# Ragrig — RAG framework for Research and Prototyping
A **trait-driven Retrieval-Augmented Generation library** 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 and researchers.** The default build compiles with
zero external dependencies — no C++ toolchain, no `cmake`, no `protoc`.
Install Rust, install Ollama, and you're done.
> **🔧 ragrig is the library crate.** The terminal REPL binary lives in
> [**ragrig‑cli**](https://github.com/schmettow/ragrig-cli) — install with
> `cargo install ragrig-cli` (on crates.io soon) or clone from GitHub.
> Build your own application on top of the same traits the REPL uses.
- **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)
- **Hot-swappable** — switch chat, memory, or embedding engines at runtime
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 TD
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 (Library)
Add ragrig to your `Cargo.toml`:
```toml
[dependencies]
ragrig = "0.9"
```
### You need two things
1. **Rust** — [rustup.rs](https://rustup.rs)
2. **Ollama** — [ollama.com/download](https://ollama.com/download) (provides models at runtime)
Pull the models you need:
```bash
ollama pull gemma2:latest # chat
ollama pull nomic-embed-text # embeddings
```
### Index and query in 4 function calls
```rust
use ragrig::{
embed::EmbedderSpec,
agents::ChatAgentSpec,
parsers::{DocumentParsers, build_parsers},
store::open_store,
types::ChunkConfig,
vector::{collect_documents, search_similar},
};
use std::path::Path;
let embedder = EmbedderSpec::Ollama { model: "nomic-embed-text".into() }.build()?;
let chat = ChatAgentSpec::Ollama { model: "gemma2:latest".into(), params: Default::default() }.build()?;
let parsers = DocumentParsers::new(build_parsers());
let store = open_store(Path::new("./my_docs")).await?;
// Index
collect_documents(&*embedder, &parsers, Path::new("./my_docs"), &ChunkConfig::default(), &*store).await?;
// Search
let results = search_similar(&*embedder, 5, 0.0, &*store, "quantum computing").await?;
// Generate
chat.generate("Summarise the following:\n\n...").await?;
```
### Model parameters
Control generation with `GenerationParams`:
```rust
use ragrig::{agents::ChatAgentSpec, GenerationParams};
let agent = ChatAgentSpec::Ollama {
model: "qwen3.5:9b".into(),
params: GenerationParams {
temperature: Some(0.1), // near-deterministic
seed: Some(42), // reproducible runs
..Default::default()
},
}.build()?;
```
See [`examples/pseudonymizer`](examples/pseudonymizer/src/main.rs) for a
complete multi-turn pseudonymization loop.
## 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
│
▼
Attach strategy (AttachStrategy trait) ← builder: .attach_strategy()
PrependAttach / AppendAttach / ReplaceAttach / NoopAttach
│
▼
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
```
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
```
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
```
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 |
| `kreuzberg` | off | Kreuzberg PDF parser (OCR, layout, DOCX) |
The `offline` feature is a convenience meta-flag that enables every local
component: chat, embeddings, and vector store all run in-process with zero
network dependencies. 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 `internal-embed` feature |
| C++ toolchain, `protoc`, `cmake` | Only with `lancedb` feature |
**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).
---
> For the REPL commands, CLI flags, and interactive usage, see the
> [`ragrig-bin` README](https://github.com/schmettow/ragrig-bin).
---
## 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
### Configuration profiles
`RagrigConfig` is the library's single format-agnostic configuration surface.
It composes four sub-configs (`ChatConfig`, `EmbedConfig`, `ParseConfig`,
`MemoryConfig`) and derives `Serialize` + `Deserialize` — so the same struct
works for CLI arguments, JSON, TOML, or programmatic construction.
**Three entry points:**
```rust
use ragrig::types::RagrigConfig;
// 1. Programmatic — builder-style via Default + struct update
let config = RagrigConfig {
folder: "./my_docs".into(),
chat: ChatConfig { model: "gemma4:e4b".into(), ..Default::default() },
..Default::default()
};
// 2. From a JSON file (zero extra dependencies — serde_json is already included)
let json = std::fs::read_to_string("profile.json")?;
let config: RagrigConfig = serde_json::from_str(&json)?;
// 3. From a TOML file (add `toml = "0.8"` to Cargo.toml)
let toml_str = std::fs::read_to_string("profile.toml")?;
let config: RagrigConfig = toml::from_str(&toml_str)?;
```
**Example TOML profile** — save this as `profile.toml` and load it at startup
via a small wrapper binary, or deserialise it programmatically:
```toml
folder = "./research-papers"
semantic_scholar_api_key = "s2k-xxxxxxxxxxxx"
[chat]
provider = "Ollama"
model = "gemma2:latest"
context_tokens = 8192
context_size_mode = "Auto"
[chat.params]
temperature = 0.1
max_tokens = 2048
[embed]
provider = "Ollama"
model = "nomic-embed-text"
top_k = 20
similarity_threshold = 0.04
[parse]
pdf_parser = "Extract"
chunk_size = 512
chunk_overlap = 64
[memory]
model = "qwen2.5:1.5b"
```
**Merging CLI overrides on top of a profile** — use `override_with()` to
apply only the fields the user explicitly changed on the command line:
```rust
// Load the base profile
let mut config: RagrigConfig = serde_json::from_str(&fs::read_to_string("profile.json")?)?;
// Parse CLI args and convert to a RagrigConfig (binary-side code)
let cli_config = RagrigConfig::from(Cli::parse());
// Merge: CLI values override profile values where they differ from defaults
config.override_with(&cli_config);
```
### 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()` and `name()` — `dyn_clone::DynClone` (required supertrait) provides
`Clone` for free:
```rust
use ragrig::{Ranker, ScoredChunk, store::StoredChunk};
#[derive(Debug, Clone)]
struct LengthRanker;
// Tell dyn_clone how to clone your ranker.
dyn_clone::clone_trait_object!(Ranker);
#[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: DocumentChunk {
text: chunks[idx].text.clone(),
source_file: chunks[idx].source_file.clone(),
meta: Default::default(),
},
})
.collect()
}
fn name(&self) -> &'static str { "length" }
}
```
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 |
### Document attachments
Attach external documents to a query without indexing them. The attachment
is parsed, chunked, and injected into the prompt alongside vector store
results via an [`AttachStrategy`](crate::attach::AttachStrategy).
Four built-in strategies control how attachments are merged:
| [`NoopAttach`](crate::attach::NoopAttach) | Ignore attachments (default when `None`) |
| [`PrependAttach`](crate::attach::PrependAttach) | Attachments first, then `---` separator, then retrieved chunks |
| [`AppendAttach`](crate::attach::AppendAttach) | Retrieved chunks first, then `---` separator, then attachments |
| [`ReplaceAttach`](crate::attach::ReplaceAttach) | Attachments only — discards retrieved chunks |
Set a strategy at build time or hot-swap at runtime:
```rust
use ragrig::{RagAgent, AttachedDocument};
use ragrig::attach::PrependAttach;
// Build-time
let agent = RagAgent::builder()
// … chat, embed, store …
.attach_strategy(Box::new(PrependAttach))
.build()?;
// Runtime hot-swap
agent.set_attach_strategy(Some(Box::new(PrependAttach)));
// Query with an attachment
let docs = vec![AttachedDocument {
name: "paper.pdf".into(),
content: "Full parsed text of the paper…".into(),
}];
let response = agent.generate_with_attachment(
"Compare this paper to the existing literature.",
&[],
&docs,
).await?;
```
Attachments are **one-shot** — they are not added to conversation memory
and evaporate after the call. The [`AttachStrategy`] trait has no access
to the query, transcript, or rewriter, so attachment content cannot leak
into query rewriting and cause context overflow.
### Similarity search by document
Use a document file as the search query to find similar papers in the
vector store:
```rust
use ragrig::vector::search_by_document;
use ragrig::parsers::DocumentParsers;
use ragrig::ChunkConfig;
let results = search_by_document(
embedder,
store,
&parsers,
Path::new("query-paper.pdf"),
&ChunkConfig::default(),
50, // top_k
0.04, // similarity_threshold
).await?;
```
The function parses the file, chunks it, embeds each chunk, searches the
store with every chunk, and fuses the results by keeping the maximum score
per unique (source, text) pair.
### 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.
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) => { … }
}
```
### Why does `Generator::generate_stream` take `&self` when my backend needs `&mut self`?
The trait is designed for stateless LLM backends (Ollama, DeepSeek) where
a prompt-in/response-out call has no observable side-effects on the client.
If your backend tracks session state — rule-usage counters, connection
pools, an internal memory queue — you need interior mutability.
**Solution:** wrap the mutable state in `Arc<Mutex<T>>`:
```rust
use std::sync::{Arc, Mutex};
struct MyStatefulBackend {
inner: Arc<Mutex<ThirdPartyEngine>>,
}
#[async_trait]
impl Generator for MyStatefulBackend {
async fn generate_stream(
&self,
prompt: &str,
on_token: &(dyn Fn(String) + Sync),
) -> Result<()> {
let response = self.inner.lock().unwrap().respond(prompt);
on_token(response);
Ok(())
}
// ...
}
```
`Arc` also gives you a trivial `clone_box` implementation — just
`Arc::clone` the inner handle. See
[`examples/eliza_generator`](examples/eliza_generator/src/main.rs) for a
complete worked example wrapping the stateful `eliza` crate.
### Why do I have to implement `Debug` on my Generator?
The `Generator` trait inherits `Debug` from its supertrait bound
(`: Send + Sync + Debug`). This is so the `RagAgent` can print a
human-readable representation of the active backend in diagnostic
output and log messages.
**The problem:** many useful types (`Regex`, raw file handles, opaque
third-party structs) don't implement `Debug`. Deriving fails, and
you're left writing a manual impl.
**Solution:** implement `Debug` by hand — it's two lines:
```rust
impl fmt::Debug for MyBackend {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("MyBackend")
.field("model", &self.model)
.finish()
}
}
```
For fully opaque types, a placeholder string is fine:
```rust
.field("engine", &"<opaque>")
```
The point is that `Debug` is informational, not load-bearing — nothing
in the framework parses the output. If your backend fields are all
plain data (`String`, `usize`, `bool`), the derive works automatically
and you won't see this issue at all.
### Why do I need `async_trait` even for a synchronous backend?
The `Generator` trait is declared with `#[async_trait]` so that every
backend — network, local, or pure computation — presents the same
async interface. This lets `RagAgent` call `generate_stream().await`
uniformly without knowing whether the backend makes HTTP requests or
runs a local regex loop.
**The cost:** one extra dependency (`async-trait = "0.1"`) and one
extra annotation (`#[async_trait]` on the impl block). The method
body itself can be completely synchronous — async doesn't force you
to spawn tasks or touch the network:
```rust
#[async_trait]
impl Generator for MySyncBackend {
async fn generate_stream(
&self,
prompt: &str,
on_token: &(dyn Fn(String) + Sync),
) -> Result<()> {
// Purely synchronous computation — no .await anywhere.
let response = compute_response(prompt);
on_token(response);
Ok(())
}
}
```
The `async` keyword on the method signature is a promise to the caller
(the framework), not a requirement that your code be asynchronous.
---
## License
MIT License — see [LICENSE](LICENSE).