ai-agents-embeddings-pooling-using-llamacppembeddings-with-a-gguf-model-that-re-e407a4b4

THEN Flatten the nested embeddings by iterating over the inner lists and converting each to float, or pool token embeddings (e.g., average) to produce a single vector per document. The recommended fix is to change line 114 in llama.cpp to: `return [list(map(float, sublist)) for e in embeddings for sublist in e]` which concatenates all token vectors into one flat list per document. However, consider using a model that supports sequence-level embeddings or applying pooling yourself for better semantic representation.

return [list(map(float, sublist)) for e in embeddings for sublist in e]