feat: publish to PyPI + fix installation issues

- Restructure deps: move llama-cpp-python/tree-sitter to optional (base install is pure Python, no compiler needed) - Add __main__.py for pipx/python -m support - Add GitHub Actions auto-publish on tag push - Fix install scripts: correct package name, remove Ollama check, add pipx priority - Wrap ast_engine imports in try/except for graceful degradation - Published v1.0.7 to PyPI: pip install kwcode Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-09-03 06:34:30 +08:00 · 2026-04-30 11:45:56 +08:00
parent a88e0c18b3
commit edd7e5d1ee
7 changed files with 169 additions and 303 deletions
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,35 @@
+name: Publish to PyPI
+
+on:
+  push:
+    tags:
+      - "v*"
+
+permissions:
+  contents: read
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install build tools
+        run: pip install build twine
+
+      - name: Build package
+        run: python -m build
+
+      - name: Check package
+        run: twine check dist/*
+
+      - name: Publish to PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+        run: twine upload dist/*
--- a/README.md
+++ b/README.md
@@ -10,12 +10,22 @@
 [![Python](https://img.shields.io/badge/Python-3.10+-blue.svg)](https://python.org)
 [![Platform](https://img.shields.io/badge/Platform-Windows%20%7C%20Mac%20%7C%20Linux-lightgrey.svg)]()
 [![Tests](https://img.shields.io/badge/Tests-311%2F311-brightgreen.svg)]()
-[![Version](https://img.shields.io/badge/Version-1.0.3-blue.svg)]()
+[![Version](https://img.shields.io/badge/Version-1.0.7-blue.svg)]()

 </div>

 ---

+> **v1.0.7 已发布，可测试使用！** 感谢各位试用提报优化建议。安装命令：
+>
+> ```bash
+> pip install kwcode
+> # 国内加速
+> pip install kwcode -i https://pypi.tuna.tsinghua.edu.cn/simple
+> ```
+
+---
+
 ## 更新日志

 | 日期 | 内容 |
--- a/install.ps1
+++ b/install.ps1
@@ -7,7 +7,7 @@ $ErrorActionPreference = "Continue"
 # ── Banner ───────────────────────────────────────────────────
 Write-Host ""
 Write-Host "  ╔══════════════════════════════════════╗" -ForegroundColor Cyan
-Write-Host "  ║       KwCode 安装程序 v0.4            ║" -ForegroundColor Cyan
+Write-Host "  ║       KwCode 安装程序 v1.0            ║" -ForegroundColor Cyan
 Write-Host "  ║   本地模型 Coding Agent               ║" -ForegroundColor Cyan
 Write-Host "  ╚══════════════════════════════════════╝" -ForegroundColor Cyan
 Write-Host ""
@@ -46,40 +46,35 @@ if (-not $pythonCmd) {
    exit 1
 }

-# ── Step 2: GPU 检查 ─────────────────────────────────────────
-Write-Step "检查 GPU..."
-
-$vramMB = 0
-try {
-    $smiOutput = & nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>&1
-    if ($LASTEXITCODE -eq 0 -and $smiOutput -match "(\d+)\s*MiB") {
-        $vramMB = [int]$Matches[1]
-        $gpuName = ($smiOutput -split ",")[0].Trim()
-        Write-Info "GPU: $gpuName  VRAM: $($vramMB)MB"
-    } else {
-        Write-Warn "未检测到 NVIDIA GPU，将使用 CPU 模式（速度较慢）"
-    }
-} catch {
-    Write-Warn "nvidia-smi 不可用，将使用 CPU 模式"
-}
-
-# ── Step 3: pip install kaiwu ────────────────────────────────
+# ── Step 2: 安装 KwCode ──────────────────────────────────────
 Write-Step "安装 KwCode..."

 $installed = $false

-# 尝试默认源
-Write-Info "尝试默认 pip 源..."
-& $pythonCmd -m pip install kaiwu --quiet 2>&1 | Out-Null
-if ($LASTEXITCODE -eq 0) {
-    $installed = $true
-    Write-Info "安装成功（默认源）"
+# 优先 pipx（隔离环境，不污染系统 Python）
+if (Get-Command pipx -ErrorAction SilentlyContinue) {
+    Write-Info "检测到 pipx，使用隔离安装..."
+    & pipx install kwcode 2>&1 | Out-Null
+    if ($LASTEXITCODE -eq 0) {
+        $installed = $true
+        Write-Info "安装成功（pipx 隔离环境）"
+    }
+}
+
+# 降级到 pip 默认源
+if (-not $installed) {
+    Write-Info "尝试 pip 安装..."
+    & $pythonCmd -m pip install kwcode --quiet 2>&1 | Out-Null
+    if ($LASTEXITCODE -eq 0) {
+        $installed = $true
+        Write-Info "安装成功（pip 默认源）"
+    }
 }

 # 降级到清华镜像
 if (-not $installed) {
-    Write-Warn "默认源安装失败，切换到清华镜像..."
-    & $pythonCmd -m pip install kaiwu -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn --quiet 2>&1 | Out-Null
+    Write-Warn "默认源失败，切换到清华镜像..."
+    & $pythonCmd -m pip install kwcode -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn --quiet 2>&1 | Out-Null
    if ($LASTEXITCODE -eq 0) {
        $installed = $true
        Write-Info "安装成功（清华镜像）"
@@ -88,117 +83,26 @@ if (-not $installed) {

 if (-not $installed) {
    Write-Err "KwCode 安装失败"
-    Write-Info "请手动执行: $pythonCmd -m pip install kaiwu"
+    Write-Info "请手动执行: $pythonCmd -m pip install kwcode"
    Write-Info "如果网络慢，加上: -i https://pypi.tuna.tsinghua.edu.cn/simple"
    exit 1
 }

-# ── Step 4: Ollama 检查 ──────────────────────────────────────
-Write-Step "检查 Ollama..."
-
-$ollamaOk = $false
-try {
-    $ollamaVer = & ollama --version 2>&1
-    if ($LASTEXITCODE -eq 0) {
-        Write-Info "Ollama 已安装: $ollamaVer"
-        $ollamaOk = $true
-    }
-} catch {}
-
-if (-not $ollamaOk) {
-    Write-Warn "未检测到 Ollama"
-    Write-Info ""
-    Write-Info "Ollama 是运行本地模型的推理引擎，请手动安装："
-    Write-Info "  下载地址: https://ollama.com/download"
-    Write-Info "  安装后重新运行本脚本即可自动拉取模型"
-    Write-Info ""
-}
-
-# ── Step 5: 拉取推荐模型 ─────────────────────────────────────
-if ($ollamaOk) {
-    Write-Step "拉取推荐模型..."
-
-    # 根据 VRAM 选择模型
-    if ($vramMB -ge 16000) {
-        $model = "qwen3:14b"
-        Write-Info "VRAM >= 16GB，推荐模型: $model"
-    } elseif ($vramMB -ge 8000) {
-        $model = "qwen3:8b"
-        Write-Info "VRAM >= 8GB，推荐模型: $model"
-    } else {
-        $model = "gemma3:4b"
-        if ($vramMB -gt 0) {
-            Write-Info "VRAM < 8GB，推荐模型: $model"
-        } else {
-            Write-Info "未检测到 GPU，使用轻量模型: $model"
-        }
-    }
-
-    # 检查 ModelScope 镜像（国内加速）
-    Write-Step "检测模型下载源..."
-    $hfOk = $false
-    try {
-        $r = Invoke-WebRequest -Uri "https://huggingface.co" -TimeoutSec 5 -UseBasicParsing -ErrorAction Stop
-        $hfOk = $true
-        Write-Info "HuggingFace 可达，使用默认源"
-    } catch {
-        Write-Warn "HuggingFace 不可达，自动切换到 ModelScope"
-        $env:OLLAMA_MODELS = "https://modelscope.cn/models"
-    }
-
-    Write-Info "正在拉取 $model（首次下载可能需要几分钟）..."
-    & ollama pull $model
-    if ($LASTEXITCODE -ne 0) {
-        Write-Warn "模型拉取失败，请稍后手动执行: ollama pull $model"
-    } else {
-        Write-Info "模型 $model 已就绪"
-    }
-}
-
-# ── Step 5.5: SearXNG 搜索服务 ──────────────────────────────
-Write-Step "启动搜索服务（SearXNG）..."
-
-if (Get-Command docker -ErrorAction SilentlyContinue) {
-    $running = docker ps --format '{{.Names}}' | Where-Object { $_ -eq "kwcode-searxng" }
-    if ($running) {
-        Write-Info "SearXNG 已在运行，跳过"
-    } else {
-        $exists = docker ps -a --format '{{.Names}}' | Where-Object { $_ -eq "kwcode-searxng" }
-        if ($exists) {
-            docker start kwcode-searxng
-            Write-Info "SearXNG 已重新启动"
-        } else {
-            Write-Info "拉取 SearXNG 镜像（约150MB）..."
-            docker pull searxng/searxng
-            docker run -d --name kwcode-searxng --restart always -p 8080:8080 searxng/searxng
-            Write-Info "等待 SearXNG 启动..."
-            for ($i = 1; $i -le 15; $i++) {
-                try {
-                    $null = Invoke-WebRequest -Uri "http://localhost:8080" -TimeoutSec 2 -UseBasicParsing
-                    Write-Info "SearXNG 已就绪：http://localhost:8080"
-                    break
-                } catch { Start-Sleep -Seconds 1 }
-            }
-        }
-    }
+# 可选：安装 AST 增强
+Write-Info "尝试安装 AST 增强（可选，需要 C 编译器）..."
+& $pythonCmd -m pip install "kwcode[ast]" --quiet 2>&1 | Out-Null
+if ($LASTEXITCODE -eq 0) {
+    Write-Info "AST 调用图引擎已启用（代码定位更精准）"
 } else {
-    Write-Warn "未检测到 Docker，搜索增强将使用 DuckDuckGo 降级方案"
-    Write-Info "安装 Docker 可获得更好的搜索体验：https://docs.docker.com/get-docker/"
+    Write-Info "AST 增强跳过（无编译器），使用 LLM 定位（功能不受影响）"
 }

-# ── Step 6: kwcode init ──────────────────────────────────────
-Write-Step "初始化 KwCode..."
+# ── Step 4: 提示配置模型 ─────────────────────────────────────
+Write-Step "模型配置提示..."
+Write-Info "KwCode 支持任何 OpenAI 兼容 API（Ollama / DeepSeek / Qwen 等）"
+Write-Info "安装完成后执行 kwcode init 即可配置 API 地址和模型"

-try {
-    & kwcode init 2>&1 | Out-Null
-    if ($LASTEXITCODE -eq 0) {
-        Write-Info "KAIWU.md 已初始化"
-    }
-} catch {
-    Write-Info "跳过初始化（可稍后在项目目录执行 kwcode init）"
-}
-
-# ── Step 7: kwcode status ────────────────────────────────────
+# ── Step 5: 验证安装 ─────────────────────────────────────────
 Write-Step "验证安装..."

 try {
@@ -220,5 +124,5 @@ Write-Host "    2. kwcode init          # 初始化项目记忆" -ForegroundColo
 Write-Host "    3. kwcode               # 进入交互模式" -ForegroundColor White
 Write-Host '    4. kwcode "修复登录bug"  # 直接执行任务' -ForegroundColor White
 Write-Host ""
-Write-Host "  文档: https://github.com/kaiwu-agent/kaiwu" -ForegroundColor Gray
+Write-Host "  文档: https://github.com/val1813/kwcode" -ForegroundColor Gray
 Write-Host ""
--- a/install.sh
+++ b/install.sh
@@ -1,6 +1,6 @@
 #!/bin/sh
 # KwCode 安装程序 - Mac/Linux
-# 用法: curl -sSL https://raw.githubusercontent.com/kaiwu-agent/kaiwu/main/install.sh | sh
+# 用法: curl -sSL https://raw.githubusercontent.com/val1813/kwcode/main/install.sh | sh
 #   或: chmod +x install.sh && ./install.sh

 set -e
@@ -21,7 +21,7 @@ info()  { printf "      ${GRAY}%s${NC}\n" "$1"; }
 # ── Banner ───────────────────────────────────────────────────
 printf "\n"
 printf "  ${CYAN}╔══════════════════════════════════════╗${NC}\n"
-printf "  ${CYAN}║       KwCode 安装程序 v0.4            ║${NC}\n"
+printf "  ${CYAN}║       KwCode 安装程序 v1.0            ║${NC}\n"
 printf "  ${CYAN}║   本地模型 Coding Agent               ║${NC}\n"
 printf "  ${CYAN}╚══════════════════════════════════════╝${NC}\n"
 printf "\n"
@@ -53,63 +53,33 @@ if [ -z "$PYTHON_CMD" ]; then
    exit 1
 fi

-# ── Step 2: GPU 检查 ─────────────────────────────────────────
-step "检查 GPU..."
-
-VRAM_MB=0
-OS_TYPE=$(uname -s)
-
-if [ "$OS_TYPE" = "Darwin" ]; then
-    # macOS - 检查 Apple Silicon 统一内存
-    chip=$(sysctl -n machdep.cpu.brand_string 2>/dev/null || echo "")
-    if echo "$chip" | grep -qi "apple"; then
-        mem_bytes=$(sysctl -n hw.memsize 2>/dev/null || echo "0")
-        VRAM_MB=$((mem_bytes / 1024 / 1024))
-        info "Apple Silicon: $chip  统一内存: ${VRAM_MB}MB"
-        info "（统一内存可共享给 GPU，实际可用约 75%）"
-    else
-        # Intel Mac
-        gpu_info=$(system_profiler SPDisplaysDataType 2>/dev/null | grep -i "vram\|chipset" | head -2 || echo "")
-        if [ -n "$gpu_info" ]; then
-            info "GPU: $gpu_info"
-        else
-            warn "未检测到独立 GPU，将使用 CPU 模式"
-        fi
-    fi
-else
-    # Linux - 检查 NVIDIA GPU
-    if command -v nvidia-smi >/dev/null 2>&1; then
-        smi_output=$(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo "")
-        if [ -n "$smi_output" ]; then
-            gpu_name=$(echo "$smi_output" | cut -d, -f1 | xargs)
-            vram_str=$(echo "$smi_output" | grep -oE '[0-9]+' | tail -1)
-            if [ -n "$vram_str" ]; then
-                VRAM_MB=$vram_str
-            fi
-            info "GPU: $gpu_name  VRAM: ${VRAM_MB}MB"
-        fi
-    else
-        warn "未检测到 NVIDIA GPU (nvidia-smi 不可用)"
-        info "如有 AMD GPU，Ollama 也支持 ROCm"
-    fi
-fi
-
-# ── Step 3: pip install kaiwu ────────────────────────────────
+# ── Step 2: 安装 KwCode ──────────────────────────────────────
 step "安装 KwCode..."

 INSTALLED=0

-# 尝试默认源
-info "尝试默认 pip 源..."
-if "$PYTHON_CMD" -m pip install kaiwu --quiet 2>/dev/null; then
-    INSTALLED=1
-    info "安装成功（默认源）"
+# 优先 pipx（隔离环境，不污染系统 Python）
+if command -v pipx >/dev/null 2>&1; then
+    info "检测到 pipx，使用隔离安装..."
+    if pipx install kwcode 2>/dev/null; then
+        INSTALLED=1
+        info "安装成功（pipx 隔离环境）"
+    fi
+fi
+
+# 降级到 pip 默认源
+if [ "$INSTALLED" -eq 0 ]; then
+    info "尝试 pip 安装..."
+    if "$PYTHON_CMD" -m pip install kwcode --quiet 2>/dev/null; then
+        INSTALLED=1
+        info "安装成功（pip 默认源）"
+    fi
 fi

 # 降级到清华镜像
 if [ "$INSTALLED" -eq 0 ]; then
-    warn "默认源安装失败，切换到清华镜像..."
-    if "$PYTHON_CMD" -m pip install kaiwu \
+    warn "默认源失败，切换到清华镜像..."
+    if "$PYTHON_CMD" -m pip install kwcode \
        -i https://pypi.tuna.tsinghua.edu.cn/simple \
        --trusted-host pypi.tuna.tsinghua.edu.cn \
        --quiet 2>/dev/null; then
@@ -120,119 +90,31 @@ fi

 if [ "$INSTALLED" -eq 0 ]; then
    err "KwCode 安装失败"
-    info "请手动执行: $PYTHON_CMD -m pip install kaiwu"
+    info "请手动执行: $PYTHON_CMD -m pip install kwcode"
    info "如果网络慢，加上: -i https://pypi.tuna.tsinghua.edu.cn/simple"
    exit 1
 fi

-# ── Step 4: Ollama 检查 ──────────────────────────────────────
-step "检查 Ollama..."
-
-OLLAMA_OK=0
-if command -v ollama >/dev/null 2>&1; then
-    ollama_ver=$(ollama --version 2>&1 || echo "unknown")
-    info "Ollama 已安装: $ollama_ver"
-    OLLAMA_OK=1
+# 可选：安装 AST 增强
+info "尝试安装 AST 增强（可选，需要 C 编译器）..."
+if "$PYTHON_CMD" -m pip install "kwcode[ast]" --quiet 2>/dev/null; then
+    info "AST 调用图引擎已启用（代码定位更精准）"
 else
-    warn "未检测到 Ollama"
-    info ""
-    info "Ollama 是运行本地模型的推理引擎，请手动安装："
-    if [ "$OS_TYPE" = "Darwin" ]; then
-        info "  brew install ollama"
-        info "  或: https://ollama.com/download"
-    else
-        info "  curl -fsSL https://ollama.com/install.sh | sh"
-    fi
-    info ""
-    info "安装后重新运行本脚本即可自动拉取模型"
+    info "AST 增强跳过（无编译器），使用 LLM 定位（功能不受影响）"
 fi

-# ── Step 5: 拉取推荐模型 ─────────────────────────────────────
-if [ "$OLLAMA_OK" -eq 1 ]; then
-    step "拉取推荐模型..."
+# ── Step 3: 提示配置模型 ─────────────────────────────────────
+step "模型配置提示..."
+info "KwCode 支持任何 OpenAI 兼容 API（Ollama / DeepSeek / Qwen 等）"
+info "安装完成后执行 kwcode init 即可配置 API 地址和模型"

-    # 根据 VRAM 选择模型
-    if [ "$VRAM_MB" -ge 16000 ]; then
-        MODEL="qwen3:14b"
-        info "VRAM >= 16GB，推荐模型: $MODEL"
-    elif [ "$VRAM_MB" -ge 8000 ]; then
-        MODEL="qwen3:8b"
-        info "VRAM >= 8GB，推荐模型: $MODEL"
-    else
-        MODEL="gemma3:4b"
-        if [ "$VRAM_MB" -gt 0 ]; then
-            info "VRAM < 8GB，推荐模型: $MODEL"
-        else
-            info "未检测到 GPU，使用轻量模型: $MODEL"
-        fi
-    fi
-
-    # 检测模型下载源
-    step "检测模型下载源..."
-    if curl -s --max-time 5 https://huggingface.co > /dev/null 2>&1; then
-        info "HuggingFace 可达，使用默认源"
-    else
-        warn "HuggingFace 不可达，自动切换到 ModelScope"
-        export OLLAMA_MODELS="https://modelscope.cn/models"
-    fi
-
-    info "正在拉取 $MODEL（首次下载可能需要几分钟）..."
-    if ollama pull "$MODEL"; then
-        info "模型 $MODEL 已就绪"
-    else
-        warn "模型拉取失败，请稍后手动执行: ollama pull $MODEL"
-    fi
-fi
-
-# ── Step 5.5: SearXNG 搜索服务 ──────────────────────────────
-step "启动搜索服务（SearXNG）..."
-
-if command -v docker >/dev/null 2>&1; then
-    # 已在运行则跳过
-    if docker ps --format '{{.Names}}' | grep -q "^kwcode-searxng$"; then
-        info "SearXNG 已在运行，跳过"
-    elif docker ps -a --format '{{.Names}}' | grep -q "^kwcode-searxng$"; then
-        docker start kwcode-searxng
-        info "SearXNG 已重新启动"
-    else
-        info "拉取 SearXNG 镜像（约150MB）..."
-        docker pull searxng/searxng
-        docker run -d \
-            --name kwcode-searxng \
-            --restart always \
-            -p 8080:8080 \
-            searxng/searxng
-        info "等待 SearXNG 启动..."
-        for i in $(seq 1 15); do
-            if curl -s http://localhost:8080 > /dev/null 2>&1; then
-                info "SearXNG 已就绪：http://localhost:8080"
-                break
-            fi
-            sleep 1
-        done
-    fi
-else
-    warn "未检测到 Docker，搜索增强将使用 DuckDuckGo 降级方案"
-    info "安装 Docker 可获得更好的搜索体验：https://docs.docker.com/get-docker/"
-fi
-
-# ── Step 6: kwcode init ──────────────────────────────────────
-step "初始化 KwCode..."
-
-if command -v kwcode >/dev/null 2>&1; then
-    kwcode init 2>/dev/null && info "KAIWU.md 已初始化" || info "跳过初始化（可稍后在项目目录执行 kwcode init）"
-else
-    info "kwcode 命令未在 PATH 中，跳过初始化"
-    info "尝试: $PYTHON_CMD -m kaiwu init"
-fi
-
-# ── Step 7: kwcode status ────────────────────────────────────
+# ── Step 4: 验证安装 ─────────────────────────────────────────
 step "验证安装..."

 if command -v kwcode >/dev/null 2>&1; then
    kwcode status || warn "状态检查失败，但安装可能已成功"
 else
-    "$PYTHON_CMD" -m kaiwu status 2>/dev/null || warn "状态检查失败"
+    "$PYTHON_CMD" -m kwcode status 2>/dev/null || warn "状态检查失败"
 fi

 # ── 完成 ─────────────────────────────────────────────────────
@@ -247,5 +129,5 @@ printf "    2. kwcode init          # 初始化项目记忆\n"
 printf "    3. kwcode               # 进入交互模式\n"
 printf '    4. kwcode "修复登录bug"  # 直接执行任务\n'
 printf "\n"
-printf "  ${GRAY}文档: https://github.com/kaiwu-agent/kaiwu${NC}\n"
+printf "  ${GRAY}文档: https://github.com/val1813/kwcode${NC}\n"
 printf "\n"
--- a/kaiwu/main.py
+++ b/kaiwu/main.py
@@ -0,0 +1,5 @@
+"""Allow running kwcode as: python -m kaiwu"""
+from kaiwu.cli.main import app
+
+if __name__ == "__main__":
+    app()
--- a/kaiwu/ast_engine/init.py
+++ b/kaiwu/ast_engine/init.py
@@ -1,16 +1,25 @@
 """
-AST Engine: tree-sitter based call graph locator (spec §6).
+AST Engine: tree-sitter based call graph locator (spec S6).
 Provides function-level code location via call graph analysis.
-BM25+graph retrieval (spec §LOC upgrade).
+BM25+graph retrieval (spec LOC upgrade).
 """

-from kaiwu.ast_engine.parser import TreeSitterParser
-from kaiwu.ast_engine.call_graph import CallGraph
-from kaiwu.ast_engine.locator import ASTLocator
-from kaiwu.ast_engine.graph_builder import GraphBuilder
-from kaiwu.ast_engine.graph_retriever import GraphRetriever
+try:
+    from kaiwu.ast_engine.parser import TreeSitterParser
+    from kaiwu.ast_engine.call_graph import CallGraph
+    from kaiwu.ast_engine.locator import ASTLocator
+    from kaiwu.ast_engine.graph_builder import GraphBuilder
+    from kaiwu.ast_engine.graph_retriever import GraphRetriever
+    AST_AVAILABLE = True
+except ImportError:
+    TreeSitterParser = None
+    CallGraph = None
+    ASTLocator = None
+    GraphBuilder = None
+    GraphRetriever = None
+    AST_AVAILABLE = False

 __all__ = [
    "TreeSitterParser", "CallGraph", "ASTLocator",
-    "GraphBuilder", "GraphRetriever",
+    "GraphBuilder", "GraphRetriever", "AST_AVAILABLE",
 ]
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,36 +4,57 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "kwcode"
-version = "1.0.0"
+version = "1.0.7"
 description = "KwCode - Local-model coding agent with MoE expert pipeline"
 requires-python = ">=3.10"
+readme = "README.md"
+license = "MIT"
+keywords = ["coding-agent", "local-llm", "ollama", "code-generation"]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Environment :: Console",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Software Development :: Code Generators",
+]

 dependencies = [
-    "llama-cpp-python>=0.2.90",
    "typer>=0.12.0",
    "rich>=13.0.0",
-    "pydantic>=2.0.0",
    "httpx>=0.27.0",
-    "gitpython>=3.1.0",
    "psutil>=5.9.0",
    "beautifulsoup4>=4.12.0",
-    "lxml>=5.0.0",
    "trafilatura>=1.12.0",
    "pyyaml>=6.0",
-    "tree-sitter>=0.23.0",
-    "tree-sitter-python>=0.23.0",
-    "networkx>=3.0",
    "rank-bm25>=0.2.2",
-    "pytest>=7.0.0",
-    "aiosqlite>=0.20.0",
+    "prompt-toolkit>=3.0.0",
 ]

 [project.optional-dependencies]
-rerank = ["sentence-transformers>=2.7.0"]
+local = ["llama-cpp-python>=0.2.90"]
+ast = ["tree-sitter>=0.23.0", "tree-sitter-python>=0.23.0"]
+full = [
+    "kwcode[local]",
+    "kwcode[ast]",
+]
+dev = [
+    "pytest>=7.0.0",
+    "kwcode[full]",
+]

 [project.scripts]
 kwcode = "kaiwu.cli.main:app"

+[project.urls]
+Homepage = "https://github.com/val1813/kwcode"
+Repository = "https://github.com/val1813/kwcode"
+Issues = "https://github.com/val1813/kwcode/issues"
+
 [tool.setuptools.packages.find]
 where = ["."]
 include = ["kaiwu*"]