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fix: mlx when searching, added to embedding_server

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This commit is contained in:
Andy Lee
2025-07-14 01:11:21 -07:00
parent 8b4654921b
commit 3da5b44d7f
8 changed files with 315 additions and 885 deletions
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18
test/build_mlx_index.py
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@@ -12,7 +12,7 @@ else:
builder = LeannBuilder(
backend_name="hnsw",
embedding_model="mlx-community/Qwen3-Embedding-0.6B-4bit-DWQ",
use_mlx=True
use_mlx=True,
)
# 2. Add documents
@@ -22,7 +22,7 @@ else:
"It was designed by Apple's machine learning research team.",
"The mlx-community organization provides pre-trained models in MLX format.",
"It supports operations on multi-dimensional arrays.",
"Leann can now use MLX for its embedding models."
"Leann can now use MLX for its embedding models.",
]
for doc in docs:
builder.add_text(doc)
@@ -34,9 +34,11 @@ else:
print(f"Check the metadata file: {INDEX_PATH}.meta.json")
chat = LeannChat(index_path=INDEX_PATH)
# add query
query = "MLX is an array framework for machine learning on Apple silicon."
print(f"Query: {query}")
response = chat.ask(query, top_k=3, recompute_beighbor_embeddings=True, complexity=3, beam_width=1)
print(f"Response: {response}")
chat = LeannChat(index_path=INDEX_PATH)
# add query
query = "MLX is an array framework for machine learning on Apple silicon."
print(f"Query: {query}")
response = chat.ask(
query, top_k=3, recompute_beighbor_embeddings=True, complexity=3, beam_width=1
)
print(f"Response: {response}")