Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 38 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a mechanism for the `ContextAnalyst` agent to store and retrieve user preferences, historical interactions, and important facts in a Qdrant vector database. The agent should use embeddings (e.g., from Gemini's embedding models) to store these memories and retrieve relevant ones based on the current user query. Provide a Python class or functions demonstrating this integration.
```python
# Example Qdrant integration sketch
from qdrant_client import QdrantClient, models
# from your_embedding_model_library import get_embedding # e.g., from Gemini
class UserMemory:
def __init__(self, collection_name="user_memories"):
self.client = QdrantClient("localhost", port=6333) # Or your Qdrant instance
self.collection_name = collection_name
self.client.recreate_collection(
collection_name=self.collection_name,
vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE), # Adjust size for Gemini embeddings
)
def store_memory(self, user_id: str, text: str, metadata: dict = None):
# vector = get_embedding(text) # Replace with actual embedding call
vector = [0.1] * 768 # Placeholder
self.client.upsert(
collection_name=self.collection_name,
points=[
models.PointStruct(
id=str(uuid.uuid4()),
vector=vector,
payload={"user_id": user_id, "text": text, **(metadata or {})}
)
]
)
def retrieve_memory(self, user_id: str, query_text: str, top_k: int = 3):
# query_vector = get_embedding(query_text) # Replace with actual embedding call
query_vector = [0.2] * 768 # Placeholder
search_result = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=models.Filter(must=[models.FieldCondition(key="user_id", match=models.MatchValue(value=user_id))]),
limit=top_k
)
return [point.payload['text'] for point in search_result]
# The ContextAnalyst agent would then use this class.
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Build Proactive Personalized Assistant with AutoGen & Gemini 2.5 Pro
Inspired by Apple's shift towards a personalized, Gemini-powered Siri, this challenge tasks you with building a sophisticated multi-agent system using Microsoft's AutoGen framework. The goal is to create a proactive digital assistant that anticipates user needs, learns from interactions, and leverages dynamic tool use to provide personalized assistance in real-time. This system should be capable of understanding complex user contexts, synthesizing information from various sources, and initiating relevant actions without explicit prompting, mimicking a truly intelligent personal assistant.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.