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| """ | |
| Tool for analyzing legal evidence using DEEPresearch. | |
| """ | |
| import requests | |
| import json | |
| from langchain.tools import BaseTool | |
| from AI_core.config import DEEPSEARCH_API_URL | |
| class EvidenceAnalysisTool(BaseTool): | |
| """Tool to analyze legal evidence using DEEPresearch.""" | |
| name: str = "evidence_analysis_tool" | |
| description: str = "Analyzes legal evidence using DEEPresearch and builds relationships between entities. Input should be a legal query about evidence or case analysis." | |
| def _run(self, query: str) -> str: | |
| """ | |
| Analyze legal evidence using DEEPresearch. | |
| Args: | |
| query: Legal query about evidence or case analysis | |
| Returns: | |
| str: Analysis results from DEEPresearch | |
| """ | |
| headers = { | |
| 'Content-Type': 'application/json' | |
| } | |
| data = { | |
| "model": "jina-deepsearch-v1", | |
| "messages": [ | |
| { | |
| "role": "system", | |
| "content": "You are a legal evidence analyst. Your task is to analyze legal evidence, extract key entities, and establish relationships between them." | |
| }, | |
| { | |
| "role": "user", | |
| "content": query | |
| } | |
| ], | |
| "stream": False, | |
| "reasoning_effort": "low", | |
| "max_attempts": 2, | |
| "no_direct_answer": False | |
| } | |
| try: | |
| # In production, use actual API call | |
| response = requests.post(DEEPSEARCH_API_URL, headers=headers, json=data) | |
| result = response.json()['choices'][0]['message']['content'] | |
| return result | |
| except Exception as e: | |
| return f"Error analyzing evidence with DEEPresearch: {str(e)}" |