Dicklesworthstone/swiss_army_llama

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1056 stars · 66 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A FastAPI-based REST service that exposes local LLM capabilities including text embeddings, completions, semantic similarity, and semantic search via llama.cpp and FAISS. It automatically computes and caches embeddings for common document types (PDFs with OCR, Word files, audio transcriptions) in SQLite.

Use cases

• run a self-hosted embeddings API for local LLMs
• semantic search across my documents
• compute embeddings for PDFs and Word files automatically
• transcribe audio files and embed the text
• compare semantic similarity between text strings with advanced measures
• cache embeddings so I don't recompute them
• expose llama.cpp models behind a REST API

When to choose

• you want a single self-hosted service combining embeddings, completions, and semantic search
• you need automatic embedding of diverse file types including OCR'd PDFs and audio
• you want advanced similarity measures beyond cosine similarity
• you prefer local LLMs without cloud API dependencies

When to avoid

• you need a managed or cloud-hosted embedding service
• you require a permissively licensed dependency (no license is specified)
• you need GPU-scale high-throughput inference rather than local convenience
• you want a library to embed in your own code rather than a standalone service

Facets

service · maturity active

llm-inference rag search-engine http-server ocr speech-recognition caching machine-learning large-language-models apis self-hosted python self-hosted embeddings semantic-search fastapi llama-cpp faiss vector-similarity whisper textract swagger-ui search natural-language-processing docker linux macos

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markdown · JSON · MCP: product_card(name="Dicklesworthstone/swiss_army_llama")