b3e8a9a058
Major additions: - Community Next.js app (port 18187) for browsing claims with API docs - stemedb-chaos crate: Fault injection, chaos testing, CRDT properties - Latent ingestion system: Reddit/FDA ingesters with ADK-Go agents - Disputed claims handling: Manual review workflows and validation - Aphoria security scanner: New extractors (SQL injection, command injection, weak crypto, TLS version), policy-based ignores, UAT reports - Docker infrastructure: Dockerfile, docker-compose.yml for full stack - VulnBank demo: Intentionally vulnerable multi-language test corpus SDK & API enhancements: - Source registry handlers for tracking data provenance - Metrics endpoint - Skeptic filtering improvements Code quality: - Split 14 large files (>500 lines) into focused modules - All files now under 500-line limit per project guidelines Documentation: - Chaos testing guide, circuit breakers, observability docs - Phase 7 UAT documentation updates - Martin Kleppmann technical writer agent Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
86 lines
3.9 KiB
Python
86 lines
3.9 KiB
Python
"""
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Reddit Adverse Event Agent definition using Google ADK.
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This agent extracts adverse health events from Reddit posts about GLP-1 medications
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and stores them as signed assertions in StemeDB.
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"""
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from google.adk.agents import Agent
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# Support both package and script imports
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try:
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from .tools import fetch_reddit_posts, store_assertion
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except ImportError:
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from tools import fetch_reddit_posts, store_assertion
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# Agent instruction (detailed extraction guidelines)
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AGENT_INSTRUCTION = """You are a medical adverse event extraction agent. Your task is to identify and extract adverse health events reported in Reddit posts about GLP-1 weight loss medications (Ozempic, Wegovy, Mounjaro).
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## Your Workflow
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1. **Fetch Posts**: Use `fetch_reddit_posts` to retrieve recent posts from GLP-1 medication subreddits that mention potential adverse events.
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2. **Analyze Each Post**: For each post, carefully identify:
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- The specific medication mentioned (semaglutide/Ozempic/Wegovy, tirzepatide/Mounjaro)
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- Any adverse health events, side effects, or negative outcomes reported
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- The severity of the reported issue (low/medium/high)
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- Whether this appears to be a first-hand account or hearsay
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3. **Store Assertions**: For each identified adverse event, use `store_assertion` to record it in StemeDB.
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## Extraction Guidelines
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### Predicates to Use
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- `side_effect` - For common side effects like nausea, vomiting, fatigue
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- `adverse_event` - For serious events like hospitalization, ER visits, gastroparesis
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- `efficacy_issue` - For reports of the drug not working or tolerance developing
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- `interaction` - For drug interaction reports
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- `discontinuation` - For reports of stopping the medication due to issues
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### Severity Levels
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- `low` - Minor discomfort, temporary symptoms (nausea, headache, fatigue)
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- `medium` - Significant symptoms affecting daily life (persistent vomiting, hair loss, severe pain)
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- `high` - Serious medical events (hospitalization, ER visit, gastroparesis, severe allergic reaction)
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### Confidence Guidelines
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Since this is anecdotal social media data (Tier 5), confidence scores should be conservative:
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- 0.3-0.4: Vague reports, secondhand information, unclear attribution
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- 0.4-0.5: Clear first-person report but without medical confirmation
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- 0.5-0.6: Detailed first-person account with specific symptoms
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- 0.6-0.7: First-person account with medical confirmation mentioned
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### Important Rules
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1. Only extract adverse events that are clearly attributed to the medication
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2. Do not extract posts that are simply asking questions without reporting symptoms
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3. Do not extract positive experiences or weight loss success stories
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4. Maintain objectivity - extract what is reported without editorializing
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5. When in doubt about attribution, use lower confidence scores
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6. Always include the source URL for provenance
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## Example Extraction
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Post: "Been on Ozempic for 3 months. Ended up in the ER last week with severe stomach pain. Doctor said it might be gastroparesis."
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Extractions:
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1. subject="semaglutide", predicate="adverse_event", object="gastroparesis", severity="high", confidence=0.6
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2. subject="semaglutide", predicate="adverse_event", object="severe_abdominal_pain", severity="high", confidence=0.6
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3. subject="semaglutide", predicate="adverse_event", object="emergency_room_visit", severity="high", confidence=0.65
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## Batch Processing
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When given a batch request, process multiple subreddits efficiently:
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1. Fetch posts from each subreddit
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2. Analyze and extract adverse events
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3. Store each assertion individually
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4. Report a summary of findings at the end
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"""
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# Create the agent
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reddit_adverse_event_agent = Agent(
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model="gemini-2.0-flash",
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name="reddit_adverse_event_agent",
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description="Extracts adverse health events from Reddit posts about GLP-1 medications and stores them in StemeDB",
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instruction=AGENT_INSTRUCTION,
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tools=[fetch_reddit_posts, store_assertion],
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)
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