| import re | |
| from graphrag.index import build_knowledge_graph_chunks | |
| from rag.app import naive | |
| from rag.nlp import rag_tokenizer, tokenize_chunks | |
| def chunk(filename, binary, tenant_id, from_page=0, to_page=100000, | |
| lang="Chinese", callback=None, **kwargs): | |
| parser_config = kwargs.get( | |
| "parser_config", { | |
| "chunk_token_num": 512, "delimiter": "\n!?;。；！？", "layout_recognize": True}) | |
| eng = lang.lower() == "english" | |
| parser_config["layout_recognize"] = True | |
| sections = naive.chunk(filename, binary, from_page=from_page, to_page=to_page, section_only=True, | |
| parser_config=parser_config, callback=callback) | |
| chunks = build_knowledge_graph_chunks(tenant_id, sections, callback, | |
| parser_config.get("entity_types", ["organization", "person", "location", "event", "time"]) | |
| ) | |
| for c in chunks: | |
| c["docnm_kwd"] = filename | |
| doc = { | |
| "docnm_kwd": filename, | |
| "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)), | |
| "knowledge_graph_kwd": "text" | |
| } | |
| doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) | |
| chunks.extend(tokenize_chunks(sections, doc, eng)) | |
| return chunks | |