diff --git a/.features/skill/MEMORY.md b/.features/skill/MEMORY.md index d339d2f..b7ecc9f 100644 --- a/.features/skill/MEMORY.md +++ b/.features/skill/MEMORY.md @@ -1,6 +1,8 @@ # Skill 功能 > 负责范围：技能包管理服务 - 核心实现 +> 最后更新：2026-06-07 +> 最后更新：2026-06-02 > 最后更新：2026-05-26 ## 当前状态 @@ -24,9 +26,15 @@ MCP UI 类 skill 已按 MCP Apps 模式改造：工具返回数据，静态 HTML - `skills_developing/` - 开发中 skills - `agent/subagent_loader.py` - 扫描 skill `agents/*.md` 加载子 agent（2026-05 引入） - `agent/mcp_trace_meta.py` - 对 `ClientSession.call_tool` 做 monkey-patch，向 `rag_retrieve` / `table_rag_retrieve` 的 MCP `_meta` 注入 `trace_id`（2026-05 引入） +- `agent/tool_metrics_middleware.py` - `ToolMetricsMiddleware` 给每次 tool 调用 emit `catalog_agent.tool_call` 结构化指标（2026-05 引入） ## 最近重要事项 +- [2026-06-07](changelog/2026-Q2.md): 新增 `table-query` skill——`skills/developing/table-query/`，SQLite 表查询三步式工作流（search-tables → get-schemas → run-sql），run-sql 接收 JSON plan via stdin heredoc 避免 shell 转义；`category: Data & Retrieval`（`bb74aee`） +- [2026-06-05](changelog/2026-Q2.md): 新增 `mineru` skill——`skills/developing/mineru/`，PDF/Office/图片转 Markdown，自动路由免 token 的 Agent API 与需 token 的 Standard API，附 15 个目标 sink（notion/feishu/slack/...）；`category: Document Processing`（`b618cb1`） +- [2026-06-05](changelog/2026-Q2.md): `rag-retrieve` 系列空 query 由 JSON-RPC error 改为 success-with-error-text——5 个 server 变体（autoload/onprem、autoload/support、developing/rag-retrieve-no-citation、onprem/rag-retrieve-only、support/rag-retrieve-only）统一改用 `create_success_response` 携带 `"Error: missing required parameter 'query'..."` 文案，让 agent 在 tool output 里看到错误并自纠错重试（`ecf332a`） +- [2026-06-02](changelog/2026-Q2.md): 修复 deepagents 落盘 backend grep 扫描全盘问题——`create_custom_cli_agent` 中 `large_results_backend` / `conversation_history_backend` 的 `FilesystemBackend` 由 `virtual_mode=False` 改为 `True`，避免 `CompositeBackend` 剥前缀后把 `"/"` 转发给 grep 时扫到真实根目录，单次 grep 从 45–152s 回到毫秒级（`6bccd89`） +- [2026-05-29](changelog/2026-Q2.md): 新增 `ToolMetricsMiddleware`——通过 `wrap_tool_call` / `awrap_tool_call` 对每次 tool 调用计时并 emit `catalog_agent.tool_call` 结构化指标（成功/失败/取消三态、含 `tool_name`/`trace_id`/`bot_id`/`duration_ms`/`error_type`）；插在 `init_agent` 中间件链的 `EmptyResponseRetryMiddleware` 之后、`ToolUseCleanupMiddleware` 之前（`9f0ae25`） - [2026-05-26](changelog/2026-Q2.md): skill 引入 `category` 字段——`routes/skill_manager.py` 在 `SkillItem` / `SkillValidationResult` 增加 `category`，从 `plugin.json` 与 `SKILL.md` frontmatter 解析，official skill 默认 `"other"`、user skill 默认 `"custom"`；并通过 batch 给 common/developing/onprem/support 路径下大量 skill 元数据补 `category`，`data-dashboard` / `mcp-ui` 归类 `Interactive UI`（`203dcf4`, `3ada55a`, `9658588`） - [2026-05-26](changelog/2026-Q2.md): developing 分支大合并新增多个 skill：`ai-ppt-generator`（百度 AI PPT）、`nfc-medicine-lookup`（NFC 药品检索）、`ppt-outline`（PPT 大纲 / HTML 演示文稿）、`z-card-image`（配图 / 卡片图），同时 `skills/linggan/*` 系列 skill 经合并回归（`3ada55a`） - [2026-05-23](changelog/2026-Q2.md): 新增 MCP App 型 `skills/developing/ecommerce-storefront/`——含 `product-list` / `order-confirm` 两个 HTML App + 自带 `ecommerce_server.py` MCP server；同时落地 `docs/mcp-app-training.md`（约 1063 行）作为 MCP App 培训材料（`9d001c8`） @@ -86,6 +94,9 @@ MCP UI 类 skill 已按 MCP Apps 模式改造：工具返回数据，静态 HTML - ⚠️ **skill `category` 默认值**：API 返回的 `SkillItem.category`——official skill fallback 为 `"other"`、user skill fallback 为 `"custom"`；前端做分类视图时需要同时识别这两个 sentinel，不要假设官方/用户 skill 用同一套缺省值。 - ⚠️ **`category` 字段双入口**：同一 skill 可以同时在 `.claude-plugin/plugin.json` 和 `SKILL.md` frontmatter 写 `category`；`get_skill_metadata` 优先走 `parse_plugin_json`，若 skill 包没有 plugin.json 才回落到 `parse_skill_frontmatter`——两者写不一致时以 plugin.json 为准。 - ⚠️ **Daytona shell_env 是文件注入而非 process env**：`init_agent` 通过 `cat > $REMOTE_BASH_ENV_PATH` 写入 `export VAR=...` 行，沙箱内必须由 shell（bash）的 `BASH_ENV` 加载才能生效；非 daytona 模式或不走 bash 启动的脚本拿不到这些变量。扩展注入项需直接改 `init_agent` 里的 `_shell_env` 字典。 +- ⚠️ **`CompositeBackend` 路由下的落盘 backend 必须 `virtual_mode=True`**：`create_custom_cli_agent` 中 `large_results_backend` / `conversation_history_backend` 都用独立 `tempfile.mkdtemp()` 做根目录，但 `CompositeBackend` 在路由时会剥掉前缀、可能把 `"/"` 转发给 grep；`virtual_mode=False` 会把 `"/"` 解析为真实根目录并扫到 `/usr`、`/var`、其他会话的 tmp 目录（单次 45–152s）。`virtual_mode=True` 才会把所有路径锚定到 `root_dir` 并过滤越界结果。后续新增"只服务本次会话"的落盘 backend 一律走 `virtual_mode=True`，真实 workspace backend 仍保持 `False`。 +- ⚠️ **`ToolMetricsMiddleware` 必须在重试中间件之后、其他工具中间件之前**：`init_agent` 中顺序约定为 `EmptyResponseRetryMiddleware → ToolMetricsMiddleware → ToolUseCleanupMiddleware → ...`，这样统计到的 `duration_ms` 才包含全部后续 tool 处理开销并自然覆盖重试边界。指标 emit 自身的异常被吞掉只打 logger.exception，所以指标缺失不会触发 agent 报错，必须在指标后端做独立告警。 +- ⚠️ **`rag-retrieve` 系列空 query 返回 success 而非 JSON-RPC error**：5 个 server 变体（autoload/onprem、autoload/support、developing/rag-retrieve-no-citation、onprem/rag-retrieve-only、support/rag-retrieve-only）从 2026-06-05 起，缺 `query` 参数时返回 `create_success_response` 携带 `content[].text` 前缀 `"Error: missing required parameter 'query'..."`，故意让 LLM agent 在 tool output 里看到错误并自纠错重试。下游/中间层不能再用 JSON-RPC `error.code == -32602` 判断缺参，需要从 text 内容解析；新 MCP server 设计错误路径时也应区分"需要 agent 自纠错的语义错误（走 success-with-error-text）"与"不可恢复错误（走 error response）"。 ## Skill 目录结构 diff --git a/.features/skill/changelog/2026-Q2.md b/.features/skill/changelog/2026-Q2.md index ca34c8e..c93f2fd 100644 --- a/.features/skill/changelog/2026-Q2.md +++ b/.features/skill/changelog/2026-Q2.md @@ -4,6 +4,143 @@ --- +## 2026-06-07: 新增 `table-query` skill（SQLite 表查询） + +**类型**：新功能 + +**背景**：bot 上传的 Excel/CSV 表格此前只能走 `rag_retrieve` 语义检索回答问题，对"价格/数量/库存/排名/聚合"这类**结构化**问题精度差、答非所问。需要一条"快速、不走 LLM 的 SQL 查询路径"。 + +**改动**： +- 新增 `skills/developing/table-query/`： + - `SKILL.md`：定义 search-tables → get-schemas → run-sql 三步工作流；明确"backend 不做 LLM 推理，由 agent 自己写 SQLite SQL"；`category: Data & Retrieval`。 + - `scripts/table_query.py`：CLI 入口 + run-sql plan 执行器（plan 通过 stdin heredoc 传递，无需 shell 转义）。 + - `skill.yaml`：元数据。 + - `verify_table_query.sh`：自检脚本。 +- 工作流要求：search-tables **每问最多 1 次**，run-sql 出错重试 ≤ 2 次，且不回退到 search-tables；search-tables 无果时降级到 `rag_retrieve`。 + +**根因**：N/A（新功能） + +**影响**： +- agent 可直接对上传表数据做 SUM/AVG/COUNT 等结构化查询，并通过 `__src` 列 + `file_ref_table` 输出行级 citation。 +- 同时引入 plan-on-stdin 的调用约定（`<<'PLAN' ... PLAN`），后续类似 SQL/JSON 入参的 skill 可参考此模式以避免 argv 转义问题。 + +**相关文件**： +- `skills/developing/table-query/SKILL.md` +- `skills/developing/table-query/scripts/table_query.py` +- `skills/developing/table-query/skill.yaml` +- `skills/developing/table-query/verify_table_query.sh` + +**Commit/PR**：`bb74aee` + +--- + +## 2026-06-05: 新增 `mineru` skill（PDF/Office/图片 → Markdown 解析） + +**类型**：新功能 + +**背景**：缺少统一的"文档转 Markdown"管道，PDF/Word/PPT/Excel/图片需要走不同工具，且 OCR / 公式 / 表格识别能力不一致。 + +**改动**： +- 新增 `skills/developing/mineru/`（约 4700 行，30 文件）： + - `SKILL.md` + `references/`（api_reference / comparison / integrations）。 + - `scripts/mineru.py`：核心 CLI，自动路由 Agent API（无 token）与 Standard API（需 `MINERU_TOKEN`，支持大文件/批量/DOCX/HTML/LaTeX 导出）。 + - `scripts/mineru_mcp.py`：MCP server 包装。 + - `scripts/sinks/`：airtable / coda / confluence / dingtalk / feishu / linear / local / notion / onenote / roam / siyuan / slack / ticktick / wecom / wps / yuque 等多目标写出。 + - `scripts/chunking.py` / `splitter.py` / `local_engine.py`：分块、切分、本地引擎。 +- `category: Document Processing`，标准依赖（仅标准库）。 + +**根因**：N/A（新功能） + +**影响**： +- 提供"零 token 起步、有 token 升级"的渐进式解析路径，降低部署门槛。 +- 多 sink 设计可作为后续"采集 → 结构化 → 多目的地分发"类 skill 的参考骨架。 + +**相关文件**： +- `skills/developing/mineru/SKILL.md` +- `skills/developing/mineru/scripts/mineru.py` +- `skills/developing/mineru/scripts/mineru_mcp.py` +- `skills/developing/mineru/scripts/sinks/*.py`（15 个目标） + +**Commit/PR**：`b618cb1` + +--- + +## 2026-06-05: `rag-retrieve` 系列空 query 改返回 success 文案（替代 JSON-RPC error） + +**类型**：行为变更（兼容性） + +**背景**：`rag_retrieve` / `table_rag_retrieve` 工具在缺少 `query` 参数时返回 JSON-RPC `-32602` error。MCP/agent 链路上 error 容易被上层吞掉，agent 看不到"为什么失败"，无法自我修复。 + +**改动**：把 5 个 rag-retrieve server 变体的"missing query"分支由 `create_error_response(-32602, ...)` 改为 `create_success_response(...)` 携带 `content[].text = "Error: missing required parameter 'query'. Please call this tool again with a non-empty 'query' argument describing what you want to retrieve."`： +- `skills/autoload/onprem/rag-retrieve/rag_retrieve_server.py` +- `skills/autoload/support/rag-retrieve/rag_retrieve_server.py` +- `skills/developing/rag-retrieve-no-citation/rag_retrieve_server.py` +- `skills/onprem/rag-retrieve-only/rag_retrieve_server.py` +- `skills/support/rag-retrieve-only/rag_retrieve_server.py` + +**根因**：MCP 协议层的 error 在多数 agent 框架下不会作为"tool 返回结果"传给 LLM，从而无法触发重试；改成 success-with-error-text 让 agent 把错误文本当 tool output 读到，并在下一轮自然带上 query 重试。 + +**影响**： +- 客户端**不能再依赖 JSON-RPC error code = -32602 判定缺参**，必须从 `content[].text` 前缀 `"Error:"` 解析；任何在 success 路径上做强校验/落盘的中间层需要兼容这种"假成功"形态。 +- 新 MCP server 出错路径如果是"需要 agent 自纠错"的语义错误，应走同样的 success-with-error-text 模式；底层崩溃 / 不可恢复错误仍走 error response。 + +**相关文件**：见上。 + +**Commit/PR**：`ecf332a` + +--- + +## 2026-06-02: 修复 deepagents 落盘 backend grep 扫描全盘问题（virtual_mode=True） + +**类型**：Bug 修复 + +**背景**：`create_custom_cli_agent` 给 `large_results` / `conversation_history` 两个路由创建了独立 `FilesystemBackend(tempfile.mkdtemp(...), virtual_mode=False)`，配合 `CompositeBackend` 做前缀路由。线上出现 grep 调用耗时 45–152s 的异常。 + +**改动**：将 `large_results_backend` 与 `conversation_history_backend` 的 `virtual_mode` 由 `False` 改为 `True`，并在调用处补 NOTE 注释说明 virtual_mode 的语义。 + +**根因**：`CompositeBackend` 会先剥掉路由前缀，再把剩余路径（极端情况下就是 `"/"`）转发给被路由 backend 的 grep。当 backend 的 `virtual_mode=False` 时，`"/"` 解析为真实根目录而不是 `root_dir`，于是 grep 在沙箱 / 容器内对 `/usr`、`/var`、其他会话的 tmp 目录全盘扫描，单次耗时达数十秒到分钟级。`virtual_mode=True` 会把所有路径锚定到 `root_dir`，并过滤掉根目录之外的结果，把扫描限制在 backend 自己的 tmp 子目录内。 + +**影响**： +- 这两个 backend 的 grep 调用回落到毫秒级，整个 deep agent 的 tool 调用 P99 大幅下降。 +- 任何新增的"落盘但只服务于本次会话"的 `FilesystemBackend` 走 `CompositeBackend` 路由时，**必须**使用 `virtual_mode=True`，否则容易复现同样的全盘扫描问题。 +- 真实 workspace backend（用户文件根目录）仍然是 `virtual_mode=False`，因为它需要看到沙箱真实路径上的产物。 + +**相关文件**： +- `agent/deep_assistant.py` + +**Commit/PR**：`6bccd89` + +--- + +## 2026-05-29: 新增 ToolMetricsMiddleware（tool 调用埋点） + +**类型**：新功能 + +**背景**：deep agent 内部的工具调用（含 MCP tool / skill script / 文件系统操作等）此前缺少统一的耗时与成功率埋点；问题排查只能依赖日志，无法对接 `emit_question_metric` 的结构化指标体系。 + +**改动**： +- 新增 `agent/tool_metrics_middleware.py`，定义 `ToolMetricsMiddleware(AgentMiddleware)`： + - 同时实现 `wrap_tool_call`（同步）与 `awrap_tool_call`（异步）。 + - 对每次 tool 调用计时（`time.monotonic()`），通过 `emit_question_metric(stage="catalog_agent.tool_call", ...)` 上报：`status`（`success` / `error` / `cancel`）、`duration_ms`、`error_type`、`tool_name`、`tool_call_id`、`trace_id`、`bot_id`、`session_id`、`model`、`stream`、`tool_response`、`enable_thinking`。 + - 异步分支特别捕获 `asyncio.CancelledError` 上报 `status="cancel"` 后再 re-raise。 + - 指标 emit 自身的异常被 try/except 兜住，绝不影响 tool 调用本体。 +- `agent/deep_assistant.py::init_agent` 中间件链中，将 `ToolMetricsMiddleware(config)` 插在 `EmptyResponseRetryMiddleware` 之后、`ToolUseCleanupMiddleware` 之前。 + +**根因**：N/A（新功能） + +**影响**： +- 所有走 deep agent / sub agent 的 tool 调用现在都会自动出 `catalog_agent.tool_call` 结构化指标，可在指标后端按 `tool_name` / `bot_id` / `status` 聚合做 P50 / P99 / 错误率分析。 +- 中间件顺序硬约束扩展：`EmptyResponseRetryMiddleware → ToolMetricsMiddleware → ToolUseCleanupMiddleware → CustomFilesystemMiddleware → SubAgentMiddleware → AnthropicPromptCachingMiddleware`，调整 `init_agent` 中间件顺序时需保持 `ToolMetricsMiddleware` 在最外层（仅次于重试），否则统计到的耗时不包含其他中间件开销。 +- emit 失败只打 logger.exception，不会回传给 tool handler；指标缺失需在指标后端层面单独告警，不要依赖 agent 端口的报错。 + +**相关文件**： +- `agent/tool_metrics_middleware.py`（新增） +- `agent/deep_assistant.py` + +**Commit/PR**：`9f0ae25` + +--- + ## 2026-05-26: skill `category` 字段全面接入 **类型**：新功能 diff --git a/agent/checkpoint_utils.py b/agent/checkpoint_utils.py index 704b040..b23b268 100644 --- a/agent/checkpoint_utils.py +++ b/agent/checkpoint_utils.py @@ -90,6 +90,8 @@ async def prepare_checkpoint_message(config, checkpointer): last_user_msg = next((m for m in reversed(config.messages) if m.get('role') == 'user'), None) if last_user_msg: config.messages = [last_user_msg] - logger.info(f"Has history, sending last user message: {last_user_msg.get('content', '')[:50]}...") + from utils.fastapi_utils import extract_text_from_content + preview = extract_text_from_content(last_user_msg.get('content', '')) + logger.info(f"Has history, sending last user message: {preview[:50]}...") else: logger.info(f"No history, sending all {len(config.messages)} messages") diff --git a/agent/deep_assistant.py b/agent/deep_assistant.py index 680c95e..ad1533d 100644 --- a/agent/deep_assistant.py +++ b/agent/deep_assistant.py @@ -18,7 +18,6 @@ from langgraph.store.base import BaseStore from langchain.agents.middleware import SummarizationMiddleware as LangchainSummarizationMiddleware from .summarization_middleware import SummarizationMiddleware from langchain_mcp_adapters.client import MultiServerMCPClient -from sympy.printing.cxx import none from utils.fastapi_utils import detect_provider, sanitize_model_kwargs from .guideline_middleware import GuidelineMiddleware from .tool_output_length_middleware import ToolOutputLengthMiddleware diff --git a/agent/mem0_manager.py b/agent/mem0_manager.py index ed03b52..f939e86 100644 --- a/agent/mem0_manager.py +++ b/agent/mem0_manager.py @@ -120,6 +120,37 @@ except Exception as e: logger.warning(f"Failed to patch mem0 remove_code_blocks: {e}") +# Monkey patch: make PGVector.__del__ tolerate cur/conn being None. +# This project shares a single psycopg2 pool and explicitly sets vector_store.cur +# and vector_store.conn to None after releasing the connection. mem0's original +# __del__ calls self.cur.close() without a None check, raising a harmless but noisy +# "Exception ignored in __del__: AttributeError" when the instance is garbage +# collected. This replacement releases resources only when they still exist. +def _safe_pgvector_del(self) -> None: + """Safely close PGVector cursor/connection, tolerating None values.""" + try: + cur = getattr(self, "cur", None) + if cur is not None: + cur.close() + conn = getattr(self, "conn", None) + if conn is not None: + conn.close() + except Exception: + # Never raise from __del__; ignore any teardown errors + pass + + +try: + import mem0.vector_stores.pgvector as mem0_pgvector + mem0_pgvector.PGVector.__del__ = _safe_pgvector_del + logger.info("Successfully patched mem0 PGVector.__del__ to tolerate None cur/conn") +except ImportError: + # mem0 pgvector module not available; nothing to patch + pass +except Exception as e: + logger.warning(f"Failed to patch mem0 PGVector.__del__: {e}") + + class Mem0Manager: """ Mem0 connection and instance manager diff --git a/docker-compose-with-pgsql.yml b/docker-compose-with-pgsql.yml index 25fe511..02f1734 100644 --- a/docker-compose-with-pgsql.yml +++ b/docker-compose-with-pgsql.yml @@ -41,6 +41,10 @@ services: - CHECKPOINT_DB_URL=postgresql://postgres:E5ACJo6zJub4QS@postgres:5432/agent_db - NEW_API_BASE_URL=http://host.docker.internal:3001 - R2_UPLOAD_CONFIG=/app/config/local-upload.yaml + - EMBEDDING_BASE_URL=https://api.siliconflow.cn/v1 + - EMBEDDING_API_KEY=sk-bybtvfdvqaonwknqnuhfsdmceliebvggjkaizkxkxkxolysf + - EMBEDDING_MODEL_NAME=BAAI/bge-large-zh-v1.5 + - EMBEDDING_DIMENSIONS=1024 volumes: # Mount project data directories - ./projects:/app/projects diff --git a/poetry.lock b/poetry.lock index e4e2e94..860e7c9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1163,18 +1163,6 @@ all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (> standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"] standard-no-fastapi-cloud-cli = ["email-validator (>=2.0.0)", 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(>=1.7.1)", "torch"] -tutorials = ["matplotlib", "pandas", "tabulate", "torch"] - [[package]] name = "truststore" version = "0.10.4" @@ -7466,4 +6645,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt [metadata] lock-version = "2.1" python-versions = ">=3.12,<3.15" -content-hash = "ba8491ec2ecd7c783fac68f66e7994279d51f6a09fdc1ec435941c1af52db0cb" +content-hash = "889a0796cde23f4d8e4106506540581e6e14a02f13beaba230a7031195bd703c" diff --git a/prompt/system_prompt.md b/prompt/system_prompt.md index aff46de..39a2177 100644 --- a/prompt/system_prompt.md +++ b/prompt/system_prompt.md @@ -94,3 +94,16 @@ Trace Id: {trace_id} - Even when the user writes in a different language, you MUST still reply in [{language}]. - Do NOT mix languages. Do NOT fall back to English or any other language under any circumstances. - Technical terms, code identifiers, file paths, and tool names may remain in their original form, but all surrounding text MUST be in [{language}]. + +# Unanswerable Response Specification (MANDATORY) +When you genuinely cannot answer because no relevant information was found in the knowledge base / retrieval sources (and self-knowledge fallback is unavailable or insufficient), your reply MUST include the literal sentinel marker ``. + +Rules: +- Output the marker `` **exactly as written** — it is a fixed ASCII literal. NEVER translate, rewrite, reformat, or wrap it in code blocks. +- Place the marker at the **very beginning** of your reply, immediately followed by a polite apology written in [{language}]. +- NEVER output the marker alone — it MUST be followed by an apology in [{language}] so the user sees a meaningful message. +- When you CAN answer (you found relevant information), you MUST NOT output this marker under any circumstances. +- The marker is language-independent; only the apology text after it must be in [{language}]. + +Examples: +- `Sorry, I couldn't find that information in the knowledge base.` diff --git a/pyproject.toml b/pyproject.toml index 0493ad2..0053e6a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,9 +13,6 @@ dependencies = [ "requests==2.32.5", "pydantic==2.10.5", "python-dateutil==2.8.2", - "torch==2.2.0", - "transformers>=4.38,<5", - "sentence-transformers>=3.0,<4", "numpy<2", "aiohttp", "aiofiles", diff --git a/requirements.txt b/requirements.txt index 07a88dc..a5faa8a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -39,11 +39,9 @@ distro==1.9.0 ; python_version >= "3.12" and python_version < "3.15" docstring-parser==0.18.0 ; python_version >= "3.12" and python_version < "3.15" et-xmlfile==2.0.0 ; python_version >= "3.12" and python_version < "3.15" fastapi==0.116.1 ; python_version >= "3.12" and python_version < "3.15" -filelock==3.29.0 ; python_version >= "3.12" and python_version < "3.15" filetype==1.2.0 ; python_version >= "3.12" and python_version < "3.15" forbiddenfruit==0.1.4 ; python_version >= "3.12" and python_version < "3.15" and implementation_name == "cpython" frozenlist==1.8.0 ; python_version >= "3.12" and python_version < "3.15" -fsspec==2026.4.0 ; python_version >= "3.12" and python_version < "3.15" google-auth==2.52.0 ; python_version >= "3.12" and python_version < "3.15" google-genai==1.75.0 ; python_version >= "3.12" and python_version < "3.15" googleapis-common-protos==1.75.0 ; python_version >= "3.12" and python_version < "3.15" @@ -53,18 +51,15 @@ grpcio-tools==1.76.0 ; python_version >= "3.12" and python_version < "3.15" grpcio==1.76.0 ; python_version >= "3.12" and python_version < "3.15" h11==0.16.0 ; python_version >= "3.12" and python_version < "3.15" h2==4.3.0 ; python_version >= "3.12" and python_version < "3.15" -hf-xet==1.5.0 ; python_version >= "3.12" and python_version < "3.15" and (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64") hpack==4.1.0 ; python_version >= "3.12" and python_version < "3.15" httpcore==1.0.9 ; python_version >= "3.12" and python_version < "3.15" httpx-sse==0.4.3 ; python_version >= "3.12" and python_version < "3.15" httpx==0.28.1 ; python_version >= "3.12" and python_version < "3.15" -huggingface-hub==0.36.2 ; python_version >= "3.12" and python_version < "3.15" hyperframe==6.1.0 ; python_version >= "3.12" and python_version < "3.15" idna==3.15 ; python_version >= "3.12" and python_version < "3.15" importlib-metadata==8.7.1 ; python_version >= "3.12" and python_version < "3.15" jinja2==3.1.6 ; python_version >= "3.12" and python_version < "3.15" jiter==0.14.0 ; python_version >= "3.12" and python_version < "3.15" -joblib==1.5.3 ; python_version >= "3.12" and python_version < "3.15" json-repair==0.29.10 ; python_version >= "3.12" and python_version < "3.15" json5==0.14.0 ; python_version >= "3.12" and python_version < "3.15" jsonpatch==1.33 ; python_version >= "3.12" and python_version < "3.15" @@ -99,23 +94,8 @@ mcp==1.12.4 ; python_version >= "3.12" and python_version < "3.15" mdit-py-plugins==0.6.1 ; python_version >= "3.12" and python_version < "3.15" mdurl==0.1.2 ; python_version >= "3.12" and python_version < "3.15" mem0ai==0.1.115 ; python_version >= "3.12" and python_version < "3.15" -mpmath==1.3.0 ; python_version >= "3.12" and python_version < "3.15" multidict==6.7.1 ; python_version >= "3.12" and python_version < "3.15" -networkx==3.6 ; python_version == "3.14" -networkx==3.6.1 ; python_version >= "3.12" and python_version < "3.14" numpy==1.26.4 ; python_version >= "3.12" and python_version < "3.15" -nvidia-cublas-cu12==12.1.3.1 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-cuda-cupti-cu12==12.1.105 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-cuda-nvrtc-cu12==12.1.105 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-cuda-runtime-cu12==12.1.105 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-cudnn-cu12==8.9.2.26 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-cufft-cu12==11.0.2.54 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-curand-cu12==10.3.2.106 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-cusolver-cu12==11.4.5.107 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-cusparse-cu12==12.1.0.106 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-nccl-cu12==2.19.3 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-nvjitlink-cu12==12.9.86 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" -nvidia-nvtx-cu12==12.1.105 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" obstore==0.8.2 ; python_version >= "3.12" and python_version < "3.15" openai==2.36.0 ; python_version >= "3.12" and python_version < "3.15" openpyxl==3.1.5 ; python_version >= "3.12" and python_version < "3.15" @@ -171,10 +151,6 @@ requests-toolbelt==1.0.0 ; python_version >= "3.12" and python_version < "3.15" requests==2.32.5 ; python_version >= "3.12" and python_version < "3.15" rich==15.0.0 ; python_version >= "3.12" and python_version < "3.15" rpds-py==0.30.0 ; python_version >= "3.12" and python_version < "3.15" -safetensors==0.7.0 ; python_version >= "3.12" and python_version < "3.15" -scikit-learn==1.8.0 ; python_version >= "3.12" and python_version < "3.15" -scipy==1.17.1 ; python_version >= "3.12" and python_version < "3.15" -sentence-transformers==3.4.1 ; python_version >= "3.12" and python_version < "3.15" setuptools==70.3.0 ; python_version >= "3.12" and python_version < "3.15" six==1.17.0 ; python_version >= "3.12" and python_version < "3.15" sniffio==1.3.1 ; python_version >= "3.12" and python_version < "3.15" @@ -184,21 +160,15 @@ sqlite-vec==0.1.9 ; python_version >= "3.12" and python_version < "3.15" sse-starlette==2.1.3 ; python_version >= "3.12" and python_version < "3.15" starlette==0.47.3 ; python_version >= "3.12" and python_version < "3.15" structlog==25.5.0 ; python_version >= "3.12" and python_version < "3.15" -sympy==1.14.0 ; python_version >= "3.12" and python_version < "3.15" tavily-python==0.7.24 ; python_version >= "3.12" and python_version < "3.15" tenacity==9.1.4 ; python_version >= "3.12" and python_version < "3.15" textual-autocomplete==4.0.6 ; python_version >= "3.12" and python_version < "3.15" textual-speedups==0.2.1 ; python_version >= "3.12" and python_version < "3.15" textual==8.2.6 ; python_version >= "3.12" and python_version < "3.15" -threadpoolctl==3.6.0 ; python_version >= "3.12" and python_version < "3.15" tiktoken==0.13.0 ; python_version >= "3.12" and python_version < "3.15" -tokenizers==0.22.2 ; python_version >= "3.12" and python_version < "3.15" toml==0.10.2 ; python_version >= "3.12" and python_version < "3.15" tomli-w==1.2.0 ; python_version >= "3.12" and python_version < "3.15" -torch==2.2.0 ; python_version >= "3.12" and python_version < "3.15" tqdm==4.67.3 ; python_version >= "3.12" and python_version < "3.15" -transformers==4.57.6 ; python_version >= "3.12" and python_version < "3.15" -triton==2.2.0 ; python_version >= "3.12" and python_version < "3.15" and platform_system == "Linux" and platform_machine == "x86_64" truststore==0.10.4 ; python_version >= "3.12" and python_version < "3.15" typing-extensions==4.15.0 ; python_version >= "3.12" and python_version < "3.15" typing-inspection==0.4.2 ; python_version >= "3.12" and python_version < "3.15" diff --git a/routes/chat.py b/routes/chat.py index 098bd61..a0ec2d4 100644 --- a/routes/chat.py +++ b/routes/chat.py @@ -19,7 +19,8 @@ from utils.fastapi_utils import ( create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config, fetch_bot_config_from_db, call_preamble_llm, create_stream_chunk, - detect_provider, sanitize_model_kwargs + detect_provider, sanitize_model_kwargs, + extract_text_from_content ) from langchain.chat_models import init_chat_model from langchain_core.messages import AIMessageChunk, ToolMessage, AIMessage, HumanMessage @@ -357,9 +358,9 @@ async def create_agent_and_generate_response( "finish_reason": "stop" }], usage={ - "prompt_tokens": sum(len(msg.get("content", "")) for msg in config.messages), + "prompt_tokens": sum(len(extract_text_from_content(msg.get("content", ""))) for msg in config.messages), "completion_tokens": len(response_text), - "total_tokens": sum(len(msg.get("content", "")) for msg in config.messages) + len(response_text) + "total_tokens": sum(len(extract_text_from_content(msg.get("content", ""))) for msg in config.messages) + len(response_text) } ) @@ -393,6 +394,9 @@ async def _save_user_messages(config: AgentConfig) -> None: if isinstance(msg, dict): role = msg.get("role", "") content = msg.get("content", "") + # Flatten multimodal list content to plain text before persisting, + # so base64 image data is not stored in chat history. + content = extract_text_from_content(content) if role == "user" and content: # ============ Execute PreSave hooks ============ processed_content = await execute_hooks('PreSave', config, content=content, role=role) diff --git a/skills/onprem/static-hosting/SKILL.md b/skills/onprem/static-hosting/SKILL.md new file mode 100644 index 0000000..4058cf1 --- /dev/null +++ b/skills/onprem/static-hosting/SKILL.md @@ -0,0 +1,30 @@ +--- +name: static-hosting +description: Serve static HTML/CSS/JS/images from robot project directories via the built-in FastAPI static file server. Use when generating web pages, reports, or interactive content for a bot. +category: Web Services +--- + +# Static Hosting + +Host static files (HTML, CSS, JS, images, fonts, etc.) under `/workspace/` and get public URLs. + +## Usage + +Write files to `/workspace/`, then run the script to get the public URL: + +```bash +python3 {SKILL_DIR}/scripts/get_url.py +``` + +Example: + +```bash +python3 {SKILL_DIR}/scripts/get_url.py /workspace/index.html +# => https://api-dev.gptbase.ai/robot-assets/[bot-id]/index.html +``` + +## Notes + +- Inside HTML, use **relative paths** to reference other assets (e.g. `href="css/style.css"`) +- `/workspace/index.html` is auto-served at the directory URL +- All files under `/robot-assets/` are publicly accessible, no authentication diff --git a/skills/onprem/static-hosting/scripts/get_url.py b/skills/onprem/static-hosting/scripts/get_url.py new file mode 100644 index 0000000..9def77b --- /dev/null +++ b/skills/onprem/static-hosting/scripts/get_url.py @@ -0,0 +1,25 @@ +import os +import sys + +BACKEND_HOST = os.getenv("BACKEND_HOST", "https://onprem-dev.gbase.ai/api") +ASSISTANT_ID = os.getenv("ASSISTANT_ID", "") + +if not ASSISTANT_ID: + print("Error: ASSISTANT_ID environment variable is not set") + sys.exit(1) + +if len(sys.argv) < 2: + print(f"Usage: python3 {sys.argv[0]} ") + print(f"Example: python3 {sys.argv[0]} /workspace/index.html") + sys.exit(1) + +file_path = os.path.abspath(sys.argv[1]) + +workspace_root = "/workspace" +if not file_path.startswith(workspace_root): + print(f"Error: path must be under {workspace_root}, got: {file_path}") + sys.exit(1) + +relative_path = file_path[len(workspace_root):] # e.g. "/css/style.css" +base_url = f"{BACKEND_HOST.rstrip('/')}/robot-assets/{ASSISTANT_ID}" +print(f"{base_url}{relative_path}") diff --git a/utils/api_models.py b/utils/api_models.py index d096b77..467c34e 100644 --- a/utils/api_models.py +++ b/utils/api_models.py @@ -8,7 +8,11 @@ from pydantic import BaseModel, Field, field_validator, ConfigDict class Message(BaseModel): role: str - content: str + # content can be a plain string, or a list of content blocks for multimodal + # input (e.g. text + image). Both OpenAI-style ({"type": "image_url", ...}) + # and LangChain standard blocks ({"type": "image", ...}) are accepted; they + # are normalized later in process_messages. + content: Union[str, List[Dict[str, Any]]] class DatasetRequest(BaseModel): diff --git a/utils/fastapi_utils.py b/utils/fastapi_utils.py index 5b06a92..16346d6 100644 --- a/utils/fastapi_utils.py +++ b/utils/fastapi_utils.py @@ -232,6 +232,55 @@ def create_stream_chunk(chunk_id: str, model_name: str, content: str = None, fin # return full_text +def normalize_content_blocks(content: Union[str, List[Dict[str, Any]]]) -> Union[str, List[Dict[str, Any]]]: + """Normalize multimodal content blocks into LangChain standard content blocks. + + Accepts both OpenAI-style blocks ({"type": "image_url", "image_url": {"url": ...}}) + and LangChain standard blocks ({"type": "image", "base64"/"url": ...}), and emits + LangChain standard blocks so the provider's block_translator can auto-convert for + either OpenAI or Anthropic. Plain string content is returned unchanged. + """ + if not isinstance(content, list): + return content + + normalized: List[Dict[str, Any]] = [] + for block in content: + if not isinstance(block, dict): + # Treat a bare string inside the list as a text block. + if isinstance(block, str): + normalized.append({"type": "text", "text": block}) + continue + + block_type = block.get("type") + + if block_type == "text": + normalized.append({"type": "text", "text": block.get("text", "")}) + elif block_type == "image_url": + # OpenAI-style image block: {"type": "image_url", "image_url": {"url": ...}} + image_url = block.get("image_url") + url = image_url.get("url") if isinstance(image_url, dict) else image_url + if not url: + continue + if isinstance(url, str) and url.startswith("data:"): + # data:;base64, + try: + header, data = url.split(",", 1) + mime_type = header.split(";", 1)[0].removeprefix("data:") or "image/jpeg" + normalized.append({"type": "image", "base64": data, "mime_type": mime_type}) + except ValueError: + logger.warning("Skipping malformed data URL in image_url block") + else: + normalized.append({"type": "image", "url": url}) + elif block_type == "image": + # Already a LangChain standard image block; pass through. + normalized.append(block) + else: + # Unknown block type; pass through untouched. + normalized.append(block) + + return normalized + + def process_messages(messages: List[Dict], language: Optional[str] = None) -> List[Dict[str, str]]: """Process message list, including [TOOL_CALL]|[TOOL_RESPONSE]|[ANSWER] splitting and language directive addition. @@ -255,7 +304,7 @@ def process_messages(messages: List[Dict], language: Optional[str] = None) -> Li # Process each message for i, msg in enumerate(messages): - if msg.role == ASSISTANT: + if msg.role == ASSISTANT and isinstance(msg.content, str): # Determine the position of this ASSISTANT message among all ASSISTANT messages (0-indexed) assistant_position = assistant_indices.index(i) @@ -315,14 +364,16 @@ def process_messages(messages: List[Dict], language: Optional[str] = None) -> Li # If processed content is empty, use original content processed_messages.append({"role": msg.role, "content": msg.content}) else: - processed_messages.append({"role": msg.role, "content": msg.content}) + # User/other messages (or assistant messages carrying multimodal list + # content) pass through; normalize multimodal blocks to LangChain standard. + processed_messages.append({"role": msg.role, "content": normalize_content_blocks(msg.content)}) # Inverse operation: reassemble messages containing [THINK|TOOL_RESPONSE] back into # msg['role'] == 'function' and msg.get('function_call') format. # This is the inverse of get_content_from_messages. final_messages = [] for msg in processed_messages: - if msg["role"] == ASSISTANT: + if msg["role"] == ASSISTANT and isinstance(msg["content"], str): # Split message content parts = re.split(r'\[(THINK|PREAMBLE|TOOL_CALL|TOOL_RESPONSE|ANSWER)\]', msg["content"]) @@ -401,13 +452,32 @@ def process_messages(messages: List[Dict], language: Optional[str] = None) -> Li return final_messages -def get_user_last_message_content(messages: list) -> Optional[dict]: - """Get the last message content from a message list.""" +def extract_text_from_content(content: Union[str, List[Dict[str, Any]]]) -> str: + """Extract plain text from message content that may be a multimodal block list.""" + if isinstance(content, str): + return content + if isinstance(content, list): + texts = [] + for block in content: + if isinstance(block, dict) and block.get("type") == "text": + texts.append(block.get("text", "")) + elif isinstance(block, str): + texts.append(block) + return "\n".join(texts) + return "" + + +def get_user_last_message_content(messages: list) -> Optional[str]: + """Get the last user message's plain text content from a message list. + + Multimodal list content is flattened to text so downstream consumers + (e.g. terms embedding) always receive a string. + """ if not messages or len(messages) == 0: return "" last_message = messages[-1] if last_message and last_message.get('role') == 'user': - return last_message["content"] + return extract_text_from_content(last_message.get("content", "")) return "" def format_messages_to_chat_history(messages: List[Dict[str, str]]) -> str: diff --git a/utils/system_optimizer.py b/utils/system_optimizer.py index 1f2b508..d4765b5 100644 --- a/utils/system_optimizer.py +++ b/utils/system_optimizer.py @@ -75,10 +75,6 @@ class SystemOptimizer: # Network optimizations 'TCP_NODELAY': '1', # Disable Nagle's algorithm - # Hugging Face optimizations - 'TRANSFORMERS_CACHE': '/tmp/transformers_cache', # Use tmpfs for faster cache access - 'HF_OFFLINE': '0', # Online mode - # CUDA optimizations if GPU is used 'CUDA_LAUNCH_BLOCKING': '0', # Asynchronous CUDA launch @@ -310,4 +306,4 @@ def get_global_system_optimizer() -> SystemOptimizer: global _global_system_optimizer if _global_system_optimizer is None: _global_system_optimizer = SystemOptimizer() - return _global_system_optimizer \ No newline at end of file + return _global_system_optimizer