Initial commit: Momentry Core v0.1

- Rust-based digital asset management system
- Video analysis: ASR, OCR, YOLO, Face, Pose
- RAG capabilities with Qdrant vector database
- Multi-database support: PostgreSQL, Redis, MongoDB
- Monitoring system with launchd plists
- n8n workflow automation integration

src/core/embedding/comic_embed.rs

@@ -0,0 +1,66 @@
+use anyhow::Result;
+
+pub struct Embedder {
+    model_path: String,
+}
+
+impl Embedder {
+    pub fn new(model_path: String) -> Self {
+        Self { model_path }
+    }
+
+    pub async fn embed_text(&self, text: &str) -> Result<Vec<f32>> {
+        // TODO: Implement comic-embed-text model loading and inference
+        // This is a placeholder that generates a random 768-dimensional vector
+        //
+        // Implementation would use:
+        // - candle (Rust ML framework) or
+        // - ort (ONNX Runtime) to run the model
+        //
+        // Example with ort:
+        // let session = Session::builder()?
+        //     .with_execution_providers([CPUExecutionProvider::default().build()])?
+        //     .with_model_from_file(&self.model_path)?;
+        //
+        // // Preprocess text to tensor
+        // let input = preprocess_text(text);
+        //
+        // // Run inference
+        // let output = session.run(vec![input])?;
+        //
+        // // Extract embeddings
+        // let embedding = output[0].view()[..768].to_vec();
+
+        let dim = 768;
+        let mut embedding = vec![0.0f32; dim];
+
+        // Simple hash-based embedding for now
+        let hash = self.hash_text(text);
+        for i in 0..dim {
+            embedding[i] = ((hash >> i) & 1) as f32;
+        }
+
+        // Normalize
+        let norm: f32 = embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
+        if norm > 0.0 {
+            for v in &mut embedding {
+                *v /= norm;
+            }
+        }
+
+        Ok(embedding)
+    }
+
+    pub async fn embed_chunk_content(&self, chunk: &crate::core::chunk::Chunk) -> Result<Vec<f32>> {
+        let text = serde_json::to_string(&chunk.content)?;
+        self.embed_text(&text).await
+    }
+
+    fn hash_text(&self, text: &str) -> u64 {
+        use std::collections::hash_map::DefaultHasher;
+        use std::hash::{Hash, Hasher};
+        let mut hasher = DefaultHasher::new();
+        text.hash(&mut hasher);
+        hasher.finish()
+    }
+}

src/core/embedding/mod.rs

@@ -0,0 +1,3 @@
+pub mod comic_embed;
+
+pub use comic_embed::Embedder;