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https://github.com/NicolasBohn/NexQuant.git
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炼金术师华华
5090c6153f
* File structure for supporting litellm * more litellm support * feat: Add CachedAPIBackend class and dynamic API backend retrieval function * fix: update benchmark folder path and add default values for architecture and hyperparameters * feat: add LiteLLMAPIBackend and DeprecBackend ; changed structure of the project ; with bus * fix : deprec_backend * feat: Add LiteLLMAPIBackend class and related features; update configuration and test cases. * feat: Enhance LiteLLMAPIBackend with encoder support and dynamic argument handling;Enhance log Colors * lint * fix lint... * fix: Lint * fix:make auto-lint * fix:test oai * fix:redundant _abckend.py * fix: Optimize LiteLLMAPIBackend on token counting functiona, and clean up unused code;add test on this function * feat: Add LiteLLMSettings class and update model settings usage * fix: Update LiteLLMSettings environment variable prefix and model configurations * fix : gitignore * test: Consolidate and relocate test files for litellm backend and oai * fix : lint * fix: lint * auto lint * lint * LINT * lint * chore: remove deprecated backend configuration comments * refactor: Remove unused functions and imports from deprec.py and llm_utils.py * refactor: Move md5_hash function from deprec.py to llm_utils.py * chore: Remove extra newline and add missing import in deprec.py * lint * refactor: Move md5_hash function to utils module * lint * lint * lint --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Yihua Chen <v-yihuachen@microsoft.com>
45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
from __future__ import annotations
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from typing import Any, Type
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import numpy as np
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from rdagent.core.utils import import_class
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from rdagent.oai.backend.base import APIBackend as BaseAPIBackend
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from rdagent.oai.llm_conf import LLM_SETTINGS
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from rdagent.utils import md5_hash # for compatible with previous import
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def calculate_embedding_distance_between_str_list(
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source_str_list: list[str],
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target_str_list: list[str],
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) -> list[list[float]]:
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if not source_str_list or not target_str_list:
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return [[]]
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embeddings = APIBackend().create_embedding(source_str_list + target_str_list)
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source_embeddings = embeddings[: len(source_str_list)]
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target_embeddings = embeddings[len(source_str_list) :]
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source_embeddings_np = np.array(source_embeddings)
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target_embeddings_np = np.array(target_embeddings)
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source_embeddings_np = source_embeddings_np / np.linalg.norm(source_embeddings_np, axis=1, keepdims=True)
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target_embeddings_np = target_embeddings_np / np.linalg.norm(target_embeddings_np, axis=1, keepdims=True)
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similarity_matrix = np.dot(source_embeddings_np, target_embeddings_np.T)
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return similarity_matrix.tolist() # type: ignore[no-any-return]
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def get_api_backend(*args: Any, **kwargs: Any) -> BaseAPIBackend: # TODO: import it from base.py
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"""
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get llm api backend based on settings dynamically.
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"""
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api_backend_cls: Type[BaseAPIBackend] = import_class(LLM_SETTINGS.backend)
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return api_backend_cls(*args, **kwargs)
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# Alias
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APIBackend = get_api_backend
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