DB cache in form of decorator

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📥 Commits
Reviewing files that changed from the base of the PR and between e8d00ec6b3b28f7410b389c058b8ed3969854c6f and 1c8028fbe7cf4d87558eac168881b0b8fe6f2d60.

Walkthrough

This pull request introduces a new caching mechanism using a PostgreSQL database through the db_cache decorator, which replaces previous in-memory caching strategies in various functions across multiple files. It includes the creation of a FunctionCache model to store function metadata and results, and updates several existing functions to utilize this new caching approach. Additionally, it removes certain classes related to data storage and adds comprehensive unit tests to validate the caching behavior.

Changes

File	Change Summary
`prediction_market_agent_tooling/tools/caches/db_cache.py`	Introduced a new caching mechanism with `FunctionCache` model and `db_cache` decorator, supporting various parameters for flexible caching behavior.
`prediction_market_agent_tooling/tools/google.py`	Updated `search_google` function to use `@db_cache(max_age=timedelta(days=1))` instead of in-memory caching, modifying the import statement accordingly.
`prediction_market_agent_tooling/tools/is_invalid.py`	Replaced `@persistent_inmemory_cache` with `@db_cache` in `is_invalid` function, maintaining the function's internal logic.
`prediction_market_agent_tooling/tools/is_predictable.py`	Updated `@persistent_inmemory_cache` to `@db_cache` for both `is_predictable_binary` and `is_predictable_without_description` functions, preserving their signatures and logic.
`prediction_market_agent_tooling/tools/relevant_news_analysis/relevant_news_analysis.py`	Modified date handling in `analyse_news_relevance` and `get_certified_relevant_news_since` functions, changing parameter types and simplifying logic.
`prediction_market_agent_tooling/tools/tavily/tavily_models.py`	Removed `TavilyResponseModel` class, eliminating structured representation of Tavily responses in the database.
`prediction_market_agent_tooling/tools/tavily/tavily_search.py`	Added `@db_cache` to `tavily_search`, updated parameter types from `days` to `news_since`, and modified related logic for handling date parameters.
`prediction_market_agent_tooling/tools/tavily/tavily_storage.py`	Deleted `TavilyStorage` class, which managed storage and retrieval of TavilyResponse data in SQL database.
`tests/conftest.py`	Added a new pytest fixture `keys_with_sqlalchemy_db_url` to provide an instance of `APIKeys` initialized with a SQLAlchemy database URL.
`tests/tools/test_db_cache.py`	Introduced unit tests for `db_cache` decorator, covering various input types and caching scenarios, ensuring correct behavior of the caching mechanism.

Possibly related PRs

#337: This PR discusses caching mechanisms related to the Tavily service, which is directly relevant to the new caching functionality introduced in the main PR's db_cache.py.
#380: This PR enhances the caching logic in the tavily_storage module, which aligns with the caching strategies implemented in the main PR.
#524: This PR improves error handling and logic in the tavily_search function, which is relevant as it interacts with the caching mechanisms introduced in the main PR.
#529: This PR introduces a utility function that utilizes caching for retrieving relevant news, directly connecting to the caching functionality established in the main PR.

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gnosis / prediction-market-agent-tooling