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| """List index. | |
| A simple data structure where LlamaIndex iterates through document chunks | |
| in sequence in order to answer a given query. | |
| """ | |
| from typing import Any, Optional, Sequence | |
| from gpt_index.data_structs.data_structs_v2 import IndexList | |
| from gpt_index.data_structs.node_v2 import Node | |
| from gpt_index.indices.base import BaseGPTIndex, QueryMap | |
| from gpt_index.indices.list.embedding_query import GPTListIndexEmbeddingQuery | |
| from gpt_index.indices.list.query import GPTListIndexQuery | |
| from gpt_index.indices.query.schema import QueryMode | |
| from gpt_index.indices.service_context import ServiceContext | |
| from gpt_index.prompts.default_prompts import DEFAULT_TEXT_QA_PROMPT | |
| from gpt_index.prompts.prompts import QuestionAnswerPrompt | |
| # This query is used to summarize the contents of the index. | |
| GENERATE_TEXT_QUERY = "What is a concise summary of this document?" | |
| class GPTListIndex(BaseGPTIndex[IndexList]): | |
| """GPT List Index. | |
| The list index is a simple data structure where nodes are stored in | |
| a sequence. During index construction, the document texts are | |
| chunked up, converted to nodes, and stored in a list. | |
| During query time, the list index iterates through the nodes | |
| with some optional filter parameters, and synthesizes an | |
| answer from all the nodes. | |
| Args: | |
| text_qa_template (Optional[QuestionAnswerPrompt]): A Question-Answer Prompt | |
| (see :ref:`Prompt-Templates`). | |
| NOTE: this is a deprecated field. | |
| """ | |
| index_struct_cls = IndexList | |
| def __init__( | |
| self, | |
| nodes: Optional[Sequence[Node]] = None, | |
| index_struct: Optional[IndexList] = None, | |
| service_context: Optional[ServiceContext] = None, | |
| text_qa_template: Optional[QuestionAnswerPrompt] = None, | |
| **kwargs: Any, | |
| ) -> None: | |
| """Initialize params.""" | |
| self.text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT | |
| super().__init__( | |
| nodes=nodes, | |
| index_struct=index_struct, | |
| service_context=service_context, | |
| **kwargs, | |
| ) | |
| def get_query_map(self) -> QueryMap: | |
| """Get query map.""" | |
| return { | |
| QueryMode.DEFAULT: GPTListIndexQuery, | |
| QueryMode.EMBEDDING: GPTListIndexEmbeddingQuery, | |
| } | |
| def _build_index_from_nodes(self, nodes: Sequence[Node]) -> IndexList: | |
| """Build the index from documents. | |
| Args: | |
| documents (List[BaseDocument]): A list of documents. | |
| Returns: | |
| IndexList: The created list index. | |
| """ | |
| index_struct = IndexList() | |
| for n in nodes: | |
| index_struct.add_node(n) | |
| return index_struct | |
| def _insert(self, nodes: Sequence[Node], **insert_kwargs: Any) -> None: | |
| """Insert a document.""" | |
| for n in nodes: | |
| print("inserting node to index struct: ", n.get_doc_id()) | |
| self._index_struct.add_node(n) | |
| def _delete(self, doc_id: str, **delete_kwargs: Any) -> None: | |
| """Delete a document.""" | |
| cur_node_ids = self._index_struct.nodes | |
| cur_nodes = self._docstore.get_nodes(cur_node_ids) | |
| nodes_to_keep = [n for n in cur_nodes if n.ref_doc_id != doc_id] | |
| self._index_struct.nodes = [n.get_doc_id() for n in nodes_to_keep] | |
| def _preprocess_query(self, mode: QueryMode, query_kwargs: Any) -> None: | |
| """Preprocess query.""" | |
| super()._preprocess_query(mode, query_kwargs) | |
| if "text_qa_template" not in query_kwargs: | |
| query_kwargs["text_qa_template"] = self.text_qa_template | |