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          <div class="badge-pill">🐍 Python execution Agent</div>
          <h1 style="font-size: 2.25rem; color: #0f172a; font-weight: 800; margin-bottom: 0.75rem;">SLM Code Interpreter</h1>
          <p style="font-size: 1.05rem; color: #334155; line-height: 1.65; font-weight: 500; margin: 0;">Lightweight local Python Code Interpreter agent with automated self-correcting feedback loops and timeout constraints inside sandboxed environments.</p>
        </section>

        <nav class="doc-nav">
          <a href="#overview">Overview</a>
          <a href="#install">
          <a href="#git">Git Checkout</a>Installation</a>
          <a href="#config">Configuration API</a>
          <a href="#performance">Performance</a>
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        <section class="doc-section" id="install">
          <h2>💻 Installation</h2>
          <div class="code-panel" style="max-width:100%">
            <div class="code-header"><div class="code-dots"><div class="code-dot"></div><div class="code-dot"></div><div class="code-dot"></div></div><div class="code-title">Terminal</div></div>
            <div class="code-content" style="display:block;padding:1.5rem">
              <pre><code><span class="comment"># Install in editable mode locally</span>
pip install -e ./slm_code_interpreter

<span class="comment"># Set performance parameters</span>
export SLM_CODE_INTERPRETER_N_THREADS=4
export SLM_CODE_INTERPRETER_N_CTX=2048</code></pre>
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          <h2>🐙 Checkout from GitHub</h2>
          <p>Clone only this agent's folder from the monorepo using Git sparse-checkout — no need to download the full repository:</p>

          <h3 style="font-size: 1.05rem; color: #0f172a; font-weight: 700; margin-top: 1.5rem; margin-bottom: 0.75rem;">Option 1 — Sparse Checkout (Recommended)</h3>
          <div class="code-panel" style="max-width:100%; background: #0f172a; border: 1px solid #1e293b; border-radius: 14px; overflow: hidden; margin: 1rem 0; box-shadow: 0 16px 40px rgba(15, 23, 42, 0.12);">
            <div class="code-header" style="background: #1e293b; padding: 10px 16px; display: flex; align-items: center; justify-content: space-between; border-bottom: 1px solid #334155;">
              <div class="code-dots"><div class="code-dot"></div><div class="code-dot"></div><div class="code-dot"></div></div>
              <div class="code-title" style="color: #94a3b8; font-weight: 700; font-size: 0.8rem; font-family: 'JetBrains Mono', monospace;">Terminal — Git Sparse Checkout</div>
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              <pre style="margin:0; background:#0f172a; color:#f8fafc; font-family:'JetBrains Mono',monospace; font-size:0.88rem; border:none; box-shadow:none; padding:0; line-height: 1.75;"><span style="color:#64748b;"># 1. Create and enter a new directory</span>
<span style="color:#34d399;">$</span> <span style="color:#c084fc; font-weight:700;">mkdir</span> <span style="color:#38bdf8;">slm_code_interpreter</span> <span style="color:#94a3b8;">&amp;&amp;</span> <span style="color:#c084fc; font-weight:700;">cd</span> <span style="color:#38bdf8;">slm_code_interpreter</span>

<span style="color:#64748b;"># 2. Initialise empty git repo and add remote</span>
<span style="color:#34d399;">$</span> <span style="color:#c084fc; font-weight:700;">git init</span>
<span style="color:#34d399;">$</span> <span style="color:#c084fc; font-weight:700;">git remote add origin</span> <span style="color:#38bdf8;">https://github.com/t00114218-stack/SLMAgents.git</span>

<span style="color:#64748b;"># 3. Enable sparse-checkout and set target folder</span>
<span style="color:#34d399;">$</span> <span style="color:#c084fc; font-weight:700;">git sparse-checkout init</span> <span style="color:#94a3b8;">--cone</span>
<span style="color:#34d399;">$</span> <span style="color:#c084fc; font-weight:700;">git sparse-checkout set</span> <span style="color:#38bdf8;">slm_code_interpreter</span>

<span style="color:#64748b;"># 4. Pull only that agent's source</span>
<span style="color:#34d399;">$</span> <span style="color:#c084fc; font-weight:700;">git pull origin</span> <span style="color:#38bdf8;">main</span></pre>
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          <h3 style="font-size: 1.05rem; color: #0f172a; font-weight: 700; margin-top: 2rem; margin-bottom: 0.75rem;">Option 2 — Full Repository Clone</h3>
          <div class="code-panel" style="max-width:100%; background: #0f172a; border: 1px solid #1e293b; border-radius: 14px; overflow: hidden; margin: 1rem 0;">
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              <div class="code-dots"><div class="code-dot"></div><div class="code-dot"></div><div class="code-dot"></div></div>
              <div class="code-title" style="color: #94a3b8; font-weight: 700; font-size: 0.8rem; font-family: 'JetBrains Mono', monospace;">Terminal — Full Clone</div>
            </div>
            <div class="code-content" style="display:block; padding: 1.25rem 1.5rem; background: #0f172a;">
              <pre style="margin:0; background:#0f172a; color:#f8fafc; font-family:'JetBrains Mono',monospace; font-size:0.88rem; border:none; box-shadow:none; padding:0; line-height: 1.75;"><span style="color:#34d399;">$</span> <span style="color:#c084fc; font-weight:700;">git clone</span> <span style="color:#38bdf8;">https://github.com/t00114218-stack/SLMAgents.git</span>
<span style="color:#34d399;">$</span> <span style="color:#c084fc; font-weight:700;">cd</span> <span style="color:#38bdf8;">SLMAgents/slm_code_interpreter</span></pre>
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          <p style="margin-top: 1.25rem; font-size: 0.9rem; color: #475569; background: #f8fafc; border: 1px solid #cbd5e1; border-radius: 10px; padding: 1rem 1.25rem;">
            💡 <strong>Tip:</strong> After checkout, install the package locally with <code style="background: #eef2ff; color: #4f46e5; border: 1px solid #c7d2fe; padding: 2px 8px; border-radius: 5px; font-weight: 700;">pip install -e ./slm_code_interpreter</code> to run in editable mode without publishing to PyPI.
          </p>
        </section>

        <section class="doc-section" id="config">
          <h2>⚙️ Configuration API</h2>
          <h3>Constructor Parameters</h3>
          <table class="param-table">
            <thead><tr><th>Parameter</th><th>Type / Default</th><th>Description</th></tr></thead>
            <tbody>
              <tr><td>model_path</td><td>str | None</td><td>Explicit path to the ONNX model directory. Defaults to caching/sharing the main model in the monorepo.</td></tr>
              <tr><td>cache_dir</td><td>str | None</td><td>HF model directory path. Also settable via <span class="env-tag">SLM_CODE_INTERPRETER_CACHE_DIR</span>.</td></tr>
              <tr><td>n_ctx</td><td>int | 2048</td><td>Context window size in tokens. Also settable via <span class="env-tag">SLM_CODE_INTERPRETER_N_CTX</span>.</td></tr>
              <tr><td>n_threads</td><td>int | 4</td><td>CPU threads for ONNX Runtime. Also settable via <span class="env-tag">SLM_CODE_INTERPRETER_N_THREADS</span>.</td></tr>
                          <tr><td>system_prompt</td><td>str | None</td><td>Optional custom system prompt instructions overriding the default template.</td></tr>
              <tr><td>user_input</td><td>str | None</td><td>Optional additional user-supplied target parameters or variables.</td></tr>
</tbody>
          </table>

          <h3>run() Parameters</h3>
          <table class="param-table">
            <thead><tr><th>Parameter</th><th>Type / Default</th><th>Description</th></tr></thead>
            <tbody>
              <tr><td>instruction</td><td>str</td><td><strong>Required.</strong> Natural language description of what you want the script to calculate or print.</td></tr>
              <tr><td>max_retries</td><td>int | 3</td><td>Number of turns the self-correction engine is allowed to attempt standard traceback fixes.</td></tr>
              <tr><td>stream</td><td>bool | False</td><td>If True, enables token streaming of the model's explanation and thinking process. Returns a Python generator.</td></tr>
                          <tr><td>system_prompt</td><td>str | None</td><td>Optional custom system prompt instructions overriding the default template.</td></tr>
              <tr><td>user_input</td><td>str | None</td><td>Optional additional user-supplied target parameters or variables.</td></tr>
</tbody>
          </table>

          <div class="code-panel" style="max-width:100%">
            <div class="code-header"><div class="code-dots"><div class="code-dot"></div><div class="code-dot"></div><div class="code-dot"></div></div><div class="code-title">Python Quick Start</div></div>
            <div class="code-content" style="display:block;padding:1.5rem">
              <pre><code><span class="keyword">from</span> slm_code_interpreter.code_interpreter <span class="keyword">import</span> SLMCodeInterpreter

interpreter = <span class="function">SLMCodeInterpreter</span>()

query = (
    <span class="string">"Load CSV text: date,department,revenue. Group by department, "</span>
    <span class="string">"extract quarter, sum revenue, filter &gt;= 40000 and print table."</span>
)
result = interpreter.<span class="function">run</span>(query)
<span class="function">print</span>(result[<span class="string">"success"</span>])
<span class="function">print</span>(result[<span class="string">"stdout"</span>])  <span class="comment"># Prints markdown summary table of results</span></code></pre>
            </div>
          </div>
        </section>

        <section class="doc-section" id="self-correcting">
          <h2>Agentic Self-Correction Loop</h2>
          <p>
            Although code writing can be difficult for small models, making it agentic through a feedback loop (writing code → executing locally → catching exceptions → feeding errors back to the model) allows a 1.5B model to achieve high execution accuracy.
          </p>
          <p>
            If <code>run_code_safely</code> captures a traceback or runtime stderr, it wraps it inside the conversation history and prompts the model with instructions to correct it:
          </p>
          <div class="tip-box">
            <strong>Traceback Feedback:</strong><br>
            <em>"The code execution failed with return code 1. Error logs: NameError: name 'x' is not defined. Correct your code errors and return the complete updated code inside ```python ```."</em>
          </div>
        </section>

        <section class="doc-section" id="sandbox">
          <h2>Sandbox Subprocess Limits</h2>
          <p>The code interpreter sandboxes execution inside restricted, resource-constrained subprocesses. It enforces standard execution limits:</p>
          <ul>
            <li><strong>Timeout Safety:</strong> Terminates infinite loops automatically after 10.0 seconds to prevent thread locking.</li>
            <li><strong>Process Isolation:</strong> Cleans up temporary files instantly after the execution returns.</li>
          </ul>
        </section>

        <section class="doc-section" id="vscode">
          <h2>🔌 Visual Studio Code Integration</h2>
          <p>You can run the Code Interpreter as a background service and trigger local executions directly from your VS Code editor workspace.</p>
          
          <h3>Step 1: Start the Background Daemon Server</h3>
          <p>Execute the local HTTP server in your terminal:</p>
          <pre><code>python -m slm_code_interpreter.server</code></pre>
          <p>The daemon activates a local REST API listening on port <code>8085</code>.</p>

          <h3>Step 2: Run the VS Code Extension Blueprint</h3>
          <p>A pre-configured extension is provided under the <code>vscode-extension/</code> directory:</p>
          <ul>
            <li>Open the extension directory in VS Code.</li>
            <li>Press <code>F5</code> to launch a debug instance of the editor.</li>
            <li>Highlight any instructions or python code blocks inside your editor workspace, right-click, and select: <strong>"SLM Code Interpreter: Execute Selected Prompt / Code"</strong>.</li>
            <li>Monitor execution progress and trace outputs inside the VS Code Output Channel under <strong>"SLM Code Interpreter"</strong>.</li>
          </ul>
        </section>

        <section class="doc-section" id="security">
          <h2>Security Warnings</h2>
          <div class="security-box">
            <strong>🔒 Subprocess Isolation:</strong> Because this runs code locally in your shell environment, never run the Code Interpreter with administrator rights or execute inputs from unauthenticated users. Consider wrapping the execution in Docker containers or firewalled machines for production systems.
          </div>
        </section>

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