NL SQL table

BaseQueryEngine

Bases: PromptMixin, DispatcherSpanMixin

Base query engine.

Source code in llama_index/core/base/base_query_engine.py

class BaseQueryEngine(PromptMixin, DispatcherSpanMixin):
    """Base query engine."""

    def __init__(
        self,
        callback_manager: Optional[CallbackManager],
    ) -> None:
        self.callback_manager = callback_manager or CallbackManager([])

    def _get_prompts(self) -> Dict[str, Any]:
        """Get prompts."""
        return {}

    def _update_prompts(self, prompts: PromptDictType) -> None:
        """Update prompts."""

    @dispatcher.span
    def query(self, str_or_query_bundle: QueryType) -> RESPONSE_TYPE:
        dispatcher.event(QueryStartEvent(query=str_or_query_bundle))
        with self.callback_manager.as_trace("query"):
            if isinstance(str_or_query_bundle, str):
                str_or_query_bundle = QueryBundle(str_or_query_bundle)
            query_result = self._query(str_or_query_bundle)
        dispatcher.event(
            QueryEndEvent(query=str_or_query_bundle, response=query_result)
        )
        return query_result

    @dispatcher.span
    async def aquery(self, str_or_query_bundle: QueryType) -> RESPONSE_TYPE:
        dispatcher.event(QueryStartEvent(query=str_or_query_bundle))
        with self.callback_manager.as_trace("query"):
            if isinstance(str_or_query_bundle, str):
                str_or_query_bundle = QueryBundle(str_or_query_bundle)
            query_result = await self._aquery(str_or_query_bundle)
        dispatcher.event(
            QueryEndEvent(query=str_or_query_bundle, response=query_result)
        )
        return query_result

    def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        raise NotImplementedError(
            "This query engine does not support retrieve, use query directly"
        )

    def synthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        raise NotImplementedError(
            "This query engine does not support synthesize, use query directly"
        )

    async def asynthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        raise NotImplementedError(
            "This query engine does not support asynthesize, use aquery directly"
        )

    @abstractmethod
    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        pass

    @abstractmethod
    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        pass

NLSQLTableQueryEngine

Bases: BaseSQLTableQueryEngine

Natural language SQL Table query engine.

Read NLStructStoreQueryEngine's docstring for more info on NL SQL.

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/indices/struct_store/sql_query.py

class NLSQLTableQueryEngine(BaseSQLTableQueryEngine):
    """
    Natural language SQL Table query engine.

    Read NLStructStoreQueryEngine's docstring for more info on NL SQL.

    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,
        markdown_response: bool = False,
        response_synthesis_prompt: Optional[BasePromptTemplate] = None,
        refine_synthesis_prompt: Optional[BasePromptTemplate] = None,
        tables: Optional[Union[List[str], List[Table]]] = None,
        table_retriever: Optional[ObjectRetriever[SQLTableSchema]] = None,
        rows_retrievers: Optional[dict[str, BaseRetriever]] = None,
        cols_retrievers: Optional[dict[str, dict[str, BaseRetriever]]] = None,
        context_str_prefix: Optional[str] = None,
        embed_model: Optional[BaseEmbedding] = None,
        sql_only: bool = False,
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = False,
        **kwargs: Any,
    ) -> None:
        """Initialize params."""
        # self._tables = tables
        self._sql_retriever = NLSQLRetriever(
            sql_database,
            llm=llm,
            text_to_sql_prompt=text_to_sql_prompt,
            context_query_kwargs=context_query_kwargs,
            tables=tables,
            table_retriever=table_retriever,
            rows_retrievers=rows_retrievers,
            cols_retrievers=cols_retrievers,
            context_str_prefix=context_str_prefix,
            embed_model=embed_model,
            sql_only=sql_only,
            callback_manager=callback_manager,
            verbose=verbose,
        )
        super().__init__(
            synthesize_response=synthesize_response,
            markdown_response=markdown_response,
            response_synthesis_prompt=response_synthesis_prompt,
            refine_synthesis_prompt=refine_synthesis_prompt,
            llm=llm,
            callback_manager=callback_manager,
            verbose=verbose,
            **kwargs,
        )

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

sql_retriever property

sql_retriever: NLSQLRetriever

Get SQL retriever.

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/indices/struct_store/sql_query.py

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,
        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,
            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,
            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.

SQLTableRetrieverQueryEngine

Bases: BaseSQLTableQueryEngine

SQL Table retriever query engine.

Source code in llama_index/core/indices/struct_store/sql_query.py

class SQLTableRetrieverQueryEngine(BaseSQLTableQueryEngine):
    """SQL Table retriever query engine."""

    def __init__(
        self,
        sql_database: SQLDatabase,
        table_retriever: ObjectRetriever[SQLTableSchema],
        rows_retrievers: Optional[dict[str, BaseRetriever]] = None,
        cols_retrievers: Optional[dict[str, dict[str, BaseRetriever]]] = None,
        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,
        context_str_prefix: Optional[str] = None,
        sql_only: bool = False,
        callback_manager: Optional[CallbackManager] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize params."""
        self._sql_retriever = NLSQLRetriever(
            sql_database,
            llm=llm,
            text_to_sql_prompt=text_to_sql_prompt,
            context_query_kwargs=context_query_kwargs,
            table_retriever=table_retriever,
            rows_retrievers=rows_retrievers,
            cols_retrievers=cols_retrievers,
            context_str_prefix=context_str_prefix,
            sql_only=sql_only,
            callback_manager=callback_manager,
            verbose=kwargs.get("verbose", False),
        )
        super().__init__(
            synthesize_response=synthesize_response,
            response_synthesis_prompt=response_synthesis_prompt,
            refine_synthesis_prompt=refine_synthesis_prompt,
            llm=llm,
            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.

CitationQueryEngine

Bases: BaseQueryEngine

Citation query engine.

Parameters:

Name	Type	Description	Default
retriever	BaseRetriever	A retriever object.	required
response_synthesizer	Optional[BaseSynthesizer]	A BaseSynthesizer object.	None
citation_chunk_size	int	Size of citation chunks, default=512. Useful for controlling granularity of sources.	DEFAULT_CITATION_CHUNK_SIZE
citation_chunk_overlap	int	Overlap of citation nodes, default=20.	DEFAULT_CITATION_CHUNK_OVERLAP
text_splitter	Optional[TextSplitter]	A text splitter for creating citation source nodes. Default is a SentenceSplitter.	None
callback_manager	Optional[CallbackManager]	A callback manager.	None
metadata_mode	MetadataMode	A MetadataMode object that controls how metadata is included in the citation prompt.	NONE

Source code in llama_index/core/query_engine/citation_query_engine.py

class CitationQueryEngine(BaseQueryEngine):
    """
    Citation query engine.

    Args:
        retriever (BaseRetriever): A retriever object.
        response_synthesizer (Optional[BaseSynthesizer]):
            A BaseSynthesizer object.
        citation_chunk_size (int):
            Size of citation chunks, default=512. Useful for controlling
            granularity of sources.
        citation_chunk_overlap (int): Overlap of citation nodes, default=20.
        text_splitter (Optional[TextSplitter]):
            A text splitter for creating citation source nodes. Default is
            a SentenceSplitter.
        callback_manager (Optional[CallbackManager]): A callback manager.
        metadata_mode (MetadataMode): A MetadataMode object that controls how
            metadata is included in the citation prompt.

    """

    def __init__(
        self,
        retriever: BaseRetriever,
        llm: Optional[LLM] = None,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        citation_chunk_size: int = DEFAULT_CITATION_CHUNK_SIZE,
        citation_chunk_overlap: int = DEFAULT_CITATION_CHUNK_OVERLAP,
        text_splitter: Optional[TextSplitter] = None,
        node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
        callback_manager: Optional[CallbackManager] = None,
        metadata_mode: MetadataMode = MetadataMode.NONE,
    ) -> None:
        self.text_splitter = text_splitter or SentenceSplitter(
            chunk_size=citation_chunk_size, chunk_overlap=citation_chunk_overlap
        )
        self._retriever = retriever

        callback_manager = callback_manager or Settings.callback_manager
        llm = llm or Settings.llm

        self._response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=llm,
            callback_manager=callback_manager,
            text_qa_template=CITATION_QA_TEMPLATE,
            refine_template=CITATION_REFINE_TEMPLATE,
            response_mode=ResponseMode.COMPACT,
            use_async=False,
            streaming=False,
        )

        self._node_postprocessors = node_postprocessors or []
        self._metadata_mode = metadata_mode

        for node_postprocessor in self._node_postprocessors:
            node_postprocessor.callback_manager = callback_manager

        super().__init__(callback_manager=callback_manager)

    @classmethod
    def from_args(
        cls,
        index: BaseGPTIndex,
        llm: Optional[LLM] = None,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        citation_chunk_size: int = DEFAULT_CITATION_CHUNK_SIZE,
        citation_chunk_overlap: int = DEFAULT_CITATION_CHUNK_OVERLAP,
        text_splitter: Optional[TextSplitter] = None,
        citation_qa_template: BasePromptTemplate = CITATION_QA_TEMPLATE,
        citation_refine_template: BasePromptTemplate = CITATION_REFINE_TEMPLATE,
        retriever: Optional[BaseRetriever] = None,
        node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
        # response synthesizer args
        response_mode: ResponseMode = ResponseMode.COMPACT,
        use_async: bool = False,
        streaming: bool = False,
        # class-specific args
        metadata_mode: MetadataMode = MetadataMode.NONE,
        **kwargs: Any,
    ) -> "CitationQueryEngine":
        """
        Initialize a CitationQueryEngine object.".

        Args:
            index: (BastGPTIndex): index to use for querying
            llm: (Optional[LLM]): LLM object to use for response generation.
            citation_chunk_size (int):
                Size of citation chunks, default=512. Useful for controlling
                granularity of sources.
            citation_chunk_overlap (int): Overlap of citation nodes, default=20.
            text_splitter (Optional[TextSplitter]):
                A text splitter for creating citation source nodes. Default is
                a SentenceSplitter.
            citation_qa_template (BasePromptTemplate): Template for initial citation QA
            citation_refine_template (BasePromptTemplate):
                Template for citation refinement.
            retriever (BaseRetriever): A retriever object.
            node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of
                node postprocessors.
            verbose (bool): Whether to print out debug info.
            response_mode (ResponseMode): A ResponseMode object.
            use_async (bool): Whether to use async.
            streaming (bool): Whether to use streaming.
            optimizer (Optional[BaseTokenUsageOptimizer]): A BaseTokenUsageOptimizer
                object.

        """
        retriever = retriever or index.as_retriever(**kwargs)

        response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=llm,
            text_qa_template=citation_qa_template,
            refine_template=citation_refine_template,
            response_mode=response_mode,
            use_async=use_async,
            streaming=streaming,
        )

        return cls(
            retriever=retriever,
            llm=llm,
            response_synthesizer=response_synthesizer,
            callback_manager=Settings.callback_manager,
            citation_chunk_size=citation_chunk_size,
            citation_chunk_overlap=citation_chunk_overlap,
            text_splitter=text_splitter,
            node_postprocessors=node_postprocessors,
            metadata_mode=metadata_mode,
        )

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {"response_synthesizer": self._response_synthesizer}

    def _create_citation_nodes(self, nodes: List[NodeWithScore]) -> List[NodeWithScore]:
        """Modify retrieved nodes to be granular sources."""
        new_nodes: List[NodeWithScore] = []
        for node in nodes:
            text_chunks = self.text_splitter.split_text(
                node.node.get_content(metadata_mode=self._metadata_mode)
            )

            for text_chunk in text_chunks:
                text = f"Source {len(new_nodes) + 1}:\n{text_chunk}\n"

                new_node = NodeWithScore(
                    node=TextNode.model_validate(node.node.model_dump()),
                    score=node.score,
                )
                new_node.node.set_content(text)
                new_nodes.append(new_node)
        return new_nodes

    def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        nodes = self._retriever.retrieve(query_bundle)

        for postprocessor in self._node_postprocessors:
            nodes = postprocessor.postprocess_nodes(nodes, query_bundle=query_bundle)

        return nodes

    async def aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        nodes = await self._retriever.aretrieve(query_bundle)

        for postprocessor in self._node_postprocessors:
            nodes = postprocessor.postprocess_nodes(nodes, query_bundle=query_bundle)

        return nodes

    @property
    def retriever(self) -> BaseRetriever:
        """Get the retriever object."""
        return self._retriever

    def synthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        nodes = self._create_citation_nodes(nodes)
        return self._response_synthesizer.synthesize(
            query=query_bundle,
            nodes=nodes,
            additional_source_nodes=additional_source_nodes,
        )

    async def asynthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        nodes = self._create_citation_nodes(nodes)
        return await self._response_synthesizer.asynthesize(
            query=query_bundle,
            nodes=nodes,
            additional_source_nodes=additional_source_nodes,
        )

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            with self.callback_manager.event(
                CBEventType.RETRIEVE,
                payload={EventPayload.QUERY_STR: query_bundle.query_str},
            ) as retrieve_event:
                nodes = self.retrieve(query_bundle)
                nodes = self._create_citation_nodes(nodes)

                retrieve_event.on_end(payload={EventPayload.NODES: nodes})

            response = self._response_synthesizer.synthesize(
                query=query_bundle,
                nodes=nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            with self.callback_manager.event(
                CBEventType.RETRIEVE,
                payload={EventPayload.QUERY_STR: query_bundle.query_str},
            ) as retrieve_event:
                nodes = await self.aretrieve(query_bundle)
                nodes = self._create_citation_nodes(nodes)

                retrieve_event.on_end(payload={EventPayload.NODES: nodes})

            response = await self._response_synthesizer.asynthesize(
                query=query_bundle,
                nodes=nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

retriever property

retriever: BaseRetriever

Get the retriever object.

from_args classmethod

from_args(index: BaseGPTIndex, llm: Optional[LLM] = None, response_synthesizer: Optional[BaseSynthesizer] = None, citation_chunk_size: int = DEFAULT_CITATION_CHUNK_SIZE, citation_chunk_overlap: int = DEFAULT_CITATION_CHUNK_OVERLAP, text_splitter: Optional[TextSplitter] = None, citation_qa_template: BasePromptTemplate = CITATION_QA_TEMPLATE, citation_refine_template: BasePromptTemplate = CITATION_REFINE_TEMPLATE, retriever: Optional[BaseRetriever] = None, node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, response_mode: ResponseMode = COMPACT, use_async: bool = False, streaming: bool = False, metadata_mode: MetadataMode = NONE, **kwargs: Any) -> CitationQueryEngine

Initialize a CitationQueryEngine object.".

Parameters:

Name	Type	Description	Default
index	BaseGPTIndex	(BastGPTIndex): index to use for querying	required
llm	Optional[LLM]	(Optional[LLM]): LLM object to use for response generation.	None
citation_chunk_size	int	Size of citation chunks, default=512. Useful for controlling granularity of sources.	DEFAULT_CITATION_CHUNK_SIZE
citation_chunk_overlap	int	Overlap of citation nodes, default=20.	DEFAULT_CITATION_CHUNK_OVERLAP
text_splitter	Optional[TextSplitter]	A text splitter for creating citation source nodes. Default is a SentenceSplitter.	None
citation_qa_template	BasePromptTemplate	Template for initial citation QA	CITATION_QA_TEMPLATE
citation_refine_template	BasePromptTemplate	Template for citation refinement.	CITATION_REFINE_TEMPLATE
retriever	BaseRetriever	A retriever object.	None
node_postprocessors	Optional[List[BaseNodePostprocessor]]	A list of node postprocessors.	None
verbose	bool	Whether to print out debug info.	required
response_mode	ResponseMode	A ResponseMode object.	COMPACT
use_async	bool	Whether to use async.	False
streaming	bool	Whether to use streaming.	False
optimizer	Optional[BaseTokenUsageOptimizer]	A BaseTokenUsageOptimizer object.	required

Source code in llama_index/core/query_engine/citation_query_engine.py

@classmethod
def from_args(
    cls,
    index: BaseGPTIndex,
    llm: Optional[LLM] = None,
    response_synthesizer: Optional[BaseSynthesizer] = None,
    citation_chunk_size: int = DEFAULT_CITATION_CHUNK_SIZE,
    citation_chunk_overlap: int = DEFAULT_CITATION_CHUNK_OVERLAP,
    text_splitter: Optional[TextSplitter] = None,
    citation_qa_template: BasePromptTemplate = CITATION_QA_TEMPLATE,
    citation_refine_template: BasePromptTemplate = CITATION_REFINE_TEMPLATE,
    retriever: Optional[BaseRetriever] = None,
    node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
    # response synthesizer args
    response_mode: ResponseMode = ResponseMode.COMPACT,
    use_async: bool = False,
    streaming: bool = False,
    # class-specific args
    metadata_mode: MetadataMode = MetadataMode.NONE,
    **kwargs: Any,
) -> "CitationQueryEngine":
    """
    Initialize a CitationQueryEngine object.".

    Args:
        index: (BastGPTIndex): index to use for querying
        llm: (Optional[LLM]): LLM object to use for response generation.
        citation_chunk_size (int):
            Size of citation chunks, default=512. Useful for controlling
            granularity of sources.
        citation_chunk_overlap (int): Overlap of citation nodes, default=20.
        text_splitter (Optional[TextSplitter]):
            A text splitter for creating citation source nodes. Default is
            a SentenceSplitter.
        citation_qa_template (BasePromptTemplate): Template for initial citation QA
        citation_refine_template (BasePromptTemplate):
            Template for citation refinement.
        retriever (BaseRetriever): A retriever object.
        node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of
            node postprocessors.
        verbose (bool): Whether to print out debug info.
        response_mode (ResponseMode): A ResponseMode object.
        use_async (bool): Whether to use async.
        streaming (bool): Whether to use streaming.
        optimizer (Optional[BaseTokenUsageOptimizer]): A BaseTokenUsageOptimizer
            object.

    """
    retriever = retriever or index.as_retriever(**kwargs)

    response_synthesizer = response_synthesizer or get_response_synthesizer(
        llm=llm,
        text_qa_template=citation_qa_template,
        refine_template=citation_refine_template,
        response_mode=response_mode,
        use_async=use_async,
        streaming=streaming,
    )

    return cls(
        retriever=retriever,
        llm=llm,
        response_synthesizer=response_synthesizer,
        callback_manager=Settings.callback_manager,
        citation_chunk_size=citation_chunk_size,
        citation_chunk_overlap=citation_chunk_overlap,
        text_splitter=text_splitter,
        node_postprocessors=node_postprocessors,
        metadata_mode=metadata_mode,
    )

CogniswitchQueryEngine

Bases: BaseQueryEngine

Source code in llama_index/core/query_engine/cogniswitch_query_engine.py

class CogniswitchQueryEngine(BaseQueryEngine):
    def __init__(self, cs_token: str, OAI_token: str, apiKey: str) -> None:
        """
        The required fields.

        Args:
            cs_token (str): Cogniswitch token.
            OAI_token (str): OpenAI token.
            apiKey (str): Oauth token.

        """
        self.cs_token = cs_token
        self.OAI_token = OAI_token
        self.apiKey = apiKey
        self.knowledge_request_endpoint = (
            "https://api.cogniswitch.ai:8243/cs-api/0.0.1/cs/knowledgeRequest"
        )
        self.headers = {
            "apiKey": self.apiKey,
            "platformToken": self.cs_token,
            "openAIToken": self.OAI_token,
        }

    def query_knowledge(self, query: str) -> Response:
        """
        Send a query to the Cogniswitch service and retrieve the response.

        Args:
            query (str): Query to be answered.

        Returns:
            dict: Response JSON from the Cogniswitch service.

        """
        data = {"query": query}
        response = requests.post(
            self.knowledge_request_endpoint,
            headers=self.headers,
            data=data,
        )
        if response.status_code == 200:
            resp = response.json()
            answer = resp["data"]["answer"]

            return Response(response=answer)
        else:
            error_message = response.json()["message"]
            return Response(response=error_message)

    def _query(self, query_bundle: QueryBundle) -> Response:
        return self.query_knowledge(query_bundle.query_str)

    async def _aquery(self, query_bundle: QueryBundle) -> Response:
        return self.query_knowledge(query_bundle.query_str)

    def _get_prompt_modules(self) -> Dict[str, Any]:
        """Get prompts."""
        return {}

query_knowledge

query_knowledge(query: str) -> Response

Send a query to the Cogniswitch service and retrieve the response.

Parameters:

Name	Type	Description	Default
query	str	Query to be answered.	required

Returns:

Name	Type	Description
dict	Response	Response JSON from the Cogniswitch service.

Source code in llama_index/core/query_engine/cogniswitch_query_engine.py

def query_knowledge(self, query: str) -> Response:
    """
    Send a query to the Cogniswitch service and retrieve the response.

    Args:
        query (str): Query to be answered.

    Returns:
        dict: Response JSON from the Cogniswitch service.

    """
    data = {"query": query}
    response = requests.post(
        self.knowledge_request_endpoint,
        headers=self.headers,
        data=data,
    )
    if response.status_code == 200:
        resp = response.json()
        answer = resp["data"]["answer"]

        return Response(response=answer)
    else:
        error_message = response.json()["message"]
        return Response(response=error_message)

CustomQueryEngine

Bases: BaseModel, BaseQueryEngine

Custom query engine.

Subclasses can define additional attributes as Pydantic fields. Subclasses must implement the custom_query method, which takes a query string and returns either a Response object or a string as output.

They can optionally implement the acustom_query method for async support.

Parameters:

Name	Type	Description	Default
callback_manager	CallbackManager		<llama_index.core.callbacks.base.CallbackManager object at 0x7f5a820ba0f0>

Source code in llama_index/core/query_engine/custom.py

class CustomQueryEngine(BaseModel, BaseQueryEngine):
    """
    Custom query engine.

    Subclasses can define additional attributes as Pydantic fields.
    Subclasses must implement the `custom_query` method, which takes a query string
    and returns either a Response object or a string as output.

    They can optionally implement the `acustom_query` method for async support.

    """

    model_config = ConfigDict(arbitrary_types_allowed=True)
    callback_manager: CallbackManager = Field(
        default_factory=lambda: CallbackManager([]), exclude=True
    )

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {}

    def query(self, str_or_query_bundle: QueryType) -> RESPONSE_TYPE:
        with self.callback_manager.as_trace("query"):
            # if query bundle, just run the query
            if isinstance(str_or_query_bundle, QueryBundle):
                query_str = str_or_query_bundle.query_str
            else:
                query_str = str_or_query_bundle
            raw_response = self.custom_query(query_str)
            return (
                Response(raw_response)
                if isinstance(raw_response, str)
                else raw_response
            )

    async def aquery(self, str_or_query_bundle: QueryType) -> RESPONSE_TYPE:
        with self.callback_manager.as_trace("query"):
            if isinstance(str_or_query_bundle, QueryBundle):
                query_str = str_or_query_bundle.query_str
            else:
                query_str = str_or_query_bundle
            raw_response = await self.acustom_query(query_str)
            return (
                Response(raw_response)
                if isinstance(raw_response, str)
                else raw_response
            )

    @abstractmethod
    def custom_query(self, query_str: str) -> STR_OR_RESPONSE_TYPE:
        """Run a custom query."""

    async def acustom_query(self, query_str: str) -> STR_OR_RESPONSE_TYPE:
        """Run a custom query asynchronously."""
        # by default, just run the synchronous version
        return self.custom_query(query_str)

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        raise NotImplementedError("This query engine does not support _query.")

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        raise NotImplementedError("This query engine does not support _aquery.")

custom_query abstractmethod

custom_query(query_str: str) -> STR_OR_RESPONSE_TYPE

Run a custom query.

Source code in llama_index/core/query_engine/custom.py

@abstractmethod
def custom_query(self, query_str: str) -> STR_OR_RESPONSE_TYPE:
    """Run a custom query."""

acustom_query async

acustom_query(query_str: str) -> STR_OR_RESPONSE_TYPE

Run a custom query asynchronously.

Source code in llama_index/core/query_engine/custom.py

async def acustom_query(self, query_str: str) -> STR_OR_RESPONSE_TYPE:
    """Run a custom query asynchronously."""
    # by default, just run the synchronous version
    return self.custom_query(query_str)

FLAREInstructQueryEngine

Bases: BaseQueryEngine

FLARE Instruct query engine.

This is the version of FLARE that uses retrieval-encouraging instructions.

NOTE: this is a beta feature. Interfaces might change, and it might not always give correct answers.

Parameters:

Name	Type	Description	Default
query_engine	BaseQueryEngine	query engine to use	required
llm	Optional[LLM]	LLM model. Defaults to None.	None
instruct_prompt	Optional[PromptTemplate]	instruct prompt. Defaults to None.	None
lookahead_answer_inserter	Optional[BaseLookaheadAnswerInserter]	lookahead answer inserter. Defaults to None.	None
done_output_parser	Optional[IsDoneOutputParser]	done output parser. Defaults to None.	None
query_task_output_parser	Optional[QueryTaskOutputParser]	query task output parser. Defaults to None.	None
max_iterations	int	max iterations. Defaults to 10.	10
max_lookahead_query_tasks	int	max lookahead query tasks. Defaults to 1.	1
callback_manager	Optional[CallbackManager]	callback manager. Defaults to None.	None
verbose	bool	give verbose outputs. Defaults to False.	False

Source code in llama_index/core/query_engine/flare/base.py

class FLAREInstructQueryEngine(BaseQueryEngine):
    """
    FLARE Instruct query engine.

    This is the version of FLARE that uses retrieval-encouraging instructions.

    NOTE: this is a beta feature. Interfaces might change, and it might not
    always give correct answers.

    Args:
        query_engine (BaseQueryEngine): query engine to use
        llm (Optional[LLM]): LLM model. Defaults to None.
        instruct_prompt (Optional[PromptTemplate]): instruct prompt. Defaults to None.
        lookahead_answer_inserter (Optional[BaseLookaheadAnswerInserter]):
            lookahead answer inserter. Defaults to None.
        done_output_parser (Optional[IsDoneOutputParser]): done output parser.
            Defaults to None.
        query_task_output_parser (Optional[QueryTaskOutputParser]):
            query task output parser. Defaults to None.
        max_iterations (int): max iterations. Defaults to 10.
        max_lookahead_query_tasks (int): max lookahead query tasks. Defaults to 1.
        callback_manager (Optional[CallbackManager]): callback manager.
            Defaults to None.
        verbose (bool): give verbose outputs. Defaults to False.

    """

    def __init__(
        self,
        query_engine: BaseQueryEngine,
        llm: Optional[LLM] = None,
        instruct_prompt: Optional[BasePromptTemplate] = None,
        lookahead_answer_inserter: Optional[BaseLookaheadAnswerInserter] = None,
        done_output_parser: Optional[IsDoneOutputParser] = None,
        query_task_output_parser: Optional[QueryTaskOutputParser] = None,
        max_iterations: int = 10,
        max_lookahead_query_tasks: int = 1,
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = False,
    ) -> None:
        """Init params."""
        super().__init__(callback_manager=callback_manager)
        self._query_engine = query_engine
        self._llm = llm or Settings.llm
        self._instruct_prompt = instruct_prompt or DEFAULT_INSTRUCT_PROMPT
        self._lookahead_answer_inserter = lookahead_answer_inserter or (
            LLMLookaheadAnswerInserter(llm=self._llm)
        )
        self._done_output_parser = done_output_parser or IsDoneOutputParser()
        self._query_task_output_parser = (
            query_task_output_parser or QueryTaskOutputParser()
        )
        self._max_iterations = max_iterations
        self._max_lookahead_query_tasks = max_lookahead_query_tasks
        self._verbose = verbose

    def _get_prompts(self) -> Dict[str, Any]:
        """Get prompts."""
        return {
            "instruct_prompt": self._instruct_prompt,
        }

    def _update_prompts(self, prompts: PromptDictType) -> None:
        """Update prompts."""
        if "instruct_prompt" in prompts:
            self._instruct_prompt = prompts["instruct_prompt"]

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {
            "query_engine": self._query_engine,
            "lookahead_answer_inserter": self._lookahead_answer_inserter,
        }

    def _get_relevant_lookahead_response(self, updated_lookahead_resp: str) -> str:
        """Get relevant lookahead response."""
        # if there's remaining query tasks, then truncate the response
        # until the start position of the first tag
        # there may be remaining query tasks because the _max_lookahead_query_tasks
        # is less than the total number of generated [Search(query)] tags
        remaining_query_tasks = self._query_task_output_parser.parse(
            updated_lookahead_resp
        )
        if len(remaining_query_tasks) == 0:
            relevant_lookahead_resp = updated_lookahead_resp
        else:
            first_task = remaining_query_tasks[0]
            relevant_lookahead_resp = updated_lookahead_resp[: first_task.start_idx]
        return relevant_lookahead_resp

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Query and get response."""
        print_text(f"Query: {query_bundle.query_str}\n", color="green")
        cur_response = ""
        source_nodes = []
        for iter in range(self._max_iterations):
            if self._verbose:
                print_text(f"Current response: {cur_response}\n", color="blue")
            # generate "lookahead response" that contains "[Search(query)]" tags
            # e.g.
            # The colors on the flag of Ghana have the following meanings. Red is
            # for [Search(Ghana flag meaning)],...
            lookahead_resp = self._llm.predict(
                self._instruct_prompt,
                query_str=query_bundle.query_str,
                existing_answer=cur_response,
            )
            lookahead_resp = lookahead_resp.strip()
            if self._verbose:
                print_text(f"Lookahead response: {lookahead_resp}\n", color="pink")

            is_done, fmt_lookahead = self._done_output_parser.parse(lookahead_resp)
            if is_done:
                cur_response = cur_response.strip() + " " + fmt_lookahead.strip()
                break

            # parse lookahead response into query tasks
            query_tasks = self._query_task_output_parser.parse(lookahead_resp)

            # get answers for each query task
            query_tasks = query_tasks[: self._max_lookahead_query_tasks]
            query_answers = []
            for _, query_task in enumerate(query_tasks):
                answer_obj = self._query_engine.query(query_task.query_str)
                if not isinstance(answer_obj, Response):
                    raise ValueError(
                        f"Expected Response object, got {type(answer_obj)} instead."
                    )
                query_answer = str(answer_obj)
                query_answers.append(query_answer)
                source_nodes.extend(answer_obj.source_nodes)

            # fill in the lookahead response template with the query answers
            # from the query engine
            updated_lookahead_resp = self._lookahead_answer_inserter.insert(
                lookahead_resp, query_tasks, query_answers, prev_response=cur_response
            )

            # get "relevant" lookahead response by truncating the updated
            # lookahead response until the start position of the first tag
            # also remove the prefix from the lookahead response, so that
            # we can concatenate it with the existing response
            relevant_lookahead_resp_wo_prefix = self._get_relevant_lookahead_response(
                updated_lookahead_resp
            )

            if self._verbose:
                print_text(
                    "Updated lookahead response: "
                    + f"{relevant_lookahead_resp_wo_prefix}\n",
                    color="pink",
                )

            # append the relevant lookahead response to the final response
            cur_response = (
                cur_response.strip() + " " + relevant_lookahead_resp_wo_prefix.strip()
            )

        # NOTE: at the moment, does not support streaming
        return Response(response=cur_response, source_nodes=source_nodes)

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        return self._query(query_bundle)

    def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        # if the query engine is a retriever, then use the retrieve method
        if isinstance(self._query_engine, RetrieverQueryEngine):
            return self._query_engine.retrieve(query_bundle)
        else:
            raise NotImplementedError(
                "This query engine does not support retrieve, use query directly"
            )

    async def aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        # if the query engine is a retriever, then use the retrieve method
        if isinstance(self._query_engine, RetrieverQueryEngine):
            return await self._query_engine.aretrieve(query_bundle)
        else:
            raise NotImplementedError(
                "This query engine does not support retrieve, use query directly"
            )

ComposableGraphQueryEngine

Bases: BaseQueryEngine

Composable graph query engine.

This query engine can operate over a ComposableGraph. It can take in custom query engines for its sub-indices.

Parameters:

Name	Type	Description	Default
graph	ComposableGraph	A ComposableGraph object.	required
custom_query_engines	Optional[Dict[str, BaseQueryEngine]]	A dictionary of custom query engines.	None
recursive	bool	Whether to recursively query the graph.	True
**kwargs	Any	additional arguments to be passed to the underlying index query engine.	{}

Source code in llama_index/core/query_engine/graph_query_engine.py

class ComposableGraphQueryEngine(BaseQueryEngine):
    """
    Composable graph query engine.

    This query engine can operate over a ComposableGraph.
    It can take in custom query engines for its sub-indices.

    Args:
        graph (ComposableGraph): A ComposableGraph object.
        custom_query_engines (Optional[Dict[str, BaseQueryEngine]]): A dictionary of
            custom query engines.
        recursive (bool): Whether to recursively query the graph.
        **kwargs: additional arguments to be passed to the underlying index query
            engine.

    """

    def __init__(
        self,
        graph: ComposableGraph,
        custom_query_engines: Optional[Dict[str, BaseQueryEngine]] = None,
        recursive: bool = True,
        **kwargs: Any,
    ) -> None:
        """Init params."""
        self._graph = graph
        self._custom_query_engines = custom_query_engines or {}
        self._kwargs = kwargs

        # additional configs
        self._recursive = recursive
        callback_manager = Settings.callback_manager
        super().__init__(callback_manager=callback_manager)

    def _get_prompt_modules(self) -> Dict[str, Any]:
        """Get prompt modules."""
        return {}

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        return self._query_index(query_bundle, index_id=None, level=0)

    @dispatcher.span
    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        return self._query_index(query_bundle, index_id=None, level=0)

    def _query_index(
        self,
        query_bundle: QueryBundle,
        index_id: Optional[str] = None,
        level: int = 0,
    ) -> RESPONSE_TYPE:
        """Query a single index."""
        index_id = index_id or self._graph.root_id

        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            # get query engine
            if index_id in self._custom_query_engines:
                query_engine = self._custom_query_engines[index_id]
            else:
                query_engine = self._graph.get_index(index_id).as_query_engine(
                    **self._kwargs
                )

            with self.callback_manager.event(
                CBEventType.RETRIEVE,
                payload={EventPayload.QUERY_STR: query_bundle.query_str},
            ) as retrieve_event:
                nodes = query_engine.retrieve(query_bundle)
                retrieve_event.on_end(payload={EventPayload.NODES: nodes})

            if self._recursive:
                # do recursion here
                nodes_for_synthesis = []
                additional_source_nodes = []
                for node_with_score in nodes:
                    node_with_score, source_nodes = self._fetch_recursive_nodes(
                        node_with_score, query_bundle, level
                    )
                    nodes_for_synthesis.append(node_with_score)
                    additional_source_nodes.extend(source_nodes)
                response = query_engine.synthesize(
                    query_bundle, nodes_for_synthesis, additional_source_nodes
                )
            else:
                response = query_engine.synthesize(query_bundle, nodes)

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    def _fetch_recursive_nodes(
        self,
        node_with_score: NodeWithScore,
        query_bundle: QueryBundle,
        level: int,
    ) -> Tuple[NodeWithScore, List[NodeWithScore]]:
        """
        Fetch nodes.

        Uses existing node if it's not an index node.
        Otherwise fetch response from corresponding index.

        """
        if isinstance(node_with_score.node, IndexNode):
            index_node = node_with_score.node
            # recursive call
            response = self._query_index(query_bundle, index_node.index_id, level + 1)

            new_node = TextNode(text=str(response))
            new_node_with_score = NodeWithScore(
                node=new_node, score=node_with_score.score
            )
            return new_node_with_score, response.source_nodes
        else:
            return node_with_score, []

JSONalyzeQueryEngine

JSONalyze query engine.

DEPRECATED: Use JSONalyzeQueryEngine from llama-index-experimental instead.

Source code in llama_index/core/query_engine/jsonalyze/jsonalyze_query_engine.py

class JSONalyzeQueryEngine:
    """
    JSONalyze query engine.

    DEPRECATED: Use `JSONalyzeQueryEngine` from `llama-index-experimental` instead.
    """

    def __init__(self, *args: Any, **kwargs: Any) -> None:
        raise DeprecationWarning(
            "JSONalyzeQueryEngine has been moved to `llama-index-experimental`.\n"
            "`pip install llama-index-experimental`\n"
            "`from llama_index.experimental.query_engine import JSONalyzeQueryEngine`\n"
            "Note that the JSONalyzeQueryEngine allows for arbitrary file creation, \n"
            "and should be used in a secure environment."
        )

KnowledgeGraphQueryEngine

Bases: BaseQueryEngine

Knowledge graph query engine.

Query engine to call a knowledge graph.

Parameters:

Name	Type	Description	Default
storage_context	Optional[StorageContext]	A storage context to use.	None
refresh_schema	bool	Whether to refresh the schema.	False
verbose	bool	Whether to print intermediate results.	False
response_synthesizer	Optional[BaseSynthesizer]	A BaseSynthesizer object.	None
**kwargs	Any	Additional keyword arguments.	{}

Source code in llama_index/core/query_engine/knowledge_graph_query_engine.py

@deprecated.deprecated(
    version="0.10.53",
    reason=(
        "KnowledgeGraphQueryEngine is deprecated. It is recommended to use "
        "the PropertyGraphIndex and associated retrievers instead."
    ),
)
class KnowledgeGraphQueryEngine(BaseQueryEngine):
    """
    Knowledge graph query engine.

    Query engine to call a knowledge graph.

    Args:
        storage_context (Optional[StorageContext]): A storage context to use.
        refresh_schema (bool): Whether to refresh the schema.
        verbose (bool): Whether to print intermediate results.
        response_synthesizer (Optional[BaseSynthesizer]):
            A BaseSynthesizer object.
        **kwargs: Additional keyword arguments.

    """

    def __init__(
        self,
        llm: Optional[LLM] = None,
        storage_context: Optional[StorageContext] = None,
        graph_query_synthesis_prompt: Optional[BasePromptTemplate] = None,
        graph_response_answer_prompt: Optional[BasePromptTemplate] = None,
        refresh_schema: bool = False,
        verbose: bool = False,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        **kwargs: Any,
    ):
        # Ensure that we have a graph store
        assert storage_context is not None, "Must provide a storage context."
        assert storage_context.graph_store is not None, (
            "Must provide a graph store in the storage context."
        )
        self._storage_context = storage_context
        self.graph_store = storage_context.graph_store

        self._llm = llm or Settings.llm

        # Get Graph schema
        self._graph_schema = self.graph_store.get_schema(refresh=refresh_schema)

        # Get graph store query synthesis prompt
        self._graph_query_synthesis_prompt = graph_query_synthesis_prompt

        self._graph_response_answer_prompt = (
            graph_response_answer_prompt or DEFAULT_KG_RESPONSE_ANSWER_PROMPT
        )
        self._verbose = verbose
        callback_manager = Settings.callback_manager
        self._response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=self._llm, callback_manager=callback_manager
        )

        super().__init__(callback_manager=callback_manager)

    def _get_prompts(self) -> Dict[str, Any]:
        """Get prompts."""
        return {
            "graph_query_synthesis_prompt": self._graph_query_synthesis_prompt,
            "graph_response_answer_prompt": self._graph_response_answer_prompt,
        }

    def _update_prompts(self, prompts: PromptDictType) -> None:
        """Update prompts."""
        if "graph_query_synthesis_prompt" in prompts:
            self._graph_query_synthesis_prompt = prompts["graph_query_synthesis_prompt"]
        if "graph_response_answer_prompt" in prompts:
            self._graph_response_answer_prompt = prompts["graph_response_answer_prompt"]

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {"response_synthesizer": self._response_synthesizer}

    def generate_query(self, query_str: str) -> str:
        """Generate a Graph Store Query from a query bundle."""
        # Get the query engine query string

        graph_store_query: str = self._llm.predict(
            self._graph_query_synthesis_prompt,
            query_str=query_str,
            schema=self._graph_schema,
        )

        return graph_store_query

    async def agenerate_query(self, query_str: str) -> str:
        """Generate a Graph Store Query from a query bundle."""
        # Get the query engine query string

        graph_store_query: str = await self._llm.apredict(
            self._graph_query_synthesis_prompt,
            query_str=query_str,
            schema=self._graph_schema,
        )

        return graph_store_query

    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Get nodes for response."""
        graph_store_query = self.generate_query(query_bundle.query_str)
        if self._verbose:
            print_text(f"Graph Store Query:\n{graph_store_query}\n", color="yellow")
        logger.debug(f"Graph Store Query:\n{graph_store_query}")

        with self.callback_manager.event(
            CBEventType.RETRIEVE,
            payload={EventPayload.QUERY_STR: graph_store_query},
        ) as retrieve_event:
            # Get the graph store response
            graph_store_response = self.graph_store.query(query=graph_store_query)
            if self._verbose:
                print_text(
                    f"Graph Store Response:\n{graph_store_response}\n",
                    color="yellow",
                )
            logger.debug(f"Graph Store Response:\n{graph_store_response}")

            retrieve_event.on_end(payload={EventPayload.RESPONSE: graph_store_response})

        retrieved_graph_context: Sequence = self._graph_response_answer_prompt.format(
            query_str=query_bundle.query_str,
            kg_query_str=graph_store_query,
            kg_response_str=graph_store_response,
        )

        node = NodeWithScore(
            node=TextNode(
                text=retrieved_graph_context,
                metadata={
                    "query_str": query_bundle.query_str,
                    "graph_store_query": graph_store_query,
                    "graph_store_response": graph_store_response,
                    "graph_schema": self._graph_schema,
                },
            ),
            score=1.0,
        )
        return [node]

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Query the graph store."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            nodes: List[NodeWithScore] = self._retrieve(query_bundle)

            response = self._response_synthesizer.synthesize(
                query=query_bundle,
                nodes=nodes,
            )

            if self._verbose:
                print_text(f"Final Response: {response}\n", color="green")

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        graph_store_query = await self.agenerate_query(query_bundle.query_str)
        if self._verbose:
            print_text(f"Graph Store Query:\n{graph_store_query}\n", color="yellow")
        logger.debug(f"Graph Store Query:\n{graph_store_query}")

        with self.callback_manager.event(
            CBEventType.RETRIEVE,
            payload={EventPayload.QUERY_STR: graph_store_query},
        ) as retrieve_event:
            # Get the graph store response
            # TBD: This is a blocking call. We need to make it async.
            graph_store_response = self.graph_store.query(query=graph_store_query)
            if self._verbose:
                print_text(
                    f"Graph Store Response:\n{graph_store_response}\n",
                    color="yellow",
                )
            logger.debug(f"Graph Store Response:\n{graph_store_response}")

            retrieve_event.on_end(payload={EventPayload.RESPONSE: graph_store_response})

        retrieved_graph_context: Sequence = self._graph_response_answer_prompt.format(
            query_str=query_bundle.query_str,
            kg_query_str=graph_store_query,
            kg_response_str=graph_store_response,
        )

        node = NodeWithScore(
            node=TextNode(
                text=retrieved_graph_context,
                metadata={
                    "query_str": query_bundle.query_str,
                    "graph_store_query": graph_store_query,
                    "graph_store_response": graph_store_response,
                    "graph_schema": self._graph_schema,
                },
            ),
            score=1.0,
        )
        return [node]

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Query the graph store."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            nodes = await self._aretrieve(query_bundle)
            response = await self._response_synthesizer.asynthesize(
                query=query_bundle,
                nodes=nodes,
            )

            if self._verbose:
                print_text(f"Final Response: {response}\n", color="green")

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

generate_query

generate_query(query_str: str) -> str

Generate a Graph Store Query from a query bundle.

Source code in llama_index/core/query_engine/knowledge_graph_query_engine.py

def generate_query(self, query_str: str) -> str:
    """Generate a Graph Store Query from a query bundle."""
    # Get the query engine query string

    graph_store_query: str = self._llm.predict(
        self._graph_query_synthesis_prompt,
        query_str=query_str,
        schema=self._graph_schema,
    )

    return graph_store_query

agenerate_query async

agenerate_query(query_str: str) -> str

Generate a Graph Store Query from a query bundle.

Source code in llama_index/core/query_engine/knowledge_graph_query_engine.py

async def agenerate_query(self, query_str: str) -> str:
    """Generate a Graph Store Query from a query bundle."""
    # Get the query engine query string

    graph_store_query: str = await self._llm.apredict(
        self._graph_query_synthesis_prompt,
        query_str=query_str,
        schema=self._graph_schema,
    )

    return graph_store_query

SimpleMultiModalQueryEngine

Bases: BaseQueryEngine

Simple Multi Modal Retriever query engine.

Assumes that retrieved text context fits within context window of LLM, along with images.

Parameters:

Name	Type	Description	Default
retriever	MultiModalVectorIndexRetriever	A retriever object.	required
multi_modal_llm	Optional[LLM]	An LLM model.	None
text_qa_template	Optional[BasePromptTemplate]	Text QA Prompt Template.	None
image_qa_template	Optional[BasePromptTemplate]	Image QA Prompt Template.	None
node_postprocessors	Optional[List[BaseNodePostprocessor]]	Node Postprocessors.	None
callback_manager	Optional[CallbackManager]	A callback manager.	None

Source code in llama_index/core/query_engine/multi_modal.py

class SimpleMultiModalQueryEngine(BaseQueryEngine):
    """
    Simple Multi Modal Retriever query engine.

    Assumes that retrieved text context fits within context window of LLM, along with images.

    Args:
        retriever (MultiModalVectorIndexRetriever): A retriever object.
        multi_modal_llm (Optional[LLM]): An LLM model.
        text_qa_template (Optional[BasePromptTemplate]): Text QA Prompt Template.
        image_qa_template (Optional[BasePromptTemplate]): Image QA Prompt Template.
        node_postprocessors (Optional[List[BaseNodePostprocessor]]): Node Postprocessors.
        callback_manager (Optional[CallbackManager]): A callback manager.

    """

    def __init__(
        self,
        retriever: "MultiModalVectorIndexRetriever",
        multi_modal_llm: Optional[LLM] = None,
        text_qa_template: Optional[BasePromptTemplate] = None,
        image_qa_template: Optional[BasePromptTemplate] = None,
        node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
        callback_manager: Optional[CallbackManager] = None,
        **kwargs: Any,
    ) -> None:
        self._retriever = retriever
        if multi_modal_llm:
            self._multi_modal_llm = multi_modal_llm
        else:
            try:
                from llama_index.llms.openai import (
                    OpenAIResponses,
                )  # pants: no-infer-dep

                self._multi_modal_llm = OpenAIResponses(
                    model="gpt-4.1", max_output_tokens=1000
                )
            except ImportError as e:
                raise ImportError(
                    "`llama-index-llms-openai` package cannot be found. "
                    "Please install it by using `pip install `llama-index-llms-openai`"
                )
        self._text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT
        self._image_qa_template = image_qa_template or DEFAULT_TEXT_QA_PROMPT

        self._node_postprocessors = node_postprocessors or []
        callback_manager = callback_manager or CallbackManager([])
        for node_postprocessor in self._node_postprocessors:
            node_postprocessor.callback_manager = callback_manager

        super().__init__(callback_manager)

    def _get_prompts(self) -> Dict[str, Any]:
        """Get prompts."""
        return {"text_qa_template": self._text_qa_template}

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {}

    def _apply_node_postprocessors(
        self, nodes: List[NodeWithScore], query_bundle: QueryBundle
    ) -> List[NodeWithScore]:
        for node_postprocessor in self._node_postprocessors:
            nodes = node_postprocessor.postprocess_nodes(
                nodes, query_bundle=query_bundle
            )
        return nodes

    def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        nodes = self._retriever.retrieve(query_bundle)
        return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)

    async def aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        nodes = await self._retriever.aretrieve(query_bundle)
        return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)

    def synthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
        context_str = "\n\n".join(
            [r.get_content(metadata_mode=MetadataMode.LLM) for r in text_nodes]
        )
        fmt_prompt = self._text_qa_template.format(
            context_str=context_str, query_str=query_bundle.query_str
        )

        blocks: List[Union[ImageBlock, TextBlock]] = [
            image_node_to_image_block(image_node.node)
            for image_node in image_nodes
            if isinstance(image_node.node, ImageNode)
        ]

        blocks.append(TextBlock(text=fmt_prompt))

        llm_response = self._multi_modal_llm.chat(
            [ChatMessage(role="user", blocks=blocks)]
        )
        return Response(
            response=llm_response.message.content,
            source_nodes=nodes,
            metadata={"text_nodes": text_nodes, "image_nodes": image_nodes},
        )

    def _get_response_with_images(
        self,
        prompt_str: str,
        image_nodes: List[NodeWithScore],
    ) -> RESPONSE_TYPE:
        assert all(isinstance(node.node, ImageNode) for node in image_nodes)

        fmt_prompt = self._image_qa_template.format(
            query_str=prompt_str,
        )

        blocks: List[Union[ImageBlock, TextBlock]] = [
            image_node_to_image_block(image_node.node)
            for image_node in image_nodes
            if isinstance(image_node.node, ImageNode)
        ]

        blocks.append(TextBlock(text=fmt_prompt))

        llm_response = self._multi_modal_llm.chat(
            [ChatMessage(role="user", blocks=blocks)]
        )
        return Response(
            response=llm_response.message.content,
            source_nodes=image_nodes,
            metadata={"image_nodes": image_nodes},
        )

    async def asynthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
        context_str = "\n\n".join(
            [r.get_content(metadata_mode=MetadataMode.LLM) for r in text_nodes]
        )
        fmt_prompt = self._text_qa_template.format(
            context_str=context_str, query_str=query_bundle.query_str
        )

        blocks: List[Union[ImageBlock, TextBlock]] = [
            image_node_to_image_block(image_node.node)
            for image_node in image_nodes
            if isinstance(image_node.node, ImageNode)
        ]

        blocks.append(TextBlock(text=fmt_prompt))

        llm_response = await self._multi_modal_llm.achat(
            [ChatMessage(role="user", blocks=blocks)]
        )
        return Response(
            response=llm_response.message.content,
            source_nodes=nodes,
            metadata={"text_nodes": text_nodes, "image_nodes": image_nodes},
        )

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            with self.callback_manager.event(
                CBEventType.RETRIEVE,
                payload={EventPayload.QUERY_STR: query_bundle.query_str},
            ) as retrieve_event:
                nodes = self.retrieve(query_bundle)

                retrieve_event.on_end(
                    payload={EventPayload.NODES: nodes},
                )

            response = self.synthesize(
                query_bundle,
                nodes=nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    def image_query(self, image_path: QueryType, prompt_str: str) -> RESPONSE_TYPE:
        """Answer a image query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: str(image_path)}
        ) as query_event:
            with self.callback_manager.event(
                CBEventType.RETRIEVE,
                payload={EventPayload.QUERY_STR: str(image_path)},
            ) as retrieve_event:
                nodes = self._retriever.image_to_image_retrieve(image_path)

                retrieve_event.on_end(
                    payload={EventPayload.NODES: nodes},
                )

            image_nodes, _ = _get_image_and_text_nodes(nodes)
            response = self._get_response_with_images(
                prompt_str=prompt_str,
                image_nodes=image_nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            with self.callback_manager.event(
                CBEventType.RETRIEVE,
                payload={EventPayload.QUERY_STR: query_bundle.query_str},
            ) as retrieve_event:
                nodes = await self.aretrieve(query_bundle)

                retrieve_event.on_end(
                    payload={EventPayload.NODES: nodes},
                )

            response = await self.asynthesize(
                query_bundle,
                nodes=nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    @property
    def retriever(self) -> "MultiModalVectorIndexRetriever":
        """Get the retriever object."""
        return self._retriever

retriever property

retriever: MultiModalVectorIndexRetriever

Get the retriever object.

image_query

image_query(image_path: QueryType, prompt_str: str) -> RESPONSE_TYPE

Answer a image query.

Source code in llama_index/core/query_engine/multi_modal.py

def image_query(self, image_path: QueryType, prompt_str: str) -> RESPONSE_TYPE:
    """Answer a image query."""
    with self.callback_manager.event(
        CBEventType.QUERY, payload={EventPayload.QUERY_STR: str(image_path)}
    ) as query_event:
        with self.callback_manager.event(
            CBEventType.RETRIEVE,
            payload={EventPayload.QUERY_STR: str(image_path)},
        ) as retrieve_event:
            nodes = self._retriever.image_to_image_retrieve(image_path)

            retrieve_event.on_end(
                payload={EventPayload.NODES: nodes},
            )

        image_nodes, _ = _get_image_and_text_nodes(nodes)
        response = self._get_response_with_images(
            prompt_str=prompt_str,
            image_nodes=image_nodes,
        )

        query_event.on_end(payload={EventPayload.RESPONSE: response})

    return response

MultiStepQueryEngine

Bases: BaseQueryEngine

Multi-step query engine.

This query engine can operate over an existing base query engine, along with the multi-step query transform.

Parameters:

Name	Type	Description	Default
query_engine	BaseQueryEngine	A BaseQueryEngine object.	required
query_transform	StepDecomposeQueryTransform	A StepDecomposeQueryTransform object.	required
response_synthesizer	Optional[BaseSynthesizer]	A BaseSynthesizer object.	None
num_steps	Optional[int]	Number of steps to run the multi-step query.	3
early_stopping	bool	Whether to stop early if the stop function returns True.	True
index_summary	str	A string summary of the index.	'None'
stop_fn	Optional[Callable[[Dict], bool]]	A stop function that takes in a dictionary of information and returns a boolean.	None

Source code in llama_index/core/query_engine/multistep_query_engine.py

class MultiStepQueryEngine(BaseQueryEngine):
    """
    Multi-step query engine.

    This query engine can operate over an existing base query engine,
    along with the multi-step query transform.

    Args:
        query_engine (BaseQueryEngine): A BaseQueryEngine object.
        query_transform (StepDecomposeQueryTransform): A StepDecomposeQueryTransform
            object.
        response_synthesizer (Optional[BaseSynthesizer]): A BaseSynthesizer
            object.
        num_steps (Optional[int]): Number of steps to run the multi-step query.
        early_stopping (bool): Whether to stop early if the stop function returns True.
        index_summary (str): A string summary of the index.
        stop_fn (Optional[Callable[[Dict], bool]]): A stop function that takes in a
            dictionary of information and returns a boolean.

    """

    def __init__(
        self,
        query_engine: BaseQueryEngine,
        query_transform: StepDecomposeQueryTransform,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        num_steps: Optional[int] = 3,
        early_stopping: bool = True,
        index_summary: str = "None",
        stop_fn: Optional[Callable[[Dict], bool]] = None,
    ) -> None:
        self._query_engine = query_engine
        self._query_transform = query_transform
        self._response_synthesizer = response_synthesizer or get_response_synthesizer(
            callback_manager=self._query_engine.callback_manager
        )

        self._index_summary = index_summary
        self._num_steps = num_steps
        self._early_stopping = early_stopping
        # TODO: make interface to stop function better
        self._stop_fn = stop_fn or default_stop_fn
        # num_steps must be provided if early_stopping is False
        if not self._early_stopping and self._num_steps is None:
            raise ValueError("Must specify num_steps if early_stopping is False.")

        callback_manager = self._query_engine.callback_manager
        super().__init__(callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {
            "response_synthesizer": self._response_synthesizer,
            "query_transform": self._query_transform,
        }

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            nodes, source_nodes, metadata = self._query_multistep(query_bundle)

            final_response = self._response_synthesizer.synthesize(
                query=query_bundle,
                nodes=nodes,
                additional_source_nodes=source_nodes,
            )
            final_response.metadata = metadata

            query_event.on_end(payload={EventPayload.RESPONSE: final_response})

        return final_response

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            nodes, source_nodes, metadata = self._query_multistep(query_bundle)

            final_response = await self._response_synthesizer.asynthesize(
                query=query_bundle,
                nodes=nodes,
                additional_source_nodes=source_nodes,
            )
            final_response.metadata = metadata

            query_event.on_end(payload={EventPayload.RESPONSE: final_response})

        return final_response

    def _combine_queries(
        self, query_bundle: QueryBundle, prev_reasoning: str
    ) -> QueryBundle:
        """Combine queries."""
        transform_metadata = {
            "prev_reasoning": prev_reasoning,
            "index_summary": self._index_summary,
        }
        return self._query_transform(query_bundle, metadata=transform_metadata)

    def _query_multistep(
        self, query_bundle: QueryBundle
    ) -> Tuple[List[NodeWithScore], List[NodeWithScore], Dict[str, Any]]:
        """Run query combiner."""
        prev_reasoning = ""
        cur_response = None
        should_stop = False
        cur_steps = 0

        # use response
        final_response_metadata: Dict[str, Any] = {"sub_qa": []}

        text_chunks = []
        source_nodes = []
        while not should_stop:
            if self._num_steps is not None and cur_steps >= self._num_steps:
                should_stop = True
                break
            elif should_stop:
                break

            updated_query_bundle = self._combine_queries(query_bundle, prev_reasoning)

            # TODO: make stop logic better
            stop_dict = {"query_bundle": updated_query_bundle}
            if self._stop_fn(stop_dict):
                should_stop = True
                break

            cur_response = self._query_engine.query(updated_query_bundle)

            # append to response builder
            cur_qa_text = (
                f"\nQuestion: {updated_query_bundle.query_str}\n"
                f"Answer: {cur_response!s}"
            )
            text_chunks.append(cur_qa_text)
            for source_node in cur_response.source_nodes:
                source_nodes.append(source_node)
            # update metadata
            final_response_metadata["sub_qa"].append(
                (updated_query_bundle.query_str, cur_response)
            )

            prev_reasoning += (
                f"- {updated_query_bundle.query_str}\n- {cur_response!s}\n"
            )
            cur_steps += 1

        nodes = [
            NodeWithScore(node=TextNode(text=text_chunk)) for text_chunk in text_chunks
        ]
        return nodes, source_nodes, final_response_metadata

PandasQueryEngine

Pandas query engine.

DEPRECATED: Use PandasQueryEngine from llama-index-experimental instead.

Source code in llama_index/core/query_engine/pandas/pandas_query_engine.py

class PandasQueryEngine:
    """
    Pandas query engine.

    DEPRECATED: Use `PandasQueryEngine` from `llama-index-experimental` instead.
    """

    def __init__(self, *args: Any, **kwargs: Any) -> None:
        raise DeprecationWarning(
            "PandasQueryEngine has been moved to `llama-index-experimental`.\n"
            "`pip install llama-index-experimental`\n"
            "`from llama_index.experimental.query_engine import PandasQueryEngine`\n"
            "Note that the PandasQueryEngine allows for arbitrary code execution, \n"
            "and should be used in a secure environment."
        )

RetrieverQueryEngine

Bases: BaseQueryEngine

Retriever query engine.

Parameters:

Name	Type	Description	Default
retriever	BaseRetriever	A retriever object.	required
response_synthesizer	Optional[BaseSynthesizer]	A BaseSynthesizer object.	None
callback_manager	Optional[CallbackManager]	A callback manager.	None

Source code in llama_index/core/query_engine/retriever_query_engine.py

class RetrieverQueryEngine(BaseQueryEngine):
    """
    Retriever query engine.

    Args:
        retriever (BaseRetriever): A retriever object.
        response_synthesizer (Optional[BaseSynthesizer]): A BaseSynthesizer
            object.
        callback_manager (Optional[CallbackManager]): A callback manager.

    """

    def __init__(
        self,
        retriever: BaseRetriever,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
        callback_manager: Optional[CallbackManager] = None,
    ) -> None:
        self._retriever = retriever
        self._response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=Settings.llm,
            callback_manager=callback_manager or Settings.callback_manager,
        )

        self._node_postprocessors = node_postprocessors or []
        callback_manager = (
            callback_manager or self._response_synthesizer.callback_manager
        )
        for node_postprocessor in self._node_postprocessors:
            node_postprocessor.callback_manager = callback_manager
        super().__init__(callback_manager=callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {"response_synthesizer": self._response_synthesizer}

    @classmethod
    def from_args(
        cls,
        retriever: BaseRetriever,
        llm: Optional[LLM] = None,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
        callback_manager: Optional[CallbackManager] = None,
        # response synthesizer args
        response_mode: ResponseMode = ResponseMode.COMPACT,
        text_qa_template: Optional[BasePromptTemplate] = None,
        refine_template: Optional[BasePromptTemplate] = None,
        summary_template: Optional[BasePromptTemplate] = None,
        simple_template: Optional[BasePromptTemplate] = None,
        output_cls: Optional[Type[BaseModel]] = None,
        use_async: bool = False,
        streaming: bool = False,
        verbose: bool = False,
        **kwargs: Any,
    ) -> "RetrieverQueryEngine":
        """
        Initialize a RetrieverQueryEngine object.".

        Args:
            retriever (BaseRetriever): A retriever object.
            llm (Optional[LLM]): An instance of an LLM.
            response_synthesizer (Optional[BaseSynthesizer]): An instance of a response
                synthesizer.
            node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of
                node postprocessors.
            callback_manager (Optional[CallbackManager]): A callback manager.
            response_mode (ResponseMode): A ResponseMode object.
            text_qa_template (Optional[BasePromptTemplate]): A BasePromptTemplate
                object.
            refine_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
            summary_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
            simple_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
            output_cls (Optional[Type[BaseModel]]): The pydantic model to pass to the
                response synthesizer.
            use_async (bool): Whether to use async.
            streaming (bool): Whether to use streaming.
            verbose (bool): Whether to print verbose output.

        """
        llm = llm or Settings.llm

        response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=llm,
            text_qa_template=text_qa_template,
            refine_template=refine_template,
            summary_template=summary_template,
            simple_template=simple_template,
            response_mode=response_mode,
            output_cls=output_cls,
            use_async=use_async,
            streaming=streaming,
            verbose=verbose,
        )

        callback_manager = callback_manager or Settings.callback_manager

        return cls(
            retriever=retriever,
            response_synthesizer=response_synthesizer,
            callback_manager=callback_manager,
            node_postprocessors=node_postprocessors,
        )

    def _apply_node_postprocessors(
        self, nodes: List[NodeWithScore], query_bundle: QueryBundle
    ) -> List[NodeWithScore]:
        for node_postprocessor in self._node_postprocessors:
            nodes = node_postprocessor.postprocess_nodes(
                nodes, query_bundle=query_bundle
            )
        return nodes

    async def _async_apply_node_postprocessors(
        self, nodes: List[NodeWithScore], query_bundle: QueryBundle
    ) -> List[NodeWithScore]:
        for node_postprocessor in self._node_postprocessors:
            nodes = await node_postprocessor.apostprocess_nodes(
                nodes, query_bundle=query_bundle
            )
        return nodes

    def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        nodes = self._retriever.retrieve(query_bundle)
        return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)

    async def aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        nodes = await self._retriever.aretrieve(query_bundle)
        return await self._async_apply_node_postprocessors(
            nodes, query_bundle=query_bundle
        )

    def with_retriever(self, retriever: BaseRetriever) -> "RetrieverQueryEngine":
        return RetrieverQueryEngine(
            retriever=retriever,
            response_synthesizer=self._response_synthesizer,
            callback_manager=self.callback_manager,
            node_postprocessors=self._node_postprocessors,
        )

    def synthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        return self._response_synthesizer.synthesize(
            query=query_bundle,
            nodes=nodes,
            additional_source_nodes=additional_source_nodes,
        )

    async def asynthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        return await self._response_synthesizer.asynthesize(
            query=query_bundle,
            nodes=nodes,
            additional_source_nodes=additional_source_nodes,
        )

    @dispatcher.span
    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            nodes = self.retrieve(query_bundle)
            response = self._response_synthesizer.synthesize(
                query=query_bundle,
                nodes=nodes,
            )
            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    @dispatcher.span
    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            nodes = await self.aretrieve(query_bundle)

            response = await self._response_synthesizer.asynthesize(
                query=query_bundle,
                nodes=nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    @property
    def retriever(self) -> BaseRetriever:
        """Get the retriever object."""
        return self._retriever

retriever property

retriever: BaseRetriever

Get the retriever object.

from_args classmethod

from_args(retriever: BaseRetriever, llm: Optional[LLM] = None, response_synthesizer: Optional[BaseSynthesizer] = None, node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, callback_manager: Optional[CallbackManager] = None, response_mode: ResponseMode = COMPACT, text_qa_template: Optional[BasePromptTemplate] = None, refine_template: Optional[BasePromptTemplate] = None, summary_template: Optional[BasePromptTemplate] = None, simple_template: Optional[BasePromptTemplate] = None, output_cls: Optional[Type[BaseModel]] = None, use_async: bool = False, streaming: bool = False, verbose: bool = False, **kwargs: Any) -> RetrieverQueryEngine

Initialize a RetrieverQueryEngine object.".

Parameters:

Name	Type	Description	Default
retriever	BaseRetriever	A retriever object.	required
llm	Optional[LLM]	An instance of an LLM.	None
response_synthesizer	Optional[BaseSynthesizer]	An instance of a response synthesizer.	None
node_postprocessors	Optional[List[BaseNodePostprocessor]]	A list of node postprocessors.	None
callback_manager	Optional[CallbackManager]	A callback manager.	None
response_mode	ResponseMode	A ResponseMode object.	COMPACT
text_qa_template	Optional[BasePromptTemplate]	A BasePromptTemplate object.	None
refine_template	Optional[BasePromptTemplate]	A BasePromptTemplate object.	None
summary_template	Optional[BasePromptTemplate]	A BasePromptTemplate object.	None
simple_template	Optional[BasePromptTemplate]	A BasePromptTemplate object.	None
output_cls	Optional[Type[BaseModel]]	The pydantic model to pass to the response synthesizer.	None
use_async	bool	Whether to use async.	False
streaming	bool	Whether to use streaming.	False
verbose	bool	Whether to print verbose output.	False

Source code in llama_index/core/query_engine/retriever_query_engine.py

@classmethod
def from_args(
    cls,
    retriever: BaseRetriever,
    llm: Optional[LLM] = None,
    response_synthesizer: Optional[BaseSynthesizer] = None,
    node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
    callback_manager: Optional[CallbackManager] = None,
    # response synthesizer args
    response_mode: ResponseMode = ResponseMode.COMPACT,
    text_qa_template: Optional[BasePromptTemplate] = None,
    refine_template: Optional[BasePromptTemplate] = None,
    summary_template: Optional[BasePromptTemplate] = None,
    simple_template: Optional[BasePromptTemplate] = None,
    output_cls: Optional[Type[BaseModel]] = None,
    use_async: bool = False,
    streaming: bool = False,
    verbose: bool = False,
    **kwargs: Any,
) -> "RetrieverQueryEngine":
    """
    Initialize a RetrieverQueryEngine object.".

    Args:
        retriever (BaseRetriever): A retriever object.
        llm (Optional[LLM]): An instance of an LLM.
        response_synthesizer (Optional[BaseSynthesizer]): An instance of a response
            synthesizer.
        node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of
            node postprocessors.
        callback_manager (Optional[CallbackManager]): A callback manager.
        response_mode (ResponseMode): A ResponseMode object.
        text_qa_template (Optional[BasePromptTemplate]): A BasePromptTemplate
            object.
        refine_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
        summary_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
        simple_template (Optional[BasePromptTemplate]): A BasePromptTemplate object.
        output_cls (Optional[Type[BaseModel]]): The pydantic model to pass to the
            response synthesizer.
        use_async (bool): Whether to use async.
        streaming (bool): Whether to use streaming.
        verbose (bool): Whether to print verbose output.

    """
    llm = llm or Settings.llm

    response_synthesizer = response_synthesizer or get_response_synthesizer(
        llm=llm,
        text_qa_template=text_qa_template,
        refine_template=refine_template,
        summary_template=summary_template,
        simple_template=simple_template,
        response_mode=response_mode,
        output_cls=output_cls,
        use_async=use_async,
        streaming=streaming,
        verbose=verbose,
    )

    callback_manager = callback_manager or Settings.callback_manager

    return cls(
        retriever=retriever,
        response_synthesizer=response_synthesizer,
        callback_manager=callback_manager,
        node_postprocessors=node_postprocessors,
    )

RetryGuidelineQueryEngine

Bases: BaseQueryEngine

Does retry with evaluator feedback if query engine fails evaluation.

Parameters:

Name	Type	Description	Default
query_engine	BaseQueryEngine	A query engine object	required
guideline_evaluator	GuidelineEvaluator	A guideline evaluator object	required
resynthesize_query	bool	Whether to resynthesize query	False
max_retries	int	Maximum number of retries	3
callback_manager	Optional[CallbackManager]	A callback manager object	None

Source code in llama_index/core/query_engine/retry_query_engine.py

class RetryGuidelineQueryEngine(BaseQueryEngine):
    """
    Does retry with evaluator feedback
    if query engine fails evaluation.

    Args:
        query_engine (BaseQueryEngine): A query engine object
        guideline_evaluator (GuidelineEvaluator): A guideline evaluator object
        resynthesize_query (bool): Whether to resynthesize query
        max_retries (int): Maximum number of retries
        callback_manager (Optional[CallbackManager]): A callback manager object

    """

    def __init__(
        self,
        query_engine: BaseQueryEngine,
        guideline_evaluator: GuidelineEvaluator,
        resynthesize_query: bool = False,
        max_retries: int = 3,
        callback_manager: Optional[CallbackManager] = None,
        query_transformer: Optional[FeedbackQueryTransformation] = None,
    ) -> None:
        self._query_engine = query_engine
        self._guideline_evaluator = guideline_evaluator
        self.max_retries = max_retries
        self.resynthesize_query = resynthesize_query
        self.query_transformer = query_transformer or FeedbackQueryTransformation(
            resynthesize_query=self.resynthesize_query
        )
        super().__init__(callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {
            "query_engine": self._query_engine,
            "guideline_evalator": self._guideline_evaluator,
        }

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        response = self._query_engine._query(query_bundle)
        assert not isinstance(response, AsyncStreamingResponse)
        if self.max_retries <= 0:
            return response
        typed_response = (
            response if isinstance(response, Response) else response.get_response()
        )
        query_str = query_bundle.query_str
        eval = self._guideline_evaluator.evaluate_response(query_str, typed_response)
        if eval.passing:
            logger.debug("Evaluation returned True.")
            return response
        else:
            logger.debug("Evaluation returned False.")
            new_query_engine = RetryGuidelineQueryEngine(
                self._query_engine,
                self._guideline_evaluator,
                self.resynthesize_query,
                self.max_retries - 1,
                self.callback_manager,
            )
            new_query = self.query_transformer.run(query_bundle, {"evaluation": eval})
            logger.debug("New query: %s", new_query.query_str)
            return new_query_engine.query(new_query)

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Not supported."""
        return self._query(query_bundle)

RetryQueryEngine

Bases: BaseQueryEngine

Does retry on query engine if it fails evaluation.

Parameters:

Name	Type	Description	Default
query_engine	BaseQueryEngine	A query engine object	required
evaluator	BaseEvaluator	An evaluator object	required
max_retries	int	Maximum number of retries	3
callback_manager	Optional[CallbackManager]	A callback manager object	None

Source code in llama_index/core/query_engine/retry_query_engine.py

class RetryQueryEngine(BaseQueryEngine):
    """
    Does retry on query engine if it fails evaluation.

    Args:
        query_engine (BaseQueryEngine): A query engine object
        evaluator (BaseEvaluator): An evaluator object
        max_retries (int): Maximum number of retries
        callback_manager (Optional[CallbackManager]): A callback manager object

    """

    def __init__(
        self,
        query_engine: BaseQueryEngine,
        evaluator: BaseEvaluator,
        max_retries: int = 3,
        callback_manager: Optional[CallbackManager] = None,
    ) -> None:
        self._query_engine = query_engine
        self._evaluator = evaluator
        self.max_retries = max_retries
        super().__init__(callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {"query_engine": self._query_engine, "evaluator": self._evaluator}

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        response = self._query_engine._query(query_bundle)
        assert not isinstance(response, AsyncStreamingResponse)
        if self.max_retries <= 0:
            return response
        typed_response = (
            response if isinstance(response, Response) else response.get_response()
        )
        query_str = query_bundle.query_str
        eval = self._evaluator.evaluate_response(query_str, typed_response)
        if eval.passing:
            logger.debug("Evaluation returned True.")
            return response
        else:
            logger.debug("Evaluation returned False.")
            new_query_engine = RetryQueryEngine(
                self._query_engine, self._evaluator, self.max_retries - 1
            )
            query_transformer = FeedbackQueryTransformation()
            new_query = query_transformer.run(query_bundle, {"evaluation": eval})
            return new_query_engine.query(new_query)

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Not supported."""
        return self._query(query_bundle)

RetrySourceQueryEngine

Bases: BaseQueryEngine

Retry with different source nodes.

Source code in llama_index/core/query_engine/retry_source_query_engine.py

class RetrySourceQueryEngine(BaseQueryEngine):
    """Retry with different source nodes."""

    def __init__(
        self,
        query_engine: RetrieverQueryEngine,
        evaluator: BaseEvaluator,
        llm: Optional[LLM] = None,
        max_retries: int = 3,
        callback_manager: Optional[CallbackManager] = None,
    ) -> None:
        """Run a BaseQueryEngine with retries."""
        self._query_engine = query_engine
        self._evaluator = evaluator
        self._llm = llm or Settings.llm
        self.max_retries = max_retries
        super().__init__(callback_manager=callback_manager or Settings.callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {"query_engine": self._query_engine, "evaluator": self._evaluator}

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        response = self._query_engine._query(query_bundle)
        assert not isinstance(response, AsyncStreamingResponse)
        if self.max_retries <= 0:
            return response
        typed_response = (
            response if isinstance(response, Response) else response.get_response()
        )
        query_str = query_bundle.query_str
        eval = self._evaluator.evaluate_response(query_str, typed_response)
        if eval.passing:
            logger.debug("Evaluation returned True.")
            return response
        else:
            logger.debug("Evaluation returned False.")
            # Test source nodes
            source_evals = [
                self._evaluator.evaluate(
                    query=query_str,
                    response=typed_response.response,
                    contexts=[source_node.get_content()],
                )
                for source_node in typed_response.source_nodes
            ]
            orig_nodes = typed_response.source_nodes
            assert len(source_evals) == len(orig_nodes)
            new_docs = []
            for node, eval_result in zip(orig_nodes, source_evals):
                if eval_result:
                    new_docs.append(Document(text=node.node.get_content()))
            if len(new_docs) == 0:
                raise ValueError("No source nodes passed evaluation.")
            new_index = SummaryIndex.from_documents(
                new_docs,
            )
            new_retriever_engine = RetrieverQueryEngine(new_index.as_retriever())
            new_query_engine = RetrySourceQueryEngine(
                new_retriever_engine,
                self._evaluator,
                self._llm,
                self.max_retries - 1,
            )
            return new_query_engine.query(query_bundle)

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Not supported."""
        return self._query(query_bundle)

RetrieverRouterQueryEngine

Bases: BaseQueryEngine

Retriever-based router query engine.

NOTE: this is deprecated, please use our new ToolRetrieverRouterQueryEngine

Use a retriever to select a set of Nodes. Each node will be converted into a ToolMetadata object, and also used to retrieve a query engine, to form a QueryEngineTool.

NOTE: this is a beta feature. We are figuring out the right interface between the retriever and query engine.

Parameters:

Name	Type	Description	Default
selector	BaseSelector	A selector that chooses one out of many options based on each candidate's metadata and query.	required
query_engine_tools	Sequence[QueryEngineTool]	A sequence of candidate query engines. They must be wrapped as tools to expose metadata to the selector.	required
callback_manager	Optional[CallbackManager]	A callback manager.	None

Source code in llama_index/core/query_engine/router_query_engine.py

class RetrieverRouterQueryEngine(BaseQueryEngine):
    """
    Retriever-based router query engine.

    NOTE: this is deprecated, please use our new ToolRetrieverRouterQueryEngine

    Use a retriever to select a set of Nodes. Each node will be converted
    into a ToolMetadata object, and also used to retrieve a query engine, to form
    a QueryEngineTool.

    NOTE: this is a beta feature. We are figuring out the right interface
    between the retriever and query engine.

    Args:
        selector (BaseSelector): A selector that chooses one out of many options based
            on each candidate's metadata and query.
        query_engine_tools (Sequence[QueryEngineTool]): A sequence of candidate
            query engines. They must be wrapped as tools to expose metadata to
            the selector.
        callback_manager (Optional[CallbackManager]): A callback manager.

    """

    def __init__(
        self,
        retriever: BaseRetriever,
        node_to_query_engine_fn: Callable,
        callback_manager: Optional[CallbackManager] = None,
    ) -> None:
        self._retriever = retriever
        self._node_to_query_engine_fn = node_to_query_engine_fn
        super().__init__(callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        # NOTE: don't include tools for now
        return {"retriever": self._retriever}

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        nodes_with_score = self._retriever.retrieve(query_bundle)
        # TODO: for now we only support retrieving one node
        if len(nodes_with_score) > 1:
            raise ValueError("Retrieved more than one node.")

        node = nodes_with_score[0].node
        query_engine = self._node_to_query_engine_fn(node)
        return query_engine.query(query_bundle)

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        return self._query(query_bundle)

RouterQueryEngine

Bases: BaseQueryEngine

Router query engine.

Selects one out of several candidate query engines to execute a query.

Parameters:

Name	Type	Description	Default
selector	BaseSelector	A selector that chooses one out of many options based on each candidate's metadata and query.	required
query_engine_tools	Sequence[QueryEngineTool]	A sequence of candidate query engines. They must be wrapped as tools to expose metadata to the selector.	required
summarizer	Optional[TreeSummarize]	Tree summarizer to summarize sub-results.	None

Source code in llama_index/core/query_engine/router_query_engine.py

class RouterQueryEngine(BaseQueryEngine):
    """
    Router query engine.

    Selects one out of several candidate query engines to execute a query.

    Args:
        selector (BaseSelector): A selector that chooses one out of many options based
            on each candidate's metadata and query.
        query_engine_tools (Sequence[QueryEngineTool]): A sequence of candidate
            query engines. They must be wrapped as tools to expose metadata to
            the selector.
        summarizer (Optional[TreeSummarize]): Tree summarizer to summarize sub-results.

    """

    def __init__(
        self,
        selector: BaseSelector,
        query_engine_tools: Sequence[QueryEngineTool],
        llm: Optional[LLM] = None,
        summarizer: Optional[TreeSummarize] = None,
        verbose: bool = False,
    ) -> None:
        self._llm = llm or Settings.llm
        self._selector = selector
        self._query_engines = [x.query_engine for x in query_engine_tools]
        self._metadatas = [x.metadata for x in query_engine_tools]
        self._summarizer = summarizer or TreeSummarize(
            llm=self._llm,
            summary_template=DEFAULT_TREE_SUMMARIZE_PROMPT_SEL,
        )
        self._verbose = verbose

        super().__init__(callback_manager=Settings.callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        # NOTE: don't include tools for now
        return {"summarizer": self._summarizer, "selector": self._selector}

    @classmethod
    def from_defaults(
        cls,
        query_engine_tools: Sequence[QueryEngineTool],
        llm: Optional[LLM] = None,
        selector: Optional[BaseSelector] = None,
        summarizer: Optional[TreeSummarize] = None,
        select_multi: bool = False,
        **kwargs: Any,
    ) -> "RouterQueryEngine":
        llm = llm or Settings.llm

        selector = selector or get_selector_from_llm(llm, is_multi=select_multi)

        assert selector is not None

        return cls(
            selector,
            query_engine_tools,
            llm=llm,
            summarizer=summarizer,
            **kwargs,
        )

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            result = self._selector.select(self._metadatas, query_bundle)

            if len(result.inds) > 1:
                responses = []
                for i, engine_ind in enumerate(result.inds):
                    log_str = (
                        f"Selecting query engine {engine_ind}: {result.reasons[i]}."
                    )
                    logger.info(log_str)
                    if self._verbose:
                        print_text(log_str + "\n", color="pink")

                    selected_query_engine = self._query_engines[engine_ind]
                    responses.append(selected_query_engine.query(query_bundle))

                if len(responses) > 1:
                    final_response = combine_responses(
                        self._summarizer, responses, query_bundle
                    )
                else:
                    final_response = responses[0]
            else:
                try:
                    selected_query_engine = self._query_engines[result.ind]
                    log_str = f"Selecting query engine {result.ind}: {result.reason}."
                    logger.info(log_str)
                    if self._verbose:
                        print_text(log_str + "\n", color="pink")
                except ValueError as e:
                    raise ValueError("Failed to select query engine") from e

                final_response = selected_query_engine.query(query_bundle)

            # add selected result
            final_response.metadata = final_response.metadata or {}
            final_response.metadata["selector_result"] = result

            query_event.on_end(payload={EventPayload.RESPONSE: final_response})

        return final_response

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            result = await self._selector.aselect(self._metadatas, query_bundle)

            if len(result.inds) > 1:
                tasks = []
                for i, engine_ind in enumerate(result.inds):
                    log_str = (
                        f"Selecting query engine {engine_ind}: {result.reasons[i]}."
                    )
                    logger.info(log_str)
                    if self._verbose:
                        print_text(log_str + "\n", color="pink")
                    selected_query_engine = self._query_engines[engine_ind]
                    tasks.append(selected_query_engine.aquery(query_bundle))

                responses = run_async_tasks(tasks)
                if len(responses) > 1:
                    final_response = await acombine_responses(
                        self._summarizer, responses, query_bundle
                    )
                else:
                    final_response = responses[0]
            else:
                try:
                    selected_query_engine = self._query_engines[result.ind]
                    log_str = f"Selecting query engine {result.ind}: {result.reason}."
                    logger.info(log_str)
                    if self._verbose:
                        print_text(log_str + "\n", color="pink")
                except ValueError as e:
                    raise ValueError("Failed to select query engine") from e

                final_response = await selected_query_engine.aquery(query_bundle)

            # add selected result
            final_response.metadata = final_response.metadata or {}
            final_response.metadata["selector_result"] = result

            query_event.on_end(payload={EventPayload.RESPONSE: final_response})

        return final_response

ToolRetrieverRouterQueryEngine

Bases: BaseQueryEngine

Tool Retriever router query engine.

Selects a set of candidate query engines to execute a query.

Parameters:

Name	Type	Description	Default
retriever	ObjectRetriever	A retriever that retrieves a set of query engine tools.	required
summarizer	Optional[TreeSummarize]	Tree summarizer to summarize sub-results.	None

Source code in llama_index/core/query_engine/router_query_engine.py

class ToolRetrieverRouterQueryEngine(BaseQueryEngine):
    """
    Tool Retriever router query engine.

    Selects a set of candidate query engines to execute a query.

    Args:
        retriever (ObjectRetriever): A retriever that retrieves a set of
            query engine tools.
        summarizer (Optional[TreeSummarize]): Tree summarizer to summarize sub-results.

    """

    def __init__(
        self,
        retriever: ObjectRetriever[QueryEngineTool],
        llm: Optional[LLM] = None,
        summarizer: Optional[TreeSummarize] = None,
    ) -> None:
        llm = llm or Settings.llm
        self._summarizer = summarizer or TreeSummarize(
            llm=llm,
            summary_template=DEFAULT_TREE_SUMMARIZE_PROMPT_SEL,
        )
        self._retriever = retriever

        super().__init__(Settings.callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        # NOTE: don't include tools for now
        return {"summarizer": self._summarizer}

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            query_engine_tools = self._retriever.retrieve(query_bundle)
            responses = []
            for query_engine_tool in query_engine_tools:
                query_engine = query_engine_tool.query_engine
                responses.append(query_engine.query(query_bundle))

            if len(responses) > 1:
                final_response = combine_responses(
                    self._summarizer, responses, query_bundle
                )
            else:
                final_response = responses[0]

            # add selected result
            final_response.metadata = final_response.metadata or {}
            final_response.metadata["retrieved_tools"] = query_engine_tools

            query_event.on_end(payload={EventPayload.RESPONSE: final_response})

        return final_response

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            query_engine_tools = self._retriever.retrieve(query_bundle)
            tasks = []
            for query_engine_tool in query_engine_tools:
                query_engine = query_engine_tool.query_engine
                tasks.append(query_engine.aquery(query_bundle))
            responses = run_async_tasks(tasks)
            if len(responses) > 1:
                final_response = await acombine_responses(
                    self._summarizer, responses, query_bundle
                )
            else:
                final_response = responses[0]

            # add selected result
            final_response.metadata = final_response.metadata or {}
            final_response.metadata["retrieved_tools"] = query_engine_tools

            query_event.on_end(payload={EventPayload.RESPONSE: final_response})

        return final_response

SQLJoinQueryEngine

Bases: BaseQueryEngine

SQL Join Query Engine.

This query engine can "Join" a SQL database results with another query engine. It can decide it needs to query the SQL database or the other query engine. If it decides to query the SQL database, it will first query the SQL database, whether to augment information with retrieved results from the other query engine.

Parameters:

Name	Type	Description	Default
sql_query_tool	QueryEngineTool	Query engine tool for SQL database. other_query_tool (QueryEngineTool): Other query engine tool.	required
selector	Optional[Union[LLMSingleSelector, PydanticSingleSelector]]	Selector to use.	None
sql_join_synthesis_prompt	Optional[BasePromptTemplate]	PromptTemplate to use for SQL join synthesis.	None
sql_augment_query_transform	Optional[SQLAugmentQueryTransform]	Query transform to use for SQL augmentation.	None
use_sql_join_synthesis	bool	Whether to use SQL join synthesis.	True
callback_manager	Optional[CallbackManager]	Callback manager to use.	None
verbose	bool	Whether to print intermediate results.	True

Source code in llama_index/core/query_engine/sql_join_query_engine.py

class SQLJoinQueryEngine(BaseQueryEngine):
    """
    SQL Join Query Engine.

    This query engine can "Join" a SQL database results
    with another query engine.
    It can decide it needs to query the SQL database or the other query engine.
    If it decides to query the SQL database, it will first query the SQL database,
    whether to augment information with retrieved results from the other query engine.

    Args:
        sql_query_tool (QueryEngineTool): Query engine tool for SQL database.
            other_query_tool (QueryEngineTool): Other query engine tool.
        selector (Optional[Union[LLMSingleSelector, PydanticSingleSelector]]):
            Selector to use.
        sql_join_synthesis_prompt (Optional[BasePromptTemplate]):
            PromptTemplate to use for SQL join synthesis.
        sql_augment_query_transform (Optional[SQLAugmentQueryTransform]): Query
            transform to use for SQL augmentation.
        use_sql_join_synthesis (bool): Whether to use SQL join synthesis.
        callback_manager (Optional[CallbackManager]): Callback manager to use.
        verbose (bool): Whether to print intermediate results.

    """

    def __init__(
        self,
        sql_query_tool: QueryEngineTool,
        other_query_tool: QueryEngineTool,
        selector: Optional[Union[LLMSingleSelector, PydanticSingleSelector]] = None,
        llm: Optional[LLM] = None,
        sql_join_synthesis_prompt: Optional[BasePromptTemplate] = None,
        sql_augment_query_transform: Optional[SQLAugmentQueryTransform] = None,
        use_sql_join_synthesis: bool = True,
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = True,
        streaming: bool = False,
    ) -> None:
        """Initialize params."""
        super().__init__(callback_manager=callback_manager)
        # validate that the query engines are of the right type
        if not isinstance(
            sql_query_tool.query_engine,
            (BaseSQLTableQueryEngine, NLSQLTableQueryEngine),
        ):
            raise ValueError(
                "sql_query_tool.query_engine must be an instance of "
                "BaseSQLTableQueryEngine or NLSQLTableQueryEngine"
            )
        self._sql_query_tool = sql_query_tool
        self._other_query_tool = other_query_tool

        self._llm = llm or Settings.llm

        self._selector = selector or get_selector_from_llm(self._llm, is_multi=False)  # type: ignore
        assert isinstance(self._selector, (LLMSingleSelector, PydanticSingleSelector))

        self._sql_join_synthesis_prompt = (
            sql_join_synthesis_prompt or DEFAULT_SQL_JOIN_SYNTHESIS_PROMPT
        )
        self._sql_augment_query_transform = (
            sql_augment_query_transform or SQLAugmentQueryTransform(llm=self._llm)
        )
        self._use_sql_join_synthesis = use_sql_join_synthesis
        self._verbose = verbose
        self._streaming = streaming

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {
            "selector": self._selector,
            "sql_augment_query_transform": self._sql_augment_query_transform,
        }

    def _get_prompts(self) -> PromptDictType:
        """Get prompts."""
        return {"sql_join_synthesis_prompt": self._sql_join_synthesis_prompt}

    def _update_prompts(self, prompts: PromptDictType) -> None:
        """Update prompts."""
        if "sql_join_synthesis_prompt" in prompts:
            self._sql_join_synthesis_prompt = prompts["sql_join_synthesis_prompt"]

    def _query_sql_other(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Query SQL database + other query engine in sequence."""
        # first query SQL database
        sql_response = self._sql_query_tool.query_engine.query(query_bundle)
        if not self._use_sql_join_synthesis:
            return sql_response

        sql_query = (
            sql_response.metadata["sql_query"] if sql_response.metadata else None
        )
        if self._verbose:
            print_text(f"SQL query: {sql_query}\n", color="yellow")
            print_text(f"SQL response: {sql_response}\n", color="yellow")

        # 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),
            },
        )

        if self._verbose:
            print_text(
                f"Transformed query given SQL response: {new_query.query_str}\n",
                color="blue",
            )
        logger.info(f"> Transformed query given SQL response: {new_query.query_str}")
        if self._sql_augment_query_transform.check_stop(new_query):
            return sql_response

        other_response = self._other_query_tool.query_engine.query(new_query)
        if self._verbose:
            print_text(f"query engine response: {other_response}\n", color="pink")
        logger.info(f"> query engine response: {other_response}")

        if self._streaming:
            response_gen = self._llm.stream(
                self._sql_join_synthesis_prompt,
                query_str=query_bundle.query_str,
                sql_query_str=sql_query,
                sql_response_str=str(sql_response),
                query_engine_query_str=new_query.query_str,
                query_engine_response_str=str(other_response),
            )

            response_metadata = {
                **(sql_response.metadata or {}),
                **(other_response.metadata or {}),
            }
            source_nodes = other_response.source_nodes
            return StreamingResponse(
                response_gen,
                metadata=response_metadata,
                source_nodes=source_nodes,
            )
        else:
            response_str = self._llm.predict(
                self._sql_join_synthesis_prompt,
                query_str=query_bundle.query_str,
                sql_query_str=sql_query,
                sql_response_str=str(sql_response),
                query_engine_query_str=new_query.query_str,
                query_engine_response_str=str(other_response),
            )

            response_metadata = {
                **(sql_response.metadata or {}),
                **(other_response.metadata or {}),
            }
            source_nodes = other_response.source_nodes
            return Response(
                response_str,
                metadata=response_metadata,
                source_nodes=source_nodes,
            )

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Query and get response."""
        # TODO: see if this can be consolidated with logic in RouterQueryEngine
        metadatas = [self._sql_query_tool.metadata, self._other_query_tool.metadata]
        result = self._selector.select(metadatas, query_bundle)
        # pick sql query
        if result.ind == 0:
            if self._verbose:
                print_text(f"Querying SQL database: {result.reason}\n", color="blue")
            logger.info(f"> Querying SQL database: {result.reason}")
            return self._query_sql_other(query_bundle)
        elif result.ind == 1:
            if self._verbose:
                print_text(
                    f"Querying other query engine: {result.reason}\n", color="blue"
                )
            logger.info(f"> Querying other query engine: {result.reason}")
            return self._other_query_tool.query_engine.query(query_bundle)
        else:
            raise ValueError(f"Invalid result.ind: {result.ind}")

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        # TODO: make async
        return self._query(query_bundle)

SQLAutoVectorQueryEngine

Bases: SQLJoinQueryEngine

SQL + Vector Index Auto Retriever Query Engine.

This query engine can query both a SQL database as well as a vector database. It will first decide whether it needs to query the SQL database or vector store. If it decides to query the SQL database, it will also decide whether to augment information with retrieved results from the vector store. We use the VectorIndexAutoRetriever to retrieve results.

Parameters:

Name	Type	Description	Default
sql_query_tool	QueryEngineTool	Query engine tool for SQL database.	required
vector_query_tool	QueryEngineTool	Query engine tool for vector database.	required
selector	Optional[Union[LLMSingleSelector, PydanticSingleSelector]]	Selector to use.	None
sql_vector_synthesis_prompt	Optional[BasePromptTemplate]	Prompt to use for SQL vector synthesis.	None
sql_augment_query_transform	Optional[SQLAugmentQueryTransform]	Query transform to use for SQL augmentation.	None
use_sql_vector_synthesis	bool	Whether to use SQL vector synthesis.	True
callback_manager	Optional[CallbackManager]	Callback manager to use.	None
verbose	bool	Whether to print intermediate results.	True

Source code in llama_index/core/query_engine/sql_vector_query_engine.py

class SQLAutoVectorQueryEngine(SQLJoinQueryEngine):
    """
    SQL + Vector Index Auto Retriever Query Engine.

    This query engine can query both a SQL database
    as well as a vector database. It will first decide
    whether it needs to query the SQL database or vector store.
    If it decides to query the SQL database, it will also decide
    whether to augment information with retrieved results from the vector store.
    We use the VectorIndexAutoRetriever to retrieve results.

    Args:
        sql_query_tool (QueryEngineTool): Query engine tool for SQL database.
        vector_query_tool (QueryEngineTool): Query engine tool for vector database.
        selector (Optional[Union[LLMSingleSelector, PydanticSingleSelector]]):
            Selector to use.
        sql_vector_synthesis_prompt (Optional[BasePromptTemplate]):
            Prompt to use for SQL vector synthesis.
        sql_augment_query_transform (Optional[SQLAugmentQueryTransform]): Query
            transform to use for SQL augmentation.
        use_sql_vector_synthesis (bool): Whether to use SQL vector synthesis.
        callback_manager (Optional[CallbackManager]): Callback manager to use.
        verbose (bool): Whether to print intermediate results.

    """

    def __init__(
        self,
        sql_query_tool: QueryEngineTool,
        vector_query_tool: QueryEngineTool,
        selector: Optional[Union[LLMSingleSelector, PydanticSingleSelector]] = None,
        llm: Optional[LLM] = None,
        sql_vector_synthesis_prompt: Optional[BasePromptTemplate] = None,
        sql_augment_query_transform: Optional[SQLAugmentQueryTransform] = None,
        use_sql_vector_synthesis: bool = True,
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = True,
    ) -> None:
        """Initialize params."""
        # validate that the query engines are of the right type
        if not isinstance(
            sql_query_tool.query_engine,
            (BaseSQLTableQueryEngine, NLSQLTableQueryEngine),
        ):
            raise ValueError(
                "sql_query_tool.query_engine must be an instance of "
                "BaseSQLTableQueryEngine or NLSQLTableQueryEngine"
            )
        if not isinstance(vector_query_tool.query_engine, RetrieverQueryEngine):
            raise ValueError(
                "vector_query_tool.query_engine must be an instance of "
                "RetrieverQueryEngine"
            )
        if not isinstance(
            vector_query_tool.query_engine.retriever, VectorIndexAutoRetriever
        ):
            raise ValueError(
                "vector_query_tool.query_engine.retriever must be an instance "
                "of VectorIndexAutoRetriever"
            )

        sql_vector_synthesis_prompt = (
            sql_vector_synthesis_prompt or DEFAULT_SQL_VECTOR_SYNTHESIS_PROMPT
        )
        super().__init__(
            sql_query_tool,
            vector_query_tool,
            selector=selector,
            llm=llm,
            sql_join_synthesis_prompt=sql_vector_synthesis_prompt,
            sql_augment_query_transform=sql_augment_query_transform,
            use_sql_join_synthesis=use_sql_vector_synthesis,
            callback_manager=callback_manager,
            verbose=verbose,
        )

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {
            "selector": self._selector,
            "sql_augment_query_transform": self._sql_augment_query_transform,
        }

    def _get_prompts(self) -> PromptDictType:
        """Get prompts."""
        return {"sql_join_synthesis_prompt": self._sql_join_synthesis_prompt}

    def _update_prompts(self, prompts: PromptDictType) -> None:
        """Update prompts."""
        if "sql_join_synthesis_prompt" in prompts:
            self._sql_join_synthesis_prompt = prompts["sql_join_synthesis_prompt"]

    @classmethod
    def from_sql_and_vector_query_engines(
        cls,
        sql_query_engine: Union[BaseSQLTableQueryEngine, NLSQLTableQueryEngine],
        sql_tool_name: str,
        sql_tool_description: str,
        vector_auto_retriever: RetrieverQueryEngine,
        vector_tool_name: str,
        vector_tool_description: str,
        selector: Optional[Union[LLMSingleSelector, PydanticSingleSelector]] = None,
        **kwargs: Any,
    ) -> "SQLAutoVectorQueryEngine":
        """
        From SQL and vector query engines.

        Args:
            sql_query_engine (BaseSQLTableQueryEngine): SQL query engine.
            vector_query_engine (VectorIndexAutoRetriever): Vector retriever.
            selector (Optional[Union[LLMSingleSelector, PydanticSingleSelector]]):
                Selector to use.

        """
        sql_query_tool = QueryEngineTool.from_defaults(
            sql_query_engine, name=sql_tool_name, description=sql_tool_description
        )
        vector_query_tool = QueryEngineTool.from_defaults(
            vector_auto_retriever,
            name=vector_tool_name,
            description=vector_tool_description,
        )
        return cls(sql_query_tool, vector_query_tool, selector, **kwargs)

from_sql_and_vector_query_engines classmethod

from_sql_and_vector_query_engines(sql_query_engine: Union[BaseSQLTableQueryEngine, NLSQLTableQueryEngine], sql_tool_name: str, sql_tool_description: str, vector_auto_retriever: RetrieverQueryEngine, vector_tool_name: str, vector_tool_description: str, selector: Optional[Union[LLMSingleSelector, PydanticSingleSelector]] = None, **kwargs: Any) -> SQLAutoVectorQueryEngine

From SQL and vector query engines.

Parameters:

Name	Type	Description	Default
sql_query_engine	BaseSQLTableQueryEngine	SQL query engine.	required
vector_query_engine	VectorIndexAutoRetriever	Vector retriever.	required
selector	Optional[Union[LLMSingleSelector, PydanticSingleSelector]]	Selector to use.	None

Source code in llama_index/core/query_engine/sql_vector_query_engine.py

@classmethod
def from_sql_and_vector_query_engines(
    cls,
    sql_query_engine: Union[BaseSQLTableQueryEngine, NLSQLTableQueryEngine],
    sql_tool_name: str,
    sql_tool_description: str,
    vector_auto_retriever: RetrieverQueryEngine,
    vector_tool_name: str,
    vector_tool_description: str,
    selector: Optional[Union[LLMSingleSelector, PydanticSingleSelector]] = None,
    **kwargs: Any,
) -> "SQLAutoVectorQueryEngine":
    """
    From SQL and vector query engines.

    Args:
        sql_query_engine (BaseSQLTableQueryEngine): SQL query engine.
        vector_query_engine (VectorIndexAutoRetriever): Vector retriever.
        selector (Optional[Union[LLMSingleSelector, PydanticSingleSelector]]):
            Selector to use.

    """
    sql_query_tool = QueryEngineTool.from_defaults(
        sql_query_engine, name=sql_tool_name, description=sql_tool_description
    )
    vector_query_tool = QueryEngineTool.from_defaults(
        vector_auto_retriever,
        name=vector_tool_name,
        description=vector_tool_description,
    )
    return cls(sql_query_tool, vector_query_tool, selector, **kwargs)

SubQuestionAnswerPair

Bases: BaseModel

Pair of the sub question and optionally its answer (if its been answered yet).

Parameters:

Name	Type	Description	Default
sub_q	SubQuestion		required
answer	str | None		None
sources	List[NodeWithScore]	Built-in mutable sequence. If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.	<dynamic>

Source code in llama_index/core/query_engine/sub_question_query_engine.py

class SubQuestionAnswerPair(BaseModel):
    """
    Pair of the sub question and optionally its answer (if its been answered yet).
    """

    sub_q: SubQuestion
    answer: Optional[str] = None
    sources: List[NodeWithScore] = Field(default_factory=list)

SubQuestionQueryEngine

Bases: BaseQueryEngine

Sub question query engine.

A query engine that breaks down a complex query (e.g. compare and contrast) into many sub questions and their target query engine for execution. After executing all sub questions, all responses are gathered and sent to response synthesizer to produce the final response.

Parameters:

Name	Type	Description	Default
question_gen	BaseQuestionGenerator	A module for generating sub questions given a complex question and tools.	required
response_synthesizer	BaseSynthesizer	A response synthesizer for generating the final response	required
query_engine_tools	Sequence[QueryEngineTool]	Tools to answer the sub questions.	required
verbose	bool	whether to print intermediate questions and answers. Defaults to True	True
use_async	bool	whether to execute the sub questions with asyncio. Defaults to True	False

Source code in llama_index/core/query_engine/sub_question_query_engine.py

class SubQuestionQueryEngine(BaseQueryEngine):
    """
    Sub question query engine.

    A query engine that breaks down a complex query (e.g. compare and contrast) into
        many sub questions and their target query engine for execution.
        After executing all sub questions, all responses are gathered and sent to
        response synthesizer to produce the final response.

    Args:
        question_gen (BaseQuestionGenerator): A module for generating sub questions
            given a complex question and tools.
        response_synthesizer (BaseSynthesizer): A response synthesizer for
            generating the final response
        query_engine_tools (Sequence[QueryEngineTool]): Tools to answer the
            sub questions.
        verbose (bool): whether to print intermediate questions and answers.
            Defaults to True
        use_async (bool): whether to execute the sub questions with asyncio.
            Defaults to True

    """

    def __init__(
        self,
        question_gen: BaseQuestionGenerator,
        response_synthesizer: BaseSynthesizer,
        query_engine_tools: Sequence[QueryEngineTool],
        callback_manager: Optional[CallbackManager] = None,
        verbose: bool = True,
        use_async: bool = False,
    ) -> None:
        self._question_gen = question_gen
        self._response_synthesizer = response_synthesizer
        self._metadatas = [x.metadata for x in query_engine_tools]
        self._query_engines = {
            tool.metadata.name: tool.query_engine for tool in query_engine_tools
        }
        self._verbose = verbose
        self._use_async = use_async
        super().__init__(callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {
            "question_gen": self._question_gen,
            "response_synthesizer": self._response_synthesizer,
        }

    @classmethod
    def from_defaults(
        cls,
        query_engine_tools: Sequence[QueryEngineTool],
        llm: Optional[LLM] = None,
        question_gen: Optional[BaseQuestionGenerator] = None,
        response_synthesizer: Optional[BaseSynthesizer] = None,
        verbose: bool = True,
        use_async: bool = True,
    ) -> "SubQuestionQueryEngine":
        callback_manager = Settings.callback_manager
        if len(query_engine_tools) > 0:
            callback_manager = query_engine_tools[0].query_engine.callback_manager

        llm = llm or Settings.llm
        if question_gen is None:
            try:
                from llama_index.question_gen.openai import (
                    OpenAIQuestionGenerator,
                )  # pants: no-infer-dep

                # try to use OpenAI function calling based question generator.
                # if incompatible, use general LLM question generator
                question_gen = OpenAIQuestionGenerator.from_defaults(llm=llm)

            except ImportError as e:
                raise ImportError(
                    "`llama-index-question-gen-openai` package cannot be found. "
                    "Please install it by using `pip install `llama-index-question-gen-openai`"
                )
            except ValueError:
                question_gen = LLMQuestionGenerator.from_defaults(llm=llm)

        synth = response_synthesizer or get_response_synthesizer(
            llm=llm,
            callback_manager=callback_manager,
            use_async=use_async,
        )

        return cls(
            question_gen,
            synth,
            query_engine_tools,
            callback_manager=callback_manager,
            verbose=verbose,
            use_async=use_async,
        )

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            sub_questions = self._question_gen.generate(self._metadatas, query_bundle)

            colors = get_color_mapping([str(i) for i in range(len(sub_questions))])

            if self._verbose:
                print_text(f"Generated {len(sub_questions)} sub questions.\n")

            if self._use_async:
                tasks = [
                    self._aquery_subq(sub_q, color=colors[str(ind)])
                    for ind, sub_q in enumerate(sub_questions)
                ]

                qa_pairs_all = run_async_tasks(tasks)
                qa_pairs_all = cast(List[Optional[SubQuestionAnswerPair]], qa_pairs_all)
            else:
                qa_pairs_all = [
                    self._query_subq(sub_q, color=colors[str(ind)])
                    for ind, sub_q in enumerate(sub_questions)
                ]

            # filter out sub questions that failed
            qa_pairs: List[SubQuestionAnswerPair] = list(filter(None, qa_pairs_all))

            nodes = [self._construct_node(pair) for pair in qa_pairs]

            source_nodes = [node for qa_pair in qa_pairs for node in qa_pair.sources]
            response = self._response_synthesizer.synthesize(
                query=query_bundle,
                nodes=nodes,
                additional_source_nodes=source_nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        with self.callback_manager.event(
            CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
        ) as query_event:
            sub_questions = await self._question_gen.agenerate(
                self._metadatas, query_bundle
            )

            colors = get_color_mapping([str(i) for i in range(len(sub_questions))])

            if self._verbose:
                print_text(f"Generated {len(sub_questions)} sub questions.\n")

            tasks = [
                self._aquery_subq(sub_q, color=colors[str(ind)])
                for ind, sub_q in enumerate(sub_questions)
            ]

            qa_pairs_all = await asyncio.gather(*tasks)
            qa_pairs_all = cast(List[Optional[SubQuestionAnswerPair]], qa_pairs_all)

            # filter out sub questions that failed
            qa_pairs: List[SubQuestionAnswerPair] = list(filter(None, qa_pairs_all))

            nodes = [self._construct_node(pair) for pair in qa_pairs]

            source_nodes = [node for qa_pair in qa_pairs for node in qa_pair.sources]
            response = await self._response_synthesizer.asynthesize(
                query=query_bundle,
                nodes=nodes,
                additional_source_nodes=source_nodes,
            )

            query_event.on_end(payload={EventPayload.RESPONSE: response})

        return response

    def _construct_node(self, qa_pair: SubQuestionAnswerPair) -> NodeWithScore:
        node_text = (
            f"Sub question: {qa_pair.sub_q.sub_question}\nResponse: {qa_pair.answer}"
        )
        return NodeWithScore(node=TextNode(text=node_text))

    async def _aquery_subq(
        self, sub_q: SubQuestion, color: Optional[str] = None
    ) -> Optional[SubQuestionAnswerPair]:
        try:
            with self.callback_manager.event(
                CBEventType.SUB_QUESTION,
                payload={EventPayload.SUB_QUESTION: SubQuestionAnswerPair(sub_q=sub_q)},
            ) as event:
                question = sub_q.sub_question
                query_engine = self._query_engines[sub_q.tool_name]

                if self._verbose:
                    print_text(f"[{sub_q.tool_name}] Q: {question}\n", color=color)

                response = await query_engine.aquery(question)
                response_text = str(response)

                if self._verbose:
                    print_text(f"[{sub_q.tool_name}] A: {response_text}\n", color=color)

                qa_pair = SubQuestionAnswerPair(
                    sub_q=sub_q, answer=response_text, sources=response.source_nodes
                )

                event.on_end(payload={EventPayload.SUB_QUESTION: qa_pair})

            return qa_pair
        except ValueError:
            logger.warning(f"[{sub_q.tool_name}] Failed to run {question}")
            return None

    def _query_subq(
        self, sub_q: SubQuestion, color: Optional[str] = None
    ) -> Optional[SubQuestionAnswerPair]:
        try:
            with self.callback_manager.event(
                CBEventType.SUB_QUESTION,
                payload={EventPayload.SUB_QUESTION: SubQuestionAnswerPair(sub_q=sub_q)},
            ) as event:
                question = sub_q.sub_question
                query_engine = self._query_engines[sub_q.tool_name]

                if self._verbose:
                    print_text(f"[{sub_q.tool_name}] Q: {question}\n", color=color)

                response = query_engine.query(question)
                response_text = str(response)

                if self._verbose:
                    print_text(f"[{sub_q.tool_name}] A: {response_text}\n", color=color)

                qa_pair = SubQuestionAnswerPair(
                    sub_q=sub_q, answer=response_text, sources=response.source_nodes
                )

                event.on_end(payload={EventPayload.SUB_QUESTION: qa_pair})

            return qa_pair
        except ValueError:
            logger.warning(f"[{sub_q.tool_name}] Failed to run {question}")
            return None

TransformQueryEngine

Bases: BaseQueryEngine

Transform query engine.

Applies a query transform to a query bundle before passing it to a query engine.

Parameters:

Name	Type	Description	Default
query_engine	BaseQueryEngine	A query engine object.	required
query_transform	BaseQueryTransform	A query transform object.	required
transform_metadata	Optional[dict]	metadata to pass to the query transform.	None
callback_manager	Optional[CallbackManager]	A callback manager.	None

Source code in llama_index/core/query_engine/transform_query_engine.py

class TransformQueryEngine(BaseQueryEngine):
    """
    Transform query engine.

    Applies a query transform to a query bundle before passing
        it to a query engine.

    Args:
        query_engine (BaseQueryEngine): A query engine object.
        query_transform (BaseQueryTransform): A query transform object.
        transform_metadata (Optional[dict]): metadata to pass to the
            query transform.
        callback_manager (Optional[CallbackManager]): A callback manager.

    """

    def __init__(
        self,
        query_engine: BaseQueryEngine,
        query_transform: BaseQueryTransform,
        transform_metadata: Optional[dict] = None,
        callback_manager: Optional[CallbackManager] = None,
    ) -> None:
        self._query_engine = query_engine
        self._query_transform = query_transform
        self._transform_metadata = transform_metadata
        super().__init__(callback_manager)

    def _get_prompt_modules(self) -> PromptMixinType:
        """Get prompt sub-modules."""
        return {
            "query_transform": self._query_transform,
            "query_engine": self._query_engine,
        }

    def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        query_bundle = self._query_transform.run(
            query_bundle, metadata=self._transform_metadata
        )
        return self._query_engine.retrieve(query_bundle)

    def synthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        query_bundle = self._query_transform.run(
            query_bundle, metadata=self._transform_metadata
        )
        return self._query_engine.synthesize(
            query_bundle=query_bundle,
            nodes=nodes,
            additional_source_nodes=additional_source_nodes,
        )

    async def asynthesize(
        self,
        query_bundle: QueryBundle,
        nodes: List[NodeWithScore],
        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
    ) -> RESPONSE_TYPE:
        query_bundle = self._query_transform.run(
            query_bundle, metadata=self._transform_metadata
        )
        return await self._query_engine.asynthesize(
            query_bundle=query_bundle,
            nodes=nodes,
            additional_source_nodes=additional_source_nodes,
        )

    def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        query_bundle = self._query_transform.run(
            query_bundle, metadata=self._transform_metadata
        )
        return self._query_engine.query(query_bundle)

    async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
        """Answer a query."""
        query_bundle = self._query_transform.run(
            query_bundle, metadata=self._transform_metadata
        )
        return await self._query_engine.aquery(query_bundle)

options: members: - NLSQLTableQueryEngine