List Index

Building the List Index

List-based data structures.

llama_index.indices.list.GPTListIndex

alias of ListIndex

class llama_index.indices.list.ListIndex(nodes: Optional[Sequence[Node]] = None, index_struct: Optional[IndexList] = None, service_context: Optional[ServiceContext] = None, **kwargs: Any)

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.

Parameters

text_qa_template (Optional[QuestionAnswerPrompt]) – A Question-Answer Prompt (see Prompt Templates). NOTE: this is a deprecated field.

delete_nodes(doc_ids: List[str], delete_from_docstore: bool = False, **delete_kwargs: Any) → None

Delete a list of nodes from the index.

Parameters

doc_ids (List[str]) – A list of doc_ids from the nodes to delete

delete_ref_doc(ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any) → None

Delete a document and it’s nodes by using ref_doc_id.

classmethod from_documents(documents: Sequence[Document], storage_context: Optional[StorageContext] = None, service_context: Optional[ServiceContext] = None, **kwargs: Any) → IndexType

Create index from documents.

Parameters

documents (Optional[Sequence[BaseDocument]]) – List of documents to build the index from.

property index_id: str

Get the index struct.

insert(document: Document, **insert_kwargs: Any) → None

Insert a document.

property ref_doc_info: Dict[str, RefDocInfo]

Retrieve a dict mapping of ingested documents and their nodes+metadata.

refresh(documents: Sequence[Document], **update_kwargs: Any) → List[bool]

Refresh an index with documents that have changed.

This allows users to save LLM and Embedding model calls, while only updating documents that have any changes in text or extra_info. It will also insert any documents that previously were not stored.

refresh_ref_docs(documents: Sequence[Document], **update_kwargs: Any) → List[bool]

Refresh an index with documents that have changed.

This allows users to save LLM and Embedding model calls, while only updating documents that have any changes in text or extra_info. It will also insert any documents that previously were not stored.

set_index_id(index_id: str) → None

Set the index id.

NOTE: if you decide to set the index_id on the index_struct manually, you will need to explicitly call add_index_struct on the index_store to update the index store.

Parameters

index_id (str) – Index id to set.

update(document: Document, **update_kwargs: Any) → None

Update a document and it’s corresponding nodes.

This is equivalent to deleting the document and then inserting it again.

Parameters

• document (Union[BaseDocument, BaseIndex]) – document to update

• insert_kwargs (Dict) – kwargs to pass to insert

• delete_kwargs (Dict) – kwargs to pass to delete

update_ref_doc(document: Document, **update_kwargs: Any) → None

Update a document and it’s corresponding nodes.

This is equivalent to deleting the document and then inserting it again.

Parameters

• document (Union[BaseDocument, BaseIndex]) – document to update

• insert_kwargs (Dict) – kwargs to pass to insert

• delete_kwargs (Dict) – kwargs to pass to delete

class llama_index.indices.list.ListIndexEmbeddingRetriever(index: ListIndex, similarity_top_k: Optional[int] = 1, **kwargs: Any)

Embedding based retriever for ListIndex.

Generates embeddings in a lazy fashion for all nodes that are traversed.

Parameters

• index (ListIndex) – The index to retrieve from.

• similarity_top_k (Optional[int]) – The number of top nodes to return.

retrieve(str_or_query_bundle: Union[str, QueryBundle]) → List[NodeWithScore]

Retrieve nodes given query.

Parameters

str_or_query_bundle (QueryType) – Either a query string or a QueryBundle object.

class llama_index.indices.list.ListIndexLLMRetriever(index: ListIndex, choice_select_prompt: Optional[Prompt] = None, choice_batch_size: int = 10, format_node_batch_fn: Optional[Callable] = None, parse_choice_select_answer_fn: Optional[Callable] = None, service_context: Optional[ServiceContext] = None, **kwargs: Any)

LLM retriever for ListIndex.

Parameters

• index (ListIndex) – The index to retrieve from.

• choice_select_prompt (Optional[ChoiceSelectPrompt]) – A Choice-Select Prompt (see Prompt Templates).)

• choice_batch_size (int) – The number of nodes to query at a time.

• format_node_batch_fn (Optional[Callable]) – A function that formats a batch of nodes.

• parse_choice_select_answer_fn (Optional[Callable]) – A function that parses the choice select answer.

• service_context (Optional[ServiceContext]) – A service context.

retrieve(str_or_query_bundle: Union[str, QueryBundle]) → List[NodeWithScore]

Retrieve nodes given query.

Parameters

str_or_query_bundle (QueryType) – Either a query string or a QueryBundle object.

class llama_index.indices.list.ListIndexRetriever(index: ListIndex, **kwargs: Any)

Simple retriever for ListIndex that returns all nodes.

Parameters

index (ListIndex) – The index to retrieve from.

retrieve(str_or_query_bundle: Union[str, QueryBundle]) → List[NodeWithScore]

Retrieve nodes given query.

Parameters

str_or_query_bundle (QueryType) – Either a query string or a QueryBundle object.