# HG changeset patch # User drewp@bigasterisk.com # Date 1720309356 25200 # Node ID 0e33c65f190468579c992d304d5e5ac7d211c7f8 # Parent ba1ce5921a4bd57e9f06113753153115d85afe01 playing with extractors diff -r ba1ce5921a4b -r 0e33c65f1904 extract_pdf.py --- a/extract_pdf.py Wed Jul 03 20:20:18 2024 -0700 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,31 +0,0 @@ -import re -from pathlib import Path -from typing import Iterable - -import nltk -from pdfminer.converter import PDFPageAggregator -from pdfminer.layout import LAParams, LTTextBox -from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager -from pdfminer.pdfpage import PDFPage - - -def phrasesFromFile(p: Path) -> Iterable: - fp = open(p, 'rb') - rsrcmgr = PDFResourceManager() - laparams = LAParams() - device = PDFPageAggregator(rsrcmgr, laparams=laparams) - interpreter = PDFPageInterpreter(rsrcmgr, device) - pages = PDFPage.get_pages(fp) - - for page in pages: - interpreter.process_page(page) - layout = 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client.has_collection(collection_name="demo_collection"): - client.create_collection( - collection_name="demo_collection", - dimension=dim, - ) - - docs = [] - for i, (bbox, phrase) in tqdm(enumerate( - phrasesFromFile( - Path("data") / - "Meetings2226Minutes_20240702182359526 (1).pdf"))): - [vector] = embedding_fn.encode_documents([phrase]) - doc = { - "id": i, - "vector": vector, - "text": phrase, - "bbox": json.dumps(bbox), - } - docs.append(doc) - res = client.insert(collection_name="demo_collection", data=docs) - print('insert:', res) - - -def search(q, embedding_fn, client): - query_vectors = embedding_fn.encode_queries([q]) - - [query_result] = client.search( - collection_name="demo_collection", - data=query_vectors, - limit=5, - output_fields=["text"], - ) - query_result.sort(key=lambda x: x["distance"], reverse=True) - - for row in query_result: - print(f'{row["distance"]:.6f} {row["entity"]["text"]}') - - -q, = sys.argv[1:] - -embedding_fn = OnnxEmbeddingFunction(model_name="GPTCache/paraphrase-albert-onnx") -client = MilvusClient("milvus_demo.db") -rebuild(client, embedding_fn, dim=embedding_fn.dim) -search(q, embedding_fn, client) diff -r ba1ce5921a4b -r 0e33c65f1904 search/extract_pdf.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/search/extract_pdf.py Sat Jul 06 16:42:36 2024 -0700 @@ -0,0 +1,33 @@ +import re +from pathlib import Path +from typing import Iterable + +import nltk +from pdfminer.converter import PDFPageAggregator +from pdfminer.layout import LAParams, LTTextBox +from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager +from pdfminer.pdfpage import PDFPage + + +def phrasesFromFile(p: Path) -> Iterable: + fp = open(p, 'rb') + rsrcmgr = PDFResourceManager() + laparams = LAParams() + device = PDFPageAggregator(rsrcmgr, laparams=laparams) + interpreter = PDFPageInterpreter(rsrcmgr, device) + pages = PDFPage.get_pages(fp) + + for page in pages: + interpreter.process_page(page) + layout = device.get_result() + for lobj in layout: + if isinstance(lobj, LTTextBox): + text = lobj.get_text() + for sentence in nltk.sent_tokenize(text): + sentence = re.sub(r'\s+', ' ', sentence).strip() + if len(sentence) < 5: + continue + if not re.search(r'\w\w\w\w\w', sentence): + continue + + yield list(map(int, lobj.bbox)), sentence diff -r ba1ce5921a4b -r 0e33c65f1904 search/query.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/search/query.py Sat Jul 06 16:42:36 2024 -0700 @@ -0,0 +1,70 @@ +import json +import sys +from pathlib import Path + +from tqdm import tqdm + +from pymilvus import MilvusClient +from milvus_model.dense.onnx import OnnxEmbeddingFunction + +from extract_pdf import phrasesFromFile + +from fastapi import FastAPI + +def rebuild(client, embedding_fn, dim): + client.drop_collection(collection_name="demo_collection") + if not client.has_collection(collection_name="demo_collection"): + client.create_collection( + collection_name="demo_collection", + dimension=dim, + ) + + docs = [] + for i, (bbox, phrase) in tqdm(enumerate( + phrasesFromFile( + Path("data") / + "Meetings2226Minutes_20240702182359526 (1).pdf")), + desc="rebuilding", + unit=' phrase'): + [vector] = embedding_fn.encode_documents([phrase]) + doc = { + "id": i, + "vector": vector, + "text": phrase, + "bbox": json.dumps(bbox), + } + docs.append(doc) + res = client.insert(collection_name="demo_collection", data=docs) + print('insert:', res['insert_count']) + + +def search(q, embedding_fn, client): + query_vectors = embedding_fn.encode_queries([q]) + + [query_result] = client.search( + collection_name="demo_collection", + data=query_vectors, + limit=5, + output_fields=["text"], + ) + query_result.sort(key=lambda x: x["distance"], reverse=True) + + for row in query_result: + print(f'{row["distance"]:.6f} {row["entity"]["text"]}') + + +# q, = sys.argv[1:] + +# https://huggingface.co/models?pipeline_tag=feature-extraction&library=onnx&sort=trending +# embedding_fn = OnnxEmbeddingFunction(model_name="jinaai/jina-embeddings-v2-base-en") +# client = MilvusClient("milvus_demo.db") +# rebuild(client, embedding_fn, dim=embedding_fn.dim) +# search(q, embedding_fn, client) + +app = FastAPI() + + +@app.get("/sco/query") +def read_query1(q: str|None): + print(f'1 {q=}') + return {"Hello": "World"} diff -r ba1ce5921a4b -r 0e33c65f1904 webvtt_1.py --- a/webvtt_1.py Wed Jul 03 20:20:18 2024 -0700 +++ b/webvtt_1.py Sat Jul 06 16:42:36 2024 -0700 @@ -15,6 +15,7 @@ # print(caption.end) # print(caption.text) +##https://stackoverflow.com/questions/51784232/how-do-i-convert-the-webvtt-format-to-plain-text transcript = "" lines = [] @@ -32,10 +33,18 @@ transcript += " " + line previous = line -print(transcript) +#print(transcript) # text = lobj.get_text() # for sentence in nltk.sent_tokenize(text): # sentence = re.sub(r'\s+', ' ', sentence).strip() # if len(sentence) < 5: -# continue \ No newline at end of file +# continue + +from langchain import RecursiveCharacterTextSplitter ; + +const text = transcript +const splitter = new RecursiveCharacterTextSplitter({ + chunkSize: 10, + chunkOverlap: 1, +}); \ No newline at end of file