feat: add tool use to Chainlit

src/raglite/_chainlit.py

@@ -1,27 +1,17 @@
 """Chainlit frontend for RAGLite."""
 
+import json
 import os
 from pathlib import Path
 
 import chainlit as cl
 from chainlit.input_widget import Switch, TextInput
 
-from raglite import (
-    RAGLiteConfig,
-    async_rag,
-    create_rag_instruction,
-    hybrid_search,
-    insert_document,
-    rerank_chunks,
-    retrieve_chunk_spans,
-    retrieve_chunks,
-)
+from raglite import RAGLiteConfig, async_rag, hybrid_search, insert_document, rerank_chunks
 from raglite._markdown import document_to_markdown
 
 async_insert_document = cl.make_async(insert_document)
 async_hybrid_search = cl.make_async(hybrid_search)
-async_retrieve_chunks = cl.make_async(retrieve_chunks)
-async_retrieve_chunk_spans = cl.make_async(retrieve_chunk_spans)
 async_rerank_chunks = cl.make_async(rerank_chunks)
 
 
@@ -93,31 +83,27 @@ async def handle_message(user_message: cl.Message) -> None:
             for i, attachment in enumerate(inline_attachments)
         )
         + f"\n\n{user_message.content}"
-    )
-    # Search for relevant contexts for RAG.
-    async with cl.Step(name="search", type="retrieval") as step:
-        step.input = user_message.content
-        chunk_ids, _ = await async_hybrid_search(query=user_prompt, num_results=10, config=config)
-        chunks = await async_retrieve_chunks(chunk_ids=chunk_ids, config=config)
-        step.output = chunks
-        step.elements = [  # Show the top chunks inline.
-            cl.Text(content=str(chunk), display="inline") for chunk in chunks[:5]
-        ]
-        await step.update()  # TODO: Workaround for https://github.com/Chainlit/chainlit/issues/602.
-    # Rerank the chunks and group them into chunk spans.
-    async with cl.Step(name="rerank", type="rerank") as step:
-        step.input = chunks
-        chunks = await async_rerank_chunks(query=user_prompt, chunk_ids=chunks, config=config)
-        chunk_spans = await async_retrieve_chunk_spans(chunks[:5], config=config)
-        step.output = chunk_spans
-        step.elements = [  # Show the top chunk spans inline.
-            cl.Text(content=str(chunk_span), display="inline") for chunk_span in chunk_spans
-        ]
-        await step.update()  # TODO: Workaround for https://github.com/Chainlit/chainlit/issues/602.
+    ).strip()
     # Stream the LLM response.
     assistant_message = cl.Message(content="")
     messages: list[dict[str, str]] = cl.chat_context.to_openai()[:-1]  # type: ignore[no-untyped-call]
-    messages.append(create_rag_instruction(user_prompt=user_prompt, context=chunk_spans))
+    messages.append({"role": "user", "content": user_prompt})
     async for token in async_rag(messages, config=config):
         await assistant_message.stream_token(token)
+    # Append RAG sources if any.
+    if messages[-2]["role"] == "tool":
+        rag_context = json.loads(messages[-2]["content"])
+        rag_sources: dict[str, list[str]] = {}
+        for document in rag_context["documents"]:
+            rag_sources.setdefault(document["source"], [])
+            rag_sources[document["source"]].append(
+                document["span"]["headings"] + "\n" + document["span"]["content"]
+            )
+        assistant_message.content += "\n\nSources: " + ", ".join(  # Rendered as hyperlinks.
+            f"[{i + 1}]" for i in range(len(rag_sources))
+        )
+        assistant_message.elements = [  # Markdown content is rendered in sidebar.
+            cl.Text(name=f"[{i + 1}]", content="\n\n---\n\n".join(content), display="side")  # type: ignore[misc]
+            for i, (_, content) in enumerate(rag_sources.items())
+        ]
     await assistant_message.update()  # type: ignore[no-untyped-call]