Initial commit: Vera-AI v2 with async Qdrant, singleton pattern, monthly curation, and configurable UID/GID/TZ
Features: - AsyncQdrantClient for non-blocking Qdrant operations - Singleton pattern for QdrantService - Monthly full curation (day 1 at 03:00) - Configurable UID/GID for Docker - Timezone support via TZ env var - Configurable log directory (VERA_LOG_DIR) - Volume mounts for config/, prompts/, logs/ - Standard Docker format with .env file Fixes: - Removed unused system_token_budget - Added semantic_score_threshold config - Fixed streaming response handling - Python-based healthcheck (no curl dependency)
This commit is contained in:
Vera-AI
203
app/utils.py
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203
app/utils.py
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"""Utility functions for vera-ai."""
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from .config import config
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import tiktoken
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import os
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from typing import List, Dict
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from datetime import datetime, timedelta
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from pathlib import Path
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# Use cl100k_base encoding (GPT-4 compatible)
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ENCODING = tiktoken.get_encoding("cl100k_base")
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# Configurable paths (can be overridden via environment)
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PROMPTS_DIR = Path(os.environ.get("VERA_PROMPTS_DIR", "/app/prompts"))
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STATIC_DIR = Path(os.environ.get("VERA_STATIC_DIR", "/app/static"))
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# Global qdrant_service instance for utils
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_qdrant_service = None
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def get_qdrant_service():
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"""Get or create the QdrantService singleton."""
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global _qdrant_service
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if _qdrant_service is None:
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from .config import config
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from .qdrant_service import QdrantService
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_qdrant_service = QdrantService(
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host=config.qdrant_host,
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collection=config.qdrant_collection,
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embedding_model=config.embedding_model,
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vector_size=config.vector_size,
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ollama_host=config.ollama_host
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)
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return _qdrant_service
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def count_tokens(text: str) -> int:
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"""Count tokens in text."""
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if not text:
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return 0
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return len(ENCODING.encode(text))
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def count_messages_tokens(messages: List[dict]) -> int:
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"""Count total tokens in messages."""
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total = 0
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for msg in messages:
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if "content" in msg:
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total += count_tokens(msg["content"])
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return total
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def truncate_by_tokens(text: str, max_tokens: int) -> str:
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"""Truncate text to fit within token budget."""
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if not text:
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return text
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tokens = ENCODING.encode(text)
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if len(tokens) <= max_tokens:
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return text
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return ENCODING.decode(tokens[:max_tokens])
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def filter_memories_by_time(memories: List[Dict], hours: int = 24) -> List[Dict]:
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"""Filter memories from the last N hours."""
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cutoff = datetime.utcnow() - timedelta(hours=hours)
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filtered = []
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for mem in memories:
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ts = mem.get("timestamp")
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if ts:
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try:
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# Parse ISO timestamp
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if isinstance(ts, str):
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mem_time = datetime.fromisoformat(ts.replace("Z", "+00:00").replace("+00:00", ""))
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else:
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mem_time = ts
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if mem_time > cutoff:
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filtered.append(mem)
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except Exception:
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# If timestamp parsing fails, include the memory
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filtered.append(mem)
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else:
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# Include memories without timestamp
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filtered.append(mem)
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return filtered
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def merge_memories(memories: List[Dict]) -> Dict:
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"""Merge multiple memories into one combined text."""
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if not memories:
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return {"text": "", "ids": []}
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texts = []
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ids = []
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for mem in memories:
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text = mem.get("text", "") or mem.get("content", "")
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if text:
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# Include role if available
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role = mem.get("role", "")
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if role:
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texts.append(f"[{role}]: {text}")
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else:
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texts.append(text)
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ids.append(mem.get("id"))
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return {
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"text": "\n\n".join(texts),
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"ids": ids
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}
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def calculate_token_budget(total_budget: int, system_ratio: float = 0.2,
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semantic_ratio: float = 0.5, context_ratio: float = 0.3) -> Dict[int, int]:
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"""Calculate token budgets for each layer."""
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return {
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"system": int(total_budget * system_ratio),
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"semantic": int(total_budget * semantic_ratio),
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"context": int(total_budget * context_ratio)
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}
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def load_system_prompt() -> str:
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"""Load system prompt from prompts directory."""
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import logging
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logger = logging.getLogger(__name__)
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# Try prompts directory first, then static for backward compatibility
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prompts_path = PROMPTS_DIR / "systemprompt.md"
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static_path = STATIC_DIR / "systemprompt.md"
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if prompts_path.exists():
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return prompts_path.read_text().strip()
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elif static_path.exists():
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return static_path.read_text().strip()
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else:
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logger.warning("systemprompt.md not found")
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return ""
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async def build_augmented_messages(incoming_messages: List[Dict]) -> List[Dict]:
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"""Build 4-layer augmented messages from incoming messages.
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This is a standalone version that can be used by proxy_handler.py.
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"""
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import logging
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logger = logging.getLogger(__name__)
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# Load system prompt
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system_prompt = load_system_prompt()
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# Get user question (last user message)
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user_question = ""
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for msg in reversed(incoming_messages):
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if msg.get("role") == "user":
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user_question = msg.get("content", "")
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break
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# Get search context (last few turns)
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search_context = ""
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for msg in incoming_messages[-6:]:
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if msg.get("role") in ("user", "assistant"):
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search_context += msg.get("content", "") + " "
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messages = []
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# === LAYER 1: System Prompt ===
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system_content = ""
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for msg in incoming_messages:
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if msg.get("role") == "system":
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system_content = msg.get("content", "")
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break
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if system_prompt:
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system_content += "\n\n" + system_prompt
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if system_content:
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messages.append({"role": "system", "content": system_content})
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# === LAYER 2: Semantic (curated memories) ===
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qdrant = get_qdrant_service()
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semantic_results = await qdrant.semantic_search(
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query=search_context if search_context else user_question,
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limit=20,
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score_threshold=config.semantic_score_threshold,
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entry_type="curated"
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)
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semantic_tokens = 0
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for result in semantic_results:
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payload = result.get("payload", {})
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text = payload.get("text", "")
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if text and semantic_tokens < config.semantic_token_budget:
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messages.append({"role": "user", "content": text}) # Add as context
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semantic_tokens += count_tokens(text)
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# === LAYER 3: Context (recent turns) ===
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recent_turns = await qdrant.get_recent_turns(limit=20)
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context_tokens = 0
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for turn in reversed(recent_turns):
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payload = turn.get("payload", {})
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text = payload.get("text", "")
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if text and context_tokens < config.context_token_budget:
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messages.append({"role": "user", "content": text}) # Add as context
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context_tokens += count_tokens(text)
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# === LAYER 4: Current messages (passed through) ===
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for msg in incoming_messages:
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if msg.get("role") != "system": # Do not duplicate system
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messages.append(msg)
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return messages
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