feat: integrate RAG into hypothesis generation

[Xisen-Wang]

Description

This PR integrates Retrieval-Augmented Generation (RAG) into the hypothesis generation process for Kaggle scenarios. It enhances the KGHypothesisGen class to leverage relevant information from a knowledge base when generating hypotheses.

Key changes:

• Added PDVectorBase initialization in KGHypothesisGen
• Modified prepare_context method to perform RAG search
• Incorporated RAG results into the context for hypothesis generation

Motivation and Context

This change improves the quality and relevance of generated hypotheses by providing additional context from a knowledge base. It allows the model to consider a broader range of information when formulating hypotheses, potentially leading to more insightful and accurate proposals.

Related issue: #[Issue number if applicable]

How Has This Been Tested?

• Pass the test by running: pytest qlib/tests/test_all_pipeline.py under upper directory of qlib.
• Added new test cases in tests/test_kg_hypothesis_gen.py to verify RAG integration.

Screenshots of Test Results (if appropriate):

1. Pipeline test: [Insert screenshot or log snippet]
2. New RAG integration tests: [Insert screenshot or log snippet]

Types of changes

• Fix bugs
• Add new feature
• Update documentation

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📚 Documentation preview 📚: https://RDAgent--267.org.readthedocs.build/en/267/