3a4b67304f
* Added a **pyproject.toml** file to define project metadata and dependencies. * Added **run\_maestro.py** and **osworld\_run\_maestro.py** to provide the main execution logic. * Introduced multiple new modules, including **Evaluator**, **Controller**, **Manager**, and **Sub-Worker**, supporting task planning, state management, and data analysis. * Added a **tools module** containing utility functions and tool configurations to improve code reusability. * Updated the **README** and documentation with usage examples and module descriptions. These changes lay the foundation for expanding the Maestro project’s functionality and improving the user experience. Co-authored-by: Hiroid <guoliangxuan@deepmatrix.com>
53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
import numpy as np
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from ..utils.common_utils import (
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load_embeddings,
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save_embeddings,
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)
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import os
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# List all embeddings' keys and their shapes
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def list_embeddings(embeddings_path: str):
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if not os.path.exists(embeddings_path):
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print(f"[EmbeddingManager] File not found: {embeddings_path}")
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return {}
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embeddings = load_embeddings(embeddings_path)
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info = {}
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for k, v in embeddings.items():
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arr = np.array(v)
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info[k] = {'shape': arr.shape, 'preview': arr.flatten()[:5].tolist()}
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return info
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# Delete a specific embedding by key
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def delete_embedding(embeddings_path: str, key: str) -> bool:
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if not os.path.exists(embeddings_path):
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print(f"[EmbeddingManager] File not found: {embeddings_path}")
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return False
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embeddings = load_embeddings(embeddings_path)
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if key not in embeddings:
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print(f"[EmbeddingManager] Key not found: {key}")
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return False
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del embeddings[key]
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save_embeddings(embeddings_path, embeddings)
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print(f"[EmbeddingManager] Deleted embedding for key: {key}")
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return True
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def delete_empty_shape_embeddings(embeddings_path: str) -> int:
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"""Delete all embeddings whose value is empty (shape==0), shape==(), or content is error string, and return the number deleted."""
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if not os.path.exists(embeddings_path):
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print(f"[EmbeddingManager] File not found: {embeddings_path}")
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return 0
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embeddings = load_embeddings(embeddings_path)
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to_delete = []
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for k, v in embeddings.items():
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arr = np.array(v)
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# Delete shape==0 or shape==() or content is string/error information
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if arr.size == 0 or arr.shape == () or (
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isinstance(v, list) and v and isinstance(v[0], str) and v[0].startswith('Error:')
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) or (isinstance(v, str) and v.startswith('Error:')):
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to_delete.append(k)
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for k in to_delete:
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del embeddings[k]
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print(f"[EmbeddingManager] Deleted empty or error embedding for key: {k}")
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if to_delete:
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save_embeddings(embeddings_path, embeddings)
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return len(to_delete) |