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
208
app/context_handler.py
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208
app/context_handler.py
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"""Context handler - builds 4-layer context for every request."""
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import httpx
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import logging
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from typing import List, Dict, Any, Optional
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from pathlib import Path
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from .config import Config
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from .qdrant_service import QdrantService
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from .utils import count_tokens, truncate_by_tokens
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logger = logging.getLogger(__name__)
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class ContextHandler:
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def __init__(self, config: Config):
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self.config = config
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self.qdrant = 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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ollama_host=config.ollama_host
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)
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self.system_prompt = self._load_system_prompt()
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def _load_system_prompt(self) -> str:
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"""Load system prompt from static/systemprompt.md."""
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try:
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path = Path(__file__).parent.parent / "static" / "systemprompt.md"
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return path.read_text().strip()
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except FileNotFoundError:
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logger.error("systemprompt.md not found - required file")
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raise
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async def process(self, messages: List[Dict], model: str, stream: bool = False) -> Dict:
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"""Process chat request through 4-layer context."""
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# Get user question (last user message)
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user_question = ""
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for msg in reversed(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 messages for semantic search (last N turns)
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search_messages = []
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for msg in messages[-self.config.semantic_search_turns:]:
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if msg.get("role") in ("user", "assistant"):
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search_messages.append(msg.get("content", ""))
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# Build the 4-layer context messages
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context_messages = await self.build_context_messages(
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incoming_system=next((m for m in messages if m.get("role") == "system"), None),
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user_question=user_question,
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search_context=" ".join(search_messages)
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)
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# Forward to Ollama
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async with httpx.AsyncClient(timeout=120.0) as client:
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response = await client.post(
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f"{self.config.ollama_host}/api/chat",
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json={"model": model, "messages": context_messages, "stream": stream}
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)
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result = response.json()
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# Store the Q&A turn in Qdrant
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assistant_msg = result.get("message", {}).get("content", "")
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await self.qdrant.store_qa_turn(user_question, assistant_msg)
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return result
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def _parse_curated_turn(self, text: str) -> List[Dict]:
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"""Parse a curated turn into alternating user/assistant messages.
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Input format:
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User: [question]
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Assistant: [answer]
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Timestamp: ISO datetime
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Returns list of message dicts with role and content.
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"""
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messages = []
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lines = text.strip().split("\n")
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current_role = None
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current_content = []
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for line in lines:
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line = line.strip()
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if line.startswith("User:"):
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# Save previous content if exists
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if current_role and current_content:
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messages.append({
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"role": current_role,
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"content": "\n".join(current_content).strip()
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})
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current_role = "user"
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current_content = [line[5:].strip()] # Remove "User:" prefix
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elif line.startswith("Assistant:"):
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# Save previous content if exists
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if current_role and current_content:
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messages.append({
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"role": current_role,
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"content": "\n".join(current_content).strip()
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})
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current_role = "assistant"
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current_content = [line[10:].strip()] # Remove "Assistant:" prefix
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elif line.startswith("Timestamp:"):
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# Ignore timestamp line
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continue
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elif current_role:
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# Continuation of current message
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current_content.append(line)
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# Save last message
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if current_role and current_content:
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messages.append({
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"role": current_role,
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"content": "\n".join(current_content).strip()
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})
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return messages
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async def build_context_messages(self, incoming_system: Optional[Dict], user_question: str, search_context: str) -> List[Dict]:
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"""Build 4-layer context messages array."""
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messages = []
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token_budget = {
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"semantic": self.config.semantic_token_budget,
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"context": self.config.context_token_budget
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}
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# === LAYER 1: System Prompt (pass through unchanged) ===
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# DO NOT truncate - preserve OpenClaw's system prompt entirely
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system_content = ""
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if incoming_system:
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system_content = incoming_system.get("content", "")
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logger.info(f"System layer: preserved incoming system {len(system_content)} chars, {count_tokens(system_content)} tokens")
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# Add Vera context info if present (small, just metadata)
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if self.system_prompt.strip():
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system_content += "\n\n" + self.system_prompt
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logger.info(f"System layer: added vera context {len(self.system_prompt)} chars")
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messages.append({"role": "system", "content": system_content})
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# === LAYER 2: Semantic Layer (curated memories) ===
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# Search for curated blocks only
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semantic_results = await self.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=self.config.semantic_score_threshold,
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entry_type="curated"
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)
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# Parse curated turns into alternating user/assistant messages
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semantic_messages = []
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semantic_tokens_used = 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:
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parsed = self._parse_curated_turn(text)
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for msg in parsed:
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msg_tokens = count_tokens(msg.get("content", ""))
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if semantic_tokens_used + msg_tokens <= token_budget["semantic"]:
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semantic_messages.append(msg)
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semantic_tokens_used += msg_tokens
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else:
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break
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# Add parsed messages to context
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for msg in semantic_messages:
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messages.append(msg)
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if semantic_messages:
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logger.info(f"Semantic layer: {len(semantic_messages)} messages, ~{semantic_tokens_used} tokens")
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# === LAYER 3: Context Layer (recent turns) ===
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recent_turns = await self.qdrant.get_recent_turns(limit=50)
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context_messages_parsed = []
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context_tokens_used = 0
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for turn in reversed(recent_turns): # Oldest first
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payload = turn.get("payload", {})
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text = payload.get("text", "")
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entry_type = payload.get("type", "raw")
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if text:
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# Parse turn into messages
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parsed = self._parse_curated_turn(text)
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for msg in parsed:
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msg_tokens = count_tokens(msg.get("content", ""))
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if context_tokens_used + msg_tokens <= token_budget["context"]:
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context_messages_parsed.append(msg)
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context_tokens_used += msg_tokens
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else:
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break
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for msg in context_messages_parsed:
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messages.append(msg)
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if context_messages_parsed:
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logger.info(f"Context layer: {len(context_messages_parsed)} messages, ~{context_tokens_used} tokens")
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# === LAYER 4: Current Question ===
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messages.append({"role": "user", "content": user_question})
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return messages
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