f33708534a
Initial implementation of the bot: - cmd/chat-bot: CLI entrypoint (serve, reindex, ask, version) - internal/agent: LLM provider client + agent runner with RAG injection - internal/config: YAML config loader (providers, RAG, persona, server) - internal/i18n: response-language detection (EN/ES) - internal/persona: persona system prompt assembly from YAML - internal/portfolio: heading-based chunker + SQLite FTS5 indexer - internal/server: chi router with /api/chat (SSE), /api/health, /api/info, /api/reindex, middleware (RequestID, Logging, CORS, RateLimit) - internal/streaming: SSE protocol helpers (start, chunk, sources, done, error) - web/: drop-in vanilla-JS chat widget (no build, no deps) + demo + README - bench/: reproducible driver benchmark (modernc vs mattn SQLite) - configs/portfolio-bot.yaml: llama.cpp default provider, SQLite RAG, canine persona - docs/architecture.md / .es.md: aligned with SQLite FTS5 + llama.cpp decisions - data/projects/README*.md: project data documentation - README.md / .es.md: updated for current implementation All tests pass (go test ./...). Bot is functional end-to-end with the configured LLM provider.
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1.3 KiB
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50 lines
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1.3 KiB
Markdown
# Portfolio Projects
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Place a `.md` file here for each project you want the bot to be able to answer about.
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## File naming convention
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- One file per project: `project-name.md`
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- Name in kebab-case (lowercase with hyphens)
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- Example: `rony-harness.md`, `rony-llm-agent.md`, `portfolio-astro.md`
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## Frontmatter (optional but recommended)
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```markdown
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---
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title: "Rony Harness"
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date: 2026-06
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status: "active" # active | archived | wip
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tags: ["go", "ai", "cli"]
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repo: "https://github.com/VictorVargas/rony-harness"
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demo: "https://..." # optional
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---
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# Rony Harness
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AI agent harness for software development...
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```
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## How they're processed
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1. The bot scans this directory on startup
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2. Each `.md` is split into chunks of ~500 characters
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3. Chunks are stored in a local SQLite database with **FTS5** (full-text search, BM25 ranking)
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4. When someone asks a question, the top-5 most relevant chunks are matched
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5. Those chunks are injected into the LLM context
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No embedding models or external vector DBs are required — everything runs in a single SQLite file (`data/portfolio.db`).
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## Re-index
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If you modify the `.md` files, run:
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```bash
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./bin/chat-bot reindex
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```
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This rebuilds the SQLite FTS5 index from scratch.
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## Project example
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See [`example-project.md`](./example-project.md) for a template. |