Currently `langchain.tools.sql_database.tool.QueryCheckerTool` has a
field `llm` with type `BaseLLM`. This breaks initialization for some
LLMs. For example, trying to use it with GPT4:
```python
from langchain.sql_database import SQLDatabase
from langchain.chat_models import ChatOpenAI
from langchain.tools.sql_database.tool import QueryCheckerTool
db = SQLDatabase.from_uri("some_db_uri")
llm = ChatOpenAI(model_name="gpt-4")
tool = QueryCheckerTool(db=db, llm=llm)
# pydantic.error_wrappers.ValidationError: 1 validation error for QueryCheckerTool
# llm
# Can't instantiate abstract class BaseLLM with abstract methods _agenerate, _generate, _llm_type (type=type_error)
```
Seems like much of the rest of the codebase has switched from `BaseLLM`
to `BaseLanguageModel`. This PR makes the change for QueryCheckerTool as
well
Co-authored-by: Zachary Jones <zjones@zetaglobal.com>
This PR adds
* `ZeroShotAgent.as_sql_agent`, which returns an agent for interacting
with a sql database. This builds off of `SQLDatabaseChain`. The main
advantages are 1) answering general questions about the db, 2) access to
a tool for double checking queries, and 3) recovering from errors
* `ZeroShotAgent.as_json_agent` which returns an agent for interacting
with json blobs.
* Several examples in notebooks
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Ankush Gola