Files
minne/ingestion-pipeline/src/pipeline.rs
2025-09-28 22:08:08 +02:00

239 lines
7.5 KiB
Rust

use std::{sync::Arc, time::Instant};
use chrono::Utc;
use text_splitter::TextSplitter;
use tokio::time::{sleep, Duration};
use tracing::{info, warn};
use common::{
error::AppError,
storage::{
db::SurrealDbClient,
types::{
ingestion_task::{IngestionTask, IngestionTaskStatus, MAX_ATTEMPTS},
knowledge_entity::KnowledgeEntity,
knowledge_relationship::KnowledgeRelationship,
text_chunk::TextChunk,
text_content::TextContent,
},
},
utils::{config::AppConfig, embedding::generate_embedding},
};
use crate::{
enricher::IngestionEnricher,
types::{llm_enrichment_result::LLMEnrichmentResult, to_text_content},
};
pub struct IngestionPipeline {
db: Arc<SurrealDbClient>,
openai_client: Arc<async_openai::Client<async_openai::config::OpenAIConfig>>,
config: AppConfig,
}
impl IngestionPipeline {
pub async fn new(
db: Arc<SurrealDbClient>,
openai_client: Arc<async_openai::Client<async_openai::config::OpenAIConfig>>,
config: AppConfig,
) -> Result<Self, AppError> {
Ok(Self {
db,
openai_client,
config,
})
}
pub async fn process_task(&self, task: IngestionTask) -> Result<(), AppError> {
let current_attempts = match task.status {
IngestionTaskStatus::InProgress { attempts, .. } => attempts + 1,
_ => 1,
};
// Update status to InProgress with attempt count
IngestionTask::update_status(
&task.id,
IngestionTaskStatus::InProgress {
attempts: current_attempts,
last_attempt: Utc::now(),
},
&self.db,
)
.await?;
let text_content =
to_text_content(task.content, &self.db, &self.config, &self.openai_client).await?;
match self.process(&text_content).await {
Ok(_) => {
IngestionTask::update_status(&task.id, IngestionTaskStatus::Completed, &self.db)
.await?;
Ok(())
}
Err(e) => {
if current_attempts >= MAX_ATTEMPTS {
IngestionTask::update_status(
&task.id,
IngestionTaskStatus::Error {
message: format!("Max attempts reached: {}", e),
},
&self.db,
)
.await?;
}
Err(AppError::Processing(e.to_string()))
}
}
}
pub async fn process(&self, content: &TextContent) -> Result<(), AppError> {
let now = Instant::now();
// Perform analyis, this step also includes retrieval
let analysis = self.perform_semantic_analysis(content).await?;
let end = now.elapsed();
info!(
"{:?} time elapsed during creation of entities and relationships",
end
);
// Convert analysis to application objects
let (entities, relationships) = analysis
.to_database_entities(&content.id, &content.user_id, &self.openai_client, &self.db)
.await?;
// Store everything
tokio::try_join!(
self.store_graph_entities(entities, relationships),
self.store_vector_chunks(content),
)?;
// Store original content
self.db.store_item(content.to_owned()).await?;
self.db.rebuild_indexes().await?;
Ok(())
}
async fn perform_semantic_analysis(
&self,
content: &TextContent,
) -> Result<LLMEnrichmentResult, AppError> {
let analyser = IngestionEnricher::new(self.db.clone(), self.openai_client.clone());
analyser
.analyze_content(
&content.category,
content.context.as_deref(),
&content.text,
&content.user_id,
)
.await
}
async fn store_graph_entities(
&self,
entities: Vec<KnowledgeEntity>,
relationships: Vec<KnowledgeRelationship>,
) -> Result<(), AppError> {
let entities = Arc::new(entities);
let relationships = Arc::new(relationships);
let entity_count = entities.len();
let relationship_count = relationships.len();
const STORE_GRAPH_MUTATION: &str = r#"
BEGIN TRANSACTION;
LET $entities = $entities;
LET $relationships = $relationships;
FOR $entity IN $entities {
CREATE type::thing('knowledge_entity', $entity.id) CONTENT $entity;
};
FOR $relationship IN $relationships {
LET $in_node = type::thing('knowledge_entity', $relationship.in);
LET $out_node = type::thing('knowledge_entity', $relationship.out);
RELATE $in_node->relates_to->$out_node CONTENT {
id: type::thing('relates_to', $relationship.id),
metadata: $relationship.metadata
};
};
COMMIT TRANSACTION;
"#;
const MAX_ATTEMPTS: usize = 3;
const INITIAL_BACKOFF_MS: u64 = 50;
const MAX_BACKOFF_MS: u64 = 800;
let mut backoff_ms = INITIAL_BACKOFF_MS;
let mut success = false;
for attempt in 0..MAX_ATTEMPTS {
let result = self
.db
.client
.query(STORE_GRAPH_MUTATION)
.bind(("entities", entities.clone()))
.bind(("relationships", relationships.clone()))
.await;
match result {
Ok(_) => {
success = true;
break;
}
Err(err) => {
if Self::is_retryable_conflict(&err) && attempt + 1 < MAX_ATTEMPTS {
warn!(
attempt = attempt + 1,
"Transient SurrealDB conflict while storing graph data; retrying"
);
sleep(Duration::from_millis(backoff_ms)).await;
backoff_ms = (backoff_ms * 2).min(MAX_BACKOFF_MS);
continue;
}
return Err(AppError::from(err));
}
}
}
if !success {
return Err(AppError::InternalError(
"Failed to store graph entities after retries".to_string(),
));
}
info!(
"Stored {} entities and {} relationships",
entity_count, relationship_count
);
Ok(())
}
async fn store_vector_chunks(&self, content: &TextContent) -> Result<(), AppError> {
let splitter = TextSplitter::new(500..2000);
let chunks = splitter.chunks(&content.text);
// Could potentially process chunks in parallel with a bounded concurrent limit
for chunk in chunks {
let embedding = generate_embedding(&self.openai_client, chunk, &self.db).await?;
let text_chunk = TextChunk::new(
content.id.to_string(),
chunk.to_string(),
embedding,
content.user_id.to_string(),
);
self.db.store_item(text_chunk).await?;
}
Ok(())
}
fn is_retryable_conflict(error: &surrealdb::Error) -> bool {
error
.to_string()
.contains("Failed to commit transaction due to a read or write conflict")
}
}