File size: 5,792 Bytes
6054f54 8bc2fc9 8de8827 6054f54 b6ce919 6054f54 35bb186 6054f54 73c78d3 6054f54 eae0334 b6ce919 6054f54 0c61e3b 6054f54 2118d99 6054f54 8de8827 6a49fcd 0c61e3b 6054f54 6a49fcd 6054f54 8d91371 6054f54 8bc2fc9 6054f54 6d4f792 6054f54 8bc2fc9 6054f54 b71b66c 6054f54 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
#
# 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 logging
import os
from concurrent.futures import ThreadPoolExecutor
import json
from functools import reduce
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, DEFAULT_ENTITY_TYPES
from graphrag.mind_map_extractor import MindMapExtractor
from rag.nlp import rag_tokenizer
from rag.utils import num_tokens_from_string
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_knowledge_graph_chunks(tenant_id: str, chunks: list[str], callback, entity_types=DEFAULT_ENTITY_TYPES):
_, 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 * 0.6, 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})"
BATCH_SIZE=4
texts, graphs = [], []
cnt = 0
max_workers = int(os.environ.get('GRAPH_EXTRACTOR_MAX_WORKERS', 50))
with ThreadPoolExecutor(max_workers=max_workers) as exe:
threads = []
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), BATCH_SIZE):
threads.append(exe.submit(ext, ["\n".join(texts[b:b+BATCH_SIZE])], {"entity_types": entity_types}, callback))
texts = []
cnt = 0
texts.append(chunks[i])
cnt += tkn_cnt
if texts:
for b in range(0, len(texts), BATCH_SIZE):
threads.append(exe.submit(ext, ["\n".join(texts[b:b+BATCH_SIZE])], {"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) if graphs else nx.Graph()
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:
logging.debug(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
logging.debug(json.dumps(mg, ensure_ascii=False, indent=2))
chunks.append(
{
"content_with_weight": json.dumps(mg, ensure_ascii=False, indent=2),
"knowledge_graph_kwd": "mind_map"
})
return chunks
|