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#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
Reference:
- [graphrag](https://github.com/microsoft/graphrag)
"""
import argparse
import html
import json
import logging
import numbers
import re
import traceback
from collections.abc import Callable
from dataclasses import dataclass
from graphrag.utils import ErrorHandlerFn, perform_variable_replacements
from rag.llm.chat_model import Base as CompletionLLM
import networkx as nx
from rag.utils import num_tokens_from_string
SUMMARIZE_PROMPT = """
You are a helpful assistant responsible for generating a comprehensive summary of the data provided below.
Given one or two entities, and a list of descriptions, all related to the same entity or group of entities.
Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary.
Make sure it is written in third person, and include the entity names so we the have full context.
#######
-Data-
Entities: {entity_name}
Description List: {description_list}
#######
Output:
"""
# Max token size for input prompts
DEFAULT_MAX_INPUT_TOKENS = 4_000
# Max token count for LLM answers
DEFAULT_MAX_SUMMARY_LENGTH = 128
@dataclass
class SummarizationResult:
"""Unipartite graph extraction result class definition."""
items: str | tuple[str, str]
description: str
class SummarizeExtractor:
"""Unipartite graph extractor class definition."""
_llm: CompletionLLM
_entity_name_key: str
_input_descriptions_key: str
_summarization_prompt: str
_on_error: ErrorHandlerFn
_max_summary_length: int
_max_input_tokens: int
def __init__(
self,
llm_invoker: CompletionLLM,
entity_name_key: str | None = None,
input_descriptions_key: str | None = None,
summarization_prompt: str | None = None,
on_error: ErrorHandlerFn | None = None,
max_summary_length: int | None = None,
max_input_tokens: int | None = None,
):
"""Init method definition."""
# TODO: streamline construction
self._llm = llm_invoker
self._entity_name_key = entity_name_key or "entity_name"
self._input_descriptions_key = input_descriptions_key or "description_list"
self._summarization_prompt = summarization_prompt or SUMMARIZE_PROMPT
self._on_error = on_error or (lambda _e, _s, _d: None)
self._max_summary_length = max_summary_length or DEFAULT_MAX_SUMMARY_LENGTH
self._max_input_tokens = max_input_tokens or DEFAULT_MAX_INPUT_TOKENS
def __call__(
self,
items: str | tuple[str, str],
descriptions: list[str],
) -> SummarizationResult:
"""Call method definition."""
result = ""
if len(descriptions) == 0:
result = ""
if len(descriptions) == 1:
result = descriptions[0]
else:
result = self._summarize_descriptions(items, descriptions)
return SummarizationResult(
items=items,
description=result or "",
)
def _summarize_descriptions(
self, items: str | tuple[str, str], descriptions: list[str]
) -> str:
"""Summarize descriptions into a single description."""
sorted_items = sorted(items) if isinstance(items, list) else items
# Safety check, should always be a list
if not isinstance(descriptions, list):
descriptions = [descriptions]
# Iterate over descriptions, adding all until the max input tokens is reached
usable_tokens = self._max_input_tokens - num_tokens_from_string(
self._summarization_prompt
)
descriptions_collected = []
result = ""
for i, description in enumerate(descriptions):
usable_tokens -= num_tokens_from_string(description)
descriptions_collected.append(description)
# If buffer is full, or all descriptions have been added, summarize
if (usable_tokens < 0 and len(descriptions_collected) > 1) or (
i == len(descriptions) - 1
):
# Calculate result (final or partial)
result = await self._summarize_descriptions_with_llm(
sorted_items, descriptions_collected
)
# If we go for another loop, reset values to new
if i != len(descriptions) - 1:
descriptions_collected = [result]
usable_tokens = (
self._max_input_tokens
- num_tokens_from_string(self._summarization_prompt)
- num_tokens_from_string(result)
)
return result
def _summarize_descriptions_with_llm(
self, items: str | tuple[str, str] | list[str], descriptions: list[str]
):
"""Summarize descriptions using the LLM."""
variables = {
self._entity_name_key: json.dumps(items),
self._input_descriptions_key: json.dumps(sorted(descriptions)),
}
text = perform_variable_replacements(self._summarization_prompt, variables=variables)
return self._llm.chat("", [{"role": "user", "content": text}])
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