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          <div class="badge-pill">🧠 Preference Graph</div>
          <h1 style="font-size: 2.25rem; color: #0f172a; font-weight: 800; margin-bottom: 0.75rem;">SLM Memory Manager</h1>
          <p style="font-size: 1.05rem; color: #334155; line-height: 1.65; font-weight: 500; margin: 0;">Manages long-term personal state and preference graphs. Learns and adapts to user query patterns locally without cloud synchronization.</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>
        </nav>

        <section class="doc-section" id="overview">
          <h2>🚀 Overview &amp; Capabilities</h2>
          <p>Manages long-term personal state and preference graphs. Learns and adapts to user query patterns locally without cloud synchronization.</p>
          
          <h3 style="margin-top: 1.5rem; font-size: 1.15rem; color: #0f172a; font-weight: 700;">Key Features</h3>
          <ul>
            <li><strong>Entities and relations extraction from chat history</strong></li>
<li><strong>Builds a local knowledge graph of user preferences</strong></li>
<li><strong>Prunes older irrelevant details to fit within context limits</strong></li>
<li><strong>Auto-injects user context tags into RAG sessions</strong></li>
          </ul>
        </section>

        <section class="doc-section" id="install">
          <h2>💻 Installation</h2>
          <p>Install the local CPU-optimized package using pip:</p>
          <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 local CPU-optimized package</span>
pip install slm-memory</code></pre>
            </div>
          </div>
        </section>

        
        <!-- GIT CHECKOUT -->
        <section class="doc-section" id="git">
          <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>
            </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:#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_memory</span> <span style="color:#94a3b8;">&amp;&amp;</span> <span style="color:#c084fc; font-weight:700;">cd</span> <span style="color:#38bdf8;">slm_memory</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_memory</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>
            </div>
          </div>

          <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;">
            <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 — 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_memory</span></pre>
            </div>
          </div>

          <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_memory</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>
          <p>Instantiate <code>SLMMemoryManager</code> with performance options:</p>
          <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 ONNX model weights. If omitted, downloads standard checkpoints.</td></tr>
              <tr><td>cache_dir</td><td>str | None</td><td>Directory to store model weights offline. Defaults to <code>~/.cache/slm-memory/</code>. Also settable via <span class="env-tag">SLM_MEMORY_MANAGER_CACHE_DIR</span>.</td></tr>
              <tr><td>n_threads</td><td>int | 4</td><td>CPU thread count for ONNX inference. Optimize for CPU core count. Also settable via <span class="env-tag">SLM_MEMORY_MANAGER_N_THREADS</span>.</td></tr>
            </tbody>
          </table>

          <h3>Methods</h3>
          <table class="param-table">
            <thead>
              <tr><th>Method Signature</th><th>Return Type</th><th>Description</th></tr>
            </thead>
            <tbody>
              <tr><td><code>store_fact(fact_text)</code></td><td><code>None</code></td><td>Saves semantic user parameters offline to local storage.</td></tr><tr><td><code>get_relevant_facts(query, system_prompt=None, user_input=None)</code></td><td><code>list[str]</code></td><td>Queries SQLite embedding tables to retrieve contextual preference strings.</td></tr>
            </tbody>
          </table>

          <h3>Method Parameters (Execution Customization)</h3>
          <p>All main execution methods accept optional system routing parameters:</p>
          <table class="param-table">
            <thead>
              <tr><th>Parameter</th><th>Type / Default</th><th>Description</th></tr>
            </thead>
            <tbody>
              <tr><td>system_prompt</td><td>str | None</td><td>Optional custom system prompt instruction to override the default system template response parameters.</td></tr>
              <tr><td>user_input</td><td>str | None</td><td>Optional additional user-supplied target text variables or contextual keys.</td></tr>
            </tbody>
          </table>

          <h3>Quick Start</h3>
          <pre><code class="language-python">from slm_memory import SLMMemoryManager

mem = SLMMemoryManager()
mem.store_fact("User prefers python code examples.")
print(mem.get_relevant_facts(
    "code preferences",
    system_prompt="Prioritize code formatting details",
    user_input="Sort by recency"
))</code></pre>
        </section>

        <section class="doc-section" id="env-vars">
          <h2>Environment Variables</h2>
          <p>Configure agent parameters globally using environment values:</p>
          <table class="param-table">
            <thead>
              <tr><th>Environment Variable</th><th>Default</th><th>Purpose</th></tr>
            </thead>
            <tbody>
              <tr><td>SLM_MEMORY_MANAGER_N_THREADS</td><td>4</td><td>Sets CPU inference execution threads.</td></tr>
              <tr><td>SLM_MEMORY_MANAGER_CACHE_DIR</td><td>~/.cache/slm-memory/</td><td>Default directory to store downloaded ONNX weights.</td></tr>
            </tbody>
          </table>
        </section>

        <section class="doc-section" id="performance">
          <h2>CPU Performance Tuning</h2>
          <p>To run the <code>SLMMemoryManager</code> engine efficiently on CPU under 1.5 GB memory footprint:</p>
          <ul>
            <li><strong>Match Threads to Core Count:</strong> Set <code>n_threads</code> or <code>SLM_MEMORY_MANAGER_N_THREADS</code> to match the physical CPU core count.</li>
            <li><strong>Sequential Processing:</strong> Avoid concurrent processing when batch files are large.</li>
            <li><strong>Garbage Collection:</strong> Clear variables and run <code>gc.collect()</code> to release model RAM blocks after execution.</li>
          </ul>
        </section>

        <section class="doc-section">
          <h2>Verified Input & Output Logs</h2>
          <p>Diagnostic execution console response running locally on CPU:</p>
          <div class="tip-box" style="background: rgba(30, 41, 59, 0.7); border: 1px solid rgba(255, 255, 255, 0.08);">
            <pre><code style="color: #38bdf8; font-family: monospace; font-size: 0.9rem; line-height: 1.5; display: block; white-space: pre-wrap;">→ INPUT (Store Fact):
"User prefers python code examples."

← OUTPUT (Fact Retrieval):
[
  'User prefers python code examples.'
]</code></pre>
          </div>
        </section>

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