Support Python 3.13 (#2844)

* ci: support python 3.13 on CI

* docs: update docs about support python version

* build: adds requires python <3.14

* build: explicit tokenizers dependency

Added explicit tokenizers dependency: Added tokenizers>=0.20.3 to ensure a version compatible with Python 3.13 is used.

* build: drop fastembed is not longer used

* build: attempt to build PyTorch on Python 3.13

* feat: upgrade fastavro, pyarrow and lancedb

* build: ensure tiktoken greather than 0.8.0 due Python 3.13 compatibility

src/crewai/knowledge/embedder/fastembed.py

@@ -1,93 +0,0 @@
-from pathlib import Path
-from typing import List, Optional, Union
-
-import numpy as np
-
-from .base_embedder import BaseEmbedder
-
-try:
-    from fastembed_gpu import TextEmbedding  # type: ignore
-
-    FASTEMBED_AVAILABLE = True
-except ImportError:
-    try:
-        from fastembed import TextEmbedding
-
-        FASTEMBED_AVAILABLE = True
-    except ImportError:
-        FASTEMBED_AVAILABLE = False
-
-
-class FastEmbed(BaseEmbedder):
-    """
-    A wrapper class for text embedding models using FastEmbed
-    """
-
-    def __init__(
-        self,
-        model_name: str = "BAAI/bge-small-en-v1.5",
-        cache_dir: Optional[Union[str, Path]] = None,
-    ):
-        """
-        Initialize the embedding model
-
-        Args:
-            model_name: Name of the model to use
-            cache_dir: Directory to cache the model
-            gpu: Whether to use GPU acceleration
-        """
-        if not FASTEMBED_AVAILABLE:
-            raise ImportError(
-                "FastEmbed is not installed. Please install it with: "
-                "uv pip install fastembed or uv pip install fastembed-gpu for GPU support"
-            )
-
-        self.model = TextEmbedding(
-            model_name=model_name,
-            cache_dir=str(cache_dir) if cache_dir else None,
-        )
-
-    def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]:
-        """
-        Generate embeddings for a list of text chunks
-
-        Args:
-            chunks: List of text chunks to embed
-
-        Returns:
-            List of embeddings
-        """
-        embeddings = list(self.model.embed(chunks))
-        return embeddings
-
-    def embed_texts(self, texts: List[str]) -> List[np.ndarray]:
-        """
-        Generate embeddings for a list of texts
-
-        Args:
-            texts: List of texts to embed
-
-        Returns:
-            List of embeddings
-        """
-        embeddings = list(self.model.embed(texts))
-        return embeddings
-
-    def embed_text(self, text: str) -> np.ndarray:
-        """
-        Generate embedding for a single text
-
-        Args:
-            text: Text to embed
-
-        Returns:
-            Embedding array
-        """
-        return self.embed_texts([text])[0]
-
-    @property
-    def dimension(self) -> int:
-        """Get the dimension of the embeddings"""
-        # Generate a test embedding to get dimensions
-        test_embed = self.embed_text("test")
-        return len(test_embed)