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

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- 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>
This commit is contained in:
David Blanc Brioir
2026-01-04 14:32:47 +01:00
parent 930615d239
commit 7e8367863d
1 changed files with 45 additions and 10 deletions
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55
generations/library_rag/flask_app.py
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@@ -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
print(f"[Diverse Search] Calling rag_search with limit={initial_pool}")
candidates = rag_search(query, limit=initial_pool)
# Step 1: Retrieve large initial pool (with work filter if specified)
print(f"[Diverse Search] Calling rag_search with limit={initial_pool}, selected_works={works_filter_str}")
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}")
# Fallback to standard search
return rag_search(query, limit)
print(f"[Diverse Search] Falling back to standard rag_search with limit={limit}, selected_works={works_filter_str}")
# Fallback to standard search (preserve work filter)
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")