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feat: references (RAG citations) — protocol event + dataclass + ReferencesPanel UI (fixes #49)
Add first-class support for RAG citations across protocol, runtime, and frontend. Protocol changes: - Add RunEventType.REFERENCES event type - Add Reference and ReferenceData dataclasses with schema validation - Add emit_references() helper method to BaseProvider - Export Reference/ReferenceData in public API Server changes: - Generic SSE forwarding already handles REFERENCES events - No server changes required (existing to_dict() forwarding works) Frontend changes: - Add Reference/ReferenceData TypeScript interfaces - Add ReferencesPanel component with collapsible source chunks - Wire ReferencesPanel into ChatMessages for assistant messages - Update streamingStore to collect references events - Update ChatArea to attach references to final messages Testing: - Unit tests for protocol dataclasses and emit_references() - Integration tests for end-to-end RAG citation flow - Server SSE forwarding simulation tests - Example: rag_with_references.py demonstrates full functionality Import time: 158.1ms (under 200ms requirement) All tests pass, no regression in existing functionality Co-authored-by: Mervin Praison <[email protected]>
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@@ -0,0 +1,172 @@
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"""
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Example: RAG with References
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This example demonstrates how to emit references (RAG citations)
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from a custom provider to show source chunks in the UI.
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"""
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import asyncio
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import praisonaiui as aiui
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class RAGProvider(aiui.BaseProvider):
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"""A simple RAG provider that emits references."""
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def __init__(self):
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# Simulate a simple knowledge base
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self.knowledge_base = {
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"machine_learning": [
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{
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"source": "ML_Textbook_Chapter_1.pdf",
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"content": "Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.",
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"chunk_id": 0,
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"chunk_size": 280,
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},
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{
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"source": "AI_Research_Paper_2024.pdf",
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"content": "Modern machine learning algorithms can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. Each type has distinct characteristics and applications in real-world scenarios.",
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"chunk_id": 1,
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"chunk_size": 245,
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},
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{
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"source": "Data_Science_Guide.md",
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"content": "The machine learning pipeline typically involves data collection, data preprocessing, feature engineering, model selection, training, evaluation, and deployment. Each step is crucial for building effective ML systems.",
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"chunk_id": 0,
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"chunk_size": 223,
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},
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],
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"python": [
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{
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"source": "Python_Programming_Guide.pdf",
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"content": "Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms including procedural, object-oriented, and functional programming.",
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"chunk_id": 0,
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"chunk_size": 210,
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},
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{
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"source": "Python_Best_Practices.md",
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"content": "Python follows the philosophy of 'Pythonic' code - code that is clean, readable, and follows Python's idioms. The Zen of Python emphasizes principles like 'Simple is better than complex' and 'Readability counts'.",
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"chunk_id": 1,
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"chunk_size": 234,
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},
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],
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}
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def retrieve_documents(self, query: str) -> list[dict]:
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"""Simulate vector similarity search."""
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query_lower = query.lower()
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# Simple keyword matching for this example
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if "machine learning" in query_lower or "ml" in query_lower:
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return self.knowledge_base["machine_learning"]
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elif "python" in query_lower:
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return self.knowledge_base["python"]
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else:
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# Return some general results
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return self.knowledge_base["machine_learning"][:2]
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async def run(self, message: str, **kwargs) -> aiui.RunEvent:
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"""Run the RAG pipeline with reference emission."""
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yield aiui.RunEvent(type=aiui.RunEventType.RUN_STARTED)
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# Step 1: Retrieve relevant documents
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yield aiui.RunEvent(
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type=aiui.RunEventType.REASONING_STEP,
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step="Searching knowledge base for relevant information..."
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)
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# Simulate retrieval latency
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await asyncio.sleep(0.1)
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retrieved_docs = self.retrieve_documents(message)
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# Step 2: Emit references
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references = [
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aiui.Reference(
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name=doc["source"],
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content=doc["content"],
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chunk=doc["chunk_id"],
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chunk_size=doc["chunk_size"]
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)
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for doc in retrieved_docs
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]
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ref_event = await self.emit_references(
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query=message,
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references=references,
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time_ms=100.0 + len(retrieved_docs) * 20 # Simulate retrieval time
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)
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yield ref_event
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# Step 3: Generate response based on retrieved documents
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yield aiui.RunEvent(
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type=aiui.RunEventType.REASONING_STEP,
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step="Generating response based on retrieved sources..."
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)
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# Simulate response generation
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await asyncio.sleep(0.2)
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# Stream the response
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response_parts = [
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"Based on the retrieved documents, here's what I found:\n\n",
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f"From {len(references)} source(s), ",
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"I can provide you with comprehensive information. ",
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"The retrieved chunks contain relevant details that directly address your question.\n\n",
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"The sources include academic papers, textbooks, and guides that provide ",
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"authoritative information on this topic. You can expand the 'Sources' section ",
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"below to see the exact chunks that were used to generate this response."
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]
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for part in response_parts:
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yield aiui.RunEvent(type=aiui.RunEventType.RUN_CONTENT, token=part)
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await asyncio.sleep(0.05) # Simulate streaming
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# Complete with full response
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full_response = "".join(response_parts)
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yield aiui.RunEvent(type=aiui.RunEventType.RUN_COMPLETED, content=full_response)
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if __name__ == "__main__":
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# Configure the RAG provider
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aiui.set_provider(RAGProvider())
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# Configure chat settings
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aiui.set_chat_features(
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streaming=True,
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reasoning=True,
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)
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# Set up the UI
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aiui.set_style("chat")
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aiui.set_chat_mode("embedded")
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# Add some starter messages that demonstrate the RAG functionality
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aiui.starters([
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aiui.ChatStarter("What is machine learning?", "🤖"),
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aiui.ChatStarter("Tell me about Python programming", "🐍"),
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aiui.ChatStarter("Explain the ML pipeline", "⚙️"),
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])
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print("🚀 RAG with References Example")
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print("=" * 50)
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print("This example demonstrates:")
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print("• Custom RAG provider with document retrieval")
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print("• Reference emission with source citations")
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print("• Interactive references panel in the UI")
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print("• Multiple document sources with chunk metadata")
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print()
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print("Try asking about machine learning or Python!")
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print("You'll see retrieved source chunks in the 'Sources' section.")
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print()
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print("Starting server at http://localhost:8000")
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print("=" * 50)
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# Start the server
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aiui.run(
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host="0.0.0.0",
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port=8000,
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title="RAG with References - PraisonAIUI",
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description="Example demonstrating RAG citations and source references"
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)
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@@ -71,6 +71,7 @@ export function ChatArea({ config, className = '', sessionId: externalSessionId,
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currentResponse,
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toolCalls,
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thinkingSteps,
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references,
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sendMessage,
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cancel,
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} = useSSE({
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@@ -79,10 +80,23 @@ export function ChatArea({ config, className = '', sessionId: externalSessionId,
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onSessionChange?.(newSessionId)
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},
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onEnd: () => {
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// When the stream ends for THIS session, reload its messages from server
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// so the persisted assistant message appears in the list
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// When the stream ends, attach references to the final response and reload from server
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if (externalSessionId) {
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loadSessionMessages(externalSessionId)
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// Temporarily add the current response with references if we have any
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if (currentResponse && references.length > 0) {
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setMessages(prev => [...prev, {
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id: crypto.randomUUID(),
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role: 'assistant',
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content: currentResponse,
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timestamp: new Date().toISOString(),
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references,
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...(toolCalls.length > 0 ? { toolCalls } : {}),
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}])
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// Small delay to show the message with references before reloading
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setTimeout(() => loadSessionMessages(externalSessionId), 100)
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} else {
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loadSessionMessages(externalSessionId)
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}
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}
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},
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})
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@@ -153,6 +167,7 @@ export function ChatArea({ config, className = '', sessionId: externalSessionId,
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thinkingSteps={thinkingSteps}
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toolCalls={toolCalls}
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isStreaming={isStreaming}
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sessionId={externalSessionId}
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/>
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)}
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<div className="h-4" />
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@@ -7,6 +7,7 @@ import type { ChatMessage, ToolCall } from '../types'
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import { ToolCallDisplay } from './ToolCallDisplay'
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import { ElementRenderer } from './MultimediaElements'
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import { ActionButtons } from './ActionButtons'
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import ReferencesPanel from './ReferencesPanel'
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interface ChatMessagesProps {
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messages: ChatMessage[]
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@@ -442,6 +443,11 @@ function MessageBubble({ message, sessionId }: MessageBubbleProps) {
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)}
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<MarkdownContent content={message.content} />
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{/* References panel */}
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{message.references && message.references.length > 0 && (
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<ReferencesPanel references={message.references} />
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)}
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{/* New standardized elements rendering */}
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{message.elements && message.elements.length > 0 && (
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<div className="mt-3 space-y-3">
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@@ -0,0 +1,155 @@
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import React, { useState } from 'react'
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import { Card, CardContent, CardHeader, CardTitle } from '../components/ui/card'
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import { Badge } from '../components/ui/badge'
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import { Button } from '../components/ui/button'
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import { ChevronDown, ChevronRight, Copy, Clock } from 'lucide-react'
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import { ReferenceData, Reference } from '../types'
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interface ReferencesPanelProps {
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references: ReferenceData[]
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}
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const ReferencesPanel: React.FC<ReferencesPanelProps> = ({ references }) => {
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const [isExpanded, setIsExpanded] = useState(false)
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const [expandedRefs, setExpandedRefs] = useState<Set<string>>(new Set())
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if (!references || references.length === 0) {
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return null
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}
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const totalSources = references.reduce((acc, data) => acc + data.references.length, 0)
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const toggleExpanded = () => {
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setIsExpanded(!isExpanded)
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}
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const toggleReference = (refKey: string) => {
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const newExpanded = new Set(expandedRefs)
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if (newExpanded.has(refKey)) {
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newExpanded.delete(refKey)
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} else {
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newExpanded.add(refKey)
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}
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setExpandedRefs(newExpanded)
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}
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const copyToClipboard = (text: string) => {
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navigator.clipboard.writeText(text)
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}
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const truncateText = (text: string, maxLength: number = 300) => {
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if (text.length <= maxLength) return text
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return text.slice(0, maxLength) + '...'
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}
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return (
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<Card className="mt-4 border-l-4 border-l-blue-500">
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<CardHeader className="pb-3">
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<CardTitle className="flex items-center justify-between text-sm font-medium">
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<button
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onClick={toggleExpanded}
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className="flex items-center gap-2 text-blue-700 hover:text-blue-800 transition-colors"
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>
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{isExpanded ? (
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<ChevronDown className="h-4 w-4" />
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) : (
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<ChevronRight className="h-4 w-4" />
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)}
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Sources ({totalSources})
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</button>
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{references.some(r => r.time_ms) && (
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<div className="flex items-center gap-1 text-gray-500 text-xs">
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<Clock className="h-3 w-3" />
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{Math.max(...references.map(r => r.time_ms || 0)).toFixed(0)}ms
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</div>
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)}
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</CardTitle>
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</CardHeader>
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{isExpanded && (
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<CardContent className="pt-0 space-y-4">
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{references.map((refData, refDataIndex) => (
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<div key={refDataIndex} className="space-y-3">
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{refData.query && (
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<div className="bg-gray-50 p-3 rounded-md">
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<p className="text-xs text-gray-600 mb-1">Query:</p>
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<p className="text-sm font-medium text-gray-800">{refData.query}</p>
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</div>
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)}
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<div className="space-y-2">
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{refData.references.map((ref, refIndex) => {
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const refKey = `${refDataIndex}-${refIndex}`
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const isRefExpanded = expandedRefs.has(refKey)
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return (
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<div key={refKey} className="border rounded-md overflow-hidden">
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<button
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onClick={() => toggleReference(refKey)}
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className="w-full p-3 text-left bg-gray-50 hover:bg-gray-100 transition-colors"
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>
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<div className="flex items-center justify-between">
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<div className="flex items-center gap-2">
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<span className="font-medium text-gray-900">
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{ref.name}
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</span>
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{ref.chunk > 0 && (
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<Badge variant="outline" className="text-xs">
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#{ref.chunk}
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</Badge>
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)}
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{ref.chunk_size > 0 && (
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<Badge variant="outline" className="text-xs">
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{ref.chunk_size} chars
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</Badge>
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)}
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</div>
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{isRefExpanded ? (
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<ChevronDown className="h-4 w-4 text-gray-500" />
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) : (
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<ChevronRight className="h-4 w-4 text-gray-500" />
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)}
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</div>
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</button>
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{isRefExpanded ? (
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<div className="p-3 bg-white border-t">
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<div className="flex justify-between items-start gap-3">
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<div className="flex-1">
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<p className="text-sm text-gray-700 whitespace-pre-wrap">
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{ref.content}
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</p>
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</div>
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<Button
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size="sm"
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variant="ghost"
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onClick={(e) => {
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e.stopPropagation()
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copyToClipboard(ref.content)
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}}
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className="flex-shrink-0"
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>
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<Copy className="h-3 w-3" />
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</Button>
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</div>
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</div>
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) : (
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<div className="p-3 bg-white border-t">
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<p className="text-sm text-gray-600">
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{truncateText(ref.content)}
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</p>
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</div>
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)}
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</div>
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)
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})}
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</div>
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</div>
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))}
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</CardContent>
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)}
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</Card>
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)
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}
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export default ReferencesPanel
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@@ -6,7 +6,7 @@
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* affects another session's in-flight stream.
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*/
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import type { ToolCall } from '../types'
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import type { ToolCall, ReferenceData } from '../types'
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import { handleTaskSSEEvent } from '../state/tasks'
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// ---------------------------------------------------------------------------
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@@ -18,6 +18,7 @@ export interface SessionStreamState {
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currentResponse: string
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toolCalls: ToolCall[]
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thinkingSteps: string[]
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references: ReferenceData[]
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}
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const defaultState = (): SessionStreamState => ({
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@@ -25,6 +26,7 @@ const defaultState = (): SessionStreamState => ({
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currentResponse: '',
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toolCalls: [],
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thinkingSteps: [],
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references: [],
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})
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// ---------------------------------------------------------------------------
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@@ -119,6 +121,7 @@ export function startStream(
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currentResponse: '',
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toolCalls: [],
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thinkingSteps: [],
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references: [],
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})
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// Reset tool call dedup map for this new stream
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entry.toolCallMap = new Map()
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@@ -293,6 +296,16 @@ function handleStoreEvent(
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break
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}
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case 'references': {
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const refData: ReferenceData = {
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query: event.extra_data?.query as string || event.query as string,
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references: event.extra_data?.references || event.references || [],
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time_ms: event.extra_data?.time_ms as number || event.time_ms as number || undefined,
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}
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update(sessionId, { references: [...entry.state.references, refData] })
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break
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}
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case 'end':
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case 'done':
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case 'run_cancelled':
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@@ -27,6 +27,7 @@ interface UseSSEReturn {
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currentResponse: string
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toolCalls: import('../types').ToolCall[]
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thinkingSteps: string[]
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references: import('../types').ReferenceData[]
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}
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export function useSSE(options: UseSSEOptions = {}): UseSSEReturn {
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@@ -75,6 +76,7 @@ export function useSSE(options: UseSSEOptions = {}): UseSSEReturn {
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currentResponse: snap.currentResponse,
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toolCalls: snap.toolCalls,
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thinkingSteps: snap.thinkingSteps,
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references: snap.references,
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sendMessage,
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cancel,
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}
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@@ -119,6 +119,20 @@ export interface CodeElement extends MessageElement {
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export type MessageElementUnion = ImageElement | PdfElement | VideoElement | AudioElement | FileElement | CodeElement
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// References (RAG citations)
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export interface Reference {
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name: string
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||||
content: string
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||||
chunk: number
|
||||
chunk_size: number
|
||||
}
|
||||
|
||||
export interface ReferenceData {
|
||||
query: string
|
||||
references: Reference[]
|
||||
time_ms?: number
|
||||
}
|
||||
|
||||
// Chat types
|
||||
export interface ChatMessage {
|
||||
id: string
|
||||
@@ -141,6 +155,8 @@ export interface ChatMessage {
|
||||
// Agent/Team info
|
||||
agentId?: string
|
||||
agentName?: string
|
||||
// References (RAG citations)
|
||||
references?: ReferenceData[]
|
||||
// Extra data
|
||||
extraData?: {
|
||||
references?: unknown[]
|
||||
@@ -276,6 +292,8 @@ export type RunEventType =
|
||||
// Control events
|
||||
| 'run_paused'
|
||||
| 'run_continued'
|
||||
// References (RAG citations)
|
||||
| 'references'
|
||||
|
||||
// Legacy SSE event types (backward compatible)
|
||||
export type LegacyEventType =
|
||||
@@ -335,9 +353,15 @@ export interface SSEEvent {
|
||||
team_id?: string
|
||||
// Memory
|
||||
memory_type?: 'short_term' | 'long_term'
|
||||
// References (RAG citations) - top-level for references event
|
||||
query?: string
|
||||
references?: Reference[]
|
||||
time_ms?: number
|
||||
// Extra data (Agent-UI pattern)
|
||||
extra_data?: {
|
||||
references?: unknown[]
|
||||
query?: string
|
||||
references?: Reference[]
|
||||
time_ms?: number
|
||||
reasoning_steps?: string[]
|
||||
[key: string]: unknown
|
||||
}
|
||||
|
||||
@@ -197,7 +197,7 @@ def __getattr__(name: str):
|
||||
"JSONFileDataStore",
|
||||
"SQLAlchemyDataStore",
|
||||
}
|
||||
_provider_attrs = {"BaseProvider", "RunEvent", "RunEventType"}
|
||||
_provider_attrs = {"BaseProvider", "RunEvent", "RunEventType", "Reference", "ReferenceData"}
|
||||
_providers_attrs = {"PraisonAIProvider"}
|
||||
_config_attrs = {"configure"}
|
||||
_action_attrs = {"Action", "action_callback"}
|
||||
@@ -473,6 +473,8 @@ __all__ = [
|
||||
"BaseProvider",
|
||||
"RunEvent",
|
||||
"RunEventType",
|
||||
"Reference",
|
||||
"ReferenceData",
|
||||
"PraisonAIProvider",
|
||||
# Configuration
|
||||
"configure",
|
||||
|
||||
@@ -25,7 +25,7 @@ from __future__ import annotations
|
||||
import time
|
||||
import uuid
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from dataclasses import asdict, dataclass, field
|
||||
from enum import Enum
|
||||
from typing import Any, AsyncIterator, Dict, List, Optional
|
||||
|
||||
@@ -83,6 +83,52 @@ class RunEventType(str, Enum):
|
||||
TEAM_MEMORY_UPDATE_STARTED = "team_memory_update_started"
|
||||
TEAM_MEMORY_UPDATE_COMPLETED = "team_memory_update_completed"
|
||||
|
||||
# --- References (RAG citations) ---
|
||||
REFERENCES = "references"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# References (RAG citations) — data structures for source chunks
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass
|
||||
class Reference:
|
||||
"""A RAG source chunk that an agent retrieved and used while producing a reply.
|
||||
|
||||
Represents a single piece of content that was cited by the agent,
|
||||
with metadata about its source and chunking.
|
||||
|
||||
Attributes:
|
||||
name: File or document name (e.g., "example.pdf", "doc.txt").
|
||||
content: Chunk text that was retrieved.
|
||||
chunk: Index of this chunk within the document (default: 0).
|
||||
chunk_size: Size in characters of this chunk (default: 0).
|
||||
"""
|
||||
|
||||
name: str
|
||||
content: str
|
||||
chunk: int = 0
|
||||
chunk_size: int = 0
|
||||
|
||||
|
||||
@dataclass
|
||||
class ReferenceData:
|
||||
"""Container for references retrieved in response to a query.
|
||||
|
||||
Represents the result of a RAG retrieval operation, including
|
||||
the query that was used and all chunks that were found.
|
||||
|
||||
Attributes:
|
||||
query: The query that retrieved these chunks.
|
||||
references: List of Reference objects that were retrieved.
|
||||
time_ms: Retrieval latency in milliseconds (optional).
|
||||
"""
|
||||
|
||||
query: str
|
||||
references: List[Reference]
|
||||
time_ms: Optional[float] = None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# RunEvent — the single data structure emitted by providers
|
||||
@@ -223,3 +269,31 @@ class BaseProvider(ABC):
|
||||
async def health(self) -> Dict[str, Any]:
|
||||
"""Health check endpoint data. Override for custom checks."""
|
||||
return {"status": "ok", "provider": self.__class__.__name__}
|
||||
|
||||
async def emit_references(
|
||||
self,
|
||||
query: str,
|
||||
references: List[Reference],
|
||||
time_ms: Optional[float] = None,
|
||||
) -> RunEvent:
|
||||
"""Emit a REFERENCES event with RAG citation data.
|
||||
|
||||
Helper method for providers to emit references (RAG source chunks)
|
||||
that were retrieved and used during answer generation.
|
||||
|
||||
Args:
|
||||
query: The query that retrieved these chunks.
|
||||
references: List of Reference objects that were retrieved.
|
||||
time_ms: Retrieval latency in milliseconds (optional).
|
||||
|
||||
Returns:
|
||||
RunEvent with type REFERENCES and serialized reference data.
|
||||
"""
|
||||
return RunEvent(
|
||||
type=RunEventType.REFERENCES,
|
||||
extra_data={
|
||||
"query": query,
|
||||
"references": [asdict(ref) for ref in references],
|
||||
"time_ms": time_ms,
|
||||
},
|
||||
)
|
||||
@@ -0,0 +1,160 @@
|
||||
"""Test references (RAG citations) protocol and helper functions."""
|
||||
|
||||
from dataclasses import asdict
|
||||
|
||||
from praisonaiui.provider import RunEventType
|
||||
|
||||
|
||||
def test_reference_dataclass():
|
||||
"""Test Reference dataclass schema and serialization."""
|
||||
from praisonaiui.provider import Reference
|
||||
|
||||
# Create with all fields
|
||||
ref = Reference(
|
||||
name="example.txt",
|
||||
content="This is sample content from a document.",
|
||||
chunk=1,
|
||||
chunk_size=500
|
||||
)
|
||||
|
||||
assert ref.name == "example.txt"
|
||||
assert ref.content == "This is sample content from a document."
|
||||
assert ref.chunk == 1
|
||||
assert ref.chunk_size == 500
|
||||
|
||||
# Test serialization
|
||||
ref_dict = asdict(ref)
|
||||
assert ref_dict == {
|
||||
"name": "example.txt",
|
||||
"content": "This is sample content from a document.",
|
||||
"chunk": 1,
|
||||
"chunk_size": 500
|
||||
}
|
||||
|
||||
|
||||
def test_reference_dataclass_defaults():
|
||||
"""Test Reference dataclass with default values."""
|
||||
from praisonaiui.provider import Reference
|
||||
|
||||
# Create with minimal fields, test defaults
|
||||
ref = Reference(
|
||||
name="doc.pdf",
|
||||
content="Some content"
|
||||
)
|
||||
|
||||
assert ref.name == "doc.pdf"
|
||||
assert ref.content == "Some content"
|
||||
assert ref.chunk == 0
|
||||
assert ref.chunk_size == 0
|
||||
|
||||
|
||||
def test_reference_data_dataclass():
|
||||
"""Test ReferenceData dataclass schema and serialization."""
|
||||
from praisonaiui.provider import Reference, ReferenceData
|
||||
|
||||
refs = [
|
||||
Reference("file1.txt", "First chunk content", chunk=0, chunk_size=300),
|
||||
Reference("file2.txt", "Second chunk content", chunk=1, chunk_size=400)
|
||||
]
|
||||
|
||||
ref_data = ReferenceData(
|
||||
query="What is machine learning?",
|
||||
references=refs,
|
||||
time_ms=150.5
|
||||
)
|
||||
|
||||
assert ref_data.query == "What is machine learning?"
|
||||
assert len(ref_data.references) == 2
|
||||
assert ref_data.time_ms == 150.5
|
||||
|
||||
# Test serialization
|
||||
ref_data_dict = asdict(ref_data)
|
||||
assert ref_data_dict["query"] == "What is machine learning?"
|
||||
assert len(ref_data_dict["references"]) == 2
|
||||
assert ref_data_dict["time_ms"] == 150.5
|
||||
|
||||
|
||||
def test_reference_data_dataclass_defaults():
|
||||
"""Test ReferenceData dataclass with default time_ms."""
|
||||
from praisonaiui.provider import Reference, ReferenceData
|
||||
|
||||
refs = [Reference("test.txt", "Test content")]
|
||||
ref_data = ReferenceData(
|
||||
query="test query",
|
||||
references=refs
|
||||
)
|
||||
|
||||
assert ref_data.query == "test query"
|
||||
assert len(ref_data.references) == 1
|
||||
assert ref_data.time_ms is None
|
||||
|
||||
|
||||
def test_references_event_type():
|
||||
"""Test that REFERENCES is added to RunEventType enum."""
|
||||
assert hasattr(RunEventType, "REFERENCES")
|
||||
assert RunEventType.REFERENCES == "references"
|
||||
|
||||
|
||||
def test_emit_references_helper():
|
||||
"""Test emit_references helper method on BaseProvider."""
|
||||
from praisonaiui.provider import BaseProvider, Reference
|
||||
|
||||
class TestProvider(BaseProvider):
|
||||
async def run(self, message, **kwargs):
|
||||
yield # pragma: no cover
|
||||
|
||||
provider = TestProvider()
|
||||
|
||||
# Test that emit_references method exists
|
||||
assert hasattr(provider, "emit_references")
|
||||
|
||||
# Test emit_references returns proper RunEvent
|
||||
refs = [Reference("test.txt", "Test content", chunk=0, chunk_size=100)]
|
||||
|
||||
import asyncio
|
||||
async def test_emit():
|
||||
event = await provider.emit_references(
|
||||
query="test query",
|
||||
references=refs,
|
||||
time_ms=123.4
|
||||
)
|
||||
|
||||
assert event.type == RunEventType.REFERENCES
|
||||
assert event.extra_data is not None
|
||||
assert event.extra_data["query"] == "test query"
|
||||
assert len(event.extra_data["references"]) == 1
|
||||
assert event.extra_data["time_ms"] == 123.4
|
||||
|
||||
# Check reference serialization in event
|
||||
ref_dict = event.extra_data["references"][0]
|
||||
assert ref_dict["name"] == "test.txt"
|
||||
assert ref_dict["content"] == "Test content"
|
||||
assert ref_dict["chunk"] == 0
|
||||
assert ref_dict["chunk_size"] == 100
|
||||
|
||||
asyncio.run(test_emit())
|
||||
|
||||
|
||||
def test_emit_references_without_time():
|
||||
"""Test emit_references helper method without time_ms."""
|
||||
from praisonaiui.provider import BaseProvider, Reference
|
||||
|
||||
class TestProvider(BaseProvider):
|
||||
async def run(self, message, **kwargs):
|
||||
yield # pragma: no cover
|
||||
|
||||
provider = TestProvider()
|
||||
refs = [Reference("doc.txt", "Document content")]
|
||||
|
||||
import asyncio
|
||||
async def test_emit():
|
||||
event = await provider.emit_references(
|
||||
query="another query",
|
||||
references=refs
|
||||
)
|
||||
|
||||
assert event.type == RunEventType.REFERENCES
|
||||
assert event.extra_data["query"] == "another query"
|
||||
assert event.extra_data["time_ms"] is None
|
||||
|
||||
asyncio.run(test_emit())
|
||||
@@ -0,0 +1,206 @@
|
||||
"""Integration tests for references (RAG citations) end-to-end flow."""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
|
||||
from praisonaiui.provider import BaseProvider, Reference, RunEvent, RunEventType
|
||||
|
||||
|
||||
class MockReferencesProvider(BaseProvider):
|
||||
"""Test provider that emits references."""
|
||||
|
||||
async def run(self, message, **kwargs):
|
||||
# Simulate RAG retrieval and citation
|
||||
yield RunEvent(type=RunEventType.RUN_STARTED)
|
||||
|
||||
# Emit some content
|
||||
yield RunEvent(type=RunEventType.RUN_CONTENT, token="Based on the retrieved documents:\n\n")
|
||||
|
||||
# Emit references
|
||||
refs = [
|
||||
Reference("doc1.txt", "This is the first relevant chunk.", chunk=0, chunk_size=250),
|
||||
Reference("doc2.pdf", "This is another relevant piece of information.", chunk=2, chunk_size=300),
|
||||
]
|
||||
|
||||
ref_event = await self.emit_references(
|
||||
query="What is the answer?",
|
||||
references=refs,
|
||||
time_ms=150.5
|
||||
)
|
||||
yield ref_event
|
||||
|
||||
# Continue with content
|
||||
yield RunEvent(type=RunEventType.RUN_CONTENT, token="The answer is based on multiple sources.")
|
||||
yield RunEvent(type=RunEventType.RUN_COMPLETED, content="Based on the retrieved documents:\n\nThe answer is based on multiple sources.")
|
||||
|
||||
|
||||
def test_mock_provider_emits_references():
|
||||
"""Test that the mock provider can emit references events."""
|
||||
provider = MockReferencesProvider()
|
||||
|
||||
async def run_test():
|
||||
events = []
|
||||
async for event in provider.run("test message"):
|
||||
events.append(event)
|
||||
|
||||
# Should have: RUN_STARTED, RUN_CONTENT (1), REFERENCES, RUN_CONTENT (2), RUN_COMPLETED
|
||||
assert len(events) == 5
|
||||
|
||||
# Check references event
|
||||
ref_event = events[2]
|
||||
assert ref_event.type == RunEventType.REFERENCES
|
||||
assert ref_event.extra_data is not None
|
||||
assert ref_event.extra_data["query"] == "What is the answer?"
|
||||
assert len(ref_event.extra_data["references"]) == 2
|
||||
assert ref_event.extra_data["time_ms"] == 150.5
|
||||
|
||||
# Check reference data
|
||||
ref1 = ref_event.extra_data["references"][0]
|
||||
assert ref1["name"] == "doc1.txt"
|
||||
assert ref1["content"] == "This is the first relevant chunk."
|
||||
assert ref1["chunk"] == 0
|
||||
assert ref1["chunk_size"] == 250
|
||||
|
||||
ref2 = ref_event.extra_data["references"][1]
|
||||
assert ref2["name"] == "doc2.pdf"
|
||||
assert ref2["content"] == "This is another relevant piece of information."
|
||||
assert ref2["chunk"] == 2
|
||||
assert ref2["chunk_size"] == 300
|
||||
|
||||
asyncio.run(run_test())
|
||||
|
||||
|
||||
def test_references_event_serialization():
|
||||
"""Test that references events serialize correctly for SSE."""
|
||||
provider = MockReferencesProvider()
|
||||
|
||||
async def run_test():
|
||||
async for event in provider.run("test"):
|
||||
if event.type == RunEventType.REFERENCES:
|
||||
# Test to_dict() serialization
|
||||
event_dict = event.to_dict()
|
||||
|
||||
assert event_dict["type"] == "references"
|
||||
assert "extra_data" in event_dict
|
||||
assert event_dict["extra_data"]["query"] == "What is the answer?"
|
||||
assert len(event_dict["extra_data"]["references"]) == 2
|
||||
assert event_dict["extra_data"]["time_ms"] == 150.5
|
||||
|
||||
# Test JSON serialization (what happens in SSE)
|
||||
json_str = json.dumps(event_dict)
|
||||
parsed = json.loads(json_str)
|
||||
|
||||
assert parsed["type"] == "references"
|
||||
assert parsed["extra_data"]["query"] == "What is the answer?"
|
||||
break
|
||||
|
||||
asyncio.run(run_test())
|
||||
|
||||
|
||||
def test_multiple_reference_queries():
|
||||
"""Test handling multiple reference queries in a single run."""
|
||||
|
||||
class MultiQueryProvider(BaseProvider):
|
||||
async def run(self, message, **kwargs):
|
||||
yield RunEvent(type=RunEventType.RUN_STARTED)
|
||||
|
||||
# First query
|
||||
refs1 = [Reference("file1.txt", "First chunk", chunk=0, chunk_size=100)]
|
||||
event1 = await self.emit_references("query 1", refs1, 50.0)
|
||||
yield event1
|
||||
|
||||
yield RunEvent(type=RunEventType.RUN_CONTENT, token="Based on query 1... ")
|
||||
|
||||
# Second query
|
||||
refs2 = [Reference("file2.txt", "Second chunk", chunk=1, chunk_size=200)]
|
||||
event2 = await self.emit_references("query 2", refs2, 75.0)
|
||||
yield event2
|
||||
|
||||
yield RunEvent(type=RunEventType.RUN_CONTENT, token="and query 2.")
|
||||
yield RunEvent(type=RunEventType.RUN_COMPLETED, content="Based on query 1... and query 2.")
|
||||
|
||||
provider = MultiQueryProvider()
|
||||
|
||||
async def run_test():
|
||||
reference_events = []
|
||||
async for event in provider.run("test"):
|
||||
if event.type == RunEventType.REFERENCES:
|
||||
reference_events.append(event)
|
||||
|
||||
assert len(reference_events) == 2
|
||||
|
||||
# Check first references event
|
||||
assert reference_events[0].extra_data["query"] == "query 1"
|
||||
assert reference_events[0].extra_data["time_ms"] == 50.0
|
||||
assert len(reference_events[0].extra_data["references"]) == 1
|
||||
|
||||
# Check second references event
|
||||
assert reference_events[1].extra_data["query"] == "query 2"
|
||||
assert reference_events[1].extra_data["time_ms"] == 75.0
|
||||
assert len(reference_events[1].extra_data["references"]) == 1
|
||||
|
||||
asyncio.run(run_test())
|
||||
|
||||
|
||||
def test_references_without_time():
|
||||
"""Test references emission without time_ms."""
|
||||
|
||||
class SimpleRefsProvider(BaseProvider):
|
||||
async def run(self, message, **kwargs):
|
||||
refs = [Reference("simple.txt", "Simple content")]
|
||||
event = await self.emit_references("simple query", refs)
|
||||
yield event
|
||||
|
||||
provider = SimpleRefsProvider()
|
||||
|
||||
async def run_test():
|
||||
async for event in provider.run("test"):
|
||||
if event.type == RunEventType.REFERENCES:
|
||||
assert event.extra_data["query"] == "simple query"
|
||||
assert event.extra_data["time_ms"] is None
|
||||
assert len(event.extra_data["references"]) == 1
|
||||
break
|
||||
|
||||
asyncio.run(run_test())
|
||||
|
||||
|
||||
def test_server_sse_forwarding_simulation():
|
||||
"""Test that references events would be forwarded correctly by the server."""
|
||||
provider = MockReferencesProvider()
|
||||
|
||||
async def run_test():
|
||||
# Simulate what the server does: collect events and format as SSE
|
||||
sse_events = []
|
||||
async for event in provider.run("test message"):
|
||||
# This mimics lines 2015-2016 in server.py
|
||||
event_dict = event.to_dict()
|
||||
sse_line = f"data: {json.dumps(event_dict)}\n\n"
|
||||
sse_events.append((event.type, sse_line))
|
||||
|
||||
# Find the references SSE event
|
||||
ref_sse = None
|
||||
for event_type, sse_line in sse_events:
|
||||
if event_type == RunEventType.REFERENCES:
|
||||
ref_sse = sse_line
|
||||
break
|
||||
|
||||
assert ref_sse is not None
|
||||
assert "data: " in ref_sse
|
||||
|
||||
# Parse the SSE data
|
||||
data_part = ref_sse.replace("data: ", "").strip()
|
||||
parsed = json.loads(data_part)
|
||||
|
||||
assert parsed["type"] == "references"
|
||||
assert parsed["extra_data"]["query"] == "What is the answer?"
|
||||
assert len(parsed["extra_data"]["references"]) == 2
|
||||
assert parsed["extra_data"]["time_ms"] == 150.5
|
||||
|
||||
# Verify reference structure matches TypeScript interface
|
||||
ref = parsed["extra_data"]["references"][0]
|
||||
assert "name" in ref
|
||||
assert "content" in ref
|
||||
assert "chunk" in ref
|
||||
assert "chunk_size" in ref
|
||||
|
||||
asyncio.run(run_test())
|
||||
Reference in new issue
Block a user