Lollms Artifacts And Surgical Patching

Lollms Artifacts and Surgical Patching

This skill explains how to utilize, create, and surgically update persistent artifacts using Lollms.

1. Creating and Updating Artifacts

Artifacts are persistent, versioned documents (code, text, notes) stored in discussion metadata.

• Creating new: Provide 100% of the content inside <artifact name="filename" type="type">...</artifact>.
• Updating existing: Use SEARCH/REPLACE Aider-style patches to modify targeted regions.

2. Strategic Formulation: The Coding Plan

Before outputting any <artifact> update, the model must formulate a plan inside <coding_plan> to organize its implementation strategy:

<coding_plan>
  - Goal: Implement safe multiplication
  - Specialist Persona: Python Programmer
  - Target Artifacts: math_ops.py
  - Implementation Details: Add a method compute_multiply(a, b).
</coding_plan>

3. Surgical Patch Rules

When patching an existing file, the SEARCH block must copy lines exactly as they appear in the active artifact (matching all leading indentation spaces and comments character-for-character).

<artifact name="math_ops.py" type="code" language="python">
<<<<<<< SEARCH
        def compute_sum(a, b):
            # Target region
            return a + b
=======
        def compute_sum(a, b):
            # Target region
            return a + b

        def compute_product(a, b):
            # Safe multiplication logic
            return a * b
>>>>>>> REPLACE
</artifact>

4. Indentation-Agnostic Fuzzy Matching

If the LLM outputs a patch at column-0, or with slightly imperfect indentation, the Lollms Aider patch engine automatically:

1. Calculates the alignment offset of the matched region in the file.
2. Re-indents the REPLACE block to align with the rest of the code structure on disk.

5. Controlling In-Message Status Reporting

When performing complex tasks, the system can write status updates to the chat bubble. Use enable_in_message_status to toggle this behavior:

• enable_in_message_status=True (Default): Streaming chunks emit <processing> tags showing updates in real-time.
• enable_in_message_status=False: The prose stream remains 100% clean, and updates are fired only via background structured callbacks.

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> Source: [ParisNeo/lollms_client](https://github.com/ParisNeo/lollms_client) — distributed by [TomeVault](https://tomevault.io).
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