AIMon reranker integration into LlamaIndex node postprocessors

[devvratbhardwaj]

Description

This PR integrates the AIMon reranker as a LlamaIndex postprocessor, allowing for context-aware, domain-specific reranking of retrieval results. The AIMon reranker performs strongly in benchmarks and enables users to specify a domain in the task definition, providing context-specific reranking for improved relevance.

Additionally, this implementation introduces batched reranking with a 10,000-word limit per request, ensuring efficient processing while maintaining low latency. The reranking calls are serialized to prevent concurrency issues and optimize performance.

New Package?

Did I fill in the tool.llamahub section in the pyproject.toml and provide a detailed README.md for my new integration or package?

• Yes
• No

Version Bump?

Did I bump the version in the pyproject.toml file of the package I am updating? (Except for the llama-index-core package)

• Yes
• No

Type of Change

• New feature (non-breaking change which adds functionality)

How Has This Been Tested?

• I added new unit tests to cover this change
• I believe this change is already covered by existing unit tests
• I have tested my code successfully in end-to-end testing

Suggested Checklist:

• I have performed a self-review of my own code
• I have performed a pre-commit check
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• I have added Google Colab support for the newly added notebooks.
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• I ran make format; make lint to appease the lint gods
• I will add a Google Colab notebook example in a stacked PR following this PR