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Retrieval step display + status indicator + empty state #33

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Merged
markgewhite merged 1 commit into from
Apr 11, 2026
Merged

Retrieval step display + status indicator + empty state #33

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27 changes: 18 additions & 9 deletions app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,7 @@ async def on_chat_start():

if doc_count > 0:
await cl.Message(
content=f"{doc_count} chunks indexed. Ask me anything!",
content=f"**Ready** \u2014 {doc_count} chunks indexed. Ask me anything!",
actions=actions,
).send()
else:
Expand Down Expand Up @@ -204,7 +204,7 @@ async def on_reingest(action: cl.Action):
summary = _build_ingest_summary(result, total_files)
doc_count = store.count()
await cl.Message(
content=f"{summary}\n\n{doc_count} chunks indexed."
content=f"{summary}\n\n**Ready** \u2014 {doc_count} chunks indexed."
).send()


Expand Down Expand Up @@ -238,7 +238,7 @@ async def on_settings_update(settings: dict):
cl.user_session.set("retriever", retriever)

await cl.Message(
content=f"Done. {doc_count} chunks indexed."
content=f"**Ready** \u2014 {doc_count} chunks indexed."
).send()


Expand All @@ -263,13 +263,22 @@ async def on_message(message: cl.Message):
chat_history = cl.user_session.get("chat_history")
query = condenser.condense(message.content, chat_history)

candidates = retriever.retrieve(query)
# Retrieval pipeline with collapsible step display
async with cl.Step(name="Retrieval", type="tool") as retrieval_step:
# Step 1: Hybrid search
async with cl.Step(name="Searching", type="tool") as search_step:
candidates = retriever.retrieve(query)
search_step.output = f"Found {len(candidates)} candidates via hybrid search (BM25 + semantic)"

# Step 2: Reranking
async with cl.Step(name="Reranking", type="tool") as rerank_step:
reranker = cl.user_session.get("reranker")
results = reranker.rerank(
query, candidates, top_k=config.retrieval.rerank_top_k
)
rerank_step.output = f"Reranked to top {len(results)} by cross-encoder relevance"

# Rerank candidates with cross-encoder
reranker = cl.user_session.get("reranker")
results = reranker.rerank(
query, candidates, top_k=config.retrieval.rerank_top_k
)
retrieval_step.output = f"Retrieved {len(results)} relevant chunks"

# Stream the answer token by token
msg = cl.Message(content="")
Expand Down
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