Accurate Mode

from synap import Synap

sdk = Synap(api_key="your_api_key")

# Long-range ingestion for an important strategic conversation
await sdk.memories.create(
    document=(
        "User: Let's revisit the Project Atlas timeline. I spoke with Sarah Chen "
        "from engineering yesterday, and she's concerned about the Q3 deadline. The "
        "infrastructure team hasn't finished the database migration yet, and James "
        "from DevOps says they need at least three more weeks.\n"
        "Assistant: I'll note that. So the key concerns are: Sarah Chen flagged the "
        "Q3 deadline as infeasible, the database migration is blocking progress, and "
        "James estimates three additional weeks for the infrastructure work. Would you "
        "like me to also note the proposed Q4 fallback?\n"
        "User: Yes. And note that we might bring in two engineers from the platform "
        "team to help accelerate. Maria approved the budget for that yesterday."
    ),
    document_type="ai-chat-conversation",
    user_id="user_123",
    customer_id="acme_corp",
    mode="long-range"
)

# Accurate retrieval for a complex, relationship-aware query
context = await sdk.conversation.context.fetch(
    user_id="user_123",
    customer_id="acme_corp",
    query="What do we know about Project Atlas, including who is involved and what decisions have been made?",
    mode="accurate"
)

for fact in context.facts:
    print(f"[{fact.confidence:.2f}] {fact.content}")
    if fact.entities:
        print(f"  Entities: {', '.join(e.canonical_name for e in fact.entities)}")
    if fact.relationships:
        print(f"  Relationships: {', '.join(str(r) for r in fact.relationships)}")

from synap import Synap

sdk = Synap(api_key="your_api_key")

async def get_context(user_id: str, customer_id: str, query: str, is_complex: bool = False):
    """Retrieve context with automatic mode selection."""

    mode = "accurate" if is_complex else "fast"

    context = await sdk.conversation.context.fetch(
        user_id=user_id,
        customer_id=customer_id,
        query=query,
        mode=mode
    )

    return context


async def ingest_conversation(
    content: str,
    user_id: str,
    customer_id: str,
    is_important: bool = False
):
    """Ingest with mode selection based on importance."""

    mode = "long-range" if is_important else "fast"

    await sdk.memories.create(
        document=content,
        document_type="ai-chat-conversation",
        user_id=user_id,
        customer_id=customer_id,
        mode=mode
    )

def should_use_accurate_mode(query: str) -> bool:
    """Simple heuristic for mode selection based on query characteristics."""

    # Keywords that suggest complex, multi-entity queries
    complex_indicators = [
        "summarize", "everything about", "full briefing",
        "who is involved", "related to", "connected to",
        "all the details", "comprehensive", "overview of",
        "history of", "timeline for"
    ]

    query_lower = query.lower()
    return any(indicator in query_lower for indicator in complex_indicators)