feat: Universal Local LLM & Custom Endpoint Support (Ollama, LM Studio, vLLM, etc.)

[sreenivas134]

This PR introduces comprehensive support for local LLMs and custom API endpoints. While existing proposals often focus on specific providers (like Ollama), this implementation leverages LiteLLM's full capabilities to provide a universal, config-driven interface that works with any OpenAI-compatible or local provider (LM Studio, vLLM, LocalAI, Ollama, etc.) with minimal configuration.

✨ Key Features

• Universal api_base Support: Adds a global api_base attribute that can be configured via config.yaml, environment variables, or directly through the CLI.
• Automated Provider Normalization: Implements intelligent model-name routing. If a custom endpoint is detected (like LM Studio at localhost:1234), the system automatically ensures the correct openai/ prefix is applied, removing the need for users to manually prefix their models for most local inference servers.
• Robust Direct Parameter Passing: Unlike implementations that rely solely on environment variables, this PR passes the api_base directly to LiteLLM's completion calls, ensuring reliability across multi-provider sessions and preventing environment pollution.
• Centralized Configuration: All LLM setup logic is consolidated into a new setup_litellm helper in utils.py, making the codebase cleaner and easier to maintain.

Verification

• Tested with LM Studio and Ollama local endpoints.
• Verified environment variable isolation (no cross-contamination between different base_url settings).
• Confirmed recursive support for both PDF and Markdown processing flows.

[claude]

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