compagnonsdudev/spectrallm repository overview

⁠Turn your documents into a private, fine-tuned AI — in one application.

100% local · No cloud · No API keys · No subscriptions

GitHub⁠ · Documentation⁠ · Français⁠ · AGPL-3.0

Your organization's knowledge lives in PDFs, Word docs, wikis and exports. Generic LLMs know none of it — and shipping internal documents to a cloud API is often a non-starter.

⁠What this image is

compagnonsdudev/spectrallm is the backend: Java 25 / Spring Boot — the REST API, the ingestion pipeline, the document store, the hybrid RAG engine and the fine-tuning orchestration. It listens on 8080.

It is one of two images published for this project:

Neither runs alone. A working Spectra stack is five containers: these two, a ChromaDB vector store, and two llama.cpp servers — one for chat, one for embeddings. The Compose file below wires all five together.

⁠Quick start — nothing is built on your machine

git clone https://github.com/devdownin/SpectraLLM.git && cd SpectraLLM

# GGUF weights (~1.2 GB for the defaults) — data, not image layers
./scripts/setup.sh --download-embed --download-chat

# Pull the published images and start the stack
docker compose --project-directory . \
  -f deploy/docker/docker-compose.yml -f deploy/docker/docker-compose.hub.yml pull

docker compose --project-directory . \
  -f deploy/docker/docker-compose.yml -f deploy/docker/docker-compose.hub.yml \
  up -d --wait --no-build

Then open http://localhost⁠, drop a PDF on the Ingestion page, and start asking questions.

--wait blocks until every service reports healthy through its own healthcheck, and exits non-zero if one never does. --no-build is the guardrail: if an image reference were wrong, Compose would otherwise rebuild it silently and the whole point of pulling would be lost.

Why a git clone and not a Compose file to paste here. The two llama.cpp servers mount supervisor entrypoints from scripts/, and the GGUF weights live in data/models — several gigabytes you have to download anyway. The clone carries those; the images carry the code. Nothing is compiled locally: no Maven build, no Vite build. That is exactly what these images are for.

Prefer the guided path? ./scripts/start.sh --first-run does the download, the start and opens the browser — but it builds the images rather than pulling them.

⁠Tags

Release candidates (1.0.0-rc1) are published as-is and never carry latest.

Architectures: linux/amd64 and linux/arm64 — Apple Silicon included.

Both images are built and pushed by a single GitHub Actions workflow⁠ on each version tag, with provenance and SBOM attestations attached:

docker buildx imagetools inspect compagnonsdudev/spectrallm:latest

⁠Requirements

• Docker Engine 25+ with Compose v2 (the healthchecks use start_interval, which older engines reject).
• 16 GB RAM for the default 7B chat model. Smaller models work on less.
• GPU optional — NVIDIA, AMD/ROCm and Vulkan are auto-detected by scripts/start.sh; a Compose overlay handles the offload.

⁠Configuration

Everything has a working default. The variables worth knowing:

Full list: Configuration guide⁠.

Volume. This image reads and writes /app/data — the document store, the H2 database, the GGUF models, the fine-tuning jobs and the logs. In the stack it is bind-mounted to ./data, alongside the vector index: one directory, one backup, one reset. The container runs as the non-root user spectra, so that directory must be writable by others.

Ports. By default every port is published on the loopback only. The web UI (80) and the API (8080) are the two you need; 8000/8081/8082 are there for diagnostics.

⁠What you get

• 📥 Ingest — PDF, DOCX, HTML, Markdown, CSV, JSON, XML, Avro, TXT, ZIP, URLs, even live Kafka streams. Layout-aware parsing keeps tables and headings intact.
• 🔍 Ask — Hybrid search (keyword + vector) with reranking and cited sources. Six retrieval strategies, picked adaptively per question, up to an agentic ReAct loop for multi-hop reasoning — with a live view of how each answer was built.
• 🎓 Fine-tune — Spectra builds a training dataset from your own corpus, then bakes the knowledge into the model's weights (QLoRA/DPO, CPU or GPU). Approved answers feed a continuous-learning loop.
• 📦 Deploy — Out comes a single GGUF file, runnable anywhere (llama.cpp, Ollama, LM Studio…), with built-in evaluation and A/B comparison to prove the gain.

Ask your documents — answers with cited sources	Fine-tune a model that keeps the knowledge

⁠Optional services

Three more services sit behind Compose profiles and are not part of this pull — they are built locally on demand, because each weighs several gigabytes and only matters if you turn it on:

docker compose --project-directory . -f deploy/docker/docker-compose.yml \
  --profile trainer up -d trainer      # in-container fine-tuning (torch)

layout-parser (layout-aware PDF parsing), reranker (cross-encoder reranking — the JVM ONNX engine is the default and needs no container) and kafka (streaming ingestion) work the same way.

⁠Documentation

⁠License

GNU AGPL-3.0 — use, modify and self-host freely, in production, on premises or air-gapped. AGPL is a strong copyleft: if you run a modified version as a network service, you must make the corresponding source available to its users. Full text⁠.

From raw documents to domain expertise — all on your hardware.