haiku.rag/haiku_rag_slim/haiku/rag/agents/chat/context.py
2026-01-27 10:38:18 +02:00

116 lines
3.8 KiB
Python

from datetime import datetime, timedelta
from pydantic_ai import Agent
from haiku.rag.agents.chat.prompts import SESSION_SUMMARY_PROMPT
from haiku.rag.agents.chat.state import ChatSessionState, QAResponse, SessionContext
from haiku.rag.config.models import AppConfig
from haiku.rag.utils import get_model
# Cache for session contexts (session_id -> SessionContext)
# Used to persist async summarization results between requests
_session_context_cache: dict[str, SessionContext] = {}
_cache_timestamps: dict[str, datetime] = {}
_CACHE_TTL = timedelta(hours=1)
def _cleanup_stale_cache() -> None:
"""Remove cache entries older than TTL."""
now = datetime.now()
stale = [sid for sid, ts in _cache_timestamps.items() if now - ts > _CACHE_TTL]
for sid in stale:
_session_context_cache.pop(sid, None)
_cache_timestamps.pop(sid, None)
def cache_session_context(session_id: str, context: SessionContext) -> None:
"""Store session context in cache."""
_cleanup_stale_cache()
_session_context_cache[session_id] = context
_cache_timestamps[session_id] = datetime.now()
def get_cached_session_context(session_id: str) -> SessionContext | None:
"""Get session context from server cache."""
_cleanup_stale_cache()
return _session_context_cache.get(session_id)
async def summarize_session(
qa_history: list[QAResponse],
config: AppConfig,
current_context: str | None = None,
) -> str:
"""Summarize qa_history into compact context.
Args:
qa_history: List of Q&A pairs from the conversation.
config: AppConfig for model selection.
current_context: Previous session_context.summary to incorporate.
The summarizer will build upon this.
Returns:
Markdown summary of the conversation history.
"""
if not qa_history:
return ""
model = get_model(config.qa.model, config)
agent: Agent[None, str] = Agent(
model,
output_type=str,
instructions=SESSION_SUMMARY_PROMPT,
retries=2,
)
history_text = _format_qa_history(qa_history)
if current_context:
history_text = f"## Current Context\n{current_context}\n\n{history_text}"
result = await agent.run(history_text)
return result.output
async def update_session_context(
qa_history: list[QAResponse],
config: AppConfig,
session_state: ChatSessionState,
) -> None:
"""Update session context in the session state.
Args:
qa_history: List of Q&A pairs from the conversation.
config: AppConfig for model selection.
session_state: The session state to update.
"""
# Use existing session_context summary if available, else initial_context
current_context: str | None = None
if session_state.session_context and session_state.session_context.summary:
current_context = session_state.session_context.summary
elif session_state.initial_context:
current_context = session_state.initial_context
summary = await summarize_session(
qa_history, config, current_context=current_context
)
session_state.session_context = SessionContext(
summary=summary,
last_updated=datetime.now(),
)
# Also cache for next-run delivery in stateless contexts
if session_state.session_id:
cache_session_context(session_state.session_id, session_state.session_context)
def _format_qa_history(qa_history: list[QAResponse]) -> str:
"""Format qa_history for input to summarization."""
lines: list[str] = []
for i, qa in enumerate(qa_history, 1):
lines.append(f"## Q{i}: {qa.question}")
lines.append(f"**Answer** (confidence: {qa.confidence:.0%}):")
lines.append(qa.answer)
if qa.sources:
lines.append(f"**Sources:** {', '.join(qa.sources)}")
lines.append("")
return "\n".join(lines)