[Question]: How to integrate models in tranformers in llama index

[sreenivasanm6]

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Question

How can I integrate other models for text to sql conversion. For example -How can I integrate "defog/sqlcoder-7b-2" in NLSQLTableQueryEngine method.

[dosubot[bot]]

Hey there @sreenivasanm6! Fancy seeing you here again, diving into the world of text to SQL conversions, huh? Always a pleasure to cross paths with a familiar explorer of code.

To integrate the "defog/sqlcoder-7b-2" model into the NLSQLTableQueryEngine method for text to SQL conversion in LlamaIndex, you need to follow these steps:

1. Instantiate the Model: Ensure you have access to the "defog/sqlcoder-7b-2" model. This might involve downloading the model or accessing it through an API.

2. Modify the NLSQLRetriever Class: The NLSQLRetriever class is responsible for converting natural language queries into SQL queries. You will need to modify this class to use the "defog/sqlcoder-7b-2" model for the conversion process.

3. Update the sql_retriever Property: Ensure that the sql_retriever property in the BaseSQLTableQueryEngine class returns an instance of the modified NLSQLRetriever that uses the "defog/sqlcoder-7b-2" model.

Here is an example of how you might modify the NLSQLRetriever class to use the "defog/sqlcoder-7b-2" model:

from llama_index.core.indices.struct_store.sql_retriever import NLSQLRetriever

class CustomNLSQLRetriever(NLSQLRetriever):
    def __init__(self, model_name="defog/sqlcoder-7b-2", **kwargs):
        super().__init__(**kwargs)
        self.model = load_model(model_name)  # Assuming a function to load the model

    def convert_nl_to_sql(self, nl_query: str) -> str:
        # Use the "defog/sqlcoder-7b-2" model to convert the natural language query to SQL
        sql_query = self.model.predict(nl_query)
        return sql_query

# Then, in the BaseSQLTableQueryEngine or a derived class, override the sql_retriever property
@property
def sql_retriever(self) -> CustomNLSQLRetriever:
    return CustomNLSQLRetriever()

Note: This example assumes the existence of a load_model function that can load the "defog/sqlcoder-7b-2" model and that the model has a predict method which takes a natural language query as input and returns the corresponding SQL query. You will need to adjust the implementation based on the actual interface of the "defog/sqlcoder-7b-2" model and how it is accessed or integrated into your application.

You can find more details about the NLSQLRetriever class and its role in the NLSQLTableQueryEngine method in the LlamaIndex repository.

Please let me know if you need further assistance or clarification.

Sources

• llama-index-core/llama_index/core/indices/struct_store/sql_query.py
• llama-index-legacy/llama_index/legacy/query_engine/sql_join_query_engine.py

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[logan-markewich]

We have a few examples. The main thing is ensuring proper prompt formatting

Here's an example with zephyr https://colab.research.google.com/drive/1C55IGyJNDe14DsHkAIIpIjn76NvK5pc1?usp=sharing#scrollTo=lMNaHDzPM68f