Merge pull request #10 from aevyraai/fix/add-introduction-doc

fix: add missing introduction.mdx to docs

Author: agunapal

docs/introduction.mdx

@@ -0,0 +1,129 @@
+---
+title: Introduction
+description: Autonomous vLLM deployment optimizer — tunes your serving config overnight against your real workload.
+---
+
+aevyra-forge tunes your vLLM deployment overnight. Give it a model, a GPU,
+and a workload trace. It runs an autonomous loop — propose a config, boot the
+server, benchmark against your real workload, keep or revert, repeat. By
+morning you have a deployment recipe that beats hand-tuned defaults, with a
+full audit trail of every experiment.
+
+```bash
+pip install aevyra-forge
+```
+
+vLLM exposes roughly 40 serving args. The defaults are conservative — designed
+to work safely on any GPU, not to max out any specific one. On a T4 with a
+chat workload the defaults leave significant throughput on the table:
+
+```
+Baseline (defaults):    2718 tok/s   P99: 241 ms
+After Forge (8 exps):   3421 tok/s   P99: 187 ms   (+26%)
+```
+
+Forge finds that gain by searching the joint space of batching, caching, and
+memory knobs — and keeping only the changes that improve the score on your
+actual workload.
+
+## Where Forge fits
+
+```mermaid
+flowchart LR
+    W[Witness\ncaptures trace]:::w
+    V[Verdict\ngrades it]:::v
+    O[Origin\nfinds culprits]:::o
+    R[Reflex\nfixes prompts]:::r
+    F[Forge\ntunes deployment]:::f
+
+    W --> V --> O --> R
+    W --> F
+
+    classDef w fill:#3FBFFF,color:#fff,stroke:none
+    classDef v fill:#6E3FF3,color:#fff,stroke:none
+    classDef o fill:#FF6B6B,color:#fff,stroke:none
+    classDef r fill:#2ECC71,color:#fff,stroke:none
+    classDef f fill:#FF6B35,color:#fff,stroke:none
+```
+
+Forge operates on the infrastructure layer. Where Reflex rewrites prompts and
+Origin diagnoses agent failures, Forge maximises throughput and minimises
+latency for a model that's already doing the right thing.
+
+## The loop
+
+```mermaid
+flowchart LR
+    WL[Workload\nJSONL trace]:::data
+    HW[Hardware\nauto-detected]:::data
+    PB[Playbook\nheuristics]:::forge
+    AG[Agent\n1 LLM call]:::forge
+    VL[vLLM\nserver]:::bench
+    BN[bench\nreplay workload]:::bench
+    SC([Score\nthroughput × accuracy]):::score
+    RY[best_recipe.yaml]:::output
+
+    WL & HW --> AG
+    PB --> AG
+    AG -->|mutated recipe| VL
+    VL --> BN
+    BN -->|BenchResult| SC
+    SC -->|keep / revert| AG
+    SC --> RY
+
+    classDef data    fill:#6E3FF3,color:#fff,stroke:none
+    classDef forge   fill:#9B6BFF,color:#fff,stroke:none
+    classDef bench   fill:#3FBFFF,color:#fff,stroke:none
+    classDef score   fill:#FF6B35,color:#fff,stroke:none
+    classDef output  fill:#2ECC71,color:#fff,stroke:none
+```
+
+Each iteration: the agent reads the playbook and experiment history, proposes
+one targeted change, and Forge measures whether it actually helps. The audit
+trail captures every decision — config, result, rationale — so you can see
+exactly how the winning recipe was found.
+
+## Tuning layers
+
+| Layer | What it tunes | Status |
+|---|---|---|
+| **1. Config** | vLLM serving args: batching, caching, parallelism | ✅ v0.1 |
+| **2. Quantization** | INT4/FP8/INT8, KV cache precision | 🔧 v0.2 |
+| **3. Kernel synthesis** | Custom kernels via AutoKernel | 🚧 v0.3 |
+
+Layer 1 has the highest leverage per experiment because it requires no
+recompilation. Forge escalates to Layer 2 when Layer 1 converges.
+
+## Works with any GPU and LLM
+
+Auto-detects NVIDIA and AMD GPUs via `nvidia-smi` / `rocm-smi`. Works with
+any OpenAI-compatible LLM for the agent.
+
+```bash
+pip install aevyra-forge               # Claude included by default
+pip install aevyra-forge[openai]       # add OpenAI / OpenRouter / Together / Groq
+```
+
+| Provider | Env var |
+|---|---|
+| **Anthropic** | `ANTHROPIC_API_KEY` |
+| **OpenAI** | `OPENAI_API_KEY` |
+| **OpenRouter** | `OPENROUTER_API_KEY` |
+| **Ollama** | — |
+
+Python 3.10+. Apache-2.0 licensed.
+
+<CardGroup cols={2}>
+  <Card title="Quick start" icon="bolt" href="/forge/quickstart">
+    Run your first tuning session in 15 minutes
+  </Card>
+  <Card title="Tutorial" icon="book-open" href="/forge/tutorial-colab-quickstart">
+    Dry-run walkthrough with real log output
+  </Card>
+  <Card title="Concepts: Recipe" icon="file-code" href="/forge/concepts/recipe">
+    The artifact Forge proposes, mutates, and keeps or reverts
+  </Card>
+  <Card title="Concepts: Playbook" icon="book" href="/forge/concepts/playbook">
+    The agent's instruction manual
+  </Card>
+</CardGroup>