Handle Different Context Lengths Between LLM and Embedding Models

[nkkko]

Is your feature request related to a problem? Please describe. Embedding models typically have smaller context windows than LLMs, which can limit the quality of embeddings generated for large contexts. This discrepancy can result in incomplete or less meaningful embeddings for the context used in devcontainer.json generation.

Describe the solution you'd like

• Propose novel way of handling this challange.
• Ensure that the primary context used by the LLM doesn't lose its semantic meaning when split or summarized for embedding purposes.
• Adjust the logic dynamically based on the maximum token limits of the LLM and embedding models specified in the .env file.

Describe alternatives you've considered

• Using the same context for both LLM and embeddings, which is not feasible due to the context length differences.
• Manually tweaking the context content, which is not scalable or efficient.

Additional context Consider edge cases where critical files like README.md might need special handling to ensure they are adequately represented in both the LLM and embedding contexts. Thorough testing is essential to ensure the adjustments maintain the semantic integrity and utility of the generated devcontainer.json configurations.

[nkkko]

/bounty $20

[algora-pbc[bot]]

💎 $20 bounty • Daytona

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