Intelligent Assistant Architecture
This AI-driven solution integrates multiple data sources to resolve database-specific challenges. The system follows a structured approach:
- Documentation retrieval: Direct access to official database manuals
- Community mining: Extraction of solutions from technical forums
- Intelligent enalysis: Contextual processing using language models
Prompt Engineering
# Role
Expert database consultant specializing in KingbaseES systems
## Core Capabilities
1. Query resolution: Analyze database issues using search tools
2. Clarification protocol: Request additional details for ambiguous questions
## Constraints
- Strictly database-related responses
- Adherence to specified response formats
- Concise information extraction
Knowledge Repository Construction
Building an efffective knowledge base involves:
- Curating official documentation resources
- Organizing troubleshooting guides and configuration references
- Implementing version-controlled updates
API Integration Implementation
from web_interaction import RequestParameters, ResponseHandler
import json
import httpx
def fetch_forum_data(search_term, resource_type):
session_config = {
'headers': {
'Accept': 'application/json',
'Content-Type': 'application/json;charset=UTF-8'
},
'base_url': 'https://bbs.kingbase.com.cn'
}
payload = {
'query': search_term,
'category': f'kingbase_blog_{resource_type}',
'page': 1,
'limit': 100
}
with httpx.Client(**session_config) as client:
response = client.post('/web-api/web/search/queryByKeyWord', json=payload)
return response.json()
def process_query(request: RequestParameters) -> dict:
results = fetch_forum_data(request.query, request.resource_type)
return results
Informtaion Processing Workflow
The data handling pipeline includes:
- Relevance filtering algorithms
- Language model-based prioritization
- Source attribution mechanisms
Performance Optimization
Key enhancements to address search limitations:
- Increased result volume retrieval
- Intelligent relevance scoring
- Query efficiency improvements