LRP-138: Implement Weaviate filter for selected_works in chat search

- Add selected_works parameter to rag_search() function
- Build Weaviate filter using Filter.by_property("workTitle").contains_any()
- Add selected_works parameter to diverse_author_search() function
- Pass selected_works from run_chat_generation to diverse_author_search
- Preserve work filter in fallback search path
- Add logging for applied work filters

The filter allows restricting RAG search to specific works selected by the user.
When selected_works is empty or None, all works are searched (no filter).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

generations/library_rag/flask_app.py

@@ -917,7 +917,11 @@ def search() -> str:
     )
 
 
-def rag_search(query: str, limit: int = 5) -> List[Dict[str, Any]]:
+def rag_search(
+    query: str,
+    limit: int = 5,
+    selected_works: Optional[List[str]] = None
+) -> List[Dict[str, Any]]:
     """Search passages for RAG context with formatted results.
 
     Wraps the existing search_passages() function but returns results formatted
@@ -927,6 +931,9 @@ def rag_search(query: str, limit: int = 5) -> List[Dict[str, Any]]:
     Args:
         query: The user's question or search query.
         limit: Maximum number of context chunks to retrieve. Defaults to 5.
+        selected_works: Optional list of work titles to filter results.
+            If provided and non-empty, only chunks from these works are returned.
+            If None or empty, all works are included (no filter).
 
     Returns:
         List of context dictionaries with keys:
@@ -943,10 +950,19 @@ def rag_search(query: str, limit: int = 5) -> List[Dict[str, Any]]:
         'Platon'
         >>> results[0]["work"]
         'République'
+
+        >>> # With work filter
+        >>> results = rag_search("la vertu", limit=5, selected_works=["Ménon"])
+        >>> all(r["work"] == "Ménon" for r in results)
+        True
     """
     import time
     start_time = time.time()
 
+    # Normalize selected_works
+    if selected_works is None:
+        selected_works = []
+
     try:
         with get_weaviate_client() as client:
             if client is None:
@@ -955,10 +971,18 @@ def rag_search(query: str, limit: int = 5) -> List[Dict[str, Any]]:
 
             chunks = client.collections.get("Chunk")
 
+            # Build work filter if selected_works is provided
+            work_filter: Optional[Any] = None
+            if selected_works:
+                # Use contains_any for filtering multiple work titles
+                work_filter = wvq.Filter.by_property("workTitle").contains_any(selected_works)
+                print(f"[RAG Search] Applying work filter: {selected_works}")
+
             # Query with properties needed for RAG context
             result = chunks.query.near_text(
                 query=query,
                 limit=limit,
+                filters=work_filter,
                 return_metadata=wvq.MetadataQuery(distance=True),
                 return_properties=[
                     "text",
@@ -1001,7 +1025,8 @@ def diverse_author_search(
     limit: int = 10,
     initial_pool: int = 100,
     max_authors: int = 5,
-    chunks_per_author: int = 2
+    chunks_per_author: int = 2,
+    selected_works: Optional[List[str]] = None
 ) -> List[Dict[str, Any]]:
     """Search passages with author diversity to avoid corpus imbalance bias.
 
@@ -1023,6 +1048,9 @@ def diverse_author_search(
         initial_pool: Size of initial candidate pool (default: 100).
         max_authors: Maximum number of distinct authors to include (default: 5).
         chunks_per_author: Number of chunks per selected author (default: 2).
+        selected_works: Optional list of work titles to filter results.
+            If provided and non-empty, only chunks from these works are returned.
+            If None or empty, all works are included (no filter).
 
     Returns:
         List of context dictionaries with keys:
@@ -1049,12 +1077,17 @@ def diverse_author_search(
     import time
     start_time = time.time()
 
-    print(f"[Diverse Search] CALLED with query='{query[:50]}...', initial_pool={initial_pool}, max_authors={max_authors}, chunks_per_author={chunks_per_author}")
+    # Normalize selected_works
+    if selected_works is None:
+        selected_works = []
+
+    works_filter_str = selected_works if selected_works else "all"
+    print(f"[Diverse Search] CALLED with query='{query[:50]}...', initial_pool={initial_pool}, max_authors={max_authors}, chunks_per_author={chunks_per_author}, selected_works={works_filter_str}")
 
     try:
-        # Step 1: Retrieve large initial pool
+        # Step 1: Retrieve large initial pool (with work filter if specified)
-        print(f"[Diverse Search] Calling rag_search with limit={initial_pool}")
+        print(f"[Diverse Search] Calling rag_search with limit={initial_pool}, selected_works={works_filter_str}")
-        candidates = rag_search(query, limit=initial_pool)
+        candidates = rag_search(query, limit=initial_pool, selected_works=selected_works)
         print(f"[Diverse Search] rag_search returned {len(candidates)} candidates")
 
         if not candidates:
@@ -1126,9 +1159,9 @@ def diverse_author_search(
         import traceback
         print(f"[Diverse Search] EXCEPTION CAUGHT: {e}")
         print(f"[Diverse Search] Traceback: {traceback.format_exc()}")
-        print(f"[Diverse Search] Falling back to standard rag_search with limit={limit}")
+        print(f"[Diverse Search] Falling back to standard rag_search with limit={limit}, selected_works={works_filter_str}")
-        # Fallback to standard search
+        # Fallback to standard search (preserve work filter)
-        return rag_search(query, limit)
+        return rag_search(query, limit, selected_works=selected_works)
 
 
 def build_prompt_with_context(user_question: str, rag_context: List[Dict[str, Any]]) -> str:
@@ -1707,13 +1740,15 @@ def run_chat_generation(
         # The question parameter here is the final chosen version (original or reformulated)
 
         # Step 1: Diverse author search (avoids corpus imbalance bias)
+        # Apply selected_works filter if specified
         session["status"] = "searching"
         rag_context = diverse_author_search(
             query=question,
             limit=25,  # Get 25 diverse chunks
             initial_pool=200,  # LARGE pool to find all relevant authors (increased from 100)
             max_authors=8,  # Include up to 8 distinct authors (increased from 6)
-            chunks_per_author=3  # Max 3 chunks per author for balance
+            chunks_per_author=3,  # Max 3 chunks per author for balance
+            selected_works=selected_works  # Filter by selected works (empty = all)
         )
 
         print(f"[Pipeline] diverse_author_search returned {len(rag_context)} chunks")