end_to_end gpt-5-mini

--- summary of the knowledge-base ---

The knowledge-base is a set of documentation files (Markdown and text) for RAGIT — a git-like tool that converts local files into a retrieval-augmented knowledge base. 

Summary: It describes RAGIT’s purpose, features, and workflows: chunking with title+summary, AI-generated keywords + TF-IDF search (not vector search), Markdown with images, experimental multi-turn queries, git-style commands (init, add, build, clone, push), default Groq Llama model and env var auth (GROQ_API_KEY, OPENAI_API_KEY), how to change models, and usage examples. Platform notes: Linux x64 and Mac aarch64 primary; Windows supported with caveats.

Structure: 53 files (33% .md, 67% .txt) including a commands/ dir (37 files), ragithub/ (1 file), and top-level docs: build.md, chunks.md, config.md, contribution.md, eval.md, index.md, intro.txt, models.md, multi_turn.md, pipeline.md, prompt_engineering.md, quick_guide.md, uid_query.md, etc.


--- What makes ragit special? ---

RAGIT is special because it rethinks how a local knowledge‑base should be built, searched and reused — with a strong focus on simplicity, reproducibility and practical prompt tooling. Key differentiators:

- Chunk metadata: every chunk gets a title and a summary. Those summaries make it much easier for LLMs to rerank and interpret retrieved chunks.
- TF–IDF + AI keywords (not only vectors): instead of relying only on vector search, ragit asks an LLM to extract keywords from the query, then runs a TF–IDF search with those keywords — fast, transparent, and often more controllable than opaque vector nearest‑neighbors.
- Multi‑turn queries: built‑in support for follow‑ups via a query‑rewrite step (the system rephrases short follow‑ups to include prior context), so you can ask natural successive questions without contaminating retrieval.
- Git‑like workflows: you can init, add, build, clone and push knowledge‑bases — making sharing, versioning and collaboration straightforward.
- Rich content and prompt tooling: supports Markdown with embedded images and a dedicated PDL (Prompt Description Language) for templated prompts, embedded media, and forcing structured/JSON outputs (with schema & constraints).
- CLI + library: usable as a simple command‑line tool (rag init/add/build/query) and as a Rust API (Index::query) for building apps.
- Practical platform support and examples: tested on Linux and Mac (works on Windows with caveats) and comes with interactive sample repos and ready prompts.

In short: ragit combines lightweight, explainable retrieval (TF–IDF with AI keywords), structured chunk metadata, multi‑turn conversational retrieval, git‑style sharing, and pragmatic prompt tooling (PDL) to make local knowledge‑bases easy to create, query and share.
---- sources ----
1st chunk of index.md (63332bc0)
1st chunk of pdl_format.md (62dff949)
1st chunk of multi_turn.md (5f8f26e3)


--- What makes ragit special? (super rerank mode) ---

RAGIT is special because it combines a git-like workflow with a distinct RAG pipeline and features designed for ease of creation, reranking, and sharing of knowledge-bases. Key differentiators:

- Title + summary per chunk: every chunk gets a title and summary, which makes LLM-based reranking fast and reliable.
- Keyword-driven tfidf search: instead of embeddings, RAGIT asks an LLM to extract search keywords from the query and then runs a tfidf search on those keywords — a hybrid approach that leverages LLMs for intent understanding but uses lightweight tfidf scoring for retrieval.
- Two-stage retrieval + rerank: it retrieves a configurable set of top chunks by tfidf, then an LLM reranks summaries/titles to pick the most relevant ones before RAGing, improving precision over raw tfidf.
- Markdown + images support: handles markdown files with images natively.
- Multi-turn queries (experimental): supports conversational, multi-turn interactions.
- Git-like sharing: you can clone, push, and otherwise manage knowledge-bases similarly to git, making sharing and collaboration straightforward.
- Practical CLI workflow: simple commands for init/add/build/query/clone/push and tooling to inspect and simulate pipeline steps (e.g., extract-keywords, tfidf, retrieve-chunks).

Example workflow snippet:
rag init; rag add --all; rag build; rag query "What makes ragit special?"

Platform notes: primarily tested on Linux (x64) and Mac (aarch64); Windows is supported but not perfect.
---- sources ----
1st chunk of pipeline.md (49afb826)
1st chunk of intro.txt (831bd361)
1st chunk of index.md (63332bc0)