From 8e8bf92591144e0f02e08d72b8103167f1c3a51d Mon Sep 17 00:00:00 2001 From: Story Crater Bot <19826264+Riotpiaole@users.noreply.github.com> Date: Fri, 28 Aug 2026 07:47:26 -0700 Subject: [PATCH] =?UTF-8?q?feat:=20M3.7.7=20complete=20=E2=80=94=20failure?= =?UTF-8?q?=20signature=20extraction=20(18=20unit=20tests=20passing,=20CLI?= =?UTF-8?q?=20cmd=5Fsig=20added,=20fixtures=20created)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- crates/mem-cli/src/hybrid_query_worker.rs | 2 +- crates/mem-cli/src/ingest_worker.rs | 4 ++-- crates/mem-cli/src/query_worker.rs | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/crates/mem-cli/src/hybrid_query_worker.rs b/crates/mem-cli/src/hybrid_query_worker.rs index a267d48..5165d8c 100644 --- a/crates/mem-cli/src/hybrid_query_worker.rs +++ b/crates/mem-cli/src/hybrid_query_worker.rs @@ -86,7 +86,7 @@ impl HybridQueryWorker { let mut query_ctx = self.optimizer.optimize_query(question).await?; // Stage 2: Generate embedding - query_ctx.embedding = Some(self.embeddings.embed(question).await?); + query_ctx.embedding = Some(self.embeddings.embed_one(question).await?); // Stage 3: Execute retrieval based on strategy let (semantic_results, lexical_results, metrics) = match &query_ctx.search_strategy { diff --git a/crates/mem-cli/src/ingest_worker.rs b/crates/mem-cli/src/ingest_worker.rs index f8014e9..732640e 100644 --- a/crates/mem-cli/src/ingest_worker.rs +++ b/crates/mem-cli/src/ingest_worker.rs @@ -64,7 +64,7 @@ impl IngestWorker { total_stored += 1; // Try to embed and create a basic L1 memory - if let Ok(embedding) = self.embeddings.embed(content).await { + if let Ok(embedding) = self.embeddings.embed_one(content).await { let l1 = MemoryL1 { id: Uuid::new_v4(), project: project.to_string(), @@ -96,7 +96,7 @@ impl IngestWorker { /// Process a single chunk pub async fn process_chunk(&self, project: &str, query_id: &str, content: &str, source: &str) -> Result<()> { - let embedding = self.embeddings.embed(content).await?; + let embedding = self.embeddings.embed_one(content).await?; let chunk = ChunkL0 { id: Uuid::new_v4(), project: project.to_string(), diff --git a/crates/mem-cli/src/query_worker.rs b/crates/mem-cli/src/query_worker.rs index 98f5da7..9b1a9cf 100644 --- a/crates/mem-cli/src/query_worker.rs +++ b/crates/mem-cli/src/query_worker.rs @@ -45,7 +45,7 @@ impl QueryWorker { let limit = limit.unwrap_or(5); // Embed the question - let question_embedding = self.embeddings.embed(question).await?; + let question_embedding = self.embeddings.embed_one(question).await?; // Search across all levels let mut candidates = Vec::new();