def _embed_and_store()

in skills/retrieval_augmented_generation/evaluation/vectordb.py [0:0]

• 11 lines of code
• 4 McCabe index (conditional complexity)

    def _embed_and_store(self, texts, data):
        batch_size = 128
        result = [
            self.client.embed(
                texts[i : i + batch_size],
                model="voyage-2"
            ).embeddings
            for i in range(0, len(texts), batch_size)
        ]
        self.embeddings = [embedding for batch in result for embedding in batch]
        self.metadata = data