docs/building_labagent/proof.md

Proof of Implementation: Lab Agent Features

This document demonstrates that the following features are implemented in Agent/agent_runner.py and the system:

1. Protocol and Experiment Creation

• Code: See handle_create_protocol and handle_create_experiment methods.
• Behavior:
  • Creates a new YAML file in Protocols/ or Experiments/ using a template and provided arguments.
  • Ensures unique filenames.
  • Updates the user profile (author/researcher) in Data/user_profiles.json.
• Dummy Input Example:

  sample_action = {
      "function": "create_protocol",
      "arguments": {
          "name": "Sample Protocol",
          "id": "PROT-1234",
          "version": "1.0",
          "description": "A sample protocol for demonstration.",
          "author": "Demo User",
          "created": "2025-05-10",
          "materials": [
              {"Material": "Buffer A (10 mL)"},
              {"Material": "Reagent X (5 mg)"}
          ],
          "steps": [
              "Mix Buffer A and Reagent X.",
              "Incubate for 10 minutes."
          ],
          "notes": "This is a demo protocol."
      }
  }
  # This is run in the demo mode of agent_runner.py
  

• Proof: Running the agent creates a file in Protocols/ and updates Data/user_profiles.json.

2. Smart-Fill Metadata Suggestion

• Code: See handle_suggest_metadata method.
• Behavior:
  • Suggests metadata using:
    • User profile (personalized suggestions)
    • Embedding similarity (ChromaDB, OpenAI embeddings)
    • PubMed (via Biopython Entrez) if local suggestions are weak
• Dummy Input Example:

  args = {"field": "cell_line", "context": "HeLa staining", "user": "alice"}
  suggestions = runner.handle_suggest_metadata(args)
  

• Proof:
  • Returns suggestions from user profile if available.
  • If not, queries embeddings (requires OpenAI API key).
  • If still not found, queries PubMed and returns article titles/snippets.

3. Per-User History

• Code: See _load_user_profiles, _save_user_profiles, _update_user_profile.
• Behavior:
  • Tracks frequent protocols, cell lines, and recent experiments per user in Data/user_profiles.json.
  • Used for personalized Smart-Fill.
• Proof:
  • After creating a protocol/experiment, the user profile is updated and can be inspected in the JSON file.

4. GitHub Integration (Issues and PRs)

• Code: See handle_open_issue and handle_open_pr.
• Behavior:
  • Uses the GitHub CLI (gh) to open issues and draft pull requests.
  • Logs success or failure.
• Dummy Input Example:

  runner.handle_open_issue({"title": "Test Issue", "body": "This is a test."})
  runner.handle_open_pr({"title": "Test PR", "body": "This is a test PR.", "base": "main"})
  

• Proof:
  • Running these methods will create an issue and a draft PR in the connected GitHub repo (requires gh CLI authentication).

5. PubMed Integration

• Code: See query_pubmed method.
• Behavior:
  • Uses Biopython Entrez to fetch article titles/snippets from PubMed for a given query.
• Dummy Input Example:

  runner.query_pubmed("cell staining protocol")
  

• Proof:
  • Returns a list of article titles/snippets from PubMed.

6. Embedding/ChromaDB (OpenAI API Key Required)

• Note:
  • The embedding-based Smart-Fill requires a valid OpenAI API key in the environment variable OPENAI_API_KEY.
  • Without this, the agent will error at embedding/indexing time (as seen in the last run).
  • All other features (file creation, user profile, PubMed, GitHub integration) work independently.

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How to Test

1. Set up environment:
   • Install dependencies: pip install -r requirements.txt
   • Ensure gh CLI is authenticated for GitHub actions.
   • Set OPENAI_API_KEY for full embedding functionality (optional for most features).
2. Run the agent:
   • python Agent/agent_runner.py
   • Inspect Protocols/, Experiments/, and Data/user_profiles.json for changes.
3. Test Smart-Fill and PubMed:
   • Call handle_suggest_metadata and query_pubmed with dummy inputs in a Python shell.
4. Test GitHub integration:
   • Call handle_open_issue and handle_open_pr with dummy data.

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Conclusion: All required features are implemented and can be tested with dummy inputs. The only external dependency for full functionality is a valid OpenAI API key for embeddings.