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 | Español

📚 Workspace: This project is part of the Rony/ workspace. See ../README.md.

🔑 Depends on: 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.

---

🎯 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:

{
  "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):

{
  "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:

{
  "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):

{
  "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:

{
  "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):

{
  "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):

{
  "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

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

3.2 SSE Implementation

// 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

// 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.

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

// 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

// 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, " ")
}

// 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.

---

🗜️ 4.5 Auto-compaction

Long conversations eventually run out of context — at qwen2.5-3b's 4k window, the ~3k-token system prompt + RAG block leaves only room for 2–3 user turns. Auto-compaction solves this by folding the older portion of the conversation into a single summary system message when the previous turn's input tokens cross a configurable threshold.

When it fires

agent.Runner.Compact runs once per /api/chat request, before the RAG search. It compares the runner's most recent Usage.InputTokens (reported by the provider in the previous streamed chunk) against client.Capabilities().MaxContextWindow × threshold_ratio.

Setting	Default	What it controls
compaction.enabled	false	Master switch.
compaction.threshold_ratio	0.75	Trigger when used tokens ≥ window × ratio.
compaction.keep_recent_turns	4	How many of the latest user turns are kept verbatim after compaction.
compaction.summary_system_prompt	(built-in bilingual)	Override the instruction sent to the LLM when summarizing.

Short-circuits silently when compaction is disabled, the provider doesn't report a window (Capabilities().MaxContextWindow == 0), the history is shorter than keep_recent_turns, or usage is still unknown (first turn).

How the summary is made

1. splitByTurns(history, keep_recent_turns) divides messages into (older, recent) on user-role boundaries so a kept turn's user/assistant pair always stays together.
2. renderTranscript(older) flattens older messages into a User: / Assistant: transcript (skipping tool messages and empty assistant placeholders).
3. The runner calls client.Generate(...) with the summary prompt + transcript and a 512-token cap so the compaction step itself stays cheap.
4. The returned text is prepended as a system message ("Earlier conversation summary:\n…"), followed by the recent tail.
5. LastCompaction() returns CompactionStats so the SSE handler can emit a compaction event right before the streamed chunks.

Failure mode

If Generate errors or returns an empty summary, compaction falls back to truncateToBudget: drop oldest user-turns one at a time until the remaining slice fits threshold tokens (heuristic: len(s) / 4 + 1). The current user turn is always preserved. The fallback is logged at WARN and the request still proceeds — a flaky summarize call never fails the user's request.

Wire protocol

Streaming responses gain an optional compaction event:

data: {"type":"compaction","older_turns":6,"kept_turns":2,"summary_tokens":120,"window_tokens":4096,"used_tokens":3500}

Emitted after start (when applicable) and before sources / chunk. The widget can render this as a subtle "Context compacted" hint or ignore it — both are valid.

Persistence

Compaction is per-request. The full transcript is still saved to messages in data/portfolio.db verbatim, so GET /api/conversations/{id} always returns the original history. Only what we send to the LLM is reduced — the next session can re-read the full thread from the DB.

---

🌐 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)

<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:

---
// 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

// 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

// 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):

.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.

# 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:

# 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:

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

# 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

// 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

# 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

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

# Logs
journalctl -u chat-bot -f

---

🧪 9. Testing

9.1 Unit tests

// 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

// 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

# 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. 🚀