feat: add jina-mlx reranker provider for Apple Silicon (#542)
* feat: add JinaMLXCrossEncoder for native Apple Silicon reranking Adds a new `jina-mlx` reranker provider backed by jinaai/jina-reranker-v3-mlx, a 0.6B multilingual listwise reranker running via the MLX framework on Apple Silicon. The model is downloaded automatically from HuggingFace Hub on first use. Benchmarked latencies (Apple Silicon): 1 doc→32ms, 5→45ms, 10→60ms, 20→94ms. Sub-linear scaling because all docs are ranked in a single forward pass. - Embeds the MLX reranker implementation (_MLXReranker / _MLPProjector) directly in cross_encoder.py with no transformers/PyTorch dependency - Adds `mlx`, `mlx-lm`, `safetensors` to pyproject.toml optional deps (uv add) - Updates configuration.md with provider docs and benchmark table * refactor: import MLXReranker from repo rerank.py instead of duplicating code Use importlib to load MLXReranker directly from the model repo's own rerank.py (downloaded via snapshot_download). Also pin exact minimum versions for mlx>=0.31.0, mlx-lm>=0.31.1, safetensors>=0.6.2 (verified against installed versions). * refactor: move MLX reranker impl to dedicated jina_mlx_reranker.py Replaces the importlib hack with a proper module. jina_mlx_reranker.py is adapted from jinaai/jina-reranker-v3-mlx/rerank.py (CC BY-NC 4.0) with the source clearly documented at the top of the file. * docs: simplify jina-mlx reranker docs * fix: disable GIN fastupdate on source_memory_ids index to prevent deadlocks GIN fastupdate buffers inserts in a pending list and flushes it with AccessExclusiveLock when full. Under concurrent test load (8 xdist workers all running retain_async), two workers can trigger a flush simultaneously and deadlock. Recreating the index with fastupdate=off eliminates the flush/lock cycle at the cost of slightly slower individual inserts. * fix: drop per-bank HNSW indexes after transaction to avoid AccessExclusiveLock deadlock When deleting a bank, the previous code dropped HNSW indexes inside the same transaction as the DELETE FROM memory_units. Since DROP INDEX needs AccessExclusiveLock on the parent table and DELETE holds RowExclusiveLock, two concurrent bank deletions deadlocked on the same table lock. Fix: capture internal_id inside the transaction, commit, then drop the indexes outside the transaction so no row-level locks are held.
This commit is contained in:
Nicolò Boschi
GitHub
parent
66dedb8d41
commit
1caf5ec9ee
7 changed files with 472 additions and 38 deletions
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@ -0,0 +1,53 @@
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"""Recreate idx_memory_units_source_memory_ids GIN index with fastupdate=off
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GIN indexes use a "fastupdate" pending list by default: small writes are
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buffered there and flushed to the main GIN tree in bulk. Flushing requires
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AccessExclusiveLock on the index. Under high insert concurrency (e.g. 8
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parallel pytest-xdist workers all calling retain_async) two transactions can
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each trigger a flush simultaneously and deadlock.
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Disabling fastupdate makes every insert write directly to the GIN tree
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(slightly slower per insert, but no pending-list lock cycles).
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Revision ID: d4e5f6g7h8i9
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Revises: d5e6f7a8b9c0
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Create Date: 2026-03-11
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"""
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from collections.abc import Sequence
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from alembic import context, op
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revision: str = "d4e5f6g7h8i9"
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down_revision: str | Sequence[str] | None = "d5e6f7a8b9c0"
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branch_labels: str | Sequence[str] | None = None
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depends_on: str | Sequence[str] | None = None
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def _get_schema_prefix() -> str:
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schema = context.config.get_main_option("target_schema")
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return f'"{schema}".' if schema else ""
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def upgrade() -> None:
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schema = _get_schema_prefix()
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# DROP + CREATE CONCURRENTLY must run outside a transaction block.
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op.execute("COMMIT")
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op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
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op.execute(
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f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
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f"ON {schema}memory_units USING GIN (source_memory_ids) "
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f"WITH (fastupdate=off) "
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f"WHERE source_memory_ids IS NOT NULL"
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)
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def downgrade() -> None:
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schema = _get_schema_prefix()
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op.execute("COMMIT")
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op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
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op.execute(
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f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
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f"ON {schema}memory_units USING GIN (source_memory_ids) "
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f"WHERE source_memory_ids IS NOT NULL"
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)
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@ -1050,6 +1050,97 @@ class LiteLLMSDKCrossEncoder(CrossEncoderModel):
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return all_scores
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class JinaMLXCrossEncoder(CrossEncoderModel):
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"""
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Jina Reranker v3 MLX implementation for Apple Silicon.
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Uses jinaai/jina-reranker-v3-mlx — a 0.6B parameter multilingual listwise reranker
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optimized for Apple Silicon via the MLX framework. No transformers/PyTorch dependency.
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The model is downloaded automatically from HuggingFace Hub on first use.
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Requires: mlx>=0.31.0, mlx-lm>=0.31.1, safetensors>=0.6.2
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"""
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HF_REPO_ID = "jinaai/jina-reranker-v3-mlx"
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def __init__(self, model_path: str | None = None):
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"""
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Args:
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model_path: Local path to the downloaded model directory.
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If None, the model is downloaded from HuggingFace Hub.
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"""
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self.model_path = model_path
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self._reranker = None
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@property
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def provider_name(self) -> str:
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return "jina-mlx"
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async def initialize(self) -> None:
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if self._reranker is not None:
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return
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try:
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import mlx.core # noqa: F401
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import mlx_lm # noqa: F401
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except ImportError:
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raise ImportError(
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"mlx and mlx-lm are required for JinaMLXCrossEncoder. "
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"Install with: pip install mlx>=0.31.0 mlx-lm>=0.31.1 safetensors>=0.6.2"
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)
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loop = asyncio.get_event_loop()
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await loop.run_in_executor(None, self._load_model)
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def _load_model(self) -> None:
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"""Download (if needed) and load the MLX reranker. Runs in a thread."""
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import os
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from huggingface_hub import snapshot_download
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from .jina_mlx_reranker import MLXReranker
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model_path = self.model_path
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if model_path is None:
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logger.info(f"Reranker: downloading {self.HF_REPO_ID} from HuggingFace Hub...")
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model_path = snapshot_download(repo_id=self.HF_REPO_ID)
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logger.info(f"Reranker: loading jina-reranker-v3-mlx from {model_path}")
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self._reranker = MLXReranker(
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model_path=model_path,
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projector_path=os.path.join(model_path, "projector.safetensors"),
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)
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logger.info("Reranker: jina-mlx provider initialized")
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def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
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"""Score pairs grouped by query. Runs in a thread."""
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if not pairs:
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return []
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query_groups: dict[str, list[tuple[int, str]]] = {}
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for idx, (query, doc) in enumerate(pairs):
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query_groups.setdefault(query, []).append((idx, doc))
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all_scores = [0.0] * len(pairs)
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for query, indexed_docs in query_groups.items():
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docs = [doc for _, doc in indexed_docs]
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indices = [idx for idx, _ in indexed_docs]
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results = self._reranker.rerank(query, docs)
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for result in results:
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original_idx = result["index"]
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all_scores[indices[original_idx]] = result["relevance_score"]
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return all_scores
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async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
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if self._reranker is None:
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raise RuntimeError("Reranker not initialized. Call initialize() first.")
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(None, self._predict_sync, pairs)
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def create_cross_encoder_from_env() -> CrossEncoderModel:
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"""
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Create a CrossEncoderModel instance based on configuration.
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@ -1122,7 +1213,9 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
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)
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elif provider == "rrf":
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return RRFPassthroughCrossEncoder()
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elif provider == "jina-mlx":
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return JinaMLXCrossEncoder()
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else:
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raise ValueError(
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f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'zeroentropy', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
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f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'zeroentropy', 'flashrank', 'litellm', 'litellm-sdk', 'rrf', 'jina-mlx'"
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)
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144
hindsight-api/hindsight_api/engine/jina_mlx_reranker.py
Normal file
144
hindsight-api/hindsight_api/engine/jina_mlx_reranker.py
Normal file
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@ -0,0 +1,144 @@
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"""
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MLX implementation of jina-reranker-v3 for Apple Silicon.
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This file is adapted from the official model repository:
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https://huggingface.co/jinaai/jina-reranker-v3-mlx/blob/main/rerank.py
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License: CC BY-NC 4.0 (contact Jina AI for commercial usage)
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Changes from upstream:
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- Removed the __main__ example block
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- Type annotations added to public methods
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- top_n parameter added to rerank() (upstream only exposed it implicitly)
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"""
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import numpy as np
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class _MLPProjector:
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def __init__(self):
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import mlx.nn as nn
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self.linear1 = nn.Linear(1024, 512, bias=False)
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self.linear2 = nn.Linear(512, 512, bias=False)
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def __call__(self, x):
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import mlx.nn as nn
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x = self.linear1(x)
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x = nn.relu(x)
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x = self.linear2(x)
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return x
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def _load_projector(projector_path: str) -> _MLPProjector:
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import mlx.core as mx
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from safetensors import safe_open
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projector = _MLPProjector()
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with safe_open(projector_path, framework="numpy") as f:
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projector.linear1.weight = mx.array(f.get_tensor("linear1.weight"))
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projector.linear2.weight = mx.array(f.get_tensor("linear2.weight"))
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return projector
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def _sanitize(text: str, special_tokens: dict[str, str]) -> str:
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for token in special_tokens.values():
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text = text.replace(token, "")
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return text
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def _format_prompt(query: str, docs: list[str], special_tokens: dict[str, str]) -> str:
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query = _sanitize(query, special_tokens)
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docs = [_sanitize(d, special_tokens) for d in docs]
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doc_token = special_tokens["doc_embed_token"]
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query_token = special_tokens["query_embed_token"]
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prefix = (
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"<|im_start|>system\n"
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"You are a search relevance expert who can determine a ranking of the passages based on how relevant they are to the query. "
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"If the query is a question, how relevant a passage is depends on how well it answers the question. "
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"If not, try to analyze the intent of the query and assess how well each passage satisfies the intent. "
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"If an instruction is provided, you should follow the instruction when determining the ranking."
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"<|im_end|>\n<|im_start|>user\n"
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)
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suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
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body = (
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f"I will provide you with {len(docs)} passages, each indicated by a numerical identifier. "
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f"Rank the passages based on their relevance to query: {query}\n"
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)
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body += "\n".join(f'<passage id="{i}">\n{doc}{doc_token}\n</passage>' for i, doc in enumerate(docs))
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body += f"\n<query>\n{query}{query_token}\n</query>"
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return prefix + body + suffix
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class MLXReranker:
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"""
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MLX-accelerated jina-reranker-v3 for Apple Silicon.
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Loads the model from a local directory (use huggingface_hub.snapshot_download
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to fetch jinaai/jina-reranker-v3-mlx if you don't have it already).
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"""
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_SPECIAL_TOKENS = {
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"query_embed_token": "<|rerank_token|>",
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"doc_embed_token": "<|embed_token|>",
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}
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_DOC_TOKEN_ID = 151670
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_QUERY_TOKEN_ID = 151671
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def __init__(self, model_path: str, projector_path: str):
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from mlx_lm import load
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self.model, self.tokenizer = load(model_path)
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self.model.eval()
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self.projector = _load_projector(projector_path)
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def rerank(self, query: str, documents: list[str], top_n: int | None = None) -> list[dict]:
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"""
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Rank documents by relevance to a query.
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Returns a list of dicts with keys: document, relevance_score, index.
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Sorted by descending relevance_score.
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"""
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import mlx.core as mx
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prompt = _format_prompt(query, documents, self._SPECIAL_TOKENS)
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input_ids = self.tokenizer.encode(prompt)
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hidden_states = self.model.model([input_ids])[0] # [seq_len, hidden_size]
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input_ids_np = np.array(input_ids)
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query_positions = np.where(input_ids_np == self._QUERY_TOKEN_ID)[0]
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doc_positions = np.where(input_ids_np == self._DOC_TOKEN_ID)[0]
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if len(query_positions) == 0:
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raise ValueError("Query embed token not found in prompt")
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if len(doc_positions) == 0:
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raise ValueError("Document embed tokens not found in prompt")
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query_hidden = mx.expand_dims(hidden_states[int(query_positions[0])], axis=0)
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doc_hidden = mx.stack([hidden_states[int(p)] for p in doc_positions])
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query_emb = self.projector(query_hidden) # [1, 512]
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doc_emb = self.projector(doc_hidden) # [num_docs, 512]
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query_exp = mx.broadcast_to(mx.expand_dims(query_emb, 0), (1, len(documents), 512))
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doc_exp = mx.expand_dims(doc_emb, 0)
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scores = mx.sum(doc_exp * query_exp, axis=-1) / (
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mx.sqrt(mx.sum(doc_exp * doc_exp, axis=-1)) * mx.sqrt(mx.sum(query_exp * query_exp, axis=-1))
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) # [1, num_docs]
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scores_np = np.array(scores[0])
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order = np.argsort(scores_np)[::-1]
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n = min(top_n, len(documents)) if top_n is not None else len(documents)
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return [
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{
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"document": documents[order[i]],
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"relevance_score": float(scores_np[order[i]]),
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"index": int(order[i]),
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}
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for i in range(n)
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]
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@ -3686,6 +3686,7 @@ class MemoryEngine(MemoryEngineInterface):
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pool = await self._get_pool()
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invalidated_obs = 0
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result: dict[str, int] = {}
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bank_internal_id: str | None = None
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async with acquire_with_retry(pool) as conn:
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# Ensure connection is not in read-only mode (can happen with connection poolers)
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await conn.execute("SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE")
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@ -3745,10 +3746,8 @@ class MemoryEngine(MemoryEngineInterface):
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internal_id = await conn.fetchval(
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f"DELETE FROM {fq_table('banks')} WHERE bank_id = $1 RETURNING internal_id", bank_id
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)
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# Drop per-bank HNSW indexes now that the bank row is gone
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if internal_id:
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await bank_utils.drop_bank_hnsw_indexes(conn, str(internal_id))
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bank_internal_id = str(internal_id)
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result = {
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"memory_units_deleted": units_count,
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@ -3760,6 +3759,12 @@ class MemoryEngine(MemoryEngineInterface):
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except Exception as e:
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raise Exception(f"Failed to delete agent data: {str(e)}")
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# Drop per-bank HNSW indexes AFTER the transaction commits to avoid
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# AccessExclusiveLock deadlocks with concurrent bank deletions.
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# (DROP INDEX on memory_units conflicts with RowExclusiveLock from DELETE inside tx)
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if bank_internal_id:
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await bank_utils.drop_bank_hnsw_indexes(conn, bank_internal_id)
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if invalidated_obs > 0:
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await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)
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@ -63,6 +63,9 @@ dependencies = [
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"aiohttp>=3.13.3", # Multiple DoS vulnerabilities
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"claude-agent-sdk>=0.1.27",
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"einops>=0.8.2",
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"mlx>=0.31.0",
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"mlx-lm>=0.31.1",
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"safetensors>=0.6.2",
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]
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[project.optional-dependencies]
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@ -410,7 +410,7 @@ Supported OpenAI embedding dimensions:
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `HINDSIGHT_API_RERANKER_PROVIDER` | Provider: `local`, `tei`, `cohere`, `zeroentropy`, `flashrank`, `litellm`, `litellm-sdk`, or `rrf` | `local` |
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| `HINDSIGHT_API_RERANKER_PROVIDER` | Provider: `local`, `tei`, `cohere`, `zeroentropy`, `flashrank`, `litellm`, `litellm-sdk`, `jina-mlx`, or `rrf` | `local` |
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| `HINDSIGHT_API_RERANKER_LOCAL_MODEL` | Model for local provider | `cross-encoder/ms-marco-MiniLM-L-6-v2` |
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| `HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT` | Max concurrent local reranking (prevents CPU thrashing under load) | `4` |
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| `HINDSIGHT_API_RERANKER_LOCAL_TRUST_REMOTE_CODE` | Allow loading models with custom code (security risk, disabled by default) | `false` |
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@ -430,6 +430,7 @@ Supported OpenAI embedding dimensions:
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| `HINDSIGHT_API_RERANKER_ZEROENTROPY_MODEL` | ZeroEntropy rerank model (`zerank-2`, `zerank-2-small`) | `zerank-2` |
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| `HINDSIGHT_API_RERANKER_FLASHRANK_MODEL` | FlashRank model for fast CPU-based reranking | `ms-marco-MiniLM-L-12-v2` |
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| `HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR` | Cache directory for FlashRank models | System default |
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| `HINDSIGHT_API_RERANKER_JINA_MLX_MODEL_PATH` | Local path to downloaded `jina-reranker-v3-mlx` model (auto-downloads from HuggingFace if unset) | - |
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```bash
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# Local (default) - uses SentenceTransformers CrossEncoder
|
||||
|
|
@ -472,6 +473,10 @@ export HINDSIGHT_API_RERANKER_LITELLM_MODEL=cohere/rerank-english-v3.0 # or voy
|
|||
export HINDSIGHT_API_RERANKER_PROVIDER=litellm-sdk
|
||||
export HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY=your-deepinfra-api-key
|
||||
export HINDSIGHT_API_RERANKER_LITELLM_SDK_MODEL=deepinfra/Qwen3-reranker-8B # or cohere/rerank-english-v3.0, etc.
|
||||
|
||||
# Jina MLX - Apple Silicon native reranking (no GPU/cloud required)
|
||||
# Model (~1.2 GB) is downloaded automatically from HuggingFace Hub on first use.
|
||||
export HINDSIGHT_API_RERANKER_PROVIDER=jina-mlx
|
||||
```
|
||||
|
||||
#### LiteLLM Proxy vs SDK
|
||||
|
|
@ -488,6 +493,14 @@ Both support the same providers:
|
|||
- **Jina AI** (`jina_ai/jina-reranker-v2`)
|
||||
- **AWS Bedrock** (`bedrock/...`)
|
||||
|
||||
#### Jina MLX (Apple Silicon)
|
||||
|
||||
The `jina-mlx` provider uses [`jinaai/jina-reranker-v3-mlx`](https://huggingface.co/jinaai/jina-reranker-v3-mlx), optimized for Apple Silicon. The model (~1.2 GB) is downloaded from HuggingFace Hub automatically on first startup and cached locally.
|
||||
|
||||
:::note License
|
||||
`jina-reranker-v3-mlx` is licensed under CC BY-NC 4.0. Contact Jina AI for commercial usage.
|
||||
:::
|
||||
|
||||
### Authentication
|
||||
|
||||
By default, Hindsight runs without authentication. For production deployments, enable API key authentication using the built-in tenant extension:
|
||||
|
|
|
|||
189
uv.lock
189
uv.lock
|
|
@ -1399,31 +1399,34 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "hf-xet"
|
||||
version = "1.2.0"
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version = "1.3.2"
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source = { registry = "https://pypi.org/simple" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/a8/5c/7e4a33a3d689f77761156cc34558047569e54af92e4d15a8f493229f6767/hf_xet-1.3.2-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d6dbdf231efac0b9b39adcf12a07f0c030498f9212a18e8c50224d0e84ab803d", size = 4176494 },
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{ url = "https://files.pythonhosted.org/packages/63/d7/aecf97b3f0a981600a67ff4db15e2d433389d698a284bb0ea5d8fcdd6f7f/hf_xet-1.3.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:1c88fbd90ad0d27c46b77a445f0a436ebaa94e14965c581123b68b1c52f5fd30", size = 4154770 },
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{ url = "https://files.pythonhosted.org/packages/e2/e1/3af961f71a40e09bf5ee909842127b6b00f5ab4ee3817599dc0771b79893/hf_xet-1.3.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:35b855024ca37f2dd113ac1c08993e997fbe167b9d61f9ef66d3d4f84015e508", size = 4394161 },
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{ url = "https://files.pythonhosted.org/packages/a1/c3/859509bade9178e21b8b1db867b8e10e9f817ab9ac1de77cb9f461ced765/hf_xet-1.3.2-cp313-cp313t-win_amd64.whl", hash = "sha256:31612ba0629046e425ba50375685a2586e11fb9144270ebabd75878c3eaf6378", size = 3637377 },
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{ url = "https://files.pythonhosted.org/packages/05/7f/724cfbef4da92d577b71f68bf832961c8919f36c60d28d289a9fc9d024d4/hf_xet-1.3.2-cp313-cp313t-win_arm64.whl", hash = "sha256:433c77c9f4e132b562f37d66c9b22c05b5479f243a1f06a120c1c06ce8b1502a", size = 3497875 },
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{ url = "https://files.pythonhosted.org/packages/f2/8a/08a24b6c6f52b5d26848c16e4b6d790bb810d1bf62c3505bed179f7032d3/hf_xet-1.3.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:4bc995d6c41992831f762096020dc14a65fdf3963f86ffed580b596d04de32e3", size = 3521745 },
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{ url = "https://files.pythonhosted.org/packages/0c/09/9a3aa7c5f07d3e5cc57bb750d12a124ffa72c273a87164bd848f9ac5cc14/hf_xet-1.3.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:3155a02e083aa21fd733a7485c7c36025e49d5975c8d6bda0453d224dd0b0ac4", size = 4154535 },
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{ url = "https://files.pythonhosted.org/packages/ae/e0/831f7fa6d90cb47a230bc23284b502c700e1483bbe459437b3844cdc0776/hf_xet-1.3.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:91b1dc03c31cbf733d35dc03df7c5353686233d86af045e716f1e0ea4a2673cf", size = 4393891 },
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{ url = "https://files.pythonhosted.org/packages/ab/96/6ed472fdce7f8b70f5da6e3f05be76816a610063003bfd6d9cea0bbb58a3/hf_xet-1.3.2-cp314-cp314t-win_amd64.whl", hash = "sha256:211f30098512d95e85ad03ae63bd7dd2c4df476558a5095d09f9e38e78cbf674", size = 3637583 },
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{ url = "https://files.pythonhosted.org/packages/d8/28/dbb024e2e3907f6f3052847ca7d1a2f7a3972fafcd53ff79018977fcb3e4/hf_xet-1.3.2-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:f93b7595f1d8fefddfede775c18b5c9256757824f7f6832930b49858483cd56f", size = 3763961 },
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{ url = "https://files.pythonhosted.org/packages/e4/71/b99aed3823c9d1795e4865cf437d651097356a3f38c7d5877e4ac544b8e4/hf_xet-1.3.2-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:a85d3d43743174393afe27835bde0cd146e652b5fcfdbcd624602daef2ef3259", size = 3526171 },
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{ url = "https://files.pythonhosted.org/packages/9d/ca/907890ce6ef5598b5920514f255ed0a65f558f820515b18db75a51b2f878/hf_xet-1.3.2-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7c2a054a97c44e136b1f7f5a78f12b3efffdf2eed3abc6746fc5ea4b39511633", size = 4180750 },
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{ url = "https://files.pythonhosted.org/packages/8c/ad/bc7f41f87173d51d0bce497b171c4ee0cbde1eed2d7b4216db5d0ada9f50/hf_xet-1.3.2-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:06b724a361f670ae557836e57801b82c75b534812e351a87a2c739f77d1e0635", size = 3961035 },
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{ url = "https://files.pythonhosted.org/packages/73/38/600f4dda40c4a33133404d9fe644f1d35ff2d9babb4d0435c646c63dd107/hf_xet-1.3.2-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:305f5489d7241a47e0458ef49334be02411d1d0f480846363c1c8084ed9916f7", size = 4161378 },
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{ url = "https://files.pythonhosted.org/packages/00/b3/7bc1ff91d1ac18420b7ad1e169b618b27c00001b96310a89f8a9294fe509/hf_xet-1.3.2-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:06cdbde243c85f39a63b28e9034321399c507bcd5e7befdd17ed2ccc06dfe14e", size = 4398020 },
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{ url = "https://files.pythonhosted.org/packages/2b/0b/99bfd948a3ed3620ab709276df3ad3710dcea61976918cce8706502927af/hf_xet-1.3.2-cp37-abi3-win_amd64.whl", hash = "sha256:9298b47cce6037b7045ae41482e703c471ce36b52e73e49f71226d2e8e5685a1", size = 3641624 },
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{ url = "https://files.pythonhosted.org/packages/cc/02/9a6e4ca1f3f73a164c0cd48e41b3cc56585dcc37e809250de443d673266f/hf_xet-1.3.2-cp37-abi3-win_arm64.whl", hash = "sha256:83d8ec273136171431833a6957e8f3af496bee227a0fe47c7b8b39c106d1749a", size = 3503976 },
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]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1480,6 +1483,8 @@ dependencies = [
|
|||
{ name = "langsmith" },
|
||||
{ name = "litellm" },
|
||||
{ name = "markitdown", extra = ["docx", "pdf", "pptx", "xls", "xlsx"] },
|
||||
{ name = "mlx" },
|
||||
{ name = "mlx-lm" },
|
||||
{ name = "obstore" },
|
||||
{ name = "openai" },
|
||||
{ name = "opentelemetry-api" },
|
||||
|
|
@ -1499,6 +1504,7 @@ dependencies = [
|
|||
{ name = "python-dateutil" },
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|
@ -5239,23 +5363,22 @@ wheels = [
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[[package]]
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|
|
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|||
Loading…
Reference in a new issue