errors + """ + error_message = str(e) + + # Handle botocore ClientError with specific error codes + if isinstance(e, ClientError): + error_code = e.response.get("Error", {}).get("Code", "") + error_msg = e.response.get("Error", {}).get("Message", error_message) + + # Rate limiting and throttling errors (retryable) + if error_code in [ + "ThrottlingException", + "ProvisionedThroughputExceededException", + ]: + logging.error(f"{operation} rate limit error: {error_msg}") + raise BedrockRateLimitError(f"Rate limit error: {error_msg}") + + # Server errors (retryable) + elif error_code in ["ServiceUnavailableException", "InternalServerException"]: + logging.error(f"{operation} connection error: {error_msg}") + raise BedrockConnectionError(f"Service error: {error_msg}") + + # Check for 5xx HTTP status codes (retryable) + elif e.response.get("ResponseMetadata", {}).get("HTTPStatusCode", 0) >= 500: + logging.error(f"{operation} server error: {error_msg}") + raise BedrockConnectionError(f"Server error: {error_msg}") + + # Validation and other client errors (non-retryable) + else: + logging.error(f"{operation} client error: {error_msg}") + raise BedrockError(f"Client error: {error_msg}") + + # Connection errors (retryable) + elif isinstance(e, BotocoreConnectionError): + logging.error(f"{operation} connection error: {error_message}") + raise BedrockConnectionError(f"Connection error: {error_message}") + + # Timeout errors (retryable) + elif isinstance(e, (ReadTimeoutError, TimeoutError)): + logging.error(f"{operation} timeout error: {error_message}") + raise BedrockTimeoutError(f"Timeout error: {error_message}") + + # Custom Bedrock errors (already properly typed) + elif isinstance( + e, + ( + BedrockRateLimitError, + BedrockConnectionError, + BedrockTimeoutError, + BedrockError, + ), + ): + raise + + # Unknown errors (non-retryable) + else: + logging.error(f"{operation} unexpected error: {error_message}") + raise BedrockError(f"Unexpected error: {error_message}") + + @retry( stop=stop_after_attempt(5), - wait=wait_exponential(multiplier=1, max=60), - retry=retry_if_exception_type((BedrockError)), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(BedrockRateLimitError) + | retry_if_exception_type(BedrockConnectionError) + | retry_if_exception_type(BedrockTimeoutError) + ), ) async def bedrock_complete_if_cache( model, @@ -159,9 +260,6 @@ async def bedrock_complete_if_cache( break except Exception as e: - # Log the specific error for debugging - logging.error(f"Bedrock streaming error: {e}") - # Try to clean up resources if possible if ( iteration_started @@ -176,7 +274,8 @@ async def bedrock_complete_if_cache( f"Failed to close Bedrock event stream: {close_error}" ) - raise BedrockError(f"Streaming error: {e}") + # Convert to appropriate exception type + _handle_bedrock_exception(e, "Bedrock streaming") finally: # Clean up the event stream @@ -232,10 +331,8 @@ async def bedrock_complete_if_cache( return content except Exception as e: - if isinstance(e, BedrockError): - raise - else: - raise BedrockError(f"Bedrock API error: {e}") + # Convert to appropriate exception type + _handle_bedrock_exception(e, "Bedrock converse") # Generic Bedrock completion function @@ -255,11 +352,15 @@ async def bedrock_complete( @wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) -# @retry( -# stop=stop_after_attempt(3), -# wait=wait_exponential(multiplier=1, min=4, max=10), -# retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)), # TODO: fix exceptions -# ) +@retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(BedrockRateLimitError) + | retry_if_exception_type(BedrockConnectionError) + | retry_if_exception_type(BedrockTimeoutError) + ), +) async def bedrock_embed( texts: list[str], model: str = "amazon.titan-embed-text-v2:0", @@ -282,48 +383,101 @@ async def bedrock_embed( async with session.client( "bedrock-runtime", region_name=region ) as bedrock_async_client: - if (model_provider := model.split(".")[0]) == "amazon": - embed_texts = [] - for text in texts: - if "v2" in model: + try: + if (model_provider := model.split(".")[0]) == "amazon": + embed_texts = [] + for text in texts: + try: + if "v2" in model: + body = json.dumps( + { + "inputText": text, + # 'dimensions': embedding_dim, + "embeddingTypes": ["float"], + } + ) + elif "v1" in model: + body = json.dumps({"inputText": text}) + else: + raise BedrockError(f"Model {model} is not supported!") + + response = await bedrock_async_client.invoke_model( + modelId=model, + body=body, + accept="application/json", + contentType="application/json", + ) + + response_body = await response.get("body").json() + + # Validate response structure + if not response_body or "embedding" not in response_body: + raise BedrockError( + f"Invalid embedding response structure for text: {text[:50]}..." + ) + + embedding = response_body["embedding"] + if not embedding: + raise BedrockError( + f"Received empty embedding for text: {text[:50]}..." + ) + + embed_texts.append(embedding) + + except Exception as e: + # Convert to appropriate exception type + _handle_bedrock_exception( + e, "Bedrock embedding (amazon, text chunk)" + ) + + elif model_provider == "cohere": + try: body = json.dumps( { - "inputText": text, - # 'dimensions': embedding_dim, - "embeddingTypes": ["float"], + "texts": texts, + "input_type": "search_document", + "truncate": "NONE", } ) - elif "v1" in model: - body = json.dumps({"inputText": text}) - else: - raise ValueError(f"Model {model} is not supported!") - response = await bedrock_async_client.invoke_model( - modelId=model, - body=body, - accept="application/json", - contentType="application/json", + response = await bedrock_async_client.invoke_model( + model=model, + body=body, + accept="application/json", + contentType="application/json", + ) + + response_body = json.loads(response.get("body").read()) + + # Validate response structure + if not response_body or "embeddings" not in response_body: + raise BedrockError( + "Invalid embedding response structure from Cohere" + ) + + embeddings = response_body["embeddings"] + if not embeddings or len(embeddings) != len(texts): + raise BedrockError( + f"Invalid embeddings count: expected {len(texts)}, got {len(embeddings) if embeddings else 0}" + ) + + embed_texts = embeddings + + except Exception as e: + # Convert to appropriate exception type + _handle_bedrock_exception(e, "Bedrock embedding (cohere)") + + else: + raise BedrockError( + f"Model provider '{model_provider}' is not supported!" ) - response_body = await response.get("body").json() + # Final validation + if not embed_texts: + raise BedrockError("No embeddings generated") - embed_texts.append(response_body["embedding"]) - elif model_provider == "cohere": - body = json.dumps( - {"texts": texts, "input_type": "search_document", "truncate": "NONE"} - ) + return np.array(embed_texts) - response = await bedrock_async_client.invoke_model( - model=model, - body=body, - accept="application/json", - contentType="application/json", - ) - - response_body = json.loads(response.get("body").read()) - - embed_texts = response_body["embeddings"] - else: - raise ValueError(f"Model provider '{model_provider}' is not supported!") - - return np.array(embed_texts) + except Exception as e: + # Convert to appropriate exception type + _handle_bedrock_exception(e, "Bedrock embedding") From ab4d7ac2b0bb4821f788cf321fcb6b2d68dba580 Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 19:28:36 +0800 Subject: [PATCH 29/37] Add configurable embedding token limit with validation - Add EMBEDDING_TOKEN_LIMIT env var - Set max_token_size on embedding func - Add token limit property to LightRAG - Validate summary length vs limit - Log warning when limit exceeded --- lightrag/api/config.py | 5 +++++ lightrag/api/lightrag_server.py | 5 +++++ lightrag/lightrag.py | 7 +++++++ lightrag/operate.py | 14 ++++++++++++++ 4 files changed, 31 insertions(+) diff --git a/lightrag/api/config.py b/lightrag/api/config.py index 95ab9f70..4f59d3c1 100644 --- a/lightrag/api/config.py +++ b/lightrag/api/config.py @@ -445,6 +445,11 @@ def parse_args() -> argparse.Namespace: "EMBEDDING_BATCH_NUM", DEFAULT_EMBEDDING_BATCH_NUM, int ) + # Embedding token limit configuration + args.embedding_token_limit = get_env_value( + "EMBEDDING_TOKEN_LIMIT", None, int, special_none=True + ) + ollama_server_infos.LIGHTRAG_NAME = args.simulated_model_name ollama_server_infos.LIGHTRAG_TAG = args.simulated_model_tag diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py index e5a86b3e..adbc5f28 100644 --- a/lightrag/api/lightrag_server.py +++ b/lightrag/api/lightrag_server.py @@ -784,6 +784,11 @@ def create_app(args): send_dimensions=send_dimensions, ) + # Set max_token_size if EMBEDDING_TOKEN_LIMIT is provided + if args.embedding_token_limit is not None: + embedding_func.max_token_size = args.embedding_token_limit + logger.info(f"Set embedding max_token_size to {args.embedding_token_limit}") + # Configure rerank function based on args.rerank_bindingparameter rerank_model_func = None if args.rerank_binding != "null": diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index e540e3b7..72a4dc6d 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -276,6 +276,13 @@ class LightRAG: embedding_func: EmbeddingFunc | None = field(default=None) """Function for computing text embeddings. Must be set before use.""" + @property + def embedding_token_limit(self) -> int | None: + """Get the token limit for embedding model from embedding_func.""" + if self.embedding_func and hasattr(self.embedding_func, "max_token_size"): + return self.embedding_func.max_token_size + return None + embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 10))) """Batch size for embedding computations.""" diff --git a/lightrag/operate.py b/lightrag/operate.py index ae2be49e..858553b1 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -345,6 +345,20 @@ async def _summarize_descriptions( llm_response_cache=llm_response_cache, cache_type="summary", ) + + # Check summary token length against embedding limit + embedding_token_limit = global_config.get("embedding_token_limit") + if embedding_token_limit is not None and summary: + tokenizer = global_config["tokenizer"] + summary_token_count = len(tokenizer.encode(summary)) + threshold = int(embedding_token_limit * 0.9) + + if summary_token_count > threshold: + logger.warning( + f"Summary tokens ({summary_token_count}) exceeds 90% of embedding limit " + f"({embedding_token_limit}) for {description_type}: {description_name}" + ) + return summary From 39b49e92ffa1fe62d8a00932010d5fb940dec030 Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 20:58:41 +0800 Subject: [PATCH 30/37] Convert embedding_token_limit from property to field with __post_init__ MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Remove property decorator • Add field with init=False • Set value in __post_init__ method • embedding_token_limit is now in config dictionary --- lightrag/lightrag.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index 72a4dc6d..1f0aa9d9 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -276,12 +276,8 @@ class LightRAG: embedding_func: EmbeddingFunc | None = field(default=None) """Function for computing text embeddings. Must be set before use.""" - @property - def embedding_token_limit(self) -> int | None: - """Get the token limit for embedding model from embedding_func.""" - if self.embedding_func and hasattr(self.embedding_func, "max_token_size"): - return self.embedding_func.max_token_size - return None + embedding_token_limit: int | None = field(default=None, init=False) + """Token limit for embedding model. Set automatically from embedding_func.max_token_size in __post_init__.""" embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 10))) """Batch size for embedding computations.""" @@ -532,6 +528,12 @@ class LightRAG: queue_name="Embedding func", )(self.embedding_func) + # Initialize embedding_token_limit from embedding_func + if self.embedding_func and hasattr(self.embedding_func, "max_token_size"): + self.embedding_token_limit = self.embedding_func.max_token_size + else: + self.embedding_token_limit = None + # Initialize all storages self.key_string_value_json_storage_cls: type[BaseKVStorage] = ( self._get_storage_class(self.kv_storage) From 963a0a5db154a6cbcf62c060ae6a4549d5079fe2 Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 22:29:08 +0800 Subject: [PATCH 31/37] Refactor embedding function creation with proper attribute inheritance - Extract max_token_size from providers - Avoid double-wrapping EmbeddingFunc - Improve configuration priority logic - Add comprehensive debug logging - Return complete EmbeddingFunc instance --- lightrag/api/lightrag_server.py | 173 +++++++++++++++++++++++++------- 1 file changed, 139 insertions(+), 34 deletions(-) diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py index adbc5f28..f4b0bd24 100644 --- a/lightrag/api/lightrag_server.py +++ b/lightrag/api/lightrag_server.py @@ -618,33 +618,102 @@ def create_app(args): def create_optimized_embedding_function( config_cache: LLMConfigCache, binding, model, host, api_key, args - ): + ) -> EmbeddingFunc: """ - Create optimized embedding function with pre-processed configuration for applicable bindings. - Uses lazy imports for all bindings and avoids repeated configuration parsing. + Create optimized embedding function and return an EmbeddingFunc instance + with proper max_token_size inheritance from provider defaults. + + This function: + 1. Imports the provider embedding function + 2. Extracts max_token_size and embedding_dim from provider if it's an EmbeddingFunc + 3. Creates an optimized wrapper that calls the underlying function directly (avoiding double-wrapping) + 4. Returns a properly configured EmbeddingFunc instance """ + # Step 1: Import provider function and extract default attributes + provider_func = None + default_max_token_size = None + default_embedding_dim = args.embedding_dim # Use config as default + + try: + if binding == "openai": + from lightrag.llm.openai import openai_embed + + provider_func = openai_embed + elif binding == "ollama": + from lightrag.llm.ollama import ollama_embed + + provider_func = ollama_embed + elif binding == "gemini": + from lightrag.llm.gemini import gemini_embed + + provider_func = gemini_embed + elif binding == "jina": + from lightrag.llm.jina import jina_embed + + provider_func = jina_embed + elif binding == "azure_openai": + from lightrag.llm.azure_openai import azure_openai_embed + + provider_func = azure_openai_embed + elif binding == "aws_bedrock": + from lightrag.llm.bedrock import bedrock_embed + + provider_func = bedrock_embed + elif binding == "lollms": + from lightrag.llm.lollms import lollms_embed + + provider_func = lollms_embed + + # Extract attributes if provider is an EmbeddingFunc + if provider_func and isinstance(provider_func, EmbeddingFunc): + default_max_token_size = provider_func.max_token_size + default_embedding_dim = provider_func.embedding_dim + logger.debug( + f"Extracted from {binding} provider: " + f"max_token_size={default_max_token_size}, " + f"embedding_dim={default_embedding_dim}" + ) + except ImportError as e: + logger.warning(f"Could not import provider function for {binding}: {e}") + + # Step 2: Apply priority (environment variable > provider default) + final_max_token_size = args.embedding_token_limit or default_max_token_size + + # Step 3: Create optimized embedding function (calls underlying function directly) async def optimized_embedding_function(texts, embedding_dim=None): try: if binding == "lollms": from lightrag.llm.lollms import lollms_embed - return await lollms_embed( + # Get real function, skip EmbeddingFunc wrapper if present + actual_func = ( + lollms_embed.func + if isinstance(lollms_embed, EmbeddingFunc) + else lollms_embed + ) + return await actual_func( texts, embed_model=model, host=host, api_key=api_key ) elif binding == "ollama": from lightrag.llm.ollama import ollama_embed - # Use pre-processed configuration if available, otherwise fallback to dynamic parsing + # Get real function, skip EmbeddingFunc wrapper if present + actual_func = ( + ollama_embed.func + if isinstance(ollama_embed, EmbeddingFunc) + else ollama_embed + ) + + # Use pre-processed configuration if available if config_cache.ollama_embedding_options is not None: ollama_options = config_cache.ollama_embedding_options else: - # Fallback for cases where config cache wasn't initialized properly from lightrag.llm.binding_options import OllamaEmbeddingOptions ollama_options = OllamaEmbeddingOptions.options_dict(args) - return await ollama_embed( + return await actual_func( texts, embed_model=model, host=host, @@ -654,15 +723,30 @@ def create_app(args): elif binding == "azure_openai": from lightrag.llm.azure_openai import azure_openai_embed - return await azure_openai_embed(texts, model=model, api_key=api_key) + actual_func = ( + azure_openai_embed.func + if isinstance(azure_openai_embed, EmbeddingFunc) + else azure_openai_embed + ) + return await actual_func(texts, model=model, api_key=api_key) elif binding == "aws_bedrock": from lightrag.llm.bedrock import bedrock_embed - return await bedrock_embed(texts, model=model) + actual_func = ( + bedrock_embed.func + if isinstance(bedrock_embed, EmbeddingFunc) + else bedrock_embed + ) + return await actual_func(texts, model=model) elif binding == "jina": from lightrag.llm.jina import jina_embed - return await jina_embed( + actual_func = ( + jina_embed.func + if isinstance(jina_embed, EmbeddingFunc) + else jina_embed + ) + return await actual_func( texts, embedding_dim=embedding_dim, base_url=host, @@ -671,16 +755,21 @@ def create_app(args): elif binding == "gemini": from lightrag.llm.gemini import gemini_embed - # Use pre-processed configuration if available, otherwise fallback to dynamic parsing + actual_func = ( + gemini_embed.func + if isinstance(gemini_embed, EmbeddingFunc) + else gemini_embed + ) + + # Use pre-processed configuration if available if config_cache.gemini_embedding_options is not None: gemini_options = config_cache.gemini_embedding_options else: - # Fallback for cases where config cache wasn't initialized properly from lightrag.llm.binding_options import GeminiEmbeddingOptions gemini_options = GeminiEmbeddingOptions.options_dict(args) - return await gemini_embed( + return await actual_func( texts, model=model, base_url=host, @@ -691,7 +780,12 @@ def create_app(args): else: # openai and compatible from lightrag.llm.openai import openai_embed - return await openai_embed( + actual_func = ( + openai_embed.func + if isinstance(openai_embed, EmbeddingFunc) + else openai_embed + ) + return await actual_func( texts, model=model, base_url=host, @@ -701,7 +795,15 @@ def create_app(args): except ImportError as e: raise Exception(f"Failed to import {binding} embedding: {e}") - return optimized_embedding_function + # Step 4: Wrap in EmbeddingFunc and return + embedding_func_instance = EmbeddingFunc( + embedding_dim=default_embedding_dim, + func=optimized_embedding_function, + max_token_size=final_max_token_size, + send_dimensions=False, # Will be set later based on binding requirements + ) + + return embedding_func_instance llm_timeout = get_env_value("LLM_TIMEOUT", DEFAULT_LLM_TIMEOUT, int) embedding_timeout = get_env_value( @@ -735,25 +837,24 @@ def create_app(args): **kwargs, ) - # Create embedding function with optimized configuration + # Create embedding function with optimized configuration and max_token_size inheritance import inspect - # Create the optimized embedding function - optimized_embedding_func = create_optimized_embedding_function( + # Create the EmbeddingFunc instance (now returns complete EmbeddingFunc with max_token_size) + embedding_func = create_optimized_embedding_function( config_cache=config_cache, binding=args.embedding_binding, model=args.embedding_model, host=args.embedding_binding_host, api_key=args.embedding_binding_api_key, - args=args, # Pass args object for fallback option generation + args=args, ) # Get embedding_send_dim from centralized configuration embedding_send_dim = args.embedding_send_dim - # Check if the function signature has embedding_dim parameter - # Note: Since optimized_embedding_func is an async function, inspect its signature - sig = inspect.signature(optimized_embedding_func) + # Check if the underlying function signature has embedding_dim parameter + sig = inspect.signature(embedding_func.func) has_embedding_dim_param = "embedding_dim" in sig.parameters # Determine send_dimensions value based on binding type @@ -771,23 +872,27 @@ def create_app(args): else: dimension_control = "by not hasparam" + # Set send_dimensions on the EmbeddingFunc instance + embedding_func.send_dimensions = send_dimensions + logger.info( f"Send embedding dimension: {send_dimensions} {dimension_control} " - f"(dimensions={args.embedding_dim}, has_param={has_embedding_dim_param}, " + f"(dimensions={embedding_func.embedding_dim}, has_param={has_embedding_dim_param}, " f"binding={args.embedding_binding})" ) - # Create EmbeddingFunc with send_dimensions attribute - embedding_func = EmbeddingFunc( - embedding_dim=args.embedding_dim, - func=optimized_embedding_func, - send_dimensions=send_dimensions, - ) - - # Set max_token_size if EMBEDDING_TOKEN_LIMIT is provided - if args.embedding_token_limit is not None: - embedding_func.max_token_size = args.embedding_token_limit - logger.info(f"Set embedding max_token_size to {args.embedding_token_limit}") + # Log max_token_size source + if embedding_func.max_token_size: + source = ( + "env variable" + if args.embedding_token_limit + else f"{args.embedding_binding} provider default" + ) + logger.info( + f"Embedding max_token_size: {embedding_func.max_token_size} (from {source})" + ) + else: + logger.info("Embedding max_token_size: not set (90% token warning disabled)") # Configure rerank function based on args.rerank_bindingparameter rerank_model_func = None From de4412dd4080380d9da870d271faffffebba8760 Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 22:56:03 +0800 Subject: [PATCH 32/37] Fix embedding token limit initialization order * Capture max_token_size before decorator * Apply wrapper after capturing attribute * Prevent decorator from stripping dataclass * Ensure token limit is properly set --- lightrag/lightrag.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index 1f0aa9d9..c53c98ac 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -522,18 +522,22 @@ class LightRAG: logger.debug(f"LightRAG init with param:\n {_print_config}\n") # Init Embedding + # Step 1: Capture max_token_size before applying decorator (decorator strips dataclass attributes) + embedding_max_token_size = None + if self.embedding_func and hasattr(self.embedding_func, "max_token_size"): + embedding_max_token_size = self.embedding_func.max_token_size + logger.debug( + f"Captured embedding max_token_size: {embedding_max_token_size}" + ) + self.embedding_token_limit = embedding_max_token_size + + # Step 2: Apply priority wrapper decorator self.embedding_func = priority_limit_async_func_call( self.embedding_func_max_async, llm_timeout=self.default_embedding_timeout, queue_name="Embedding func", )(self.embedding_func) - # Initialize embedding_token_limit from embedding_func - if self.embedding_func and hasattr(self.embedding_func, "max_token_size"): - self.embedding_token_limit = self.embedding_func.max_token_size - else: - self.embedding_token_limit = None - # Initialize all storages self.key_string_value_json_storage_cls: type[BaseKVStorage] = ( self._get_storage_class(self.kv_storage) From 5dec4deac7ca23d818d1a36ab1497f5eef08f3d2 Mon Sep 17 00:00:00 2001 From: yangdx Date: Fri, 14 Nov 2025 23:22:44 +0800 Subject: [PATCH 33/37] Improve embedding config priority and add debug logging MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Fix embedding_dim priority logic • Add final config logging --- lightrag/api/lightrag_server.py | 30 +++++++++++++++++++++--------- 1 file changed, 21 insertions(+), 9 deletions(-) diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py index f4b0bd24..41a07f7f 100644 --- a/lightrag/api/lightrag_server.py +++ b/lightrag/api/lightrag_server.py @@ -632,8 +632,8 @@ def create_app(args): # Step 1: Import provider function and extract default attributes provider_func = None - default_max_token_size = None - default_embedding_dim = args.embedding_dim # Use config as default + provider_max_token_size = None + provider_embedding_dim = None try: if binding == "openai": @@ -667,18 +667,24 @@ def create_app(args): # Extract attributes if provider is an EmbeddingFunc if provider_func and isinstance(provider_func, EmbeddingFunc): - default_max_token_size = provider_func.max_token_size - default_embedding_dim = provider_func.embedding_dim + provider_max_token_size = provider_func.max_token_size + provider_embedding_dim = provider_func.embedding_dim logger.debug( f"Extracted from {binding} provider: " - f"max_token_size={default_max_token_size}, " - f"embedding_dim={default_embedding_dim}" + f"max_token_size={provider_max_token_size}, " + f"embedding_dim={provider_embedding_dim}" ) except ImportError as e: logger.warning(f"Could not import provider function for {binding}: {e}") - # Step 2: Apply priority (environment variable > provider default) - final_max_token_size = args.embedding_token_limit or default_max_token_size + # Step 2: Apply priority (user config > provider default) + # For max_token_size: explicit env var > provider default > None + final_max_token_size = args.embedding_token_limit or provider_max_token_size + # For embedding_dim: user config (always has value) takes priority + # Only use provider default if user config is explicitly None (which shouldn't happen) + final_embedding_dim = ( + args.embedding_dim if args.embedding_dim else provider_embedding_dim + ) # Step 3: Create optimized embedding function (calls underlying function directly) async def optimized_embedding_function(texts, embedding_dim=None): @@ -797,12 +803,18 @@ def create_app(args): # Step 4: Wrap in EmbeddingFunc and return embedding_func_instance = EmbeddingFunc( - embedding_dim=default_embedding_dim, + embedding_dim=final_embedding_dim, func=optimized_embedding_function, max_token_size=final_max_token_size, send_dimensions=False, # Will be set later based on binding requirements ) + # Log final embedding configuration + logger.info( + f"Embedding config: binding={binding} model={model} " + f"embedding_dim={final_embedding_dim} max_token_size={final_max_token_size}" + ) + return embedding_func_instance llm_timeout = get_env_value("LLM_TIMEOUT", DEFAULT_LLM_TIMEOUT, int) From 4343db753a05995563b9ab02fcc35c49bb1ef6e3 Mon Sep 17 00:00:00 2001 From: yangdx Date: Sat, 15 Nov 2025 00:58:23 +0800 Subject: [PATCH 34/37] Add macOS fork safety check for Gunicorn multi-worker mode MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Check OBJC_DISABLE_INITIALIZE_FORK_SAFETY • Prevent NumPy/Accelerate crashes • Show detailed error message • Provide multiple fix options • Exit early if misconfigured --- lightrag/api/run_with_gunicorn.py | 33 +++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/lightrag/api/run_with_gunicorn.py b/lightrag/api/run_with_gunicorn.py index 0de2ac36..deabe7cf 100644 --- a/lightrag/api/run_with_gunicorn.py +++ b/lightrag/api/run_with_gunicorn.py @@ -76,6 +76,39 @@ def main(): print("=" * 80 + "\n") sys.exit(1) + # Check macOS fork safety environment variable for multi-worker mode + if ( + platform.system() == "Darwin" + and global_args.workers > 1 + and os.environ.get("OBJC_DISABLE_INITIALIZE_FORK_SAFETY") != "YES" + ): + print("\n" + "=" * 80) + print("❌ ERROR: Missing required environment variable on macOS!") + print("=" * 80) + print("\nmacOS with Gunicorn multi-worker mode requires:") + print(" OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES") + print("\nReason:") + print(" NumPy uses macOS's Accelerate framework (Objective-C based) for") + print(" vector computations. The Objective-C runtime has fork safety checks") + print(" that will crash worker processes when embedding functions are called.") + print("\nCurrent configuration:") + print(" - Operating System: macOS (Darwin)") + print(f" - Workers: {global_args.workers}") + print( + f" - Environment Variable: {os.environ.get('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'NOT SET')}" + ) + print("\nHow to fix:") + print(" Option 1 - Set environment variable before starting (recommended):") + print(" export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES") + print(" lightrag-server") + print("\n Option 2 - Add to your shell profile (~/.zshrc or ~/.bash_profile):") + print(" echo 'export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES' >> ~/.zshrc") + print(" source ~/.zshrc") + print("\n Option 3 - Use single worker mode (no multiprocessing):") + print(" lightrag-server --workers 1") + print("=" * 80 + "\n") + sys.exit(1) + # Check and install dependencies check_and_install_dependencies() From 8722103550648f7d67fa2f6cab9712ec56e49b96 Mon Sep 17 00:00:00 2001 From: yangdx Date: Sat, 15 Nov 2025 01:25:56 +0800 Subject: [PATCH 35/37] Update env.example MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Comment out Ollama config • Set OpenAI as active default • Add EMBEDDING_TOKEN_LIMIT option • Add Gemini embedding configuration --- env.example | 38 +++++++++++++++++++++++++------------- 1 file changed, 25 insertions(+), 13 deletions(-) diff --git a/env.example b/env.example index a95ff9bf..60aaf0ed 100644 --- a/env.example +++ b/env.example @@ -255,21 +255,23 @@ OLLAMA_LLM_NUM_CTX=32768 ### For OpenAI: Set to 'true' to enable dynamic dimension adjustment ### For OpenAI: Set to 'false' (default) to disable sending dimension parameter ### Note: Automatically ignored for backends that don't support dimension parameter (e.g., Ollama) -# EMBEDDING_SEND_DIM=false -EMBEDDING_BINDING=ollama -EMBEDDING_MODEL=bge-m3:latest -EMBEDDING_DIM=1024 -EMBEDDING_BINDING_API_KEY=your_api_key -# If LightRAG deployed in Docker uses host.docker.internal instead of localhost -EMBEDDING_BINDING_HOST=http://localhost:11434 - -### OpenAI compatible (VoyageAI embedding openai compatible) -# EMBEDDING_BINDING=openai -# EMBEDDING_MODEL=text-embedding-3-large -# EMBEDDING_DIM=3072 -# EMBEDDING_BINDING_HOST=https://api.openai.com/v1 +# Ollama embedding +# EMBEDDING_BINDING=ollama +# EMBEDDING_MODEL=bge-m3:latest +# EMBEDDING_DIM=1024 # EMBEDDING_BINDING_API_KEY=your_api_key +### If LightRAG deployed in Docker uses host.docker.internal instead of localhost +# EMBEDDING_BINDING_HOST=http://localhost:11434 + +### OpenAI compatible embedding +EMBEDDING_BINDING=openai +EMBEDDING_MODEL=text-embedding-3-large +EMBEDDING_DIM=3072 +EMBEDDING_SEND_DIM=false +EMBEDDING_TOKEN_LIMIT=8192 +EMBEDDING_BINDING_HOST=https://api.openai.com/v1 +EMBEDDING_BINDING_API_KEY=your_api_key ### Optional for Azure # AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large @@ -277,6 +279,16 @@ EMBEDDING_BINDING_HOST=http://localhost:11434 # AZURE_EMBEDDING_ENDPOINT=your_endpoint # AZURE_EMBEDDING_API_KEY=your_api_key +### Gemini embedding +# EMBEDDING_BINDING=gemini +# EMBEDDING_MODEL=gemini-embedding-001 +# EMBEDDING_DIM=1536 +# EMBEDDING_TOKEN_LIMIT=2048 +# EMBEDDING_BINDING_HOST=https://generativelanguage.googleapis.com +# EMBEDDING_BINDING_API_KEY=your_api_key +### Gemini embedding requires sending dimension to server +# EMBEDDING_SEND_DIM=true + ### Jina AI Embedding # EMBEDDING_BINDING=jina # EMBEDDING_BINDING_HOST=https://api.jina.ai/v1/embeddings From c13f9116d9cb319a8d08a4e84d21cb8dd105e9af Mon Sep 17 00:00:00 2001 From: yangdx Date: Mon, 17 Nov 2025 12:26:54 +0800 Subject: [PATCH 36/37] Add embedding dimension validation to EmbeddingFunc wrapper MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit • Validate total elements divisibility • Check vector count matches input count • Raise clear error messages on mismatch • Ensure embedding output correctness • Add docstring for EmbeddingFunc class --- lightrag/utils.py | 37 ++++++++++++++++++++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/lightrag/utils.py b/lightrag/utils.py index d653c1e3..aa04f338 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -353,6 +353,16 @@ class TaskState: @dataclass class EmbeddingFunc: + """Embedding function wrapper with dimension validation + This class wraps an embedding function to ensure that the output embeddings have the correct dimension. + This class should not be wrapped multiple times. + + Args: + embedding_dim: Expected dimension of the embeddings + func: The actual embedding function to wrap + max_token_size: Optional token limit for the embedding model + send_dimensions: Whether to inject embedding_dim as a keyword argument + """ embedding_dim: int func: callable max_token_size: int | None = None # Token limit for the embedding model @@ -379,7 +389,32 @@ class EmbeddingFunc: # Inject embedding_dim from decorator kwargs["embedding_dim"] = self.embedding_dim - return await self.func(*args, **kwargs) + # Call the actual embedding function + result = await self.func(*args, **kwargs) + + # Validate embedding dimensions using total element count + total_elements = result.size # Total number of elements in the numpy array + expected_dim = self.embedding_dim + + # Check if total elements can be evenly divided by embedding_dim + if total_elements % expected_dim != 0: + raise ValueError( + f"Embedding dimension mismatch detected: " + f"total elements ({total_elements}) cannot be evenly divided by " + f"expected dimension ({expected_dim}). " + ) + + # Optional: Verify vector count matches input text count + actual_vectors = total_elements // expected_dim + if args and isinstance(args[0], (list, tuple)): + expected_vectors = len(args[0]) + if actual_vectors != expected_vectors: + raise ValueError( + f"Vector count mismatch: " + f"expected {expected_vectors} vectors but got {actual_vectors} vectors (from embedding result)." + ) + + return result def compute_args_hash(*args: Any) -> str: From 90f52acf0cf915e75318d53798c84b3a76ec0477 Mon Sep 17 00:00:00 2001 From: yangdx Date: Mon, 17 Nov 2025 12:28:53 +0800 Subject: [PATCH 37/37] Fix linting --- lightrag/utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/lightrag/utils.py b/lightrag/utils.py index aa04f338..8c9b7776 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -363,6 +363,7 @@ class EmbeddingFunc: max_token_size: Optional token limit for the embedding model send_dimensions: Whether to inject embedding_dim as a keyword argument """ + embedding_dim: int func: callable max_token_size: int | None = None # Token limit for the embedding model