diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..14b8936 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,71 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + test-python: + name: Test Python Package + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10", "3.11", "3.12"] + + defaults: + run: + working-directory: packages/llmpane-py + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -e ".[all]" + + - name: Run linting + run: ruff check llmpane + + - name: Run type checking + run: mypy llmpane + + - name: Run tests + run: pytest -v + + test-react: + name: Test React Package + runs-on: ubuntu-latest + + defaults: + run: + working-directory: packages/llmpane-react + + steps: + - uses: actions/checkout@v4 + + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + cache: "npm" + cache-dependency-path: packages/llmpane-react/package-lock.json + + - name: Install dependencies + run: npm ci + + - name: Run linting + run: npm run lint + + - name: Run tests + run: npm run test:run + + - name: Build package + run: npm run build diff --git a/.github/workflows/publish-npm.yml b/.github/workflows/publish-npm.yml new file mode 100644 index 0000000..dba09de --- /dev/null +++ b/.github/workflows/publish-npm.yml @@ -0,0 +1,136 @@ +name: Publish React Package + +on: + release: + types: [published] + workflow_dispatch: + inputs: + tag: + description: "npm tag (latest, beta, next)" + required: false + default: "latest" + type: choice + options: + - latest + - beta + - next + dry_run: + description: "Dry run (no actual publish)" + required: false + default: false + type: boolean + +jobs: + build: + name: Build Package + runs-on: ubuntu-latest + + defaults: + run: + working-directory: packages/llmpane-react + + steps: + - uses: actions/checkout@v4 + + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + cache: "npm" + cache-dependency-path: packages/llmpane-react/package-lock.json + + - name: Install dependencies + run: npm ci + + - name: Build package + run: npm run build + + - name: Upload build artifacts + uses: actions/upload-artifact@v4 + with: + name: npm-package + path: | + packages/llmpane-react/dist/ + packages/llmpane-react/package.json + packages/llmpane-react/README.md + + test: + name: Test Package + runs-on: ubuntu-latest + needs: build + + defaults: + run: + working-directory: packages/llmpane-react + + steps: + - uses: actions/checkout@v4 + + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + cache: "npm" + cache-dependency-path: packages/llmpane-react/package-lock.json + + - name: Install dependencies + run: npm ci + + - name: Run tests + run: npm run test:run + + publish: + name: Publish to npm + runs-on: ubuntu-latest + needs: [build, test] + environment: npm + permissions: + contents: read + id-token: write + + defaults: + run: + working-directory: packages/llmpane-react + + steps: + - uses: actions/checkout@v4 + + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + registry-url: "https://registry.npmjs.org" + cache: "npm" + cache-dependency-path: packages/llmpane-react/package-lock.json + + - name: Install dependencies + run: npm ci + + - name: Build package + run: npm run build + + - name: Determine npm tag + id: npm-tag + run: | + if [ "${{ github.event_name }}" == "release" ]; then + # Check if it's a prerelease + if [ "${{ github.event.release.prerelease }}" == "true" ]; then + echo "tag=beta" >> $GITHUB_OUTPUT + else + echo "tag=latest" >> $GITHUB_OUTPUT + fi + else + echo "tag=${{ inputs.tag }}" >> $GITHUB_OUTPUT + fi + + - name: Publish to npm (dry run) + if: inputs.dry_run + run: npm publish --access public --tag ${{ steps.npm-tag.outputs.tag }} --dry-run + env: + NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }} + + - name: Publish to npm + if: "!inputs.dry_run" + run: npm publish --access public --tag ${{ steps.npm-tag.outputs.tag }} + env: + NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }} diff --git a/.github/workflows/publish-python.yml b/.github/workflows/publish-python.yml new file mode 100644 index 0000000..cf2aa5e --- /dev/null +++ b/.github/workflows/publish-python.yml @@ -0,0 +1,108 @@ +name: Publish Python Package + +on: + release: + types: [published] + workflow_dispatch: + inputs: + test_pypi: + description: "Publish to TestPyPI instead of PyPI" + required: false + default: false + type: boolean + +jobs: + build: + name: Build Package + runs-on: ubuntu-latest + + defaults: + run: + working-directory: packages/llmpane-py + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install build dependencies + run: | + python -m pip install --upgrade pip + pip install build + + - name: Build package + run: python -m build + + - name: Upload build artifacts + uses: actions/upload-artifact@v4 + with: + name: python-package + path: packages/llmpane-py/dist/ + + test: + name: Test Package + runs-on: ubuntu-latest + needs: build + + defaults: + run: + working-directory: packages/llmpane-py + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -e ".[all]" + + - name: Run tests + run: pytest -v + + publish-testpypi: + name: Publish to TestPyPI + runs-on: ubuntu-latest + needs: [build, test] + if: github.event_name == 'workflow_dispatch' && inputs.test_pypi + environment: testpypi + permissions: + id-token: write + + steps: + - name: Download build artifacts + uses: actions/download-artifact@v4 + with: + name: python-package + path: dist/ + + - name: Publish to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ + + publish-pypi: + name: Publish to PyPI + runs-on: ubuntu-latest + needs: [build, test] + if: github.event_name == 'release' || (github.event_name == 'workflow_dispatch' && !inputs.test_pypi) + environment: pypi + permissions: + id-token: write + + steps: + - name: Download build artifacts + uses: actions/download-artifact@v4 + with: + name: python-package + path: dist/ + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.gitignore b/.gitignore index b7faf40..2bcb118 100644 --- a/.gitignore +++ b/.gitignore @@ -205,3 +205,6 @@ cython_debug/ marimo/_static/ marimo/_lsp/ __marimo__/ + +# Claude +.claude \ No newline at end of file diff --git a/README.md b/README.md index 4045319..c21493d 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,205 @@ # llmpane -The embeddable LLM chat component for Python backends + +The embeddable LLM chat component for Python backends. + +**Not another ChatGPT clone.** llmpane is a lightweight React component +that connects to your FastAPI endpoint. You control the LLM, the logic, +the data—llmpane just handles the conversation UI. + +## When to use llmpane vs. alternatives + +| Use case | Tool | +| -------------------------------------------- | ------------------------ | +| "I want a self-hosted ChatGPT" | Open WebUI, LibreChat | +| "I want to build a full AI app from scratch" | Vercel AI SDK, LangChain | +| "I have a Python backend and need a chat UI" | **llmpane** | + +## Features + +- **Type-safe end-to-end** - Pydantic models in Python, TypeScript types in React +- **SSE streaming** - Real-time token streaming that works naturally with FastAPI +- **Server-side message IDs** - Simplifies database storage and conversation history +- **Built-in patterns** - ActionMessage for confirmations, RefinementMessage for iterative outputs +- **Fully customizable** - CSS variables for theming, headless mode for complete control + +## Quick Start + +### Python Backend + +```bash +pip install llmpane[fastapi] +``` + +```python +from fastapi import FastAPI +from llmpane import ChatRequest, StreamChunk, create_sse_response + +app = FastAPI() + +@app.post("/chat") +async def chat(request: ChatRequest): + async def generate(): + # Your LLM logic here + yield StreamChunk(delta="Hello ") + yield StreamChunk(delta="world!") + yield StreamChunk(done=True, message_id="msg_123") + + return create_sse_response(generate()) +``` + +### React Frontend + +```bash +npm install @llmpane/react +``` + +```tsx +import { ChatPane } from "@llmpane/react"; +import "@llmpane/react/styles"; + +function App() { + return ; +} +``` + +## Packages + +| Package | Description | +|---------|-------------| +| [`llmpane`](packages/llmpane-py) | Python package with Pydantic models and FastAPI utilities | +| [`@llmpane/react`](packages/llmpane-react) | React component with hooks and pre-built patterns | + +## Patterns + +### ActionMessage + +For "LLM proposes, user confirms" flows: + +```tsx + applyFilter(action)} + onReject={(action) => console.log("Rejected")} +/> +``` + +### RefinementMessage + +For iterative structured output: + +```tsx + executeQuery(query)} +/> +``` + +## Examples + +See the [examples/fastapi-example](examples/fastapi-example) directory for a complete working demo. + +```bash +# Clone and navigate to the example +cd examples/fastapi-example + +# Create and activate a virtual environment +python -m venv venv +source venv/bin/activate # On Windows: venv\Scripts\activate + +# Install Python dependencies +pip install -r requirements.txt + +# Start the backend server +python main.py +``` + +```bash +# In another terminal, start the frontend +cd examples/fastapi-example/frontend +npm install +npm run dev +``` + +Then open http://localhost:5173 to see the demo. + +## Documentation + +- [Python Package](packages/llmpane-py/README.md) +- [React Package](packages/llmpane-react/README.md) + +## Development + +### Running Tests + +```bash +# Python package +cd packages/llmpane-py +pip install -e ".[all]" +pytest -v + +# React package +cd packages/llmpane-react +npm install +npm run test:run +``` + +### Linting + +```bash +# Python +cd packages/llmpane-py +ruff check llmpane +mypy llmpane + +# React +cd packages/llmpane-react +npm run lint +``` + +## Publishing + +Both packages use GitHub Actions for automated publishing. + +### Automatic Publishing (Recommended) + +1. Update the version in `packages/llmpane-py/pyproject.toml` and/or `packages/llmpane-react/package.json` +2. Create a GitHub Release with a tag matching the version (e.g., `v0.1.0`) +3. The workflows will automatically build, test, and publish to PyPI and npm + +### Manual Publishing + +You can also trigger publishing manually via the GitHub Actions "Run workflow" button: + +- **Python**: Go to Actions > "Publish Python Package" > Run workflow + - Option to publish to TestPyPI first for validation +- **React**: Go to Actions > "Publish React Package" > Run workflow + - Supports `latest`, `beta`, and `next` npm tags + - Dry run option to validate without publishing + +### Required Secrets + +Configure these in your repository settings under Settings > Secrets and variables > Actions: + +| Secret | Description | +|--------|-------------| +| `NPM_TOKEN` | npm automation token with publish access | + +For PyPI, we use [Trusted Publishing](https://docs.pypi.org/trusted-publishers/) with OIDC. Configure the GitHub publisher in your PyPI project settings: +- Owner: `hartbrook` +- Repository: `llmpane` +- Workflow: `publish-python.yml` +- Environment: `pypi` (or `testpypi` for TestPyPI) + +### Environments + +Create these environments in Settings > Environments: + +| Environment | Used For | +|-------------|----------| +| `pypi` | Production PyPI publishing | +| `testpypi` | TestPyPI publishing (optional) | +| `npm` | npm publishing | + +## License + +MIT diff --git a/examples/fastapi-example/.gitignore b/examples/fastapi-example/.gitignore new file mode 100644 index 0000000..6016931 --- /dev/null +++ b/examples/fastapi-example/.gitignore @@ -0,0 +1,20 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +venv/ +.venv/ +env/ +.env + +# Build output (frontend builds here) +static/ + +# Logs +*.log + +# OS +.DS_Store +Thumbs.db diff --git a/examples/fastapi-example/frontend/.gitignore b/examples/fastapi-example/frontend/.gitignore new file mode 100644 index 0000000..a7a0e64 --- /dev/null +++ b/examples/fastapi-example/frontend/.gitignore @@ -0,0 +1,21 @@ +# Dependencies +node_modules/ + +# Build output +dist/ + +# Vite +*.local + +# Logs +*.log +npm-debug.log* + +# Editor +.vscode/* +!.vscode/extensions.json +.idea/ + +# OS +.DS_Store +Thumbs.db diff --git a/examples/fastapi-example/frontend/index.html b/examples/fastapi-example/frontend/index.html new file mode 100644 index 0000000..921969c --- /dev/null +++ b/examples/fastapi-example/frontend/index.html @@ -0,0 +1,12 @@ + + + + + + llmpane Example + + +
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build", + "preview": "vite preview" + }, + "dependencies": { + "react": "^18.2.0", + "react-dom": "^18.2.0" + }, + "devDependencies": { + "@types/react": "^18.2.0", + "@types/react-dom": "^18.2.0", + "@vitejs/plugin-react": "^4.0.0", + "typescript": "^5.0.0", + "vite": "^5.0.0" + } +} diff --git a/examples/fastapi-example/frontend/src/App.tsx b/examples/fastapi-example/frontend/src/App.tsx new file mode 100644 index 0000000..634fdf4 --- /dev/null +++ b/examples/fastapi-example/frontend/src/App.tsx @@ -0,0 +1,207 @@ +import { useState } from "react"; +import { + ChatPane, + ActionMessage, + RefinementMessage, + useChat, + type DisplayMessage, + type ActionMetadata, + type RefinementMetadata, +} from "@llmpane/react"; +import "@llmpane/react/styles/llmpane.css"; + +type TabType = "basic" | "action" | "refinement"; + +// --- Filter Action Types --- +interface FilterAction { + field: string; + operator: string; + value: string; +} + +// --- SQL Query Types --- +interface SQLQuery { + query: string; + tables: string[]; + estimatedRows: number; +} + +// --- Basic Chat Demo --- +function BasicChatDemo() { + return ( + + ); +} + +// --- Action Message Demo --- +function ActionChatDemo() { + const { messages, isLoading, sendMessage } = useChat>({ + endpoint: "/api/chat/action", + }); + + const handleAccept = (action: FilterAction) => { + console.log("Accepted action:", action); + alert(`Applied filter: ${action.field} ${action.operator} ${action.value}`); + }; + + const handleReject = (action: FilterAction) => { + console.log("Rejected action:", action); + sendMessage("Let's try a different filter approach"); + }; + + const renderMessage = (message: DisplayMessage>, index: number) => { + const key = "tempId" in message ? message.tempId : message.id ?? `msg-${index}`; + + // Render action messages with special component + if (message.role === "assistant" && message.metadata?.action) { + return ( + + key={key} + message={message as any} + onAccept={handleAccept} + onReject={handleReject} + renderAction={(action) => ( +
+ Filter: {action.field} {action.operator} "{action.value}" +
+ )} + /> + ); + } + + // Default rendering for other messages + return ( +
+
+ {message.role === "user" ? "You" : "Assistant"} +
+
+ {message.content || (message.role === "assistant" && "...")} +
+
+ ); + }; + + return ( + + ); +} + +// --- Refinement Message Demo --- +function RefinementChatDemo() { + const { messages, isLoading, sendMessage } = useChat>({ + endpoint: "/api/chat/refinement", + conversationId: "refinement-demo", + }); + + const handleAccept = (query: SQLQuery) => { + console.log("Accepted query:", query); + alert(`Executing query:\n${query.query}`); + }; + + const renderMessage = (message: DisplayMessage>, index: number) => { + const key = "tempId" in message ? message.tempId : message.id ?? `msg-${index}`; + + // Render refinement messages with special component + if (message.role === "assistant" && message.metadata?.output) { + return ( + + key={key} + message={message as any} + onAccept={handleAccept} + renderOutput={(output, iteration) => ( +
+ 
+                {output.query}
+
+
+ Tables: {output.tables.join(", ")} | Est. rows: {output.estimatedRows.toLocaleString()} +
+
+ )} + /> + ); + } + + // Default rendering for other messages + return ( +
+
+ {message.role === "user" ? "You" : "Assistant"} +
+
+ {message.content || (message.role === "assistant" && "...")} +
+
+ ); + }; + + return ( + + ); +} + +// --- Main App --- +export default function App() { + const [activeTab, setActiveTab] = useState("basic"); + + return ( +
+

llmpane Demo

+

A lightweight chat UI component for LLM-powered applications

+ +
+ + + +
+ +
+ {activeTab === "basic" && } + {activeTab === "action" && } + {activeTab === "refinement" && } +
+
+ ); +} diff --git a/examples/fastapi-example/frontend/src/index.css b/examples/fastapi-example/frontend/src/index.css new file mode 100644 index 0000000..923534e --- /dev/null +++ b/examples/fastapi-example/frontend/src/index.css @@ -0,0 +1,75 @@ +* { + box-sizing: border-box; + margin: 0; + padding: 0; +} + +html, +body, +#root { + height: 100%; +} + +body { + font-family: system-ui, -apple-system, sans-serif; + background: #f3f4f6; +} + +.app { + max-width: 1200px; + margin: 0 auto; + padding: 2rem; + height: 100%; + display: flex; + flex-direction: column; +} + +.app h1 { + text-align: center; + margin-bottom: 1rem; + color: #111827; +} + +.app p { + text-align: center; + color: #6b7280; + margin-bottom: 2rem; +} + +.tabs { + display: flex; + gap: 0.5rem; + margin-bottom: 1rem; +} + +.tabs button { + padding: 0.5rem 1rem; + border: none; + border-radius: 0.5rem; + background: #e5e7eb; + color: #374151; + cursor: pointer; + font-size: 0.875rem; + font-weight: 500; + transition: all 0.15s; +} + +.tabs button:hover { + background: #d1d5db; +} + +.tabs button.active { + background: #3b82f6; + color: white; +} + +.chat-container { + flex: 1; + background: white; + border-radius: 1rem; + box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); + overflow: hidden; + display: flex; + flex-direction: column; + min-height: 500px; +} diff --git a/examples/fastapi-example/frontend/src/main.tsx b/examples/fastapi-example/frontend/src/main.tsx new file mode 100644 index 0000000..9b67590 --- /dev/null +++ b/examples/fastapi-example/frontend/src/main.tsx @@ -0,0 +1,10 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import App from "./App"; +import "./index.css"; + +ReactDOM.createRoot(document.getElementById("root")!).render( + + + +); diff --git a/examples/fastapi-example/frontend/tsconfig.json b/examples/fastapi-example/frontend/tsconfig.json new file mode 100644 index 0000000..3e703d9 --- /dev/null +++ b/examples/fastapi-example/frontend/tsconfig.json @@ -0,0 +1,25 @@ +{ + "compilerOptions": { + "target": "ES2020", + "useDefineForClassFields": true, + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true, + "paths": { + "@llmpane/react": ["../../../packages/llmpane-react/src"], + "@llmpane/react/*": ["../../../packages/llmpane-react/src/*"] + } + }, + "include": ["src"], + "references": [{ "path": "./tsconfig.node.json" }] +} diff --git a/examples/fastapi-example/frontend/tsconfig.node.json b/examples/fastapi-example/frontend/tsconfig.node.json new file mode 100644 index 0000000..97ede7e --- /dev/null +++ b/examples/fastapi-example/frontend/tsconfig.node.json @@ -0,0 +1,11 @@ +{ + "compilerOptions": { + "composite": true, + "skipLibCheck": true, + "module": "ESNext", + "moduleResolution": "bundler", + "allowSyntheticDefaultImports": true, + "strict": true + }, + "include": ["vite.config.ts"] +} diff --git a/examples/fastapi-example/frontend/vite.config.ts b/examples/fastapi-example/frontend/vite.config.ts new file mode 100644 index 0000000..fa9a78e --- /dev/null +++ b/examples/fastapi-example/frontend/vite.config.ts @@ -0,0 +1,24 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; +import path from "path"; + +export default defineConfig({ + plugins: [react()], + resolve: { + alias: { + "@llmpane/react": path.resolve(__dirname, "../../../packages/llmpane-react/src"), + }, + }, + server: { + proxy: { + "/api": { + target: "http://localhost:8000", + changeOrigin: true, + }, + }, + }, + build: { + outDir: "../static", + emptyOutDir: true, + }, +}); diff --git a/examples/fastapi-example/main.py b/examples/fastapi-example/main.py new file mode 100644 index 0000000..d0eb097 --- /dev/null +++ b/examples/fastapi-example/main.py @@ -0,0 +1,260 @@ +"""FastAPI example demonstrating all llmpane patterns with a mock LLM.""" + +import asyncio +import json +import random +import uuid +from collections.abc import AsyncGenerator +from pathlib import Path + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from fastapi.staticfiles import StaticFiles +from fastapi.responses import FileResponse +from pydantic import BaseModel + +# Add parent packages to path for development +import sys + +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "packages" / "llmpane-py")) + +from llmpane import ChatRequest, StreamChunk +from llmpane.streaming import create_sse_response + +app = FastAPI(title="llmpane Example", version="0.1.0") + +# CORS for development +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +# --- Mock LLM Responses --- + + +MOCK_RESPONSES = [ + "That's a great question! Let me think about it...\n\nBased on my analysis, I'd say the answer involves considering multiple factors. First, we need to understand the context. Then, we can break down the problem into smaller parts.", + "I'd be happy to help with that! Here's what I think:\n\n1. Start by defining the problem clearly\n2. Gather relevant information\n3. Analyze the options\n4. Make a decision based on the evidence", + "Interesting point! From my perspective, there are several ways to approach this. The most effective method depends on your specific situation and goals.", + "Thanks for asking! This is a topic I find fascinating. Let me share some thoughts:\n\nThe key insight here is that simplicity often leads to better outcomes. Focus on the fundamentals and build from there.", +] + + +async def generate_mock_response(message: str) -> AsyncGenerator[str, None]: + """Simulate realistic LLM streaming with variable-sized chunks. + + Real LLMs stream in chunks of varying sizes (1-10 words typically), + with the delay being the model's generation time, not network latency. + """ + response = random.choice(MOCK_RESPONSES) + words = response.split(" ") + i = 0 + + while i < len(words): + # Random chunk size (1-5 words) - mimics real LLM behavior + chunk_size = random.randint(1, 5) + chunk_words = words[i : i + chunk_size] + + # Build chunk with spaces + if i > 0: + chunk = " " + " ".join(chunk_words) + else: + chunk = " ".join(chunk_words) + + yield chunk + i += chunk_size + + # Minimal delay - real LLMs stream as fast as they generate + # This simulates ~50-100 tokens/second which is typical + await asyncio.sleep(random.uniform(0.01, 0.03)) + + +# --- Basic Chat Endpoint --- + + +@app.post("/api/chat") +async def chat(request: ChatRequest): + """Basic streaming chat endpoint.""" + + async def generate() -> AsyncGenerator[StreamChunk, None]: + message_id = f"msg_{uuid.uuid4().hex[:12]}" + accumulated = "" + + async for char in generate_mock_response(request.message): + accumulated += char + yield StreamChunk(delta=char) + + yield StreamChunk(done=True, message_id=message_id) + + return create_sse_response(generate()) + + +# --- Action Message Pattern --- + + +class FilterAction(BaseModel): + """Example action: filter data by a field.""" + + field: str + operator: str + value: str + + +class ActionMetadataPayload(BaseModel): + """Metadata for action messages.""" + + action: FilterAction + status: str = "pending" + explanation: str + + +FILTER_SUGGESTIONS = [ + FilterAction(field="date", operator=">=", value="2024-01-01"), + FilterAction(field="status", operator="=", value="active"), + FilterAction(field="amount", operator=">", value="1000"), + FilterAction(field="category", operator="in", value="electronics,clothing"), +] + + +@app.post("/api/chat/action") +async def chat_with_action(request: ChatRequest): + """Chat endpoint that returns proposed actions.""" + + async def generate() -> AsyncGenerator[StreamChunk, None]: + message_id = f"msg_{uuid.uuid4().hex[:12]}" + + # Stream explanation in realistic chunks + explanation = "Based on your request, I suggest applying the following filter:" + async for chunk in generate_mock_response(explanation): + yield StreamChunk(delta=chunk) + # Small pause before showing the action card + await asyncio.sleep(0.05) + + # Then send the action metadata + action = random.choice(FILTER_SUGGESTIONS) + metadata = ActionMetadataPayload( + action=action, + status="pending", + explanation=f"Filter {action.field} where it {action.operator} {action.value}", + ) + + yield StreamChunk(metadata=metadata) + yield StreamChunk(done=True, message_id=message_id) + + return create_sse_response(generate()) + + +# --- Refinement Message Pattern --- + + +class SQLQuery(BaseModel): + """Example output: a SQL query.""" + + query: str + tables: list[str] + estimated_rows: int + + +class RefinementMetadataPayload(BaseModel): + """Metadata for refinement messages.""" + + output: SQLQuery + iteration: int + is_final: bool = False + + +# Simulate refining queries over iterations +QUERY_ITERATIONS = [ + SQLQuery( + query="SELECT * FROM orders", + tables=["orders"], + estimated_rows=10000, + ), + SQLQuery( + query="SELECT id, customer_id, total FROM orders WHERE status = 'completed'", + tables=["orders"], + estimated_rows=5000, + ), + SQLQuery( + query=( + "SELECT o.id, c.name, o.total " + "FROM orders o " + "JOIN customers c ON o.customer_id = c.id " + "WHERE o.status = 'completed' AND o.total > 100" + ), + tables=["orders", "customers"], + estimated_rows=1500, + ), +] + +# Track iteration per conversation (simplified - in production use proper session management) +iteration_tracker: dict[str, int] = {} + + +@app.post("/api/chat/refinement") +async def chat_with_refinement(request: ChatRequest): + """Chat endpoint that returns iteratively refined output.""" + conv_id = request.conversation_id or "default" + + # Get or initialize iteration count + if conv_id not in iteration_tracker: + iteration_tracker[conv_id] = 0 + + current_iteration = iteration_tracker[conv_id] + iteration_tracker[conv_id] = (current_iteration + 1) % len(QUERY_ITERATIONS) + + async def generate() -> AsyncGenerator[StreamChunk, None]: + message_id = f"msg_{uuid.uuid4().hex[:12]}" + + # Stream explanation in realistic chunks + explanation = f"Here's iteration {current_iteration + 1} of the query based on your feedback:" + async for chunk in generate_mock_response(explanation): + yield StreamChunk(delta=chunk) + # Small pause before showing the refinement card + await asyncio.sleep(0.05) + + # Send refinement metadata + query = QUERY_ITERATIONS[current_iteration] + metadata = RefinementMetadataPayload( + output=query, + iteration=current_iteration + 1, + is_final=current_iteration == len(QUERY_ITERATIONS) - 1, + ) + + yield StreamChunk(metadata=metadata) + yield StreamChunk(done=True, message_id=message_id) + + return create_sse_response(generate()) + + +# --- Serve Static Files (for the React example) --- + +static_dir = Path(__file__).parent / "static" +if static_dir.exists(): + app.mount("/static", StaticFiles(directory=static_dir), name="static") + + +@app.get("/") +async def serve_index(): + """Serve the React app.""" + index_path = static_dir / "index.html" + if index_path.exists(): + return FileResponse(index_path) + return { + "message": "llmpane example API", + "endpoints": [ + "/api/chat - Basic streaming chat", + "/api/chat/action - Action message pattern", + "/api/chat/refinement - Refinement message pattern", + ], + } + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/examples/fastapi-example/requirements.txt b/examples/fastapi-example/requirements.txt new file mode 100644 index 0000000..1293d0e --- /dev/null +++ b/examples/fastapi-example/requirements.txt @@ -0,0 +1,4 @@ +fastapi>=0.100.0 +uvicorn[standard]>=0.23.0 +sse-starlette>=1.6.0 +pydantic>=2.0.0 diff --git a/packages/llmpane-py/README.md b/packages/llmpane-py/README.md new file mode 100644 index 0000000..5191b7b --- /dev/null +++ b/packages/llmpane-py/README.md @@ -0,0 +1,147 @@ +# llmpane + +A lightweight, embeddable chat UI component for Python/FastAPI backends serving LLM features. + +## Installation + +```bash +pip install llmpane + +# With FastAPI support +pip install llmpane[fastapi] +``` + +## Quick Start + +```python +from fastapi import FastAPI +from llmpane import ChatRequest, StreamChunk, create_sse_response + +app = FastAPI() + +@app.post("/chat") +async def chat(request: ChatRequest): + async def generate(): + # Your LLM logic here + yield StreamChunk(delta="Hello ") + yield StreamChunk(delta="world!") + yield StreamChunk(done=True, message_id="msg_123") + + return create_sse_response(generate()) +``` + +## Models + +### ChatMessage + +A single message in a conversation. + +```python +from llmpane import ChatMessage, MessageRole + +message = ChatMessage( + id="msg_123", + role=MessageRole.USER, + content="Hello!", + metadata=None, # Optional custom metadata +) +``` + +### ChatRequest + +Request payload for chat endpoints. + +```python +from llmpane import ChatRequest + +request = ChatRequest( + message="What's the weather?", + conversation_id="conv_456", # Optional + history=[], # Previous messages + metadata=None, # Optional custom metadata +) +``` + +### StreamChunk + +A single chunk in a streaming response. + +```python +from llmpane import StreamChunk + +# Text chunk +chunk = StreamChunk(delta="Hello ") + +# Metadata chunk +chunk = StreamChunk(metadata={"action": "search"}) + +# Final chunk +chunk = StreamChunk(done=True, message_id="msg_123") + +# Error chunk +chunk = StreamChunk(error="Something went wrong", done=True) +``` + +## Custom Metadata + +Use generics to add type-safe custom metadata: + +```python +from pydantic import BaseModel +from llmpane import ChatMessage, ChatRequest, StreamChunk + +class MyMetadata(BaseModel): + confidence: float + sources: list[str] + +# Type-safe message with metadata +message: ChatMessage[MyMetadata] = ChatMessage( + id="msg_123", + role="assistant", + content="The answer is 42.", + metadata=MyMetadata(confidence=0.95, sources=["doc1.pdf"]), +) +``` + +## Action Message Pattern + +For "LLM proposes, user confirms" flows: + +```python +from pydantic import BaseModel +from llmpane.models import ActionMetadata, ActionStatus + +class FilterAction(BaseModel): + field: str + operator: str + value: str + +metadata = ActionMetadata[FilterAction]( + action=FilterAction(field="date", operator=">=", value="2024-01-01"), + status=ActionStatus.PENDING, + explanation="Filter orders from the last year", +) +``` + +## Refinement Message Pattern + +For iterative structured output: + +```python +from pydantic import BaseModel +from llmpane.models import RefinementMetadata + +class SQLQuery(BaseModel): + query: str + tables: list[str] + +metadata = RefinementMetadata[SQLQuery]( + output=SQLQuery(query="SELECT * FROM orders", tables=["orders"]), + iteration=1, + is_final=False, +) +``` + +## License + +MIT diff --git a/packages/llmpane-py/llmpane/__init__.py b/packages/llmpane-py/llmpane/__init__.py new file mode 100644 index 0000000..396537d --- /dev/null +++ b/packages/llmpane-py/llmpane/__init__.py @@ -0,0 +1,38 @@ +"""llmpane - A lightweight, embeddable chat UI component for Python/FastAPI backends.""" + +from typing import Any + +from llmpane.models import ( + ActionMetadata, + ActionStatus, + ChatMessage, + ChatRequest, + ChatResponse, + MessageRole, + RefinementMetadata, + StreamChunk, +) + +__version__ = "0.1.0" + +__all__ = [ + "ActionMetadata", + "ActionStatus", + "ChatMessage", + "ChatRequest", + "ChatResponse", + "MessageRole", + "RefinementMetadata", + "StreamChunk", + "__version__", +] + + +def __getattr__(name: str) -> Any: + if name == "create_sse_response": + from llmpane.streaming import create_sse_response + return create_sse_response + if name == "stream_chunks": + from llmpane.streaming import stream_chunks + return stream_chunks + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/packages/llmpane-py/llmpane/models.py b/packages/llmpane-py/llmpane/models.py new file mode 100644 index 0000000..2e94f08 --- /dev/null +++ b/packages/llmpane-py/llmpane/models.py @@ -0,0 +1,133 @@ +"""Pydantic models for llmpane - the source of truth for type definitions.""" + +from enum import Enum +from typing import Generic, TypeVar + +from pydantic import BaseModel, ConfigDict, Field + + +class MessageRole(str, Enum): + """Role of a message in a conversation.""" + + USER = "user" + ASSISTANT = "assistant" + SYSTEM = "system" + + +# Generic type for custom metadata +MetadataT = TypeVar("MetadataT", bound=BaseModel | None) + + +class ChatMessage(BaseModel, Generic[MetadataT]): + """A single message in a conversation.""" + + model_config = ConfigDict(use_enum_values=True) + + id: str | None = Field( + default=None, + description="Server-assigned message ID. None for pending messages.", + ) + role: MessageRole + content: str + metadata: MetadataT | None = Field( + default=None, + description="Optional custom metadata attached to this message.", + ) + + +class ChatRequest(BaseModel, Generic[MetadataT]): + """Request to send a message and get a streaming response.""" + + model_config = ConfigDict(use_enum_values=True) + + message: str = Field(description="The user's message content.") + conversation_id: str | None = Field( + default=None, + description="Optional conversation ID for multi-turn conversations.", + ) + history: list[ChatMessage[MetadataT]] = Field( + default_factory=list, + description="Previous messages in this conversation.", + ) + metadata: MetadataT | None = Field( + default=None, + description="Optional custom metadata for this request.", + ) + + +class StreamChunk(BaseModel, Generic[MetadataT]): + """A single chunk in a streaming response.""" + + delta: str = Field( + default="", + description="Text content to append to the message.", + ) + metadata: MetadataT | None = Field( + default=None, + description="Optional metadata update. Replaces previous metadata when set.", + ) + done: bool = Field( + default=False, + description="True when the stream is complete.", + ) + error: str | None = Field( + default=None, + description="Error message if something went wrong.", + ) + message_id: str | None = Field( + default=None, + description="Server-assigned message ID. Typically sent in the final chunk.", + ) + + +class ChatResponse(BaseModel, Generic[MetadataT]): + """Non-streaming response (for simple use cases).""" + + message: ChatMessage[MetadataT] + conversation_id: str | None = None + + +# --- Action Message Pattern --- + + +class ActionStatus(str, Enum): + """Status of a proposed action.""" + + PENDING = "pending" + ACCEPTED = "accepted" + REJECTED = "rejected" + REFINED = "refined" + + +ActionT = TypeVar("ActionT", bound=BaseModel) + + +class ActionMetadata(BaseModel, Generic[ActionT]): + """Metadata for action-based messages (LLM proposes, user confirms).""" + + action: ActionT + status: ActionStatus = ActionStatus.PENDING + explanation: str | None = Field( + default=None, + description="Human-readable explanation of what the action does.", + ) + + +# --- Refinement Message Pattern --- + + +OutputT = TypeVar("OutputT", bound=BaseModel) + + +class RefinementMetadata(BaseModel, Generic[OutputT]): + """Metadata for iterative refinement messages.""" + + output: OutputT + iteration: int = Field( + default=1, + description="Which iteration of refinement this is.", + ) + is_final: bool = Field( + default=False, + description="True if the user has accepted this as final.", + ) diff --git a/packages/llmpane-py/llmpane/py.typed b/packages/llmpane-py/llmpane/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/packages/llmpane-py/llmpane/streaming.py b/packages/llmpane-py/llmpane/streaming.py new file mode 100644 index 0000000..d5cbdff --- /dev/null +++ b/packages/llmpane-py/llmpane/streaming.py @@ -0,0 +1,117 @@ +"""FastAPI SSE streaming utilities for llmpane.""" + +from collections.abc import AsyncGenerator +from typing import Any + +from pydantic import BaseModel + +from llmpane.models import StreamChunk + +try: + from sse_starlette.sse import EventSourceResponse +except ImportError as e: + raise ImportError( + "FastAPI streaming utilities require the 'fastapi' extra. " + "Install with: pip install llmpane[fastapi]" + ) from e + + +def _to_camel(string: str) -> str: + """Convert snake_case to camelCase.""" + components = string.split("_") + return components[0] + "".join(x.title() for x in components[1:]) + + +def _serialize_chunk(chunk: StreamChunk[Any]) -> str: + """Serialize a StreamChunk to JSON with camelCase keys.""" + data = chunk.model_dump(exclude_none=True, by_alias=True) + # Convert keys to camelCase for JavaScript consumption + camel_data = {_to_camel(k): v for k, v in data.items()} + # Handle nested metadata + if "metadata" in camel_data and isinstance(camel_data["metadata"], dict): + camel_data["metadata"] = { + _to_camel(k): v for k, v in camel_data["metadata"].items() + } + import json + + return json.dumps(camel_data) + + +async def stream_chunks( + generator: AsyncGenerator[StreamChunk[Any], None], +) -> AsyncGenerator[dict[str, str], None]: + """Convert StreamChunk async generator to SSE event format.""" + async for chunk in generator: + yield {"data": _serialize_chunk(chunk)} + + +def create_sse_response( + generator: AsyncGenerator[StreamChunk[Any], None], + **kwargs: Any, +) -> EventSourceResponse: + """Create an SSE response from a StreamChunk generator. + + Args: + generator: Async generator yielding StreamChunk objects. + **kwargs: Additional arguments passed to EventSourceResponse. + + Returns: + EventSourceResponse ready to be returned from a FastAPI endpoint. + + Example: + @app.post("/chat") + async def chat(request: ChatRequest): + async def generate(): + yield StreamChunk(delta="Hello ") + yield StreamChunk(delta="world!") + yield StreamChunk(done=True, message_id="msg_123") + + return create_sse_response(generate()) + """ + return EventSourceResponse( + stream_chunks(generator), + media_type="text/event-stream", + **kwargs, + ) + + +class StreamBuilder: + """Helper class for building streaming responses.""" + + def __init__(self, metadata_class: type[BaseModel] | None = None): + """Initialize the stream builder. + + Args: + metadata_class: Optional Pydantic model class for metadata validation. + """ + self._metadata_class = metadata_class + self._accumulated_text = "" + self._metadata: Any = None + + def text(self, delta: str) -> StreamChunk[Any]: + """Create a text delta chunk.""" + self._accumulated_text += delta + return StreamChunk(delta=delta) + + def metadata(self, metadata: BaseModel) -> StreamChunk[Any]: + """Create a metadata update chunk.""" + self._metadata = metadata + return StreamChunk(metadata=metadata) + + def done(self, message_id: str) -> StreamChunk[Any]: + """Create the final chunk with message ID.""" + return StreamChunk(done=True, message_id=message_id) + + def error(self, message: str) -> StreamChunk[Any]: + """Create an error chunk.""" + return StreamChunk(error=message, done=True) + + @property + def accumulated_text(self) -> str: + """Get the accumulated text from all delta chunks.""" + return self._accumulated_text + + @property + def current_metadata(self) -> Any: + """Get the current metadata value.""" + return self._metadata diff --git a/packages/llmpane-py/pyproject.toml b/packages/llmpane-py/pyproject.toml new file mode 100644 index 0000000..253b394 --- /dev/null +++ b/packages/llmpane-py/pyproject.toml @@ -0,0 +1,61 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "llmpane" +version = "0.1.0" +description = "A lightweight, embeddable chat UI component for Python/FastAPI backends serving LLM features" +readme = "README.md" +license = "MIT" +requires-python = ">=3.10" +authors = [{ name = "Cody Hart" }] +keywords = ["llm", "chat", "fastapi", "sse", "streaming", "pydantic"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Framework :: FastAPI", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Software Development :: Libraries :: Python Modules", + "Typing :: Typed", +] +dependencies = ["pydantic>=2.0.0"] + +[project.optional-dependencies] +fastapi = ["fastapi>=0.100.0", "sse-starlette>=1.6.0"] +dev = [ + "pytest>=7.0.0", + "pytest-asyncio>=0.21.0", + "httpx>=0.24.0", + "ruff>=0.1.0", + "mypy>=1.0.0", +] +all = ["llmpane[fastapi,dev]"] + +[project.urls] +Homepage = "https://github.com/hartbrook/llmpane" +Documentation = "https://github.com/hartbrook/llmpane#readme" +Repository = "https://github.com/hartbrook/llmpane" + +[tool.hatch.build.targets.wheel] +packages = ["llmpane"] + +[tool.ruff] +target-version = "py310" +line-length = 100 + +[tool.ruff.lint] +select = ["E", "F", "I", "UP", "B", "SIM", "RUF"] +ignore = ["E501"] + +[tool.mypy] +python_version = "3.10" +strict = true + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] diff --git a/packages/llmpane-py/tests/__init__.py b/packages/llmpane-py/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/packages/llmpane-py/tests/test_models.py b/packages/llmpane-py/tests/test_models.py new file mode 100644 index 0000000..ebb1bb8 --- /dev/null +++ b/packages/llmpane-py/tests/test_models.py @@ -0,0 +1,197 @@ +"""Tests for llmpane Pydantic models.""" + +from pydantic import BaseModel + +from llmpane.models import ( + ActionMetadata, + ActionStatus, + ChatMessage, + ChatRequest, + ChatResponse, + MessageRole, + RefinementMetadata, + StreamChunk, +) + + +class TestMessageRole: + def test_enum_values(self): + assert MessageRole.USER.value == "user" + assert MessageRole.ASSISTANT.value == "assistant" + assert MessageRole.SYSTEM.value == "system" + + def test_string_comparison(self): + assert MessageRole.USER == "user" + assert MessageRole.ASSISTANT == "assistant" + + +class TestChatMessage: + def test_basic_message(self): + msg = ChatMessage(role=MessageRole.USER, content="Hello!") + assert msg.role == "user" + assert msg.content == "Hello!" + assert msg.id is None + assert msg.metadata is None + + def test_message_with_id(self): + msg = ChatMessage(id="msg_123", role=MessageRole.ASSISTANT, content="Hi there!") + assert msg.id == "msg_123" + assert msg.role == "assistant" + + def test_message_serialization(self): + msg = ChatMessage(id="msg_123", role=MessageRole.USER, content="Test") + data = msg.model_dump() + assert data["id"] == "msg_123" + assert data["role"] == "user" + assert data["content"] == "Test" + + def test_message_with_custom_metadata(self): + class CustomMeta(BaseModel): + confidence: float + + msg = ChatMessage[CustomMeta]( + role=MessageRole.ASSISTANT, + content="Answer", + metadata=CustomMeta(confidence=0.95), + ) + assert msg.metadata is not None + assert msg.metadata.confidence == 0.95 + + def test_message_from_dict(self): + data = {"role": "user", "content": "Hello from dict"} + msg = ChatMessage.model_validate(data) + assert msg.role == "user" + assert msg.content == "Hello from dict" + + +class TestChatRequest: + def test_minimal_request(self): + req = ChatRequest(message="What is Python?") + assert req.message == "What is Python?" + assert req.conversation_id is None + assert req.history == [] + assert req.metadata is None + + def test_request_with_conversation_id(self): + req = ChatRequest(message="Follow up", conversation_id="conv_456") + assert req.conversation_id == "conv_456" + + def test_request_with_history(self): + history = [ + ChatMessage(role=MessageRole.USER, content="Hi"), + ChatMessage(role=MessageRole.ASSISTANT, content="Hello!"), + ] + req = ChatRequest(message="How are you?", history=history) + assert len(req.history) == 2 + assert req.history[0].content == "Hi" + assert req.history[1].role == "assistant" + + def test_request_serialization(self): + req = ChatRequest(message="Test", conversation_id="conv_123") + data = req.model_dump() + assert data["message"] == "Test" + assert data["conversation_id"] == "conv_123" + assert data["history"] == [] + + +class TestStreamChunk: + def test_text_chunk(self): + chunk = StreamChunk(delta="Hello ") + assert chunk.delta == "Hello " + assert chunk.done is False + assert chunk.error is None + assert chunk.message_id is None + + def test_done_chunk(self): + chunk = StreamChunk(done=True, message_id="msg_789") + assert chunk.done is True + assert chunk.message_id == "msg_789" + assert chunk.delta == "" + + def test_error_chunk(self): + chunk = StreamChunk(error="Something went wrong", done=True) + assert chunk.error == "Something went wrong" + assert chunk.done is True + + def test_metadata_chunk(self): + class QueryMeta(BaseModel): + query: str + + chunk = StreamChunk[QueryMeta](metadata=QueryMeta(query="SELECT *")) + assert chunk.metadata is not None + assert chunk.metadata.query == "SELECT *" + + def test_chunk_serialization_excludes_none(self): + chunk = StreamChunk(delta="Hi") + data = chunk.model_dump(exclude_none=True) + assert "delta" in data + assert "error" not in data + assert "message_id" not in data + + +class TestChatResponse: + def test_basic_response(self): + msg = ChatMessage(id="msg_1", role=MessageRole.ASSISTANT, content="Response") + resp = ChatResponse(message=msg) + assert resp.message.content == "Response" + assert resp.conversation_id is None + + def test_response_with_conversation_id(self): + msg = ChatMessage(id="msg_1", role=MessageRole.ASSISTANT, content="Response") + resp = ChatResponse(message=msg, conversation_id="conv_abc") + assert resp.conversation_id == "conv_abc" + + +class TestActionMetadata: + def test_action_metadata(self): + class FilterAction(BaseModel): + field: str + value: str + + action = FilterAction(field="status", value="active") + meta = ActionMetadata[FilterAction](action=action) + assert meta.action.field == "status" + assert meta.status == ActionStatus.PENDING + assert meta.explanation is None + + def test_action_with_explanation(self): + class DeleteAction(BaseModel): + target_id: str + + action = DeleteAction(target_id="123") + meta = ActionMetadata[DeleteAction]( + action=action, + status=ActionStatus.PENDING, + explanation="Delete record 123", + ) + assert meta.explanation == "Delete record 123" + + def test_action_status_values(self): + assert ActionStatus.PENDING.value == "pending" + assert ActionStatus.ACCEPTED.value == "accepted" + assert ActionStatus.REJECTED.value == "rejected" + assert ActionStatus.REFINED.value == "refined" + + +class TestRefinementMetadata: + def test_refinement_metadata(self): + class SQLOutput(BaseModel): + query: str + + output = SQLOutput(query="SELECT * FROM users") + meta = RefinementMetadata[SQLOutput](output=output) + assert meta.output.query == "SELECT * FROM users" + assert meta.iteration == 1 + assert meta.is_final is False + + def test_refinement_iteration(self): + class Config(BaseModel): + setting: str + + meta = RefinementMetadata[Config]( + output=Config(setting="value"), + iteration=3, + is_final=True, + ) + assert meta.iteration == 3 + assert meta.is_final is True diff --git a/packages/llmpane-py/tests/test_streaming.py b/packages/llmpane-py/tests/test_streaming.py new file mode 100644 index 0000000..7cb67fe --- /dev/null +++ b/packages/llmpane-py/tests/test_streaming.py @@ -0,0 +1,142 @@ +"""Tests for llmpane SSE streaming utilities.""" + +import json + +import pytest +from pydantic import BaseModel + +from llmpane.models import StreamChunk +from llmpane.streaming import StreamBuilder, _serialize_chunk, _to_camel, stream_chunks + + +class TestCamelCaseConversion: + def test_simple_conversion(self): + assert _to_camel("message_id") == "messageId" + assert _to_camel("conversation_id") == "conversationId" + assert _to_camel("is_final") == "isFinal" + + def test_single_word(self): + assert _to_camel("delta") == "delta" + assert _to_camel("done") == "done" + + def test_multiple_underscores(self): + assert _to_camel("some_long_variable_name") == "someLongVariableName" + + +class TestSerializeChunk: + def test_serialize_text_chunk(self): + chunk = StreamChunk(delta="Hello") + result = _serialize_chunk(chunk) + data = json.loads(result) + assert data["delta"] == "Hello" + assert "done" in data + assert data["done"] is False + + def test_serialize_done_chunk(self): + chunk = StreamChunk(done=True, message_id="msg_123") + result = _serialize_chunk(chunk) + data = json.loads(result) + assert data["done"] is True + assert data["messageId"] == "msg_123" + + def test_serialize_excludes_none(self): + chunk = StreamChunk(delta="Hi") + result = _serialize_chunk(chunk) + data = json.loads(result) + assert "error" not in data + assert "messageId" not in data + + def test_serialize_with_metadata(self): + class Meta(BaseModel): + filter_value: str + is_active: bool + + chunk = StreamChunk[Meta](metadata=Meta(filter_value="test", is_active=True)) + result = _serialize_chunk(chunk) + data = json.loads(result) + assert "metadata" in data + assert data["metadata"]["filterValue"] == "test" + assert data["metadata"]["isActive"] is True + + +class TestStreamChunks: + @pytest.mark.asyncio + async def test_stream_chunks_generator(self): + async def generate(): + yield StreamChunk(delta="Hello ") + yield StreamChunk(delta="world!") + yield StreamChunk(done=True, message_id="msg_1") + + events = [] + async for event in stream_chunks(generate()): + events.append(event) + + assert len(events) == 3 + assert "data" in events[0] + + # Verify each event is valid JSON + for event in events: + data = json.loads(event["data"]) + assert isinstance(data, dict) + + @pytest.mark.asyncio + async def test_stream_chunks_content(self): + async def generate(): + yield StreamChunk(delta="Test") + + async for event in stream_chunks(generate()): + data = json.loads(event["data"]) + assert data["delta"] == "Test" + + +class TestStreamBuilder: + def test_text_accumulation(self): + builder = StreamBuilder() + chunk1 = builder.text("Hello ") + chunk2 = builder.text("world!") + + assert chunk1.delta == "Hello " + assert chunk2.delta == "world!" + assert builder.accumulated_text == "Hello world!" + + def test_metadata_update(self): + class Meta(BaseModel): + count: int + + builder = StreamBuilder() + chunk = builder.metadata(Meta(count=5)) + + assert chunk.metadata is not None + assert builder.current_metadata.count == 5 + + def test_done_chunk(self): + builder = StreamBuilder() + chunk = builder.done("msg_abc") + + assert chunk.done is True + assert chunk.message_id == "msg_abc" + + def test_error_chunk(self): + builder = StreamBuilder() + chunk = builder.error("Connection failed") + + assert chunk.error == "Connection failed" + assert chunk.done is True + + def test_full_stream_flow(self): + class QueryMeta(BaseModel): + sql: str + + builder = StreamBuilder() + + # Simulate a stream + chunks = [ + builder.text("Here's your query: "), + builder.metadata(QueryMeta(sql="SELECT * FROM users")), + builder.done("msg_xyz"), + ] + + assert len(chunks) == 3 + assert builder.accumulated_text == "Here's your query: " + assert builder.current_metadata.sql == "SELECT * FROM users" + assert chunks[-1].done is True diff --git a/packages/llmpane-react/.eslintrc.cjs b/packages/llmpane-react/.eslintrc.cjs new file mode 100644 index 0000000..166c542 --- /dev/null +++ b/packages/llmpane-react/.eslintrc.cjs @@ -0,0 +1,32 @@ +module.exports = { + root: true, + env: { + browser: true, + es2020: true, + }, + extends: [ + 'eslint:recommended', + 'plugin:@typescript-eslint/recommended', + 'plugin:react/recommended', + 'plugin:react-hooks/recommended', + ], + ignorePatterns: ['dist', '.eslintrc.cjs'], + parser: '@typescript-eslint/parser', + parserOptions: { + ecmaVersion: 'latest', + sourceType: 'module', + ecmaFeatures: { + jsx: true, + }, + }, + plugins: ['react', 'react-hooks', '@typescript-eslint'], + settings: { + react: { + version: '18.2', + }, + }, + rules: { + 'react/react-in-jsx-scope': 'off', + 'react/prop-types': 'off', + }, +}; diff --git a/packages/llmpane-react/.gitignore b/packages/llmpane-react/.gitignore new file mode 100644 index 0000000..5433605 --- /dev/null +++ b/packages/llmpane-react/.gitignore @@ -0,0 +1,21 @@ +# Dependencies +node_modules/ + +# Build output +dist/ + +# Logs +*.log +npm-debug.log* + +# Editor +.vscode/* +!.vscode/extensions.json +.idea/ + +# OS +.DS_Store +Thumbs.db + +# Test coverage +coverage/ diff --git a/packages/llmpane-react/README.md b/packages/llmpane-react/README.md new file mode 100644 index 0000000..af5e2c9 --- /dev/null +++ b/packages/llmpane-react/README.md @@ -0,0 +1,200 @@ +# @llmpane/react + +A lightweight, embeddable chat UI component for LLM-powered React applications. + +## Installation + +```bash +npm install @llmpane/react +``` + +## Quick Start + +```tsx +import { ChatPane } from "@llmpane/react"; +import "@llmpane/react/styles"; + +function App() { + return ( + + ); +} +``` + +## Components + +### ChatPane + +The main chat component with built-in message display and input handling. + +```tsx + console.log("Received:", message)} +/> +``` + +#### Props + +| Prop | Type | Description | +|------|------|-------------| +| `endpoint` | `string` | Required. The API endpoint for chat requests. | +| `conversationId` | `string` | Optional conversation ID for multi-turn chats. | +| `placeholder` | `string` | Input placeholder text. | +| `initialMessages` | `ChatMessage[]` | Initial messages to display. | +| `renderMessage` | `function` | Custom message renderer. | +| `renderInput` | `function` | Custom input renderer. | +| `autoScroll` | `boolean` | Auto-scroll to new messages (default: true). | +| `multiline` | `boolean` | Allow multiline input with Shift+Enter. | +| `disabled` | `boolean` | Disable the input. | + +### ActionMessage + +For "LLM proposes, user confirms" flows. + +```tsx +import { ActionMessage } from "@llmpane/react"; + +interface FilterAction { + field: string; + operator: string; + value: string; +} + + + message={message} + onAccept={(action) => applyFilter(action)} + onReject={(action) => console.log("Rejected")} + renderAction={(action) => ( + {`${action.field} ${action.operator} ${action.value}`} + )} +/> +``` + +### RefinementMessage + +For iterative structured output with preview. + +```tsx +import { RefinementMessage } from "@llmpane/react"; + +interface SQLQuery { + query: string; + tables: string[]; +} + + + message={message} + onAccept={(query) => executeQuery(query)} + renderOutput={(output) => 
{output.query}
} +/> +``` + +## Hooks + +### useChat + +Core hook for managing chat state and SSE streaming. + +```tsx +import { useChat } from "@llmpane/react"; + +function MyChat() { + const { + messages, + isLoading, + error, + sendMessage, + stopStream, + clearMessages, + } = useChat({ + endpoint: "/api/chat", + onComplete: (message) => console.log("Done:", message), + }); + + return ( +
+ {messages.map(msg =>
{msg.content}
)} + +
+ ); +} +``` + +## Custom Metadata + +Use TypeScript generics for type-safe custom metadata: + +```tsx +interface MyMetadata { + confidence: number; + sources: string[]; +} + +const { messages } = useChat({ + endpoint: "/api/chat", +}); + +// messages is typed as DisplayMessage[] +``` + +## Styling + +### Using Default Styles + +```tsx +import "@llmpane/react/styles"; +``` + +### CSS Variables + +Override these CSS variables for theming: + +```css +:root { + --llmpane-bg: #ffffff; + --llmpane-text: #111827; + --llmpane-user-bg: #3b82f6; + --llmpane-user-text: #ffffff; + --llmpane-assistant-bg: #f3f4f6; + --llmpane-button-bg: #3b82f6; + /* ... see full list in llmpane.css */ +} +``` + +### Headless Mode + +Don't import the CSS and provide your own class names: + +```tsx + ( +
{msg.content}
+ )} +/> +``` + +## TypeScript Types + +All types mirror the Python Pydantic models: + +```tsx +import type { + ChatMessage, + ChatRequest, + StreamChunk, + ActionMetadata, + RefinementMetadata, +} from "@llmpane/react"; +``` + +## License + +MIT diff --git a/packages/llmpane-react/package-lock.json b/packages/llmpane-react/package-lock.json new file mode 100644 index 0000000..94694c9 --- /dev/null +++ b/packages/llmpane-react/package-lock.json @@ -0,0 +1,8144 @@ +{ + "name": "@llmpane/react", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "@llmpane/react", + "version": "0.1.0", + "license": "MIT", + "devDependencies": { + "@testing-library/jest-dom": "^6.0.0", + "@testing-library/react": "^14.0.0", + "@testing-library/user-event": "^14.0.0", + "@types/react": "^18.2.0", + "@types/react-dom": "^18.2.0", + "@typescript-eslint/eslint-plugin": "^6.0.0", + "@typescript-eslint/parser": "^6.0.0", + "@vitejs/plugin-react": "^4.0.0", + 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"react": ">=18.0.0", + "react-dom": ">=18.0.0" + }, + "devDependencies": { + "@testing-library/jest-dom": "^6.0.0", + "@testing-library/react": "^14.0.0", + "@testing-library/user-event": "^14.0.0", + "@types/react": "^18.2.0", + "@types/react-dom": "^18.2.0", + "@typescript-eslint/eslint-plugin": "^6.0.0", + "@typescript-eslint/parser": "^6.0.0", + "@vitejs/plugin-react": "^4.0.0", + "eslint": "^8.0.0", + "eslint-plugin-react": "^7.33.0", + "eslint-plugin-react-hooks": "^4.6.0", + "jsdom": "^22.0.0", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "typescript": "^5.0.0", + "vitest": "^1.0.0" + }, + "keywords": [ + "react", + "chat", + "llm", + "ai", + "streaming", + "sse", + "component" + ], + "author": "Cody Hart", + "license": "MIT", + "repository": { + "type": "git", + "url": "https://github.com/HartBrook/llmpane.git", + "directory": "packages/llmpane-react" + }, + "bugs": { + "url": "https://github.com/HartBrook/llmpane/issues" + }, + "homepage": "https://github.com/HartBrook/llmpane#readme" +} diff --git a/packages/llmpane-react/src/components/ActionMessage.test.tsx b/packages/llmpane-react/src/components/ActionMessage.test.tsx new file mode 100644 index 0000000..6f8d1d4 --- /dev/null +++ b/packages/llmpane-react/src/components/ActionMessage.test.tsx @@ -0,0 +1,190 @@ +import { describe, it, expect, vi } from "vitest"; +import { render, screen } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { ActionMessage } from "./ActionMessage"; +import type { ChatMessage, ActionMetadata } from "../types"; + +interface TestAction { + field: string; + value: string; +} + +describe("ActionMessage", () => { + const createMessage = ( + status: "pending" | "accepted" | "rejected" | "refined" = "pending" + ): ChatMessage> => ({ + id: "msg_1", + role: "assistant", + content: "Here's a suggested action:", + metadata: { + action: { field: "status", value: "active" }, + status, + explanation: "Filter by active status", + }, + }); + + it("renders message content", () => { + render(); + + expect(screen.getByText("Here's a suggested action:")).toBeInTheDocument(); + }); + + it("renders explanation", () => { + render(); + + expect(screen.getByText("Filter by active status")).toBeInTheDocument(); + }); + + it("renders action as JSON by default", () => { + render(); + + // Check that the JSON preview contains the action data + const codeBlock = screen.getByText(/"field": "status"/); + expect(codeBlock).toBeInTheDocument(); + expect(codeBlock).toHaveTextContent(/"value": "active"/); + }); + + it("renders custom action preview", () => { + render( + ( +
+ {action.field} = {action.value} +
+ )} + /> + ); + + expect(screen.getByTestId("custom-action")).toHaveTextContent( + "status = active" + ); + }); + + it("shows pending status badge", () => { + render(); + + expect(screen.getByText("Pending")).toBeInTheDocument(); + }); + + it("shows accepted status badge", () => { + render(); + + expect(screen.getByText("Accepted")).toBeInTheDocument(); + }); + + it("shows reject status badge", () => { + render(); + + expect(screen.getByText("Rejected")).toBeInTheDocument(); + }); + + it("shows action buttons when pending", () => { + render( + {}} + onReject={() => {}} + /> + ); + + expect(screen.getByRole("button", { name: /accept/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /reject/i })).toBeInTheDocument(); + }); + + it("hides action buttons when not pending", () => { + render( + {}} + onReject={() => {}} + /> + ); + + expect(screen.queryByRole("button", { name: /accept/i })).not.toBeInTheDocument(); + expect(screen.queryByRole("button", { name: /reject/i })).not.toBeInTheDocument(); + }); + + it("calls onAccept with action when accept clicked", async () => { + const onAccept = vi.fn(); + render( + + ); + + await userEvent.click(screen.getByRole("button", { name: /accept/i })); + + expect(onAccept).toHaveBeenCalledWith({ field: "status", value: "active" }); + }); + + it("calls onReject with action when reject clicked", async () => { + const onReject = vi.fn(); + render( + + ); + + await userEvent.click(screen.getByRole("button", { name: /reject/i })); + + expect(onReject).toHaveBeenCalledWith({ field: "status", value: "active" }); + }); + + it("shows refine button when enabled", () => { + render( + {}} + showRefineButton + /> + ); + + expect(screen.getByRole("button", { name: /refine/i })).toBeInTheDocument(); + }); + + it("calls onRefine with action when refine clicked", async () => { + const onRefine = vi.fn(); + render( + + ); + + await userEvent.click(screen.getByRole("button", { name: /refine/i })); + + expect(onRefine).toHaveBeenCalledWith({ field: "status", value: "active" }); + }); + + it("uses custom labels", () => { + render( + {}} + onReject={() => {}} + labels={{ + accept: "Confirm", + reject: "Cancel", + pending: "Awaiting", + }} + /> + ); + + expect(screen.getByRole("button", { name: /confirm/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /cancel/i })).toBeInTheDocument(); + expect(screen.getByText("Awaiting")).toBeInTheDocument(); + }); + + it("returns null when no metadata", () => { + const messageWithoutMetadata: ChatMessage> = { + id: "msg_1", + role: "assistant", + content: "No metadata here", + metadata: undefined, + }; + + const { container } = render( + + ); + + expect(container.firstChild).toBeNull(); + }); +}); diff --git a/packages/llmpane-react/src/components/ActionMessage.tsx b/packages/llmpane-react/src/components/ActionMessage.tsx new file mode 100644 index 0000000..0835eae --- /dev/null +++ b/packages/llmpane-react/src/components/ActionMessage.tsx @@ -0,0 +1,190 @@ +import { useCallback, type ReactNode } from "react"; + +import type { ActionMetadata, ActionStatus, ChatMessage } from "../types"; + +/** Props for the ActionMessage component */ +export interface ActionMessageProps { + /** The chat message with action metadata */ + message: ChatMessage>; + /** Callback when the user accepts the action */ + onAccept?: (action: TAction) => void; + /** Callback when the user rejects the action */ + onReject?: (action: TAction) => void; + /** Callback when the user wants to refine the action */ + onRefine?: (action: TAction) => void; + /** Custom renderer for the action preview */ + renderAction?: (action: TAction) => ReactNode; + /** Custom renderer for the explanation */ + renderExplanation?: (explanation: string) => ReactNode; + /** Labels for the action buttons */ + labels?: { + accept?: string; + reject?: string; + refine?: string; + pending?: string; + accepted?: string; + rejected?: string; + refined?: string; + }; + /** Custom class name */ + className?: string; + /** Whether to show the refine button */ + showRefineButton?: boolean; +} + +const defaultLabels = { + accept: "Accept", + reject: "Reject", + refine: "Refine", + pending: "Pending", + accepted: "Accepted", + rejected: "Rejected", + refined: "Refined", +}; + +/** Status badge component */ +function StatusBadge({ + status, + labels, +}: { + status: ActionStatus; + labels: typeof defaultLabels; +}) { + return ( + + {labels[status]} + + ); +} + +/** + * Component for displaying action-based messages with accept/reject UI. + * + * @example + * ```tsx + * interface FilterAction { + * field: string; + * operator: string; + * value: string; + * } + * + * + * message={message} + * onAccept={(action) => applyFilter(action)} + * onReject={(action) => console.log("Rejected:", action)} + * renderAction={(action) => ( + * {`${action.field} ${action.operator} ${action.value}`} + * )} + * /> + * ``` + */ +export function ActionMessage({ + message, + onAccept, + onReject, + onRefine, + renderAction, + renderExplanation, + labels: customLabels = {}, + className = "", + showRefineButton = true, +}: ActionMessageProps) { + const labels = { ...defaultLabels, ...customLabels }; + const metadata = message.metadata; + + const handleAccept = useCallback(() => { + if (metadata?.action) { + onAccept?.(metadata.action); + } + }, [metadata, onAccept]); + + const handleReject = useCallback(() => { + if (metadata?.action) { + onReject?.(metadata.action); + } + }, [metadata, onReject]); + + const handleRefine = useCallback(() => { + if (metadata?.action) { + onRefine?.(metadata.action); + } + }, [metadata, onRefine]); + + if (!metadata) { + return null; + } + + const { action, status, explanation } = metadata; + const isPending = status === "pending"; + + return ( +
+
Assistant
+ + {/* Message content */} + {message.content && ( +
{message.content}
+ )} + + {/* Action card */} +
+ {/* Explanation */} + {explanation && ( +
+ {renderExplanation ? renderExplanation(explanation) : explanation} +
+ )} + + {/* Action preview */} +
+ {renderAction ? ( + renderAction(action) + ) : ( + 
+              {JSON.stringify(action, null, 2)}
+
+ )} +
+ + {/* Status and buttons */} +
+ + + {isPending && ( +
+ {onAccept && ( + + )} + {onReject && ( + + )} + {showRefineButton && onRefine && ( + + )} +
+ )} +
+
+
+ ); +} diff --git a/packages/llmpane-react/src/components/ChatPane.test.tsx b/packages/llmpane-react/src/components/ChatPane.test.tsx new file mode 100644 index 0000000..1db1be8 --- /dev/null +++ b/packages/llmpane-react/src/components/ChatPane.test.tsx @@ -0,0 +1,225 @@ +import { describe, it, expect, vi, beforeEach } from "vitest"; +import { render, screen, waitFor } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { ChatPane } from "./ChatPane"; + +describe("ChatPane", () => { + const mockEndpoint = "/api/chat"; + + function createMockSSEResponse(chunks: string[]): Response { + let index = 0; + const encoder = new TextEncoder(); + + const stream = new ReadableStream({ + pull(controller) { + if (index < chunks.length) { + controller.enqueue(encoder.encode(chunks[index])); + index++; + } else { + controller.close(); + } + }, + }); + + return new Response(stream, { + status: 200, + headers: { "Content-Type": "text/event-stream" }, + }); + } + + beforeEach(() => { + vi.clearAllMocks(); + }); + + it("renders empty state", () => { + render(); + + expect(screen.getByText(/no messages yet/i)).toBeInTheDocument(); + expect(screen.getByRole("textbox")).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /send/i })).toBeInTheDocument(); + }); + + it("renders with custom placeholder", () => { + render(); + + expect(screen.getByPlaceholderText("Ask me anything...")).toBeInTheDocument(); + }); + + it("renders initial messages", () => { + const initialMessages = [ + { id: "1", role: "user" as const, content: "Hello!" }, + { id: "2", role: "assistant" as const, content: "Hi there!" }, + ]; + + render(); + + expect(screen.getByText("Hello!")).toBeInTheDocument(); + expect(screen.getByText("Hi there!")).toBeInTheDocument(); + }); + + it("disables send button when input is empty", () => { + render(); + + const sendButton = screen.getByRole("button", { name: /send/i }); + expect(sendButton).toBeDisabled(); + }); + + it("enables send button when input has text", async () => { + render(); + + const input = screen.getByRole("textbox"); + await userEvent.type(input, "Hello"); + + const sendButton = screen.getByRole("button", { name: /send/i }); + expect(sendButton).not.toBeDisabled(); + }); + + it("sends message on form submit", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockSSEResponse(['data: {"done":true,"messageId":"msg_1"}\n\n']) + ); + global.fetch = mockFetch; + + render(); + + const input = screen.getByRole("textbox"); + await userEvent.type(input, "Hello world"); + + const sendButton = screen.getByRole("button", { name: /send/i }); + await userEvent.click(sendButton); + + expect(mockFetch).toHaveBeenCalledWith( + mockEndpoint, + expect.objectContaining({ + method: "POST", + body: expect.stringContaining("Hello world"), + }) + ); + }); + + it("sends message on Enter key", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockSSEResponse(['data: {"done":true}\n\n']) + ); + global.fetch = mockFetch; + + render(); + + const input = screen.getByRole("textbox"); + await userEvent.type(input, "Test message{enter}"); + + expect(mockFetch).toHaveBeenCalled(); + }); + + it("clears input after sending", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockSSEResponse(['data: {"done":true}\n\n']) + ); + global.fetch = mockFetch; + + render(); + + const input = screen.getByRole("textbox"); + await userEvent.type(input, "Hello"); + await userEvent.click(screen.getByRole("button", { name: /send/i })); + + expect(input).toHaveValue(""); + }); + + it("shows stop button while loading", async () => { + // Create a response that takes time + const mockFetch = vi.fn().mockImplementation( + () => + new Promise((resolve) => + setTimeout( + () => resolve(createMockSSEResponse(['data: {"done":true}\n\n'])), + 1000 + ) + ) + ); + global.fetch = mockFetch; + + render(); + + const input = screen.getByRole("textbox"); + await userEvent.type(input, "Hello"); + await userEvent.click(screen.getByRole("button", { name: /send/i })); + + expect(screen.getByRole("button", { name: /stop/i })).toBeInTheDocument(); + }); + + it("keeps input enabled while loading", async () => { + const mockFetch = vi.fn().mockImplementation( + () => + new Promise((resolve) => + setTimeout( + () => resolve(createMockSSEResponse(['data: {"done":true}\n\n'])), + 1000 + ) + ) + ); + global.fetch = mockFetch; + + render(); + + const input = screen.getByRole("textbox"); + await userEvent.type(input, "Hello"); + await userEvent.click(screen.getByRole("button", { name: /send/i })); + + // Input should still be enabled + expect(input).not.toBeDisabled(); + }); + + it("displays streamed response", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockSSEResponse([ + 'data: {"delta":"Hello "}\n\n', + 'data: {"delta":"world!"}\n\n', + 'data: {"done":true,"messageId":"msg_1"}\n\n', + ]) + ); + global.fetch = mockFetch; + + render(); + + const input = screen.getByRole("textbox"); + await userEvent.type(input, "Hi"); + await userEvent.click(screen.getByRole("button", { name: /send/i })); + + await waitFor(() => { + expect(screen.getByText("Hello world!")).toBeInTheDocument(); + }); + }); + + it("uses custom renderMessage", () => { + const initialMessages = [ + { id: "1", role: "user" as const, content: "Test" }, + ]; + + render( + ( +
{msg.content.toUpperCase()}
+ )} + /> + ); + + expect(screen.getByTestId("custom-message")).toHaveTextContent("TEST"); + }); + + it("applies custom className", () => { + const { container } = render( + + ); + + expect(container.querySelector(".my-custom-class")).toBeInTheDocument(); + }); + + it("disables input when disabled prop is true", () => { + render(); + + expect(screen.getByRole("textbox")).toBeDisabled(); + }); +}); diff --git a/packages/llmpane-react/src/components/ChatPane.tsx b/packages/llmpane-react/src/components/ChatPane.tsx new file mode 100644 index 0000000..19f2cbd --- /dev/null +++ b/packages/llmpane-react/src/components/ChatPane.tsx @@ -0,0 +1,265 @@ +import React, { + useCallback, + useEffect, + useRef, + useState, + type FormEvent, + type KeyboardEvent, + type ReactNode, +} from "react"; + +import { useChat, type UseChatOptions } from "../hooks/useChat"; +import type { DisplayMessage } from "../types"; +import { isPendingMessage } from "../types"; + +/** Props for the ChatPane component */ +export interface ChatPaneProps + extends UseChatOptions { + /** Custom class name for the container */ + className?: string; + /** Placeholder text for the input */ + placeholder?: string; + /** Label for the send button */ + sendButtonLabel?: string; + /** Label for the stop button */ + stopButtonLabel?: string; + /** Whether to show timestamps on messages */ + showTimestamps?: boolean; + /** Custom message renderer for full control */ + renderMessage?: ( + message: DisplayMessage, + index: number + ) => ReactNode; + /** Custom input renderer for full control */ + renderInput?: (props: { + value: string; + onChange: (value: string) => void; + onSubmit: () => void; + isLoading: boolean; + placeholder: string; + }) => ReactNode; + /** Whether to auto-scroll to new messages */ + autoScroll?: boolean; + /** Whether to allow multiline input (Shift+Enter for newline) */ + multiline?: boolean; + /** Maximum height for the input in multiline mode */ + maxInputHeight?: number; + /** Whether to disable the input */ + disabled?: boolean; +} + +/** Default message renderer */ +function DefaultMessage({ + message, +}: { + message: DisplayMessage; +}) { + const isPending = isPendingMessage(message); + const isAssistantPending = isPending && message.role === "assistant"; + const showCursor = isAssistantPending && !message.content; + + return ( +
+
+ {message.role === "user" ? "You" : "Assistant"} +
+
+ {message.content} + {showCursor && } +
+
+ ); +} + +/** + * Main chat pane component that handles message display and input. + * + * @example + * ```tsx + * + * ``` + * + * @example With custom message rendering + * ```tsx + * ( + * + * )} + * /> + * ``` + */ +export function ChatPane({ + className = "", + placeholder = "Type a message...", + sendButtonLabel = "Send", + stopButtonLabel = "Stop", + // Note: showTimestamps is accepted for API compatibility but not yet implemented + // eslint-disable-next-line @typescript-eslint/no-unused-vars + showTimestamps = false, + renderMessage, + renderInput, + autoScroll = true, + multiline = false, + maxInputHeight = 150, + disabled = false, + ...chatOptions +}: ChatPaneProps) { + const { + messages, + isLoading, + error, + sendMessage, + stopStream, + clearError, + } = useChat(chatOptions); + + const [inputValue, setInputValue] = useState(""); + const messagesEndRef = useRef(null); + const inputRef = useRef(null); + + // Auto-scroll to bottom when new messages arrive + useEffect(() => { + if (autoScroll && messagesEndRef.current) { + messagesEndRef.current.scrollIntoView({ behavior: "smooth" }); + } + }, [messages, autoScroll]); + + // Auto-resize textarea for multiline mode + useEffect(() => { + if (multiline && inputRef.current) { + inputRef.current.style.height = "auto"; + const scrollHeight = inputRef.current.scrollHeight; + inputRef.current.style.height = `${Math.min(scrollHeight, maxInputHeight)}px`; + } + }, [inputValue, multiline, maxInputHeight]); + + const handleSubmit = useCallback( + async (e?: FormEvent) => { + e?.preventDefault(); + const trimmed = inputValue.trim(); + if (!trimmed || isLoading || disabled) return; + + setInputValue(""); + await sendMessage(trimmed); + }, + [inputValue, isLoading, disabled, sendMessage] + ); + + const handleKeyDown = useCallback( + (e: KeyboardEvent) => { + if (e.key === "Enter") { + if (multiline) { + // Shift+Enter for newline, Enter to submit + if (!e.shiftKey) { + e.preventDefault(); + handleSubmit(); + } + } else { + // Enter to submit (no newlines allowed) + e.preventDefault(); + handleSubmit(); + } + } + }, + [multiline, handleSubmit] + ); + + const handleInputChange = useCallback((value: string) => { + setInputValue(value); + }, []); + + return ( +
+ {/* Messages area */} +
+ {messages.length === 0 ? ( +
+

No messages yet. Start a conversation!

+
+ ) : ( + messages.map((message, index) => { + const key = + "tempId" in message + ? message.tempId + : message.id ?? `msg-${index}`; + + return renderMessage ? ( + + {renderMessage(message, index)} + + ) : ( + + ); + }) + )} +
+
+ + {/* Error display */} + {error && ( +
+ {error.message} + +
+ )} + + {/* Input area */} +
+ {renderInput ? ( + renderInput({ + value: inputValue, + onChange: handleInputChange, + onSubmit: handleSubmit, + isLoading, + placeholder, + }) + ) : ( + <> +