Use lancedb for chat retriever

src/paperless_ai/chat.py

@@ -80,7 +80,7 @@ def _get_document_filtered_retriever(index, doc_ids: set[str], similarity_top_k:
     from llama_index.core.schema import NodeWithScore
     from llama_index.core.vector_stores import VectorStoreQuery
 
-    class DocumentFilteredFaissRetriever(BaseRetriever):
+    class DocumentFilteredLanceDBRetriever(BaseRetriever):
         def __init__(self):
             super().__init__()
             self._cached_query_str = None
@@ -97,8 +97,7 @@ def _get_document_filtered_retriever(index, doc_ids: set[str], similarity_top_k:
                     )
                 )
 
-            faiss_index = index.vector_store._faiss_index
-            max_top_k = faiss_index.ntotal
+            max_top_k = index.vector_store.table.count_rows()
             if max_top_k == 0:
                 self._cached_query_str = query_bundle.query_str
                 self._cached_nodes = []
@@ -109,20 +108,24 @@ def _get_document_filtered_retriever(index, doc_ids: set[str], similarity_top_k:
             seen_node_ids: set[str] = set()
 
             while query_top_k <= max_top_k:
-                query_result = index.vector_store.query(
-                    VectorStoreQuery(
-                        query_embedding=query_bundle.embedding,
-                        similarity_top_k=query_top_k,
-                    ),
-                )
+                try:
+                    query_result = index.vector_store.query(
+                        VectorStoreQuery(
+                            query_embedding=query_bundle.embedding,
+                            similarity_top_k=query_top_k,
+                        ),
+                    )
+                except Warning:
+                    self._cached_query_str = query_bundle.query_str
+                    self._cached_nodes = []
+                    return []
 
-                for vector_id, score in zip(
+                for node_id, score in zip(
                     query_result.ids or [],
                     query_result.similarities or [],
                     strict=False,
                 ):
-                    node_id = index.index_struct.nodes_dict.get(vector_id)
-                    if node_id is None or node_id in seen_node_ids:
+                    if node_id in seen_node_ids:
                         continue
 
                     node = index.docstore.docs.get(node_id)
@@ -148,7 +151,7 @@ def _get_document_filtered_retriever(index, doc_ids: set[str], similarity_top_k:
             self._cached_nodes = allowed_nodes
             return allowed_nodes
 
-    return DocumentFilteredFaissRetriever()
+    return DocumentFilteredLanceDBRetriever()
 
 
 def stream_chat_with_documents(query_str: str, documents: list[Document]):

src/paperless_ai/tests/test_chat.py

@@ -59,15 +59,12 @@ def assert_chat_output(
 
 
 def add_vector_query_results(mock_index, nodes: list[TextNode]) -> None:
-    mock_index.index_struct.nodes_dict = {
-        str(vector_id): node.node_id for vector_id, node in enumerate(nodes)
-    }
     mock_index.docstore.docs.get.side_effect = {
         node.node_id: node for node in nodes
     }.get
-    mock_index.vector_store._faiss_index.ntotal = len(nodes)
+    mock_index.vector_store.table.count_rows.return_value = len(nodes)
     mock_index.vector_store.query.return_value = MagicMock(
-        ids=list(mock_index.index_struct.nodes_dict),
+        ids=[node.node_id for node in nodes],
         similarities=[0.1] * len(nodes),
     )
     mock_index._embed_model.get_agg_embedding_from_queries.return_value = [0.1] * 1536
@@ -92,21 +89,21 @@ def test_document_filtered_retriever_expands_filters_and_caches() -> None:
     )
 
     mock_index = MagicMock()
-    mock_index.index_struct.nodes_dict = {
-        "0": foreign_node.node_id,
-        "1": missing_node.node_id,
-        "2": allowed_node1.node_id,
-        "3": allowed_node2.node_id,
-    }
     mock_index.docstore.docs.get.side_effect = {
         allowed_node1.node_id: allowed_node1,
         allowed_node2.node_id: allowed_node2,
         foreign_node.node_id: foreign_node,
     }.get
-    mock_index.vector_store._faiss_index.ntotal = 4
+    mock_index.vector_store.table.count_rows.return_value = 4
     mock_index.vector_store.query.side_effect = [
-        MagicMock(ids=["0", "2"], similarities=[0.9, 0.8]),
-        MagicMock(ids=["0", "1", "3"], similarities=[0.9, 0.7, 0.6]),
+        MagicMock(
+            ids=[foreign_node.node_id, allowed_node1.node_id],
+            similarities=[0.9, 0.8],
+        ),
+        MagicMock(
+            ids=[foreign_node.node_id, missing_node.node_id, allowed_node2.node_id],
+            similarities=[0.9, 0.7, 0.6],
+        ),
     ]
     mock_index._embed_model.get_agg_embedding_from_queries.return_value = [0.1] * 1536
 
@@ -128,9 +125,9 @@ def test_document_filtered_retriever_expands_filters_and_caches() -> None:
     assert mock_index._embed_model.get_agg_embedding_from_queries.call_count == 1
 
 
-def test_document_filtered_retriever_handles_empty_faiss_index() -> None:
+def test_document_filtered_retriever_handles_empty_vector_store() -> None:
     mock_index = MagicMock()
-    mock_index.vector_store._faiss_index.ntotal = 0
+    mock_index.vector_store.table.count_rows.return_value = 0
     mock_index._embed_model.get_agg_embedding_from_queries.return_value = [0.1] * 1536
 
     retriever = _get_document_filtered_retriever(