Auto prev next

Node PostProcessor module.

AutoPrevNextNodePostprocessor

Bases: BaseNodePostprocessor

Previous/Next Node post-processor.

Allows users to fetch additional nodes from the document store, based on the prev/next relationships of the nodes.

NOTE: difference with PrevNextPostprocessor is that this infers forward/backwards direction.

NOTE: this is a beta feature.

Parameters:

Name	Type	Description	Default
docstore	BaseDocumentStore	The document store.	required
num_nodes	int	The number of nodes to return (default: 1)	required
infer_prev_next_tmpl	str	The template to use for inference. Required fields are {context_str} and {query_str}.	required

Source code in llama-index-core/llama_index/core/postprocessor/node.py

class AutoPrevNextNodePostprocessor(BaseNodePostprocessor):
    """Previous/Next Node post-processor.

    Allows users to fetch additional nodes from the document store,
    based on the prev/next relationships of the nodes.

    NOTE: difference with PrevNextPostprocessor is that
    this infers forward/backwards direction.

    NOTE: this is a beta feature.

    Args:
        docstore (BaseDocumentStore): The document store.
        num_nodes (int): The number of nodes to return (default: 1)
        infer_prev_next_tmpl (str): The template to use for inference.
            Required fields are {context_str} and {query_str}.

    """

    docstore: BaseDocumentStore
    service_context: ServiceContext
    num_nodes: int = Field(default=1)
    infer_prev_next_tmpl: str = Field(default=DEFAULT_INFER_PREV_NEXT_TMPL)
    refine_prev_next_tmpl: str = Field(default=DEFAULT_REFINE_INFER_PREV_NEXT_TMPL)
    verbose: bool = Field(default=False)

    class Config:
        """Configuration for this pydantic object."""

        arbitrary_types_allowed = True

    @classmethod
    def class_name(cls) -> str:
        return "AutoPrevNextNodePostprocessor"

    def _parse_prediction(self, raw_pred: str) -> str:
        """Parse prediction."""
        pred = raw_pred.strip().lower()
        if "previous" in pred:
            return "previous"
        elif "next" in pred:
            return "next"
        elif "none" in pred:
            return "none"
        raise ValueError(f"Invalid prediction: {raw_pred}")

    def _postprocess_nodes(
        self,
        nodes: List[NodeWithScore],
        query_bundle: Optional[QueryBundle] = None,
    ) -> List[NodeWithScore]:
        """Postprocess nodes."""
        if query_bundle is None:
            raise ValueError("Missing query bundle.")

        infer_prev_next_prompt = PromptTemplate(
            self.infer_prev_next_tmpl,
        )
        refine_infer_prev_next_prompt = PromptTemplate(self.refine_prev_next_tmpl)

        all_nodes: Dict[str, NodeWithScore] = {}
        for node in nodes:
            all_nodes[node.node.node_id] = node
            # use response builder instead of llm directly
            # to be more robust to handling long context
            response_builder = get_response_synthesizer(
                service_context=self.service_context,
                text_qa_template=infer_prev_next_prompt,
                refine_template=refine_infer_prev_next_prompt,
                response_mode=ResponseMode.TREE_SUMMARIZE,
            )
            raw_pred = response_builder.get_response(
                text_chunks=[node.node.get_content()],
                query_str=query_bundle.query_str,
            )
            raw_pred = cast(str, raw_pred)
            mode = self._parse_prediction(raw_pred)

            logger.debug(f"> Postprocessor Predicted mode: {mode}")
            if self.verbose:
                print(f"> Postprocessor Predicted mode: {mode}")

            if mode == "next":
                all_nodes.update(get_forward_nodes(node, self.num_nodes, self.docstore))
            elif mode == "previous":
                all_nodes.update(
                    get_backward_nodes(node, self.num_nodes, self.docstore)
                )
            elif mode == "none":
                pass
            else:
                raise ValueError(f"Invalid mode: {mode}")

        sorted_nodes = sorted(all_nodes.values(), key=lambda x: x.node.node_id)
        return list(sorted_nodes)

Config

Configuration for this pydantic object.

Source code in llama-index-core/llama_index/core/postprocessor/node.py

class Config:
    """Configuration for this pydantic object."""

    arbitrary_types_allowed = True