dosubot[bot]
commented
1 week ago Hey there, @tpanza! I'm here to help you with any issues or questions you have while we wait for a human maintainer. Let's squash those bugs together! 🤖
To resolve the OperationalError: near "sql": syntax error when using Azure OpenAI with the JSONalyze Query Engine from the Llama Index library, you need to ensure that the SQL query generated by the LLM is valid. The error typically indicates that the SQL syntax is incorrect.
Here are the steps to troubleshoot and resolve the issue:
- Check the Generated SQL Query: Print the SQL query generated by the LLM to verify its correctness.
- Validate the SQL Syntax: Ensure that the SQL query adheres to the correct syntax for SQLite.
- Adjust the Prompt: Modify the prompt used to generate the SQL query to provide better context or constraints to the LLM.
Here is an example of how you can print the SQL query for debugging purposes:
try:
# Execute the SQL query
results = list(db.query(sql_query))
except sqlite_utils.db_exceptions.OperationalError as exc:
print_text(f"Error executing query: {sql_query}")
raise ValueError(f"Invalid query: {sql_query}") from excAdditionally, you can adjust the prompt to provide more specific instructions to the LLM:
prompt = prompt or DEFAULT_JSONALYZE_PROMPT
response_str = await service_context.llm.apredict(
prompt=prompt,
table_name=table_name,
table_schema=table_schema,
question=query,
)
# Print the response string for debugging
print_text(f"Generated SQL Query: {response_str}")
sql_query = sql_parser.parse_response_to_sql(response_str, query_bundle)By printing the generated SQL query, you can identify any syntax errors and adjust the prompt or the SQL parser accordingly.
The JSONalyzeQueryEngine class in the Llama Index library provides methods to handle both synchronous and asynchronous query execution. Ensure you are using the correct method (_query for synchronous or _aquery for asynchronous) based on your implementation. Here is a snippet from the JSONalyzeQueryEngine class that shows how the SQL query is generated and executed:
def _query(self, query_bundle: QueryBundle) -> Response:
query = query_bundle.query_str
if self._verbose:
print_text(f"Query: {query}\n", color="green")
# Perform the analysis
sql_query, table_schema, results = self._analyzer(
self._list_of_dict,
query_bundle,
self._service_context,
table_name=self._table_name,
prompt=self._jsonalyze_prompt,
sql_parser=self._sql_parser,
)
if self._verbose:
print_text(f"SQL Query: {sql_query}\n", color="blue")
print_text(f"Table Schema: {table_schema}\n", color="cyan")
print_text(f"SQL Response: {results}\n", color="yellow")
if self._synthesize_response:
response_str = self._service_context.llm.predict(
self._response_synthesis_prompt,
sql_query=sql_query,
table_schema=table_schema,
sql_response=results,
query_str=query_bundle.query_str,
)
if self._verbose:
print_text(f"Response: {response_str}", color="magenta")
else:
response_str = str(results)
response_metadata = {"sql_query": sql_query, "table_schema": str(table_schema)}
return Response(response=response_str, metadata=response_metadata)By following these steps and utilizing the provided methods, you should be able to resolve the OperationalError and ensure that the SQL queries generated by the LLM are valid and executable [1][2][3].
To continue talking to Dosu, mention @dosu.
logan-markewich
tpanza
guptadhaval18
Bug Description
Tried following this example for the JSONalyze Query Engine (except for the LLM being Azure OpenAI), and
OperationalError: near "sql": syntax erroris raised.Version
0.10.38
Steps to Reproduce
Just run the code from the docs: https://docs.llamaindex.ai/en/latest/examples/query_engine/JSONalyze_query_engine/
My setup required setting the LLM to Azure OpenAI.
Python version 3.11.9.
Relevant Logs/Tracbacks