Skip to content

PGVector SQL

PGVectorSQLQueryEngine #

Bases: BaseSQLTableQueryEngine

PGvector SQL query engine.

A modified version of the normal text-to-SQL query engine because we can infer embedding vectors in the sql query.

NOTE: this is a beta feature

NOTE: Any Text-to-SQL application should be aware that executing arbitrary SQL queries can be a security risk. It is recommended to take precautions as needed, such as using restricted roles, read-only databases, sandboxing, etc.

Source code in llama-index-core/llama_index/core/indices/struct_store/sql_query.py
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
class PGVectorSQLQueryEngine(BaseSQLTableQueryEngine):
    """PGvector SQL query engine.

    A modified version of the normal text-to-SQL query engine because
    we can infer embedding vectors in the sql query.

    NOTE: this is a beta feature

    NOTE: Any Text-to-SQL application should be aware that executing
    arbitrary SQL queries can be a security risk. It is recommended to
    take precautions as needed, such as using restricted roles, read-only
    databases, sandboxing, etc.

    """

    def __init__(
        self,
        sql_database: SQLDatabase,
        llm: Optional[LLM] = None,
        text_to_sql_prompt: Optional[BasePromptTemplate] = None,
        context_query_kwargs: Optional[dict] = None,
        synthesize_response: bool = True,
        response_synthesis_prompt: Optional[BasePromptTemplate] = None,
        refine_synthesis_prompt: Optional[BasePromptTemplate] = None,
        tables: Optional[Union[List[str], List[Table]]] = None,
        service_context: Optional[ServiceContext] = None,
        context_str_prefix: Optional[str] = None,
        sql_only: bool = False,
        callback_manager: Optional[CallbackManager] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize params."""
        text_to_sql_prompt = text_to_sql_prompt or DEFAULT_TEXT_TO_SQL_PGVECTOR_PROMPT
        self._sql_retriever = NLSQLRetriever(
            sql_database,
            llm=llm,
            text_to_sql_prompt=text_to_sql_prompt,
            context_query_kwargs=context_query_kwargs,
            tables=tables,
            sql_parser_mode=SQLParserMode.PGVECTOR,
            context_str_prefix=context_str_prefix,
            service_context=service_context,
            sql_only=sql_only,
            callback_manager=callback_manager,
        )
        super().__init__(
            synthesize_response=synthesize_response,
            response_synthesis_prompt=response_synthesis_prompt,
            refine_synthesis_prompt=refine_synthesis_prompt,
            llm=llm,
            service_context=service_context,
            callback_manager=callback_manager,
            **kwargs,
        )

    @property
    def sql_retriever(self) -> NLSQLRetriever:
        """Get SQL retriever."""
        return self._sql_retriever

sql_retriever property #

sql_retriever: NLSQLRetriever

Get SQL retriever.