add dataset features

python/python/lancedb/embeddings/fine_tuner/dataset.py

@@ -1,13 +1,13 @@
 import re
 import uuid
 from pathlib import Path
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, List, Tuple, Optional
 
 import lance
 import pyarrow as pa
 from pydantic import BaseModel
 from tqdm import tqdm
-
+from lancedb.utils.general import LOGGER
 from .llm import BaseLLM
 
 DEFAULT_PROMPT_TMPL = """\
@@ -37,7 +37,7 @@ class QADataset(BaseModel):
         relevant_docs (Dict[str, List[str]]): Dict query id -> list of doc ids.
 
     """
-
+    path: Optional[str] = None
     queries: Dict[str, str]  # id -> query
     corpus: Dict[str, str]  # id -> text
     relevant_docs: Dict[str, List[str]]  # query id -> list of retrieved doc ids
@@ -53,6 +53,7 @@ class QADataset(BaseModel):
 
     def save(self, path: str, mode: str = "overwrite") -> None:
         """Save to lance dataset"""
+        self.path = path
         save_dir = Path(path)
         save_dir.mkdir(parents=True, exist_ok=True)
 
@@ -86,20 +87,28 @@ class QADataset(BaseModel):
         )
 
     @classmethod
-    def load(cls, path: str) -> "QADataset":
+    def load(cls, path: str, version: Optional[int] = None) -> "QADataset":
         """Load from .lance data"""
         load_dir = Path(path)
-        queries = lance.dataset(load_dir / "queries.lance").to_table().to_pydict()
+        queries = lance.dataset(load_dir / "queries.lance", version=version).to_table().to_pydict()
-        corpus = lance.dataset(load_dir / "corpus.lance").to_table().to_pydict()
+        corpus = lance.dataset(load_dir / "corpus.lance", version=version).to_table().to_pydict()
         relevant_docs = (
-            lance.dataset(load_dir / "relevant_docs.lance").to_table().to_pydict()
+            lance.dataset(load_dir / "relevant_docs.lance", version=version).to_table().to_pydict()
         )
         return cls(
+            path=str(path),
             queries=dict(zip(queries["id"], queries["query"])),
             corpus=dict(zip(corpus["id"], corpus["text"])),
             relevant_docs=dict(zip(relevant_docs["query_id"], relevant_docs["doc_id"])),
         )
 
+    @classmethod
+    def switch_version(cls, version: int) -> "QADataset":
+        """Switch version of a dataset."""
+        if not cls.path:
+            raise ValueError("Path not set. You need to call save() first.")
+        return cls.load(cls.path, version=version)
+    
     # generate queries as a convenience function
     @classmethod
     def from_llm(
@@ -142,6 +151,23 @@ class QADataset(BaseModel):
         """Create a QADataset from a list of TextChunks and a list of questions."""
         node_dict = {node.id: node.text for node in docs}
         return cls(queries=queries, corpus=node_dict, relevant_docs=relevant_docs)
+    
+    def versions(self) -> List[int]:
+        """Get the versions of the dataset."""
+        # TODO: tidy this up
+        data_paths = self._get_data_file_paths()
+        return lance.dataset(data_paths[0]).versions()
+    
+    
+    def _get_data_file_paths(self) -> str:
+        """Get the absolute path of the dataset."""
+        queries = self.path / "queries.lance"
+        corpus = self.path / "corpus.lance"
+        relevant_docs = self.path / "relevant_docs.lance"
+
+        return queries, corpus, relevant_docs
+
+        
 
 
 class TextChunk(BaseModel):