Regarding chatbot one query engine for all structure, unstructure and semi-structure data

[PradiptaAtByteIQMind-Graph2023]

Hi team

I want to get connected with multiples structure db, multiple unstructure db i.e. mongodb, and multiple semistructure db for my chatbot.
how can i build the generalized SQLAutoVectorQueryEngine or is there any other way to create it?

[dosubot[bot]]

🤖

Hello @PradiptaAtByteIQMind-Graph2023,

Nice to meet you! I'm Dosu, an AI bot here to assist you with your queries and issues regarding the LlamaIndex repository while you wait for a human maintainer. Feel free to ask me any questions or report any bugs you encounter. We're in this together!

Please note, I'm a bot and aim to create a friendly, efficient environment for all contributors. Looking forward to working with you!

To build a generalized SQLAutoVectorQueryEngine that connects your chatbot with multiple structured, unstructured, and semi-structured databases like MongoDB, you would need to create a custom database adapter for MongoDB and integrate it into the LlamaIndex framework.

The current example provided in the context uses SQLDatabase, which is a SQL-specific adapter. You would need to create a similar adapter for MongoDB. This adapter should be able to translate natural language queries into MongoDB queries and execute them.

Here's a simplified example of how you might create a MongoDB adapter:

from pymongo import MongoClient
from llama_index.indices.struct_store.sql_query import NLTableQueryEngine

class MongoDBDatabase:
    def __init__(self, db_name):
        self.client = MongoClient()
        self.db = self.client[db_name]

    def execute_query(self, query):
        # Translate and execute the MongoDB query here
        pass

class NLMongoDBQueryEngine(NLTableQueryEngine):
    def __init__(self, mongo_database, tables):
        super().__init__(mongo_database, tables)

You would then use this MongoDB adapter in the same way the SQLDatabase is used in the example:

mongo_database = MongoDBDatabase('my_database')
mongo_query_engine = NLMongoDBQueryEngine(mongo_database, ['my_collection'])

Please note that this is a simplified example and the actual implementation would require handling the translation of natural language queries into MongoDB queries, which can be complex depending on the nature of the queries and the structure of your MongoDB database.

Also, this example assumes that you are using pymongo, the Python driver for MongoDB. If you are using a different driver, you would need to adjust the code accordingly.

Finally, please note that LlamaIndex currently does not support MongoDB out of the box, so this would require significant custom development. You can refer to the SQLAutoVectorQueryEngine example for more details on how to implement a custom query engine.

I hope this helps! If you have any further questions, feel free to ask.

Sources

• docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb
• llama_index/query_engine/init.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.