4511f4c801
🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
import requests
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import json
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class LLMAnalyzer:
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"""Analyzes text for secrets using a local LLM via Ollama."""
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def __init__(self, endpoint: str, model: str, system_prompt: str):
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self.endpoint = endpoint
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self.model = model
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self.system_prompt = system_prompt
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def analyze(self, text: str) -> dict:
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"""
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Sends text to the Ollama API for analysis and returns a structured JSON response.
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Returns:
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A dictionary like:
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{
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"secret_found": bool,
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"secret_type": str,
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"confidence_score": float,
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"severity": str
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}
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Returns a default "not found" response on error.
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"""
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prompt = f"Log entry: \"{text}\"\n\nAnalyze this for secrets and respond with only the required JSON."
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payload = {
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"model": self.model,
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"system": self.system_prompt,
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"prompt": prompt,
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"format": "json",
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"stream": False
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}
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try:
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response = requests.post(self.endpoint, json=payload, timeout=15)
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response.raise_for_status()
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# The response from Ollama is a JSON string, which needs to be parsed.
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analysis = json.loads(response.json().get("response", "{}"))
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return analysis
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except (requests.exceptions.RequestException, json.JSONDecodeError) as e:
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print(f"[ERROR] LLMAnalyzer failed: {e}")
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# Fallback: If LLM fails, assume no secret was found to avoid blocking the pipeline.
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return {"secret_found": False}
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