ragflow/graphrag/general/community_reports_extractor.py

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License
"""
Reference:
 - [graphrag](https://github.com/microsoft/graphrag)
"""

import logging
import json
import re
from typing import Callable
from dataclasses import dataclass
import networkx as nx
import pandas as pd
from graphrag.general import leiden
from graphrag.general.community_report_prompt import COMMUNITY_REPORT_PROMPT
from graphrag.general.extractor import Extractor
from graphrag.general.leiden import add_community_info2graph
from rag.llm.chat_model import Base as CompletionLLM
from graphrag.utils import perform_variable_replacements, dict_has_keys_with_types, chat_limiter
from rag.utils import num_tokens_from_string
import trio


@dataclass
class CommunityReportsResult:
    """Community reports result class definition."""

    output: list[str]
    structured_output: list[dict]


class CommunityReportsExtractor(Extractor):
    """Community reports extractor class definition."""

    _extraction_prompt: str
    _output_formatter_prompt: str
    _max_report_length: int

    def __init__(
            self,
            llm_invoker: CompletionLLM,
            max_report_length: int | None = None,
    ):
        super().__init__(llm_invoker)
        """Init method definition."""
        self._llm = llm_invoker
        self._extraction_prompt = COMMUNITY_REPORT_PROMPT
        self._max_report_length = max_report_length or 1500

    async def __call__(self, graph: nx.Graph, callback: Callable | None = None):
        for node_degree in graph.degree:
            graph.nodes[str(node_degree[0])]["rank"] = int(node_degree[1])

        communities: dict[str, dict[str, list]] = leiden.run(graph, {})
        total = sum([len(comm.items()) for _, comm in communities.items()])
        res_str = []
        res_dict = []
        over, token_count = 0, 0
        async def extract_community_report(community):
            nonlocal res_str, res_dict, over, token_count
            cm_id, cm = community
            weight = cm["weight"]
            ents = cm["nodes"]
            if len(ents) < 2:
                return
            ent_list = [{"entity": ent, "description": graph.nodes[ent]["description"]} for ent in ents]
            ent_df = pd.DataFrame(ent_list)

            rela_list = []
            k = 0
            for i in range(0, len(ents)):
                if k >= 10000:
                    break
                for j in range(i + 1, len(ents)):
                    if k >= 10000:
                        break
                    edge = graph.get_edge_data(ents[i], ents[j])
                    if edge is None:
                        continue
                    rela_list.append({"source": ents[i], "target": ents[j], "description": edge["description"]})
                    k += 1
            rela_df = pd.DataFrame(rela_list)

            prompt_variables = {
                "entity_df": ent_df.to_csv(index_label="id"),
                "relation_df": rela_df.to_csv(index_label="id")
            }
            text = perform_variable_replacements(self._extraction_prompt, variables=prompt_variables)
            gen_conf = {"temperature": 0.3}
            async with chat_limiter:
                response = await trio.to_thread.run_sync(lambda: self._chat(text, [{"role": "user", "content": "Output:"}], gen_conf))
            token_count += num_tokens_from_string(text + response)
            response = re.sub(r"^[^\{]*", "", response)
            response = re.sub(r"[^\}]*$", "", response)
            response = re.sub(r"\{\{", "{", response)
            response = re.sub(r"\}\}", "}", response)
            logging.debug(response)
            try:
                response = json.loads(response)
            except json.JSONDecodeError as e:
                logging.error(f"Failed to parse JSON response: {e}")
                logging.error(f"Response content: {response}")
                return
            if not dict_has_keys_with_types(response, [
                        ("title", str),
                        ("summary", str),
                        ("findings", list),
                        ("rating", float),
                        ("rating_explanation", str),
                    ]):
                return
            response["weight"] = weight
            response["entities"] = ents
            add_community_info2graph(graph, ents, response["title"])
            res_str.append(self._get_text_output(response))
            res_dict.append(response)
            over += 1
            if callback:
                callback(msg=f"Communities: {over}/{total}, used tokens: {token_count}")

        st = trio.current_time()
        async with trio.open_nursery() as nursery:
            for level, comm in communities.items():
                logging.info(f"Level {level}: Community: {len(comm.keys())}")
                for community in comm.items():
                    nursery.start_soon(lambda: extract_community_report(community))
        if callback:
            callback(msg=f"Community reports done in {trio.current_time() - st:.2f}s, used tokens: {token_count}")

        return CommunityReportsResult(
            structured_output=res_dict,
            output=res_str,
        )

    def _get_text_output(self, parsed_output: dict) -> str:
        title = parsed_output.get("title", "Report")
        summary = parsed_output.get("summary", "")
        findings = parsed_output.get("findings", [])

        def finding_summary(finding: dict):
            if isinstance(finding, str):
                return finding
            return finding.get("summary")

        def finding_explanation(finding: dict):
            if isinstance(finding, str):
                return ""
            return finding.get("explanation")

        report_sections = "\n\n".join(
            f"## {finding_summary(f)}\n\n{finding_explanation(f)}" for f in findings
        )
        return f"# {title}\n\n{summary}\n\n{report_sections}"