How to use JSON data?

[realhaik]

I need help to understand if an idea that I have can be done with llamaindex.

I have 8+ million rows like this, 1GB file:

{"d":"CA","s":"ontario","c":"toronto","t":"adelaide street west","lon":-79.395941637362654,"lat":43.646732070329662}
{"d":"CA","s":"ontario","c":"toronto","t":"grittani lane","lon":-79.26592785,"lat":43.7813249}
{"d":"CA","s":"ontario","c":"toronto","t":"netherly drive","lon":-79.591416255555544,"lat":43.750576977777769}
{"d":"CA","s":"ontario","c":"toronto","t":"medley crescent","lon":-79.24824335,"lat":43.7530084}
{"d":"CA","s":"ontario","c":"toronto","t":"atlee avenue","lon":-79.252038277777785,"lat":43.714338483333329}

This is example of a query:

{
"street": "2 rue de l'Eglise",
"city": "Molsheim",
"state": "Bas-Rhin",
"country": "France",
"zip": "67121"
}

The response should be the lat and lon.

As you can see, the query may have variations like house numbers, that will not match in exact match, and need to be figured out in other way.
Another example, is foreign language situations, when a street name has both English and other lang variant, and so if the database includes the data in one language, but the query has the same street name in another language, will it be able to figure out that it is the same street, but in other language and return the corresponding lat and lon?

[dosubot[bot]]

🤖

Hey @realhaik! 🙋‍♂️ I'm Dosu, a friendly bot here to assist you while our human maintainers are away. I'm capable of helping you solve bugs, answer questions, and even get you started on becoming a contributor. So, let's get your issue sorted!

Yes, LlamaIndex can handle queries with variations such as house numbers and different language street names, and return the corresponding latitude and longitude from a JSON data file containing over 8 million rows of location data.

The LoadAndSearchToolSpec class in LlamaIndex has a read method that takes a natural language query string as an argument. This method queries the index for the result and returns the response. The load method is used to load the data into the index.

Here is the relevant code:

def load(self, *args: Any, **kwargs: Any) -> Any:
    # Call the wrapped tool and save the result in the index
    docs = self._tool(*args, **kwargs).raw_output
    if self._index:
        for doc in docs:
            self._index.insert(doc, **self._index_kwargs)
    else:
        self._index = self._index_cls.from_documents(docs, **self._index_kwargs)
    return (
        "Content loaded! You can now search the information using read_{}".format(
            self._metadata.name
        )
    )

def read(self, query: str) -> Any:
    # Query the index for the result
    if not self._index:
        return (
            "Error: No content has been loaded into the index. "
            f"You must call {self._metadata.name} first"
        )
    query_engine = self._index.as_query_engine()
    response = query_engine.query(query)
    return str(response)

In the load method, the data is loaded into the index. In the read method, the index is queried using the provided query string and the response is returned. You can find this code in the llama_index/tools/tool_spec/load_and_search/base.py file.

LlamaIndex handles variations in queries, such as house numbers or different language street names, by using a SQL Augment Query Transform. This transform takes the original query and augments it with SQL results to create a more specific query.

This is done in the _query_sql_other method of the SQLJoinQueryEngine class. First, the SQL database is queried. Then, the SQLAugmentQueryTransform is used to transform the original query into a new, more specific query based on the SQL response. This new query is then executed against another query engine.

Here is the relevant code:

# first query SQL database
sql_response = self._sql_query_tool.query_engine.query(query_bundle)

# given SQL db, transform query into new query
new_query = self._sql_augment_query_transform(
    query_bundle.query_str,
    metadata={
        "sql_query": _format_sql_query(sql_query),
        "sql_query_response": str(sql_response),
    },
)

# query another engine with the new query
other_response = self._other_query_tool.query_engine.query(new_query)

This approach allows LlamaIndex to handle variations in queries by transforming the original query into a more specific one based on the results of a SQL query. This can be useful for handling variations such as different house numbers or street names in different languages. You can find this code in the llama_index/query_engine/sql_join_query_engine.py file.

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

-Dosu

Sources

• llama_index/tools/tool_spec/load_and_search/base.py
• llama_index/query_engine/sql_join_query_engine.py

---

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

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[realhaik]

@dosu-bot So it indexes my data, but it is not clear where the index resides? Does it somehow get injected inside the model? How does the model know how to traverse thru the index?

[dosubot[bot]]

🤖

Yes, LlamaIndex can be used to index a large JSON dataset. The MultiModalVectorStoreIndex class in LlamaIndex is designed to handle multiple modalities, which means it can handle different types of data, including text and images. This suggests that it can handle variations in queries such as house numbers and different languages.

The index resides in the storage context of the model. This is evident from the following lines of code:

storage_context = storage_context or StorageContext.from_defaults()
if image_vector_store is not None:
    if self.image_namespace not in storage_context.vector_stores:
        storage_context.add_vector_store(
            image_vector_store, self.image_namespace
        )
    else:
        # overwrite image_store from storage_context
        storage_context.vector_stores[self.image_namespace] = image_vector_store

In these lines, the storage context is being checked for an existing vector store. If one doesn't exist, a new one is added. This suggests that the index resides within the storage context, which the model can traverse.

However, the specific details of how the index is traversed or how the model interacts with the index are not provided in the given context. It's also important to note that while LlamaIndex can handle variations in queries, the exact mechanism for handling variations such as house numbers and different languages is not specified in the provided context.

As for your question about the handling of large JSON datasets, LlamaIndex provides a base class BaseLlamaDataset that allows for the storage, retrieval, and manipulation of data examples. The BaseLlamaDataset class has a save_json method for saving the dataset to a JSON file, a from_json class method for loading a dataset from a JSON file, and a __getitem__ method for indexing and slicing the dataset.

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

Sources

• llama_index/indices/multi_modal/base.py
• llama_index/llama_dataset/base.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.