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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.
#
import re
from concurrent.futures import ThreadPoolExecutor
import json
from functools import reduce
from typing import List
import networkx as nx
from api.db import LLMType
from api.db.services.llm_service import LLMBundle
from api.db.services.user_service import TenantService
from graphrag.community_reports_extractor import CommunityReportsExtractor
from graphrag.entity_resolution import EntityResolution
from graphrag.graph_extractor import GraphExtractor
from graphrag.mind_map_extractor import MindMapExtractor
from rag.nlp import rag_tokenizer
from rag.utils import num_tokens_from_string
def be_children(obj: dict, keyset:set):
if isinstance(obj, str):
obj = [obj]
if isinstance(obj, list):
for i in obj: keyset.add(i)
return [{"id": re.sub(r"\*+", "", i), "children":[]} for i in obj]
arr = []
for k,v in obj.items():
k = re.sub(r"\*+", "", k)
if not k or k in keyset:continue
keyset.add(k)
arr.append({
"id": k,
"children": be_children(v, keyset)
})
return arr
def graph_merge(g1, g2):
g = g2.copy()
for n, attr in g1.nodes(data=True):
if n not in g2.nodes():
g.add_node(n, **attr)
continue
g.nodes[n]["weight"] += 1
if g.nodes[n]["description"].lower().find(attr["description"][:32].lower()) < 0:
g.nodes[n]["description"] += "\n" + attr["description"]
for source, target, attr in g1.edges(data=True):
if g.has_edge(source, target):
g[source][target].update({"weight": attr["weight"]+1})
continue
g.add_edge(source, target, **attr)
for node_degree in g.degree:
g.nodes[str(node_degree[0])]["rank"] = int(node_degree[1])
return g
def build_knowlege_graph_chunks(tenant_id: str, chunks: List[str], callback, entity_types=["organization", "person", "location", "event", "time"]):
_, tenant = TenantService.get_by_id(tenant_id)
llm_bdl = LLMBundle(tenant_id, LLMType.CHAT, tenant.llm_id)
ext = GraphExtractor(llm_bdl)
left_token_count = llm_bdl.max_length - ext.prompt_token_count - 1024
left_token_count = max(llm_bdl.max_length * 0.8, left_token_count)
assert left_token_count > 0, f"The LLM context length({llm_bdl.max_length}) is smaller than prompt({ext.prompt_token_count})"
texts, graphs = [], []
cnt = 0
threads = []
exe = ThreadPoolExecutor(max_workers=12)
for i in range(len(chunks)):
tkn_cnt = num_tokens_from_string(chunks[i])
if cnt+tkn_cnt >= left_token_count and texts:
for b in range(0, len(texts), 16):
threads.append(exe.submit(ext, ["\n".join(texts[b:b+16])], {"entity_types": entity_types}, callback))
texts = []
cnt = 0
texts.append(chunks[i])
cnt += tkn_cnt
if texts:
for b in range(0, len(texts), 16):
threads.append(exe.submit(ext, ["\n".join(texts[b:b+16])], {"entity_types": entity_types}, callback))
callback(0.5, "Extracting entities.")
graphs = []
for i, _ in enumerate(threads):
graphs.append(_.result().output)
callback(0.5 + 0.1*i/len(threads), f"Entities extraction progress ... {i+1}/{len(threads)}")
graph = reduce(graph_merge, graphs)
er = EntityResolution(llm_bdl)
graph = er(graph).output
_chunks = chunks
chunks = []
for n, attr in graph.nodes(data=True):
if attr.get("rank", 0) == 0:
print(f"Ignore entity: {n}")
continue
chunk = {
"name_kwd": n,
"important_kwd": [n],
"title_tks": rag_tokenizer.tokenize(n),
"content_with_weight": json.dumps({"name": n, **attr}, ensure_ascii=False),
"content_ltks": rag_tokenizer.tokenize(attr["description"]),
"knowledge_graph_kwd": "entity",
"rank_int": attr["rank"],
"weight_int": attr["weight"]
}
chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(chunk["content_ltks"])
chunks.append(chunk)
callback(0.6, "Extracting community reports.")
cr = CommunityReportsExtractor(llm_bdl)
cr = cr(graph, callback=callback)
for community, desc in zip(cr.structured_output, cr.output):
chunk = {
"title_tks": rag_tokenizer.tokenize(community["title"]),
"content_with_weight": desc,
"content_ltks": rag_tokenizer.tokenize(desc),
"knowledge_graph_kwd": "community_report",
"weight_flt": community["weight"],
"entities_kwd": community["entities"],
"important_kwd": community["entities"]
}
chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(chunk["content_ltks"])
chunks.append(chunk)
chunks.append(
{
"content_with_weight": json.dumps(nx.node_link_data(graph), ensure_ascii=False, indent=2),
"knowledge_graph_kwd": "graph"
})
callback(0.75, "Extracting mind graph.")
mindmap = MindMapExtractor(llm_bdl)
mg = mindmap(_chunks).output
if not len(mg.keys()): return chunks
if len(mg.keys()) > 1:
keyset = set([re.sub(r"\*+", "", k) for k,v in mg.items() if isinstance(v, dict) and re.sub(r"\*+", "", k)])
md_map = {"id": "root", "children": [{"id": re.sub(r"\*+", "", k), "children": be_children(v, keyset)} for k,v in mg.items() if isinstance(v, dict) and re.sub(r"\*+", "", k)]}
else:
k = re.sub(r"\*+", "", list(mg.keys())[0])
md_map = {"id": k, "children": be_children(list(mg.items())[0][1], set([k]))}
print(json.dumps(md_map, ensure_ascii=False, indent=2))
chunks.append(
{
"content_with_weight": json.dumps(md_map, ensure_ascii=False, indent=2),
"knowledge_graph_kwd": "mind_map"
})
return chunks
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