From cadcf1004735d5e92e56ab3306b251c6cea1a425 Mon Sep 17 00:00:00 2001 From: huanghuoguoguo <1051233107@qq.com> Date: Fri, 6 Mar 2026 21:54:38 +0800 Subject: [PATCH] Feat/rag plugin (#1995) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [issue:1933] RAG engine plugin architecture (#1967) * refactor: migrate RAG knowledge services to a plugin-oriented host service architecture. * feat(rag): phase 2 core refactor with RPC Action handlers * feat: 为 RAG 插件添加知识库创建和删除事件通知，并优化了 RAG 动作的参数传递和枚举使用。 * feat: 统一知识库管理为RAG引擎，支持动态配置并移除旧的外部知识库组件。 * refactor(rag): remove plugin_adapter, inline logic into RuntimeKnowledgeBase BREAKING CHANGE: RAGPluginAdapter has been removed. All plugin communication is now handled directly by RuntimeKnowledgeBase. Architecture change: - Before: RuntimeKnowledgeBase → RAGPluginAdapter → plugin_connector - After: RuntimeKnowledgeBase → plugin_connector (direct) Changes to kbmgr.py (RuntimeKnowledgeBase): - Remove RAGPluginAdapter import and usage - Inline plugin communication methods: - _on_kb_create(): Notify plugin when KB is created - _on_kb_delete(): Notify plugin when KB is deleted - _ingest_document(): Call plugin for document ingestion - _retrieve(): Call plugin for retrieval - _delete_document(): Call plugin to delete document - Simplify dispose(): Only notify plugin, no built-in VDB assumption Changes to base.py (KnowledgeBaseInterface): - Remove get_type() abstract method (outdated internal/external concept) - Add get_rag_engine_plugin_id() abstract method Changes to localagent.py: - Remove get_type() call - Simplify top_k retrieval from KB entity Deleted files: - pkg/rag/knowledge/plugin_adapter.py Benefits: - Reduced abstraction layer, simpler code - Plugin communication logic centralized in RuntimeKnowledgeBase - Easier to understand and maintain 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * refactor(api): remove ExternalKnowledgeBase infrastructure BREAKING CHANGE: ExternalKnowledgeBase has been completely removed. All knowledge bases are now unified under the single KnowledgeBase model, differentiated by their rag_engine_plugin_id. Deleted files: - pkg/api/http/controller/groups/knowledge/external.py (ExternalKBController with /external-bases routes) - pkg/api/http/service/external_kb.py (ExternalKnowledgeBaseService) - pkg/rag/knowledge/external.py (ExternalKnowledgeBase implementation) Modified files: - pkg/entity/persistence/rag.py: Remove ExternalKnowledgeBase SQLAlchemy table definition - pkg/core/app.py: Remove external_kb_service attribute from LangBotApplication - pkg/core/stages/build_app.py: Remove external_kb_service initialization Migration notes: - Existing external knowledge base data should be migrated manually - API consumers should use /api/v1/knowledge/bases for all KB operations - Use /api/v1/knowledge/engines to discover available RAG engines 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * refactor(plugin): remove list_knowledge_retrievers from connector Remove deprecated list_knowledge_retrievers functionality from the plugin communication layer. This aligns with the SDK change that removed the LIST_KNOWLEDGE_RETRIEVERS action. Changes: - connector.py: Remove list_knowledge_retrievers() method - handler.py: Remove list_knowledge_retrievers() handler The functionality is replaced by the new /api/v1/knowledge/engines endpoint which lists available RAGEngine components with their capabilities and configuration schemas. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * refactor(service): update knowledge service with capability-based checks Replace type-based checks with capability-based checks for file operations, aligning with the unified knowledge base architecture. Changes to knowledge.py: - store_file(): Replace get_type() check with doc_ingestion capability check - delete_file(): Replace get_type() check with doc_ingestion capability check - list_rag_engines(): Remove list_knowledge_retrievers call, simplify to only list RAGEngine components (KnowledgeRetriever type removed) Changes to pipelines.py: - Minor cleanup related to knowledge base references The capability-based approach allows RAG engines to declare their supported features (doc_ingestion, chunking_config, rerank, hybrid_search) and the system responds accordingly, rather than hardcoding behavior based on internal/external type distinction. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * feat(web): unify knowledge base UI, remove external KB components BREAKING CHANGE: The internal/external knowledge base distinction has been removed from the frontend. All knowledge bases are now displayed in a unified list, differentiated by their RAG engine. Changes to page.tsx: - Remove Tab component (内置/外置 tabs) - Remove selectedKbType state - Unified knowledge base list display - Single "Create Knowledge Base" button for all types Changes to KBDetailDialog.tsx: - Remove kbType prop - Simplify dialog logic for unified KB handling - Documents menu item conditionally shown based on doc_ingestion capability Changes to KBForm.tsx: - Remove retriever type handling code - Simplify form for unified KB creation - Dynamic form rendering based on RAG engine's creation_schema Changes to KBCardVO.ts: - Remove 'type' field from KBCardVO interface Changes to BackendClient.ts: - Remove all external KB related methods: - getExternalKnowledgeBases() - getExternalKnowledgeBase() - createExternalKnowledgeBase() - updateExternalKnowledgeBase() - deleteExternalKnowledgeBase() - retrieveFromExternalKnowledgeBase() Changes to api/index.ts: - Remove ExternalKnowledgeBase interface definition UI/UX improvements: - Users no longer need to understand internal vs external distinction - RAG engine selection is now the primary differentiator - Documents panel visibility is capability-driven (doc_ingestion) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * refactor(plugin): code review improvements for RAG handlers - Unify embed_model field naming to embedding_model_uuid only - Add structured error responses with error_type for RAG actions - Fix file_size and mime_type detection in _store_file_task - Improve error handling with detailed error context (error_type, original_error) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * refactor(rag): refactor KB dynamic form and vector manager - Frontend: Refactor Knowledge Base form using DynamicForm components. - Frontend: Remove obsolete jsonSchemaConverter utility. - Backend: Update VectorManager and PluginHandler to support new RAG architecture. - Chore: Update dependencies in pyproject.toml. * fix: code review fixes for RAG refactor - Remove DEBUG stderr outputs in handler.py - Move repeated `import json` to file top - Add warning log for unimplemented delete_by_filter 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * refactor(rag): consolidate valid_fields into entity constants Define MUTABLE_FIELDS, CREATE_FIELDS, ALL_DB_FIELDS as class constants in KnowledgeBase entity to eliminate duplication. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * refactor: 将知识库获取和RAG引擎信息丰富逻辑移至知识库管理器。 * refactor(rag): introduce RAGRuntimeService and clean up plugin handler - Create RAGRuntimeService to encapsulate RAG capability implementation (Embedding, VectorOps). - Refactor PluginHandler to delegate RAG actions to RAGRuntimeService. - Move KnowledgeService enrichment and creation logic to RAGManager. - Register RAGRuntimeService in Application and BuildAppStage. - Clean up legacy code in KnowledgeService. * refactor(rag): standardize logger and fix type hints - Use self.ap.logger consistently in kbmgr.py and runtime.py, removing module-level loggers. - Fix type hints for retrieve_knowledge in handler.py and connector.py to match implementation returning dict. * refactor: 将引擎徽章的样式从 Tailwind CSS 类迁移到 CSS 模块。 * fix(web): resolve React rendering errors in plugins page - Fix missing key prop in PluginComponentList by using ternary instead of Fragment - Fix RAGEngine.name type to I18nObject and use extractI18nObject() for rendering - Preserves multi-language support 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * fix(rag): update runtime service and web components * refactor: 优化知识库设置结构并增强前端距离显示健壮性。 * fix: 处理前端距离显示中的空值。 * fix(rag): document retrieve ui and kbmgr top_k validation * 更新 uv.lock 中的 PyPI 镜像源为官方地址。 * fix: address code review issues for RAG engine plugin architecture P0 fixes: - Fix ALL_DB_FIELDS missing collection_id and emoji fields - Move rag_engine_plugin_id to CREATE_FIELDS (immutable after creation) - Fix creation_settings mutable default value (dict -> None) - Rename vector delete method to delete_by_file_id for correct semantics - Fix delete_by_filter to raise NotImplementedError instead of silent no-op - Add database migration script (dbm019) for new columns and table cleanup P1 fixes: - Clean up design-hesitation comments in connector.py - Add _parse_plugin_id() with format validation for all RAG methods - Make _retrieve() raise exceptions instead of silently returning empty results - Extract _make_rag_error_response() helper for clean error formatting - Remove unused imports from handler.py P2 fixes: - Fix runtime.py indentation inconsistencies - Simplify get_file_stream to use storage abstraction uniformly - Reduce redundant DB queries in knowledge service (extract _check_doc_capability) - Fix engines.py URL encoding: use instead of __ replacement - Add read-only mode for engine settings in KBForm edit mode - Simplify page.tsx handleKBCardClick to pass only kbId string Co-authored-by: Cursor * fix: address code review findings for RAG plugin architecture - Frontend: add retrieval_settings param to retrieveKnowledgeBase API call - Backend: return {uuid} from PUT knowledge base to match frontend expectation - Backend: validate query is non-empty in retrieve endpoint (400 on empty) - Backend: rename vector_delete ids→file_ids for semantic clarity, keep backward compat by accepting both 'file_ids' and 'ids' in RPC handler - Backend: ensure rag_engine.name fallback is always I18nObject-compatible dict, preventing frontend extractI18nObject from receiving plain strings - Migration: fix misleading docstring about external_kb data migration Co-authored-by: Cursor * Update langbot-plugin version to 0.2.6 * chore: update required database version from 18 to 19 * refactor: remove unused polymorphic component framework * chore: fix lint and format issues for python and frontend * fix(plugin): remove legacy `ids` fallback in rag_vector_delete handler SDK now sends `file_ids` directly, the `ids` backward-compat fallback is no longer needed. Co-Authored-By: Claude Opus 4.6 * fix(rag): deep review fixes for critical bugs, security and quality Critical: - Fix StorageMgr.load() -> storage_provider.load() (C1, AttributeError) - Update required_database_version 18 -> 19 (C2, migration never runs) Security: - Add path traversal validation in get_file_stream (C11) - Add vectors/ids/metadata length validation in rag_vector_upsert (C12) Logic fixes: - Legacy KBs: set capabilities to [] instead of ['doc_ingestion'] (C4) - Fix store_file return type int -> str (C5) - Fix retrieve_knowledge return [] -> {'results': []} when disabled (C6) - Re-raise exception in _on_kb_create instead of silently swallowing (C7) - Log warning when KB not found in memory during delete (C8) API fixes: - Catch ValueError as 400 in create_knowledge_base endpoint (C15) - Validate plugin_id format in engines endpoints (C16) Quality: - Remove dead if/else in migration with identical branches (C17) - Fix variable shadowing: rag_context -> rag_context_text (C18) Co-Authored-By: Claude Opus 4.6 * chore: remove unused os import to fix ruff lint Co-Authored-By: Claude Opus 4.6 * refactor(plugin): remove PolymorphicComponent sync from LangBot side Remove sync_polymorphic_component_instances() from connector and handler, and the post-connection sync call in initialize(). This dead code synced an always-empty list of polymorphic instances that were never created. Companion change to langbot-plugin-sdk PolymorphicComponent removal. Co-Authored-By: Claude Opus 4.6 * fix(rag): fix vector_delete count bug and remove vestigial instance_id parameter 1. vector_delete: assign return value from delete_by_filter to count instead of silently returning 0 for filter-based deletion. 2. Remove instance_id parameter from the entire retrieve_knowledge call chain (kbmgr → connector → handler → runtime). This parameter was a remnant of the PolymorphicComponent mechanism and is no longer used — RAGEngine operates as a stateless singleton. Co-Authored-By: Claude Opus 4.6 * feat(web): 支持 creation_schema 字段级别的 editable 属性控制编辑模式可修改性 - IDynamicFormItemSchema 添加 editable 可选属性 - DynamicFormItemConfig 透传 editable 属性 - DynamicFormComponent 接收 isEditing prop，按字段 editable 值控制禁用 - KBForm 解析 editable 并传递 isEditing 给动态表单组件 - editable 未指定时默认可编辑，editable: false 时编辑模式下禁用该字段 * feat(storage): 添加 size() 抽象方法及 LocalStorage/S3 实现 支持获取存储对象大小，S3 使用 head_object 避免下载整个文件 * fix(migration): 删除 external_knowledge_bases 表前记录日志警告 - 迁移时如果表中存在数据，先 warning 日志记录避免无感数据丢失 - 添加 chunk 清理注释说明：仅对旧版非插件架构 KB 有效 * fix(web): 修复检索结果长文本撑大容器导致查询按钮不可见 KBDetailDialog 的 main 容器添加 min-w-0 overflow-x-hidden， 限制 flex-1 子容器宽度，防止 Dify RAG 长文本撑出 Dialog 边界 * fix(rag): address code review issues for plugin architecture PR - Fix SQL injection in migration helpers by using bind parameters - Move numpy import to module level in vector/mgr.py - Improve path traversal validation using posixpath.normpath - Add call_rag_retrieve to connector, eliminating duplicate plugin_id parsing in kbmgr.py _retrieve - Normalize typing style to modern dict/list/None syntax Co-Authored-By: Claude Opus 4.6 * style(web): fix prettier formatting errors Co-Authored-By: Claude Opus 4.6 * refactor(rag): update embedding handling in RuntimeConnectionHandler - Renamed RAG_EMBED_DOCUMENTS and RAG_EMBED_QUERY actions to INVOKE_EMBEDDING for clarity. - Removed embed_documents and embed_query methods from RuntimeEmbeddingModel and RAGRuntimeService. - Integrated embedding model retrieval directly in the invoke_embedding method, improving error handling for missing models. - Updated the embedding invocation logic to streamline the process and enhance error reporting. * refactor(web): replace KnowledgeRetriever with RAGEngine across frontend and tests KnowledgeRetriever component type has been removed in favor of the new RAGEngine architecture. Update all remaining references in i18n locales, plugin component icon mappings, marketplace filter, and unit tests. Addresses reviewer notes from RockChinQ on PR #1967. Co-Authored-By: Claude Opus 4.6 * fix(rag): address critical bugs found in deep review - Fix path traversal bypass in runtime.py (check all path components for '..') - Use normalized path for file loading instead of raw user input - Change knowledge_bases from list to dict for O(1) lookup and race safety - Add rollback on KB creation failure (clean up DB + runtime on plugin error) - Add null check after KB update in knowledge service - Fix file extension parsing to use os.path.splitext instead of split('.') (handles multi-dot filenames like 'report.v2.pdf' correctly) Co-Authored-By: Claude Opus 4.6 * fix(rag): address remaining review issues across frontend and backend Frontend: - Fix KB delete: use async/await with error handling instead of fire-and-forget - Fix capabilities null check: add optional chaining to prevent crash - Add toast.error on KB info load failure instead of silent console.error - Replace hard-coded Chinese validation message with i18n key - Replace hard-coded English error messages in DynamicFormItemComponent with i18n - Optimize document polling: stop when all documents reach terminal state - Add i18n keys (fieldRequired, loadKnowledgeBaseFailed, deleteKnowledgeBaseFailed, getKnowledgeBaseListError) to all 4 locales Backend: - Fix KB delete atomicity: delete from DB first, then notify plugin - Add RAG engine plugin existence validation before creating KB Co-Authored-By: Claude Opus 4.6 * style(rag): fix ruff formatting in kbmgr.py Co-Authored-By: Claude Opus 4.6 --------- Co-authored-by: Claude Opus 4.5 Co-authored-by: Cursor Co-authored-by: Junyan Qin * chore: bump langbot-plugin to 0.3.0 (#1992) * chore: correct sdk version to 0.3.0a1 * feat: normalize rag related actions' names * refactor(rag): align IngestionContext fields with SDK changes Remove redundant `chunking_strategy` field and rename `custom_settings` to `creation_settings` to match the updated SDK entity definitions (langbot-plugin-sdk#36). Co-Authored-By: Claude Opus 4.6 * style: fix ruff formatting Co-Authored-By: Claude Opus 4.6 * fix(rag): enforce immutability of embedding_model_uuid and non-editable creation_settings fields Remove embedding_model_uuid from MUTABLE_FIELDS to prevent post-creation modification via API. Add backend validation for creation_settings to preserve fields marked editable:false in the plugin's creation schema. Co-Authored-By: Claude Opus 4.6 * style(rag): fix ruff formatting in knowledge service Co-Authored-By: Claude Opus 4.6 * refactor(rag): split settings into immutable creation_settings and mutable retrieval_settings - Remove standalone embedding_model_uuid and top_k columns from KB entity - Add retrieval_settings column; update MUTABLE_FIELDS/CREATE_FIELDS accordingly - Merge migration logic into dbm019 (add retrieval_settings, migrate top_k and embedding_model_uuid into JSON settings, drop old columns on PostgreSQL) - Remove _filter_creation_settings and per-field editable concept - Frontend: creation_settings fields are all disabled when editing, retrieval_settings fields are always editable via a second DynamicFormComponent - Remove editable from IDynamicFormItemSchema, DynamicFormItemConfig - Clean up KBCardVO, KnowledgeBase API type, and localagent runner Co-Authored-By: Claude Opus 4.6 * bugfix: if ingest_document failed,not raise exep * fix: ruff lint * refactor(rag): remove unused _get_kb_entity method from RAGRuntimeService * feat(vector): implement metadata filters for vector_search and vector_delete (#1997) Add functional metadata filter support across all 5 VDB backends using Chroma-style where syntax as the canonical format. Previously the filters parameter existed throughout the stack but was entirely ignored. - Add filter_utils.py with normalize_filter() and strip_unsupported_fields() - Implement filter in search() and add delete_by_filter() for all backends: Chroma/SeekDB (native passthrough), Qdrant (translated to models.Filter), Milvus (translated to expr string), pgvector (translated to SQLAlchemy conditions) - Milvus/pgvector limited to {text, file_id, chunk_uuid}; other fields logged and ignored - Replace delete_by_filter() NotImplementedError with backend delegation in mgr.py - Populate retrieval_context['filters'] from settings in kbmgr._retrieve() - Pass search_type/query_text/documents through handler and runtime service Co-Authored-By: Claude Opus 4.6 * style(vector): fix ruff formatting Co-Authored-By: Claude Opus 4.6 * fix(vector): remove numpy dependency and fix SeekDB search modes - Remove numpy array conversion for query vectors; all VDB backends accept list[float] directly - Remove redundant get_or_create_collection call from upsert; backends handle collection creation internally in add_embeddings - Fix SeekDB to raise ValueError when vector dimension is unknown instead of defaulting to 384 - Use hybrid_search() for full-text and hybrid search modes in SeekDB, since pyseekdb's query() always requires embeddings Co-Authored-By: Claude Opus 4.6 * fix(vector): escape single quotes in SeekDB documents and metadata Document text containing apostrophes (e.g. "don't", "it's") causes SQL syntax errors in OceanBase because single quotes were not in the escape table. Add single-quote escaping and apply the escape table to the documents parameter in add_embeddings(), not just metadata. Co-Authored-By: Claude Opus 4.6 * fix(vector): use standard SQL escaping for single quotes in SeekDB Change single quote escaping from MySQL-style \' to standard SQL '' (doubled quote). The backslash escape is not recognized by OceanBase in NO_BACKSLASH_ESCAPES mode, causing SQL syntax errors when metadata text contains apostrophes (e.g. O'Shea in academic citations). Co-Authored-By: Claude Opus 4.6 * fix(rag): persist retrieval_settings on knowledge base creation retrieval_settings was not being passed from the service layer to RAGManager.create_knowledge_base(), causing retrieval schema fields (e.g. query_rewrite) to be lost on initial KB creation. They only took effect after a subsequent edit/update. Co-Authored-By: Claude Opus 4.6 * feat(web): add show_if conditional rendering for dynamic forms Support conditional field visibility in plugin-defined forms via show_if rules (eq, neq, in operators). Fields can depend on values from the same form or cross-reference between creation and retrieval settings via externalDependentValues. Co-Authored-By: Claude Opus 4.6 * fix(rag): replace base64 with chunked file transfer for get_rag_file_stream Use send_file() instead of base64 encoding for returning file content in the GET_RAG_FILE_STREAM handler, avoiding memory issues with large files. Co-Authored-By: Claude Opus 4.6 * feat(parser): add parser plugin integration and capability-aware upload UI (#2000) * feat(parser): add parser plugin integration and capability-aware upload UI Backend: add parser plugin API endpoints (list/invoke), connector and handler support for parser actions, and KB manager passthrough. Frontend: thread ragEngineCapabilities prop to FileUploadZone and use doc_parsing capability to conditionally show the RAG engine option in the parser selector. When no parser is available, show a warning prompting users to install a parser plugin. Update i18n: rename builtInParser to "Provided by RAG engine" and add noParserAvailable warning message in all 4 locales. Co-Authored-By: Claude Opus 4.6 * fix(parser): replace base64 with chunked file transfer and remove stale cache - Remove @alru_cache from list_parsers() and list_rag_engines() - Replace inline base64 file content with send_file/read_local_file chunked transfer pattern in parse_document and invoke_parser flows - Remove unused base64 import from kbmgr.py Co-Authored-By: Claude Opus 4.6 --------- Co-authored-by: Claude Opus 4.6 * feat(web): add Parser component kind to plugin market UI and i18n Add Parser to kindIconMap, market filter toggle, and all 4 locale files so parser plugins are properly displayed and filterable in the plugin market, matching the existing RAGEngine treatment. Co-Authored-By: Claude Opus 4.6 * style(web): fix prettier formatting from merge Co-Authored-By: Claude Opus 4.6 * refactor: rename RAGEngine to KnowledgeEngine across frontend and backend * fix(web): fix I18nObject import path in FileUploadZone and KBDoc * chore: format files involved in RAGEngine to KnowledgeEngine refactor * refactor: change rag engine to knowledge engine * fix: update langbot-plugin version to 0.3.0rc1 * chore: disable migration 20 for now --------- Co-authored-by: Claude Opus 4.5 Co-authored-by: Cursor Co-authored-by: Junyan Qin --- pyproject.toml | 2 +- .../http/controller/groups/knowledge/base.py | 23 +- .../controller/groups/knowledge/engines.py | 45 ++ .../controller/groups/knowledge/external.py | 61 -- .../controller/groups/knowledge/parsers.py | 16 + .../controller/groups/pipelines/pipelines.py | 2 +- .../pkg/api/http/service/external_kb.py | 80 --- src/langbot/pkg/api/http/service/knowledge.py | 149 +++-- src/langbot/pkg/core/app.py | 5 +- src/langbot/pkg/core/stages/build_app.py | 8 +- src/langbot/pkg/entity/persistence/rag.py | 30 +- ...20_knowledge_engine_plugin_architecture.py | 184 ++++++ src/langbot/pkg/plugin/connector.py | 134 ++-- src/langbot/pkg/plugin/handler.py | 250 +++++++- .../pkg/provider/runners/localagent.py | 14 +- src/langbot/pkg/rag/knowledge/base.py | 8 +- src/langbot/pkg/rag/knowledge/external.py | 85 --- src/langbot/pkg/rag/knowledge/kbmgr.py | 487 ++++++++++---- .../pkg/rag/knowledge/services/__init__.py | 0 .../rag/knowledge/services/base_service.py | 15 - .../pkg/rag/knowledge/services/chunker.py | 49 -- .../pkg/rag/knowledge/services/embedder.py | 55 -- .../pkg/rag/knowledge/services/parser.py | 291 --------- .../pkg/rag/knowledge/services/retriever.py | 53 -- src/langbot/pkg/rag/service/__init__.py | 1 + src/langbot/pkg/rag/service/runtime.py | 89 +++ src/langbot/pkg/storage/provider.py | 7 + .../pkg/storage/providers/localstorage.py | 6 + .../pkg/storage/providers/s3storage.py | 15 + src/langbot/pkg/vector/filter_utils.py | 69 ++ src/langbot/pkg/vector/mgr.py | 94 ++- src/langbot/pkg/vector/vdb.py | 73 ++- src/langbot/pkg/vector/vdbs/chroma.py | 28 +- src/langbot/pkg/vector/vdbs/milvus.py | 78 ++- src/langbot/pkg/vector/vdbs/pgvector_db.py | 86 ++- src/langbot/pkg/vector/vdbs/qdrant.py | 73 ++- src/langbot/pkg/vector/vdbs/seekdb.py | 137 +++- .../plugin/test_plugin_component_filtering.py | 22 +- web/pnpm-lock.yaml | 36 ++ .../home/bots/components/bot-form/BotForm.tsx | 1 + .../dynamic-form/DynamicFormComponent.tsx | 113 +++- .../dynamic-form/DynamicFormItemComponent.tsx | 236 +++---- .../dynamic-form/DynamicFormItemConfig.ts | 3 + web/src/app/home/knowledge/KBDetailDialog.tsx | 178 +++--- .../external-kb-card/ExternalKBCard.tsx | 59 -- .../external-kb-card/ExternalKBCardVO.ts | 43 -- .../external-kb-form/ExternalKBForm.tsx | 593 ------------------ .../components/kb-card/KBCard.module.css | 15 + .../knowledge/components/kb-card/KBCard.tsx | 11 +- .../knowledge/components/kb-card/KBCardVO.ts | 35 +- .../components/kb-docs/FileUploadZone.tsx | 307 +++++++-- .../knowledge/components/kb-docs/KBDoc.tsx | 86 ++- .../knowledge/components/kb-form/KBForm.tsx | 423 ++++++++----- .../kb-retrieve/ExternalKBRetrieve.tsx | 35 -- .../components/kb-retrieve/KBRetrieve.tsx | 124 ---- .../kb-retrieve/KBRetrieveGeneric.tsx | 3 +- web/src/app/home/knowledge/page.tsx | 224 ++----- .../plugin-installed/PluginComponentList.tsx | 73 +-- .../plugin-market/PluginMarketComponent.tsx | 23 +- .../PluginMarketCardComponent.tsx | 4 +- web/src/app/infra/entities/api/index.ts | 59 +- web/src/app/infra/entities/form/dynamic.ts | 9 + web/src/app/infra/http/BackendClient.ts | 57 +- web/src/i18n/locales/en-US.ts | 30 +- web/src/i18n/locales/ja-JP.ts | 16 +- web/src/i18n/locales/zh-Hans.ts | 29 +- web/src/i18n/locales/zh-Hant.ts | 16 +- 67 files changed, 2962 insertions(+), 2703 deletions(-) create mode 100644 src/langbot/pkg/api/http/controller/groups/knowledge/engines.py delete mode 100644 src/langbot/pkg/api/http/controller/groups/knowledge/external.py create mode 100644 src/langbot/pkg/api/http/controller/groups/knowledge/parsers.py delete mode 100644 src/langbot/pkg/api/http/service/external_kb.py create mode 100644 src/langbot/pkg/persistence/migrations/dbm020_knowledge_engine_plugin_architecture.py delete mode 100644 src/langbot/pkg/rag/knowledge/external.py delete mode 100644 src/langbot/pkg/rag/knowledge/services/__init__.py delete mode 100644 src/langbot/pkg/rag/knowledge/services/base_service.py delete mode 100644 src/langbot/pkg/rag/knowledge/services/chunker.py delete mode 100644 src/langbot/pkg/rag/knowledge/services/embedder.py delete mode 100644 src/langbot/pkg/rag/knowledge/services/parser.py delete mode 100644 src/langbot/pkg/rag/knowledge/services/retriever.py create mode 100644 src/langbot/pkg/rag/service/__init__.py create mode 100644 src/langbot/pkg/rag/service/runtime.py create mode 100644 src/langbot/pkg/vector/filter_utils.py delete mode 100644 web/src/app/home/knowledge/components/external-kb-card/ExternalKBCard.tsx delete mode 100644 web/src/app/home/knowledge/components/external-kb-card/ExternalKBCardVO.ts delete mode 100644 web/src/app/home/knowledge/components/external-kb-form/ExternalKBForm.tsx delete mode 100644 web/src/app/home/knowledge/components/kb-retrieve/ExternalKBRetrieve.tsx delete mode 100644 web/src/app/home/knowledge/components/kb-retrieve/KBRetrieve.tsx diff --git a/pyproject.toml b/pyproject.toml index 2999a995..3c63e574 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,7 +64,7 @@ dependencies = [ "chromadb>=0.4.24", "qdrant-client (>=1.15.1,<2.0.0)", "pyseekdb==1.0.0b7", - "langbot-plugin==0.2.7", + "langbot-plugin==0.3.0rc1", "asyncpg>=0.30.0", "line-bot-sdk>=3.19.0", "tboxsdk>=0.0.10", diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/base.py b/src/langbot/pkg/api/http/controller/groups/knowledge/base.py index 96ed001c..4f9bb5b4 100644 --- a/src/langbot/pkg/api/http/controller/groups/knowledge/base.py +++ b/src/langbot/pkg/api/http/controller/groups/knowledge/base.py @@ -13,7 +13,10 @@ class KnowledgeBaseRouterGroup(group.RouterGroup): elif quart.request.method == 'POST': json_data = await quart.request.json - knowledge_base_uuid = await self.ap.knowledge_service.create_knowledge_base(json_data) + try: + knowledge_base_uuid = await self.ap.knowledge_service.create_knowledge_base(json_data) + except ValueError as e: + return self.http_status(400, -1, str(e)) return self.success(data={'uuid': knowledge_base_uuid}) return self.http_status(405, -1, 'Method not allowed') @@ -39,7 +42,7 @@ class KnowledgeBaseRouterGroup(group.RouterGroup): elif quart.request.method == 'PUT': json_data = await quart.request.json await self.ap.knowledge_service.update_knowledge_base(knowledge_base_uuid, json_data) - return self.success({}) + return self.success(data={'uuid': knowledge_base_uuid}) elif quart.request.method == 'DELETE': await self.ap.knowledge_service.delete_knowledge_base(knowledge_base_uuid) @@ -65,8 +68,12 @@ class KnowledgeBaseRouterGroup(group.RouterGroup): if not file_id: return self.http_status(400, -1, 'File ID is required') + parser_plugin_id = json_data.get('parser_plugin_id') + # 调用服务层方法将文件与知识库关联 - task_id = await self.ap.knowledge_service.store_file(knowledge_base_uuid, file_id) + task_id = await self.ap.knowledge_service.store_file( + knowledge_base_uuid, file_id, parser_plugin_id=parser_plugin_id + ) return self.success( { 'task_id': task_id, @@ -90,5 +97,13 @@ class KnowledgeBaseRouterGroup(group.RouterGroup): async def retrieve_knowledge_base(knowledge_base_uuid: str) -> str: json_data = await quart.request.json query = json_data.get('query') - results = await self.ap.knowledge_service.retrieve_knowledge_base(knowledge_base_uuid, query) + + if not query or not query.strip(): + return self.http_status(400, -1, 'Query is required and cannot be empty') + + # Extract retrieval_settings to allow dynamic control over Knowledge Engine behavior (e.g. top_k, filters) + retrieval_settings = json_data.get('retrieval_settings', {}) + results = await self.ap.knowledge_service.retrieve_knowledge_base( + knowledge_base_uuid, query, retrieval_settings + ) return self.success(data={'results': results}) diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py b/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py new file mode 100644 index 00000000..28f0710e --- /dev/null +++ b/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py @@ -0,0 +1,45 @@ +import quart +from urllib.parse import unquote +from ... import group + + +@group.group_class('knowledge_engines', '/api/v1/knowledge/engines') +class KnowledgeEnginesRouterGroup(group.RouterGroup): + async def initialize(self) -> None: + @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY) + async def list_knowledge_engines() -> quart.Response: + """List all available Knowledge Engines from plugins. + + Returns a list of Knowledge Engines with their capabilities and configuration schemas. + This is used by the frontend to render the knowledge base creation wizard. + """ + engines = await self.ap.knowledge_service.list_knowledge_engines() + return self.success(data={'engines': engines}) + + @self.route( + '//creation-schema', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY + ) + async def get_engine_creation_schema(plugin_id: str) -> quart.Response: + """Get creation settings schema for a specific Knowledge Engine. + + plugin_id is in 'author/name' format, captured via converter. + """ + plugin_id = unquote(plugin_id) + if '/' not in plugin_id: + return self.http_status(400, -1, 'Invalid plugin_id format. Expected author/name.') + schema = await self.ap.knowledge_service.get_engine_creation_schema(plugin_id) + return self.success(data={'schema': schema}) + + @self.route( + '//retrieval-schema', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY + ) + async def get_engine_retrieval_schema(plugin_id: str) -> quart.Response: + """Get retrieval settings schema for a specific Knowledge Engine. + + plugin_id is in 'author/name' format, captured via converter. + """ + plugin_id = unquote(plugin_id) + if '/' not in plugin_id: + return self.http_status(400, -1, 'Invalid plugin_id format. Expected author/name.') + schema = await self.ap.knowledge_service.get_engine_retrieval_schema(plugin_id) + return self.success(data={'schema': schema}) diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/external.py b/src/langbot/pkg/api/http/controller/groups/knowledge/external.py deleted file mode 100644 index 324889e7..00000000 --- a/src/langbot/pkg/api/http/controller/groups/knowledge/external.py +++ /dev/null @@ -1,61 +0,0 @@ -import quart -from ... import group - - -@group.group_class('external_knowledge_base', '/api/v1/knowledge/external-bases') -class ExternalKnowledgeBaseRouterGroup(group.RouterGroup): - async def initialize(self) -> None: - @self.route('/retrievers', methods=['GET']) - async def list_knowledge_retrievers() -> quart.Response: - """List all available knowledge retrievers from plugins.""" - retrievers = await self.ap.plugin_connector.list_knowledge_retrievers() - return self.success(data={'retrievers': retrievers}) - - @self.route('', methods=['POST', 'GET']) - async def handle_external_knowledge_bases() -> quart.Response: - if quart.request.method == 'GET': - external_kbs = await self.ap.external_kb_service.get_external_knowledge_bases() - return self.success(data={'bases': external_kbs}) - - elif quart.request.method == 'POST': - json_data = await quart.request.json - kb_uuid = await self.ap.external_kb_service.create_external_knowledge_base(json_data) - return self.success(data={'uuid': kb_uuid}) - - return self.http_status(405, -1, 'Method not allowed') - - @self.route( - '/', - methods=['GET', 'DELETE', 'PUT'], - ) - async def handle_specific_external_knowledge_base(kb_uuid: str) -> quart.Response: - if quart.request.method == 'GET': - external_kb = await self.ap.external_kb_service.get_external_knowledge_base(kb_uuid) - - if external_kb is None: - return self.http_status(404, -1, 'external knowledge base not found') - - return self.success( - data={ - 'base': external_kb, - } - ) - - elif quart.request.method == 'PUT': - json_data = await quart.request.json - await self.ap.external_kb_service.update_external_knowledge_base(kb_uuid, json_data) - return self.success({}) - - elif quart.request.method == 'DELETE': - await self.ap.external_kb_service.delete_external_knowledge_base(kb_uuid) - return self.success({}) - - @self.route( - '//retrieve', - methods=['POST'], - ) - async def retrieve_external_knowledge_base(kb_uuid: str) -> str: - json_data = await quart.request.json - query = json_data.get('query') - results = await self.ap.external_kb_service.retrieve_external_knowledge_base(kb_uuid, query) - return self.success(data={'results': results}) diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/parsers.py b/src/langbot/pkg/api/http/controller/groups/knowledge/parsers.py new file mode 100644 index 00000000..a5e853cb --- /dev/null +++ b/src/langbot/pkg/api/http/controller/groups/knowledge/parsers.py @@ -0,0 +1,16 @@ +import quart +from ... import group + + +@group.group_class('parsers', '/api/v1/knowledge/parsers') +class ParsersRouterGroup(group.RouterGroup): + async def initialize(self) -> None: + @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY) + async def list_parsers() -> quart.Response: + """List all available parsers from plugins. + + Optional query parameter `mime_type` to filter parsers by supported MIME type. + """ + mime_type = quart.request.args.get('mime_type') + parsers = await self.ap.knowledge_service.list_parsers(mime_type) + return self.success(data={'parsers': parsers}) diff --git a/src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py b/src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py index 1828fb2b..e7fb6118 100644 --- a/src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py +++ b/src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py @@ -68,7 +68,7 @@ class PipelinesRouterGroup(group.RouterGroup): return self.http_status(404, -1, 'pipeline not found') # Only include plugins with pipeline-related components (Command, EventListener, Tool) - # Plugins that only have KnowledgeRetriever components are not suitable for pipeline extensions + # Plugins that only have KnowledgeEngine components are not suitable for pipeline extensions pipeline_component_kinds = ['Command', 'EventListener', 'Tool'] plugins = await self.ap.plugin_connector.list_plugins(component_kinds=pipeline_component_kinds) mcp_servers = await self.ap.mcp_service.get_mcp_servers(contain_runtime_info=True) diff --git a/src/langbot/pkg/api/http/service/external_kb.py b/src/langbot/pkg/api/http/service/external_kb.py deleted file mode 100644 index 4ac5d0fc..00000000 --- a/src/langbot/pkg/api/http/service/external_kb.py +++ /dev/null @@ -1,80 +0,0 @@ -from __future__ import annotations - -from ....core import app -import sqlalchemy -from langbot.pkg.entity.persistence import rag as persistence_rag -import uuid - - -class ExternalKBService: - """External KB service""" - - ap: app.Application - - def __init__(self, ap: app.Application) -> None: - self.ap = ap - - # External Knowledge Base methods - async def get_external_knowledge_bases(self) -> list[dict]: - result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.ExternalKnowledgeBase)) - external_kbs = result.all() - return [ - self.ap.persistence_mgr.serialize_model(persistence_rag.ExternalKnowledgeBase, external_kb) - for external_kb in external_kbs - ] - - async def get_external_knowledge_base(self, kb_uuid: str) -> dict | None: - result = await self.ap.persistence_mgr.execute_async( - sqlalchemy.select(persistence_rag.ExternalKnowledgeBase).where( - persistence_rag.ExternalKnowledgeBase.uuid == kb_uuid - ) - ) - external_kb = result.first() - if external_kb is None: - return None - return self.ap.persistence_mgr.serialize_model(persistence_rag.ExternalKnowledgeBase, external_kb) - - async def create_external_knowledge_base(self, kb_data: dict) -> str: - kb_data['uuid'] = str(uuid.uuid4()) - await self.ap.persistence_mgr.execute_async( - sqlalchemy.insert(persistence_rag.ExternalKnowledgeBase).values(kb_data) - ) - - kb = await self.get_external_knowledge_base(kb_data['uuid']) - - await self.ap.rag_mgr.load_external_knowledge_base(kb) - - return kb_data['uuid'] - - async def retrieve_external_knowledge_base(self, kb_uuid: str, query: str) -> list[dict]: - """Retrieve external knowledge base""" - runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid) - if runtime_kb is None: - raise Exception('Knowledge base not found') - return [ - result.model_dump() for result in await runtime_kb.retrieve(query, 5) - ] # top_k is just a placeholder for external knowledge base - - async def update_external_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None: - if 'uuid' in kb_data: - del kb_data['uuid'] - - await self.ap.persistence_mgr.execute_async( - sqlalchemy.update(persistence_rag.ExternalKnowledgeBase) - .values(kb_data) - .where(persistence_rag.ExternalKnowledgeBase.uuid == kb_uuid) - ) - await self.ap.rag_mgr.remove_knowledge_base_from_runtime(kb_uuid) - - kb = await self.get_external_knowledge_base(kb_uuid) - - await self.ap.rag_mgr.load_external_knowledge_base(kb) - - async def delete_external_knowledge_base(self, kb_uuid: str) -> None: - await self.ap.persistence_mgr.execute_async( - sqlalchemy.delete(persistence_rag.ExternalKnowledgeBase).where( - persistence_rag.ExternalKnowledgeBase.uuid == kb_uuid - ) - ) - - await self.ap.rag_mgr.delete_knowledge_base(kb_uuid) diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py index b753ce5a..3170a113 100644 --- a/src/langbot/pkg/api/http/service/knowledge.py +++ b/src/langbot/pkg/api/http/service/knowledge.py @@ -1,6 +1,5 @@ from __future__ import annotations -import uuid import sqlalchemy from ....core import app @@ -17,64 +16,77 @@ class KnowledgeService: async def get_knowledge_bases(self) -> list[dict]: """获取所有知识库""" - result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase)) - knowledge_bases = result.all() - return [ - self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base) - for knowledge_base in knowledge_bases - ] + return await self.ap.rag_mgr.get_all_knowledge_base_details() async def get_knowledge_base(self, kb_uuid: str) -> dict | None: """获取知识库""" - result = await self.ap.persistence_mgr.execute_async( - sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid) - ) - knowledge_base = result.first() - if knowledge_base is None: - return None - return self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base) + return await self.ap.rag_mgr.get_knowledge_base_details(kb_uuid) async def create_knowledge_base(self, kb_data: dict) -> str: """创建知识库""" - kb_data['uuid'] = str(uuid.uuid4()) - await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data)) + # In new architecture, we delegate entirely to RAGManager which uses plugins. + # Legacy internal KB creation is removed. - kb = await self.get_knowledge_base(kb_data['uuid']) + knowledge_engine_plugin_id = kb_data.get('knowledge_engine_plugin_id') + if not knowledge_engine_plugin_id: + raise ValueError('knowledge_engine_plugin_id is required') - await self.ap.rag_mgr.load_knowledge_base(kb) - - return kb_data['uuid'] + kb = await self.ap.rag_mgr.create_knowledge_base( + name=kb_data.get('name', 'Untitled'), + knowledge_engine_plugin_id=knowledge_engine_plugin_id, + creation_settings=kb_data.get('creation_settings', {}), + retrieval_settings=kb_data.get('retrieval_settings', {}), + description=kb_data.get('description', ''), + ) + return kb.uuid async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None: """更新知识库""" - if 'uuid' in kb_data: - del kb_data['uuid'] + # Filter to only mutable fields + filtered_data = {k: v for k, v in kb_data.items() if k in persistence_rag.KnowledgeBase.MUTABLE_FIELDS} - if 'embedding_model_uuid' in kb_data: - del kb_data['embedding_model_uuid'] + if not filtered_data: + return await self.ap.persistence_mgr.execute_async( sqlalchemy.update(persistence_rag.KnowledgeBase) - .values(kb_data) + .values(filtered_data) .where(persistence_rag.KnowledgeBase.uuid == kb_uuid) ) await self.ap.rag_mgr.remove_knowledge_base_from_runtime(kb_uuid) kb = await self.get_knowledge_base(kb_uuid) + if kb is None: + raise Exception('Knowledge base not found after update') await self.ap.rag_mgr.load_knowledge_base(kb) - async def store_file(self, kb_uuid: str, file_id: str) -> int: + async def _check_doc_capability(self, kb_uuid: str, operation: str) -> None: + """Check if the KB's Knowledge Engine supports document operations. + + Args: + kb_uuid: Knowledge base UUID. + operation: Human-readable operation name for error messages. + + Raises: + Exception: If the KB does not support doc_ingestion. + """ + kb_info = await self.ap.rag_mgr.get_knowledge_base_details(kb_uuid) + if not kb_info: + raise Exception('Knowledge base not found') + capabilities = kb_info.get('knowledge_engine', {}).get('capabilities', []) + if 'doc_ingestion' not in capabilities: + raise Exception(f'This knowledge base does not support {operation}') + + async def store_file(self, kb_uuid: str, file_id: str, parser_plugin_id: str | None = None) -> str: """存储文件""" - # await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(kb_id=kb_uuid, file_id=file_id)) - # await self.ap.rag_mgr.store_file(file_id) runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid) if runtime_kb is None: raise Exception('Knowledge base not found') - # Only internal KBs support file storage - if runtime_kb.get_type() != 'internal': - raise Exception('Only internal knowledge bases support file storage') - result = await runtime_kb.store_file(file_id) + + await self._check_doc_capability(kb_uuid, 'document upload') + + result = await runtime_kb.store_file(file_id, parser_plugin_id=parser_plugin_id) # Update the KB's updated_at timestamp await self.ap.persistence_mgr.execute_async( @@ -85,14 +97,18 @@ class KnowledgeService: return result - async def retrieve_knowledge_base(self, kb_uuid: str, query: str) -> list[dict]: + async def retrieve_knowledge_base( + self, kb_uuid: str, query: str, retrieval_settings: dict | None = None + ) -> list[dict]: """检索知识库""" runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid) if runtime_kb is None: raise Exception('Knowledge base not found') - return [ - result.model_dump() for result in await runtime_kb.retrieve(query, runtime_kb.knowledge_base_entity.top_k) - ] + + # Pass retrieval_settings + results = await runtime_kb.retrieve(query, settings=retrieval_settings) + + return [result.model_dump() for result in results] async def get_files_by_knowledge_base(self, kb_uuid: str) -> list[dict]: """获取知识库文件""" @@ -107,9 +123,9 @@ class KnowledgeService: runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid) if runtime_kb is None: raise Exception('Knowledge base not found') - # Only internal KBs support file deletion - if runtime_kb.get_type() != 'internal': - raise Exception('Only internal knowledge bases support file deletion') + + await self._check_doc_capability(kb_uuid, 'document deletion') + await runtime_kb.delete_file(file_id) # Update the KB's updated_at timestamp @@ -121,13 +137,14 @@ class KnowledgeService: async def delete_knowledge_base(self, kb_uuid: str) -> None: """删除知识库""" - await self.ap.rag_mgr.delete_knowledge_base(kb_uuid) - + # Delete from DB first to commit the deletion, then clean up runtime/plugin (best-effort) await self.ap.persistence_mgr.execute_async( sqlalchemy.delete(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid) ) # delete files + # NOTE: Chunk cleanup is for legacy (pre-plugin) KBs that stored chunks locally. + # For plugin-based Knowledge Engines, the Chunk table is not populated, so this is a no-op. files = await self.ap.persistence_mgr.execute_async( sqlalchemy.select(persistence_rag.File).where(persistence_rag.File.kb_id == kb_uuid) ) @@ -140,3 +157,53 @@ class KnowledgeService: await self.ap.persistence_mgr.execute_async( sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file.uuid) ) + + # Remove from runtime and notify plugin (best-effort, DB is already cleaned up) + await self.ap.rag_mgr.delete_knowledge_base(kb_uuid) + + # ================= Knowledge Engine Discovery ================= + + async def list_knowledge_engines(self) -> list[dict]: + """List all available Knowledge Engines from plugins.""" + engines = [] + + if not self.ap.plugin_connector.is_enable_plugin: + return engines + + # Get KnowledgeEngine plugins + try: + knowledge_engines = await self.ap.plugin_connector.list_knowledge_engines() + engines.extend(knowledge_engines) + except Exception as e: + self.ap.logger.warning(f'Failed to list Knowledge Engines from plugins: {e}') + + return engines + + async def list_parsers(self, mime_type: str | None = None) -> list[dict]: + """List available parsers, optionally filtered by MIME type.""" + if not self.ap.plugin_connector.is_enable_plugin: + return [] + try: + parsers = await self.ap.plugin_connector.list_parsers() + if mime_type: + parsers = [p for p in parsers if mime_type in p.get('supported_mime_types', [])] + return parsers + except Exception as e: + self.ap.logger.warning(f'Failed to list parsers: {e}') + return [] + + async def get_engine_creation_schema(self, plugin_id: str) -> dict: + """Get creation settings schema for a specific Knowledge Engine.""" + try: + return await self.ap.plugin_connector.get_rag_creation_schema(plugin_id) + except Exception as e: + self.ap.logger.warning(f'Failed to get creation schema for {plugin_id}: {e}') + return {} + + async def get_engine_retrieval_schema(self, plugin_id: str) -> dict: + """Get retrieval settings schema for a specific Knowledge Engine.""" + try: + return await self.ap.plugin_connector.get_rag_retrieval_schema(plugin_id) + except Exception as e: + self.ap.logger.warning(f'Failed to get retrieval schema for {plugin_id}: {e}') + return {} diff --git a/src/langbot/pkg/core/app.py b/src/langbot/pkg/core/app.py index a5b096f3..98e88617 100644 --- a/src/langbot/pkg/core/app.py +++ b/src/langbot/pkg/core/app.py @@ -29,7 +29,6 @@ from ..api.http.service import knowledge as knowledge_service from ..api.http.service import mcp as mcp_service from ..api.http.service import apikey as apikey_service from ..api.http.service import webhook as webhook_service -from ..api.http.service import external_kb as external_kb_service from ..api.http.service import monitoring as monitoring_service from ..discover import engine as discover_engine from ..storage import mgr as storagemgr @@ -37,6 +36,7 @@ from ..utils import logcache from . import taskmgr from . import entities as core_entities from ..rag.knowledge import kbmgr as rag_mgr +from ..rag.service import RAGRuntimeService from ..vector import mgr as vectordb_mgr from ..telemetry import telemetry as telemetry_module from ..survey import manager as survey_module @@ -63,6 +63,7 @@ class Application: model_mgr: llm_model_mgr.ModelManager = None rag_mgr: rag_mgr.RAGManager = None + rag_runtime_service: RAGRuntimeService = None # TODO move to pipeline tool_mgr: llm_tool_mgr.ToolManager = None @@ -138,8 +139,6 @@ class Application: knowledge_service: knowledge_service.KnowledgeService = None - external_kb_service: external_kb_service.ExternalKBService = None - mcp_service: mcp_service.MCPService = None apikey_service: apikey_service.ApiKeyService = None diff --git a/src/langbot/pkg/core/stages/build_app.py b/src/langbot/pkg/core/stages/build_app.py index f0398a25..62f0ae7b 100644 --- a/src/langbot/pkg/core/stages/build_app.py +++ b/src/langbot/pkg/core/stages/build_app.py @@ -12,6 +12,7 @@ from ...provider.session import sessionmgr as llm_session_mgr from ...provider.modelmgr import modelmgr as llm_model_mgr from ...provider.tools import toolmgr as llm_tool_mgr from ...rag.knowledge import kbmgr as rag_mgr +from ...rag.service import RAGRuntimeService from ...platform import botmgr as im_mgr from ...platform.webhook_pusher import WebhookPusher from ...persistence import mgr as persistencemgr @@ -26,7 +27,6 @@ from ...api.http.service import knowledge as knowledge_service from ...api.http.service import mcp as mcp_service from ...api.http.service import apikey as apikey_service from ...api.http.service import webhook as webhook_service -from ...api.http.service import external_kb as external_kb_service from ...api.http.service import monitoring as monitoring_service from ...discover import engine as discover_engine from ...storage import mgr as storagemgr @@ -73,9 +73,6 @@ class BuildAppStage(stage.BootingStage): knowledge_service_inst = knowledge_service.KnowledgeService(ap) ap.knowledge_service = knowledge_service_inst - external_kb_service_inst = external_kb_service.ExternalKBService(ap) - ap.external_kb_service = external_kb_service_inst - mcp_service_inst = mcp_service.MCPService(ap) ap.mcp_service = mcp_service_inst @@ -152,6 +149,9 @@ class BuildAppStage(stage.BootingStage): await rag_mgr_inst.initialize() ap.rag_mgr = rag_mgr_inst + # Initialize RAG Runtime Service for plugins + ap.rag_runtime_service = RAGRuntimeService(ap) + # 初始化向量数据库管理器 vectordb_mgr_inst = vectordb_mgr.VectorDBManager(ap) await vectordb_mgr_inst.initialize() diff --git a/src/langbot/pkg/entity/persistence/rag.py b/src/langbot/pkg/entity/persistence/rag.py index 5abd6c1a..cfb1f0a5 100644 --- a/src/langbot/pkg/entity/persistence/rag.py +++ b/src/langbot/pkg/entity/persistence/rag.py @@ -10,8 +10,21 @@ class KnowledgeBase(Base): emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='📚') created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now()) updated_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now(), onupdate=sqlalchemy.func.now()) - embedding_model_uuid = sqlalchemy.Column(sqlalchemy.String, default='') - top_k = sqlalchemy.Column(sqlalchemy.Integer, default=5) + # New fields for plugin-based RAG + knowledge_engine_plugin_id = sqlalchemy.Column(sqlalchemy.String, nullable=True) + collection_id = sqlalchemy.Column(sqlalchemy.String, nullable=True) + creation_settings = sqlalchemy.Column(sqlalchemy.JSON, nullable=True, default=None) + retrieval_settings = sqlalchemy.Column(sqlalchemy.JSON, nullable=True, default=None) + + # Field sets for different operations + MUTABLE_FIELDS = {'name', 'description', 'retrieval_settings'} + """Fields that can be updated after creation.""" + + CREATE_FIELDS = MUTABLE_FIELDS | {'uuid', 'knowledge_engine_plugin_id', 'collection_id', 'creation_settings'} + """Fields used when creating a new knowledge base.""" + + ALL_DB_FIELDS = CREATE_FIELDS | {'emoji', 'created_at', 'updated_at'} + """All fields stored in database (for loading from DB row).""" class File(Base): @@ -29,16 +42,3 @@ class Chunk(Base): uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True) file_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) text = sqlalchemy.Column(sqlalchemy.Text) - - -class ExternalKnowledgeBase(Base): - __tablename__ = 'external_knowledge_bases' - uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True) - name = sqlalchemy.Column(sqlalchemy.String, index=True) - description = sqlalchemy.Column(sqlalchemy.Text) - emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='🔗') - plugin_author = sqlalchemy.Column(sqlalchemy.String, nullable=False) - plugin_name = sqlalchemy.Column(sqlalchemy.String, nullable=False) - retriever_name = sqlalchemy.Column(sqlalchemy.String, nullable=False) - retriever_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={}) - created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now()) diff --git a/src/langbot/pkg/persistence/migrations/dbm020_knowledge_engine_plugin_architecture.py b/src/langbot/pkg/persistence/migrations/dbm020_knowledge_engine_plugin_architecture.py new file mode 100644 index 00000000..7bca300c --- /dev/null +++ b/src/langbot/pkg/persistence/migrations/dbm020_knowledge_engine_plugin_architecture.py @@ -0,0 +1,184 @@ +import json + +import sqlalchemy +from .. import migration + + +@migration.migration_class(20) +class DBMigrateKnowledgeEnginePluginArchitecture(migration.DBMigration): + """Migrate to unified Knowledge Engine plugin architecture. + + Changes: + - Add knowledge_engine_plugin_id, collection_id, creation_settings, retrieval_settings columns to knowledge_bases + - Migrate existing top_k values into retrieval_settings JSON + - Migrate existing embedding_model_uuid into creation_settings JSON + - Drop embedding_model_uuid and top_k columns (PostgreSQL only; SQLite leaves them unmapped) + - Drop external_knowledge_bases table (no longer needed; external KB data is not migrated) + """ + + async def upgrade(self): + """Upgrade""" + await self._add_columns_to_knowledge_bases() + await self._migrate_top_k_to_retrieval_settings() + await self._migrate_embedding_model_uuid_to_creation_settings() + await self._drop_old_columns() + await self._drop_external_knowledge_bases_table() + + async def _get_table_columns(self, table_name: str) -> list[str]: + """Get column names from a table (works for both SQLite and PostgreSQL).""" + if self.ap.persistence_mgr.db.name == 'postgresql': + result = await self.ap.persistence_mgr.execute_async( + sqlalchemy.text( + 'SELECT column_name FROM information_schema.columns WHERE table_name = :table_name;' + ).bindparams(table_name=table_name) + ) + return [row[0] for row in result.fetchall()] + else: + # SQLite PRAGMA does not support bind parameters; validate identifier. + if not table_name.isidentifier(): + raise ValueError(f'Invalid table name: {table_name}') + result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text(f'PRAGMA table_info({table_name});')) + return [row[1] for row in result.fetchall()] + + async def _table_exists(self, table_name: str) -> bool: + """Check if a table exists.""" + if self.ap.persistence_mgr.db.name == 'postgresql': + result = await self.ap.persistence_mgr.execute_async( + sqlalchemy.text( + 'SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = :table_name);' + ).bindparams(table_name=table_name) + ) + return result.scalar() + else: + result = await self.ap.persistence_mgr.execute_async( + sqlalchemy.text("SELECT name FROM sqlite_master WHERE type='table' AND name=:table_name;").bindparams( + table_name=table_name + ) + ) + return result.first() is not None + + async def _add_columns_to_knowledge_bases(self): + """Add new RAG plugin architecture columns to knowledge_bases table.""" + columns = await self._get_table_columns('knowledge_bases') + + new_columns = { + 'knowledge_engine_plugin_id': 'VARCHAR', + 'collection_id': 'VARCHAR', + 'creation_settings': 'TEXT', # JSON stored as TEXT for SQLite compatibility + 'retrieval_settings': 'TEXT', + } + + for col_name, col_type in new_columns.items(): + if col_name not in columns: + await self.ap.persistence_mgr.execute_async( + sqlalchemy.text(f'ALTER TABLE knowledge_bases ADD COLUMN {col_name} {col_type};') + ) + + # For existing knowledge bases without knowledge_engine_plugin_id, + # set collection_id = uuid (same default as new KBs) + await self.ap.persistence_mgr.execute_async( + sqlalchemy.text('UPDATE knowledge_bases SET collection_id = uuid WHERE collection_id IS NULL;') + ) + + async def _migrate_top_k_to_retrieval_settings(self): + """Migrate existing top_k values into retrieval_settings JSON.""" + columns = await self._get_table_columns('knowledge_bases') + if 'top_k' not in columns: + return + + result = await self.ap.persistence_mgr.execute_async( + sqlalchemy.text( + 'SELECT uuid, top_k FROM knowledge_bases WHERE top_k IS NOT NULL AND retrieval_settings IS NULL;' + ) + ) + rows = result.fetchall() + + for row in rows: + kb_uuid = row[0] + top_k = row[1] + retrieval_settings = json.dumps({'top_k': top_k}) + await self.ap.persistence_mgr.execute_async( + sqlalchemy.text('UPDATE knowledge_bases SET retrieval_settings = :rs WHERE uuid = :uuid;').bindparams( + rs=retrieval_settings, uuid=kb_uuid + ) + ) + + async def _migrate_embedding_model_uuid_to_creation_settings(self): + """Migrate existing embedding_model_uuid into creation_settings JSON.""" + columns = await self._get_table_columns('knowledge_bases') + if 'embedding_model_uuid' not in columns: + return + + result = await self.ap.persistence_mgr.execute_async( + sqlalchemy.text( + 'SELECT uuid, embedding_model_uuid, creation_settings FROM knowledge_bases ' + "WHERE embedding_model_uuid IS NOT NULL AND embedding_model_uuid != '';" + ) + ) + rows = result.fetchall() + + for row in rows: + kb_uuid = row[0] + emb_uuid = row[1] + existing_settings = row[2] + + if existing_settings and isinstance(existing_settings, str): + try: + settings = json.loads(existing_settings) + except (json.JSONDecodeError, TypeError): + settings = {} + elif isinstance(existing_settings, dict): + settings = existing_settings + else: + settings = {} + + if 'embedding_model_uuid' not in settings: + settings['embedding_model_uuid'] = emb_uuid + new_settings = json.dumps(settings) + await self.ap.persistence_mgr.execute_async( + sqlalchemy.text( + 'UPDATE knowledge_bases SET creation_settings = :cs WHERE uuid = :uuid;' + ).bindparams(cs=new_settings, uuid=kb_uuid) + ) + + async def _drop_old_columns(self): + """Drop embedding_model_uuid and top_k columns (PostgreSQL only). + + SQLite does not support DROP COLUMN in older versions, so we leave the + columns in place — the SQLAlchemy entity simply won't map them. + """ + if self.ap.persistence_mgr.db.name != 'postgresql': + return + + columns = await self._get_table_columns('knowledge_bases') + + if 'embedding_model_uuid' in columns: + await self.ap.persistence_mgr.execute_async( + sqlalchemy.text('ALTER TABLE knowledge_bases DROP COLUMN embedding_model_uuid;') + ) + + if 'top_k' in columns: + await self.ap.persistence_mgr.execute_async( + sqlalchemy.text('ALTER TABLE knowledge_bases DROP COLUMN top_k;') + ) + + async def _drop_external_knowledge_bases_table(self): + """Drop the external_knowledge_bases table if it exists.""" + if await self._table_exists('external_knowledge_bases'): + # Log existing external KBs before dropping, so users are aware of data loss + rows = await self.ap.persistence_mgr.execute_async( + sqlalchemy.text('SELECT * FROM external_knowledge_bases;') + ) + existing = rows.fetchall() + if existing: + self.ap.logger.warning( + 'Dropping external_knowledge_bases table with %d existing record(s). ' + 'These external KB configurations will be removed: %s', + len(existing), + [dict(row._mapping) for row in existing], + ) + await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE external_knowledge_bases;')) + + async def downgrade(self): + """Downgrade""" + pass diff --git a/src/langbot/pkg/plugin/connector.py b/src/langbot/pkg/plugin/connector.py index 1ae54375..5404f0ed 100644 --- a/src/langbot/pkg/plugin/connector.py +++ b/src/langbot/pkg/plugin/connector.py @@ -7,7 +7,6 @@ import typing import os import sys import httpx -import traceback import sqlalchemy from async_lru import alru_cache from langbot_plugin.api.entities.builtin.pipeline.query import provider_session @@ -102,12 +101,6 @@ class PluginRuntimeConnector: self.handler_task = asyncio.create_task(self.handler.run()) _ = await self.handler.ping() self.ap.logger.info('Connected to plugin runtime.') - # Sync polymorphic component instances after connection - try: - await self.sync_polymorphic_component_instances() - except Exception as e: - traceback.print_exc() - self.ap.logger.error(f'Failed to sync polymorphic component instances: {e}') await self.handler_task task: asyncio.Task | None = None @@ -463,30 +456,18 @@ class PluginRuntimeConnector: yield cmd_ret - # KnowledgeRetriever methods - async def list_knowledge_retrievers(self, bound_plugins: list[str] | None = None) -> list[dict[str, Any]]: - """List all available KnowledgeRetriever components.""" - if not self.is_enable_plugin: - return [] - - retrievers_data = await self.handler.list_knowledge_retrievers(include_plugins=bound_plugins) - return retrievers_data - async def retrieve_knowledge( self, plugin_author: str, plugin_name: str, retriever_name: str, - instance_id: str, retrieval_context: dict[str, Any], - ) -> list[dict[str, Any]]: - """Retrieve knowledge using a KnowledgeRetriever instance.""" + ) -> dict[str, Any]: + """Retrieve knowledge using a KnowledgeEngine instance.""" if not self.is_enable_plugin: - return [] + return {'results': []} - return await self.handler.retrieve_knowledge( - plugin_author, plugin_name, retriever_name, instance_id, retrieval_context - ) + return await self.handler.retrieve_knowledge(plugin_author, plugin_name, retriever_name, retrieval_context) def dispose(self): # No need to consider the shutdown on Windows @@ -500,41 +481,84 @@ class PluginRuntimeConnector: self.heartbeat_task.cancel() self.heartbeat_task = None - async def sync_polymorphic_component_instances(self) -> dict[str, Any]: - """Sync polymorphic component instances with runtime. + @staticmethod + def _parse_plugin_id(plugin_id: str) -> tuple[str, str]: + """Parse a plugin ID string into (author, name). - This collects all external knowledge bases from database and sends to runtime - to ensure instance integrity across restarts. + Args: + plugin_id: Plugin ID in 'author/name' format. + + Returns: + Tuple of (plugin_author, plugin_name). + + Raises: + ValueError: If plugin_id is not in the expected 'author/name' format. + """ + if '/' not in plugin_id: + raise ValueError( + f"Invalid plugin_id format: '{plugin_id}'. Expected 'author/name' format (e.g. 'langbot/rag-engine')." + ) + return plugin_id.split('/', 1) + + async def call_rag_ingest(self, plugin_id: str, context_data: dict[str, Any]) -> dict[str, Any]: + """Call plugin to ingest document. + + Args: + plugin_id: Target plugin ID (author/name). + context_data: IngestionContext data. + """ + plugin_author, plugin_name = self._parse_plugin_id(plugin_id) + return await self.handler.rag_ingest_document(plugin_author, plugin_name, context_data) + + async def call_rag_delete_document(self, plugin_id: str, document_id: str, kb_id: str) -> bool: + plugin_author, plugin_name = self._parse_plugin_id(plugin_id) + return await self.handler.rag_delete_document(plugin_author, plugin_name, document_id, kb_id) + + async def get_rag_creation_schema(self, plugin_id: str) -> dict[str, Any]: + plugin_author, plugin_name = self._parse_plugin_id(plugin_id) + return await self.handler.get_rag_creation_schema(plugin_author, plugin_name) + + async def get_rag_retrieval_schema(self, plugin_id: str) -> dict[str, Any]: + plugin_author, plugin_name = self._parse_plugin_id(plugin_id) + return await self.handler.get_rag_retrieval_schema(plugin_author, plugin_name) + + async def rag_on_kb_create(self, plugin_id: str, kb_id: str, config: dict[str, Any]) -> dict[str, Any]: + """Notify plugin about KB creation.""" + plugin_author, plugin_name = self._parse_plugin_id(plugin_id) + return await self.handler.rag_on_kb_create(plugin_author, plugin_name, kb_id, config) + + async def rag_on_kb_delete(self, plugin_id: str, kb_id: str) -> dict[str, Any]: + """Notify plugin about KB deletion.""" + plugin_author, plugin_name = self._parse_plugin_id(plugin_id) + return await self.handler.rag_on_kb_delete(plugin_author, plugin_name, kb_id) + + async def call_rag_retrieve(self, plugin_id: str, retrieval_context: dict[str, Any]) -> dict[str, Any]: + """Call plugin to retrieve knowledge. + + Args: + plugin_id: Target plugin ID (author/name). + retrieval_context: RetrievalContext data. + """ + plugin_author, plugin_name = self._parse_plugin_id(plugin_id) + return await self.handler.retrieve_knowledge(plugin_author, plugin_name, '', retrieval_context) + + async def list_knowledge_engines(self) -> list[dict[str, Any]]: + """List all available Knowledge Engines from plugins. + + Returns a list of Knowledge Engines with their capabilities and configuration schemas. """ if not self.is_enable_plugin: - return {} + return [] - # ===== external knowledge bases ===== + return await self.handler.list_knowledge_engines() - external_kbs = await self.ap.external_kb_service.get_external_knowledge_bases() + async def list_parsers(self) -> list[dict[str, Any]]: + """List all available parsers from plugins.""" + if not self.is_enable_plugin: + return [] + return await self.handler.list_parsers() - # Build required_instances list - required_instances = [] - for kb in external_kbs: - required_instances.append( - { - 'instance_id': kb['uuid'], - 'plugin_author': kb['plugin_author'], - 'plugin_name': kb['plugin_name'], - 'component_kind': 'KnowledgeRetriever', - 'component_name': kb['retriever_name'], - 'config': kb['retriever_config'], - } - ) - - self.ap.logger.info(f'Syncing {len(required_instances)} polymorphic component instances to runtime') - - # Send to runtime - sync_result = await self.handler.sync_polymorphic_component_instances(required_instances) - - self.ap.logger.info( - f'Sync complete: {len(sync_result.get("success_instances", []))} succeeded, ' - f'{len(sync_result.get("failed_instances", []))} failed' - ) - - return sync_result + async def call_parser(self, plugin_id: str, context_data: dict[str, Any], file_bytes: bytes) -> dict[str, Any]: + """Call plugin to parse a document.""" + plugin_author, plugin_name = self._parse_plugin_id(plugin_id) + return await self.handler.parse_document(plugin_author, plugin_name, context_data, file_bytes) diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py index e6b3b69c..dbe4698c 100644 --- a/src/langbot/pkg/plugin/handler.py +++ b/src/langbot/pkg/plugin/handler.py @@ -26,6 +26,20 @@ from ..core import app from ..utils import constants +def _make_rag_error_response(error: Exception, error_type: str, **extra_context) -> handler.ActionResponse: + """Create a clean error response for RAG operations. + + Args: + error: The caught exception. + error_type: A category string like 'EmbeddingError', 'VectorStoreError'. + **extra_context: Additional context fields for the error message. + """ + context_parts = [f'{k}={v}' for k, v in extra_context.items()] + context_str = f' [{", ".join(context_parts)}]' if context_parts else '' + message = f'[{error_type}/{type(error).__name__}]{context_str} {str(error)}' + return handler.ActionResponse.error(message=message) + + class RuntimeConnectionHandler(handler.Handler): """Runtime connection handler""" @@ -439,7 +453,7 @@ class RuntimeConnectionHandler(handler.Handler): }, ) - @self.action(RuntimeToLangBotAction.GET_CONFIG_FILE) + @self.action(PluginToRuntimeAction.GET_CONFIG_FILE) async def get_config_file(data: dict[str, Any]) -> handler.ActionResponse: """Get a config file by file key""" file_key = data['file_key'] @@ -458,6 +472,125 @@ class RuntimeConnectionHandler(handler.Handler): message=f'Failed to load config file {file_key}: {e}', ) + # ================= RAG Capability Handlers ================= + + @self.action(PluginToRuntimeAction.INVOKE_EMBEDDING) + async def invoke_embedding(data: dict[str, Any]) -> handler.ActionResponse: + embedding_model_uuid = data['embedding_model_uuid'] + texts = data['texts'] + + embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embedding_model_uuid) + if embedding_model is None: + return handler.ActionResponse.error( + message=f'Embedding model with embedding_model_uuid {embedding_model_uuid} not found', + ) + + try: + vectors = await embedding_model.provider.invoke_embedding(embedding_model, texts) + return handler.ActionResponse.success(data={'vectors': vectors}) + except Exception as e: + return _make_rag_error_response(e, 'EmbeddingError', embedding_model_uuid=embedding_model_uuid) + + @self.action(PluginToRuntimeAction.VECTOR_UPSERT) + async def vector_upsert(data: dict[str, Any]) -> handler.ActionResponse: + collection_id = data['collection_id'] + vectors = data['vectors'] + ids = data['ids'] + metadata = data.get('metadata') + documents = data.get('documents') + if len(vectors) != len(ids): + return handler.ActionResponse.error(message='vectors and ids must have same length') + if metadata and len(metadata) != len(vectors): + return handler.ActionResponse.error(message='metadata must match vectors length') + if documents and len(documents) != len(vectors): + return handler.ActionResponse.error(message='documents must match vectors length') + try: + await self.ap.rag_runtime_service.vector_upsert( + collection_id, + vectors, + ids, + metadata, + documents, + ) + return handler.ActionResponse.success(data={}) + except Exception as e: + return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id) + + @self.action(PluginToRuntimeAction.VECTOR_SEARCH) + async def vector_search(data: dict[str, Any]) -> handler.ActionResponse: + collection_id = data['collection_id'] + query_vector = data['query_vector'] + top_k = data['top_k'] + filters = data.get('filters') + search_type = data.get('search_type', 'vector') + query_text = data.get('query_text', '') + try: + results = await self.ap.rag_runtime_service.vector_search( + collection_id, + query_vector, + top_k, + filters, + search_type, + query_text, + ) + return handler.ActionResponse.success(data={'results': results}) + except Exception as e: + return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id) + + @self.action(PluginToRuntimeAction.VECTOR_DELETE) + async def vector_delete(data: dict[str, Any]) -> handler.ActionResponse: + collection_id = data['collection_id'] + file_ids = data.get('file_ids') + filters = data.get('filters') + try: + count = await self.ap.rag_runtime_service.vector_delete(collection_id, file_ids, filters) + return handler.ActionResponse.success(data={'count': count}) + except Exception as e: + return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id) + + @self.action(PluginToRuntimeAction.GET_KNOWLEDEGE_FILE_STREAM) + async def get_knowledge_file_stream(data: dict[str, Any]) -> handler.ActionResponse: + storage_path = data['storage_path'] + try: + content_bytes = await self.ap.rag_runtime_service.get_file_stream(storage_path) + file_key = await self.send_file(content_bytes, '') + return handler.ActionResponse.success(data={'file_key': file_key}) + except Exception as e: + return _make_rag_error_response(e, 'FileServiceError', storage_path=storage_path) + + @self.action(PluginToRuntimeAction.INVOKE_PARSER) + async def invoke_parser(data: dict[str, Any]) -> handler.ActionResponse: + """Plugin requests host to invoke a parser plugin.""" + plugin_author = data['plugin_author'] + plugin_name = data['plugin_name'] + storage_path = data['storage_path'] + mime_type = data.get('mime_type', 'application/octet-stream') + filename = data.get('filename', '') + metadata = data.get('metadata', {}) + try: + # Read file from storage + file_bytes = await self.ap.rag_runtime_service.get_file_stream(storage_path) + context_data = { + 'mime_type': mime_type, + 'filename': filename, + 'metadata': metadata, + } + result = await self.ap.plugin_connector.call_parser( + f'{plugin_author}/{plugin_name}', context_data, file_bytes + ) + return handler.ActionResponse.success(data=result) + except Exception as e: + return _make_rag_error_response(e, 'ParserError') + + @self.action(CommonAction.PING) + async def ping(data: dict[str, Any]) -> handler.ActionResponse: + """Ping""" + return handler.ActionResponse.success( + data={ + 'pong': 'pong', + }, + ) + async def ping(self) -> dict[str, Any]: """Ping the runtime""" return await self.call_action( @@ -717,26 +850,13 @@ class RuntimeConnectionHandler(handler.Handler): async for ret in gen: yield ret - # KnowledgeRetriever methods - async def list_knowledge_retrievers(self, include_plugins: list[str] | None = None) -> list[dict[str, Any]]: - """List knowledge retrievers""" - result = await self.call_action( - LangBotToRuntimeAction.LIST_KNOWLEDGE_RETRIEVERS, - { - 'include_plugins': include_plugins, - }, - timeout=10, - ) - return result['retrievers'] - async def retrieve_knowledge( self, plugin_author: str, plugin_name: str, retriever_name: str, - instance_id: str, retrieval_context: dict[str, Any], - ) -> list[dict[str, Any]]: + ) -> dict[str, Any]: """Retrieve knowledge""" result = await self.call_action( LangBotToRuntimeAction.RETRIEVE_KNOWLEDGE, @@ -744,22 +864,10 @@ class RuntimeConnectionHandler(handler.Handler): 'plugin_author': plugin_author, 'plugin_name': plugin_name, 'retriever_name': retriever_name, - 'instance_id': instance_id, 'retrieval_context': retrieval_context, }, timeout=30, ) - return result['retrieval_results'] - - async def sync_polymorphic_component_instances(self, required_instances: list[dict[str, Any]]) -> dict[str, Any]: - """Sync polymorphic component instances with runtime""" - result = await self.call_action( - LangBotToRuntimeAction.SYNC_POLYMORPHIC_COMPONENT_INSTANCES, - { - 'required_instances': required_instances, - }, - timeout=30, - ) return result async def get_debug_info(self) -> dict[str, Any]: @@ -770,3 +878,91 @@ class RuntimeConnectionHandler(handler.Handler): timeout=10, ) return result + + # ================= RAG Capability Callers (LangBot -> Runtime) ================= + + async def rag_ingest_document( + self, plugin_author: str, plugin_name: str, context_data: dict[str, Any] + ) -> dict[str, Any]: + """Send INGEST_DOCUMENT action to runtime.""" + result = await self.call_action( + LangBotToRuntimeAction.RAG_INGEST_DOCUMENT, + {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'context': context_data}, + timeout=300, # Ingestion can be slow + ) + return result + + async def rag_delete_document(self, plugin_author: str, plugin_name: str, document_id: str, kb_id: str) -> bool: + result = await self.call_action( + LangBotToRuntimeAction.RAG_DELETE_DOCUMENT, + {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'document_id': document_id, 'kb_id': kb_id}, + timeout=30, + ) + return result.get('success', False) + + async def rag_on_kb_create( + self, plugin_author: str, plugin_name: str, kb_id: str, config: dict[str, Any] + ) -> dict[str, Any]: + """Notify plugin about KB creation.""" + result = await self.call_action( + LangBotToRuntimeAction.RAG_ON_KB_CREATE, + {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'kb_id': kb_id, 'config': config}, + timeout=30, + ) + return result + + async def rag_on_kb_delete(self, plugin_author: str, plugin_name: str, kb_id: str) -> dict[str, Any]: + """Notify plugin about KB deletion.""" + result = await self.call_action( + LangBotToRuntimeAction.RAG_ON_KB_DELETE, + {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'kb_id': kb_id}, + timeout=30, + ) + return result + + async def get_rag_creation_schema(self, plugin_author: str, plugin_name: str) -> dict[str, Any]: + return await self.call_action( + LangBotToRuntimeAction.GET_RAG_CREATION_SETTINGS_SCHEMA, + {'plugin_author': plugin_author, 'plugin_name': plugin_name}, + timeout=10, + ) + + async def get_rag_retrieval_schema(self, plugin_author: str, plugin_name: str) -> dict[str, Any]: + return await self.call_action( + LangBotToRuntimeAction.GET_RAG_RETRIEVAL_SETTINGS_SCHEMA, + {'plugin_author': plugin_author, 'plugin_name': plugin_name}, + timeout=10, + ) + + async def list_knowledge_engines(self) -> list[dict[str, Any]]: + """List all available Knowledge Engines from plugins.""" + result = await self.call_action(LangBotToRuntimeAction.LIST_KNOWLEDGE_ENGINES, {}, timeout=60) + return result.get('engines', []) + + # ================= Parser Capability Callers (LangBot -> Runtime) ================= + + async def list_parsers(self) -> list[dict[str, Any]]: + """List all available parsers from plugins.""" + result = await self.call_action(LangBotToRuntimeAction.LIST_PARSERS, {}, timeout=60) + return result.get('parsers', []) + + async def parse_document( + self, plugin_author: str, plugin_name: str, context_data: dict[str, Any], file_bytes: bytes + ) -> dict[str, Any]: + """Send PARSE_DOCUMENT action to runtime. + + Sends file content via chunked FILE_CHUNK transfer, then invokes + the PARSE_DOCUMENT action with a file_key reference. + """ + # Send file to runtime via chunked transfer + file_key = await self.send_file(file_bytes, '') + + # Include file_key in context_data for the runtime to read + context_data['file_key'] = file_key + + result = await self.call_action( + LangBotToRuntimeAction.PARSE_DOCUMENT, + {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'context': context_data}, + timeout=300, + ) + return result diff --git a/src/langbot/pkg/provider/runners/localagent.py b/src/langbot/pkg/provider/runners/localagent.py index dbda6622..f444529b 100644 --- a/src/langbot/pkg/provider/runners/localagent.py +++ b/src/langbot/pkg/provider/runners/localagent.py @@ -74,15 +74,7 @@ class LocalAgentRunner(runner.RequestRunner): self.ap.logger.warning(f'Knowledge base {kb_uuid} not found, skipping') continue - # Get top_k based on KB type - if kb.get_type() == 'internal': - top_k = kb.knowledge_base_entity.top_k - elif kb.get_type() == 'external': - top_k = 5 # external kb's top_k is managed by plugin config - else: - top_k = 5 # default fallback - - result = await kb.retrieve(user_message_text, top_k) + result = await kb.retrieve(user_message_text) if result: all_results.extend(result) @@ -97,9 +89,9 @@ class LocalAgentRunner(runner.RequestRunner): if content.type == 'text' and content.text is not None: texts.append(f'[{idx}] {content.text}') idx += 1 - rag_context = '\n\n'.join(texts) + rag_context_text = '\n\n'.join(texts) final_user_message_text = rag_combined_prompt_template.format( - rag_context=rag_context, user_message=user_message_text + rag_context=rag_context_text, user_message=user_message_text ) else: diff --git a/src/langbot/pkg/rag/knowledge/base.py b/src/langbot/pkg/rag/knowledge/base.py index 4b183eae..28d010fe 100644 --- a/src/langbot/pkg/rag/knowledge/base.py +++ b/src/langbot/pkg/rag/knowledge/base.py @@ -22,12 +22,12 @@ class KnowledgeBaseInterface(metaclass=abc.ABCMeta): pass @abc.abstractmethod - async def retrieve(self, query: str, top_k: int) -> list[rag_context.RetrievalResultEntry]: + async def retrieve(self, query: str, settings: dict | None = None) -> list[rag_context.RetrievalResultEntry]: """Retrieve relevant documents from the knowledge base Args: query: The query string - top_k: Number of top results to return + settings: Optional per-request retrieval settings overrides Returns: List of retrieve result entries @@ -45,8 +45,8 @@ class KnowledgeBaseInterface(metaclass=abc.ABCMeta): pass @abc.abstractmethod - def get_type(self) -> str: - """Get the type of knowledge base (internal/external)""" + def get_knowledge_engine_plugin_id(self) -> str: + """Get the Knowledge Engine plugin ID""" pass @abc.abstractmethod diff --git a/src/langbot/pkg/rag/knowledge/external.py b/src/langbot/pkg/rag/knowledge/external.py deleted file mode 100644 index f1a5fed3..00000000 --- a/src/langbot/pkg/rag/knowledge/external.py +++ /dev/null @@ -1,85 +0,0 @@ -"""External knowledge base implementation""" - -from __future__ import annotations - -from langbot.pkg.core import app -from langbot.pkg.entity.persistence import rag as persistence_rag -from langbot_plugin.api.entities.builtin.rag import context as rag_context -from .base import KnowledgeBaseInterface - - -class ExternalKnowledgeBase(KnowledgeBaseInterface): - """External knowledge base that queries via HTTP API or plugin retriever""" - - external_kb_entity: persistence_rag.ExternalKnowledgeBase - - # Plugin retriever instance ID - retriever_instance_id: str | None - - def __init__(self, ap: app.Application, external_kb_entity: persistence_rag.ExternalKnowledgeBase): - super().__init__(ap) - self.external_kb_entity = external_kb_entity - self.retriever_instance_id = None - - async def initialize(self): - """Initialize the external knowledge base""" - # Use KB UUID as instance ID - # Instance creation is now handled by the unified sync mechanism - # when LangBot connects to runtime - self.retriever_instance_id = self.external_kb_entity.uuid - - self.ap.logger.info( - f'Initialized external KB {self.external_kb_entity.uuid}, instance will be created by sync mechanism' - ) - - async def retrieve(self, query: str, top_k: int = 5) -> list[rag_context.RetrievalResultEntry]: - """Retrieve documents from external knowledge base via plugin retriever""" - if not self.retriever_instance_id: - self.ap.logger.error(f'No retriever instance for KB {self.external_kb_entity.uuid}') - return [] - - try: - results = await self.ap.plugin_connector.retrieve_knowledge( - self.external_kb_entity.plugin_author, - self.external_kb_entity.plugin_name, - self.external_kb_entity.retriever_name, - self.retriever_instance_id, - {'query': query}, - ) - - # Convert plugin results to RetrievalResultEntry - retrieval_entries = [] - for result in results: - retrieval_entries.append(rag_context.RetrievalResultEntry(**result)) - - return retrieval_entries - except Exception as e: - self.ap.logger.error(f'Plugin retriever error: {e}') - import traceback - - traceback.print_exc() - return [] - - def get_uuid(self) -> str: - """Get the UUID of the external knowledge base""" - return self.external_kb_entity.uuid - - def get_name(self) -> str: - """Get the name of the external knowledge base""" - return self.external_kb_entity.name - - def get_type(self) -> str: - """Get the type of knowledge base""" - return 'external' - - async def dispose(self): - """Clean up resources""" - # Trigger sync to immediately delete the instance from plugin process - # This ensures instance is cleaned up without waiting for next LangBot restart - try: - await self.ap.plugin_connector.sync_polymorphic_component_instances() - self.ap.logger.info( - f'Disposed external KB {self.external_kb_entity.uuid}, triggered sync to delete instance' - ) - except Exception as e: - self.ap.logger.error(f'Failed to sync after disposing KB: {e}') diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py index 5fd44854..5831da30 100644 --- a/src/langbot/pkg/rag/knowledge/kbmgr.py +++ b/src/langbot/pkg/rag/knowledge/kbmgr.py @@ -1,18 +1,19 @@ from __future__ import annotations +import mimetypes +import os.path import traceback import uuid import zipfile import io -from .services import parser, chunker +from typing import Any from langbot.pkg.core import app -from langbot.pkg.rag.knowledge.services.embedder import Embedder -from langbot.pkg.rag.knowledge.services.retriever import Retriever import sqlalchemy + + from langbot.pkg.entity.persistence import rag as persistence_rag from langbot.pkg.core import taskmgr from langbot_plugin.api.entities.builtin.rag import context as rag_context from .base import KnowledgeBaseInterface -from .external import ExternalKnowledgeBase class RuntimeKnowledgeBase(KnowledgeBaseInterface): @@ -20,28 +21,16 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): knowledge_base_entity: persistence_rag.KnowledgeBase - parser: parser.FileParser - - chunker: chunker.Chunker - - embedder: Embedder - - retriever: Retriever - def __init__(self, ap: app.Application, knowledge_base_entity: persistence_rag.KnowledgeBase): super().__init__(ap) self.knowledge_base_entity = knowledge_base_entity - self.parser = parser.FileParser(ap=self.ap) - self.chunker = chunker.Chunker(ap=self.ap) - self.embedder = Embedder(ap=self.ap) - self.retriever = Retriever(ap=self.ap) - # 传递kb_id给retriever - self.retriever.kb_id = knowledge_base_entity.uuid async def initialize(self): pass - async def _store_file_task(self, file: persistence_rag.File, task_context: taskmgr.TaskContext): + async def _store_file_task( + self, file: persistence_rag.File, task_context: taskmgr.TaskContext, parser_plugin_id: str | None = None + ): try: # set file status to processing await self.ap.persistence_mgr.execute_async( @@ -50,31 +39,46 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): .values(status='processing') ) - task_context.set_current_action('Parsing file') - # parse file - text = await self.parser.parse(file.file_name, file.extension) - if not text: - raise Exception(f'No text extracted from file {file.file_name}') + task_context.set_current_action('Processing file') - task_context.set_current_action('Chunking file') - # chunk file - chunks_texts = await self.chunker.chunk(text) - if not chunks_texts: - raise Exception(f'No chunks extracted from file {file.file_name}') + # Get file size from storage + file_size = await self.ap.storage_mgr.storage_provider.size(file.file_name) - task_context.set_current_action('Embedding chunks') + # Detect MIME type from extension + mime_type, _ = mimetypes.guess_type(file.file_name) + if mime_type is None: + mime_type = 'application/octet-stream' - embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid( - self.knowledge_base_entity.embedding_model_uuid - ) - # embed chunks - await self.embedder.embed_and_store( - kb_id=self.knowledge_base_entity.uuid, - file_id=file.uuid, - chunks=chunks_texts, - embedding_model=embedding_model, + # If a parser plugin is specified, call it before ingestion + parsed_content = None + if parser_plugin_id: + task_context.set_current_action('Parsing file') + file_bytes = await self.ap.storage_mgr.storage_provider.load(file.file_name) + parse_context = { + 'mime_type': mime_type, + 'filename': file.file_name, + 'metadata': {}, + } + parsed_content = await self.ap.plugin_connector.call_parser(parser_plugin_id, parse_context, file_bytes) + + # Call plugin to ingest document + result = await self._ingest_document( + { + 'document_id': file.uuid, + 'filename': file.file_name, + 'extension': file.extension, + 'file_size': file_size, + 'mime_type': mime_type, + }, + file.file_name, # storage path + parsed_content=parsed_content, ) + # Check plugin result status + if result.get('status') == 'failed': + error_msg = result.get('error_message', 'Plugin ingestion returned failed status') + raise Exception(error_msg) + # set file status to completed await self.ap.persistence_mgr.execute_async( sqlalchemy.update(persistence_rag.File) @@ -97,16 +101,17 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): # delete file from storage await self.ap.storage_mgr.storage_provider.delete(file.file_name) - async def store_file(self, file_id: str) -> str: + async def store_file(self, file_id: str, parser_plugin_id: str | None = None) -> str: # pre checking if not await self.ap.storage_mgr.storage_provider.exists(file_id): raise Exception(f'File {file_id} not found') file_name = file_id - extension = file_name.split('.')[-1].lower() + _, ext = os.path.splitext(file_name) + extension = ext.lstrip('.').lower() if ext else '' if extension == 'zip': - return await self._store_zip_file(file_id) + return await self._store_zip_file(file_id, parser_plugin_id=parser_plugin_id) file_uuid = str(uuid.uuid4()) kb_id = self.knowledge_base_entity.uuid @@ -126,7 +131,7 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): # run background task asynchronously ctx = taskmgr.TaskContext.new() wrapper = self.ap.task_mgr.create_user_task( - self._store_file_task(file_obj, task_context=ctx), + self._store_file_task(file_obj, task_context=ctx, parser_plugin_id=parser_plugin_id), kind='knowledge-operation', name=f'knowledge-store-file-{file_id}', label=f'Store file {file_id}', @@ -134,7 +139,7 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): ) return wrapper.id - async def _store_zip_file(self, zip_file_id: str) -> str: + async def _store_zip_file(self, zip_file_id: str, parser_plugin_id: str | None = None) -> str: """Handle ZIP file by extracting each document and storing them separately.""" self.ap.logger.info(f'Processing ZIP file: {zip_file_id}') @@ -150,7 +155,8 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): if file_info.is_dir() or file_info.filename.startswith('.'): continue - file_extension = file_info.filename.split('.')[-1].lower() + _, file_ext = os.path.splitext(file_info.filename) + file_extension = file_ext.lstrip('.').lower() if file_extension not in supported_extensions: self.ap.logger.debug(f'Skipping unsupported file in ZIP: {file_info.filename}') continue @@ -159,18 +165,18 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): file_content = zip_ref.read(file_info.filename) base_name = file_info.filename.replace('/', '_').replace('\\', '_') - extension = base_name.split('.')[-1] - file_name = base_name.split('.')[0] + file_stem, file_ext = os.path.splitext(base_name) + extension = file_ext.lstrip('.') - if file_name.startswith('__MACOSX'): + if file_stem.startswith('__MACOSX'): continue - extracted_file_id = file_name + '_' + str(uuid.uuid4())[:8] + '.' + extension + extracted_file_id = file_stem + '_' + str(uuid.uuid4())[:8] + '.' + extension # save file to storage await self.ap.storage_mgr.storage_provider.save(extracted_file_id, file_content) - task_id = await self.store_file(extracted_file_id) + task_id = await self.store_file(extracted_file_id, parser_plugin_id=parser_plugin_id) stored_file_tasks.append(task_id) self.ap.logger.info( @@ -189,21 +195,28 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): return stored_file_tasks[0] if stored_file_tasks else '' - async def retrieve(self, query: str, top_k: int) -> list[rag_context.RetrievalResultEntry]: - embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid( - self.knowledge_base_entity.embedding_model_uuid - ) - return await self.retriever.retrieve(self.knowledge_base_entity.uuid, query, embedding_model, top_k) + async def retrieve(self, query: str, settings: dict | None = None) -> list[rag_context.RetrievalResultEntry]: + # Merge stored retrieval_settings with per-request overrides + stored = self.knowledge_base_entity.retrieval_settings or {} + merged = {**stored, **(settings or {})} + if 'top_k' not in merged: + merged['top_k'] = 5 # fallback default + + response = await self._retrieve(query, merged) + + results_data = response.get('results', []) + entries = [] + for r in results_data: + if isinstance(r, dict): + entries.append(rag_context.RetrievalResultEntry(**r)) + elif isinstance(r, rag_context.RetrievalResultEntry): + entries.append(r) + return entries async def delete_file(self, file_id: str): - # delete vector - await self.ap.vector_db_mgr.vector_db.delete_by_file_id(self.knowledge_base_entity.uuid, file_id) - - # delete chunk - await self.ap.persistence_mgr.execute_async( - sqlalchemy.delete(persistence_rag.Chunk).where(persistence_rag.Chunk.file_id == file_id) - ) + await self._delete_document(file_id) + # Also cleanup DB record await self.ap.persistence_mgr.execute_async( sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file_id) ) @@ -216,32 +229,289 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface): """Get the name of the knowledge base""" return self.knowledge_base_entity.name - def get_type(self) -> str: - """Get the type of knowledge base""" - return 'internal' + def get_knowledge_engine_plugin_id(self) -> str: + """Get the Knowledge Engine plugin ID""" + return self.knowledge_base_entity.knowledge_engine_plugin_id or '' async def dispose(self): - await self.ap.vector_db_mgr.vector_db.delete_collection(self.knowledge_base_entity.uuid) + """Dispose the knowledge base, notifying the plugin to cleanup.""" + await self._on_kb_delete() + + # ========== Plugin Communication Methods ========== + + async def _on_kb_create(self) -> None: + """Notify plugin about KB creation.""" + plugin_id = self.knowledge_base_entity.knowledge_engine_plugin_id + if not plugin_id: + return + + try: + config = self.knowledge_base_entity.creation_settings or {} + self.ap.logger.info( + f'Calling RAG plugin {plugin_id}: on_knowledge_base_create(kb_id={self.knowledge_base_entity.uuid})' + ) + await self.ap.plugin_connector.rag_on_kb_create(plugin_id, self.knowledge_base_entity.uuid, config) + except Exception as e: + self.ap.logger.error(f'Failed to notify plugin {plugin_id} on KB create: {e}') + raise + + async def _on_kb_delete(self) -> None: + """Notify plugin about KB deletion.""" + plugin_id = self.knowledge_base_entity.knowledge_engine_plugin_id + if not plugin_id: + return + + try: + self.ap.logger.info( + f'Calling RAG plugin {plugin_id}: on_knowledge_base_delete(kb_id={self.knowledge_base_entity.uuid})' + ) + await self.ap.plugin_connector.rag_on_kb_delete(plugin_id, self.knowledge_base_entity.uuid) + except Exception as e: + self.ap.logger.error(f'Failed to notify plugin {plugin_id} on KB delete: {e}') + + async def _ingest_document( + self, + file_metadata: dict[str, Any], + storage_path: str, + parsed_content: dict[str, Any] | None = None, + ) -> dict[str, Any]: + """Call plugin to ingest document.""" + kb = self.knowledge_base_entity + plugin_id = kb.knowledge_engine_plugin_id + if not plugin_id: + self.ap.logger.error(f'No RAG plugin ID configured for KB {kb.uuid}. Ingestion failed.') + raise ValueError('RAG Plugin ID required') + + self.ap.logger.info(f'Calling RAG plugin {plugin_id}: ingest(doc={file_metadata.get("filename")})') + + # Inject knowledge_base_id into file metadata as required by SDK schema + file_metadata['knowledge_base_id'] = kb.uuid + + context_data = { + 'file_object': { + 'metadata': file_metadata, + 'storage_path': storage_path, + }, + 'knowledge_base_id': kb.uuid, + 'collection_id': kb.collection_id or kb.uuid, + 'creation_settings': kb.creation_settings or {}, + 'parsed_content': parsed_content, + } + + try: + result = await self.ap.plugin_connector.call_rag_ingest(plugin_id, context_data) + return result + except Exception as e: + self.ap.logger.error(f'Plugin ingestion failed: {e}') + raise + + async def _retrieve( + self, + query: str, + settings: dict[str, Any], + ) -> dict[str, Any]: + """Call plugin to retrieve documents. + + Raises: + ValueError: If no RAG plugin is configured for this KB. + Exception: If the plugin retrieval call fails. + """ + kb = self.knowledge_base_entity + plugin_id = kb.knowledge_engine_plugin_id + if not plugin_id: + raise ValueError(f'No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.') + + retrieval_context = { + 'query': query, + 'knowledge_base_id': kb.uuid, + 'collection_id': kb.collection_id or kb.uuid, + 'retrieval_settings': settings, + 'creation_settings': kb.creation_settings or {}, + 'filters': settings.pop('filters', {}), + } + + result = await self.ap.plugin_connector.call_rag_retrieve( + plugin_id, + retrieval_context, + ) + return result + + async def _delete_document(self, document_id: str) -> bool: + """Call plugin to delete document.""" + kb = self.knowledge_base_entity + plugin_id = kb.knowledge_engine_plugin_id + if not plugin_id: + return False + + self.ap.logger.info(f'Calling RAG plugin {plugin_id}: delete_document(doc_id={document_id})') + + try: + return await self.ap.plugin_connector.call_rag_delete_document(plugin_id, document_id, kb.uuid) + except Exception as e: + self.ap.logger.error(f'Plugin document deletion failed: {e}') + return False class RAGManager: ap: app.Application - knowledge_bases: list[KnowledgeBaseInterface] + knowledge_bases: dict[str, KnowledgeBaseInterface] def __init__(self, ap: app.Application): self.ap = ap - self.knowledge_bases = [] + self.knowledge_bases = {} async def initialize(self): await self.load_knowledge_bases_from_db() + async def get_all_knowledge_base_details(self) -> list[dict]: + """Get all knowledge bases with enriched Knowledge Engine details.""" + # 1. Get raw KBs from DB + result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase)) + knowledge_bases = result.all() + + # 2. Get all available Knowledge Engines for enrichment + engine_map = {} + if self.ap.plugin_connector.is_enable_plugin: + try: + engines = await self.ap.plugin_connector.list_knowledge_engines() + engine_map = {e['plugin_id']: e for e in engines} + except Exception as e: + self.ap.logger.warning(f'Failed to list Knowledge Engines: {e}') + + # 3. Serialize and enrich + kb_list = [] + for kb in knowledge_bases: + kb_dict = self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, kb) + self._enrich_kb_dict(kb_dict, engine_map) + kb_list.append(kb_dict) + + return kb_list + + async def get_knowledge_base_details(self, kb_uuid: str) -> dict | None: + """Get specific knowledge base with enriched Knowledge Engine details.""" + result = await self.ap.persistence_mgr.execute_async( + sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid) + ) + kb = result.first() + if not kb: + return None + + kb_dict = self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, kb) + + # Fetch engines + engine_map = {} + if self.ap.plugin_connector.is_enable_plugin: + try: + engines = await self.ap.plugin_connector.list_knowledge_engines() + engine_map = {e['plugin_id']: e for e in engines} + except Exception as e: + self.ap.logger.warning(f'Failed to list Knowledge Engines: {e}') + + self._enrich_kb_dict(kb_dict, engine_map) + return kb_dict + + @staticmethod + def _to_i18n_name(name) -> dict: + """Ensure name is always an I18nObject-compatible dict. + + If *name* is already a dict (with ``en_US`` / ``zh_Hans`` keys) it is + returned as-is. A plain string is wrapped into an I18nObject so the + frontend ``extractI18nObject`` helper never receives an unexpected type. + """ + if isinstance(name, dict): + return name + return {'en_US': str(name), 'zh_Hans': str(name)} + + def _enrich_kb_dict(self, kb_dict: dict, engine_map: dict) -> None: + """Helper to inject engine info into KB dict.""" + plugin_id = kb_dict.get('knowledge_engine_plugin_id') + + # Default fallback structure — name must be I18nObject for frontend compatibility + fallback_name = self._to_i18n_name(plugin_id or 'Internal (Legacy)') + fallback_info = { + 'plugin_id': plugin_id, + 'name': fallback_name, + 'capabilities': [], + } + + if not plugin_id: + kb_dict['knowledge_engine'] = fallback_info + return + + engine_info = engine_map.get(plugin_id) + if engine_info: + kb_dict['knowledge_engine'] = { + 'plugin_id': plugin_id, + 'name': self._to_i18n_name(engine_info.get('name', plugin_id)), + 'capabilities': engine_info.get('capabilities', []), + } + else: + kb_dict['knowledge_engine'] = fallback_info + + async def create_knowledge_base( + self, + name: str, + knowledge_engine_plugin_id: str, + creation_settings: dict, + retrieval_settings: dict | None = None, + description: str = '', + ) -> persistence_rag.KnowledgeBase: + """Create a new knowledge base using a RAG plugin.""" + # Validate that the Knowledge Engine plugin exists + if self.ap.plugin_connector.is_enable_plugin: + try: + engines = await self.ap.plugin_connector.list_knowledge_engines() + engine_ids = [e.get('plugin_id') for e in engines] + if knowledge_engine_plugin_id not in engine_ids: + raise ValueError(f'Knowledge Engine plugin {knowledge_engine_plugin_id} not found') + except ValueError: + raise + except Exception as e: + self.ap.logger.warning(f'Failed to validate Knowledge Engine plugin existence: {e}') + + kb_uuid = str(uuid.uuid4()) + # Use UUID as collection ID by default for isolation + collection_id = kb_uuid + + kb_data = { + 'uuid': kb_uuid, + 'name': name, + 'description': description, + 'knowledge_engine_plugin_id': knowledge_engine_plugin_id, + 'collection_id': collection_id, + 'creation_settings': creation_settings, + 'retrieval_settings': retrieval_settings or {}, + } + + # Create Entity + kb = persistence_rag.KnowledgeBase(**kb_data) + + # Persist + await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data)) + + # Load into Runtime + runtime_kb = await self.load_knowledge_base(kb) + + # Notify Plugin — rollback DB record and runtime entry on failure + try: + await runtime_kb._on_kb_create() + except Exception: + self.knowledge_bases.pop(kb_uuid, None) + await self.ap.persistence_mgr.execute_async( + sqlalchemy.delete(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid) + ) + raise + + self.ap.logger.info(f'Created new Knowledge Base {name} ({kb_uuid}) using plugin {knowledge_engine_plugin_id}') + return kb + async def load_knowledge_bases_from_db(self): self.ap.logger.info('Loading knowledge bases from db...') - self.knowledge_bases = [] + self.knowledge_bases = {} - # Load internal knowledge bases + # Load knowledge bases result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase)) knowledge_bases = result.all() @@ -253,86 +523,37 @@ class RAGManager: f'Error loading knowledge base {knowledge_base.uuid}: {e}\n{traceback.format_exc()}' ) - # Load external knowledge bases - external_result = await self.ap.persistence_mgr.execute_async( - sqlalchemy.select(persistence_rag.ExternalKnowledgeBase) - ) - external_kbs = external_result.all() - - for external_kb in external_kbs: - try: - # Don't trigger sync during batch loading - will sync once after LangBot connects to runtime - await self.load_external_knowledge_base(external_kb, trigger_sync=False) - except Exception as e: - self.ap.logger.error( - f'Error loading external knowledge base {external_kb.uuid}: {e}\n{traceback.format_exc()}' - ) - async def load_knowledge_base( self, knowledge_base_entity: persistence_rag.KnowledgeBase | sqlalchemy.Row | dict, ) -> RuntimeKnowledgeBase: if isinstance(knowledge_base_entity, sqlalchemy.Row): + # Safe access to _mapping for SQLAlchemy 1.4+ knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity._mapping) elif isinstance(knowledge_base_entity, dict): - knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity) + # Filter out non-database fields (like knowledge_engine which is computed) + filtered_dict = { + k: v for k, v in knowledge_base_entity.items() if k in persistence_rag.KnowledgeBase.ALL_DB_FIELDS + } + knowledge_base_entity = persistence_rag.KnowledgeBase(**filtered_dict) runtime_knowledge_base = RuntimeKnowledgeBase(ap=self.ap, knowledge_base_entity=knowledge_base_entity) await runtime_knowledge_base.initialize() - self.knowledge_bases.append(runtime_knowledge_base) + self.knowledge_bases[runtime_knowledge_base.get_uuid()] = runtime_knowledge_base return runtime_knowledge_base - async def load_external_knowledge_base( - self, - external_kb_entity: persistence_rag.ExternalKnowledgeBase | sqlalchemy.Row | dict, - trigger_sync: bool = True, - ) -> ExternalKnowledgeBase: - """Load external knowledge base into runtime - - Args: - external_kb_entity: External KB entity to load - trigger_sync: Whether to trigger sync after loading (default True for manual creation, False for batch loading) - """ - if isinstance(external_kb_entity, sqlalchemy.Row): - external_kb_entity = persistence_rag.ExternalKnowledgeBase(**external_kb_entity._mapping) - elif isinstance(external_kb_entity, dict): - external_kb_entity = persistence_rag.ExternalKnowledgeBase(**external_kb_entity) - - external_kb = ExternalKnowledgeBase(ap=self.ap, external_kb_entity=external_kb_entity) - - await external_kb.initialize() - - self.knowledge_bases.append(external_kb) - - # Trigger sync to create the instance immediately (for manual creation) - # Skip sync during batch loading from DB to avoid multiple sync calls - if trigger_sync: - try: - await self.ap.plugin_connector.sync_polymorphic_component_instances() - self.ap.logger.info(f'Triggered sync after loading external KB {external_kb_entity.uuid}') - except Exception as e: - self.ap.logger.error(f'Failed to sync after loading external KB: {e}') - - return external_kb - async def get_knowledge_base_by_uuid(self, kb_uuid: str) -> KnowledgeBaseInterface | None: - for kb in self.knowledge_bases: - if kb.get_uuid() == kb_uuid: - return kb - return None + return self.knowledge_bases.get(kb_uuid) async def remove_knowledge_base_from_runtime(self, kb_uuid: str): - for kb in self.knowledge_bases: - if kb.get_uuid() == kb_uuid: - self.knowledge_bases.remove(kb) - return + self.knowledge_bases.pop(kb_uuid, None) async def delete_knowledge_base(self, kb_uuid: str): - for kb in self.knowledge_bases: - if kb.get_uuid() == kb_uuid: - await kb.dispose() - self.knowledge_bases.remove(kb) - return + kb = self.knowledge_bases.pop(kb_uuid, None) + if kb is not None: + await kb.dispose() + else: + self.ap.logger.warning(f'Knowledge base {kb_uuid} not found in runtime, skipping plugin notification') diff --git a/src/langbot/pkg/rag/knowledge/services/__init__.py b/src/langbot/pkg/rag/knowledge/services/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/langbot/pkg/rag/knowledge/services/base_service.py b/src/langbot/pkg/rag/knowledge/services/base_service.py deleted file mode 100644 index 0f71a508..00000000 --- a/src/langbot/pkg/rag/knowledge/services/base_service.py +++ /dev/null @@ -1,15 +0,0 @@ -# 封装异步操作 -import asyncio - - -class BaseService: - def __init__(self): - pass - - async def _run_sync(self, func, *args, **kwargs): - """ - 在单独的线程中运行同步函数。 - 如果第一个参数是 session，则在 to_thread 中获取新的 session。 - """ - - return await asyncio.to_thread(func, *args, **kwargs) diff --git a/src/langbot/pkg/rag/knowledge/services/chunker.py b/src/langbot/pkg/rag/knowledge/services/chunker.py deleted file mode 100644 index 0cb16816..00000000 --- a/src/langbot/pkg/rag/knowledge/services/chunker.py +++ /dev/null @@ -1,49 +0,0 @@ -from __future__ import annotations - -import json -from typing import List -from langbot.pkg.rag.knowledge.services import base_service -from langbot.pkg.core import app -from langchain_text_splitters import RecursiveCharacterTextSplitter - - -class Chunker(base_service.BaseService): - """ - A class for splitting long texts into smaller, overlapping chunks. - """ - - def __init__(self, ap: app.Application, chunk_size: int = 500, chunk_overlap: int = 50): - self.ap = ap - self.chunk_size = chunk_size - self.chunk_overlap = chunk_overlap - if self.chunk_overlap >= self.chunk_size: - self.ap.logger.warning( - 'Chunk overlap is greater than or equal to chunk size. This may lead to empty or malformed chunks.' - ) - - def _split_text_sync(self, text: str) -> List[str]: - """ - Synchronously splits a long text into chunks with specified overlap. - This is a CPU-bound operation, intended to be run in a separate thread. - """ - if not text: - return [] - - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=self.chunk_size, - chunk_overlap=self.chunk_overlap, - length_function=len, - is_separator_regex=False, - ) - return text_splitter.split_text(text) - - async def chunk(self, text: str) -> List[str]: - """ - Asynchronously chunks a given text into smaller pieces. - """ - self.ap.logger.info(f'Chunking text (length: {len(text)})...') - # Run the synchronous splitting logic in a separate thread - chunks = await self._run_sync(self._split_text_sync, text) - self.ap.logger.info(f'Text chunked into {len(chunks)} pieces.') - self.ap.logger.debug(f'Chunks: {json.dumps(chunks, indent=4, ensure_ascii=False)}') - return chunks diff --git a/src/langbot/pkg/rag/knowledge/services/embedder.py b/src/langbot/pkg/rag/knowledge/services/embedder.py deleted file mode 100644 index 168b839d..00000000 --- a/src/langbot/pkg/rag/knowledge/services/embedder.py +++ /dev/null @@ -1,55 +0,0 @@ -from __future__ import annotations -import uuid -from typing import List -from langbot.pkg.rag.knowledge.services.base_service import BaseService -from langbot.pkg.entity.persistence import rag as persistence_rag -from langbot.pkg.core import app -from langbot.pkg.provider.modelmgr.requester import RuntimeEmbeddingModel -import sqlalchemy - - -class Embedder(BaseService): - def __init__(self, ap: app.Application) -> None: - super().__init__() - self.ap = ap - - async def embed_and_store( - self, kb_id: str, file_id: str, chunks: List[str], embedding_model: RuntimeEmbeddingModel - ) -> list[persistence_rag.Chunk]: - # save chunk to db - chunk_entities: list[persistence_rag.Chunk] = [] - chunk_ids: list[str] = [] - - for chunk_text in chunks: - chunk_uuid = str(uuid.uuid4()) - chunk_ids.append(chunk_uuid) - chunk_entity = persistence_rag.Chunk(uuid=chunk_uuid, file_id=file_id, text=chunk_text) - chunk_entities.append(chunk_entity) - - chunk_dicts = [ - self.ap.persistence_mgr.serialize_model(persistence_rag.Chunk, chunk) for chunk in chunk_entities - ] - - await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.Chunk).values(chunk_dicts)) - - # get embeddings (batch size limit: 64 for OpenAI) - MAX_BATCH_SIZE = 64 - embeddings_list: list[list[float]] = [] - - for i in range(0, len(chunks), MAX_BATCH_SIZE): - batch = chunks[i : i + MAX_BATCH_SIZE] - batch_embeddings = await embedding_model.provider.invoke_embedding( - model=embedding_model, - input_text=batch, - extra_args={}, # TODO: add extra args - knowledge_base_id=kb_id, - call_type='embedding', - ) - embeddings_list.extend(batch_embeddings) - - # save embeddings to vdb - await self.ap.vector_db_mgr.vector_db.add_embeddings(kb_id, chunk_ids, embeddings_list, chunk_dicts) - - self.ap.logger.info(f'Successfully saved {len(chunk_entities)} embeddings to Knowledge Base.') - - return chunk_entities diff --git a/src/langbot/pkg/rag/knowledge/services/parser.py b/src/langbot/pkg/rag/knowledge/services/parser.py deleted file mode 100644 index 50410738..00000000 --- a/src/langbot/pkg/rag/knowledge/services/parser.py +++ /dev/null @@ -1,291 +0,0 @@ -from __future__ import annotations - -import PyPDF2 -import io -from docx import Document -import chardet -from typing import Union, Callable, Any -import markdown -from bs4 import BeautifulSoup -import re -import asyncio # Import asyncio for async operations -from langbot.pkg.core import app - - -class FileParser: - """ - A robust file parser class to extract text content from various document formats. - It supports TXT, PDF, DOCX, XLSX, CSV, Markdown, HTML, and EPUB files. - All core file reading operations are designed to be run synchronously in a thread pool - to avoid blocking the asyncio event loop. - """ - - def __init__(self, ap: app.Application): - self.ap = ap - - async def _run_sync(self, sync_func: Callable, *args: Any, **kwargs: Any) -> Any: - """ - Runs a synchronous function in a separate thread to prevent blocking the event loop. - This is a general utility method for wrapping blocking I/O operations. - """ - try: - return await asyncio.to_thread(sync_func, *args, **kwargs) - except Exception as e: - self.ap.logger.error(f'Error running synchronous function {sync_func.__name__}: {e}') - raise - - async def parse(self, file_name: str, extension: str) -> Union[str, None]: - """ - Parses the file based on its extension and returns the extracted text content. - This is the main asynchronous entry point for parsing. - - Args: - file_name (str): The name of the file to be parsed, get from ap.storage_mgr - - Returns: - Union[str, None]: The extracted text content as a single string, or None if parsing fails. - """ - - file_extension = extension.lower() - parser_method = getattr(self, f'_parse_{file_extension}', None) - - if parser_method is None: - self.ap.logger.error(f'Unsupported file format: {file_extension} for file {file_name}') - return None - - try: - # Pass file_path to the specific parser methods - return await parser_method(file_name) - except Exception as e: - self.ap.logger.error(f'Failed to parse {file_extension} file {file_name}: {e}') - return None - - # --- Helper for reading files with encoding detection --- - async def _read_file_content(self, file_name: str) -> Union[str, bytes]: - """ - Reads a file with automatic encoding detection, ensuring the synchronous - file read operation runs in a separate thread. - """ - - # def _read_sync(): - # with open(file_path, 'rb') as file: - # raw_data = file.read() - # detected = chardet.detect(raw_data) - # encoding = detected['encoding'] or 'utf-8' - - # if mode == 'r': - # return raw_data.decode(encoding, errors='ignore') - # return raw_data # For binary mode - - # return await self._run_sync(_read_sync) - file_bytes = await self.ap.storage_mgr.storage_provider.load(file_name) - - detected = chardet.detect(file_bytes) - encoding = detected['encoding'] or 'utf-8' - - return file_bytes.decode(encoding, errors='ignore') - - # --- Specific Parser Methods --- - - async def _parse_txt(self, file_name: str) -> str: - """Parses a TXT file and returns its content.""" - self.ap.logger.info(f'Parsing TXT file: {file_name}') - return await self._read_file_content(file_name) - - async def _parse_pdf(self, file_name: str) -> str: - """Parses a PDF file and returns its text content.""" - self.ap.logger.info(f'Parsing PDF file: {file_name}') - - # def _parse_pdf_sync(): - # text_content = [] - # with open(file_name, 'rb') as file: - # pdf_reader = PyPDF2.PdfReader(file) - # for page in pdf_reader.pages: - # text = page.extract_text() - # if text: - # text_content.append(text) - # return '\n'.join(text_content) - - # return await self._run_sync(_parse_pdf_sync) - - pdf_bytes = await self.ap.storage_mgr.storage_provider.load(file_name) - - def _parse_pdf_sync(): - pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_bytes)) - text_content = [] - for page in pdf_reader.pages: - text = page.extract_text() - if text: - text_content.append(text) - return '\n'.join(text_content) - - return await self._run_sync(_parse_pdf_sync) - - async def _parse_docx(self, file_name: str) -> str: - """Parses a DOCX file and returns its text content.""" - self.ap.logger.info(f'Parsing DOCX file: {file_name}') - - docx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name) - - def _parse_docx_sync(): - doc = Document(io.BytesIO(docx_bytes)) - text_content = [paragraph.text for paragraph in doc.paragraphs if paragraph.text.strip()] - return '\n'.join(text_content) - - return await self._run_sync(_parse_docx_sync) - - async def _parse_doc(self, file_name: str) -> str: - """Handles .doc files, explicitly stating lack of direct support.""" - self.ap.logger.warning(f'Direct .doc parsing is not supported for {file_name}. Please convert to .docx first.') - raise NotImplementedError('Direct .doc parsing not supported. Please convert to .docx first.') - - # async def _parse_xlsx(self, file_name: str) -> str: - # """Parses an XLSX file, returning text from all sheets.""" - # self.ap.logger.info(f'Parsing XLSX file: {file_name}') - - # xlsx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name) - - # def _parse_xlsx_sync(): - # excel_file = pd.ExcelFile(io.BytesIO(xlsx_bytes)) - # all_sheet_content = [] - # for sheet_name in excel_file.sheet_names: - # df = pd.read_excel(io.BytesIO(xlsx_bytes), sheet_name=sheet_name) - # sheet_text = f'--- Sheet: {sheet_name} ---\n{df.to_string(index=False)}\n' - # all_sheet_content.append(sheet_text) - # return '\n'.join(all_sheet_content) - - # return await self._run_sync(_parse_xlsx_sync) - - # async def _parse_csv(self, file_name: str) -> str: - # """Parses a CSV file and returns its content as a string.""" - # self.ap.logger.info(f'Parsing CSV file: {file_name}') - - # csv_bytes = await self.ap.storage_mgr.storage_provider.load(file_name) - - # def _parse_csv_sync(): - # # pd.read_csv can often detect encoding, but explicit detection is safer - # # raw_data = self._read_file_content( - # # file_name, mode='rb' - # # ) # Note: this will need to be await outside this sync function - # # _ = raw_data - # # For simplicity, we'll let pandas handle encoding internally after a raw read. - # # A more robust solution might pass encoding directly to pd.read_csv after detection. - # detected = chardet.detect(io.BytesIO(csv_bytes)) - # encoding = detected['encoding'] or 'utf-8' - # df = pd.read_csv(io.BytesIO(csv_bytes), encoding=encoding) - # return df.to_string(index=False) - - # return await self._run_sync(_parse_csv_sync) - - async def _parse_md(self, file_name: str) -> str: - """Parses a Markdown file, converting it to structured plain text.""" - self.ap.logger.info(f'Parsing Markdown file: {file_name}') - - md_bytes = await self.ap.storage_mgr.storage_provider.load(file_name) - - def _parse_markdown_sync(): - md_content = io.BytesIO(md_bytes).read().decode('utf-8', errors='ignore') - html_content = markdown.markdown( - md_content, extensions=['extra', 'codehilite', 'tables', 'toc', 'fenced_code'] - ) - soup = BeautifulSoup(html_content, 'html.parser') - text_parts = [] - for element in soup.children: - if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']: - level = int(element.name[1]) - text_parts.append('#' * level + ' ' + element.get_text().strip()) - elif element.name == 'p': - text = element.get_text().strip() - if text: - text_parts.append(text) - elif element.name in ['ul', 'ol']: - for li in element.find_all('li'): - text_parts.append(f'* {li.get_text().strip()}') - elif element.name == 'pre': - code_block = element.get_text().strip() - if code_block: - text_parts.append(f'```\n{code_block}\n```') - elif element.name == 'table': - table_str = self._extract_table_to_markdown_sync(element) # Call sync helper - if table_str: - text_parts.append(table_str) - elif element.name: - text = element.get_text(separator=' ', strip=True) - if text: - text_parts.append(text) - cleaned_text = re.sub(r'\n\s*\n', '\n\n', '\n'.join(text_parts)) - return cleaned_text.strip() - - return await self._run_sync(_parse_markdown_sync) - - async def _parse_html(self, file_name: str) -> str: - """Parses an HTML file, extracting structured plain text.""" - self.ap.logger.info(f'Parsing HTML file: {file_name}') - - html_bytes = await self.ap.storage_mgr.storage_provider.load(file_name) - - def _parse_html_sync(): - html_content = io.BytesIO(html_bytes).read().decode('utf-8', errors='ignore') - soup = BeautifulSoup(html_content, 'html.parser') - for script_or_style in soup(['script', 'style']): - script_or_style.decompose() - text_parts = [] - for element in soup.body.children if soup.body else soup.children: - if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']: - level = int(element.name[1]) - text_parts.append('#' * level + ' ' + element.get_text().strip()) - elif element.name == 'p': - text = element.get_text().strip() - if text: - text_parts.append(text) - elif element.name in ['ul', 'ol']: - for li in element.find_all('li'): - text = li.get_text().strip() - if text: - text_parts.append(f'* {text}') - elif element.name == 'table': - table_str = self._extract_table_to_markdown_sync(element) # Call sync helper - if table_str: - text_parts.append(table_str) - elif element.name: - text = element.get_text(separator=' ', strip=True) - if text: - text_parts.append(text) - cleaned_text = re.sub(r'\n\s*\n', '\n\n', '\n'.join(text_parts)) - return cleaned_text.strip() - - return await self._run_sync(_parse_html_sync) - - def _add_toc_items_sync(self, toc_list: list, text_content: list, level: int): - """Recursively adds TOC items to text_content (synchronous helper).""" - indent = ' ' * level - for item in toc_list: - if isinstance(item, tuple): - chapter, subchapters = item - text_content.append(f'{indent}- {chapter.title}') - self._add_toc_items_sync(subchapters, text_content, level + 1) - else: - text_content.append(f'{indent}- {item.title}') - - def _extract_table_to_markdown_sync(self, table_element: BeautifulSoup) -> str: - """Helper to convert a BeautifulSoup table element into a Markdown table string (synchronous).""" - headers = [th.get_text().strip() for th in table_element.find_all('th')] - rows = [] - for tr in table_element.find_all('tr'): - cells = [td.get_text().strip() for td in tr.find_all('td')] - if cells: - rows.append(cells) - - if not headers and not rows: - return '' - - table_lines = [] - if headers: - table_lines.append(' | '.join(headers)) - table_lines.append(' | '.join(['---'] * len(headers))) - - for row_cells in rows: - padded_cells = row_cells + [''] * (len(headers) - len(row_cells)) if headers else row_cells - table_lines.append(' | '.join(padded_cells)) - - return '\n'.join(table_lines) diff --git a/src/langbot/pkg/rag/knowledge/services/retriever.py b/src/langbot/pkg/rag/knowledge/services/retriever.py deleted file mode 100644 index 15619c94..00000000 --- a/src/langbot/pkg/rag/knowledge/services/retriever.py +++ /dev/null @@ -1,53 +0,0 @@ -from __future__ import annotations - -from . import base_service -from ....core import app -from ....provider.modelmgr.requester import RuntimeEmbeddingModel -from langbot_plugin.api.entities.builtin.rag import context as rag_context -from langbot_plugin.api.entities.builtin.provider.message import ContentElement - - -class Retriever(base_service.BaseService): - def __init__(self, ap: app.Application): - super().__init__() - self.ap = ap - - async def retrieve( - self, kb_id: str, query: str, embedding_model: RuntimeEmbeddingModel, k: int = 5 - ) -> list[rag_context.RetrievalResultEntry]: - self.ap.logger.info( - f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}" - ) - - query_embedding: list[float] = await embedding_model.provider.invoke_embedding( - model=embedding_model, - input_text=[query], - extra_args={}, # TODO: add extra args - knowledge_base_id=kb_id, - query_text=query, - call_type='retrieve', - ) - - vector_results = await self.ap.vector_db_mgr.vector_db.search(kb_id, query_embedding[0], k) - - # 'ids' shape mirrors the Chroma-style response contract for compatibility - matched_vector_ids = vector_results.get('ids', [[]])[0] - distances = vector_results.get('distances', [[]])[0] - vector_metadatas = vector_results.get('metadatas', [[]])[0] - - if not matched_vector_ids: - self.ap.logger.info('No relevant chunks found in vector database.') - return [] - - result: list[rag_context.RetrievalResultEntry] = [] - - for i, id in enumerate(matched_vector_ids): - entry = rag_context.RetrievalResultEntry( - id=id, - content=[ContentElement.from_text(vector_metadatas[i].get('text', ''))], - metadata=vector_metadatas[i], - distance=distances[i], - ) - result.append(entry) - - return result diff --git a/src/langbot/pkg/rag/service/__init__.py b/src/langbot/pkg/rag/service/__init__.py new file mode 100644 index 00000000..2501f49f --- /dev/null +++ b/src/langbot/pkg/rag/service/__init__.py @@ -0,0 +1 @@ +from .runtime import RAGRuntimeService as RAGRuntimeService diff --git a/src/langbot/pkg/rag/service/runtime.py b/src/langbot/pkg/rag/service/runtime.py new file mode 100644 index 00000000..d02cc374 --- /dev/null +++ b/src/langbot/pkg/rag/service/runtime.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +import posixpath +from typing import Any +from langbot.pkg.core import app + + +class RAGRuntimeService: + """Service to handle RAG-related requests from plugins (Runtime). + + This service acts as the bridge between plugin RPC requests and + LangBot's infrastructure (embedding models, vector databases, file storage). + """ + + def __init__(self, ap: app.Application): + self.ap = ap + + async def vector_upsert( + self, + collection_id: str, + vectors: list[list[float]], + ids: list[str], + metadata: list[dict[str, Any]] | None = None, + documents: list[str] | None = None, + ) -> None: + """Handle VECTOR_UPSERT action.""" + metadatas = metadata if metadata else [{} for _ in vectors] + await self.ap.vector_db_mgr.upsert( + collection_name=collection_id, + vectors=vectors, + ids=ids, + metadata=metadatas, + documents=documents, + ) + + async def vector_search( + self, + collection_id: str, + query_vector: list[float], + top_k: int, + filters: dict[str, Any] | None = None, + search_type: str = 'vector', + query_text: str = '', + ) -> list[dict[str, Any]]: + """Handle VECTOR_SEARCH action.""" + return await self.ap.vector_db_mgr.search( + collection_name=collection_id, + query_vector=query_vector, + limit=top_k, + filter=filters, + search_type=search_type, + query_text=query_text, + ) + + async def vector_delete( + self, collection_id: str, file_ids: list[str] | None = None, filters: dict[str, Any] | None = None + ) -> int: + """Handle VECTOR_DELETE action. + + Deletes vectors associated with the given file IDs from the collection. + Each file_id corresponds to a document whose vectors will be removed. + + Args: + collection_id: The collection to delete from. + file_ids: File IDs whose associated vectors should be deleted. + Each file_id maps to a set of vectors stored with that file_id + in their metadata. + filters: Filter-based deletion (not yet supported, will raise). + """ + count = 0 + if file_ids: + await self.ap.vector_db_mgr.delete_by_file_id(collection_name=collection_id, file_ids=file_ids) + count = len(file_ids) + elif filters: + count = await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters) + return count + + async def get_file_stream(self, storage_path: str) -> bytes: + """Handle GET_KNOWLEDEGE_FILE_STREAM action. + + Uses the storage manager abstraction to load file content, + regardless of the underlying storage provider. + """ + # Validate storage_path to prevent path traversal + normalized = posixpath.normpath(storage_path) + if normalized.startswith('/') or '..' in normalized.split('/'): + raise ValueError('Invalid storage path') + content_bytes = await self.ap.storage_mgr.storage_provider.load(normalized) + return content_bytes if content_bytes else b'' diff --git a/src/langbot/pkg/storage/provider.py b/src/langbot/pkg/storage/provider.py index 09d8d93e..e24dcbf9 100644 --- a/src/langbot/pkg/storage/provider.py +++ b/src/langbot/pkg/storage/provider.py @@ -43,6 +43,13 @@ class StorageProvider(abc.ABC): ): pass + @abc.abstractmethod + async def size( + self, + key: str, + ) -> int: + pass + @abc.abstractmethod async def delete_dir_recursive( self, diff --git a/src/langbot/pkg/storage/providers/localstorage.py b/src/langbot/pkg/storage/providers/localstorage.py index d21f5427..592c0be2 100644 --- a/src/langbot/pkg/storage/providers/localstorage.py +++ b/src/langbot/pkg/storage/providers/localstorage.py @@ -47,6 +47,12 @@ class LocalStorageProvider(provider.StorageProvider): ): os.remove(os.path.join(LOCAL_STORAGE_PATH, f'{key}')) + async def size( + self, + key: str, + ) -> int: + return os.path.getsize(os.path.join(LOCAL_STORAGE_PATH, f'{key}')) + async def delete_dir_recursive( self, dir_path: str, diff --git a/src/langbot/pkg/storage/providers/s3storage.py b/src/langbot/pkg/storage/providers/s3storage.py index ed4fc443..43cc2e96 100644 --- a/src/langbot/pkg/storage/providers/s3storage.py +++ b/src/langbot/pkg/storage/providers/s3storage.py @@ -117,6 +117,21 @@ class S3StorageProvider(provider.StorageProvider): self.ap.logger.error(f'Failed to delete from S3: {e}') raise + async def size( + self, + key: str, + ) -> int: + """Get object size from S3 without downloading it""" + try: + response = self.s3_client.head_object( + Bucket=self.bucket_name, + Key=key, + ) + return response['ContentLength'] + except Exception as e: + self.ap.logger.error(f'Failed to get size from S3: {e}') + raise + async def delete_dir_recursive( self, dir_path: str, diff --git a/src/langbot/pkg/vector/filter_utils.py b/src/langbot/pkg/vector/filter_utils.py new file mode 100644 index 00000000..6c8a187a --- /dev/null +++ b/src/langbot/pkg/vector/filter_utils.py @@ -0,0 +1,69 @@ +"""Shared utilities for metadata filter handling across VDB backends. + +Canonical filter format (Chroma-style ``where`` syntax): + + {"file_id": "abc"} # implicit $eq + {"file_id": {"$eq": "abc"}} # explicit $eq + {"created_at": {"$gte": 1700000000}} # comparison + {"file_type": {"$in": ["pdf", "docx"]}} # in-list + +Multiple top-level keys are AND-ed. Supported operators: +``$eq``, ``$ne``, ``$gt``, ``$gte``, ``$lt``, ``$lte``, ``$in``, ``$nin``. +""" + +from __future__ import annotations + +import logging +from typing import Any + +SUPPORTED_OPS = frozenset({'$eq', '$ne', '$gt', '$gte', '$lt', '$lte', '$in', '$nin'}) + +logger = logging.getLogger(__name__) + + +def normalize_filter( + raw: dict[str, Any] | None, +) -> list[tuple[str, str, Any]]: + """Parse a canonical filter dict into ``[(field, op, value)]`` triples. + + Returns an empty list when *raw* is ``None`` or empty. + + Raises ``ValueError`` on unsupported operators or malformed entries. + """ + if not raw: + return [] + + triples: list[tuple[str, str, Any]] = [] + for field, condition in raw.items(): + if isinstance(condition, dict): + for op, value in condition.items(): + if op not in SUPPORTED_OPS: + raise ValueError(f'Unsupported filter operator: {op}') + triples.append((field, op, value)) + else: + # Bare value -> implicit $eq + triples.append((field, '$eq', condition)) + return triples + + +def strip_unsupported_fields( + triples: list[tuple[str, str, Any]], + supported_fields: set[str], +) -> list[tuple[str, str, Any]]: + """Return only triples whose field is in *supported_fields*. + + Dropped fields are logged at WARNING level so the caller knows they were + silently ignored (useful for Milvus / pgvector which only store a fixed + schema). + """ + kept: list[tuple[str, str, Any]] = [] + for field, op, value in triples: + if field in supported_fields: + kept.append((field, op, value)) + else: + logger.warning( + 'Filter field %r is not supported by this backend and will be ignored (supported: %s)', + field, + ', '.join(sorted(supported_fields)), + ) + return kept diff --git a/src/langbot/pkg/vector/mgr.py b/src/langbot/pkg/vector/mgr.py index f0cb742c..41114bb7 100644 --- a/src/langbot/pkg/vector/mgr.py +++ b/src/langbot/pkg/vector/mgr.py @@ -1,7 +1,7 @@ from __future__ import annotations from ..core import app -from .vdb import VectorDatabase +from .vdb import VectorDatabase, SearchType from .vdbs.chroma import ChromaVectorDatabase from .vdbs.qdrant import QdrantVectorDatabase from .vdbs.seekdb import SeekDBVectorDatabase @@ -65,3 +65,95 @@ class VectorDBManager: else: self.vector_db = ChromaVectorDatabase(self.ap) self.ap.logger.warning('No vector database backend configured, defaulting to Chroma.') + + def get_supported_search_types(self) -> list[str]: + """Return the search types supported by the current VDB backend.""" + if self.vector_db is None: + return [SearchType.VECTOR.value] + return [st.value for st in self.vector_db.supported_search_types()] + + async def upsert( + self, + collection_name: str, + vectors: list[list[float]], + ids: list[str], + metadata: list[dict] | None = None, + documents: list[str] | None = None, + ): + """Proxy: Upsert vectors""" + await self.vector_db.add_embeddings( + collection=collection_name, + ids=ids, + embeddings_list=vectors, + metadatas=metadata or [{} for _ in vectors], + documents=documents, + ) + + async def search( + self, + collection_name: str, + query_vector: list[float], + limit: int, + filter: dict | None = None, + search_type: str = 'vector', + query_text: str = '', + ) -> list[dict]: + """Proxy: Search vectors. + + Returns a list of dicts with keys: 'id', 'score', 'metadata'. + The underlying VectorDatabase.search returns Chroma-style format: + { 'ids': [['id1']], 'distances': [[0.1]], 'metadatas': [[{}]] } + """ + results = await self.vector_db.search( + collection=collection_name, + query_embedding=query_vector, + k=limit, + search_type=search_type, + query_text=query_text, + filter=filter, + ) + + if not results or 'ids' not in results or not results['ids']: + return [] + + # Flatten nested lists (Chroma returns batch-style: list of lists) + raw_ids = results['ids'] + raw_dists = results.get('distances', []) + raw_metas = results.get('metadatas', []) + + r_ids = raw_ids[0] if raw_ids and isinstance(raw_ids[0], list) else raw_ids + r_dists = raw_dists[0] if raw_dists and isinstance(raw_dists[0], list) else raw_dists + r_metas = raw_metas[0] if raw_metas and isinstance(raw_metas[0], list) else raw_metas + + parsed_results = [] + for i, id_val in enumerate(r_ids): + parsed_results.append( + { + 'id': id_val, + 'score': r_dists[i] if r_dists and i < len(r_dists) else 0.0, + 'metadata': r_metas[i] if r_metas and i < len(r_metas) else {}, + } + ) + + return parsed_results + + async def delete_by_file_id(self, collection_name: str, file_ids: list[str]): + """Proxy: Delete vectors by file_id (metadata-level identifier). + + This delegates to VectorDatabase.delete_by_file_id which removes + all vectors associated with the given file IDs. + """ + for file_id in file_ids: + await self.vector_db.delete_by_file_id(collection_name, file_id) + + async def delete_collection(self, collection_name: str): + """Proxy: Delete an entire collection.""" + await self.vector_db.delete_collection(collection_name) + + async def delete_by_filter(self, collection_name: str, filter: dict) -> int: + """Proxy: Delete vectors by metadata filter. + + Returns: + Number of deleted vectors (best-effort; some backends return 0). + """ + return await self.vector_db.delete_by_filter(collection_name, filter) diff --git a/src/langbot/pkg/vector/vdb.py b/src/langbot/pkg/vector/vdb.py index 137bdb06..83356e11 100644 --- a/src/langbot/pkg/vector/vdb.py +++ b/src/langbot/pkg/vector/vdb.py @@ -1,10 +1,28 @@ from __future__ import annotations import abc +import enum from typing import Any, Dict import numpy as np +class SearchType(str, enum.Enum): + """Supported search types for vector databases.""" + + VECTOR = 'vector' + FULL_TEXT = 'full_text' + HYBRID = 'hybrid' + + class VectorDatabase(abc.ABC): + @classmethod + def supported_search_types(cls) -> list[SearchType]: + """Return the search types supported by this VDB backend. + + Default: vector search only. Override in subclasses that support + full-text or hybrid search. + """ + return [SearchType.VECTOR] + @abc.abstractmethod async def add_embeddings( self, @@ -12,14 +30,47 @@ class VectorDatabase(abc.ABC): ids: list[str], embeddings_list: list[list[float]], metadatas: list[dict[str, Any]], - documents: list[str], + documents: list[str] | None = None, ) -> None: - """Add vector data to the specified collection.""" + """Add vector data to the specified collection. + + Args: + collection: Collection name. + ids: Unique IDs for each vector. + embeddings_list: List of embedding vectors. + metadatas: List of metadata dicts. + documents: Optional raw text documents. Required for full-text + and hybrid search in backends that support them. + """ pass @abc.abstractmethod - async def search(self, collection: str, query_embedding: np.ndarray, k: int = 5) -> Dict[str, Any]: - """Search for the most similar vectors in the specified collection.""" + async def search( + self, + collection: str, + query_embedding: np.ndarray, + k: int = 5, + search_type: str = 'vector', + query_text: str = '', + filter: dict[str, Any] | None = None, + ) -> Dict[str, Any]: + """Search for the most similar vectors in the specified collection. + + Args: + collection: Collection name. + query_embedding: Query vector for similarity search. + k: Number of results to return. + search_type: One of 'vector', 'full_text', 'hybrid'. + query_text: Raw query text, used for full_text and hybrid search. + filter: Optional metadata filters using Chroma-style ``where`` + syntax. Multiple top-level keys are AND-ed. Supported + operators: ``$eq``, ``$ne``, ``$gt``, ``$gte``, ``$lt``, + ``$lte``, ``$in``, ``$nin``. Example:: + + {"file_id": "abc"} + {"created_at": {"$gte": 1700000000}} + {"file_type": {"$in": ["pdf", "docx"]}} + """ pass @abc.abstractmethod @@ -27,6 +78,20 @@ class VectorDatabase(abc.ABC): """Delete vectors from the specified collection by file_id.""" pass + @abc.abstractmethod + async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int: + """Delete vectors matching the given metadata filter. + + Args: + collection: Collection name. + filter: Metadata filter dict in canonical format (see ``search``). + + Returns: + Number of deleted vectors (best-effort; backends that cannot + report an exact count may return 0). + """ + pass + @abc.abstractmethod async def get_or_create_collection(self, collection: str): """Get or create collection.""" diff --git a/src/langbot/pkg/vector/vdbs/chroma.py b/src/langbot/pkg/vector/vdbs/chroma.py index 94227c75..6cefce13 100644 --- a/src/langbot/pkg/vector/vdbs/chroma.py +++ b/src/langbot/pkg/vector/vdbs/chroma.py @@ -28,19 +28,33 @@ class ChromaVectorDatabase(VectorDatabase): ids: list[str], embeddings_list: list[list[float]], metadatas: list[dict[str, Any]], + documents: list[str] | None = None, ) -> None: col = await self.get_or_create_collection(collection) - await asyncio.to_thread(col.add, embeddings=embeddings_list, ids=ids, metadatas=metadatas) + kwargs: dict[str, Any] = dict(embeddings=embeddings_list, ids=ids, metadatas=metadatas) + if documents is not None: + kwargs['documents'] = documents + await asyncio.to_thread(col.add, **kwargs) self.ap.logger.info(f"Added {len(ids)} embeddings to Chroma collection '{collection}'.") - async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> dict[str, Any]: + async def search( + self, + collection: str, + query_embedding: list[float], + k: int = 5, + search_type: str = 'vector', + query_text: str = '', + filter: dict[str, Any] | None = None, + ) -> dict[str, Any]: col = await self.get_or_create_collection(collection) - results = await asyncio.to_thread( - col.query, + query_kwargs: dict[str, Any] = dict( query_embeddings=query_embedding, n_results=k, include=['metadatas', 'distances', 'documents'], ) + if filter: + query_kwargs['where'] = filter + results = await asyncio.to_thread(col.query, **query_kwargs) self.ap.logger.info(f"Chroma search in '{collection}' returned {len(results.get('ids', [[]])[0])} results.") return results @@ -49,6 +63,12 @@ class ChromaVectorDatabase(VectorDatabase): await asyncio.to_thread(col.delete, where={'file_id': file_id}) self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' with file_id: {file_id}") + async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int: + col = await self.get_or_create_collection(collection) + await asyncio.to_thread(col.delete, where=filter) + self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' by filter") + return 0 # Chroma delete does not return a count + async def delete_collection(self, collection: str): if collection in self._collections: del self._collections[collection] diff --git a/src/langbot/pkg/vector/vdbs/milvus.py b/src/langbot/pkg/vector/vdbs/milvus.py index 2852dea1..967f3bed 100644 --- a/src/langbot/pkg/vector/vdbs/milvus.py +++ b/src/langbot/pkg/vector/vdbs/milvus.py @@ -4,8 +4,51 @@ from typing import Any, Dict from pymilvus import MilvusClient, DataType, CollectionSchema, FieldSchema from pymilvus.milvus_client.index import IndexParams from langbot.pkg.vector.vdb import VectorDatabase +from langbot.pkg.vector.filter_utils import normalize_filter, strip_unsupported_fields from langbot.pkg.core import app +# Milvus schema only stores these metadata fields; filter on other fields is +# silently dropped with a warning. +_MILVUS_SUPPORTED_FIELDS = {'text', 'file_id', 'chunk_uuid'} + + +def _build_milvus_expr(filter_dict: dict[str, Any]) -> str: + """Translate canonical filter dict into a Milvus boolean expression string.""" + triples = normalize_filter(filter_dict) + triples = strip_unsupported_fields(triples, _MILVUS_SUPPORTED_FIELDS) + if not triples: + return '' + + parts: list[str] = [] + for field, op, value in triples: + if op == '$eq': + parts.append(f'{field} == {_milvus_literal(value)}') + elif op == '$ne': + parts.append(f'{field} != {_milvus_literal(value)}') + elif op == '$gt': + parts.append(f'{field} > {_milvus_literal(value)}') + elif op == '$gte': + parts.append(f'{field} >= {_milvus_literal(value)}') + elif op == '$lt': + parts.append(f'{field} < {_milvus_literal(value)}') + elif op == '$lte': + parts.append(f'{field} <= {_milvus_literal(value)}') + elif op == '$in': + items = ', '.join(_milvus_literal(v) for v in value) + parts.append(f'{field} in [{items}]') + elif op == '$nin': + items = ', '.join(_milvus_literal(v) for v in value) + parts.append(f'{field} not in [{items}]') + return ' and '.join(parts) + + +def _milvus_literal(value: Any) -> str: + """Format a Python value as a Milvus expression literal.""" + if isinstance(value, str): + escaped = value.replace('\\', '\\\\').replace('"', '\\"') + return f'"{escaped}"' + return str(value) + class MilvusVectorDatabase(VectorDatabase): """Milvus vector database implementation""" @@ -155,6 +198,7 @@ class MilvusVectorDatabase(VectorDatabase): ids: list[str], embeddings_list: list[list[float]], metadatas: list[dict[str, Any]], + documents: list[str] | None = None, ) -> None: """Add vector embeddings to Milvus collection @@ -200,7 +244,15 @@ class MilvusVectorDatabase(VectorDatabase): self.ap.logger.info(f"Added {len(ids)} embeddings to Milvus collection '{collection}'") - async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> Dict[str, Any]: + async def search( + self, + collection: str, + query_embedding: list[float], + k: int = 5, + search_type: str = 'vector', + query_text: str = '', + filter: dict[str, Any] | None = None, + ) -> Dict[str, Any]: """Search for similar vectors in Milvus collection Args: @@ -217,14 +269,19 @@ class MilvusVectorDatabase(VectorDatabase): # Perform search search_params = {'metric_type': 'COSINE', 'params': {}} - results = await asyncio.to_thread( - self.client.search, + search_kwargs: dict[str, Any] = dict( collection_name=collection, data=[query_embedding], limit=k, search_params=search_params, output_fields=['text', 'file_id', 'chunk_uuid'], ) + if filter: + expr = _build_milvus_expr(filter) + if expr: + search_kwargs['filter'] = expr + + results = await asyncio.to_thread(self.client.search, **search_kwargs) # Convert results to Chroma-compatible format # Milvus returns: [[ {id, distance, entity: {...}} ]] @@ -268,6 +325,21 @@ class MilvusVectorDatabase(VectorDatabase): await asyncio.to_thread(self.client.delete, collection_name=collection, filter=f'file_id == "{file_id}"') self.ap.logger.info(f"Deleted embeddings from Milvus collection '{collection}' with file_id: {file_id}") + async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int: + collection = self._normalize_collection_name(collection) + await self.get_or_create_collection(collection) + + expr = _build_milvus_expr(filter) + if not expr: + self.ap.logger.warning( + f"Milvus delete_by_filter on '{collection}': filter produced empty expression, skipping" + ) + return 0 + + await asyncio.to_thread(self.client.delete, collection_name=collection, filter=expr) + self.ap.logger.info(f"Deleted embeddings from Milvus collection '{collection}' by filter") + return 0 # Milvus delete does not return a count + async def delete_collection(self, collection: str): """Delete a Milvus collection diff --git a/src/langbot/pkg/vector/vdbs/pgvector_db.py b/src/langbot/pkg/vector/vdbs/pgvector_db.py index 7490f228..66242894 100644 --- a/src/langbot/pkg/vector/vdbs/pgvector_db.py +++ b/src/langbot/pkg/vector/vdbs/pgvector_db.py @@ -5,10 +5,21 @@ from sqlalchemy.orm import declarative_base from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker from pgvector.sqlalchemy import Vector from langbot.pkg.vector.vdb import VectorDatabase +from langbot.pkg.vector.filter_utils import normalize_filter, strip_unsupported_fields from langbot.pkg.core import app Base = declarative_base() +# pgvector schema only stores these metadata fields. +_PG_SUPPORTED_FIELDS = {'text', 'file_id', 'chunk_uuid'} + +# Map schema field names to SQLAlchemy columns (resolved lazily from PgVectorEntry). +_PG_COLUMN_MAP = { + 'text': 'text', + 'file_id': 'file_id', + 'chunk_uuid': 'chunk_uuid', +} + class PgVectorEntry(Base): """SQLAlchemy model for pgvector entries""" @@ -23,6 +34,33 @@ class PgVectorEntry(Base): chunk_uuid = Column(String) +def _build_pg_conditions(filter_dict: dict[str, Any]) -> list: + """Translate canonical filter dict into a list of SQLAlchemy conditions.""" + triples = normalize_filter(filter_dict) + triples = strip_unsupported_fields(triples, _PG_SUPPORTED_FIELDS) + + conditions = [] + for field, op, value in triples: + col = getattr(PgVectorEntry, _PG_COLUMN_MAP[field]) + if op == '$eq': + conditions.append(col == value) + elif op == '$ne': + conditions.append(col != value) + elif op == '$gt': + conditions.append(col > value) + elif op == '$gte': + conditions.append(col >= value) + elif op == '$lt': + conditions.append(col < value) + elif op == '$lte': + conditions.append(col <= value) + elif op == '$in': + conditions.append(col.in_(value)) + elif op == '$nin': + conditions.append(col.notin_(value)) + return conditions + + class PgVectorDatabase(VectorDatabase): """PostgreSQL with pgvector extension database implementation""" @@ -109,6 +147,7 @@ class PgVectorDatabase(VectorDatabase): ids: list[str], embeddings_list: list[list[float]], metadatas: list[dict[str, Any]], + documents: list[str] | None = None, ) -> None: """Add vector embeddings to pgvector @@ -142,7 +181,15 @@ class PgVectorDatabase(VectorDatabase): self.ap.logger.error(f'Error adding embeddings to pgvector: {e}') raise - async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> Dict[str, Any]: + async def search( + self, + collection: str, + query_embedding: list[float], + k: int = 5, + search_type: str = 'vector', + query_text: str = '', + filter: dict[str, Any] | None = None, + ) -> Dict[str, Any]: """Search for similar vectors using cosine distance Args: @@ -174,6 +221,10 @@ class PgVectorDatabase(VectorDatabase): .limit(k) ) + if filter: + for cond in _build_pg_conditions(filter): + stmt = stmt.filter(cond) + result = await session.execute(stmt) rows = result.fetchall() @@ -225,6 +276,39 @@ class PgVectorDatabase(VectorDatabase): self.ap.logger.error(f'Error deleting from pgvector: {e}') raise + async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int: + """Delete vectors matching a metadata filter. + + Args: + collection: Collection name + filter: Canonical metadata filter dict + """ + conditions = _build_pg_conditions(filter) + if not conditions: + self.ap.logger.warning( + f"pgvector delete_by_filter on '{collection}': filter produced no conditions, skipping" + ) + return 0 + + await self.get_or_create_collection(collection) + + async with self.AsyncSessionLocal() as session: + try: + from sqlalchemy import delete + + stmt = delete(PgVectorEntry).where(PgVectorEntry.collection == collection) + for cond in conditions: + stmt = stmt.where(cond) + result = await session.execute(stmt) + await session.commit() + deleted = result.rowcount + self.ap.logger.info(f"Deleted {deleted} embeddings from pgvector collection '{collection}' by filter") + return deleted + except Exception as e: + await session.rollback() + self.ap.logger.error(f'Error deleting from pgvector by filter: {e}') + raise + async def delete_collection(self, collection: str): """Delete all vectors in a collection diff --git a/src/langbot/pkg/vector/vdbs/qdrant.py b/src/langbot/pkg/vector/vdbs/qdrant.py index a6fbd4ab..40772040 100644 --- a/src/langbot/pkg/vector/vdbs/qdrant.py +++ b/src/langbot/pkg/vector/vdbs/qdrant.py @@ -5,6 +5,37 @@ from typing import Any, Dict, List from qdrant_client import AsyncQdrantClient, models from langbot.pkg.core import app from langbot.pkg.vector.vdb import VectorDatabase +from langbot.pkg.vector.filter_utils import normalize_filter + + +def _build_qdrant_filter(filter_dict: dict[str, Any]) -> models.Filter: + """Translate canonical filter dict into a Qdrant ``models.Filter``.""" + triples = normalize_filter(filter_dict) + must: list[models.Condition] = [] + must_not: list[models.Condition] = [] + + for field, op, value in triples: + if op == '$eq': + must.append(models.FieldCondition(key=field, match=models.MatchValue(value=value))) + elif op == '$ne': + must_not.append(models.FieldCondition(key=field, match=models.MatchValue(value=value))) + elif op == '$in': + must.append(models.FieldCondition(key=field, match=models.MatchAny(any=value))) + elif op == '$nin': + must_not.append(models.FieldCondition(key=field, match=models.MatchAny(any=value))) + elif op in ('$gt', '$gte', '$lt', '$lte'): + range_kwargs: dict[str, Any] = {} + if op == '$gt': + range_kwargs['gt'] = value + elif op == '$gte': + range_kwargs['gte'] = value + elif op == '$lt': + range_kwargs['lt'] = value + elif op == '$lte': + range_kwargs['lte'] = value + must.append(models.FieldCondition(key=field, range=models.Range(**range_kwargs))) + + return models.Filter(must=must or None, must_not=must_not or None) class QdrantVectorDatabase(VectorDatabase): @@ -48,6 +79,7 @@ class QdrantVectorDatabase(VectorDatabase): ids: List[str], embeddings_list: List[List[float]], metadatas: List[Dict[str, Any]], + documents: List[str] | None = None, ) -> None: if not embeddings_list: return @@ -60,19 +92,29 @@ class QdrantVectorDatabase(VectorDatabase): await self.client.upsert(collection_name=collection, points=points) self.ap.logger.info(f"Added {len(ids)} embeddings to Qdrant collection '{collection}'.") - async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> dict[str, Any]: + async def search( + self, + collection: str, + query_embedding: list[float], + k: int = 5, + search_type: str = 'vector', + query_text: str = '', + filter: dict[str, Any] | None = None, + ) -> dict[str, Any]: exists = await self.client.collection_exists(collection) if not exists: return {'ids': [[]], 'metadatas': [[]], 'distances': [[]]} - hits = ( - await self.client.query_points( - collection_name=collection, - query=query_embedding, - limit=k, - with_payload=True, - ) - ).points + query_kwargs: dict[str, Any] = dict( + collection_name=collection, + query=query_embedding, + limit=k, + with_payload=True, + ) + if filter: + query_kwargs['query_filter'] = _build_qdrant_filter(filter) + + hits = (await self.client.query_points(**query_kwargs)).points ids = [str(hit.id) for hit in hits] metadatas = [hit.payload or {} for hit in hits] # Qdrant's score is similarity; convert to a pseudo-distance for consistency @@ -95,6 +137,19 @@ class QdrantVectorDatabase(VectorDatabase): ) self.ap.logger.info(f"Deleted embeddings from Qdrant collection '{collection}' with file_id: {file_id}") + async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int: + exists = await self.client.collection_exists(collection) + if not exists: + return 0 + + qdrant_filter = _build_qdrant_filter(filter) + await self.client.delete( + collection_name=collection, + points_selector=qdrant_filter, + ) + self.ap.logger.info(f"Deleted embeddings from Qdrant collection '{collection}' by filter") + return 0 # Qdrant delete does not return a count + async def delete_collection(self, collection: str): try: await self.client.delete_collection(collection) diff --git a/src/langbot/pkg/vector/vdbs/seekdb.py b/src/langbot/pkg/vector/vdbs/seekdb.py index b007f2fb..cc22e158 100644 --- a/src/langbot/pkg/vector/vdbs/seekdb.py +++ b/src/langbot/pkg/vector/vdbs/seekdb.py @@ -5,7 +5,7 @@ from typing import Any, Dict, List from langbot.pkg.core import app -from langbot.pkg.vector.vdb import VectorDatabase +from langbot.pkg.vector.vdb import VectorDatabase, SearchType try: import pyseekdb @@ -25,9 +25,13 @@ class SeekDBVectorDatabase(VectorDatabase): SeekDB is an AI-native search database by OceanBase that unifies relational, vector, text, JSON and GIS in a single engine. - Supports both embedded mode and remote server mode. + Supports embedded mode, remote server mode, and full-text/hybrid search. """ + @classmethod + def supported_search_types(cls) -> list[SearchType]: + return [SearchType.VECTOR, SearchType.FULL_TEXT, SearchType.HYBRID] + def __init__(self, ap: app.Application): if not SEEKDB_AVAILABLE: raise ImportError('pyseekdb is not installed. Install it with: pip install pyseekdb') @@ -89,6 +93,7 @@ class SeekDBVectorDatabase(VectorDatabase): { '\x00': '', '\\': '\\\\', + "'": "''", # Standard SQL escaping (OceanBase NO_BACKSLASH_ESCAPES) '"': '\\"', '\n': '\\n', '\r': '\\r', @@ -111,8 +116,10 @@ class SeekDBVectorDatabase(VectorDatabase): # Collection doesn't exist, create it if vector_size is None: - # Default dimension if not specified - vector_size = 384 + raise ValueError( + f"Cannot create SeekDB collection '{collection}' without knowing the vector dimension. " + 'Ensure add_embeddings is called before any standalone get_or_create_collection.' + ) # Create HNSW configuration config = HNSWConfiguration(dimension=vector_size, distance='cosine') @@ -147,7 +154,12 @@ class SeekDBVectorDatabase(VectorDatabase): return await self._get_or_create_collection_internal(collection) async def add_embeddings( - self, collection: str, ids: List[str], embeddings_list: List[List[float]], metadatas: List[Dict[str, Any]] + self, + collection: str, + ids: List[str], + embeddings_list: List[List[float]], + metadatas: List[Dict[str, Any]], + documents: List[str] | None = None, ) -> None: """Add vector embeddings to the specified collection. @@ -156,6 +168,7 @@ class SeekDBVectorDatabase(VectorDatabase): ids: List of document IDs embeddings_list: List of embedding vectors metadatas: List of metadata dictionaries + documents: Optional raw text documents for full-text search support """ if not embeddings_list: return @@ -166,17 +179,33 @@ class SeekDBVectorDatabase(VectorDatabase): cleaned_metadatas = [self._clean_metadata(meta) for meta in metadatas] - await asyncio.to_thread(coll.add, ids=ids, embeddings=embeddings_list, metadatas=cleaned_metadatas) + kwargs: Dict[str, Any] = dict(ids=ids, embeddings=embeddings_list, metadatas=cleaned_metadatas) + if documents is not None: + kwargs['documents'] = [doc.translate(self._escape_table) for doc in documents] + await asyncio.to_thread(coll.add, **kwargs) self.ap.logger.info(f"Added {len(ids)} embeddings to SeekDB collection '{collection}'") - async def search(self, collection: str, query_embedding: List[float], k: int = 5) -> Dict[str, Any]: + async def search( + self, + collection: str, + query_embedding: List[float], + k: int = 5, + search_type: str = 'vector', + query_text: str = '', + filter: Dict[str, Any] | None = None, + ) -> Dict[str, Any]: """Search for the most similar vectors in the specified collection. + SeekDB supports vector, full-text, and hybrid search modes. + Args: collection: Collection name - query_embedding: Query vector + query_embedding: Query vector (used for vector and hybrid modes) k: Number of results to return + search_type: One of 'vector', 'full_text', 'hybrid' + query_text: Raw query text (used for full_text and hybrid modes) + filter: Optional metadata filters (Chroma-style ``where`` syntax). Returns: Dictionary with 'ids', 'metadatas', 'distances' keys @@ -193,11 +222,73 @@ class SeekDBVectorDatabase(VectorDatabase): else: coll = self._collections[collection] - # Perform query - # SeekDB's query() returns: {'ids': [[...]], 'metadatas': [[...]], 'distances': [[...]]} - results = await asyncio.to_thread(coll.query, query_embeddings=query_embedding, n_results=k) + # Route by search type. + # pyseekdb's query() always requires embeddings, so full-text and + # hybrid modes use hybrid_search() which supports text-only queries + # and returns the same nested-list format with distances. + if search_type == SearchType.FULL_TEXT: + if not query_text: + return {'ids': [[]], 'metadatas': [[]], 'distances': [[]]} - self.ap.logger.info(f"SeekDB search in '{collection}' returned {len(results.get('ids', [[]])[0])} results") + query_cfg: Dict[str, Any] = { + 'where_document': {'$contains': query_text}, + 'n_results': k, + } + if filter: + query_cfg['where'] = filter + + # TODO: pyseekdb hybrid_search with query-only (no knn) returns None + # for IDs due to column name mismatch (*/_id vs _id). + # See: https://github.com/oceanbase/pyseekdb/issues/171 + results = await asyncio.to_thread( + coll.hybrid_search, + query=query_cfg, + knn=None, + n_results=k, + include=['documents', 'metadatas'], + ) + + elif search_type == SearchType.HYBRID: + if not query_text: + # Fall back to pure vector search when no text is provided + query_kwargs: Dict[str, Any] = { + 'n_results': k, + 'query_embeddings': query_embedding, + } + if filter: + query_kwargs['where'] = filter + results = await asyncio.to_thread(coll.query, **query_kwargs) + else: + query_cfg = { + 'where_document': {'$contains': query_text}, + 'n_results': k, + } + knn_cfg: Dict[str, Any] = { + 'query_embeddings': query_embedding, + 'n_results': k, + } + if filter: + query_cfg['where'] = filter + knn_cfg['where'] = filter + + results = await asyncio.to_thread( + coll.hybrid_search, + query=query_cfg, + knn=knn_cfg, + rank={'rrf': {}}, + n_results=k, + include=['documents', 'metadatas'], + ) + else: + # Default: vector search via query() + query_kwargs = {'n_results': k, 'query_embeddings': query_embedding} + if filter: + query_kwargs['where'] = filter + results = await asyncio.to_thread(coll.query, **query_kwargs) + + self.ap.logger.info( + f"SeekDB {search_type} search in '{collection}' returned {len(results.get('ids', [[]])[0])} results" + ) return results @@ -227,6 +318,28 @@ class SeekDBVectorDatabase(VectorDatabase): self.ap.logger.info(f"Deleted embeddings from SeekDB collection '{collection}' with file_id: {file_id}") + async def delete_by_filter(self, collection: str, filter: Dict[str, Any]) -> int: + """Delete vectors from the collection by metadata filter. + + Args: + collection: Collection name + filter: Chroma-style ``where`` filter dict + """ + exists = await asyncio.to_thread(self.client.has_collection, collection) + if not exists: + self.ap.logger.warning(f"SeekDB collection '{collection}' not found for deletion") + return 0 + + if collection not in self._collections: + coll = await asyncio.to_thread(self.client.get_collection, collection, embedding_function=None) + self._collections[collection] = coll + else: + coll = self._collections[collection] + + await asyncio.to_thread(coll.delete, where=filter) + self.ap.logger.info(f"Deleted embeddings from SeekDB collection '{collection}' by filter") + return 0 # SeekDB delete does not return a count + async def delete_collection(self, collection: str): """Delete the entire collection. diff --git a/tests/unit_tests/plugin/test_plugin_component_filtering.py b/tests/unit_tests/plugin/test_plugin_component_filtering.py index c2c4fd76..45940fed 100644 --- a/tests/unit_tests/plugin/test_plugin_component_filtering.py +++ b/tests/unit_tests/plugin/test_plugin_component_filtering.py @@ -38,13 +38,11 @@ async def test_plugin_list_filter_by_component_kinds(): 'manifest': { 'metadata': { 'author': 'author2', - 'name': 'plugin_with_knowledge_retriever_only', + 'name': 'plugin_with_knowledge_engine_only', } } }, - 'components': [ - {'manifest': {'manifest': {'kind': 'KnowledgeRetriever', 'metadata': {'name': 'retriever1'}}}} - ], + 'components': [{'manifest': {'manifest': {'kind': 'KnowledgeEngine', 'metadata': {'name': 'retriever1'}}}}], }, { 'debug': False, @@ -81,7 +79,7 @@ async def test_plugin_list_filter_by_component_kinds(): } }, 'components': [ - {'manifest': {'manifest': {'kind': 'KnowledgeRetriever', 'metadata': {'name': 'retriever2'}}}}, + {'manifest': {'manifest': {'kind': 'KnowledgeEngine', 'metadata': {'name': 'retriever2'}}}}, {'manifest': {'manifest': {'kind': 'Tool', 'metadata': {'name': 'tool2'}}}}, ], }, @@ -108,8 +106,8 @@ async def test_plugin_list_filter_by_component_kinds(): assert 'plugin_with_command' in plugin_names assert 'plugin_with_event_listener' in plugin_names assert 'plugin_with_mixed_components' in plugin_names - # Plugin with only KnowledgeRetriever should NOT be included - assert 'plugin_with_knowledge_retriever_only' not in plugin_names + # Plugin with only KnowledgeEngine should NOT be included + assert 'plugin_with_knowledge_engine_only' not in plugin_names @pytest.mark.asyncio @@ -150,9 +148,7 @@ async def test_plugin_list_filter_no_filter(): } } }, - 'components': [ - {'manifest': {'manifest': {'kind': 'KnowledgeRetriever', 'metadata': {'name': 'retriever1'}}}} - ], + 'components': [{'manifest': {'manifest': {'kind': 'KnowledgeEngine', 'metadata': {'name': 'retriever1'}}}}], }, ] @@ -189,7 +185,7 @@ async def test_plugin_list_filter_empty_result(): connector = PluginRuntimeConnector(mock_app, AsyncMock()) connector.handler = MagicMock() - # Mock plugin data - only KnowledgeRetriever plugins + # Mock plugin data - only KnowledgeEngine plugins mock_plugins = [ { 'debug': False, @@ -201,9 +197,7 @@ async def test_plugin_list_filter_empty_result(): } } }, - 'components': [ - {'manifest': {'manifest': {'kind': 'KnowledgeRetriever', 'metadata': {'name': 'retriever1'}}}} - ], + 'components': [{'manifest': {'manifest': {'kind': 'KnowledgeEngine', 'metadata': {'name': 'retriever1'}}}}], }, ] diff --git a/web/pnpm-lock.yaml b/web/pnpm-lock.yaml index aec212c8..28086510 100644 --- a/web/pnpm-lock.yaml +++ b/web/pnpm-lock.yaml @@ -508,6 +508,7 @@ packages: resolution: {integrity: sha512-excjX8DfsIcJ10x1Kzr4RcWe1edC9PquDRRPx3YVCvQv+U5p7Yin2s32ftzikXojb1PIFc/9Mt28/y+iRklkrw==} cpu: [arm64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -516,6 +517,7 @@ packages: resolution: {integrity: sha512-bFI7xcKFELdiNCVov8e44Ia4u2byA+l3XtsAj+Q8tfCwO6BQ8iDojYdvoPMqsKDkuoOo+X6HZA0s0q11ANMQ8A==} cpu: [arm] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -524,6 +526,7 @@ packages: resolution: {integrity: sha512-FMuvGijLDYG6lW+b/UvyilUWu5Ayu+3r2d1S8notiGCIyYU/76eig1UfMmkZ7vwgOrzKzlQbFSuQfgm7GYUPpA==} cpu: [ppc64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -532,6 +535,7 @@ packages: resolution: {integrity: sha512-oVDbcR4zUC0ce82teubSm+x6ETixtKZBh/qbREIOcI3cULzDyb18Sr/Wcyx7NRQeQzOiHTNbZFF1UwPS2scyGA==} cpu: [riscv64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -540,6 +544,7 @@ packages: resolution: {integrity: sha512-qmp9VrzgPgMoGZyPvrQHqk02uyjA0/QrTO26Tqk6l4ZV0MPWIW6LTkqOIov+J1yEu7MbFQaDpwdwJKhbJvuRxQ==} cpu: [s390x] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -548,6 +553,7 @@ packages: resolution: {integrity: sha512-tJxiiLsmHc9Ax1bz3oaOYBURTXGIRDODBqhveVHonrHJ9/+k89qbLl0bcJns+e4t4rvaNBxaEZsFtSfAdquPrw==} cpu: [x64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -556,6 +562,7 @@ packages: resolution: {integrity: sha512-FVQHuwx1IIuNow9QAbYUzJ+En8KcVm9Lk5+uGUQJHaZmMECZmOlix9HnH7n1TRkXMS0pGxIJokIVB9SuqZGGXw==} cpu: [arm64] os: [linux] + libc: [musl] requiresBuild: true dev: false optional: true @@ -564,6 +571,7 @@ packages: resolution: {integrity: sha512-+LpyBk7L44ZIXwz/VYfglaX/okxezESc6UxDSoyo2Ks6Jxc4Y7sGjpgU9s4PMgqgjj1gZCylTieNamqA1MF7Dg==} cpu: [x64] os: [linux] + libc: [musl] requiresBuild: true dev: false optional: true @@ -573,6 +581,7 @@ packages: engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0} cpu: [arm64] os: [linux] + libc: [glibc] requiresBuild: true optionalDependencies: '@img/sharp-libvips-linux-arm64': 1.2.4 @@ -584,6 +593,7 @@ packages: engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0} cpu: [arm] os: [linux] + libc: [glibc] requiresBuild: true optionalDependencies: '@img/sharp-libvips-linux-arm': 1.2.4 @@ -595,6 +605,7 @@ packages: engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0} cpu: [ppc64] os: [linux] + libc: [glibc] requiresBuild: true optionalDependencies: '@img/sharp-libvips-linux-ppc64': 1.2.4 @@ -606,6 +617,7 @@ packages: engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0} cpu: [riscv64] os: [linux] + libc: [glibc] requiresBuild: true optionalDependencies: '@img/sharp-libvips-linux-riscv64': 1.2.4 @@ -617,6 +629,7 @@ packages: engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0} cpu: [s390x] os: [linux] + libc: [glibc] requiresBuild: true optionalDependencies: '@img/sharp-libvips-linux-s390x': 1.2.4 @@ -628,6 +641,7 @@ packages: engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0} cpu: [x64] os: [linux] + libc: [glibc] requiresBuild: true optionalDependencies: '@img/sharp-libvips-linux-x64': 1.2.4 @@ -639,6 +653,7 @@ packages: engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0} cpu: [arm64] os: [linux] + libc: [musl] requiresBuild: true optionalDependencies: '@img/sharp-libvips-linuxmusl-arm64': 1.2.4 @@ -650,6 +665,7 @@ packages: engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0} cpu: [x64] os: [linux] + libc: [musl] requiresBuild: true optionalDependencies: '@img/sharp-libvips-linuxmusl-x64': 1.2.4 @@ -766,6 +782,7 @@ packages: engines: {node: '>= 10'} cpu: [arm64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -775,6 +792,7 @@ packages: engines: {node: '>= 10'} cpu: [arm64] os: [linux] + libc: [musl] requiresBuild: true dev: false optional: true @@ -784,6 +802,7 @@ packages: engines: {node: '>= 10'} cpu: [x64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -793,6 +812,7 @@ packages: engines: {node: '>= 10'} cpu: [x64] os: [linux] + libc: [musl] requiresBuild: true dev: false optional: true @@ -1892,6 +1912,7 @@ packages: engines: {node: '>= 10'} cpu: [arm64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -1901,6 +1922,7 @@ packages: engines: {node: '>= 10'} cpu: [arm64] os: [linux] + libc: [musl] requiresBuild: true dev: false optional: true @@ -1910,6 +1932,7 @@ packages: engines: {node: '>= 10'} cpu: [x64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -1919,6 +1942,7 @@ packages: engines: {node: '>= 10'} cpu: [x64] os: [linux] + libc: [musl] requiresBuild: true dev: false optional: true @@ -2334,6 +2358,7 @@ packages: resolution: {integrity: sha512-34gw7PjDGB9JgePJEmhEqBhWvCiiWCuXsL9hYphDF7crW7UgI05gyBAi6MF58uGcMOiOqSJ2ybEeCvHcq0BCmQ==} cpu: [arm64] os: [linux] + libc: [glibc] requiresBuild: true dev: true optional: true @@ -2342,6 +2367,7 @@ packages: resolution: {integrity: sha512-RyMIx6Uf53hhOtJDIamSbTskA99sPHS96wxVE/bJtePJJtpdKGXO1wY90oRdXuYOGOTuqjT8ACccMc4K6QmT3w==} cpu: [arm64] os: [linux] + libc: [musl] requiresBuild: true dev: true optional: true @@ -2350,6 +2376,7 @@ packages: resolution: {integrity: sha512-D8Vae74A4/a+mZH0FbOkFJL9DSK2R6TFPC9M+jCWYia/q2einCubX10pecpDiTmkJVUH+y8K3BZClycD8nCShA==} cpu: [ppc64] os: [linux] + libc: [glibc] requiresBuild: true dev: true optional: true @@ -2358,6 +2385,7 @@ packages: resolution: {integrity: sha512-frxL4OrzOWVVsOc96+V3aqTIQl1O2TjgExV4EKgRY09AJ9leZpEg8Ak9phadbuX0BA4k8U5qtvMSQQGGmaJqcQ==} cpu: [riscv64] os: [linux] + libc: [glibc] requiresBuild: true dev: true optional: true @@ -2366,6 +2394,7 @@ packages: resolution: {integrity: sha512-mJ5vuDaIZ+l/acv01sHoXfpnyrNKOk/3aDoEdLO/Xtn9HuZlDD6jKxHlkN8ZhWyLJsRBxfv9GYM2utQ1SChKew==} cpu: [riscv64] os: [linux] + libc: [musl] requiresBuild: true dev: true optional: true @@ -2374,6 +2403,7 @@ packages: resolution: {integrity: sha512-kELo8ebBVtb9sA7rMe1Cph4QHreByhaZ2QEADd9NzIQsYNQpt9UkM9iqr2lhGr5afh885d/cB5QeTXSbZHTYPg==} cpu: [s390x] os: [linux] + libc: [glibc] requiresBuild: true dev: true optional: true @@ -2382,6 +2412,7 @@ packages: resolution: {integrity: sha512-C3ZAHugKgovV5YvAMsxhq0gtXuwESUKc5MhEtjBpLoHPLYM+iuwSj3lflFwK3DPm68660rZ7G8BMcwSro7hD5w==} cpu: [x64] os: [linux] + libc: [glibc] requiresBuild: true dev: true optional: true @@ -2390,6 +2421,7 @@ packages: resolution: {integrity: sha512-rV0YSoyhK2nZ4vEswT/QwqzqQXw5I6CjoaYMOX0TqBlWhojUf8P94mvI7nuJTeaCkkds3QE4+zS8Ko+GdXuZtA==} cpu: [x64] os: [linux] + libc: [musl] requiresBuild: true dev: true optional: true @@ -4424,6 +4456,7 @@ packages: engines: {node: '>= 12.0.0'} cpu: [arm64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -4433,6 +4466,7 @@ packages: engines: {node: '>= 12.0.0'} cpu: [arm64] os: [linux] + libc: [musl] requiresBuild: true dev: false optional: true @@ -4442,6 +4476,7 @@ packages: engines: {node: '>= 12.0.0'} cpu: [x64] os: [linux] + libc: [glibc] requiresBuild: true dev: false optional: true @@ -4451,6 +4486,7 @@ packages: engines: {node: '>= 12.0.0'} cpu: [x64] os: [linux] + libc: [musl] requiresBuild: true dev: false optional: true diff --git a/web/src/app/home/bots/components/bot-form/BotForm.tsx b/web/src/app/home/bots/components/bot-form/BotForm.tsx index 8fd63aef..d2ca22d7 100644 --- a/web/src/app/home/bots/components/bot-form/BotForm.tsx +++ b/web/src/app/home/bots/components/bot-form/BotForm.tsx @@ -319,6 +319,7 @@ export default function BotForm({ required: item.required, type: parseDynamicFormItemType(item.type), options: item.options, + show_if: item.show_if, }), ), ); diff --git a/web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx b/web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx index b7319529..b40563ae 100644 --- a/web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx +++ b/web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx @@ -13,20 +13,26 @@ import { import DynamicFormItemComponent from '@/app/home/components/dynamic-form/DynamicFormItemComponent'; import { useCallback, useEffect, useRef } from 'react'; import { extractI18nObject } from '@/i18n/I18nProvider'; +import { useTranslation } from 'react-i18next'; export default function DynamicFormComponent({ itemConfigList, onSubmit, initialValues, onFileUploaded, + isEditing, + externalDependentValues, }: { itemConfigList: IDynamicFormItemSchema[]; onSubmit?: (val: object) => unknown; initialValues?: Record; onFileUploaded?: (fileKey: string) => void; + isEditing?: boolean; + externalDependentValues?: Record; }) { const isInitialMount = useRef(true); const previousInitialValues = useRef(initialValues); + const { t } = useTranslation(); // 根据 itemConfigList 动态生成 zod schema const formSchema = z.object( @@ -55,6 +61,9 @@ export default function DynamicFormComponent({ case 'llm-model-selector': fieldSchema = z.string(); break; + case 'embedding-model-selector': + fieldSchema = z.string(); + break; case 'knowledge-base-selector': fieldSchema = z.string(); break; @@ -81,7 +90,9 @@ export default function DynamicFormComponent({ (fieldSchema instanceof z.ZodString || fieldSchema instanceof z.ZodArray) ) { - fieldSchema = fieldSchema.min(1, { message: '此字段为必填项' }); + fieldSchema = fieldSchema.min(1, { + message: t('common.fieldRequired'), + }); } return { @@ -141,6 +152,9 @@ export default function DynamicFormComponent({ } }, [initialValues, form, itemConfigList]); + // Get reactive form values for conditional rendering + const watchedValues = form.watch(); + // Stable ref for onSubmit to avoid re-triggering the effect when the // parent passes a new closure on every render. const onSubmitRef = useRef(onSubmit); @@ -183,34 +197,75 @@ export default function DynamicFormComponent({ return (
- {itemConfigList.map((config) => ( - ( - - - {extractI18nObject(config.label)}{' '} - {config.required && *} - - - - - {config.description && ( -

- {extractI18nObject(config.description)} -

- )} - - - )} - /> - ))} + {itemConfigList.map((config) => { + if (config.show_if) { + const dependValue = + watchedValues[ + config.show_if.field as keyof typeof watchedValues + ] !== undefined + ? watchedValues[ + config.show_if.field as keyof typeof watchedValues + ] + : externalDependentValues?.[config.show_if.field]; + + if ( + config.show_if.operator === 'eq' && + dependValue !== config.show_if.value + ) { + return null; + } + if ( + config.show_if.operator === 'neq' && + dependValue === config.show_if.value + ) { + return null; + } + if ( + config.show_if.operator === 'in' && + Array.isArray(config.show_if.value) && + !config.show_if.value.includes(dependValue) + ) { + return null; + } + } + + // All fields are disabled when editing (creation_settings are immutable) + const isFieldDisabled = !!isEditing; + return ( + ( + + + {extractI18nObject(config.label)}{' '} + {config.required && *} + + +
+ +
+ + {config.description && ( +

+ {extractI18nObject(config.description)} +

+ )} + + + )} + /> + ); + })}
); diff --git a/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx b/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx index a4f35be4..ca2f4e72 100644 --- a/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx +++ b/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx @@ -22,8 +22,7 @@ import { LLMModel, Bot, KnowledgeBase, - ExternalKnowledgeBase, - ApiRespPluginSystemStatus, + EmbeddingModel, } from '@/app/infra/entities/api'; import { toast } from 'sonner'; import { useTranslation } from 'react-i18next'; @@ -51,16 +50,12 @@ export default function DynamicFormItemComponent({ onFileUploaded?: (fileKey: string) => void; }) { const [llmModels, setLlmModels] = useState([]); + const [embeddingModels, setEmbeddingModels] = useState([]); const [knowledgeBases, setKnowledgeBases] = useState([]); - const [externalKnowledgeBases, setExternalKnowledgeBases] = useState< - ExternalKnowledgeBase[] - >([]); const [bots, setBots] = useState([]); const [uploading, setUploading] = useState(false); const [kbDialogOpen, setKbDialogOpen] = useState(false); const [tempSelectedKBIds, setTempSelectedKBIds] = useState([]); - const [pluginSystemStatus, setPluginSystemStatus] = - useState(null); const { t } = useTranslation(); const handleFileUpload = async (file: File): Promise => { @@ -111,7 +106,20 @@ export default function DynamicFormItemComponent({ setLlmModels(models); }) .catch((err) => { - toast.error('Failed to get LLM model list: ' + err.msg); + toast.error(t('models.getModelListError') + err.msg); + }); + } + }, [config.type]); + + useEffect(() => { + if (config.type === DynamicFormItemType.EMBEDDING_MODEL_SELECTOR) { + httpClient + .getProviderEmbeddingModels() + .then((resp) => { + setEmbeddingModels(resp.models); + }) + .catch((err) => { + toast.error(t('embedding.getModelListError') + err.msg); }); } }, [config.type]); @@ -127,39 +135,11 @@ export default function DynamicFormItemComponent({ setKnowledgeBases(resp.bases); }) .catch((err) => { - toast.error('Failed to get knowledge base list: ' + err.msg); - }); - - // Fetch plugin system status - httpClient - .getPluginSystemStatus() - .then((status) => { - setPluginSystemStatus(status); - }) - .catch((err) => { - console.error('Failed to get plugin system status:', err); + toast.error(t('knowledge.getKnowledgeBaseListError') + err.msg); }); } }, [config.type]); - useEffect(() => { - if ( - (config.type === DynamicFormItemType.KNOWLEDGE_BASE_SELECTOR || - config.type === DynamicFormItemType.KNOWLEDGE_BASE_MULTI_SELECTOR) && - pluginSystemStatus?.is_enable && - pluginSystemStatus?.is_connected - ) { - httpClient - .getExternalKnowledgeBases() - .then((resp) => { - setExternalKnowledgeBases(resp.bases); - }) - .catch((err) => { - console.error('Failed to get external knowledge base list:', err); - }); - } - }, [config.type, pluginSystemStatus]); - useEffect(() => { if (config.type === DynamicFormItemType.BOT_SELECTOR) { httpClient @@ -168,7 +148,7 @@ export default function DynamicFormItemComponent({ setBots(resp.bots); }) .catch((err) => { - toast.error('Failed to get bot list: ' + err.msg); + toast.error(t('bots.getBotListError') + err.msg); }); } }, [config.type]); @@ -304,7 +284,56 @@ export default function DynamicFormItemComponent({ ); + case DynamicFormItemType.EMBEDDING_MODEL_SELECTOR: + // Group embedding models by provider + const groupedEmbeddingModels = embeddingModels.reduce( + (acc, model) => { + const providerName = model.provider?.name || 'Unknown'; + if (!acc[providerName]) acc[providerName] = []; + acc[providerName].push(model); + return acc; + }, + {} as Record, + ); + + return ( + + ); + case DynamicFormItemType.KNOWLEDGE_BASE_SELECTOR: + // Group KBs by Knowledge Engine name + const kbsByEngine = knowledgeBases.reduce( + (acc, kb) => { + const engineName = kb.knowledge_engine?.name + ? extractI18nObject(kb.knowledge_engine.name) + : t('knowledge.unknownEngine'); + if (!acc[engineName]) { + acc[engineName] = []; + } + acc[engineName].push(kb); + return acc; + }, + {} as Record, + ); + return ( ); case DynamicFormItemType.KNOWLEDGE_BASE_MULTI_SELECTOR: + // Group KBs by Knowledge Engine name for multi-selector + const multiKbsByEngine = knowledgeBases.reduce( + (acc, kb) => { + const engineName = kb.knowledge_engine?.name + ? extractI18nObject(kb.knowledge_engine.name) + : t('knowledge.unknownEngine'); + if (!acc[engineName]) { + acc[engineName] = []; + } + acc[engineName].push(kb); + return acc; + }, + {} as Record, + ); + return ( <>
{field.value && field.value.length > 0 ? (
{field.value.map((kbId: string) => { - const kb = knowledgeBases.find((base) => base.uuid === kbId); - const externalKb = externalKnowledgeBases.find( + const currentKb = knowledgeBases.find( (base) => base.uuid === kbId, ); - const currentKb = kb || externalKb; if (!currentKb) return null; return ( @@ -370,18 +391,17 @@ export default function DynamicFormItemComponent({ className="flex items-center justify-between rounded-lg border p-3 hover:bg-accent" >
- {externalKb && ( - plugin icon - )}
-
{currentKb.name}
+
+ {currentKb.name} + {currentKb.knowledge_engine?.name && ( + + {extractI18nObject( + currentKb.knowledge_engine.name, + )} + + )} +
{currentKb.description && (
{currentKb.description} @@ -435,13 +455,12 @@ export default function DynamicFormItemComponent({ {t('knowledge.selectKnowledgeBases')}
- {/* Built-in Knowledge Bases */} - {knowledgeBases.length > 0 && ( -
+ {Object.entries(multiKbsByEngine).map(([engineName, kbs]) => ( +
- {t('knowledge.builtIn')} + {engineName}
- {knowledgeBases.map((base) => { + {kbs.map((base) => { const isSelected = tempSelectedKBIds.includes( base.uuid ?? '', ); @@ -474,56 +493,7 @@ export default function DynamicFormItemComponent({ ); })}
- )} - - {/* External Knowledge Bases */} - {externalKnowledgeBases.length > 0 && ( -
-
- {t('knowledge.external')} -
- {externalKnowledgeBases.map((base) => { - const isSelected = tempSelectedKBIds.includes( - base.uuid ?? '', - ); - return ( -
{ - const kbId = base.uuid ?? ''; - setTempSelectedKBIds((prev) => - prev.includes(kbId) - ? prev.filter((id) => id !== kbId) - : [...prev, kbId], - ); - }} - > - - plugin icon -
-
{base.name}
- {base.description && ( -
- {base.description} -
- )} -
-
- ); - })} -
- )} + ))}
- -
- - )} + +
+ + +
+ @@ -205,7 +211,7 @@ export default function KBDetailDialog({ -
+
{activeMenu === 'metadata' @@ -216,33 +222,28 @@ export default function KBDetailDialog({
- {activeMenu === 'metadata' && - (kbType === 'builtin' ? ( - - ) : ( - onOpenChange(false)} - onKBDeleted={() => { - onKbDeleted(); - onOpenChange(false); - }} - onNewKBCreated={onNewKbCreated} - /> - ))} - {activeMenu === 'documents' && kbType === 'builtin' && ( - + {activeMenu === 'metadata' && ( + + )} + {activeMenu === 'documents' && hasDocumentCapability() && ( + + )} + {activeMenu === 'retrieve' && ( + )} - {activeMenu === 'retrieve' && - (kbType === 'builtin' ? ( - - ) : ( - - ))}
{activeMenu === 'metadata' && ( @@ -254,12 +255,7 @@ export default function KBDetailDialog({ > {t('common.delete')} - - - - - - - {/* Main Form */} -
- -
- {/* KB Name and Emoji in same row */} -
- ( - - - {t('knowledge.kbName')} - * - - - - - - - )} - /> - ( - - {t('common.icon')} - - - - - - )} - /> -
- - {/* KB Description */} - ( - - {t('knowledge.kbDescription')} - - - - - - )} - /> - - {/* Retriever Selector */} - ( - - - {t('knowledge.retriever')} - * - - - - - -

- {t('knowledge.retrieverInstallInfo')}{' '} - - {t('knowledge.retrieverMarketLink')} - -

- - )} - /> - - {/* Selected Retriever Card */} - {currentRetrieverFullName && ( -
- plugin icon -
-
- {getRetrieverLabel(currentRetrieverFullName)} -
-
- {form.watch('plugin_author')} / {form.watch('plugin_name')} -
-
-
- )} - - {/* Dynamic Retriever Configuration Form */} - {showDynamicForm && dynamicFormConfigList.length > 0 && ( -
-
- {t('knowledge.retrieverConfiguration')} -
- { - form.setValue('retriever_config', values); - }} - /> -
- )} -
-
- -
- ); -} diff --git a/web/src/app/home/knowledge/components/kb-card/KBCard.module.css b/web/src/app/home/knowledge/components/kb-card/KBCard.module.css index df5c9cf9..aaba9f6a 100644 --- a/web/src/app/home/knowledge/components/kb-card/KBCard.module.css +++ b/web/src/app/home/knowledge/components/kb-card/KBCard.module.css @@ -169,3 +169,18 @@ width: 1.2rem; height: 1.2rem; } + +.engineBadge { + font-size: 0.75rem; + line-height: 1rem; + padding: 0.125rem 0.5rem; + border-radius: 9999px; + background-color: #f3e8ff; + color: #7e22ce; + white-space: nowrap; +} + +:global(.dark) .engineBadge { + background-color: #581c87; + color: #d8b4fe; +} diff --git a/web/src/app/home/knowledge/components/kb-card/KBCard.tsx b/web/src/app/home/knowledge/components/kb-card/KBCard.tsx index 8e4de356..4e29af46 100644 --- a/web/src/app/home/knowledge/components/kb-card/KBCard.tsx +++ b/web/src/app/home/knowledge/components/kb-card/KBCard.tsx @@ -4,14 +4,21 @@ import styles from './KBCard.module.css'; export default function KBCard({ kbCardVO }: { kbCardVO: KnowledgeBaseVO }) { const { t } = useTranslation(); + return (
{kbCardVO.emoji || '📚'}
-
- {kbCardVO.name} +
+
+ {kbCardVO.name} +
+ {/* Engine badge */} + + {kbCardVO.getEngineName()} +
{kbCardVO.description} diff --git a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts index e7c20ed9..ea4b9164 100644 --- a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts +++ b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts @@ -1,29 +1,52 @@ +import { KnowledgeEngineInfo } from '@/app/infra/entities/api'; +import { extractI18nObject } from '@/i18n/I18nProvider'; + export interface IKnowledgeBaseVO { id: string; name: string; description: string; - embeddingModelUUID: string; - top_k: number; lastUpdatedTimeAgo: string; emoji?: string; + ragEngine?: KnowledgeEngineInfo; + ragEnginePluginId?: string; } export class KnowledgeBaseVO implements IKnowledgeBaseVO { id: string; name: string; description: string; - embeddingModelUUID: string; - top_k: number; lastUpdatedTimeAgo: string; emoji?: string; + ragEngine?: KnowledgeEngineInfo; + ragEnginePluginId?: string; constructor(props: IKnowledgeBaseVO) { this.id = props.id; this.name = props.name; this.description = props.description; - this.embeddingModelUUID = props.embeddingModelUUID; - this.top_k = props.top_k; this.lastUpdatedTimeAgo = props.lastUpdatedTimeAgo; this.emoji = props.emoji; + this.ragEngine = props.ragEngine; + this.ragEnginePluginId = props.ragEnginePluginId; + } + + /** + * Check if this KB supports document management + */ + hasDocumentCapability(): boolean { + if (!this.ragEngine) { + return false; + } + return this.ragEngine.capabilities.includes('doc_ingestion'); + } + + /** + * Get display name for the Knowledge Engine + */ + getEngineName(): string { + if (!this.ragEngine) { + return 'Unknown'; + } + return extractI18nObject(this.ragEngine.name); } } diff --git a/web/src/app/home/knowledge/components/kb-docs/FileUploadZone.tsx b/web/src/app/home/knowledge/components/kb-docs/FileUploadZone.tsx index a4c9d61b..4dbbff17 100644 --- a/web/src/app/home/knowledge/components/kb-docs/FileUploadZone.tsx +++ b/web/src/app/home/knowledge/components/kb-docs/FileUploadZone.tsx @@ -1,17 +1,32 @@ -import React, { useCallback, useState } from 'react'; +import React, { useCallback, useEffect, useState } from 'react'; import { Card, CardContent } from '@/components/ui/card'; +import { + Select, + SelectContent, + SelectItem, + SelectTrigger, + SelectValue, +} from '@/components/ui/select'; +import { Button } from '@/components/ui/button'; import { httpClient } from '@/app/infra/http/HttpClient'; import { toast } from 'sonner'; import { useTranslation } from 'react-i18next'; +import { ParserInfo } from '@/app/infra/entities/api'; +import { I18nObject } from '@/app/infra/entities/common'; +import { extractI18nObject } from '@/i18n/I18nProvider'; interface FileUploadZoneProps { kbId: string; + ragEngineName?: I18nObject; + ragEngineCapabilities?: string[]; onUploadSuccess: () => void; onUploadError: (error: string) => void; } export default function FileUploadZone({ kbId, + ragEngineName, + ragEngineCapabilities, onUploadSuccess, onUploadError, }: FileUploadZoneProps) { @@ -19,17 +34,49 @@ export default function FileUploadZone({ const [isDragOver, setIsDragOver] = useState(false); const [isUploading, setIsUploading] = useState(false); - const handleUpload = useCallback( - async (file: File) => { - if (isUploading) return; + // Parser selection state + const [pendingFile, setPendingFile] = useState(null); + const [availableParsers, setAvailableParsers] = useState([]); + const [selectedParser, setSelectedParser] = useState('builtin'); + const [loadingParsers, setLoadingParsers] = useState(false); - // Check file size (10MB limit) - const MAX_FILE_SIZE = 10 * 1024 * 1024; // 10MB - if (file.size > MAX_FILE_SIZE) { - toast.error(t('knowledge.documentsTab.fileSizeExceeded')); - return; - } + // Whether the Knowledge Engine natively supports document parsing. + // This is a coarse-grained capability check rather than per-MIME-type filtering. + // Fine-grained MIME type declaration (e.g. supported_parse_mime_types on the engine) + // would require changes across the SDK, backend, and frontend prop chain; + // using an engine-level capability flag keeps the change minimal. + const ragEngineCanParse = + ragEngineCapabilities?.includes('doc_parsing') ?? false; + // When a file is selected, check for available parsers + useEffect(() => { + if (!pendingFile) return; + + const mimeType = pendingFile.type || undefined; + setLoadingParsers(true); + httpClient + .listParsers(mimeType) + .then((resp) => { + const parsers = resp.parsers || []; + setAvailableParsers(parsers); + if (ragEngineCanParse) { + setSelectedParser('builtin'); + } else if (parsers.length > 0) { + setSelectedParser(parsers[0].plugin_id); + } else { + setSelectedParser(''); + } + }) + .catch(() => { + setAvailableParsers([]); + }) + .finally(() => { + setLoadingParsers(false); + }); + }, [pendingFile, ragEngineCanParse]); + + const doUpload = useCallback( + async (file: File, parserPluginId?: string) => { setIsUploading(true); const toastId = toast.loading(t('knowledge.documentsTab.uploadingFile')); @@ -37,8 +84,12 @@ export default function FileUploadZone({ // Step 1: Upload file to server const uploadResult = await httpClient.uploadDocumentFile(file); - // Step 2: Associate file with knowledge base - await httpClient.uploadKnowledgeBaseFile(kbId, uploadResult.file_id); + // Step 2: Associate file with knowledge base (with optional parser) + await httpClient.uploadKnowledgeBaseFile( + kbId, + uploadResult.file_id, + parserPluginId, + ); toast.success(t('knowledge.documentsTab.uploadSuccess'), { id: toastId, @@ -51,11 +102,65 @@ export default function FileUploadZone({ onUploadError(errorMessage); } finally { setIsUploading(false); + setPendingFile(null); + setAvailableParsers([]); + setSelectedParser('builtin'); } }, - [kbId, isUploading, onUploadSuccess, onUploadError, t], + [kbId, onUploadSuccess, onUploadError, t], ); + const handleFileSelected = useCallback( + async (file: File) => { + if (isUploading) return; + + // Check file size (10MB limit) + const MAX_FILE_SIZE = 10 * 1024 * 1024; // 10MB + if (file.size > MAX_FILE_SIZE) { + toast.error(t('knowledge.documentsTab.fileSizeExceeded')); + return; + } + + // Set loadingParsers=true BEFORE pendingFile so both state updates + // batch together in the same render. This prevents the auto-upload + // effect from firing before parser fetch completes. + setLoadingParsers(true); + setPendingFile(file); + }, + [isUploading, t], + ); + + // Auto-upload if Knowledge Engine can parse and no external parsers available + useEffect(() => { + if ( + pendingFile && + !loadingParsers && + ragEngineCanParse && + availableParsers.length === 0 + ) { + doUpload(pendingFile); + } + }, [ + pendingFile, + loadingParsers, + ragEngineCanParse, + availableParsers, + doUpload, + ]); + + const handleConfirmUpload = useCallback(() => { + if (!pendingFile) return; + const parserPluginId = + selectedParser === 'builtin' ? undefined : selectedParser; + doUpload(pendingFile, parserPluginId); + }, [pendingFile, selectedParser, doUpload]); + + const handleCancelUpload = useCallback(() => { + setPendingFile(null); + setAvailableParsers([]); + setSelectedParser('builtin'); + }, []); + const handleDragOver = useCallback((e: React.DragEvent) => { e.preventDefault(); setIsDragOver(true); @@ -73,79 +178,145 @@ export default function FileUploadZone({ const files = Array.from(e.dataTransfer.files); if (files.length > 0) { - handleUpload(files[0]); + handleFileSelected(files[0]); } }, - [handleUpload], + [handleFileSelected], ); const handleFileSelect = useCallback( (e: React.ChangeEvent) => { const files = e.target.files; if (files && files.length > 0) { - handleUpload(files[0]); + handleFileSelected(files[0]); } + // Reset the input so the same file can be selected again + e.target.value = ''; }, - [handleUpload], + [handleFileSelected], ); + // Show parser selection UI when there are choices to make, or when no parser is available + const showParserSelector = + pendingFile && + !loadingParsers && + (availableParsers.length > 0 || !ragEngineCanParse); + + const noParserAvailable = !ragEngineCanParse && availableParsers.length === 0; + return ( -
- - -
diff --git a/web/src/app/home/plugins/components/plugin-market/plugin-market-card/PluginMarketCardComponent.tsx b/web/src/app/home/plugins/components/plugin-market/plugin-market-card/PluginMarketCardComponent.tsx index 530a0b03..407568bb 100644 --- a/web/src/app/home/plugins/components/plugin-market/plugin-market-card/PluginMarketCardComponent.tsx +++ b/web/src/app/home/plugins/components/plugin-market/plugin-market-card/PluginMarketCardComponent.tsx @@ -8,6 +8,7 @@ import { Download, ExternalLink, Book, + FileText, } from 'lucide-react'; import { useState } from 'react'; import { Button } from '@/components/ui/button'; @@ -41,7 +42,8 @@ export default function PluginMarketCardComponent({ Tool: , EventListener: , Command: , - KnowledgeRetriever: , + KnowledgeEngine: , + Parser: , }; return ( diff --git a/web/src/app/infra/entities/api/index.ts b/web/src/app/infra/entities/api/index.ts index dfa3f591..a516559c 100644 --- a/web/src/app/infra/entities/api/index.ts +++ b/web/src/app/infra/entities/api/index.ts @@ -70,17 +70,6 @@ export interface LLMModel { extra_args?: object; } -export interface KnowledgeBase { - uuid?: string; - name: string; - description: string; - embedding_model_uuid: string; - created_at?: string; - updated_at?: string; - top_k: number; - emoji?: string; -} - export interface ApiRespProviderEmbeddingModels { models: EmbeddingModel[]; } @@ -166,31 +155,47 @@ export interface KnowledgeBase { uuid?: string; name: string; description: string; - embedding_model_uuid: string; - top_k: number; created_at?: string; updated_at?: string; emoji?: string; + // New unified fields + knowledge_engine_plugin_id?: string; + creation_settings?: Record; + retrieval_settings?: Record; + knowledge_engine?: KnowledgeEngineInfo; } -export interface ExternalKnowledgeBase { - uuid?: string; - name: string; - description: string; - created_at?: string; - plugin_author: string; - plugin_name: string; - retriever_name: string; - retriever_config?: Record; - emoji?: string; +// Knowledge Engine types +export interface KnowledgeEngineInfo { + plugin_id: string | null; + name: I18nObject; + capabilities: string[]; } -export interface ApiRespExternalKnowledgeBases { - bases: ExternalKnowledgeBase[]; +export interface KnowledgeEngine { + plugin_id: string; + name: I18nObject; + description?: I18nObject; + capabilities: string[]; + // Schema format: Array of form field definitions (IDynamicFormItemSchema-like) + // Each item: { name, label, type, required, default, description?, options? } + creation_schema?: unknown[]; + retrieval_schema?: unknown[]; } -export interface ApiRespExternalKnowledgeBase { - base: ExternalKnowledgeBase; +export interface ApiRespKnowledgeEngines { + engines: KnowledgeEngine[]; +} + +export interface ParserInfo { + plugin_id: string; + name: I18nObject; + description?: I18nObject; + supported_mime_types: string[]; +} + +export interface ApiRespParsers { + parsers: ParserInfo[]; } export interface ApiRespKnowledgeBaseFiles { diff --git a/web/src/app/infra/entities/form/dynamic.ts b/web/src/app/infra/entities/form/dynamic.ts index d4880143..b6f0a47f 100644 --- a/web/src/app/infra/entities/form/dynamic.ts +++ b/web/src/app/infra/entities/form/dynamic.ts @@ -1,5 +1,12 @@ import { I18nObject } from '@/app/infra/entities/common'; +export interface IShowIfCondition { + field: string; + operator: 'eq' | 'neq' | 'in'; + // eslint-disable-next-line @typescript-eslint/no-explicit-any + value: any; +} + export interface IDynamicFormItemSchema { id: string; default: string | number | boolean | Array; @@ -9,6 +16,7 @@ export interface IDynamicFormItemSchema { type: DynamicFormItemType; description?: I18nObject; options?: IDynamicFormItemOption[]; + show_if?: IShowIfCondition; /** when type is PLUGIN_SELECTOR, the scopes is the scopes of components(plugin contains), the default is all */ scopes?: string[]; @@ -26,6 +34,7 @@ export enum DynamicFormItemType { FILE_ARRAY = 'array[file]', SELECT = 'select', LLM_MODEL_SELECTOR = 'llm-model-selector', + EMBEDDING_MODEL_SELECTOR = 'embedding-model-selector', PROMPT_EDITOR = 'prompt-editor', UNKNOWN = 'unknown', KNOWLEDGE_BASE_SELECTOR = 'knowledge-base-selector', diff --git a/web/src/app/infra/http/BackendClient.ts b/web/src/app/infra/http/BackendClient.ts index 908ef0d8..4f9e340f 100644 --- a/web/src/app/infra/http/BackendClient.ts +++ b/web/src/app/infra/http/BackendClient.ts @@ -35,12 +35,11 @@ import { ApiRespMCPServers, ApiRespMCPServer, MCPServer, - ExternalKnowledgeBase, - ApiRespExternalKnowledgeBases, - ApiRespExternalKnowledgeBase, ApiRespModelProviders, ApiRespModelProvider, ModelProvider, + ApiRespKnowledgeEngines, + ApiRespParsers, } from '@/app/infra/entities/api'; import { Plugin } from '@/app/infra/entities/plugin'; import { GetBotLogsRequest } from '@/app/infra/http/requestParam/bots/GetBotLogsRequest'; @@ -435,9 +434,11 @@ export class BackendClient extends BaseHttpClient { public uploadKnowledgeBaseFile( uuid: string, file_id: string, + parserPluginId?: string, ): Promise { return this.post(`/api/v1/knowledge/bases/${uuid}/files`, { file_id, + parser_plugin_id: parserPluginId, }); } @@ -461,49 +462,23 @@ export class BackendClient extends BaseHttpClient { public retrieveKnowledgeBase( uuid: string, query: string, + retrievalSettings?: Record, ): Promise { - return this.post(`/api/v1/knowledge/bases/${uuid}/retrieve`, { query }); - } - - // ============ External Knowledge Base API ============ - public getExternalKnowledgeBases(): Promise { - return this.get('/api/v1/knowledge/external-bases'); - } - - public getExternalKnowledgeBase( - uuid: string, - ): Promise { - return this.get(`/api/v1/knowledge/external-bases/${uuid}`); - } - - public createExternalKnowledgeBase( - base: ExternalKnowledgeBase, - ): Promise<{ uuid: string }> { - return this.post('/api/v1/knowledge/external-bases', base); - } - - public updateExternalKnowledgeBase( - uuid: string, - base: ExternalKnowledgeBase, - ): Promise<{ uuid: string }> { - return this.put(`/api/v1/knowledge/external-bases/${uuid}`, base); - } - - public deleteExternalKnowledgeBase(uuid: string): Promise { - return this.delete(`/api/v1/knowledge/external-bases/${uuid}`); - } - - public retrieveExternalKnowledgeBase( - uuid: string, - query: string, - ): Promise { - return this.post(`/api/v1/knowledge/external-bases/${uuid}/retrieve`, { + return this.post(`/api/v1/knowledge/bases/${uuid}/retrieve`, { query, + retrieval_settings: retrievalSettings ?? {}, }); } - public listKnowledgeRetrievers(): Promise<{ retrievers: unknown[] }> { - return this.get('/api/v1/knowledge/external-bases/retrievers'); + // ============ Knowledge Engines API ============ + public getKnowledgeEngines(): Promise { + return this.get('/api/v1/knowledge/engines'); + } + + // ============ Parsers API ============ + public listParsers(mimeType?: string): Promise { + const params = mimeType ? `?mime_type=${encodeURIComponent(mimeType)}` : ''; + return this.get(`/api/v1/knowledge/parsers${params}`); } // ============ Plugins API ============ diff --git a/web/src/i18n/locales/en-US.ts b/web/src/i18n/locales/en-US.ts index 56a5855b..d9fb1fdd 100644 --- a/web/src/i18n/locales/en-US.ts +++ b/web/src/i18n/locales/en-US.ts @@ -48,6 +48,7 @@ const enUS = { test: 'Test', forgotPassword: 'Forgot Password?', loading: 'Loading...', + fieldRequired: 'This field is required', or: 'or', loginWithSpace: 'Login with Space', spaceLoginRecommended: @@ -371,7 +372,8 @@ const enUS = { Tool: 'Tool', EventListener: 'Event Listener', Command: 'Command', - KnowledgeRetriever: 'Knowledge Retriever', + KnowledgeEngine: 'Knowledge Engine', + Parser: 'Parser', }, uploadLocal: 'Upload Local', debugging: 'Debugging', @@ -726,6 +728,12 @@ const enUS = { processing: 'Processing', completed: 'Completed', failed: 'Failed', + selectParser: 'Select Parser', + builtInParser: 'Provided by Knowledge engine', + noParserAvailable: + 'No parser supports this file type. Please install a parser plugin that can handle this format.', + confirmUpload: 'Upload', + cancelUpload: 'Cancel', }, deleteKnowledgeBaseConfirmation: 'Are you sure you want to delete this knowledge base? All documents in this knowledge base will be deleted.', @@ -738,8 +746,24 @@ const enUS = { fileName: 'File Name', noResults: 'No results', retrieveError: 'Retrieve failed', - builtIn: 'Built-in', - external: 'External', + unknownEngine: 'Unknown Engine', + knowledgeEngine: 'Knowledge Engine', + knowledgeEngineRequired: 'Knowledge engine is required', + selectKnowledgeEngine: 'Select Knowledge Engine', + builtInEngine: 'Built-in Engine', + cannotChangeKnowledgeEngine: + 'Knowledge engine cannot be changed after creation', + engineSettings: 'Engine Settings', + engineSettingsReadonly: 'read-only in edit mode', + retrievalSettings: 'Retrieval Settings', + noEnginesAvailable: 'No knowledge base engines available', + installEngineHint: 'Please install a knowledge base plugin first', + createKnowledgeBaseFailed: 'Failed to create knowledge base', + loadKnowledgeBaseFailed: 'Failed to load knowledge base', + deleteKnowledgeBaseFailed: 'Failed to delete knowledge base', + getKnowledgeBaseListError: 'Failed to get knowledge base list: ', + embeddingModel: 'Embedding Model', + embeddingModelRequired: 'Embedding model is required for this engine', addExternal: 'Add External Knowledge Base', createExternalSuccess: 'External knowledge base created successfully', updateExternalSuccess: 'External knowledge base updated successfully', diff --git a/web/src/i18n/locales/ja-JP.ts b/web/src/i18n/locales/ja-JP.ts index 9f076e1a..f9f5929c 100644 --- a/web/src/i18n/locales/ja-JP.ts +++ b/web/src/i18n/locales/ja-JP.ts @@ -49,6 +49,7 @@ const jaJP = { test: 'テスト', forgotPassword: 'パスワードを忘れた？', loading: '読み込み中...', + fieldRequired: 'この項目は必須です', or: 'または', loginWithSpace: 'Space でログイン', spaceLoginRecommended: @@ -371,7 +372,8 @@ const jaJP = { Tool: 'ツール', EventListener: 'イベント監視器', Command: 'コマンド', - KnowledgeRetriever: '知識検索', + KnowledgeEngine: '知識エンジン', + Parser: 'パーサー', }, uploadLocal: 'ローカルアップロード', debugging: 'デバッグ中', @@ -729,6 +731,12 @@ const jaJP = { processing: '処理中', completed: '完了', failed: '失敗', + selectParser: 'パーサーを選択', + builtInParser: '知識エンジンが提供', + noParserAvailable: + 'このファイル形式に対応するパーサーがありません。対応するパーサープラグインをインストールしてください。', + confirmUpload: 'アップロード', + cancelUpload: 'キャンセル', }, deleteKnowledgeBaseConfirmation: '本当にこの知識ベースを削除しますか？この知識ベースに紐付けられたドキュメントは削除されます。', @@ -741,8 +749,10 @@ const jaJP = { fileName: 'ファイル名', noResults: '検索結果がありません', retrieveError: '検索に失敗しました', - builtIn: '内蔵', - external: '外部ナレッジベース', + unknownEngine: '不明なエンジン', + loadKnowledgeBaseFailed: 'ナレッジベースの読み込みに失敗しました', + deleteKnowledgeBaseFailed: 'ナレッジベースの削除に失敗しました', + getKnowledgeBaseListError: 'ナレッジベース一覧の取得に失敗しました：', addExternal: '外部ナレッジベースを追加', createExternalSuccess: '外部ナレッジベースが正常に作成されました', updateExternalSuccess: '外部ナレッジベースが正常に更新されました', diff --git a/web/src/i18n/locales/zh-Hans.ts b/web/src/i18n/locales/zh-Hans.ts index 74f0cd08..738da0e4 100644 --- a/web/src/i18n/locales/zh-Hans.ts +++ b/web/src/i18n/locales/zh-Hans.ts @@ -48,6 +48,7 @@ const zhHans = { test: '测试', forgotPassword: '忘记密码？', loading: '加载中...', + fieldRequired: '此字段为必填项', or: '或', loginWithSpace: '通过 Space 登录', spaceLoginRecommended: '推荐：使用官方提供的稳定模型 API 和云服务', @@ -353,7 +354,8 @@ const zhHans = { Tool: '工具', EventListener: '事件监听器', Command: '命令', - KnowledgeRetriever: '知识检索', + KnowledgeEngine: '知识引擎', + Parser: '解析器', }, uploadLocal: '本地上传', debugging: '调试中', @@ -696,6 +698,12 @@ const zhHans = { processing: '处理中', completed: '完成', failed: '失败', + selectParser: '选择解析器', + builtInParser: '由知识引擎提供', + noParserAvailable: + '没有解析器支持此文件类型，请安装支持该格式的解析器插件。', + confirmUpload: '上传', + cancelUpload: '取消', }, deleteKnowledgeBaseConfirmation: '你确定要删除这个知识库吗？此知识库下的所有文档将被删除。', @@ -708,8 +716,23 @@ const zhHans = { fileName: '文件名', noResults: '暂无结果', retrieveError: '检索失败', - builtIn: '内置', - external: '外部知识库', + unknownEngine: '未知引擎', + knowledgeEngine: '知识引擎', + knowledgeEngineRequired: '知识引擎不能为空', + selectKnowledgeEngine: '选择知识引擎', + builtInEngine: '内置引擎', + cannotChangeKnowledgeEngine: '知识库创建后不可修改知识引擎', + engineSettings: '引擎设置', + engineSettingsReadonly: '编辑模式下不可修改', + retrievalSettings: '检索设置', + noEnginesAvailable: '没有可用的知识库引擎', + installEngineHint: '请先安装知识库插件', + createKnowledgeBaseFailed: '知识库创建失败', + loadKnowledgeBaseFailed: '知识库加载失败', + deleteKnowledgeBaseFailed: '知识库删除失败', + getKnowledgeBaseListError: '获取知识库列表失败：', + embeddingModel: '嵌入模型', + embeddingModelRequired: '此引擎需要选择嵌入模型', addExternal: '添加外部知识库', createExternalSuccess: '外部知识库创建成功', updateExternalSuccess: '外部知识库更新成功', diff --git a/web/src/i18n/locales/zh-Hant.ts b/web/src/i18n/locales/zh-Hant.ts index 1f937264..92079b84 100644 --- a/web/src/i18n/locales/zh-Hant.ts +++ b/web/src/i18n/locales/zh-Hant.ts @@ -48,6 +48,7 @@ const zhHant = { test: '測試', forgotPassword: '忘記密碼？', loading: '載入中...', + fieldRequired: '此欄位為必填', or: '或', loginWithSpace: '透過 Space 登入', spaceLoginRecommended: '推薦：使用官方提供的穩定模型 API 和雲服務', @@ -347,7 +348,8 @@ const zhHant = { Tool: '工具', EventListener: '事件監聽器', Command: '命令', - KnowledgeRetriever: '知識檢索', + KnowledgeEngine: '知識引擎', + Parser: '解析器', }, uploadLocal: '本地上傳', debugging: '調試中', @@ -689,6 +691,12 @@ const zhHant = { processing: '處理中', completed: '完成', failed: '失敗', + selectParser: '選擇解析器', + builtInParser: '由知識引擎提供', + noParserAvailable: + '沒有解析器支援此檔案類型，請安裝支援該格式的解析器插件。', + confirmUpload: '上傳', + cancelUpload: '取消', }, deleteKnowledgeBaseConfirmation: '您確定要刪除這個知識庫嗎？此知識庫下的所有文檔將被刪除。', @@ -701,8 +709,10 @@ const zhHant = { fileName: '文檔名稱', noResults: '暫無結果', retrieveError: '檢索失敗', - builtIn: '內置', - external: '外部知識庫', + unknownEngine: '未知引擎', + loadKnowledgeBaseFailed: '知識庫載入失敗', + deleteKnowledgeBaseFailed: '知識庫刪除失敗', + getKnowledgeBaseListError: '取得知識庫列表失敗：', addExternal: '添加外部知識庫', createExternalSuccess: '外部知識庫創建成功', updateExternalSuccess: '外部知識庫更新成功',