How can I resolve the abstract method error when using LangChain SQLDatabaseChain?

The error indicates that the llm instance you’re passing doesn’t implement the concrete methods required by the abstract BaseLanguageModel. In other words, you’re likely passing an instance of a class that remains abstract, and it hasn’t been properly initialized as a concrete language model that supports methods like invoke, predict, etc.

Suggestions to Resolve the Issue

1. Use a Concrete LLM Class:
   Instead of using an uninstantiated or abstract LLM, make sure you pass a concrete implementation. For example, if you’re using OpenAI’s chat model, instantiate it like so:

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   from langchain.chat_models import ChatOpenAI
   
   llm = ChatOpenAI(temperature=0)

2. Check Your LangChain Version:
   Since you’re using LangChain version 0.3.10, verify that the concrete LLM classes you’re trying to use are available and fully integrated at that version. Documentation for the specific version may highlight any differences in available APIs.

3. Review Your LLM Initialization:
   Ensure that when you create your llm instance, you’re not inadvertently passing a placeholder or incomplete configuration that leaves it abstract. The error suggests that methods like generate_prompt, invoke, etc., remain unimplemented.

Final Example

Here’s an example of how you might correctly create a SQLDatabaseChain with a concrete LLM:

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from langchain.chat_models import ChatOpenAI
from langchain_experimental.sql import SQLDatabaseChain

# Initialize a concrete LLM (for example, ChatOpenAI)
llm = ChatOpenAI(temperature=0)

# Assuming your 'db' is already set up
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)

# Now you can use db_chain as intended.

Using a concrete model should resolve the abstract method error. If you continue experiencing issues, double-check that your dependencies are correctly installed and that no conflicting versions are present.