Implement the EvalQueryEngineTool:
* Inherits from QueryEngineTool
* Uses a given evaluator to evaluate query engine responses
* If the response fails evaluation:
    * The tool output is manipulated to deter the LLM
      from using it
    * The evaluator feedback is used as a reason for failure

* Simplify eval_query_engine_tool imports and avoid circular dependencies

* Fix EvalQueryEngineTool notebook by increasing similarity_top_k to 5
* The above resulted in the lyft query engine to return a response
  that passed evaluation