# Ragrig — RAG framework for Research and Prototyping
A terminal-based Retrieval-Augmented Generation system built around three
independently swappable AI agents — **Embed**, **Memory**, and **Chat** —
each behind a Rust trait that allows hot-swapping backends at runtime.
**Designed for students.** The default build compiles with zero external
dependencies — no C++ toolchain, no `cmake`, no `protoc`. Install Rust,
install Ollama, run `cargo build --release`, and you're done. The binary
weighs ~15 MB and runs on any desktop OS.
- **Zero extra dependencies** — default build is pure Rust; Ollama provides
models at runtime
- **Trait-driven** — every pipeline stage is a `Box<dyn Trait>`; add new
backends (OpenAI, Anthropic, Groq, …) or document parsers without touching
existing code
- **Hardware-aware** — delegate heavy models to the cloud, run small models
locally, or go fully offline with CPU-only Fastembed (compiled into the binary) (`--features internal-embed`)
- **Hot-swappable** — switch chat, memory, or embedding engines mid-session
without losing document index or conversation context
- **Token-efficient cloud usage** — use a tiny local model for query rewriting
and only send the final prompt + context to an expensive cloud API
- **Hybrid retrieval** — BM25 full-text search fused with cosine vector
similarity via Reciprocal Rank Fusion. Hot-swappable ranking algorithms
(RRF, weighted linear, MMR diversity, LLM re-rank) let you experiment
with retrieval strategies without changing your document index
- **Pluggable ranking** — the `Ranker` trait decouples scoring from storage;
compare Cosine vs. BM25 vs. RRF fusion on the same document set at runtime
- **Cross-platform** — Linux, macOS, WSL, and Windows (MSVC / MinGW)
---
## What is RAG?
**Retrieval-Augmented Generation** lets an LLM answer questions about documents
it has never seen before. Instead of stuffing every document into the prompt
(which would overflow the context window), RAG works in two phases:
1. **Indexing** — Documents are split into overlapping *chunks*, each chunk is
converted to a numeric *embedding vector* (a list of floats that captures
its meaning), and both the text and its vector are saved in a *vector store*.
2. **Querying** — When you ask a question, the same embedding model converts
your query to a vector. The store finds the *k* most similar chunks (via
cosine similarity, BM25 keyword matching, or a hybrid of both). Those chunks
are injected into the LLM prompt alongside your question, so the model can
ground its answer in your documents.
Ragrig wraps this pipeline in three swappable agents — **Embed** (vectorise),
**Memory** (optionally rewrite the query for better retrieval), and **Chat**
(generate the final answer). The diagram below shows how data flows through
the system:
```mermaid
flowchart LR
U[User query] --> M[Memory]
M -->|rewritten query| E[Embed]
E -->|query vector| V[(Vector Store)]
V -->|top-k chunks| C[Chat]
U -->|original query| C
C -->|grounded answer| R[Response]
```
If you are new to these concepts, a more detailed introduction can be found at
[retrieval-augmented-generation](https://www.promptingguide.ai/techniques/rag)
on the Prompt Engineering Guide.
## Quick Start
### You need three things
1. **Rust** — [rustup.rs](https://rustup.rs)
2. **Ollama** — [ollama.com/download](https://ollama.com/download)
3. **Three models** (run these once):
```bash
ollama pull gemma2:latest # chat
ollama pull nomic-embed-text # embeddings
ollama pull qwen2.5:1.5b # memory / query-rewriting
```
### Install
```bash
cargo install ragrig
```
This downloads and compiles the latest release from
[crates.io](https://crates.io/crates/ragrig). The binary ends up in
`~/.cargo/bin/ragrig` — make sure that directory is on your `PATH`.
### Or build from source
```bash
git clone https://github.com/schmettow/ragrig
cd ragrig
cargo build --release
./target/release/ragrig --folder ~/Documents/papers
```
### Index and query
```bash
ragrig --folder ~/Documents/papers
```
First launch indexes all PDFs, EPUBs, DOCXs, and HTMLs in the folder.
Subsequent launches are instant — only changed files are re-indexed.
```
Query > What are the key findings about forced-choice paradigms?
```
> **Students:** if you only have Rust and Ollama installed, you already have
> everything you need. The default build adds nothing else.
### Model parameters
Special operations, like a context-aware pseudonymizer, require fine-tuning of
model parameters (`temperature`, `top_p`, etc.) to get deterministic, reproducible
output. Ragrig supports this at every level — CLI, REPL, and library API.
```bash
# From the command line:
ragrig --folder ~/Documents/papers --temperature 0.1 --seed 42
# Or hot-swap at runtime from the REPL:
Query > /chat temperature 0.1
Query > /chat seed 42
Query > /chat top_p 0.9
Query > /chat max_tokens 2048
```
In library code, pass a `GenerationParams` struct when building your agent:
```rust
use ragrig::{agents::ChatAgentSpec, GenerationParams};
use std::convert::TryFrom;
let agent = Box::<dyn ragrig::agents::Generator>::try_from(ChatAgentSpec::Ollama {
model: "qwen3.5:9b".into(),
params: GenerationParams {
temperature: Some(0.1), // near-deterministic
seed: Some(42), // reproducible runs
..Default::default()
},
})?;
```
You can also use `.try_into()?` instead of `.build()?` — both `ChatAgentSpec`
and `EmbedderSpec` implement `TryFrom` for their respective trait objects, so
they integrate with Rust's standard conversion ecosystem.
See [`examples/pseudonymizer`](examples/pseudonymizer/src/main.rs) for a
complete multi-turn pseudonymization loop that uses `temperature: 0.1` to
produce consistent, privacy-preserving transcript rewrites.
## Three-Agent Architecture
Every pipeline stage is a **trait object** — swap any agent at runtime
without losing your document index or conversation memory.
```
Documents (PDF/EPUB/DOCX/HTML)
│
▼
chunkedrs — token-accurate splitting with overlap
│
├── Embedder trait ──────────────────────────────────────────┐
│ OllamaEmbedder (local, nomic-embed-text) │
│ FastembedEmbedder (CPU-only, Nomic-Embed-Text-v1.5) │
│ NoopEmbedder (pure chat, no document search) │
│ │
▼ │
VectorStore trait ────────────────────────────────────────────────┤
BruteForceStore (pure Rust, MessagePack on disk) ← default │
LanceDbStore (Arrow columnar, hybrid BM25+vector) │
│ │
▼ │
Query
│
▼
Memory strategy (MemoryStrategy trait) ← hot-swap: /memory
RewriteMemory / TranscriptMemory
│
▼
Embed → VectorStore.search → Ranker → top-k chunks
│
▼
Chat agent (Generator trait) ← hot-swap: /chat
OllamaGenerator / DeepSeekGenerator
│
▼
Streamed response with retrieved context + conversation memory
```
### Hybrid Search Tuning
The vector store uses **Reciprocal Rank Fusion** (RRF, k=60) to combine
cosine vector similarity with BM25 full-text search. Two parameters
control retrieval quality:
| `top_k` | 50 | Maximum chunks injected into the prompt |
| `similarity_threshold` | 0.04 | Cosine pre‑filter — chunks with cosine < threshold are excluded from RRF fusion |
**Understanding the threshold**:
- The threshold operates on **cosine similarity** (range: 0.0–1.0).
- RRF fusion produces scores in the **0.0–0.03** range (rank‑based, not
similarity‑based). The trace output shows RRF scores, not cosine scores.
- A threshold of `0.0` passes everything; `0.04` filters out chunks with
negligible vector overlap while letting BM25 keyword matches through.
- Values above ~0.05 will aggressively prune — use when you have
high‑quality embeddings and want strictly semantic results.
Tune at runtime:
```
Query > /search # show current values
Query > /search topk 10 # fewer chunks, tighter context
Query > /search threshold 0.08 # stricter semantic filter
```
### Hot-Swap Examples
**Start with everything local, switch chat to cloud mid-session:**
```
Query > /chat deepseek deepseek-chat sk-...
Chat agent swapped: Ollama (gemma2:latest) → DeepSeek (deepseek-chat)
```
**Forgetful mode — ask Alice's name, then make her forget:**
```
Query > My name is Alice
Assistant > Nice to meet you, Alice!
Query > /memory off
Memory disabled (was: Ollama qwen2.5:1.5b)
Query > What's my name?
Assistant > I don't know — you haven't told me yet.
```
**Raw transcript — no query rewriting, test context-window pressure:**
```
Query > /memory transcript
Memory strategy: rewrite → transcript
Query > What is a vector database?
Assistant > A vector database stores embeddings ...
Query > Can you summarize that?
# "that" is NOT rewritten — the raw transcript in the prompt
# provides context. Good for testing how models handle growing
# context windows with full conversation memory appended.
```
**Session persistence — exit, restart, and recall past context:**
```
Query > What are random effects in meta-analysis?
Assistant > Random effects models assume that the true effect size
varies across studies, as opposed to a single fixed effect …
Query > /exit
# next day …
$ ragrig --folder ~/papers
Session: 1718400000
Query > /memory log
History diffusion: off → log
Query > What was I asking about yesterday?
# The chat prompt now includes the raw transcript of the previous
# session, so the model can pick up the thread without you
# repeating yourself.
Assistant > Yesterday you asked about random effects in
meta-analysis. We discussed how they differ from fixed-effect
models …
```
**Pure chat — no document search, no memory, cloud-only:**
```
Query > /embed none
Query > /memory off
Query > /chat deepseek deepseek-v4-pro
Query > Explain quantum entanglement in one paragraph.
```
**Switch embeddings to CPU-only (no network):**
```
Query > /embed fastembed
Embedder swapped: Ollama (nomic-embed-text) → Fastembed (Nomic-Embed-Text-v1.5)
```
**Experiment with ranking algorithms — same index, different retrieval:**
```
Query > /search rank Cosine
Ranker set to Cosine.
Query > /search rank BM25
Ranker set to BM25.
Query > /search rank Weighted alpha 0.7
Ranker set to Weighted.
Query > /search rank MMR lambda 0.7 inner Cosine
Ranker set to MMR.
Query > /search rank LLM inner Cosine model qwen2.5:0.5b
LLM reranker using Ollama (qwen2.5:0.5b)
Ranker set to LLM.
```
---
## Compilation Paths
### Default — Zero extra dependencies (recommended)
```bash
cargo build --release
```
Binary: ~15 MB. Nothing to install beyond Rust itself. Uses a pure-Rust
vector store (custom BM25 + cosine similarity + RRF fusion, persisted to
MessagePack). Embeddings come from Ollama over HTTP at runtime.
This is the path we ship to students. It compiles without a C++ toolchain,
`cmake`, or `protoc` — works on Windows, macOS, and Linux with zero platform
friction.
### Internal embeddings — Fastembed (CPU-only)
```bash
cargo build --release --features internal-embed
```
Binary: ~35 MB. Adds `FastembedEmbedder` — runs Nomic-Embed-Text-v1.5 on
the CPU. Zero network overhead for embeddings. Needs a C compiler (`gcc`
or `cl.exe`) at build time. Use `/embed fastembed` at runtime.
### LanceDB backend (large collections)
```bash
cargo build --release --no-default-features --features lancedb,ollama-embed
```
Binary: ~88 MB. Adds Arrow C++, protobuf, and compression codecs.
Requires `cmake` and `protoc` at build time. Faster hybrid search for
collections with 100k+ chunks.
### Feature flags
| `ollama-embed` | **on** | Local embeddings via Ollama HTTP (no extra deps) |
| `internal` | **on** | Pure-Rust vector store (MessagePack + cosine + BM25) |
| `internal-embed` | off | In-process Fastembed embeddings (needs C compiler) |
| `internal-generate` | off | In-process Candle LLM — zero network inference |
| `offline` | off | Meta: enables `internal` + `internal-embed` + `internal-generate` |
| `lancedb` | off | LanceDB hybrid index (needs protoc, Arrow C++) |
| `test-fixtures` | off | Compile-time embedded test documents for downstream crates |
### Binary size (release)
| Default (`ollama-embed`, `internal`) | ~15 MB | None — pure Rust |
| `+ internal-embed` | ~35 MB | ONNX Runtime (prebuilt binary) |
| `--features offline` | ~250 MB | Candle + ONNX Runtime — fully offline |
| `+ lancedb` | ~88 MB | Arrow C++, protobuf, compression |
The `offline` feature is a convenience meta-flag: `--features offline` compiles
ragrig into a fully self-contained binary with no network dependencies — every
component (chat, embeddings, vector store) runs locally in-process. Use
`offline-cuda`, `offline-metal`, or `offline-mkl` for GPU-accelerated variants.
---
## Requirements
| Rust 1.94+ | Build (always) |
| Ollama | Runtime — provides chat, embed, and memory models |
| C compiler (`gcc`/`cl.exe`) | Only with `--features internal-embed` |
| C++ toolchain, `protoc`, `cmake` | Only with `--features lancedb` |
**Default build: Rust + Ollama. Nothing else.**
---
## Platform Setup
### Linux / macOS / WSL
```bash
```
### Windows
1. Install Rust from [rustup.rs](https://rustup.rs) (MSVC host triple, the default)
2. Run `cargo build --release`
No extra tools needed. If you later want Fastembed (`--features internal-embed`),
install the [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
(select "C++ build tools" workload).
---
## Commands
| Any text | RAG query against your document pool |
| `/download <url>` | Download and ingest a document by URL |
| `/get 1,2,3-4,8` | Bulk-download papers from last search results |
| `/search <query>` | Search Semantic Scholar |
| `/arxiv <query>` | Search arXiv (no rate limits) |
| `/refs [topic]` | Extract references from last RAG results |
| `/chat <b> [model] [key] \| context <N>` | Hot-swap chat engine, set context window |
| `/embed <b> [model] \| purge \| index \| topk <N> \| threshold <F>` | Hot-swap embedding, clear store, re-index, tune search |
| `/memory <b> [model] [key] \| transcript \| off \| purge` | Hot-swap memory, raw-transcript mode, disable memory, or clear it |
| `/prompt chat\|rewrite <file> \| reset` | Load custom system prompts |
| `/parser pdf unpdf\|sink\|extract\|internal \| epub epub` | Hot-swap document parser per format |
| `/log [off\|error\|warn\|info\|debug\|trace]` | Show or change log verbosity at runtime |
| `/search rank <name> [key value]*` | Hot-swap chunk ranking algorithm |
| `/help` | Show available commands |
| `exit` / `quit` | End session |
### Runtime log levels
By default ragrig prints informational messages (`info` level) — agent
swaps, indexing progress, and errors. You can change the verbosity at any
time without restarting:
```text
/log # show current level
/log debug # enable diagnostic output from the ragrig crate only
/log trace # enable full pipeline observability (rewrite, chunks, tokens)
/log warn # back to quiet operation
```
At `trace` level the REPL logs every pipeline stage:
```text
[TRACE] Query: "What is RAG?"
[TRACE] Rewrite: "What is RAG?" → "retrieval augmented generation definition"
[TRACE] Retrieved 5 chunks (cosine threshold 0.040):
[TRACE] [0.0164] intro.pdf — "Retrieval-Augmented Generation (RAG) is a..."
[TRACE] [0.0141] survey.pdf — "RAG systems have become the standard..."
```
The initial level can also be set via the `RUST_LOG` environment variable:
```bash
RUST_LOG=debug ragrig --folder ~/papers
```
---
## CLI Flags
```
Usage: ragrig --folder <FOLDER>
Options:
-f, --folder <FOLDER> Document directory (PDFs, EPUBs, DOCXs, HTMLs)
--provider <PROVIDER> Chat backend: ollama (default) or deepseek
--deepseek-api-key <KEY> DeepSeek API key [env: DEEPSEEK_API_KEY]
--deepseek-model <MODEL> DeepSeek model [default: deepseek-v4-pro]
-m, --model <MODEL> Ollama chat model [default: gemma2:latest]
--embedding-provider <P> Embedding: ollama (default) or fastembed
-e, --embedding-model <MODEL> Ollama embedding model [default: nomic-embed-text]
--memory-model <MODEL> Memory/rewrite model [default: qwen2.5:1.5b]
--prompt-chat <FILE> Custom system prompt for chat agent
--prompt-rewrite <FILE> Custom system prompt for rewrite agent
--pdf-parser <BACKEND> PDF parser: unpdf (default), sink, extract, internal
-t, --threads <N> Worker threads [default: 4]
--embedding-concurrency <N> Concurrent embedding requests [default: 32]
--chunk-size <TOKENS> Max tokens per chunk [default: 1024]
--chunk-overlap <TOKENS> Overlap between chunks [default: 128]
--top-k <N> Chunks per query [default: 50]
--similarity-threshold <FL> Cosine similarity pre‑filter [default: 0.04]
--model-ctx-tokens <N> Context window budget for prompt truncation [default: 4096]
--semantic-scholar-api-key <K> API key [env: SEMANTIC_SCHOLAR_API_KEY]
```
---
## API Usage (Developers)
ragrig is a library. Build your own frontend — GUI, web server, headless
bot — on top of the same traits.
```rust
use ragrig::{
embed::{EmbedderSpec, OllamaEmbedder},
agents::{ChatAgentSpec, Generator},
parsers::{DocumentParsers, build_parsers},
store::open_store,
types::ChunkConfig,
vector::{collect_documents, search_similar},
};
use std::path::Path;
// Build agents and parser registry
let embedder = EmbedderSpec::Ollama { model: "nomic-embed-text".into() }.build()?;
let chat_agent = ChatAgentSpec::Ollama { model: "gemma2:latest".into(), params: Default::default() }
.build()?;
let parsers = DocumentParsers::new(build_parsers());
let folder = Path::new("./my_docs");
let chunk_cfg = ChunkConfig::default();
let store = open_store(folder).await?;
// Index documents
let _stats = collect_documents(&*embedder, &parsers, folder, &chunk_cfg, &*store).await?;
// Search
let results = search_similar(&*embedder, 5, 0.0, &*store, "quantum computing").await?;
// Chat
### Adding a new backend
Implement the `Generator`, `Embedder`, `VectorStore`, or `DocumentParser` trait:
```rust
struct OpenAiChat { model: String, api_key: String }
#[async_trait]
impl Generator for OpenAiChat {
async fn generate_stream(&self, prompt: &str, on_token: &(dyn Fn(String) + Sync)) -> Result<()> {
// POST to https://api.openai.com/v1/chat/completions, stream SSE chunks
}
fn backend_name(&self) -> &'static str { "OpenAI" }
fn model_name(&self) -> &str { &self.model }
}
```
Then wire it into `ChatAgentSpec::parse("openai", ...)` — no other code changes needed.
### Implementing a new document parser
Add support for a new PDF backend or file format (~30 lines). Example using
`justpdf` (pure-Rust PDF library):
```rust
use ragrig::parsers::DocumentParser;
use std::path::Path;
struct JustpdfParser;
impl DocumentParser for JustpdfParser {
fn extensions(&self) -> &[&str] { &["pdf"] }
fn parse(&self, path: &Path) -> anyhow::Result<String> {
let bytes = std::fs::read(path)?;
let doc = justpdf::Document::load(&bytes)?;
let mut md = String::new();
for page in doc.pages() {
md.push_str(&page.text());
md.push_str("\n\n");
}
Ok(md)
}
fn name(&self) -> &'static str { "justpdf" }
}
```
Then register it in `parsers::build_parsers()` (or hot-swap via `/parser pdf justpdf`
once you add the variant to `PdfParserBackend`). The chunker, embedder, and search
pipeline all work unchanged — they only see Markdown.
### Implementing a custom memory strategy
Memory backends implement the [`MemoryStrategy`] trait. The trait controls
only query rewriting — the session always replays the raw transcript whenever
*a strategy is active, regardless of whether rewriting happened.
Example: a strategy that rewrites using only the immediately preceding turn,
discarding older turns so the rewriter isn't distracted by stale context:
```rust
use async_trait::async_trait;
use ragrig::{agents::Generator, memory::MemoryStrategy};
struct LastTurnOnly {
agent: Box<dyn Generator>,
}
#[async_trait]
impl MemoryStrategy for LastTurnOnly {
async fn generate_rewrite(&self, prompt: &str) -> Option<String> {
// The prompt is "Conversation:\nUser: …\nAssistant: …\n\n"
// followed by the system rewrite prompt. Split at the
// double-newline, then grab only the last User/Assistant pair.
if let Some((memory_part, rest)) = prompt.split_once("\n\n") {
let lines: Vec<&str> = memory_part.lines().collect();
let mut tail = Vec::new();
for line in lines.iter().rev() {
if line.starts_with("User: ") || line.starts_with("Assistant: ") {
tail.push(*line);
if tail.len() >= 2 {
break;
}
}
}
tail.reverse();
let trimmed = format!("Conversation:\n{}\n\n{}", tail.join("\n"), rest);
self.agent.generate(&trimmed).await.ok()
} else {
None
}
}
fn name(&self) -> &'static str { "last-turn" }
}
```
The trait provides three methods:
| `generate_rewrite(prompt) -> Option<String>` | Return `Some(rewritten)` to replace the query before vector search, or `None` to use the raw query. |
| `clear()` | Wipe persistent state (default no-op). |
| `name()` | Label displayed in `/memory` output. |
Built-in strategies (`RewriteMemory`, `TranscriptMemory`) cover the common
cases; implement the trait directly when you need custom truncation, keyword
extraction, or external rewriter services.
### Implementing a custom history strategy
History backends implement the [`HistoryStrategy`] trait from
`ragrig::longterm_memory`. The trait controls how past sessions are
diffused into the current chat prompt.
Example: a strategy that loads only the most recent session, formats a
compact summary header, and skips the full transcript:
```rust
use async_trait::async_trait;
use ragrig::longterm_memory::{HistoryStrategy, SessionStore};
struct LatestSessionOnly;
#[async_trait]
impl HistoryStrategy for LatestSessionOnly {
async fn build_context(
&self,
store: &dyn SessionStore,
current_query: &str,
) -> anyhow::Result<String> {
let manifests = store.list().await?;
let Some(latest) = manifests.last() else {
return Ok(String::new());
};
let Some(session) = store.load(&latest.id).await? else {
return Ok(String::new());
};
// Extract just the questions the user asked last session.
let questions: Vec<&str> = session
.turns
.iter()
.filter(|t| matches!(t.role, ragrig::TurnRole::User))
.map(|t| t.text.as_str())
.collect();
Ok(format!(
"[Last session ({:?}): {} turn(s), topics: {}]\n",
session.created,
session.turns.len(),
questions.join("; "),
))
}
fn name(&self) -> &'static str {
"latest-session-only"
}
}
```
The trait provides two methods:
| `build_context(store, query) -> String` | Return a preamble injected into the system prompt. Return `""` to skip. |
| `name()` | Label displayed in `/memory` output. |
Built-in strategies (`LogHistory`, `SummaryHistory`) cover the common cases;
implement the trait directly when you need custom filtering, selection from
multiple sessions, or non-LLM recombination.
### Implementing a custom ranker
The [`Ranker`] trait lets you plug in any scoring algorithm. Implement
`rank()`, `name()`, and `clone_box()`:
```rust
use ragrig::{Ranker, ScoredChunk, store::StoredChunk};
#[derive(Debug, Clone)]
struct LengthRanker;
#[async_trait]
impl Ranker for LengthRanker {
async fn rank(
&self,
chunks: &[StoredChunk],
_query_vec: &[f32],
_query_text: &str,
top_k: usize,
_threshold: f64,
) -> Vec<ScoredChunk> {
// Trivial baseline: prefer shorter chunks (less context bloat).
let mut indexed: Vec<(usize, f64)> = chunks
.iter()
.enumerate()
.map(|(i, c)| (i, -(c.text.len() as f64)))
.collect();
indexed.sort_by(|a, b| a.1.total_cmp(&b.1));
indexed.truncate(top_k);
indexed
.into_iter()
.map(|(idx, _)| ScoredChunk {
score: 0.0.into(),
chunk: chunks[idx].text.clone().into(),
})
.collect()
}
fn name(&self) -> &'static str { "length" }
fn clone_box(&self) -> Box<dyn Ranker> { Box::new(self.clone()) }
}
```
The built-in rankers — `HybridRrfRanker`, `WeightedFusionRanker`,
`MmrDiversityRanker`, and `LlmReranker` — already cover the most common
retrieval strategies. The decorator pattern (`MmrDiversityRanker` and
`LlmReranker`) lets you wrap any inner ranker for diversity or LLM re-ranking.
### Test fixtures for downstream crates
Enable the `test-fixtures` feature to get compile-time embedded copies of
ragrig's own test documents — PDF, R Markdown, and HTML files suitable for
writing parser integration tests without shipping your own files.
```toml
# Cargo.toml
[dev-dependencies]
ragrig = { version = "0.5", features = ["test-fixtures"] }
```
```rust
use ragrig::fixtures;
#[test]
fn parse_all_pdf_fixtures() {
// fixtures::pdf::DIR is an include_dir::Dir with all files baked in.
for entry in fixtures::pdf::DIR.files() {
let name = entry.path().to_str().unwrap();
let tmp = std::env::temp_dir().join(name);
std::fs::write(&tmp, entry.contents()).unwrap();
let parsers = ragrig::DocumentParsers::new(ragrig::parsers::build_parsers());
let markdown = parsers.parse(&tmp).unwrap();
assert!(!markdown.is_empty(), "{} produced no text", name);
let _ = std::fs::remove_file(&tmp);
}
}
// Also available as named constants:
assert!(fixtures::rmd::GETTING_STARTED.len() > 1000);
assert!(fixtures::html::INDEX.len() > 100);
```
### Reactive UI integration (egui, ratatui, web, …)
Streaming generation to a GUI or TUI is a 4-call pattern. The same slim
API works identically in egui, ratatui, a web server (SSE), or any
reactive framework:
```rust
use ragrig::agents::{ChatAgentSpec, Generator};
use tokio::sync::mpsc;
// 1. Build the agent — one line
let agent = ChatAgentSpec::Ollama { model: "gemma2:latest".into() }.build()?;
// 2. Run generation on a background runtime, bridge to UI via channel
let (tx, mut rx) = mpsc::unbounded_channel::<String>();
let agent = std::sync::Arc::new(agent);
let agent_clone = agent.clone();
tokio::runtime::Runtime::new()?.spawn(async move {
let _ = agent_clone.generate_stream(&prompt, &|token| {
let _ = tx.send(token); // callback is sync, channel decouples
}).await;
});
// 3. Drain tokens in the UI loop (called every frame / event loop tick)
fn poll_stream(rx: &mut mpsc::UnboundedReceiver<String>, buffer: &mut String) -> bool {
loop {
match rx.try_recv() {
Ok(t) => buffer.push_str(&t), // more tokens coming
Err(Empty) => return false, // nothing right now
Err(Disconnected) => return true, // generation done
}
}
}
```
That's it — 4 ragrig calls: `build`, `spawn`, `generate_stream`, `try_recv`.
The remaining 95% of a chat UI is framework-specific layout and input
handling, not ragrig. See `examples/streaming_chat_egui/` and
`examples/streaming_chat_ratatui/` for complete runnable demos.
### Typed errors
ragrig defines typed error variants in [`RagrigError`] that carry
structured payloads so callers can recover programmatically:
| `ContextSizeExceeded` | `current`, `max` (tokens) | Reduce `top_k` or expand context window |
| `EmbedModelNotFound` | `model: String` | Run `ollama pull {model}` and retry |
| `StoreCorrupt` | `path: String` | Delete the store file and re-index |
| `NoDocumentsFound` | `folder: String` | Add PDF, EPUB, or HTML files to the folder |
| `OllamaUnreachable` | `context: String` | Start Ollama with `ollama serve` — see [troubleshooting](#ollama-is-unreachable--what-should-i-check) for common causes |
| `GenerationFailed` | `backend`, `model`, `detail` | Check model is pulled and fits in VRAM |
Every variant provides a [`suggested_action()`] method with a
human-readable recovery hint. Use [`RagrigError::log_or()`] to
log the error and its action in one call.
Downcast from `anyhow::Error` and switch on the variant:
```rust
use ragrig::RagrigError;
let result = agent.generate_with_context("query", &[]).await;
match result {
Err(e) => {
if let Some(ce) = e.downcast_ref::<RagrigError>() {
match ce {
RagrigError::ContextSizeExceeded { current, max } => {
eprintln!("Prompt ({current} tk) exceeds model limit ({max} tk). Truncating.");
}
RagrigError::EmbedModelNotFound { model } => {
eprintln!("Run: ollama pull {model}");
}
RagrigError::StoreCorrupt { path } => {
std::fs::remove_file(path).ok();
eprintln!("Removed corrupt store. Re-index on next run.");
}
RagrigError::NoDocumentsFound { folder } => {
eprintln!("No supported files in {folder}. Add PDFs, EPUBs, or HTML.");
}
}
} else {
eprintln!("Unexpected: {e}");
}
}
Ok(answer) => println!("{answer}"),
}
```
### Runnable examples
Clone the repo and run any example with `cargo run` in its directory
(an Ollama server must be running):
```sh
git clone https://github.com/schmettow/ragrig.git
cd ragrig
# Single-shot RAG query — index fixtures, search, generate
cargo run --manifest-path examples/rag_query/Cargo.toml -- "What is RAG?"
# Two-agent dialog with shared vector store and transcript
cargo run --manifest-path examples/dialog/Cargo.toml -- "What is a p-value?"
# Streaming chat GUI with markdown bubbles (egui)
cargo run --manifest-path examples/streaming_chat_egui/Cargo.toml
# Streaming chat TUI with two-color bubbles (ratatui)
cargo run --manifest-path examples/streaming_chat_ratatui/Cargo.toml
# Streaming chat GUI with chat bubbles, provider/model picker, and RAG folder (Iced)
cargo run --manifest-path examples/streaming_chat_iced/Cargo.toml
# Binary with embedded vector store — indexed at build time
cargo run --manifest-path examples/embedded_togo/Cargo.toml -- "What is RAG?"
```
| `rag_query` | Single-shot pipeline: index → embed → search → generate via `RagAgent` |
| `dialog` | Multi-agent orchestration: two `RagAgent` instances sharing one vector store and one transcript |
| `streaming_chat_egui` | Reactive GUI: `generate_stream` + channel bridge → egui markdown bubbles |
| `streaming_chat_ratatui` | Reactive TUI: same channel pattern → ratatui two-color bubbles with scroll |
| `streaming_chat_iced` | Reactive GUI: Iced native GUI with provider/model picker, RAG folder picker, and streaming chat bubbles |
| `embedded_togo` | Embedded store: `build.rs` indexes fixtures at compile time, `include_bytes!` bakes it into the binary |
### Transcripts
The `TurnPairs` newtype converts a session's `Vec<Turn>` into a slice of
`(&str, &str)` pairs suitable for `RagAgent::generate_with_context()`:
```rust
use ragrig::{Turn, TurnRole, TurnPairs};
let turns = vec![
Turn { role: TurnRole::User, text: "Hello".into(), perf: None },
Turn { role: TurnRole::Assistant, text: "Hi!".into(), perf: None },
];
let pairs = TurnPairs::from(&turns[..]);
agent.generate_with_context("What is RAG?", &pairs.0).await?;
```
---
## Q & A
### What is unique about ragrig and why should I use it?
Ragrig tries to be a flexible and zero-friction prototyping tool for researchers and students, not an enterprise-grade framework with all bells and whistles. Here are the points that distinguish Ragrig from other crates:
Zero native dependencies in default build.** Every other crate needs at minimum a C compiler (for tokenizers, ONNX runtime, tree-sitter, etc.) or an API key. Ragrig builds with `cargo build --release` and nothing else. This is a **genuinely unique** selling point for students, workshops, and quick-start scenarios.
2. **Runtime hot-swapping via trait objects.** Every other crate uses compile-time feature flags to select backends. Ragrig lets you switch chat/embed/memory engines *mid-session* without losing state. `langchainrust` has multiple providers but you pick them at `Cargo.toml` time. ragrig's `/chat deepseek`, `/embed fastembed`, `/memory off` commands have no equivalent in any competitor.
3. **Panic-safe multi-parser PDF pipeline.** Three PDF parsers (pdfsink for layout-aware, pdf-extract for flat text, sloppy binary scavenger as fallback) with `catch_unwind` wrapping. No other crate does this — they pick one parser and crash on malformed PDFs.
4. **Token-efficient cloud usage pattern.** Use a tiny local model for query rewriting, only send the final prompt + context to the cloud. This is described in the README hot-swap examples and baked into the MemoryStrategy trait. No competitor has this pattern explicitly designed in.
5. **Student-focused UX.** The README's quick-start is 3 commands (`rustup`, `ollama pull ×3`, `cargo build --release`). The REPL has 15+ slash commands with clear transition messages. Session persistence works out of the box.
### When should I not use it?
Ragrig is designed as an accessible framework to build multi-agent interactive prototypes. It is not intended for production use or highly scalable deployments. For these purposes, you should use a dedicated RAG framework like [rig-core](https://crates.io/crates/rig-core) on which Ragrig is heavily based.
### I am a Python programmer. I am not able to program in Rust. How can I use Ragrig?
Ragrig provides a fully documented API with numerous examples and a dedicated agent skill (only available on Github). With this information, a good coding agent can produce working Ragrig applications with not more than a few instructions.
For version 2.0, we plan to provide Python (and possibly R) bindings.
### Ollama is unreachable — what should I check?
If ragrig reports `OllamaUnreachable`, work through these in order:
1. **Ollama isn't running.** Start it in a terminal:
```bash
ollama serve
```
On macOS and Windows, launching the Ollama desktop app also starts the server.
2. **Ollama is running on a non-default port.** By default ragrig connects to
`localhost:11434`. If you changed the port (e.g. via `OLLAMA_HOST`), set the
same variable in the terminal where ragrig runs:
```bash
export OLLAMA_HOST=127.0.0.1:11435
cargo run -- --folder ./my_docs
```
3. **A model pull was interrupted.** Partial downloads can leave the Ollama
registry in a broken state. Re-pull the model:
```bash
ollama pull nomic-embed-text
ollama pull gemma2:latest
```
If that fails, remove the partial model and pull fresh:
```bash
ollama rm nomic-embed-text && ollama pull nomic-embed-text
```
4. **Firewall or port conflict.** Ensure port 11434 is not blocked by a
firewall or already bound by another process:
```bash
lsof -i :11434
netstat -ano | findstr :11434
```
5. **WSL → Windows networking.** When Ollama runs on Windows and ragrig runs
inside WSL, `localhost` does not automatically forward. Find the Windows
host IP from inside WSL and set `OLLAMA_HOST`:
```bash
export OLLAMA_HOST=$(cat /etc/resolv.conf | grep nameserver | awk '{print $2}'):11434
```
Alternatively, install Ollama directly inside WSL so both processes share
the same network namespace.
### When the context size exceeds the model's maximum, how can I adjust this?
Context-size errors happen for two reasons:
1. **Hardware VRAM limits** — Ollama caps the context window at 4096 tokens
on GPUs with less than 24 GB VRAM to prevent out-of-memory crashes.
2. **Architectural limits** — some distilled reasoning models (e.g. DeepSeek
R1 8B/14B) have a hard-coded 4096-token maximum that even Ollama cannot
override.
Ragrig detects context overflows automatically. By default, when the model
reports a [`RagrigError::ContextSizeExceeded`], the binary auto-adjusts its
budget to the model's actual maximum, rebuilds the prompt with fewer chunks,
and retries once. You see:
```
[INFO] Context overflow — shrinking budget to 9216 chars, retrying.
```
If the retry also fails, pass `--context-size-forced` to keep the original
error path, then set a manual budget:
```bash
./target/release/ragrig --folder ~/papers --model-ctx-tokens 4096
# or mid-session:
Query > /chat context 4096
```
Library consumers can catch the typed error directly:
```rust
match chat_agent.generate(prompt).await {
Err(e) => {
if let Some(ce) = e.downcast_ref::<ragrig::RagrigError>() {
// ce.current_size(), ce.max_size() — use these to trim
// your embedding results before retrying.
}
}
Ok(response) => { … }
}
```
---
## License
MIT License — see [LICENSE](LICENSE).