rony-chat-bot/docs/architecture.md

# 📋 Rony Chat Bot — Technical Design Document

**Version:** 1.0
**Author:** Victor Hugo Vargas
**Date:** 2026-06-28
**Status:** Complete specification for implementation
**Path:** `rony-chat-bot/docs/architecture.md`

> 🌐 **Language:** [English](./architecture.md) | [Español](./architecture.es.md)
>
> 📚 **Workspace:** This project is part of the `Rony/` workspace. See [`../README.md`](../../README.md).
>
> 🔑 **Depends on:** [`rony-llm-agent`](https://github.com/VictorVargas/rony-llm-agent) — core library that provides agent loop, LLM clients, RAG, persona system.
>
> 📐 **Methodology:** This project follows the **SDD + DDD + Hexagonal Architecture** approach. Functional Requirements are numbered as `CRF-XXX`. See [`../../METHODOLOGY.md`](../../METHODOLOGY.md).

---

## 🎯 1. Project Vision

### 1.1 What is Chat-Bot?

An **HTTP chatbot** that answers questions about Victor Hugo Vargas and his projects. Uses **RAG (Retrieval-Augmented Generation)** over markdown files describing each project, and a local LLM (or cloud) to generate responses.

### 1.2 Primary use case

Victor has a portfolio website (Astro + React). On the site there's a chat widget where visitors can ask:

- "What projects has Victor done?"
- "What's his experience with Go?"
- "How does Rony Harness work?"
- "Has Victor worked with PostgreSQL?"

The bot responds with accurate information extracted from the projects' markdown files + bio + skills.

### 1.3 Secondary use cases (future)

- **Client adaptation:** The same bot, with other data and another persona, serves car dealerships, restaurants, etc.
- **Standalone CLI:** `./chat-bot ask "what do you know about X?"` for terminal use.
- **Slack/Discord bot:** Wrapper that consumes the HTTP API.

### 1.4 Philosophy

- **Self-hosted by default** — works 100% local with Ollama + 1-3B models
- **Cloud optional** — if you need more quality, swap to Anthropic API
- **Portable** — easy to fork/customize for other contexts
- **Streaming** — token-by-token responses with SSE (no waiting for complete response)
- **Reuses `rony-llm-agent`** — doesn't reinvent the agent loop

---

## 🏗️ 2. Architecture

### 2.1 Overview

```
┌─────────────────────────────────────────────────────────────────┐
│  Browser (Astro site)                                            │
│      ↓ HTTP POST /api/chat                                       │
│  Astro SSR (proxy)  ←────────── Serves portfolio + proxy chat    │
│      ↓ HTTP POST /api/chat                                       │
│  Chat-Bot HTTP server (:7331)                                    │
│      ↓                                                           │
│  Agent loop (rony-llm-agent)                                       │
│      ↓                                                           │
│  RAG retrieval → SQLite FTS5 over data/projects/*.md              │
│      ↓                                                           │
│  LLM (llama.cpp local default / Ollama or Anthropic optional)   │
└─────────────────────────────────────────────────────────────────┘
```

### 2.2 Main components

| Component | Path | Responsibility |
|---|---|---|
| **HTTP server** | `internal/server/` | Gin/chi handlers, SSE streaming |
| **Agent runner** | `internal/agent/` | Wrapper over `rony-llm-agent` with specific config |
| **Portfolio loader** | `internal/portfolio/` | Reads `data/projects/*.md`, indexes in SQLite FTS5 |
| **Persona** | `internal/persona/` | Loads persona from `configs/portfolio-bot.yaml` |
| **CLI** | `cmd/chat-bot/` | Commands: `serve`, `reindex`, `ask`, `version` |

### 2.3 Tech stack

| Layer | Technology | Reason |
|---|---|---|
| **Language** | Go 1.26+ | Same as rony-harness, leverage `os.Root`, `iter.Seq` |
| **HTTP router** | `net/http` + `chi` | Stdlib + chi for middleware (CORS, logging) |
| **SSE** | `net/http` Flusher | Stdlib is enough, no external library needed |
| **Config** | `gopkg.in/yaml.v3` | Same as harness |
| **RAG backend** | SQLite + FTS5 (BM25) | Zero external deps, single file, fast |
| **LLM** | llama.cpp (qwen2.5:1.5b GGUF) — default; Ollama as alt | Self-hosted by default |
| **Tests** | stdlib + testify | Consistency with the rest |

---

## 🔌 3. HTTP API

### 3.1 Endpoints

#### `POST /api/chat` — Chat with SSE streaming

**Request:**
```json
{
  "messages": [
    {"role": "user", "content": "What projects does Victor have?"}
  ],
  "stream": true,
  "conversation_id": "57f4aa3c7fab466bc4de9c43b296903e"
}
```

| Field | Required | Notes |
|---|---|---|
| `messages` | yes | At least one user message; alternation is not enforced. |
| `stream` | no, default `true` | `false` returns a single JSON body instead of SSE. |
| `conversation_id` | no | Hex string. If omitted, the server mints a new one and returns it (see below). Pass an existing ID to keep the thread. |

**Response (SSE):**
```
data: {"type":"start","conversation_id":"57f4aa3c7fab466bc4de9c43b296903e"}

data: {"type":"chunk","content":"Victor"}
data: {"type":"chunk","content":" has"}
data: {"type":"chunk","content":" several"}
data: {"type":"chunk","content":" projects"}

data: {"type":"sources","documents":["rony-harness.md","rony-llm-agent.md"]}

data: {"type":"done","usage":{"input_tokens":245,"output_tokens":38}}
```

The `conversation_id` in the `start` event is what the client should store
(see §3.4 — *Conversation persistence*). When the client passed an
existing ID the server echoes it back; otherwise it's freshly minted.

**Without streaming** (`"stream": false`):
```json
{
  "conversation_id": "57f4aa3c7fab466bc4de9c43b296903e",
  "content": "Victor has several projects...",
  "sources": ["rony-harness.md", "rony-llm-agent.md"],
  "usage": {"input_tokens": 245, "output_tokens": 38}
}
```

#### `GET /api/conversations` — List recent conversations

Returns the most recent conversation summaries, newest first. Useful for a
"show my chats" sidebar in a custom UI.

**Query params:**
- `limit` (1–200, default 50)

**Response:**
```json
{
  "count": 2,
  "conversations": [
    {
      "id": "57f4aa3c7fab466bc4de9c43b296903e",
      "created_at": "2026-07-17T05:02:07Z",
      "updated_at": "2026-07-17T05:04:31Z",
      "preview": "What projects does Victor have?"
    }
  ]
}
```

#### `GET /api/conversations/{id}` — Fetch one conversation

Returns the full history of a conversation with all messages in
chronological order.

**Response (200):**
```json
{
  "id": "57f4aa3c7fab466bc4de9c43b296903e",
  "created_at": "2026-07-17T05:02:07Z",
  "updated_at": "2026-07-17T05:04:31Z",
  "messages": [
    {"id": 1, "role": "user",      "content": "What projects does Victor have?", "created_at": "..."},
    {"id": 2, "role": "assistant", "content": "Victor has several projects...", "sources": ["..."], "created_at": "..."}
  ]
}
```

**Response (404):** when the ID is unknown (e.g. server DB was wiped or the
client lost sync). The widget treats this as "start fresh".

> ⚠️ **Auth note:** the conversation ID is the only access token. For a
> public bot this is fine; for private contexts add auth at the proxy layer
> (e.g. require a session cookie before forwarding to this endpoint).

#### `DELETE /api/conversations/{id}` — Delete a conversation

Removes the conversation and all its messages (cascade). Returns 204 on
success, 404 if the ID doesn't exist.

#### `POST /api/reindex` — Re-index portfolio

Useful when files in `data/projects/` are modified.

**Request:** empty
**Response:**
```json
{
  "indexed_files": 12,
  "total_chunks": 87,
  "duration_ms": 4321
}
```

#### `GET /api/health` — Health check (real)

Probes the LLM provider and the SQLite store in parallel and returns their
states. Designed for monitoring/load balancers. **Returns 200 when healthy
or degraded, 503 when unhealthy.**

- `?deep=true` adds a chunk count to the store probe (same latency budget).

**Status taxonomy:**

| `status` | HTTP | Meaning |
|---|---|---|
| `healthy` | 200 | LLM up, store up |
| `degraded` | 200 | LLM up, store down — bot still answers, just without RAG |
| `unhealthy` | 503 | LLM down — bot cannot answer, no point routing traffic here |

**Probe details:**

| Component | Probe | Latency |
|---|---|---|
| `llm` | `GET {provider}/health` (llamacpp, ollama) or `/models` (openai) | ~1ms for local llama-server |
| `store` | `SELECT 1` on the SQLite handle | ~100µs |

Each probe has a 2s timeout; the whole call returns within ~2.5s even if a
dependency hangs.

**Response shape (healthy):**

```json
{
  "status": "healthy",
  "version": "0.2.0-dev",
  "checked_at": "2026-07-17T05:02:07Z",
  "components": {
    "llm": {
      "status": "up",
      "latency": "1.028ms",
      "details": {"provider": "llamacpp", "model": "qwen2.5-3b-instruct", "url": "http://localhost:9100/health"}
    },
    "store": {
      "status": "up",
      "latency": "107µs"
    }
  }
}
```

**Response shape (degraded, with `?deep=true`):**

```json
{
  "status": "degraded",
  "version": "0.2.0-dev",
  "checked_at": "2026-07-17T05:02:07Z",
  "components": {
    "llm": {"status": "up", "latency": "0.8ms", "details": {...}},
    "store": {"status": "up", "latency": "70µs", "details": {"chunks": 28}}
  }
}
```

**Response shape (unhealthy):** HTTP 503, same JSON with `"status": "unhealthy"` and the failed component reporting `"status": "down"` plus an `error` field.

#### `GET /api/info` — Bot metadata

```json
{
  "name": "Rony Chat Bot",
  "model": "qwen2.5:1.5b",
  "persona": "...",
  "topics": ["projects", "experience", "technical skills"]
}
```

### 3.2 SSE Implementation

```go
// internal/server/chat.go
package server

import (
    "encoding/json"
    "fmt"
    "net/http"
    "github.com/VictorVargas/rony-llm-agent/pkg/agent"
)

func (s *Server) handleChat(w http.ResponseWriter, r *http.Request) {
    // SSE headers
    w.Header().Set("Content-Type", "text/event-stream")
    w.Header().Set("Cache-Control", "no-cache")
    w.Header().Set("Connection", "keep-alive")
    w.Header().Set("X-Accel-Buffering", "no")
    
    flusher, ok := w.(http.Flusher)
    if !ok {
        http.Error(w, "SSE not supported", http.StatusInternalServerError)
        return
    }
    
    // Parse request
    var req ChatRequest
    if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
        writeError(w, flusher, "invalid request", err)
        return
    }
    
    // Start event
    writeSSE(w, flusher, "start", map[string]string{
        "conversation_id": generateConvID(),
    })
    
    // Run agent with streaming
    sources := []string{}
    for chunk, err := range s.agent.RunStream(r.Context(), req.Messages) {
        if err != nil {
            writeSSE(w, flusher, "error", map[string]string{"message": err.Error()})
            return
        }
        if chunk.Type == "source" {
            sources = append(sources, chunk.Source)
        }
        writeSSE(w, flusher, chunk.Type, chunk.Data)
    }
    
    // Done event
    writeSSE(w, flusher, "done", map[string]any{
        "usage": map[string]int{
            "input_tokens":  245,
            "output_tokens": 38,
        },
    })
}

func writeSSE(w http.ResponseWriter, flusher http.Flusher, eventType string, data any) {
    payload, _ := json.Marshal(data)
    fmt.Fprintf(w, "data: {\"type\":%q,\"data\":%s}\n\n", eventType, payload)
    flusher.Flush()
}
```

### 3.3 Middleware

```go
// internal/server/middleware.go
package server

func (s *Server) loggingMiddleware(next http.Handler) http.Handler {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        start := time.Now()
        // Wrap response writer to capture status
        rw := &statusRecorder{ResponseWriter: w, status: 200}
        next.ServeHTTP(rw, r)
        
        slog.Info("http.request",
            "method", r.Method,
            "path", r.URL.Path,
            "status", rw.status,
            "duration_ms", time.Since(start).Milliseconds(),
            "ip", r.RemoteAddr,
        )
    })
}

func (s *Server) corsMiddleware(next http.Handler) http.Handler {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        origin := r.Header.Get("Origin")
        for _, allowed := range s.config.Server.CORSOrigins {
            if origin == allowed {
                w.Header().Set("Access-Control-Allow-Origin", origin)
                w.Header().Set("Access-Control-Allow-Methods", "POST, GET, OPTIONS")
                w.Header().Set("Access-Control-Allow-Headers", "Content-Type")
                break
            }
        }
        if r.Method == "OPTIONS" {
            w.WriteHeader(204)
            return
        }
        next.ServeHTTP(w, r)
    })
}

func (s *Server) rateLimitMiddleware(next http.Handler) http.Handler {
    limiter := rate.NewLimiter(rate.Every(time.Minute/time.Duration(s.config.Server.RateLimit.RequestsPerMinute)), s.config.Server.RateLimit.Burst)
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        if !limiter.Allow() {
            http.Error(w, "rate limit exceeded", http.StatusTooManyRequests)
            return
        }
        next.ServeHTTP(w, r)
    })
}
```

### 3.4 Conversation persistence

The bot persists conversation threads in the same SQLite database as the
RAG index (`./data/portfolio.db`). Schema lives in `internal/portfolio/conversations.go`.

```sql
CREATE TABLE conversations (
    id         TEXT PRIMARY KEY,         -- 16-byte random hex (32 chars)
    created_at INTEGER NOT NULL,
    updated_at INTEGER NOT NULL
);

CREATE TABLE messages (
    id              INTEGER PRIMARY KEY AUTOINCREMENT,
    conversation_id TEXT    NOT NULL,
    role            TEXT    NOT NULL,    -- user | assistant | system
    content         TEXT    NOT NULL,
    sources         TEXT,                 -- JSON array, nullable
    created_at      INTEGER NOT NULL,
    FOREIGN KEY (conversation_id) REFERENCES conversations(id) ON DELETE CASCADE
);
CREATE INDEX idx_messages_conv ON messages(conversation_id, id);
```

**Lifecycle:**

| When | What |
|---|---|
| `POST /api/chat` (no `conversation_id`) | Server mints a new hex ID, returns it in the `start` SSE event (or `conversation_id` field of the JSON response) |
| `POST /api/chat` (with `conversation_id`) | Server reuses the existing row; both user message and assistant reply are appended |
| User message | Persisted **before** the LLM runs, so it survives a model failure |
| Assistant message | Persisted **after** the stream completes, with the RAG sources attached |
| `GET /api/conversations/{id}` | Returns the full thread; 404 if unknown |
| `DELETE /api/conversations/{id}` | Cascade-deletes messages |

**Client responsibilities:**

1. On the first message, omit `conversation_id`. Capture the one the server
   returns in the `start` SSE event.
2. Store it client-side (`localStorage["rony-chat-conv"]` in the widget).
3. On every subsequent message, send the ID back.
4. On page load, if you have a stored ID, call `GET /api/conversations/{id}`
   to restore the thread. If 404, clear the stored ID and start fresh.

The widget (`web/chat-widget.js`) implements all four steps. Any other
client (a custom React component, an Astro endpoint, a CLI replay tool)
follows the same protocol.

**Auth model:**

The conversation ID is the only access token for `GET /api/conversations/{id}`.
It is 128 bits of random entropy, so guessing one is infeasible. For a
public portfolio bot this is the right trade-off — anyone who knows the
URL can read its history. For private contexts, add an auth layer in front
of the bot (proxy) that gates the conversation endpoints.

---

## 🧠 4. RAG (Retrieval-Augmented Generation)

> ⚠️ **Decisiones pendientes de validar antes de implementar este módulo:**
>
> - **Tokenizer FTS5** — el spec asume `unicode61 remove_diacritics 2`. Confirmar con datos reales si conviene cambiar a `porter` (stemming EN), `trigram` (sub-string matching) o un tokenizer custom para español. **Validar:** ejecutar queries representativas contra `data/projects/` y comparar recall antes de cerrar esta elección.
> - **Driver SQLite** — ✅ **DECIDIDO: `modernc.org/sqlite`** (puro Go, sin CGO). Ver benchmark abajo.
> - **Chunking** — el split por tamaño fijo (500 chars / 50 overlap) corta headings y code blocks arbitrariamente. **Validar:** medir recall con chunks por sección markdown (split por `#`/`##`) vs por tamaño.
> - **Sin similitud semántica** — BM25 no matchea "IA" con "machine learning" salvo que la palabra esté literal. **Validar:** tamaño del corpus y types of questions esperadas; si el corpus crece o las queries se vuelven abstractas, considerar agregar embeddings como capa secundaria.

### 4.0 Driver decision: benchmark results

Reproducible con `CGO_ENABLED=1 go test -tags sqlite_fts5 -bench=. ./bench/`. Datos: 4 markdowns → 11 chunks.

| Operación | mattn (CGO) | modernc (puro Go) | Diferencia |
|---|---|---|---|
| **Insert** (11 chunks) | 2,802,843 ns/op | **1,465,646 ns/op** | modernc 1.9× más rápido |
| Insert alloc | 2,124,299 B/op | **9,770 B/op** | modernc usa 217× menos memoria |
| **Query** (8 queries BM25) | **244,047 ns/op** | 555,162 ns/op | mattn 2.3× más rápido |
| **Round-trip** (insert + 8 queries) | 3,543,417 ns/op | **2,267,669 ns/op** | modernc 1.6× más rápido |
| Binary size | 11 MB | 11 MB | igual |
| Build deps | gcc, CGO=1 | nada | modernc gana |
| CI/CD portable | requiere toolchain C | `go build` puro | modernc gana |

**Decisión: `modernc.org/sqlite`**.

Justificación:
1. Ambas latencias de query (~250µs vs ~550µs) son **2 órdenes de magnitud por debajo** del target de 50ms — imperceptible vs el LLM (varios segundos).
2. modernc gana en inserts (1.9×) y round-trip (1.6×), que es el path de reindex.
3. Sin CGO = CI/CD más simple (sin gcc, sin Alpine musl-dev, binarios reproducibles).
4. Si en el futuro el cuello de botella pasa a ser query latency (corpus >10k chunks), se puede reconsiderar. Hoy no.

### 4.1 Indexing pipeline

```
data/projects/*.md
    ↓ (read all files)
Raw markdown content
    ↓ (split into chunks, ~500 chars, 50 overlap)
Chunks []
    ↓ (insert into SQLite FTS5 virtual table "portfolio_chunks")
Indexed corpus
```

**When it runs:**
- On bot startup (if `--reindex-on-start` flag)
- Manually: `./chat-bot reindex`
- Via HTTP: `POST /api/reindex`

### 4.2 Retrieval pipeline

```
User query "what projects does Victor have?"
    ↓ (FTS5 MATCH query, BM25 ranking, top_k=5)
Top 5 relevant chunks
    ↓ (format as context block)
System prompt += relevant chunks
    ↓ (send to LLM)
LLM generates answer
```

### 4.3 Implementation

```go
// internal/portfolio/indexer.go
package portfolio

import (
    "context"
    "database/sql"
    "fmt"
    "log/slog"
    "os"
    "path/filepath"
    "strings"
)

type Indexer struct {
    dataPath     string
    db           *sql.DB
    chunkSize    int
    chunkOverlap int
}

func (i *Indexer) IndexAll(ctx context.Context) (int, error) {
    files, err := filepath.Glob(filepath.Join(i.dataPath, "*.md"))
    if err != nil {
        return 0, err
    }

    // Rebuild FTS5 index from scratch (delete + insert is faster than diff for small corpora)
    if _, err := i.db.ExecContext(ctx, `DELETE FROM portfolio_chunks`); err != nil {
        return 0, fmt.Errorf("clear index: %w", err)
    }

    totalChunks := 0
    for _, file := range files {
        chunks, err := i.indexFile(ctx, file)
        if err != nil {
            slog.Warn("failed to index file", "file", file, "err", err)
            continue
        }
        totalChunks += chunks
    }

    return totalChunks, nil
}

func (i *Indexer) indexFile(ctx context.Context, path string) (int, error) {
    content, err := os.ReadFile(path)
    if err != nil {
        return 0, err
    }

    projectID := strings.TrimSuffix(filepath.Base(path), ".md")
    chunks := splitIntoChunks(string(content), i.chunkSize, i.chunkOverlap)

    tx, err := i.db.BeginTx(ctx, nil)
    if err != nil {
        return 0, err
    }
    defer tx.Rollback()

    stmt, err := tx.PrepareContext(ctx, `
        INSERT INTO portfolio_chunks (id, project_id, source_file, chunk_index, content)
        VALUES (?, ?, ?, ?, ?)
    `)
    if err != nil {
        return 0, err
    }
    defer stmt.Close()

    for idx, chunk := range chunks {
        id := fmt.Sprintf("%s-chunk-%d", projectID, idx)
        if _, err := stmt.ExecContext(ctx, id, projectID, path, idx, chunk); err != nil {
            return idx, err
        }
    }

    if err := tx.Commit(); err != nil {
        return 0, err
    }
    return len(chunks), nil
}

// schema.go — applied at startup
const schema = `
CREATE VIRTUAL TABLE IF NOT EXISTS portfolio_chunks USING fts5(
    id UNINDEXED,
    project_id UNINDEXED,
    source_file UNINDEXED,
    chunk_index UNINDEXED,
    content,
    tokenize = 'unicode61 remove_diacritics 2'
);
`

func splitIntoChunks(text string, size, overlap int) []string {
    // Simple implementation: split by size with overlap
    // Production version uses tokenizer-aware chunking
    var chunks []string
    for i := 0; i < len(text); i += size - overlap {
        end := i + size
        if end > len(text) {
            end = len(text)
        }
        chunks = append(chunks, text[i:end])
    }
    return chunks
}
```

### 4.4 Retrieval in the agent loop

```go
// internal/portfolio/search.go
package portfolio

type Hit struct {
    ProjectID  string
    SourceFile string
    ChunkIndex int
    Content    string
    Score      float64 // BM25 score from FTS5
}

func (s *Store) Search(ctx context.Context, query string, topK int) ([]Hit, error) {
    // Escape user input: FTS5 syntax can break with special chars
    ftsQuery := sanitizeFTS5(query)

    rows, err := s.db.QueryContext(ctx, `
        SELECT project_id, source_file, chunk_index, content, bm25(portfolio_chunks) AS score
        FROM portfolio_chunks
        WHERE portfolio_chunks MATCH ?
        ORDER BY score
        LIMIT ?
    `, ftsQuery, topK)
    if err != nil {
        return nil, err
    }
    defer rows.Close()

    var hits []Hit
    for rows.Next() {
        var h Hit
        if err := rows.Scan(&h.ProjectID, &h.SourceFile, &h.ChunkIndex, &h.Content, &h.Score); err != nil {
            return nil, err
        }
        hits = append(hits, h)
    }
    return hits, rows.Err()
}

// sanitizeFTS5 wraps the user query so reserved chars and unquoted strings don't crash FTS5.
// A pragmatic choice for a Q&A bot: append prefix-match wildcard to each token.
func sanitizeFTS5(q string) string {
    tokens := strings.FieldsFunc(q, func(r rune) bool {
        return !(r == '-' || r == '_' || (r >= '0' && r <= '9') ||
            (r >= 'a' && r <= 'z') || (r >= 'A' && r <= 'Z') ||
            r > 0x7F) // keep accented chars
    })
    if len(tokens) == 0 {
        return `""`
    }
    for i, t := range tokens {
        tokens[i] = `"` + strings.ToLower(t) + `"*`
    }
    return strings.Join(tokens, " ")
}
```

```go
// internal/agent/runner.go
package agent

func (r *Runner) buildSystemPrompt(ctx context.Context, query string) (string, error) {
    basePrompt := r.persona.SystemPrompt

    hits, err := r.store.Search(ctx, query, r.config.RAG.TopK)
    if err != nil {
        return "", err
    }
    if len(hits) == 0 {
        return basePrompt, nil
    }

    var contextBlock strings.Builder
    contextBlock.WriteString(basePrompt)
    contextBlock.WriteString("\n\n## Relevant context\n\n")
    for _, h := range hits {
        contextBlock.WriteString(fmt.Sprintf("### Source: %s\n%s\n\n",
            h.SourceFile, h.Content))
    }
    return contextBlock.String(), nil
}

func (r *Runner) RunStream(ctx context.Context, messages []llm.Message) iter.Seq2[Chunk, error] {
    return func(yield func(Chunk, error) bool) {
        lastUserMsg := getLastUserMessage(messages)
        systemPrompt, err := r.buildSystemPrompt(ctx, lastUserMsg)
        if err != nil {
            yield(Chunk{}, err)
            return
        }

        messages = prependSystem(messages, systemPrompt)

        for chunk, err := range r.loop.RunStream(ctx, messages) {
            if !yield(chunk, err) {
                return
            }
        }
    }
}
```

**Why this is simpler than embeddings:**
- No embedding model to download or run (saves ~270MB of RAM and ~200ms per query)
- One file (`data/portfolio.db`), one driver, no extra process
- BM25 ranking is excellent for keyword-based retrieval over structured docs like project READMEs
- Trade-off: no semantic similarity ("projects about AI" won't match "machine learning" without the literal words). Mitigation: `trigram` tokenizer handles morphology well for English/Spanish.

---

## 🌐 5. Embedding the widget

The bot ships with a drop-in vanilla-JS widget. Add two files to your site and it works.

### 5.1 The widget (any site)

```html
<link rel="stylesheet" href="/path/to/chat-widget.css">
<script src="/path/to/chat-widget.js"
        data-api-url="https://chat.example.com"
        data-title="Ask me anything"
        data-greeting="Hi! Ask me about the projects."
        data-position="bottom-right"
        data-theme="auto"
        defer></script>
```

A bubble appears bottom-right, opens a panel, talks SSE to `/api/chat`, streams the response, and cites sources. No build step, no React/Vue, no framework lock-in.

**Browser→bot options:**

| Topology | Trade-offs |
|---|---|
| **Direct** (browser → bot, same domain or CORS) | Simplest. Add the bot's origin to `cors_origins` in YAML. |
| **Reverse proxy** (nginx/Caddy in front) | Bot stays on private network, single public domain, no CORS to manage. |
| **Site proxies the bot** (Astro/Next API route) | Adds a hop and a bit of code, but gives you auth/session hooks in your site. |

The widget works the same in all three. Pick the topology that matches your infra.

> **Default dev setup is direct + CORS.** `cors_origins` in `configs/portfolio-bot.yaml` controls which sites can call the bot. Add your site's origin there.

### 5.2 Astro: drop-in via Layout

The widget works in Astro without writing a React component. Add this to your shared layout:

```astro
---
// src/layouts/BaseLayout.astro
import "../path/to/chat-widget.css";
const apiUrl = import.meta.env.PUBLIC_CHAT_API_URL || "http://localhost:7331";
---
<html>
  <body>
    <slot />
    <script src="/path/to/chat-widget.js"
            data-api-url={apiUrl}
            data-title="Ask me anything"
            data-position="bottom-right"
            data-theme="auto"
            defer is:inline></script>
  </body>
</html>
```

`is:inline` keeps Astro from hashing/transforming the script tag, so the `data-*` attributes survive.

### 5.3 React / Next.js: same script tag

```tsx
// app/layout.tsx
import Script from "next/script";

export default function RootLayout({ children }) {
  return (
    <html>
      <head>
        <link rel="stylesheet" href="/chat-widget.css" />
        <Script src="/chat-widget.js"
                data-api-url={process.env.NEXT_PUBLIC_CHAT_API_URL}
                data-title="Ask me anything"
                data-position="bottom-right"
                data-theme="auto"
                strategy="afterInteractive" />
      </head>
      <body>{children}</body>
    </html>
  );
}
```

### 5.4 If you want a server proxy (Astro/Next API route)

The widget can also call a same-origin endpoint that forwards to the bot. This is the right call when you need:
- Auth on `/api/chat` (logged-in users only)
- Centralized rate limiting at the site level
- Hiding the bot's origin from the browser

```typescript
// src/pages/api/chat.ts (Astro) or app/api/chat/route.ts (Next)
const CHAT_BOT_URL = process.env.CHAT_BOT_URL || "http://localhost:7331";

export const POST = async ({ request }) => {
    const body = await request.json();
    // (optional) auth check, rate limit, session lookup here

    const resp = await fetch(`${CHAT_BOT_URL}/api/chat`, {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify(body),
    });

    return new Response(resp.body, {
        status: resp.status,
        headers: {
            "Content-Type": "text/event-stream",
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
        },
    });
};
```

Then point the widget at `/api/chat` (same origin) instead of the bot's URL.

### 5.5 Widget configuration reference

All options are `data-*` attributes on the `<script>` tag:

| Attribute | Default | Notes |
|---|---|---|
| `data-api-url` | *(required)* | Base URL of the bot. No trailing slash. |
| `data-title` | `"Chat"` | Header text. |
| `data-greeting` | `""` | First assistant message when the panel opens. |
| `data-position` | `"bottom-right"` | `"bottom-right"` or `"bottom-left"`. |
| `data-theme` | `"auto"` | `"auto"` (follows OS), `"light"`, `"dark"`. |

Theming is via CSS custom properties on `.rony-chat-widget-root` (see `web/chat-widget.css`):

```css
.rony-chat-widget-root {
  --rony-accent: #ff6b35;
  --rony-radius: 4px;
  --rony-font: "Inter", sans-serif;
}
```

### 5.6 What the widget doesn't do (yet)

- **Richer markdown** (tables, images) — the built-in renderer handles the common cases; for full CommonMark, swap `renderMarkdown` in `chat-widget.js` for `marked` or `markdown-it`.
- **Mobile swipe-to-dismiss** — panel goes full-screen on phones.
- **Conversation history sidebar** — only the active conversation is shown (the backend exposes `GET /api/conversations` for a future sidebar).

---

## 🤖 6. Self-hosting with llama.cpp (default)

### 6.1 Setup

llama-server is a separate process that the bot connects to over HTTP. **Both ports (the bot's and llama-server's) are configurable** — pick what fits your environment.

```bash
# 1. Make sure you have a GGUF model available
# Download from Hugging Face, e.g.:
#   https://huggingface.co/Qwen/Qwen2.5-3B-Instruct-GGUF
export RONY_MODELS_PATH=/path/to/models
ls $RONY_MODELS_PATH/qwen2.5-3b-instruct-q4_k_m.gguf

# 2. Start llama-server (port is configurable; default llama.cpp is 8080)
llama-server \
  -m $RONY_MODELS_PATH/qwen2.5-3b-instruct-q4_k_m.gguf \
  --port 9100 \
  --host 127.0.0.1 \
  --ctx-size 4096 \
  --mlock            # prevents swap, critical on shared VPS

# 3. Make sure configs/portfolio-bot.yaml points to the same port
#    providers[0].endpoint: http://localhost:9100/v1

# 4. Start the bot (default port 7331, also configurable)
./bin/chat-bot serve
# → Serves on http://localhost:7331
# → Override with: ./bin/chat-bot serve --port 9101 --host 127.0.0.1
```

**Port reference:**

| What | Default | How to change |
|---|---|---|
| `llama-server` HTTP port | 8080 (llama.cpp convention) | `--port N` flag when starting `llama-server` |
| chat-bot HTTP port | 7331 | `--port N` flag on `serve`, or `server.port` in YAML |
| chat-bot → llama-server URL | `http://localhost:8080/v1` | `endpoint` field on the provider in YAML |

The `llamacpp` provider is imported from `rony-llm-agent/pkg/llm/providers/llamacpp` and is compiled against `llama.cpp` via CGO or external binary.

### 6.2 Alternative: Ollama (easier for development)

If you don't want to manage GGUF files manually, Ollama provides the same models with a simpler workflow:

```bash
# 1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 2. Download chat model
ollama pull qwen2.5:1.5b

# 3. Verify
ollama list

# 4. Edit configs/portfolio-bot.yaml to mark ollama-local as default:
#    providers[0].default: true (and remove default from llamacpp-local)
#    Ollama exposes an OpenAI-compatible API on :11434/v1

# 5. Start the bot
ollama serve &
./bin/chat-bot serve
```

### 6.3 Alternative: llama.cpp direct (advanced)

For more control or if Ollama doesn't work in your setup:

```yaml
providers:
  - name: llamacpp-local
    type: llamacpp
    model: qwen2.5-3b-instruct
    endpoint: http://localhost:9100/v1   # configurable, see §6.1
    context_size: 4096
    max_tokens: 2048
    default: true
```

The `llamacpp` adapter is imported from `rony-llm-agent/pkg/llm/providers/llamacpp` and is compiled against `llama.cpp` via CGO or external binary.

---

## 📦 7. Bot CLI

### 7.1 Commands

```bash
# Start HTTP server
chat-bot serve [--port 7331] [--host 0.0.0.0] [--reindex-on-start]

# Re-index portfolio (reads data/projects/*.md → SQLite FTS5)
chat-bot reindex

# Single question (no server, useful for tests)
chat-bot ask "What projects does Victor have?" [--no-rag]

# Validate config
chat-bot config validate

# Health check (useful for monitoring)
chat-bot health

# Version
chat-bot version
```

### 7.2 Implementation with Cobra

```go
// cmd/chat-bot/main.go
package main

import (
    "github.com/spf13/cobra"
)

func main() {
    root := &cobra.Command{
        Use:   "chat-bot",
        Short: "Portfolio chatbot HTTP server",
    }
    
    root.AddCommand(serveCmd())
    root.AddCommand(reindexCmd())
    root.AddCommand(askCmd())
    root.AddCommand(configCmd())
    root.AddCommand(healthCmd())
    root.AddCommand(versionCmd())
    
    if err := root.Execute(); err != nil {
        os.Exit(1)
    }
}

func serveCmd() *cobra.Command {
    var port int
    var host string
    var reindexOnStart bool
    
    cmd := &cobra.Command{
        Use:   "serve",
        Short: "Start HTTP server",
        RunE: func(cmd *cobra.Command, args []string) error {
            return server.Serve(server.Config{
                Port:           port,
                Host:           host,
                ReindexOnStart: reindexOnStart,
            })
        },
    }
    
    cmd.Flags().IntVar(&port, "port", 7331, "HTTP port")
    cmd.Flags().StringVar(&host, "host", "0.0.0.0", "HTTP host")
    cmd.Flags().BoolVar(&reindexOnStart, "reindex-on-start", false, "Re-index RAG before serving")
    
    return cmd
}
```

---

## 🚀 8. Deployment

### 8.1 Recommendation: Self-hosted on VPS

```bash
# 1. Install dependencies
sudo apt install golang-go ollama
ollama pull qwen2.5:1.5b

# 2. Build
go build -o /usr/local/bin/chat-bot ./cmd/chat-bot

# 3. systemd service
cat > /etc/systemd/system/chat-bot.service <<EOF
[Unit]
Description=Portfolio Chat Bot
After=network.target ollama.service

[Service]
Type=simple
User=chatbot
WorkingDirectory=/opt/chat-bot
ExecStart=/usr/local/bin/chat-bot serve
Restart=on-failure
Environment=RONY_MODELS_PATH=/opt/models

[Install]
WantedBy=multi-user.target
EOF

sudo systemctl enable --now chat-bot
```

### 8.2 Reverse proxy (Caddy)

```
# /etc/caddy/Caddyfile
chat.victorvargas.dev {
    reverse_proxy localhost:7331
}
```

### 8.3 Monitoring

```bash
# Health check periodic
curl -s http://localhost:7331/api/health | jq

# Logs
journalctl -u chat-bot -f
```

---

## 🧪 9. Testing

### 9.1 Unit tests

```go
// internal/server/chat_test.go
package server

func TestHandleChat_ValidRequest(t *testing.T) {
    s := newTestServer(t)
    
    req := httptest.NewRequest("POST", "/api/chat", strings.NewReader(`{
        "messages": [{"role": "user", "content": "hello"}]
    }`))
    req.Header.Set("Content-Type", "application/json")
    
    w := httptest.NewRecorder()
    s.handleChat(w, req)
    
    assert.Equal(t, 200, w.Code)
    assert.Equal(t, "text/event-stream", w.Header().Get("Content-Type"))
}

func TestHandleChat_RateLimit(t *testing.T) {
    s := newTestServerWithConfig(t, server.Config{
        RateLimit: 1, // 1 request per minute
    })
    
    // First request OK
    req1 := newChatRequest("hello")
    w1 := httptest.NewRecorder()
    s.handleChat(w1, req1)
    assert.Equal(t, 200, w1.Code)
    
    // Second request denied
    req2 := newChatRequest("hello again")
    w2 := httptest.NewRecorder()
    s.handleChat(w2, req2)
    assert.Equal(t, 429, w2.Code)
}
```

### 9.2 Integration tests with mock LLM

```go
// internal/agent/runner_test.go
func TestRunner_RAGContextIsInjected(t *testing.T) {
    mockLLM := mock.New(mock.Responses{
        {Match: "projects", Response: "Victor has several projects..."},
    })
    
    memory := newMockMemoryWithDocs(t, []rag.Fragment{
        {Content: "Rony Harness: AI agent harness...", ProjectID: "rony-harness"},
        {Content: "rony-llm-agent: Go library...", ProjectID: "rony-llm-agent"},
    })
    
    runner := agent.NewRunner(agent.Config{
        LLM:    mockLLM,
        Memory: memory,
        Persona: testPersona,
    })
    
    resp, _ := runner.Run(context.Background(), []llm.Message{
        {Role: llm.RoleUser, Content: "what projects does Victor have?"},
    })
    
    // Verify LLM received context chunks in system prompt
    lastReq := mockLLM.LastRequest()
    assert.Contains(t, lastReq.Messages[0].Content, "Rony Harness")
    assert.Contains(t, lastReq.Messages[0].Content, "rony-llm-agent")
}
```

### 9.3 E2E test with Astro

```bash
# 1. Start chat-bot on :7331
./bin/chat-bot serve &

# 2. Start Astro on :4321
cd ../portfolio && npm run dev &

# 3. Make request to Astro's proxy
curl -X POST http://localhost:4321/api/chat \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"hello"}]}'

# 4. Verify SSE stream
```

---

## 📂 10. Project Structure

```
rony-chat-bot/
├── cmd/
│   └── chat-bot/
│       └── main.go                 # CLI entrypoint
│
├── internal/
│   ├── server/                     # HTTP handlers
│   │   ├── server.go               # chi router + middleware
│   │   ├── handlers.go             # /api/chat, /api/health, /api/info, /api/reindex, /api/conversations
│   │   ├── conversations_test.go   # round-trip, continue, list, 404, delete, streaming
│   │   └── middleware.go           # RequestID, Logging, CORS, RateLimit
│   │
│   ├── agent/                      # LLM client + RAG runner
│   │   ├── runner.go               # Stream wrapper, RAG injection into system prompt
│   │   └── client.go               # NewClient factory: llamacpp / ollama / openai / anthropic
│   │
│   ├── portfolio/                  # RAG: markdown → SQLite FTS5 + conversation persistence
│   │   ├── chunker.go              # Heading-based splitter
│   │   ├── indexer.go              # Store: schema, Reindex, Search (BM25)
│   │   ├── conversations.go        # Conversation + Message CRUD, persisted alongside RAG
│   │   └── chunker_test.go / store_test.go
│   │
│   ├── persona/                    # Persona bridge to rony-llm-agent
│   │   └── persona.go              # FromConfig, BuildSystemPrompt (with RAG context)
│   │
│   ├── streaming/                  # SSE protocol helpers
│   │   └── sse.go                  # WriteStart/Chunk/Sources/Done/Error
│   │
│   ├── i18n/                       # Language detection (ES/EN) for the response
│   │
│   └── config/                     # YAML loader + validation
│
├── web/                            # ← DROP-IN CHAT WIDGET
│   ├── chat-widget.js              # Vanilla JS, ~12 KB
│   ├── chat-widget.css             # Scoped styles, CSS-custom-prop themable
│   ├── example.html                # Local demo (python -m http.server)
│   └── README.md                   # Integration guide (HTML, Astro, Next.js)
│
├── data/
│   └── projects/                   # ← Markdown per project (one .md per project)
│       ├── rony-harness.md
│       ├── rony-llm-agent.md
│       └── example-project.md
│
├── configs/
│   └── portfolio-bot.yaml          # Provider + RAG + persona config
│
├── docs/
│   ├── architecture.md             # ← THIS FILE
│   └── architecture.es.md
│
├── bench/                          # Reproducible SQLite driver benchmark
│
├── go.mod                          # require rony-llm-agent, modernc.org/sqlite
└── README.md
```

---

## 📅 11. Roadmap

### Phase 1: MVP (2-3 weeks)

- [ ] Project setup (`go mod init`, structure)
- [ ] Basic HTTP server with `/api/chat` endpoint
- [ ] Functional SSE streaming
- [ ] RAG indexer (reads `data/projects/*.md` → SQLite FTS5)
- [ ] RAG retriever (query → top-k chunks)
- [ ] Persona loader from YAML
- [ ] llama.cpp integration (qwen2.5:1.5b GGUF)
- [ ] CLI: `serve`, `reindex`, `ask`
- [ ] Basic tests

### Phase 2: Integration with Astro (1 week)

- [ ] Astro API route of the proxy
- [ ] React component of the chat widget
- [ ] E2E test: Astro → chat-bot → response
- [ ] Widget styling (TailwindCSS)

### Phase 3: Polish (1 week)

- [ ] Robust rate limiting
- [ ] Structured logging (JSON)
- [ ] Health checks for monitoring
- [ ] systemd service file
- [ ] README + deployment docs

### Phase 4: Optionals

- [ ] Support for multiple conversations (session ID)
- [ ] Persisted chat history
- [ ] Analysis of frequent questions
- [ ] Multi-language (EN/ES switch)
- [ ] More polished standalone CLI version (`chat-bot ask`)

---

## 📐 12. Quality Specifications

### 12.1 Performance metrics

| Metric | Target |
|---|---|
| TTFT (Time-to-first-token) | <500ms with llama.cpp local |
| End-to-end (question → complete response) | <3s for typical responses |
| Memory at rest | <150MB |
| RAG indexing speed | ~100 docs/second |
| Retrieval latency | <50ms for top-5 |

### 12.2 Required tests

- Unit tests: coverage ≥70%
- Integration tests: with mock LLM + in-memory SQLite FTS5
- E2E: at least one complete Astro → chat-bot flow

---

## 🔒 13. Security

### 13.1 Implemented

- **Rate limiting** per IP (default 30 req/min)
- **Restrictive CORS** — only configured origins
- **Input validation** — JSON schema validation on requests
- **No PII storage** — we don't save conversations by default
- **Local-only by default** — no calls to cloud APIs

### 13.2 Deferred / Optional

- Auth with API key (for private use)
- Query logging for analytics
- IP anonymization in logs
- HTTPS via reverse proxy (Caddy/nginx)

---

## 📚 14. References

- **SSE Spec:** https://html.spec.whatwg.org/multipage/server-sent-events.html
- **Ollama API:** https://github.com/ollama/ollama/blob/main/docs/api.md
- **SQLite FTS5:** https://www.sqlite.org/fts5.html
- **Go SQLite driver:** https://github.com/mattn/go-sqlite3 (CGO) or https://modernc.org/sqlite (pure Go)
- **qwen2.5:** https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
- **Astro API routes:** https://docs.astro.build/en/guides/endpoints/
- **rony-llm-agent:** https://github.com/VictorVargas/rony-llm-agent

---

**Document ready for implementation. 🚀**