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"""Common classes/functions for tree index operations."""
import asyncio
import logging
from typing import Dict, List, Optional, Sequence, Tuple
from gpt_index.async_utils import run_async_tasks
from gpt_index.data_structs.data_structs_v2 import IndexGraph
from gpt_index.data_structs.node_v2 import Node
from gpt_index.docstore import DocumentStore
from gpt_index.indices.service_context import ServiceContext
from gpt_index.indices.utils import get_sorted_node_list, truncate_text
from gpt_index.prompts.prompts import SummaryPrompt
logger = logging.getLogger(__name__)
class GPTTreeIndexBuilder:
"""GPT tree index builder.
Helper class to build the tree-structured index,
or to synthesize an answer.
"""
def __init__(
self,
num_children: int,
summary_prompt: SummaryPrompt,
service_context: ServiceContext,
docstore: Optional[DocumentStore] = None,
use_async: bool = False,
) -> None:
"""Initialize with params."""
if num_children < 2:
raise ValueError("Invalid number of children.")
self.num_children = num_children
self.summary_prompt = summary_prompt
self._service_context = service_context
self._use_async = use_async
self._docstore = docstore or DocumentStore()
@property
def docstore(self) -> DocumentStore:
"""Return docstore."""
return self._docstore
def build_from_nodes(
self,
nodes: Sequence[Node],
build_tree: bool = True,
) -> IndexGraph:
"""Build from text.
Returns:
IndexGraph: graph object consisting of all_nodes, root_nodes
"""
index_graph = IndexGraph()
for node in nodes:
index_graph.insert(node)
if build_tree:
return self.build_index_from_nodes(
index_graph, index_graph.all_nodes, index_graph.all_nodes, level=0
)
else:
return index_graph
def _prepare_node_and_text_chunks(
self, cur_node_ids: Dict[int, str]
) -> Tuple[List[int], List[List[Node]], List[str]]:
"""Prepare node and text chunks."""
cur_nodes = {
index: self._docstore.get_node(node_id)
for index, node_id in cur_node_ids.items()
}
cur_node_list = get_sorted_node_list(cur_nodes)
logger.info(
f"> Building index from nodes: {len(cur_nodes) // self.num_children} chunks"
)
indices, cur_nodes_chunks, text_chunks = [], [], []
for i in range(0, len(cur_node_list), self.num_children):
cur_nodes_chunk = cur_node_list[i : i + self.num_children]
text_chunk = self._service_context.prompt_helper.get_text_from_nodes(
cur_nodes_chunk, prompt=self.summary_prompt
)
indices.append(i)
cur_nodes_chunks.append(cur_nodes_chunk)
text_chunks.append(text_chunk)
return indices, cur_nodes_chunks, text_chunks
def _construct_parent_nodes(
self,
index_graph: IndexGraph,
indices: List[int],
cur_nodes_chunks: List[List[Node]],
summaries: List[str],
) -> Dict[int, str]:
"""Construct parent nodes.
Save nodes to docstore.
"""
new_node_dict = {}
for i, cur_nodes_chunk, new_summary in zip(
indices, cur_nodes_chunks, summaries
):
logger.debug(
f"> {i}/{len(cur_nodes_chunk)}, "
f"summary: {truncate_text(new_summary, 50)}"
)
new_node = Node(
text=new_summary,
)
index_graph.insert(new_node, children_nodes=cur_nodes_chunk)
index = index_graph.get_index(new_node)
new_node_dict[index] = new_node.get_doc_id()
self._docstore.add_documents([new_node], allow_update=False)
return new_node_dict
def build_index_from_nodes(
self,
index_graph: IndexGraph,
cur_node_ids: Dict[int, str],
all_node_ids: Dict[int, str],
level: int = 0,
) -> IndexGraph:
"""Consolidates chunks recursively, in a bottoms-up fashion."""
if len(cur_node_ids) <= self.num_children:
index_graph.root_nodes = cur_node_ids
return index_graph
indices, cur_nodes_chunks, text_chunks = self._prepare_node_and_text_chunks(
cur_node_ids
)
if self._use_async:
tasks = [
self._service_context.llm_predictor.apredict(
self.summary_prompt, context_str=text_chunk
)
for text_chunk in text_chunks
]
outputs: List[Tuple[str, str]] = run_async_tasks(tasks)
summaries = [output[0] for output in outputs]
else:
summaries = [
self._service_context.llm_predictor.predict(
self.summary_prompt, context_str=text_chunk
)[0]
for text_chunk in text_chunks
]
self._service_context.llama_logger.add_log(
{"summaries": summaries, "level": level}
)
new_node_dict = self._construct_parent_nodes(
index_graph, indices, cur_nodes_chunks, summaries
)
all_node_ids.update(new_node_dict)
index_graph.root_nodes = new_node_dict
if len(new_node_dict) <= self.num_children:
return index_graph
else:
return self.build_index_from_nodes(
index_graph, new_node_dict, all_node_ids, level=level + 1
)
async def abuild_index_from_nodes(
self,
index_graph: IndexGraph,
cur_node_ids: Dict[int, str],
all_node_ids: Dict[int, str],
level: int = 0,
) -> IndexGraph:
"""Consolidates chunks recursively, in a bottoms-up fashion."""
if len(cur_node_ids) <= self.num_children:
index_graph.root_nodes = cur_node_ids
return index_graph
indices, cur_nodes_chunks, text_chunks = self._prepare_node_and_text_chunks(
cur_node_ids
)
tasks = [
self._service_context.llm_predictor.apredict(
self.summary_prompt, context_str=text_chunk
)
for text_chunk in text_chunks
]
outputs: List[Tuple[str, str]] = await asyncio.gather(*tasks)
summaries = [output[0] for output in outputs]
self._service_context.llama_logger.add_log(
{"summaries": summaries, "level": level}
)
new_node_dict = self._construct_parent_nodes(
index_graph, indices, cur_nodes_chunks, summaries
)
all_node_ids.update(new_node_dict)
index_graph.root_nodes = new_node_dict
if len(new_node_dict) <= self.num_children:
return index_graph
else:
return await self.abuild_index_from_nodes(
index_graph, new_node_dict, all_node_ids, level=level + 1
)
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