Incomplete answer with GPTSQLStructStoreIndex compared to ChainLang

[k4ycer]

I'm following the get started guide for the structured data example: https://gpt-index.readthedocs.io/en/latest/guides/sql_guide.html

The output is only given in array format: [('Tokyo',)]. I would expect a final answer, such as "Tokyo has the highest population..."

Furthermore, looks like other simple queries do not return enough information, such as "Does Berlin have a higher population than Tokyo?". While if I enter the same query to ChainLang directy (without indexing), it does give me a more complete answer. How can I get a more complete answer with llama-ai, similar to what I get with Chain Lang?

LLama-index code: index = GPTSQLStructStoreIndex( [], sql_database=sql_database, table_name="city_stats", ) response = index.query("Does Berlin have a higher population than Tokyo?", mode="default") print(response)

LLama-index output:

INFO:root:> [query] Total LLM token usage: 180 tokens

[query] Total LLM token usage: 180 tokens INFO:root:> [query] Total embedding token usage: 0 tokens [query] Total embedding token usage: 0 tokens [('No',)]

---

Chain-lang:

Code: db_chain = SQLDatabaseChain(llm=llm, database=sql_database, verbose=True) db_chain.run("Does Berlin have a higher population than Tokyo?")

Output:

Entering new SQLDatabaseChain chain... Does Berlin have a higher population than Tokyo? SQLQuery: SELECT city_name, population FROM city_stats WHERE city_name IN ('Berlin', 'Tokyo') ORDER BY population DESC LIMIT 5; SQLResult: [('Tokyo', 13929286), ('Berlin', 600000)] Answer: No, Tokyo has a higher population than Berlin. Finished chain.

[randaller]

@k4ycer may be, the best idea is to ask for this at gpt-index project page? :)

Here we are chatting with LLaMA and it replies well :)

[k4ycer]

Sorry, I posted it in the wrong page. Thanks.