Enhancement: dont re-build vector index with each chat

src/paperless_ai/chat.py

@@ -68,6 +68,84 @@ def _format_chat_metadata_trailer(references: list[dict[str, int | str]]) -> str
     )
 
 
+def _get_document_filtered_retriever(index, doc_ids: set[str], similarity_top_k: int):
+    from llama_index.core.base.base_retriever import BaseRetriever
+    from llama_index.core.schema import NodeWithScore
+    from llama_index.core.vector_stores import VectorStoreQuery
+
+    class DocumentFilteredFaissRetriever(BaseRetriever):
+        def __init__(self):
+            super().__init__()
+            self._cached_query_str = None
+            self._cached_nodes = []
+
+        def _retrieve(self, query_bundle):
+            if query_bundle.query_str == self._cached_query_str:
+                return self._cached_nodes
+
+            if query_bundle.embedding is None:
+                query_bundle.embedding = (
+                    index._embed_model.get_agg_embedding_from_queries(
+                        query_bundle.embedding_strs,
+                    )
+                )
+
+            faiss_index = index.vector_store._faiss_index
+            max_top_k = faiss_index.ntotal
+            if max_top_k == 0:
+                self._cached_query_str = query_bundle.query_str
+                self._cached_nodes = []
+                return []
+
+            query_top_k = min(max(similarity_top_k, 1), max_top_k)
+            allowed_nodes: list[NodeWithScore] = []
+            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,
+                    ),
+                )
+
+                allowed_nodes = []
+                seen_node_ids = set()
+                for vector_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:
+                        continue
+
+                    node = index.docstore.docs.get(node_id)
+                    if node is None or node.metadata.get("document_id") not in doc_ids:
+                        continue
+
+                    seen_node_ids.add(node_id)
+                    allowed_nodes.append(NodeWithScore(node=node, score=score))
+
+                    if len(allowed_nodes) >= similarity_top_k:
+                        self._cached_query_str = query_bundle.query_str
+                        self._cached_nodes = allowed_nodes
+                        return allowed_nodes
+
+                if query_top_k == max_top_k:
+                    self._cached_query_str = query_bundle.query_str
+                    self._cached_nodes = allowed_nodes
+                    return allowed_nodes
+
+                query_top_k = min(query_top_k * 2, max_top_k)
+
+            self._cached_query_str = query_bundle.query_str
+            self._cached_nodes = allowed_nodes
+            return allowed_nodes
+
+    return DocumentFilteredFaissRetriever()
+
+
 def stream_chat_with_documents(query_str: str, documents: list[Document]):
     client = AIClient()
     index = load_or_build_index()
@@ -86,14 +164,14 @@ def stream_chat_with_documents(query_str: str, documents: list[Document]):
         yield "Sorry, I couldn't find any content to answer your question."
         return
 
-    from llama_index.core import VectorStoreIndex
     from llama_index.core.prompts import PromptTemplate
     from llama_index.core.query_engine import RetrieverQueryEngine
     from llama_index.core.response_synthesizers import get_response_synthesizer
 
-    local_index = VectorStoreIndex(nodes=nodes)
-    retriever = local_index.as_retriever(
-        similarity_top_k=CHAT_RETRIEVER_TOP_K,
+    retriever = _get_document_filtered_retriever(
+        index,
+        set(doc_ids),
+        CHAT_RETRIEVER_TOP_K,
     )
 
     top_nodes = retriever.retrieve(query_str)

src/paperless_ai/tests/test_chat.py

@@ -3,7 +3,6 @@ from unittest.mock import MagicMock
 from unittest.mock import patch
 
 import pytest
-from llama_index.core import VectorStoreIndex
 from llama_index.core.schema import TextNode
 
 from paperless_ai.chat import CHAT_METADATA_DELIMITER
@@ -29,7 +28,7 @@ def patch_embed_nodes():
         mock_embed_nodes.side_effect = lambda nodes, *_args, **_kwargs: {
             node.node_id: [0.1] * 1536 for node in nodes
         }
-        yield
+        yield mock_embed_nodes
 
 
 @pytest.fixture
@@ -57,7 +56,25 @@ def assert_chat_output(
     }
 
 
-def test_stream_chat_with_one_document_retrieval(mock_document) -> None:
+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.query.return_value = MagicMock(
+        ids=list(mock_index.index_struct.nodes_dict),
+        similarities=[0.1] * len(nodes),
+    )
+    mock_index._embed_model.get_agg_embedding_from_queries.return_value = [0.1] * 1536
+
+
+def test_stream_chat_with_one_document_retrieval(
+    mock_document,
+    patch_embed_nodes,
+) -> None:
     with (
         patch("paperless_ai.chat.AIClient") as mock_client_cls,
         patch("paperless_ai.chat.load_or_build_index") as mock_load_index,
@@ -75,6 +92,7 @@ def test_stream_chat_with_one_document_retrieval(mock_document) -> None:
         )
         mock_index = MagicMock()
         mock_index.docstore.docs.values.return_value = [mock_node]
+        add_vector_query_results(mock_index, [mock_node])
         mock_load_index.return_value = mock_index
 
         mock_response_stream = MagicMock()
@@ -86,6 +104,7 @@ def test_stream_chat_with_one_document_retrieval(mock_document) -> None:
         output = list(stream_chat_with_documents("What is this?", [mock_document]))
 
         mock_query_engine.query.assert_called_once_with("What is this?")
+        patch_embed_nodes.assert_not_called()
         assert_chat_output(
             output,
             expected_chunks=["chunk1", "chunk2"],
@@ -102,7 +121,6 @@ def test_stream_chat_with_multiple_documents_retrieval(patch_embed_nodes) -> Non
         patch(
             "llama_index.core.query_engine.RetrieverQueryEngine.from_args",
         ) as mock_query_engine_cls,
-        patch.object(VectorStoreIndex, "as_retriever") as mock_as_retriever,
     ):
         # Mock AIClient and LLM
         mock_client = MagicMock()
@@ -118,12 +136,6 @@ def test_stream_chat_with_multiple_documents_retrieval(patch_embed_nodes) -> Non
             text="Content for doc 2.",
             metadata={"document_id": "2", "title": "Document 2"},
         )
-        mock_index = MagicMock()
-        mock_index.docstore.docs.values.return_value = [mock_node1, mock_node2]
-        mock_load_index.return_value = mock_index
-
-        # Patch as_retriever to return a retriever whose retrieve() returns mock_node1 and mock_node2
-        mock_retriever = MagicMock()
         mock_duplicate_node = TextNode(
             text="More content for doc 1.",
             metadata={"document_id": "1", "title": "Document 1 Duplicate"},
@@ -132,13 +144,18 @@ def test_stream_chat_with_multiple_documents_retrieval(patch_embed_nodes) -> Non
             text="Content for doc 3.",
             metadata={"document_id": "3", "title": "Document 3"},
         )
-        mock_retriever.retrieve.return_value = [
+        mock_index = MagicMock()
+        mock_index.docstore.docs.values.return_value = [
             mock_node1,
-            mock_duplicate_node,
             mock_node2,
+            mock_duplicate_node,
             mock_foreign_node,
         ]
-        mock_as_retriever.return_value = mock_retriever
+        add_vector_query_results(
+            mock_index,
+            [mock_node1, mock_duplicate_node, mock_node2, mock_foreign_node],
+        )
+        mock_load_index.return_value = mock_index
 
         # Mock response stream
         mock_response_stream = MagicMock()
@@ -156,6 +173,7 @@ def test_stream_chat_with_multiple_documents_retrieval(patch_embed_nodes) -> Non
         output = list(stream_chat_with_documents("What's up?", [doc1, doc2]))
 
         mock_query_engine.query.assert_called_once_with("What's up?")
+        patch_embed_nodes.assert_not_called()
         assert_chat_output(
             output,
             expected_chunks=["chunk1", "chunk2"],