little-librarian/src/query.rs

69 lines
2 KiB
Rust

//! Query processing and document retrieval.
use snafu::{ResultExt, Snafu};
use tokio::task::JoinError;
use crate::{
storage::{
self,
queries::{DocumentMatch, Queries},
},
text_encoder::{self, TextEncoder},
tokenize::{self, Tokenizer},
};
/// Errors that occur during query processing.
#[derive(Debug, Snafu)]
pub enum AskError {
#[snafu(display("Failed to encode query."))]
Encode { source: tokenize::EncodeError },
#[snafu(display("Failed to embed query."))]
Embed { source: text_encoder::EmbedError },
#[snafu(display("Embedding task failed to execute."))]
EmbedJoin { source: JoinError },
#[snafu(display("Failed to retrieve similar documents."))]
Query {
source: storage::queries::QueryError,
},
#[snafu(display("Failed to rerank documents."))]
Rerank { source: text_encoder::RerankError },
#[snafu(display("Reranking task failed to execute."))]
RerankJoin { source: JoinError },
}
/// Process a user query and return ranked document matches.
pub async fn ask(
query: &str,
db: &Queries,
tokenizer: &Tokenizer,
embedder: &TextEncoder,
reranker: &TextEncoder,
chunk_size: usize,
limit: usize,
) -> Result<Vec<DocumentMatch>, AskError> {
let encodings = tokenizer.encode(query, chunk_size).context(EncodeSnafu)?;
let embedder = embedder.clone();
let encoding = encodings[0].clone();
let embeddings = tokio::task::spawn_blocking(move || embedder.embed(encoding))
.await
.context(EmbedJoinSnafu)?
.context(EmbedSnafu)?;
let documents = db
.query(embeddings, (limit * 10) as i32)
.await
.context(QuerySnafu)?;
let reranker = reranker.clone();
let tokenizer = tokenizer.clone();
let query = query.to_string();
let reranked_docs = tokio::task::spawn_blocking(move || {
reranker.rerank(&query, documents, &tokenizer, limit)
})
.await
.context(RerankJoinSnafu)?
.context(RerankSnafu)?;
Ok(reranked_docs)
}