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towardsai-tutors
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ai-tutor-chatbot

Running

omarsol commited on Jun 9

Commit

d5ed5c9

1 Parent(s): 3f613d2

Update project structure

- Renamed the example environment file to reflect the FastAPI backend usage.
- Removed the Gradio UI and associated files, consolidating the project to a single Next.js frontend served by FastAPI.
- Added a pre-commit configuration for local linting and formatting.
- Updated AGENTS.md and README.md to clarify the architecture and deployment process, emphasizing the new structure and removing references to the Gradio UI.
- Introduced CI workflows for automated linting, testing, and deployment to Hugging Face Spaces.

Files changed (17) hide show

.env.example +1 -1
.github/workflows/ci.yml +65 -0
.github/workflows/deploy-prod-to-hf.yml +22 -0
.github/workflows/sync-to-hf.yml +7 -22
.pre-commit-config.yaml +10 -0
AGENTS.md +13 -13
Dockerfile.gradio +0 -27
README.md +11 -87
data/scraping_scripts/README.md +4 -4
pyproject.toml +5 -1
scripts/chat_service.py +91 -157
scripts/gradio_presenter.py +0 -375
scripts/main.py +0 -151
tests/manual_e2e_langsmith.md +0 -60
tests/test_gradio_kb_e2e.py +0 -209
tests/test_gradio_presenter.py +0 -91
uv.lock +80 -271

.env.example CHANGED Viewed

@@ -1,4 +1,4 @@
-# To run AI Tutor Gradio UI
 OPENAI_API_KEY=...
 ANTHROPIC_API_KEY=...
 GEMINI_API_KEY=...

+# To run the AI Tutor app (FastAPI backend)
 OPENAI_API_KEY=...
 ANTHROPIC_API_KEY=...
 GEMINI_API_KEY=...

.github/workflows/ci.yml ADDED Viewed

	@@ -0,0 +1,65 @@

+name: CI
+on:
+  pull_request:
+    branches: [main]
+  push:
+    branches: [main]
+concurrency:
+  group: ci-${{ github.ref }}
+  cancel-in-progress: true
+jobs:
+  qa:
+    name: Lint & format
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          enable-cache: true
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version-file: ".python-version"
+      - name: Install dev tooling
+        run: uv sync --only-dev --frozen
+      - name: Format check
+        run: uv run --no-sync ruff format --check .
+      - name: Lint check
+        run: uv run --no-sync ruff check .
+  tests:
+    name: Tests
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      # run_kb_command sandbox tests shell out to ripgrep; without it they skip.
+      - name: Install ripgrep
+        run: sudo apt-get update && sudo apt-get install -y ripgrep
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          enable-cache: true
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version-file: ".python-version"
+      - name: Install project
+        run: uv sync --frozen
+      # Live model-backed tests stay skipped here (no RUN_LIVE_* / no API keys);
+      # they are run manually/locally. CI guards the offline suite only.
+      - name: Run tests
+        run: uv run --no-sync pytest

.github/workflows/deploy-prod-to-hf.yml ADDED Viewed

	@@ -0,0 +1,22 @@

+name: Deploy prod to Hugging Face (ai-tutor-chatbot)
+# Manual-only promotion to the public prod Space. Run it from the Actions tab
+# after verifying the dev Space (ai-tutor) looks good. Deploys the ref you
+# pick in the "Run workflow" dropdown (defaults to main).
+on:
+  workflow_dispatch:
+jobs:
+  deploy-prod:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to HuggingFace Space (ai-tutor-chatbot)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          HF_USERNAME: ${{ secrets.HF_USERNAME }}
+        run: git push --force "https://$HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/towardsai-tutors/ai-tutor-chatbot" HEAD:main

.github/workflows/sync-to-hf.yml CHANGED Viewed

@@ -1,4 +1,7 @@
-name: Sync to Hugging Face Spaces
 on:
   push:
     branches: [main]
@@ -7,39 +10,21 @@ on:
       - 'README.md'
       - 'docs/**'
       - '**.md'
-      # Add any other paths you want to exclude
   # to run this workflow manually from the Actions tab
   workflow_dispatch:
 jobs:
-  sync-to-hub:
     runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - space: ai-tutor
-            dockerfile: Dockerfile
-          - space: ai-tutor-chatbot
-            dockerfile: Dockerfile.gradio
     steps:
       - uses: actions/checkout@v3
         with:
           fetch-depth: 0
           lfs: true
-      - name: Swap in target Dockerfile
-        if: matrix.dockerfile != 'Dockerfile'
-        run: |
-          git config user.email "actions@github.com"
-          git config user.name "GitHub Actions"
-          cp "${{ matrix.dockerfile }}" Dockerfile
-          git add Dockerfile
-          git commit -m "Use ${{ matrix.dockerfile }} for ${{ matrix.space }} space"
-      - name: Push to HuggingFace Space (${{ matrix.space }})
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
           HF_USERNAME: ${{ secrets.HF_USERNAME }}
-        run: git push --force "https://$HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/towardsai-tutors/${{ matrix.space }}" main:main

+name: Deploy dev to Hugging Face (ai-tutor)
+# Continuous deployment to the private dev Space. Test there, then promote
+# the same commit to prod with the "Deploy prod" workflow (manual trigger).
 on:
   push:
     branches: [main]
       - 'README.md'
       - 'docs/**'
       - '**.md'
   # to run this workflow manually from the Actions tab
   workflow_dispatch:
 jobs:
+  deploy-dev:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
         with:
           fetch-depth: 0
           lfs: true
+      - name: Push to HuggingFace Space (ai-tutor)
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
           HF_USERNAME: ${{ secrets.HF_USERNAME }}
+        run: git push --force "https://$HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/towardsai-tutors/ai-tutor" HEAD:main

.pre-commit-config.yaml ADDED Viewed

	@@ -0,0 +1,10 @@

+# Local fast-feedback hooks. One-time setup per clone: `uv run pre-commit install`.
+# CI (.github/workflows/ci.yml) is the real gate; these just fix lint/format
+# before a commit so you don't get a red CI run for formatting.
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.15.15 # keep in sync with the ruff version in pyproject dev deps
+    hooks:
+      - id: ruff-check # lint
+        args: [--fix]
+      - id: ruff-format # format

AGENTS.md CHANGED Viewed

@@ -4,17 +4,14 @@ This is the **canonical, tool-agnostic** instruction file for the repo. `CLAUDE.
 ## Project Overview
-AI tutor for applied AI, LLMs, RAG, and Python. **Agentic RAG**: a LangChain/LangGraph agent grounds answers in a curated corpus of course + library docs, can browse a local file-based knowledge base, and (optionally) search the live web. Two frontends share one agent core:
-- **Gradio** UI — `scripts/main.py` (the original chatbot).
-- **Next.js** UI — `frontend/`, served by a **FastAPI** backend (`scripts/api.py`) that streams in the Vercel AI SDK UI-message protocol.
 ChromaDB for vectors; Cohere for embeddings/rerank; chat model is provider-configurable (Gemini default, Anthropic, OpenAI). Python ≥3.13, managed with `uv`.
 ## Key URLs
 - [GitHub repo](https://github.com/towardsai/ai-tutor-app)
-- [Live demo (Gradio)](https://huggingface.co/spaces/towardsai-tutors/ai-tutor-chatbot)
 - [Vector DB + KB bundle](https://huggingface.co/datasets/towardsai-tutors/ai-tutor-vector-db) · [Private raw JSONL data](https://huggingface.co/datasets/towardsai-tutors/ai-tutor-data)
 ## Where things live
@@ -26,7 +23,6 @@ ChromaDB for vectors; Cohere for embeddings/rerank; chat model is provider-confi
 | Hybrid retrieval | `scripts/chroma_rag.py` |
 | KB browsing sandbox + citation resolution | `scripts/kb_shell.py`, `scripts/kb_manifest.py` |
 | FastAPI server (`/api/chat`, `/api/tools`, `/healthz`) | `scripts/api.py` |
-| Gradio app + renderer | `scripts/main.py`, `scripts/gradio_presenter.py` |
 | Paths, models, startup downloads | `scripts/setup.py` |
 | **Sources — single source of truth** | `data/scraping_scripts/source_registry.py` |
 | Agent tracing (LangSmith) + server logging (stdlib `logging` → stdout) | `scripts/agent_tracing.py`, `scripts/setup.py` |
@@ -35,7 +31,7 @@ ChromaDB for vectors; Cohere for embeddings/rerank; chat model is provider-confi
 ## Architecture in brief
-The agent is built with `langchain.agents.create_agent()` (LangGraph), an `InMemorySaver` checkpointer keyed by `thread_id`, and middlewares for context-editing, summarization, and source preference. `stream_chat()` is the single entry point both frontends call; it yields typed `ChatEvent`s. It always exposes two custom tools, plus provider-native web tools when enabled:
 - **`retrieve_tutor_context(query)`** — hybrid RAG over the corpus, scoped to the user's selected sources.
 - **`run_kb_command(...)`** — read-only KB file browsing (see below).
@@ -63,13 +59,12 @@ Runtime guidance the agent follows is in `data/kb/AGENTS.md` (injected into the
 ## Sources & config
-`data/scraping_scripts/source_registry.py` is the **single source of truth** for sources (`SOURCE_CONFIGS`, key groupings, UI labels, defaults); `scripts/setup.py` re-exports them and both frontends derive the picker from it. Docs sources ingest via the GitHub API or `llms.txt`; course sources are Notion exports. To add a source: add it to the registry (+ the relevant grouping tuples), then run the matching workflow — no separate UI edit needed. Models live in `setup.AVAILABLE_MODELS` (default `google-genai:gemini-3.5-flash`; also Claude Haiku 4.5; OpenAI supported in code).
 ## Running locally
 ```bash
 uv sync && cp .env.example .env      # then fill in keys
-uv run -m scripts.main               # Gradio UI (:7860)
 uv run -m scripts.api                # FastAPI backend (:8000; override AI_TUTOR_API_PORT/PORT)
 # Next.js frontend (needs the API running):
 cd frontend && npm install && cp .env.example .env.local && npm run dev   # :3000
@@ -77,7 +72,7 @@ cd frontend && npm install && cp .env.example .env.local && npm run dev   # :300
 First start downloads the vector-db/KB bundle from HF if missing (`HF_TOKEN`). The frontend is a static export (`output: 'export'`); `npm run build` emits `frontend/out`, which `scripts/api.py` mounts at `/`.
-Test & lint: `uv run pytest` · `uv run ruff check .`
 ## Data update workflows
@@ -97,12 +92,17 @@ Chat runtime: `COHERE_API_KEY` (retrieval), one chat-model provider key (`GEMINI
 ## Deployment
-`.github/workflows/sync-to-hf.yml` force-pushes to two HF Spaces on every push to `main`: **`ai-tutor`** (`Dockerfile`: FastAPI + Next.js export) and **`ai-tutor-chatbot`** (`Dockerfile.gradio`). Both install `ripgrep` for `run_kb_command` and run on :7860.
 ## Conventions
-- **No em-dashes in frontend user-facing text.** Use a comma, parentheses, a colon, or two sentences instead. This covers every string the UI renders (Next.js components and Gradio): tool descriptions, popovers, labels, `title` tooltips, placeholders, empty states. This file and other docs are exempt.
-- **Decide frontend vs. backend ownership before changing behavior, and fix it on the side that owns the data.** The backend is the single source of truth for data shape and meaning; the frontend renders what it receives instead of reshaping it. If a field is empty, that should be because the server wrote it empty, not because the UI stripped it. For example, a source with no library version must get `version: null` from `capture_source_versions.py`; it should never be filtered out client-side. Reshaping data in the frontend causes drift between the two frontends and ambiguity about why a value is missing (did the server omit it, or did the client hide it?).
 ## Gotchas

 ## Project Overview
+AI tutor for applied AI, LLMs, RAG, and Python. **Agentic RAG**: a LangChain/LangGraph agent grounds answers in a curated corpus of course + library docs, can browse a local file-based knowledge base, and (optionally) search the live web. One frontend: a **Next.js** UI (`frontend/`), served by a **FastAPI** backend (`scripts/api.py`) that streams in the Vercel AI SDK UI-message protocol. (A Gradio UI existed historically; it was removed to keep one rendering path.)
 ChromaDB for vectors; Cohere for embeddings/rerank; chat model is provider-configurable (Gemini default, Anthropic, OpenAI). Python ≥3.13, managed with `uv`.
 ## Key URLs
 - [GitHub repo](https://github.com/towardsai/ai-tutor-app)
+- [Live demo — prod Space](https://huggingface.co/spaces/towardsai-tutors/ai-tutor-chatbot) · [Dev Space (private)](https://huggingface.co/spaces/towardsai-tutors/ai-tutor)
 - [Vector DB + KB bundle](https://huggingface.co/datasets/towardsai-tutors/ai-tutor-vector-db) · [Private raw JSONL data](https://huggingface.co/datasets/towardsai-tutors/ai-tutor-data)
 ## Where things live
 | Hybrid retrieval | `scripts/chroma_rag.py` |
 | KB browsing sandbox + citation resolution | `scripts/kb_shell.py`, `scripts/kb_manifest.py` |
 | FastAPI server (`/api/chat`, `/api/tools`, `/healthz`) | `scripts/api.py` |
 | Paths, models, startup downloads | `scripts/setup.py` |
 | **Sources — single source of truth** | `data/scraping_scripts/source_registry.py` |
 | Agent tracing (LangSmith) + server logging (stdlib `logging` → stdout) | `scripts/agent_tracing.py`, `scripts/setup.py` |
 ## Architecture in brief
+The agent is built with `langchain.agents.create_agent()` (LangGraph), an `InMemorySaver` checkpointer keyed by `thread_id`, and middlewares for context-editing, summarization, and source preference. `stream_chat()` is the single entry point the API calls; it yields typed `ChatEvent`s that `scripts/api.py` encodes into the AI SDK UI-message stream. It always exposes two custom tools, plus provider-native web tools when enabled:
 - **`retrieve_tutor_context(query)`** — hybrid RAG over the corpus, scoped to the user's selected sources.
 - **`run_kb_command(...)`** — read-only KB file browsing (see below).
 ## Sources & config
+`data/scraping_scripts/source_registry.py` is the **single source of truth** for sources (`SOURCE_CONFIGS`, key groupings, UI labels, defaults); `scripts/setup.py` re-exports them and the frontend derives the picker from it (via `/api/tools`). Docs sources ingest via the GitHub API or `llms.txt`; course sources are Notion exports. To add a source: add it to the registry (+ the relevant grouping tuples), then run the matching workflow — no separate UI edit needed. Models live in `setup.AVAILABLE_MODELS` (default `google-genai:gemini-3.5-flash`; also Claude Haiku 4.5; OpenAI supported in code).
 ## Running locally
 ```bash
 uv sync && cp .env.example .env      # then fill in keys
 uv run -m scripts.api                # FastAPI backend (:8000; override AI_TUTOR_API_PORT/PORT)
 # Next.js frontend (needs the API running):
 cd frontend && npm install && cp .env.example .env.local && npm run dev   # :3000
 First start downloads the vector-db/KB bundle from HF if missing (`HF_TOKEN`). The frontend is a static export (`output: 'export'`); `npm run build` emits `frontend/out`, which `scripts/api.py` mounts at `/`.
+Test, lint & format: `uv run pytest` · `uv run ruff check .` · `uv run ruff format .`. CI (`.github/workflows/ci.yml`) enforces all three on PRs/pushes to `main`; for local auto-fix on commit, run `uv run pre-commit install` once.
 ## Data update workflows
 ## Deployment
+Both HF Spaces run the same image (`Dockerfile`: FastAPI + Next.js static export, `ripgrep` installed for `run_kb_command`, port :7860), in a dev → prod flow:
+- **Dev — `ai-tutor`** (private): `.github/workflows/sync-to-hf.yml` force-pushes on every push to `main`. Verify changes here first.
+- **Prod — `ai-tutor-chatbot`** (public): `.github/workflows/deploy-prod-to-hf.yml`, **manual trigger only** (Actions tab → "Deploy prod to Hugging Face" → Run workflow).
+Both Spaces need the same runtime secrets (`COHERE_API_KEY`, model provider key, `HF_TOKEN`, optional `LANGSMITH_*`/`MONGODB_URI`) configured in their HF settings.
 ## Conventions
+- **No em-dashes in frontend user-facing text.** Use a comma, parentheses, a colon, or two sentences instead. This covers every string the UI renders (Next.js components): tool descriptions, popovers, labels, `title` tooltips, placeholders, empty states. This file and other docs are exempt.
+- **Decide frontend vs. backend ownership before changing behavior, and fix it on the side that owns the data.** The backend is the single source of truth for data shape and meaning; the frontend renders what it receives instead of reshaping it. If a field is empty, that should be because the server wrote it empty, not because the UI stripped it. For example, a source with no library version must get `version: null` from `capture_source_versions.py`; it should never be filtered out client-side. Reshaping data in the frontend causes ambiguity about why a value is missing (did the server omit it, or did the client hide it?).
 ## Gotchas

Dockerfile.gradio DELETED Viewed

@@ -1,27 +0,0 @@
-FROM python:3.13
-# ripgrep backs run_kb_command's `rg`; without it the model falls back to find+cat.
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends ripgrep \
-    && rm -rf /var/lib/apt/lists/*
-# Install uv into /usr/local/bin (on PATH for every user, including HF's uid 1000).
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
-    && mv /root/.local/bin/uv /root/.local/bin/uvx /usr/local/bin/ \
-    && rm -rf /root/.local /root/.cache
-RUN useradd -m -u 1000 user
-WORKDIR /app
-COPY pyproject.toml uv.lock ./
-RUN uv sync --locked
-COPY . .
-ENV HOME=/home/user \
-    PORT=7860
-RUN chown -R user:user /app
-USER user
-EXPOSE 7860
-CMD ["uv", "run", "-m", "scripts.main"]

README.md CHANGED Viewed

@@ -8,15 +8,15 @@ app_port: 7860
 pinned: false
 ---
-### Gradio UI Chatbot
-A Gradio UI for the chatbot is available in [scripts/main.py](./scripts/main.py).
-The Gradio demo is deployed on Hugging Face Spaces at: [AI Tutor Chatbot on Hugging Face](https://huggingface.co/spaces/towardsai-tutors/ai-tutor-chatbot).
-**Note:** A GitHub Action automatically deploys the Gradio demo when changes are pushed to the main branch (excluding documentation and scripts in the `data/scraping_scripts` directory).
-### Gradio UI — Quick Start
 1. Install dependencies (requires [uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods)):
@@ -30,17 +30,9 @@ The Gradio demo is deployed on Hugging Face Spaces at: [AI Tutor Chatbot on Hugg
    cp .env.example .env  # then edit values
    ```
-   The chat model is provider-agnostic. Use the UI field in `provider:model` format, for example `openai:gpt-5.4-mini`. Optional provider keys include `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, and `GOOGLE_API_KEY`. Anthropic support is wired through the `anthropic` SDK, and Gemini support is wired through Google’s `google-genai` SDK.
    To trace requests in LangSmith, set `LANGSMITH_API_KEY`. The app enables tracing automatically when that key is present unless `LANGSMITH_TRACING=false` is set.
-3. Run:
-   ```bash
-   uv run -m scripts.main
-   ```
-   Starts the Gradio AI Tutor interface.
 ### LangSmith Agent Tracing
 The chatbot is built with `langchain.agents.create_agent()`, so LangSmith can trace the LangGraph/LangChain run tree without extra dependencies. Add these values to `.env`:
@@ -57,7 +49,7 @@ Tracing sends prompts, retrieved snippets, tool inputs, tool outputs, and model
 ### Next.js Frontend — Quick Start
-The repo now also includes a separate Next.js frontend in [frontend](./frontend) that talks to the FastAPI backend instead of the Gradio transport.
 1. Start the Python API:
@@ -102,79 +94,11 @@ This frontend consumes:
 and renders sources, tool activity, and reasoning as separate UI elements rather than a single markdown block.
-### Gradio API
-The chat endpoint is exposed as `chat`, so the API flow is:
-```bash
-POST /gradio_api/call/chat
-GET /gradio_api/call/chat/{event_id}
-```
-The `POST` body must send `data` in this exact order:
-1. User message: string
-2. History: array
-3. Sources: array of source labels exactly as shown in the UI
-4. Model: `provider:model` string
-5. Show Gemini thoughts: boolean
-6. Thread ID: string, empty for a new conversation
-Example first turn:
-```bash
-curl -s http://127.0.0.1:7860/gradio_api/call/chat \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "data": [
-      "What is LoRA?",
-      [],
-      ["PEFT Docs", "Transformers Docs"],
-      "openai:gpt-4o-mini",
-      false,
-      ""
-    ]
-  }'
-```
-That returns an `event_id`. Open the server-sent event stream with:
-```bash
-curl -N http://127.0.0.1:7860/gradio_api/call/chat/<event_id>
-```
-The streamed payloads look like this:
-```json
-["Partial assistant text", null, "thread-id"]
-```
-- `data[0]`: current streamed assistant text
-- `data[1]`: Gradio's hidden state placeholder, ignore this
-- `data[2]`: `thread_id` to reuse on the next turn
-Example follow-up turn on the same conversation:
-```bash
-curl -s http://127.0.0.1:7860/gradio_api/call/chat \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "data": [
-      "How is it different from adapters?",
-      [],
-      ["PEFT Docs", "Transformers Docs"],
-      "openai:gpt-4o-mini",
-      false,
-      "thread-id-from-previous-response"
-    ]
-  }'
-```
-Notes:
-- API clients should usually send `[]` for history and continue the conversation with `thread_id`.
-- The source filter is request-scoped, so you can keep the same `thread_id` while changing sources between turns.
-- Sending an empty `thread_id` starts a new backend conversation.
 ### Knowledge Base (file-based)

 pinned: false
 ---
+### AI Tutor Chatbot
+An agentic RAG tutor for applied AI, LLMs, RAG, and Python: a Next.js frontend served by a FastAPI backend, with a LangChain/LangGraph agent core. See [AGENTS.md](./AGENTS.md) for the architecture map.
+The live app is deployed on Hugging Face Spaces at: [AI Tutor Chatbot on Hugging Face](https://huggingface.co/spaces/towardsai-tutors/ai-tutor-chatbot) (prod).
+**Deployment flow:** every push to `main` auto-deploys to the private dev Space ([ai-tutor](https://huggingface.co/spaces/towardsai-tutors/ai-tutor)) for verification; the prod Space is promoted manually via the "Deploy prod to Hugging Face" workflow in the Actions tab.
+### Backend — Quick Start
 1. Install dependencies (requires [uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods)):
    cp .env.example .env  # then edit values
    ```
+   The chat model is provider-agnostic, configured in `provider:model` format, for example `google-genai:gemini-3.5-flash`. Optional provider keys include `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, and `GOOGLE_API_KEY`. Anthropic support is wired through the `anthropic` SDK, and Gemini support is wired through Google’s `google-genai` SDK.
    To trace requests in LangSmith, set `LANGSMITH_API_KEY`. The app enables tracing automatically when that key is present unless `LANGSMITH_TRACING=false` is set.
 ### LangSmith Agent Tracing
 The chatbot is built with `langchain.agents.create_agent()`, so LangSmith can trace the LangGraph/LangChain run tree without extra dependencies. Add these values to `.env`:
 ### Next.js Frontend — Quick Start
+The Next.js frontend in [frontend](./frontend) talks to the FastAPI backend.
 1. Start the Python API:
 and renders sources, tool activity, and reasoning as separate UI elements rather than a single markdown block.
+API notes:
+- API clients should usually send only the new user message and continue the conversation with `threadId` (the `data-thread` part of the stream carries it; send it back on the next request).
+- The source filter is request-scoped, so you can keep the same `threadId` while changing sources between turns.
+- Sending an empty `threadId` starts a new backend conversation.
 ### Knowledge Base (file-based)

data/scraping_scripts/README.md CHANGED Viewed

@@ -125,7 +125,7 @@ uv run -m data.scraping_scripts.add_course_workflow --courses master_ai_for_work
 Run the chatbot locally to test if the course has been added correctly.
 ```bash
-uv run -m scripts.main
 ```
 ----
@@ -284,11 +284,11 @@ leaving the source configured as active for a future rebuild.
 3. By default, only new content will have context added to save time and resources. Use `--process-all-context` only if you need to regenerate context for all documents. Use `--skip-data-upload` if you don't want to upload data files to the private HuggingFace repo (they're uploaded by default).
-4. When adding a new course, verify that it appears in the Gradio UI:
    - Add the source label and default-selection metadata in `source_registry.py`
-   - Check that the new source appears in the dropdown menu in the UI
    - Make sure it's properly included in the default selected sources if desired
-   - Restart the Gradio app to see the changes
 5. First time setup or missing files:
    - Both workflows automatically check for and download required data files:

 Run the chatbot locally to test if the course has been added correctly.
 ```bash
+uv run -m scripts.api
 ```
 ----
 3. By default, only new content will have context added to save time and resources. Use `--process-all-context` only if you need to regenerate context for all documents. Use `--skip-data-upload` if you don't want to upload data files to the private HuggingFace repo (they're uploaded by default).
+4. When adding a new course, verify that it appears in the UI:
    - Add the source label and default-selection metadata in `source_registry.py`
+   - Check that the new source appears in the source picker in the UI
    - Make sure it's properly included in the default selected sources if desired
+   - Restart the API server to see the changes
 5. First time setup or missing files:
    - Both workflows automatically check for and download required data files:

pyproject.toml CHANGED Viewed

@@ -10,7 +10,6 @@ dependencies = [
     "cohere",
     "fastapi",
     "google-genai",
-    "gradio",
     "hf-xet",
     "huggingface-hub",
     "instructor",
@@ -34,9 +33,14 @@ dependencies = [
 [dependency-groups]
 dev = [
     "httpx",
     "pytest>=9.0.3",
     "ruff>=0.15.10",
 ]
 [tool.pytest.ini_options]
 pythonpath = ["."]

     "cohere",
     "fastapi",
     "google-genai",
     "hf-xet",
     "huggingface-hub",
     "instructor",
 [dependency-groups]
 dev = [
     "httpx",
+    "pre-commit",
     "pytest>=9.0.3",
     "ruff>=0.15.10",
 ]
 [tool.pytest.ini_options]
 pythonpath = ["."]
+[tool.ruff]
+line-length = 88
+target-version = "py313"

scripts/chat_service.py CHANGED Viewed

@@ -57,19 +57,8 @@ from .setup import (
 logger = logging.getLogger(__name__)
-SOURCES_HEADER = "📝 Here are the sources I used to answer your question:"
-ACTIVITY_BLOCK_START = "<!-- MODEL_ACTIVITY_START -->"
-ACTIVITY_BLOCK_END = "<!-- MODEL_ACTIVITY_END -->"
-THOUGHTS_BLOCK_START = "<!-- GEMINI_THOUGHTS_START -->"
-THOUGHTS_BLOCK_END = "<!-- GEMINI_THOUGHTS_END -->"
-ANSWER_HEADER = "**Answer**"
-LEGACY_THOUGHTS_DETAILS_OPEN = "<details><summary>Gemini thoughts</summary>"
-LEGACY_THOUGHTS_DETAILS_OPEN_EXPANDED = (
-    "<details open><summary>Gemini thoughts</summary>"
-)
 CHECKPOINTER = InMemorySaver()
 _RETRIEVER_INIT_LOCK = Lock()
-KB_TOOL_NAMES = ("run_kb_command",)
 DEFAULT_KB_COMMAND_LIMIT = 20
 _KB_COMMAND_COUNTS: dict[str, int] = {}
 _KB_COMMAND_COUNT_LOCK = Lock()
@@ -246,79 +235,15 @@ def message_content_to_text(content: Any) -> str:
     return str(content)
-def strip_sources_block(text: str) -> str:
-    separator = f"\n\n{SOURCES_HEADER}"
-    body, marker, _ = text.partition(separator)
-    if marker:
-        return body.strip()
-    if text.startswith(SOURCES_HEADER):
-        return ""
-    return text.strip()
-def strip_hidden_block(text: str, start_marker: str, end_marker: str) -> str:
-    stripped = text
-    while True:
-        start = stripped.find(start_marker)
-        if start == -1:
-            return stripped
-        end = stripped.find(end_marker, start)
-        if end == -1:
-            return stripped[:start]
-        stripped = stripped[:start] + stripped[end + len(end_marker) :]
-def strip_activity_block(text: str) -> str:
-    stripped = strip_hidden_block(text, ACTIVITY_BLOCK_START, ACTIVITY_BLOCK_END)
-    stripped = strip_hidden_block(stripped, THOUGHTS_BLOCK_START, THOUGHTS_BLOCK_END)
-    for marker in (
-        LEGACY_THOUGHTS_DETAILS_OPEN_EXPANDED,
-        LEGACY_THOUGHTS_DETAILS_OPEN,
-    ):
-        separator = f"\n\n{marker}"
-        body, found, _ = stripped.partition(separator)
-        if found:
-            return body.strip()
-        if stripped.startswith(marker):
-            return ""
-    return stripped.strip()
-def strip_answer_header(text: str) -> str:
-    stripped = text.strip()
-    answer_prefix = f"{ANSWER_HEADER}\n\n"
-    if stripped.startswith(answer_prefix):
-        return stripped[len(answer_prefix) :].strip()
-    if stripped == ANSWER_HEADER:
-        return ""
-    return stripped
-def strip_display_blocks(text: str) -> str:
-    return strip_answer_header(strip_activity_block(strip_sources_block(text)))
-def normalize_history(
-    history: list[dict[str, Any]] | tuple[ChatTurn, ...] | list[ChatTurn],
-) -> tuple[ChatTurn, ...]:
-    if not history:
-        return ()
     normalized: list[ChatTurn] = []
-    for message in history:
-        if isinstance(message, ChatTurn):
-            role = message.role
-            content = message.content
-        else:
-            role = str(message.get("role", ""))
-            content = message_content_to_text(message.get("content"))
-        if role not in {"user", "assistant"}:
             continue
-        if role == "assistant":
-            content = strip_display_blocks(content)
-        normalized.append(ChatTurn(role=role, content=content))
     return tuple(normalized)
@@ -333,7 +258,7 @@ def checkpoint_messages_to_history(messages: list[BaseMessage]) -> tuple[ChatTur
             history.append(
                 ChatTurn(
                     "assistant",
-                    strip_display_blocks(message_content_to_text(message.content)),
                 )
             )
     return tuple(history)
@@ -814,7 +739,7 @@ def effective_tool_names(
     model_name: str,
     enabled_tools: tuple[str, ...],
 ) -> tuple[str, ...]:
-    names = ["retrieve_tutor_context", *KB_TOOL_NAMES]
     enabled = set(enabled_tools)
     if is_google_genai_model(model_name):
         if "web_search" in enabled:
@@ -928,6 +853,76 @@ def extract_grounding_source_matches(
 GOOGLE_SEARCH_TOOL_NAME = "google_search"
 ANTHROPIC_SERVER_TOOL_NAMES = frozenset({"web_search", "web_fetch"})
 ANTHROPIC_RESULT_BLOCK_TYPES = {
     "web_search_tool_result": ("web_search", "Web"),
@@ -1057,9 +1052,7 @@ async def stream_chat(request: ChatRequest) -> AsyncIterator[ChatEvent]:
     include_reasoning = bool(request.include_reasoning) and is_google_genai_model(
         request.model_name
     )
-    google_search_call_id = ""
-    google_search_queries: list[str] = []
-    google_search_match_count = 0
     logger.info("Running query: %s", request.query)
     agent = build_agent(
@@ -1138,33 +1131,11 @@ async def stream_chat(request: ChatRequest) -> AsyncIterator[ChatEvent]:
                         },
                     )
-                token_metadata = getattr(token, "response_metadata", None)
-                new_queries = [
-                    q
-                    for q in extract_web_search_queries(token_metadata)
-                    if q not in google_search_queries
-                ]
-                new_grounding = extract_grounding_source_matches(
-                    token_metadata,
-                    web_evidence,
                 )
-                if (new_queries or new_grounding) and not google_search_call_id:
-                    google_search_call_id = uuid4().hex
-                    yield ChatEvent(
-                        "tool_call_started",
-                        {
-                            "message_id": message_id,
-                            "call_id": google_search_call_id,
-                            "tool_name": GOOGLE_SEARCH_TOOL_NAME,
-                            "args": {
-                                "query": "; ".join(new_queries) if new_queries else ""
-                            },
-                            "args_text": "; ".join(new_queries),
-                        },
-                    )
-                if new_queries:
-                    google_search_queries.extend(new_queries)
-                google_search_match_count += len(new_grounding)
                 continue
             if chunk["type"] != "updates":
@@ -1224,33 +1195,11 @@ async def stream_chat(request: ChatRequest) -> AsyncIterator[ChatEvent]:
                 if step != "model" or getattr(message, "type", None) != "ai":
                     continue
-                message_metadata = getattr(message, "response_metadata", None)
-                new_queries = [
-                    q
-                    for q in extract_web_search_queries(message_metadata)
-                    if q not in google_search_queries
-                ]
-                new_grounding = extract_grounding_source_matches(
-                    message_metadata,
-                    web_evidence,
                 )
-                if (new_queries or new_grounding) and not google_search_call_id:
-                    google_search_call_id = uuid4().hex
-                    yield ChatEvent(
-                        "tool_call_started",
-                        {
-                            "message_id": message_id,
-                            "call_id": google_search_call_id,
-                            "tool_name": GOOGLE_SEARCH_TOOL_NAME,
-                            "args": {
-                                "query": "; ".join(new_queries) if new_queries else ""
-                            },
-                            "args_text": "; ".join(new_queries),
-                        },
-                    )
-                if new_queries:
-                    google_search_queries.extend(new_queries)
-                google_search_match_count += len(new_grounding)
                 if is_anthropic_model(request.model_name):
                     anthropic_updates, anthropic_tool_uses = (
@@ -1301,24 +1250,9 @@ async def stream_chat(request: ChatRequest) -> AsyncIterator[ChatEvent]:
     finally:
         _clear_kb_command_budget(message_id)
-    if google_search_call_id:
-        joined_queries = "; ".join(google_search_queries)
-        if google_search_match_count == 0:
-            output_text = "Google search ran but returned no grounding results."
-        else:
-            plural = "" if google_search_match_count == 1 else "s"
-            output_text = f"Google search returned {google_search_match_count} web result{plural}."
-        yield ChatEvent(
-            "tool_call_completed",
-            {
-                "message_id": message_id,
-                "call_id": google_search_call_id,
-                "tool_name": GOOGLE_SEARCH_TOOL_NAME,
-                "args": {"query": joined_queries},
-                "args_text": joined_queries,
-                "output_text": output_text,
-            },
-        )
     answer = "".join(answer_chunks).strip() or completed_answer.strip()
     matched_sources = list(

 logger = logging.getLogger(__name__)
 CHECKPOINTER = InMemorySaver()
 _RETRIEVER_INIT_LOCK = Lock()
 DEFAULT_KB_COMMAND_LIMIT = 20
 _KB_COMMAND_COUNTS: dict[str, int] = {}
 _KB_COMMAND_COUNT_LOCK = Lock()
     return str(content)
+def normalize_history(history: tuple[ChatTurn, ...]) -> tuple[ChatTurn, ...]:
+    """Keep user/assistant turns, trimming assistant whitespace so incoming
+    history compares equal to checkpoint-derived history."""
     normalized: list[ChatTurn] = []
+    for turn in history:
+        if turn.role not in {"user", "assistant"}:
             continue
+        content = turn.content.strip() if turn.role == "assistant" else turn.content
+        normalized.append(ChatTurn(role=turn.role, content=content))
     return tuple(normalized)
             history.append(
                 ChatTurn(
                     "assistant",
+                    message_content_to_text(message.content).strip(),
                 )
             )
     return tuple(history)
     model_name: str,
     enabled_tools: tuple[str, ...],
 ) -> tuple[str, ...]:
+    names = ["retrieve_tutor_context", "run_kb_command"]
     enabled = set(enabled_tools)
     if is_google_genai_model(model_name):
         if "web_search" in enabled:
 GOOGLE_SEARCH_TOOL_NAME = "google_search"
+class GoogleSearchActivity:
+    """Surface Gemini's server-side google_search activity as tool events.
+    Gemini reports search grounding via response metadata instead of tool
+    messages, so queries and grounding results are accumulated from every
+    metadata payload and exposed as a single synthetic tool call per turn.
+    """
+    def __init__(self, message_id: str, web_evidence: dict[str, SourceMatch]) -> None:
+        self._message_id = message_id
+        self._web_evidence = web_evidence
+        self._call_id = ""
+        self._queries: list[str] = []
+        self._match_count = 0
+    def observe(self, response_metadata: Any) -> ChatEvent | None:
+        """Record metadata; return a tool_call_started event on first activity."""
+        new_queries = [
+            q
+            for q in extract_web_search_queries(response_metadata)
+            if q not in self._queries
+        ]
+        new_grounding = extract_grounding_source_matches(
+            response_metadata,
+            self._web_evidence,
+        )
+        started: ChatEvent | None = None
+        if (new_queries or new_grounding) and not self._call_id:
+            self._call_id = uuid4().hex
+            joined = "; ".join(new_queries)
+            started = ChatEvent(
+                "tool_call_started",
+                {
+                    "message_id": self._message_id,
+                    "call_id": self._call_id,
+                    "tool_name": GOOGLE_SEARCH_TOOL_NAME,
+                    "args": {"query": joined},
+                    "args_text": joined,
+                },
+            )
+        self._queries.extend(new_queries)
+        self._match_count += len(new_grounding)
+        return started
+    def completed_event(self) -> ChatEvent | None:
+        if not self._call_id:
+            return None
+        joined = "; ".join(self._queries)
+        if self._match_count == 0:
+            output_text = "Google search ran but returned no grounding results."
+        else:
+            plural = "" if self._match_count == 1 else "s"
+            output_text = (
+                f"Google search returned {self._match_count} web result{plural}."
+            )
+        return ChatEvent(
+            "tool_call_completed",
+            {
+                "message_id": self._message_id,
+                "call_id": self._call_id,
+                "tool_name": GOOGLE_SEARCH_TOOL_NAME,
+                "args": {"query": joined},
+                "args_text": joined,
+                "output_text": output_text,
+            },
+        )
 ANTHROPIC_SERVER_TOOL_NAMES = frozenset({"web_search", "web_fetch"})
 ANTHROPIC_RESULT_BLOCK_TYPES = {
     "web_search_tool_result": ("web_search", "Web"),
     include_reasoning = bool(request.include_reasoning) and is_google_genai_model(
         request.model_name
     )
+    google_search = GoogleSearchActivity(message_id, web_evidence)
     logger.info("Running query: %s", request.query)
     agent = build_agent(
                         },
                     )
+                search_started = google_search.observe(
+                    getattr(token, "response_metadata", None)
                 )
+                if search_started:
+                    yield search_started
                 continue
             if chunk["type"] != "updates":
                 if step != "model" or getattr(message, "type", None) != "ai":
                     continue
+                search_started = google_search.observe(
+                    getattr(message, "response_metadata", None)
                 )
+                if search_started:
+                    yield search_started
                 if is_anthropic_model(request.model_name):
                     anthropic_updates, anthropic_tool_uses = (
     finally:
         _clear_kb_command_budget(message_id)
+    search_completed = google_search.completed_event()
+    if search_completed:
+        yield search_completed
     answer = "".join(answer_chunks).strip() or completed_answer.strip()
     matched_sources = list(

scripts/gradio_presenter.py DELETED Viewed

@@ -1,375 +0,0 @@
-from __future__ import annotations
-import json
-from dataclasses import dataclass, field
-from typing import Any
-from .chat_types import ChatEvent
-SOURCES_HEADER = "📝 Here are the sources I used to answer your question:"
-ACTIVITY_BLOCK_START = "<!-- MODEL_ACTIVITY_START -->"
-ACTIVITY_BLOCK_END = "<!-- MODEL_ACTIVITY_END -->"
-THOUGHTS_HEADER = "**Thinking**"
-THOUGHTS_HINT = "_Reasoning summary from Gemini. This is not the final answer._"
-TOOL_HEADER = "**Tool**"
-TOOL_PENDING_HINT = "_Searching the selected sources..._"
-ANSWER_HEADER = "**Answer**"
-@dataclass
-class ActivityEvent:
-    key: str
-    kind: str
-    body: str
-def as_blockquote(text: str) -> str:
-    lines = text.splitlines()
-    if not lines:
-        return ""
-    return "\n".join("> " + line if line else ">" for line in lines)
-def merge_stream_text(existing: str, incoming: str) -> str:
-    current = existing.strip()
-    update = incoming.strip()
-    if not update:
-        return current
-    if not current:
-        return update
-    if update == current or update in current:
-        return current
-    if current in update:
-        return update
-    return f"{current}\n\n{update}"
-def upsert_activity_event(
-    events: list[ActivityEvent],
-    *,
-    key: str,
-    kind: str,
-    body: str,
-    replace: bool = False,
-) -> None:
-    if not body.strip():
-        return
-    for index, event in enumerate(events):
-        if event.key != key:
-            continue
-        events[index] = ActivityEvent(
-            key=key,
-            kind=kind,
-            body=body.strip() if replace else merge_stream_text(event.body, body),
-        )
-        return
-    events.append(ActivityEvent(key=key, kind=kind, body=body.strip()))
-def format_tool_args(args: Any, args_text: str = "") -> str:
-    query = ""
-    url = ""
-    command = ""
-    if isinstance(args, dict):
-        query = str(args.get("query", "")).strip()
-        url = str(args.get("url", "")).strip()
-        command = str(args.get("command", "")).strip()
-    if command:
-        trimmed = command[:157] + "..." if len(command) > 160 else command
-        return f"Command: `{trimmed}`"
-    if not query and args_text.strip():
-        query = args_text.strip()
-    if query:
-        trimmed = query[:157] + "..." if len(query) > 160 else query
-        return f'Query: "{trimmed}"'
-    if url:
-        trimmed = url[:157] + "..." if len(url) > 160 else url
-        return f"URL: `{trimmed}`"
-    if isinstance(args, dict) and args:
-        serialized = json.dumps(args, ensure_ascii=False, sort_keys=True)
-        trimmed = serialized[:197] + "..." if len(serialized) > 200 else serialized
-        return f"Args: `{trimmed}`"
-    if args is None:
-        return ""
-    serialized = str(args).strip()
-    if not serialized:
-        return ""
-    trimmed = serialized[:197] + "..." if len(serialized) > 200 else serialized
-    return f"Args: `{trimmed}`"
-def summarize_activity_sources(sources: list[str], *, max_items: int = 3) -> str:
-    if not sources:
-        return ""
-    if len(sources) <= max_items:
-        return ", ".join(sources)
-    remaining = len(sources) - max_items
-    return f"{', '.join(sources[:max_items])}, and {remaining} more"
-def summarize_tool_result(event: ChatEvent) -> str:
-    tool_name = str(event.data.get("tool_name", ""))
-    matches = event.data.get("matches", [])
-    match_count = len(matches)
-    if tool_name == "retrieve_tutor_context":
-        if not matches:
-            try:
-                payload = json.loads(str(event.data.get("output_text", "")))
-                matches = (
-                    payload.get("matches", []) if isinstance(payload, dict) else []
-                )
-                match_count = len(matches)
-            except json.JSONDecodeError:
-                matches = []
-                match_count = 0
-        if not matches:
-            return "_No matching sources found in the selected sources._"
-        ordered_sources: list[str] = []
-        seen_sources: set[str] = set()
-        for match in matches:
-            source_label = str(
-                match.get("source_label")
-                or match.get("source_key")
-                or match.get("source")
-                or "unknown"
-            )
-            if source_label in seen_sources:
-                continue
-            seen_sources.add(source_label)
-            ordered_sources.append(source_label)
-        source_summary = summarize_activity_sources(ordered_sources)
-        match_label = "match" if match_count == 1 else "matches"
-        return f"_Found {match_count} {match_label} from {source_summary}._"
-    if tool_name == "run_kb_command":
-        output_text = str(event.data.get("output_text", "")).strip()
-        command = ""
-        exit_code = ""
-        for line in output_text.splitlines():
-            if line.startswith("$ "):
-                command = line[2:].strip()
-            elif line.startswith("exit_code:"):
-                exit_code = line.partition(":")[2].strip()
-        if "error:" in output_text:
-            return f"_KB command failed: {output_text.split('error:', 1)[1].strip()[:160]}_"
-        if command and exit_code:
-            return f"_Ran `{command}` with exit code {exit_code}._"
-        if command:
-            return f"_Ran `{command}`._"
-        return "_Ran KB command._"
-    output_text = str(event.data.get("output_text", "")).strip()
-    if output_text:
-        return f"_{output_text}_"
-    if tool_name in ("google_search", "web_search"):
-        if match_count == 0:
-            return "_Search ran but returned no results._"
-        label = "result" if match_count == 1 else "results"
-        return f"_Searched the web — {match_count} {label} used as sources._"
-    if tool_name in ("url_context", "web_fetch"):
-        if match_count == 0:
-            return "_URL fetched._"
-        label = "page" if match_count == 1 else "pages"
-        return f"_Fetched {match_count} {label}._"
-    return "_Tool completed._"
-def format_tool_event(
-    tool_name: str,
-    args: Any,
-    status_line: str,
-    *,
-    args_text: str = "",
-) -> str:
-    lines = [f"Using `{tool_name}`"]
-    args_line = format_tool_args(args, args_text=args_text)
-    if args_line:
-        lines.append(args_line)
-    lines.append(status_line)
-    return "\n".join(lines)
-def render_activity_block(events: list[ActivityEvent]) -> str:
-    sections: list[str] = []
-    for event in events:
-        if event.kind == "thinking":
-            sections.append(
-                "\n".join(
-                    [
-                        THOUGHTS_HEADER,
-                        THOUGHTS_HINT,
-                        "",
-                        as_blockquote(event.body),
-                    ]
-                ).strip()
-            )
-            continue
-        if event.kind == "tool":
-            sections.append(f"{TOOL_HEADER}\n{event.body}".strip())
-    if not sections:
-        return ""
-    rendered_sections = "\n\n".join(section for section in sections if section)
-    return f"{ACTIVITY_BLOCK_START}\n\n{rendered_sections}\n\n{ACTIVITY_BLOCK_END}"
-def format_sources(matches_by_doc_id: dict[str, dict[str, Any]]) -> str:
-    if not matches_by_doc_id:
-        return ""
-    lines = [SOURCES_HEADER]
-    sorted_matches = sorted(
-        matches_by_doc_id.values(),
-        key=lambda item: item["score"],
-        reverse=True,
-    )
-    for match in sorted_matches:
-        lines.append(
-            f"- [🔗 {match['source_label']}: {match['title']}]({match['url']}), relevance: {match['score']:.2f}"
-        )
-    return "\n".join(lines)
-def render_output(
-    answer: str,
-    activity_block: str = "",
-    sources_block: str = "",
-) -> str:
-    visible_answer = answer.strip()
-    visible_activity = activity_block.strip()
-    visible_sources = sources_block.strip()
-    output_parts = [visible_activity]
-    if visible_answer:
-        if visible_activity:
-            output_parts.append(f"{ANSWER_HEADER}\n\n{visible_answer}")
-        else:
-            output_parts.append(visible_answer)
-    output_parts.append(visible_sources)
-    return "\n\n".join(part for part in output_parts if part)
-@dataclass
-class GradioPresenterState:
-    show_activity: bool = False
-    thread_id: str = ""
-    message_completed: bool = False
-    matches_by_doc_id: dict[str, dict[str, Any]] = field(default_factory=dict)
-    activity_events: list[ActivityEvent] = field(default_factory=list)
-    answer_chunks: list[str] = field(default_factory=list)
-    completed_answer: str = ""
-    tool_matches_by_call_id: dict[str, list[dict[str, Any]]] = field(
-        default_factory=dict
-    )
-    def apply(self, event: ChatEvent) -> None:
-        if event.type == "thread_started":
-            self.thread_id = str(event.data.get("thread_id", self.thread_id))
-            return
-        if event.type == "text_delta":
-            text = str(event.data.get("text", ""))
-            if text:
-                self.answer_chunks.append(text)
-            return
-        if event.type == "reasoning_delta":
-            if not self.show_activity:
-                return
-            step = str(event.data.get("step", ""))
-            body = str(event.data.get("text", ""))
-            upsert_activity_event(
-                self.activity_events,
-                key=f"thinking:{step}",
-                kind="thinking",
-                body=body,
-            )
-            return
-        if event.type == "tool_call_started":
-            if not self.show_activity:
-                return
-            call_id = str(event.data.get("call_id", ""))
-            upsert_activity_event(
-                self.activity_events,
-                key=f"tool:{call_id}",
-                kind="tool",
-                body=format_tool_event(
-                    str(event.data.get("tool_name", "tool")),
-                    event.data.get("args"),
-                    TOOL_PENDING_HINT,
-                    args_text=str(event.data.get("args_text", "")),
-                ),
-                replace=True,
-            )
-            return
-        if event.type == "source_match":
-            doc_id = str(event.data.get("doc_id", ""))
-            existing = self.matches_by_doc_id.get(doc_id)
-            incoming_score = float(event.data.get("score", 0.0))
-            if existing and float(existing["score"]) >= incoming_score:
-                return
-            source_data = {
-                "title": str(event.data.get("title", "")),
-                "url": str(event.data.get("url", "")),
-                "source_label": str(event.data.get("source_label", "")),
-                "score": incoming_score,
-            }
-            self.matches_by_doc_id[doc_id] = source_data
-            call_id = str(event.data.get("call_id", ""))
-            if call_id:
-                self.tool_matches_by_call_id.setdefault(call_id, []).append(source_data)
-            return
-        if event.type == "tool_call_completed":
-            if not self.show_activity:
-                return
-            call_id = str(event.data.get("call_id", ""))
-            matches = self.tool_matches_by_call_id.get(call_id, [])
-            event_with_matches = ChatEvent(
-                type=event.type,
-                data={**event.data, "matches": matches},
-            )
-            upsert_activity_event(
-                self.activity_events,
-                key=f"tool:{call_id}",
-                kind="tool",
-                body=format_tool_event(
-                    str(event.data.get("tool_name", "tool")),
-                    event.data.get("args"),
-                    summarize_tool_result(event_with_matches),
-                    args_text=str(event.data.get("args_text", "")),
-                ),
-                replace=True,
-            )
-            return
-        if event.type == "message_completed":
-            self.message_completed = True
-            self.thread_id = str(event.data.get("thread_id", self.thread_id))
-            self.completed_answer = str(event.data.get("answer", "")).strip()
-    def render(self) -> str:
-        answer = "".join(self.answer_chunks).strip() or self.completed_answer
-        activity_block = (
-            render_activity_block(self.activity_events) if self.show_activity else ""
-        )
-        sources_block = (
-            format_sources(self.matches_by_doc_id) if self.message_completed else ""
-        )
-        return render_output(answer, activity_block, sources_block)

scripts/main.py DELETED Viewed

@@ -1,151 +0,0 @@
-from __future__ import annotations
-import gradio as gr
-from .chat_service import (
-    ChatRequest,
-    normalize_history,
-    stream_chat,
-    warm_up_retriever,
-)
-from .gradio_presenter import GradioPresenterState
-from .setup import (
-    AVAILABLE_SOURCES_UI,
-    DEFAULT_MODEL_NAME,
-    DEFAULT_SELECTED_SOURCES_UI,
-    SOURCE_UI_TO_KEY,
-)
-async def generate_completion(
-    query: str,
-    history,
-    sources,
-    model,
-    thread_id,
-    enable_web_search,
-    enable_url_read,
-):
-    enabled_tools: list[str] = []
-    if enable_web_search:
-        enabled_tools.append("web_search")
-    if enable_url_read:
-        enabled_tools.extend(["url_context", "web_fetch"])
-    request = ChatRequest(
-        query=query,
-        history=normalize_history(history),
-        source_keys=tuple(
-            SOURCE_UI_TO_KEY[source] for source in sources if source in SOURCE_UI_TO_KEY
-        ),
-        model_name=model,
-        include_reasoning=True,
-        thread_id=thread_id,
-        enabled_tools=tuple(enabled_tools),
-    )
-    presenter = GradioPresenterState(show_activity=True)
-    last_emitted = ""
-    async for event in stream_chat(request):
-        presenter.apply(event)
-        current_output = presenter.render()
-        if current_output and current_output != last_emitted:
-            last_emitted = current_output
-            yield current_output, presenter.thread_id
-    final_output = presenter.render()
-    if final_output and final_output != last_emitted:
-        yield final_output, presenter.thread_id
-accordion = gr.Accordion(label="Customize Sources (Click to expand)", open=False)
-sources = gr.CheckboxGroup(
-    AVAILABLE_SOURCES_UI,
-    label="Sources",
-    value=DEFAULT_SELECTED_SOURCES_UI,
-    interactive=True,
-)
-model = gr.Textbox(
-    label="Model (provider:model)",
-    value=DEFAULT_MODEL_NAME,
-    interactive=False,
-    placeholder="openai:gpt-5.4-mini | anthropic:claude-opus-4-6 | google-genai:gemini-3.5-flash",
-)
-enable_web_search = gr.Checkbox(
-    label="Web search",
-    value=True,
-    info="Let the model use its built-in web search (Gemini google_search / Claude web_search).",
-)
-enable_url_read = gr.Checkbox(
-    label="URL read",
-    value=True,
-    info="Let the model fetch URLs (Gemini url_context / Claude web_fetch).",
-)
-thread_id = gr.Textbox(
-    label="Thread ID",
-    value="",
-    visible=False,
-    container=False,
-)
-with gr.Blocks(
-    title="Towards AI 🤖",
-    analytics_enabled=True,
-    fill_height=True,
-) as demo:
-    def reset_thread_id():
-        return ""
-    chatbot = gr.Chatbot(
-        scale=20,
-        placeholder="<strong>Towards AI 🤖: A Question-Answering Bot for anything AI-related</strong><br>",
-        show_label=False,
-        buttons=["copy"],
-    )
-    chatbot.clear(
-        reset_thread_id,
-        None,
-        [thread_id],
-        api_visibility="undocumented",
-        queue=False,
-    )
-    chatbot.undo(
-        reset_thread_id,
-        None,
-        [thread_id],
-        api_visibility="undocumented",
-        queue=False,
-    )
-    chatbot.retry(
-        reset_thread_id,
-        None,
-        [thread_id],
-        api_visibility="undocumented",
-        queue=False,
-    )
-    chatbot.edit(
-        reset_thread_id,
-        None,
-        [thread_id],
-        api_visibility="undocumented",
-        queue=False,
-    )
-    gr.ChatInterface(
-        fn=generate_completion,
-        chatbot=chatbot,
-        additional_inputs=[
-            sources,
-            model,
-            thread_id,
-            enable_web_search,
-            enable_url_read,
-        ],
-        additional_outputs=[thread_id],
-        additional_inputs_accordion=accordion,
-        api_name="chat",
-    )
-if __name__ == "__main__":
-    warm_up_retriever()
-    demo.launch(server_name="0.0.0.0", server_port=7860, debug=False, share=False)

tests/manual_e2e_langsmith.md CHANGED Viewed

@@ -51,13 +51,6 @@ uv run dotenv -f .env run -- env RUN_LIVE_API_E2E=1 \
   pytest tests/test_api.py::test_live_api_stream_exposes_frontend_parts -q
 ```
-Gradio curl path:
-```bash
-uv run dotenv -f .env run -- env RUN_LIVE_GRADIO_CURL_E2E=1 \
-  pytest tests/test_gradio_kb_e2e.py::test_live_gradio_curl_can_use_retrieval_and_shell -q
-```
 ## Manual API Test
 Start the FastAPI app in one terminal:
@@ -168,59 +161,6 @@ Expected result:
 - The answer has inline citations, not only a final sources list.
 - `data-source` parts include the source cards the frontend will render.
-## Manual Gradio Test
-Start the Gradio app in one terminal:
-```bash
-uv run dotenv -f .env run -- python -m scripts.main
-```
-Submit a Gradio job:
-```bash
-cat >/tmp/ai_tutor_gradio_payload.json <<'JSON'
-{
-  "data": [
-    "Use run_kb_command to answer: can you tell me about codex?",
-    [],
-    [
-      "Agentic AI Engineering",
-      "LangChain Docs",
-      "LangGraph Docs",
-      "PEFT Docs",
-      "Transformers Docs"
-    ],
-    "google-genai:gemini-3.5-flash",
-    "",
-    false,
-    false
-  ]
-}
-JSON
-curl -s http://127.0.0.1:7860/gradio_api/call/chat \
-  -H "Content-Type: application/json" \
-  -d @/tmp/ai_tutor_gradio_payload.json \
-  -o /tmp/ai_tutor_gradio_post.json
-```
-Stream the result:
-```bash
-EVENT_ID="$(jq -r '.event_id' /tmp/ai_tutor_gradio_post.json)"
-curl -N --max-time 240 \
-  "http://127.0.0.1:7860/gradio_api/call/chat/${EVENT_ID}" \
-  -o /tmp/ai_tutor_gradio_stream.sse
-```
-Expected result:
-- The rendered text contains `Using \`retrieve_tutor_context\``.
-- The rendered text contains `Using \`run_kb_command\``.
-- The rendered text contains inline citations and source cards.
 ## Find The LangSmith Trace
 List recent traces in the project:

   pytest tests/test_api.py::test_live_api_stream_exposes_frontend_parts -q
 ```
 ## Manual API Test
 Start the FastAPI app in one terminal:
 - The answer has inline citations, not only a final sources list.
 - `data-source` parts include the source cards the frontend will render.
 ## Find The LangSmith Trace
 List recent traces in the project:

tests/test_gradio_kb_e2e.py DELETED Viewed

@@ -1,209 +0,0 @@
-from __future__ import annotations
-import socket
-import os
-import pytest
-from gradio_client import Client
-from scripts.chat_types import ChatEvent
-def free_port() -> int:
-    with socket.socket() as sock:
-        sock.bind(("127.0.0.1", 0))
-        return int(sock.getsockname()[1])
-def test_gradio_chat_api_surfaces_shell_and_retrieval_activity(monkeypatch) -> None:
-    import scripts.main as main
-    async def fake_stream_chat(_request):
-        yield ChatEvent("thread_started", {"thread_id": "thread_kb"})
-        yield ChatEvent("message_started", {"message_id": "message_kb"})
-        yield ChatEvent(
-            "tool_call_started",
-            {
-                "message_id": "message_kb",
-                "call_id": "call_search",
-                "tool_name": "retrieve_tutor_context",
-                "args": {"query": "generate_next_queries_tool"},
-                "args_text": "generate_next_queries_tool",
-            },
-        )
-        yield ChatEvent(
-            "tool_call_completed",
-            {
-                "message_id": "message_kb",
-                "call_id": "call_search",
-                "tool_name": "retrieve_tutor_context",
-                "args": {"query": "generate_next_queries_tool"},
-                "args_text": "generate_next_queries_tool",
-                "output_text": (
-                    '{"matches": [{"doc_id": "agentic_ai_engineering:lesson-18", '
-                    '"title": "Lesson 18: Research Loop", '
-                    '"url": "https://academy.towardsai.net/lesson-18", '
-                    '"source": "agentic_ai_engineering", "score": 10.0}]}'
-                ),
-            },
-        )
-        yield ChatEvent(
-            "tool_call_started",
-            {
-                "message_id": "message_kb",
-                "call_id": "call_rg",
-                "tool_name": "run_kb_command",
-                "args": {"command": "rg generate_next_queries_tool raw"},
-                "args_text": "rg generate_next_queries_tool raw",
-            },
-        )
-        yield ChatEvent(
-            "tool_call_completed",
-            {
-                "message_id": "message_kb",
-                "call_id": "call_rg",
-                "tool_name": "run_kb_command",
-                "args": {"command": "rg generate_next_queries_tool raw"},
-                "args_text": "rg generate_next_queries_tool raw",
-                "output_text": "$ rg generate_next_queries_tool raw\ncwd: data/kb\nexit_code: 0\nstdout:\nraw/courses/agentic_ai_engineering/lesson-18.md:4:Call `generate_next_queries_tool`.",
-            },
-        )
-        yield ChatEvent(
-            "text_delta",
-            {
-                "message_id": "message_kb",
-                "text": "The symbol appears in Lesson 18: Research Loop.",
-            },
-        )
-        yield ChatEvent(
-            "source_match",
-            {
-                "message_id": "message_kb",
-                "doc_id": "agentic_ai_engineering:lesson-18",
-                "title": "Lesson 18: Research Loop",
-                "url": "https://academy.towardsai.net/lesson-18",
-                "source_key": "agentic_ai_engineering",
-                "source_label": "Agentic AI Engineering",
-                "score": 10.0,
-                "group": "courses",
-            },
-        )
-        yield ChatEvent(
-            "message_completed",
-            {
-                "message_id": "message_kb",
-                "thread_id": "thread_kb",
-                "answer": "The symbol appears in Lesson 18: Research Loop.",
-            },
-        )
-    monkeypatch.setattr(main, "stream_chat", fake_stream_chat)
-    port = free_port()
-    main.demo.launch(
-        server_name="127.0.0.1",
-        server_port=port,
-        prevent_thread_lock=True,
-        quiet=True,
-    )
-    try:
-        client = Client(f"http://127.0.0.1:{port}")
-        result = client.predict(
-            "Where is generate_next_queries_tool discussed?",
-            [],
-            ["Agentic AI Engineering"],
-            "google-genai:gemini-3.5-flash",
-            "",
-            False,
-            False,
-            api_name="/chat",
-        )
-    finally:
-        main.demo.close()
-    rendered = result[0] if isinstance(result, tuple) else str(result)
-    assert "Using `retrieve_tutor_context`" in rendered
-    assert "Using `run_kb_command`" in rendered
-    assert "rg generate_next_queries_tool raw" in rendered
-    assert "Lesson 18: Research Loop" in rendered
-    assert "Agentic AI Engineering" in rendered
-@pytest.mark.skipif(
-    os.getenv("RUN_LIVE_GRADIO_CURL_E2E") != "1",
-    reason="Set RUN_LIVE_GRADIO_CURL_E2E=1 to run the live model-backed Gradio curl smoke test.",
-)
-def test_live_gradio_curl_can_use_retrieval_and_shell() -> None:
-    if os.getenv("RUN_LIVE_GRADIO_CURL_E2E") != "1":
-        pytest.skip("Set RUN_LIVE_GRADIO_CURL_E2E=1 to run this test.")
-    import scripts.main as main
-    import json
-    import shutil
-    import subprocess
-    if shutil.which("curl") is None:
-        pytest.skip("curl is required for the live Gradio curl smoke test.")
-    if not os.getenv("COHERE_API_KEY"):
-        pytest.skip("COHERE_API_KEY is required for live retrieval fallback.")
-    if not (os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")):
-        pytest.skip("GEMINI_API_KEY or GOOGLE_API_KEY is required for the live model.")
-    if not os.path.exists("data/kb/wiki/index.md"):
-        pytest.skip("data/kb artifacts must exist before running the live smoke test.")
-    port = free_port()
-    main.demo.launch(
-        server_name="127.0.0.1",
-        server_port=port,
-        prevent_thread_lock=True,
-        quiet=True,
-    )
-    try:
-        post = subprocess.run(
-            [
-                "curl",
-                "-s",
-                f"http://127.0.0.1:{port}/gradio_api/call/chat",
-                "-H",
-                "Content-Type: application/json",
-                "-d",
-                json.dumps(
-                    {
-                        "data": [
-                            "Use both retrieve_tutor_context and run_kb_command to answer: how does PEFT configure LoRA with LoraConfig?",
-                            [],
-                            ["PEFT Docs", "Transformers Docs"],
-                            os.getenv(
-                                "LIVE_GRADIO_E2E_MODEL", "google-genai:gemini-3.5-flash"
-                            ),
-                            "",
-                            False,
-                            False,
-                        ]
-                    }
-                ),
-            ],
-            check=True,
-            capture_output=True,
-            text=True,
-            timeout=30,
-        )
-        event_id = json.loads(post.stdout)["event_id"]
-        stream = subprocess.run(
-            [
-                "curl",
-                "-N",
-                f"http://127.0.0.1:{port}/gradio_api/call/chat/{event_id}",
-            ],
-            check=True,
-            capture_output=True,
-            text=True,
-            timeout=180,
-        )
-    finally:
-        main.demo.close()
-    rendered = stream.stdout
-    assert "Using `retrieve_tutor_context`" in rendered
-    assert "Using `run_kb_command`" in rendered
-    assert "LoRA" in rendered
-    assert "PEFT" in rendered or "Transformers" in rendered

tests/test_gradio_presenter.py DELETED Viewed

@@ -1,91 +0,0 @@
-from __future__ import annotations
-import unittest
-from scripts.chat_types import ChatEvent
-from scripts.gradio_presenter import (
-    GradioPresenterState,
-    SOURCES_HEADER,
-    summarize_tool_result,
-)
-class GradioPresenterTestCase(unittest.TestCase):
-    def test_sources_render_only_after_message_completion(self) -> None:
-        presenter = GradioPresenterState(show_activity=True)
-        presenter.apply(ChatEvent("thread_started", {"thread_id": "thread_1"}))
-        presenter.apply(ChatEvent("text_delta", {"message_id": "m1", "text": "Answer"}))
-        presenter.apply(
-            ChatEvent(
-                "source_match",
-                {
-                    "message_id": "m1",
-                    "call_id": "c1",
-                    "doc_id": "doc_1",
-                    "title": "RAG overview",
-                    "url": "https://example.com/rag",
-                    "source_label": "LangChain Docs",
-                    "score": 0.92,
-                },
-            )
-        )
-        in_progress = presenter.render()
-        self.assertIn("Answer", in_progress)
-        self.assertNotIn(SOURCES_HEADER, in_progress)
-        presenter.apply(
-            ChatEvent(
-                "message_completed",
-                {
-                    "message_id": "m1",
-                    "thread_id": "thread_1",
-                    "answer": "Answer",
-                },
-            )
-        )
-        completed = presenter.render()
-        self.assertIn(SOURCES_HEADER, completed)
-        self.assertIn("RAG overview", completed)
-    def test_retrieval_summary_uses_tool_payload_matches(self) -> None:
-        event = ChatEvent(
-            "tool_call_completed",
-            {
-                "tool_name": "retrieve_tutor_context",
-                "output_text": (
-                    '{"matches": [{"title": "LoRA", "source_label": "PEFT Docs"}]}'
-                ),
-            },
-        )
-        self.assertEqual(
-            summarize_tool_result(event),
-            "_Found 1 match from PEFT Docs._",
-        )
-    def test_run_kb_command_summary_confirms_command_execution(self) -> None:
-        event = ChatEvent(
-            "tool_call_completed",
-            {
-                "tool_name": "run_kb_command",
-                "output_text": (
-                    "$ rg LoraConfig raw\n"
-                    "cwd: data/kb\n"
-                    "exit_code: 0\n"
-                    "stdout:\n"
-                    "raw/docs/peft/lora.md:3:LoraConfig"
-                ),
-            },
-        )
-        self.assertEqual(
-            summarize_tool_result(event),
-            "_Ran `rg LoraConfig raw` with exit code 0._",
-        )
-if __name__ == "__main__":
-    unittest.main()

uv.lock CHANGED Viewed

@@ -20,7 +20,6 @@ dependencies = [
     { name = "cohere" },
     { name = "fastapi" },
     { name = "google-genai" },
-    { name = "gradio" },
     { name = "hf-xet" },
     { name = "huggingface-hub" },
     { name = "instructor" },
@@ -44,6 +43,7 @@ dependencies = [
 [package.dev-dependencies]
 dev = [
     { name = "httpx" },
     { name = "pytest" },
     { name = "ruff" },
 ]
@@ -55,7 +55,6 @@ requires-dist = [
     { name = "cohere" },
     { name = "fastapi" },
     { name = "google-genai" },
-    { name = "gradio" },
     { name = "hf-xet" },
     { name = "huggingface-hub" },
     { name = "instructor" },
@@ -79,6 +78,7 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [
     { name = "httpx" },
     { name = "pytest", specifier = ">=9.0.3" },
     { name = "ruff", specifier = ">=0.15.10" },
 ]
@@ -270,62 +270,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548, upload-time = "2026-03-19T14:22:23.645Z" },
 ]
-[[package]]
-name = "audioop-lts"
-version = "0.2.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/38/53/946db57842a50b2da2e0c1e34bd37f36f5aadba1a929a3971c5d7841dbca/audioop_lts-0.2.2.tar.gz", hash = "sha256:64d0c62d88e67b98a1a5e71987b7aa7b5bcffc7dcee65b635823dbdd0a8dbbd0", size = 30686, upload-time = "2025-08-05T16:43:17.409Z" }
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