add EMBEDDING_BASE_URL

Merge branch 'feature/multimodal-image-input' into developing
feat: support multimodal image (base64) input in chat API
2026-06-18 14:56:27 +08:00 · 2026-06-18 12:54:31 +08:00 · 2026-06-18 11:34:57 +08:00 · 2026-06-16 15:47:52 +08:00 · 2026-06-16 13:22:12 +08:00 · 2026-06-16 13:19:23 +08:00
16 changed files with 344 additions and 872 deletions
--- 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

--- 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
--- 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")
--- 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
--- 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
--- 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
--- a/poetry.lock
+++ b/poetry.lock
@ -1163,18 +1163,6 @@ all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>
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-dev = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "av", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "jinja2 (>=3.1.0)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<=0.9)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxconverter-common", "openai (>=1.98.0)", "optax (>=0.0.8,<=0.1.4)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "torch (>=2.2)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)", "uvicorn"]
-dev-tensorflow = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "onnxconverter-common", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "openai (>=1.98.0)", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "tf2onnx", "timeout-decorator", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "urllib3 (<2.0.0)", "uvicorn"]
-dev-torch = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "kenlm", "kernels (>=0.6.1,<=0.9)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "openai (>=1.98.0)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "torch (>=2.2)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)", "uvicorn"]
-flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
-flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-ftfy = ["ftfy"]
-hf-xet = ["hf_xet"]
-hub-kernels = ["kernels (>=0.6.1,<=0.9)"]
-integrations = ["kernels (>=0.6.1,<=0.9)", "optuna", "ray[tune] (>=2.7.0)"]
-ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)"]
-mistral-common = ["mistral-common[opencv] (>=1.6.3)"]
-modelcreation = ["cookiecutter (==1.7.3)"]
-natten = ["natten (>=0.14.6,<0.15.0)"]
-num2words = ["num2words"]
-onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
-onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
-open-telemetry = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"]
-optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (>=2.15.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.13.1)", "urllib3 (<2.0.0)"]
-ray = ["ray[tune] (>=2.7.0)"]
-retrieval = ["datasets (>=2.15.0)", "faiss-cpu"]
-ruff = ["ruff (==0.13.1)"]
-sagemaker = ["sagemaker (>=2.31.0)"]
-sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["accelerate (>=0.26.0)", "fastapi", "openai (>=1.98.0)", "pydantic (>=2)", "starlette", "torch (>=2.2)", "uvicorn"]
-sigopt = ["sigopt"]
-sklearn = ["scikit-learn"]
-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "libcst", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "openai (>=1.98.0)", "parameterized (>=0.9)", "psutil", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "tensorboard", "timeout-decorator", "torch (>=2.2)", "uvicorn"]
-tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-tiktoken = ["blobfile", "tiktoken"]
-timm = ["timm (!=1.0.18,<=1.0.19)"]
-tokenizers = ["tokenizers (>=0.22.0,<=0.23.0)"]
-torch = ["accelerate (>=0.26.0)", "torch (>=2.2)"]
-torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.34.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "tqdm (>=4.27)"]
-video = ["av"]
-vision = ["Pillow (>=10.0.1,<=15.0)"]
-
-[[package]]
-name = "triton"
-version = "2.2.0"
-description = "A language and compiler for custom Deep Learning operations"
-optional = false
-python-versions = "*"
-groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
-files = [
-    {file = "triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5"},
-    {file = "triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0"},
-    {file = "triton-2.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5"},
-    {file = "triton-2.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab"},
-    {file = "triton-2.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace"},
-    {file = "triton-2.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df"},
-]
-
-[package.dependencies]
-filelock = "*"
-
-[package.extras]
-build = ["cmake (>=3.20)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=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"
--- 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 `<NO_ANSWER>`.
+
+Rules:
+- Output the marker `<NO_ANSWER>` **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:
+- `<NO_ANSWER>Sorry, I couldn't find that information in the knowledge base.`
--- 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",
--- 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"
--- 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)
--- a/skills/onprem/static-hosting/SKILL.md
+++ 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 <absolute_path>
+```
+
+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
--- a/skills/onprem/static-hosting/scripts/get_url.py
+++ 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]} <file_path>")
+    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}")
--- 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):
--- 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:<mime_type>;base64,<data>
+                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:
--- 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
Author	SHA1	Message	Date
朱潮	01fb63c955	add EMBEDDING_BASE_URL	2026-06-18 14:56:27 +08:00
朱潮	aabb0ad072	Merge branch 'feature/multimodal-image-input' into developing	2026-06-18 12:54:31 +08:00
朱潮	13bdd9d40a	feat: support multimodal image (base64) input in chat API Normalize OpenAI-style and LangChain standard image blocks into LangChain standard content blocks so provider block_translators auto-convert for either OpenAI or Anthropic. Flatten multimodal content to plain text when persisting history and computing term embeddings. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-18 11:34:57 +08:00
朱潮	f85ddaf127	fix: remove stray sympy import causing startup failure The unused 'from sympy.printing.cxx import none' was accidentally added by IDE autocomplete. sympy is not installed in the image, so importing agent/deep_assistant.py raised ModuleNotFoundError and the API server crash-looped on startup. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-16 15:47:52 +08:00
朱潮	9bd40d9bd7	Merge branch 'feature/no_answer' into dev	2026-06-16 13:22:12 +08:00
朱潮	d8378fbb70	add no answer tag	2026-06-16 13:19:23 +08:00
朱潮	838111c0fe	onrepm static hosting	2026-06-15 15:11:03 +08:00
github-actions[bot]	79b2c35d49	chore(.features): sync feature memory (auto) (#55 ) Generated by sparticle-toolkit feature-memory-sync Co-authored-by: Denya0529 <217564326+Denya0529@users.noreply.github.com>	2026-06-12 17:00:20 +00:00
csh28	9a7d64bb59	Merge pull request #48 from sparticleinc/codex/remove-heavy-embedding-deps [codex] Remove heavy embedding dependencies	2026-06-09 18:08:42 +08:00
csh28	f42a9e484a	Remove heavy embedding dependencies	2026-06-09 08:56:31 +08:00
朱潮	cfb0babf2a	Merge branch 'prod' of https://github.com/sparticleinc/catalog-agent into prod	2026-06-08 19:54:30 +08:00
github-actions[bot]	88fa7cc05c	chore(.features): sync feature memory (auto) (#47 ) Generated by sparticle-toolkit feature-memory-sync Co-authored-by: Denya0529 <217564326+Denya0529@users.noreply.github.com>	2026-06-05 16:49:13 +00:00