feat(chunk): enhance file reading and processing logic
- Updated `read_files` function to return a list of dictionaries containing 'content' and 'title' keys. - Added logic to handle concatenation of file contents and improved handling of file prefixes. - Introduced `get_chunk_docs` function to chunk document contents using `SemanticChunker`. - Added `convert_chunks_to_document` function to convert chunked content into `Document` objects. - Integrated logging for chunking process. - Updated dependencies in `uv.lock` to include `chonkie` and other related packages.
This commit is contained in:
acano
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f6bfba5561
commit
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@ -5,6 +5,7 @@ description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"chonkie[semantic]>=1.5.6",
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"grpcio>=1.78.0",
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"grpcio-reflection>=1.78.0",
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"grpcio-tools>=1.78.0",
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@ -36,5 +37,6 @@ dev = [
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"polars>=1.38.1",
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"ragas>=0.4.3",
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"ruff>=0.15.1",
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"selenium>=4.41.0",
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"tree-sitter-language-pack>=0.13.0",
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]
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File diff suppressed because one or more lines are too long
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@ -1,59 +1,75 @@
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import json
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import time
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from bs4 import BeautifulSoup
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from selenium import webdriver
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from selenium.webdriver.chrome.options import Options
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from bs4 import BeautifulSoup
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from selenium.webdriver.common.by import By
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from selenium.webdriver.support import expected_conditions as EC
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from selenium.webdriver.support.ui import WebDriverWait
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url = "https://developer.avapframework.com/docs"
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chrome_options = Options()
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chrome_options.add_argument("--headless")
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chrome_options.add_argument("--no-sandbox")
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chrome_options.add_argument("--disable-dev-shm-usage")
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driver = webdriver.Chrome(options=chrome_options)
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try:
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driver.get(url)
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time.sleep(5)
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wait = WebDriverWait(driver, 15)
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wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#contentDoc")))
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html = driver.page_source
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finally:
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driver.quit()
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soup = BeautifulSoup(html, "html.parser")
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# Todos los bloques de contenido dentro de col-md-12
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blocks = soup.select("#contentDoc .col-md-12 div.body-md")
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main_container = soup.select_one("#contentDoc .col-md-12")
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data = []
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def html_to_markdown(elem):
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text = elem.get_text(" ", strip=True)
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for i, block in enumerate(blocks, start=1):
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parent_section = block.find_parent("div", id=True)
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if not text:
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return None
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content = []
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full_text_parts = []
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if elem.name == "h1":
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return f"# {text}\n"
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if elem.name == "h2":
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return f"## {text}\n"
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if elem.name == "h3":
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return f"### {text}\n"
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if elem.name == "h4":
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return f"#### {text}\n"
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if elem.name == "h5":
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return f"##### {text}\n"
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if elem.name == "h6":
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return f"###### {text}\n"
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# Recorremos todos los elementos en orden
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for elem in block.find_all(["h1", "h2", "h3", "h4", "h5", "h6", "p", "li", "pre", "code"], recursive=True):
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text = elem.get_text(" ", strip=True)
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if not text:
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continue
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if elem.name == "p":
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return f"{text}\n"
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item = {
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"tag": elem.name,
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"text": text
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}
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content.append(item)
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full_text_parts.append(text)
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if elem.name == "li":
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return f"- {text}"
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data.append({
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"block_index": i,
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"section_id": parent_section.get("id") if parent_section else None,
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"content": content,
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"full_text": "\n".join(full_text_parts)
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})
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if elem.name == "pre":
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code = elem.get_text("\n", strip=True)
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return f"\n```\n{code}\n```\n"
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with open("avap_docs.json", "w", encoding="utf-8") as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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return None
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print("Bloques encontrados:", len(data))
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markdown_lines = []
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tags_to_extract = ["h1", "h2", "h3", "h4", "h5", "h6", "p", "li", "pre"]
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for elem in main_container.find_all(tags_to_extract):
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md = html_to_markdown(elem)
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if md:
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markdown_lines.append(md)
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markdown = "\n".join(markdown_lines)
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with open("avap_docs.md", "w", encoding="utf-8") as f:
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f.write(markdown)
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@ -7,12 +7,16 @@ import logging
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import os
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from pathlib import Path
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from loguru import logger
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from elasticsearch import Elasticsearch
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from langchain_core.documents import Document
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from langchain_elasticsearch import ElasticsearchStore
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from src.utils.emb_factory import create_embedding_model
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_experimental.text_splitter import SemanticChunker
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from src.utils.emb_factory import create_embedding_model
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from scripts.pipelines.tasks.chunk import scrape_avap_docs
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logger = logging.getLogger(__name__)
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app = typer.Typer()
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ELASTICSEARCH_LOCAL_URL = os.getenv("ELASTICSEARCH_LOCAL_URL")
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@ -20,6 +24,7 @@ OLLAMA_LOCAL_URL = os.getenv("OLLAMA_LOCAL_URL")
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ELASTICSEARCH_INDEX = os.getenv("ELASTICSEARCH_INDEX")
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OLLAMA_URL = os.getenv("OLLAMA_URL")
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OLLAMA_EMB_MODEL_NAME = os.getenv("OLLAMA_EMB_MODEL_NAME")
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AVAP_WEB_DOCS_URL = os.getenv("AVAP_WEB_DOCS_URL")
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class DistanceStrategy(str, Enum):
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euclidean = "EUCLIDEAN_DISTANCE"
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@ -0,0 +1,143 @@
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from enum import Enum
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import typer
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import logging
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import os
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from pathlib import Path
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from loguru import logger
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from elasticsearch import Elasticsearch
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from langchain_elasticsearch import ElasticsearchStore
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from chonkie import SemanticChunker
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from src.utils.emb_factory import create_embedding_model
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from scripts.pipelines.tasks.chunk import (
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read_files,
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get_chunk_docs,
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convert_chunks_to_document
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)
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app = typer.Typer()
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ELASTICSEARCH_LOCAL_URL = os.getenv("ELASTICSEARCH_LOCAL_URL")
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OLLAMA_LOCAL_URL = os.getenv("OLLAMA_LOCAL_URL")
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ELASTICSEARCH_INDEX = os.getenv("ELASTICSEARCH_INDEX")
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OLLAMA_URL = os.getenv("OLLAMA_URL")
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OLLAMA_EMB_MODEL_NAME = os.getenv("OLLAMA_EMB_MODEL_NAME")
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AVAP_WEB_DOCS_URL = os.getenv("AVAP_WEB_DOCS_URL")
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HF_EMB_MODEL_NAME = os.getenv("HF_EMB_MODEL_NAME")
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class DistanceStrategy(str, Enum):
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euclidean = "EUCLIDEAN_DISTANCE"
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max_inner_product = "MAX_INNER_PRODUCT"
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dot_product = "DOT_PRODUCT"
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jaccard = "JACCARD"
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cosine = "COSINE"
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@app.command()
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def elasticsearch_ingestion(
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docs_folder_path: str = "docs",
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es_request_timeout: int = 120,
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es_max_retries: int = 5,
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es_retry_on_timeout: bool = True,
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distance_strategy: DistanceStrategy = DistanceStrategy.cosine,
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chunk_size: int = 2048,
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chunk_threshold: float = 0.5,
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chunk_similarity_window: int = 3,
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chunk_skip_window: int = 1,
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):
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logger.info("Starting Elasticsearch ingestion pipeline...")
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logger.info(f"Reading and concatenating files from folder: {docs_folder_path}/developer.avapframework.com")
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avap_github_docs = read_files(f"{docs_folder_path}/avap_language_github_docs", concatenate=False)
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avap_web_docs_intro = read_files(f"{docs_folder_path}/developer.avapframework.com", "intro", concatenate=True)
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# Check chapters in developer.avapframework.com folder and read and concatenate files for each chapter
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chapters = sorted({
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p.name.split("_")[0]
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for p in Path(f"{docs_folder_path}/developer.avapframework.com").glob("chapter*.md")
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})
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avap_web_docs_chapters = [
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item
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for chapter in chapters
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for item in read_files(
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f"{docs_folder_path}/developer.avapframework.com",
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f"{chapter}_",
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concatenate=True
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)
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]
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avap_web_docs_appendices = read_files(f"{docs_folder_path}/developer.avapframework.com", "appendices_", concatenate=False)
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avap_samples_docs = read_files(f"{docs_folder_path}/samples", concatenate=False)
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logger.info("Instantiating semantic chunker...")
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chunker = SemanticChunker(
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embedding_model=HF_EMB_MODEL_NAME,
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chunk_size=chunk_size,
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threshold=chunk_threshold,
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similarity_window=chunk_similarity_window,
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skip_window=chunk_skip_window
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)
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logger.info("Chunking AVAP GitHub docs...")
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avap_github_docs_chunks = get_chunk_docs(avap_github_docs, chunker)
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logger.info("Chunking AVAP web docs chapters...")
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avap_web_docs_chapters_chunks = get_chunk_docs(avap_web_docs_chapters, chunker)
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logger.info("Creating Langchain Document to index...")
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avap_github_langchain_docs = convert_chunks_to_document(avap_github_docs_chunks)
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avap_web_chapters_langchain_docs = convert_chunks_to_document(avap_web_docs_chapters_chunks)
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avap_web_intro_langchain_docs = convert_chunks_to_document(avap_web_docs_intro)
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avap_web_appendices_langchain_docs = convert_chunks_to_document(avap_web_docs_appendices)
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avap_samples_langchain_docs = convert_chunks_to_document(avap_samples_docs)
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avap_documents = avap_github_langchain_docs + avap_web_chapters_langchain_docs + avap_web_intro_langchain_docs + avap_web_appendices_langchain_docs + avap_samples_langchain_docs
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logger.info("Connecting to Elasticsearch...")
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try:
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es = Elasticsearch(
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ELASTICSEARCH_LOCAL_URL,
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request_timeout=es_request_timeout,
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max_retries=es_max_retries,
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retry_on_timeout=es_retry_on_timeout,
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)
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except:
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logger.exception("Failed to connect to Elasticsearch.")
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raise
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logger.info("Instantiating embeddings model...")
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try:
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embeddings = create_embedding_model(
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provider="ollama",
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model=OLLAMA_EMB_MODEL_NAME,
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base_url=OLLAMA_LOCAL_URL,
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)
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except:
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logger.exception("Failed to instantiate embeddings model.")
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raise
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logger.info(f"Checking if index {ELASTICSEARCH_INDEX} exists and deleting if it does...")
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if es.indices.exists(index=ELASTICSEARCH_INDEX):
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es.indices.delete(index=ELASTICSEARCH_INDEX)
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logger.info(f"Uploading documents to index {ELASTICSEARCH_INDEX}...")
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ElasticsearchStore.from_documents(
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avap_documents,
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embeddings,
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client=es,
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index_name=ELASTICSEARCH_INDEX,
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distance_strategy=distance_strategy.value,
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)
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logger.info(f"Finished uploading documents to index {ELASTICSEARCH_INDEX}.")
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if __name__ == "__main__":
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
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)
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try:
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app()
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except Exception as exc:
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logger.exception(exc)
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raise
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@ -0,0 +1,326 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f8ea7a75",
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"metadata": {},
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"outputs": [],
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"source": [
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"import re\n",
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"import hashlib\n",
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"from typing import Any\n",
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"from enum import Enum\n",
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"import typer\n",
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"import logging\n",
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"import os\n",
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"from pathlib import Path\n",
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"\n",
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"from loguru import logger\n",
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"from elasticsearch import Elasticsearch\n",
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"from langchain_core.documents import Document\n",
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"from langchain_elasticsearch import ElasticsearchStore\n",
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"from langchain_community.embeddings import HuggingFaceEmbeddings\n",
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"from chonkie import SemanticChunker\n",
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"\n",
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"from src.utils.emb_factory import create_embedding_model\n",
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"from scripts.pipelines.tasks.chunk import read_concat_files, get_chunk_docs, chunks_to_document\n",
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"from src.config import PROJ_ROOT\n",
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"\n",
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"ELASTICSEARCH_LOCAL_URL = os.getenv(\"ELASTICSEARCH_LOCAL_URL\")\n",
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"OLLAMA_LOCAL_URL = os.getenv(\"OLLAMA_LOCAL_URL\")\n",
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"ELASTICSEARCH_INDEX = os.getenv(\"ELASTICSEARCH_INDEX\")\n",
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"OLLAMA_URL = os.getenv(\"OLLAMA_URL\")\n",
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"OLLAMA_EMB_MODEL_NAME = os.getenv(\"OLLAMA_EMB_MODEL_NAME\")\n",
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"AVAP_WEB_DOCS_URL = os.getenv(\"AVAP_WEB_DOCS_URL\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a8b8de3f",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32m2026-03-10 13:58:32.657\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m3\u001b[0m - \u001b[1mStarting Elasticsearch ingestion pipeline...\u001b[0m\n",
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"\u001b[32m2026-03-10 13:58:32.658\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mReading and concatenating files from folder: docs/developer.avapframework.com\u001b[0m\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1\n",
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"1\n",
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"14\n",
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"24\n"
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]
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}
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],
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"source": [
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"docs_folder_path = \"docs\"\n",
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"\n",
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"logger.info(\"Starting Elasticsearch ingestion pipeline...\")\n",
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"logger.info(f\"Reading and concatenating files from folder: {docs_folder_path}/developer.avapframework.com\")\n",
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"avap_github_docs = read_concat_files(PROJ_ROOT / f\"{docs_folder_path}/avap_language_github_docs\", \"AVAP\", concatenate=False)\n",
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"avap_web_docs_intro = read_concat_files(PROJ_ROOT / f\"{docs_folder_path}/developer.avapframework.com\", \"intro\", concatenate=True)\n",
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"\n",
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"# Check chapters in developer.avapframework.com folder and read and concatenate files for each chapter\n",
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"chapters = sorted({\n",
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" p.name.split(\"_\")[0]\n",
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" for p in Path(f\"{docs_folder_path}/developer.avapframework.com\").glob(\"chapter*.md\")\n",
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"})\n",
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"\n",
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"avap_web_docs_chapters = [\n",
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" item\n",
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" for chapter in chapters\n",
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" for item in read_concat_files(\n",
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" f\"{docs_folder_path}/developer.avapframework.com\",\n",
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" f\"{chapter}_\",\n",
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" concatenate=True\n",
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" )\n",
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"]\n",
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"avap_web_docs_appendices = read_concat_files(PROJ_ROOT / f\"{docs_folder_path}/developer.avapframework.com\", \"appendices_\", concatenate=False)\n",
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"avap_examples_docs = read_concat_files(PROJ_ROOT / f\"{docs_folder_path}/samples\", concatenate=False)\n",
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"\n",
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"print(len(avap_github_docs))\n",
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"print(len(avap_web_docs_intro))\n",
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"print(len(avap_web_docs_chapters))\n",
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"print(len(avap_web_docs_appendices))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "36abc025",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'content': 'nivel = 5\\nes_admin = nivel >= 10\\naddResult(es_admin)',\n",
|
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" 'title': 'asignacion_booleana.avap'},\n",
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" {'content': 'subtotal = 150.50\\niva = subtotal * 0.21\\ntotal = subtotal + iva\\naddResult(total)',\n",
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" 'title': 'asignacion_matematica.avap'},\n",
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" {'content': 'startLoop(i,1,10)\\n item = \"item_%s\" % i\\n AddvariableToJSON(item,\\'valor_generado\\',mi_json)\\nendLoop()\\naddResult(mi_json)',\n",
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" 'title': 'bucle_1_10.avap'},\n",
|
||||
" {'content': \"registros = ['1','2','3']\\ngetListLen(registros, total)\\ncontador = 0\\nstartLoop(idx, 0, 2)\\n actual = registros[int(idx)]\\nendLoop()\\naddResult(actual)\",\n",
|
||||
" 'title': 'bucle_longitud_de_datos.avap'},\n",
|
||||
" {'content': 'getDateTime(\"\", 86400, \"UTC\", expira)\\naddResult(expira)',\n",
|
||||
" 'title': 'calculo_de_expiracion.avap'},\n",
|
||||
" {'content': 'addParam(\"client_id\", id_interno)\\naddResult(id_interno)',\n",
|
||||
" 'title': 'captura_de_id.avap'},\n",
|
||||
" {'content': 'addParam(emails,emails)\\ngetQueryParamList(lista_correos)\\naddResult(lista_correos)',\n",
|
||||
" 'title': 'captura_de_listas_multiples.avap'},\n",
|
||||
" {'content': 'addParam(\"lang\", l)\\nif(l, \"es\", \"=\")\\n addVar(msg, \"Hola\")\\nend()\\naddResult(msg)',\n",
|
||||
" 'title': 'comparacion_simple.avap'},\n",
|
||||
" {'content': 'nombre = \"Sistema\"\\nlog = \"Evento registrado por: %s\" % nombre\\naddResult(log)',\n",
|
||||
" 'title': 'concatenacion_dinamica.avap'},\n",
|
||||
" {'content': 'datos_cliente = \"datos\"\\naddVar(clave, \"cliente_vip\")\\nAddvariableToJSON(clave, datos_cliente, mi_json_final)\\naddResult(mi_json_final)',\n",
|
||||
" 'title': 'construccion_dinamica_de_objeto.avap'},\n",
|
||||
" {'content': 'addParam(\"data_list\", mi_lista)\\ngetListLen(mi_lista, cantidad)\\naddResult(cantidad)',\n",
|
||||
" 'title': 'contador_de_parametros.avap'},\n",
|
||||
" {'content': 'stampToDatetime(1708726162, \"%d/%m/%Y\", 0, fecha_human)\\naddResult(fecha_human)',\n",
|
||||
" 'title': 'conversion_timestamp_legible.avap'},\n",
|
||||
" {'content': 'addParam(sal_par,saldo)\\nif(saldo, 0, \">\")\\n permitir = True\\nelse()\\n permitir = False\\nend()\\naddResult(permitir)',\n",
|
||||
" 'title': 'else_estandar.avap'},\n",
|
||||
" {'content': 'addParam(userrype, user_type)\\naddParam(sells, compras)\\nif(None, None, \" user_type == \\'VIP\\' or compras > 100\")\\n addVar(descuento, 0.20)\\nend()\\naddResult(descuento)',\n",
|
||||
" 'title': 'expresion_compleja.avap'},\n",
|
||||
" {'content': 'getDateTime(\"%Y-%m-%d %H:%M:%S\", 0, \"Europe/Madrid\", sql_date)\\naddResult(sql_date)',\n",
|
||||
" 'title': 'fecha_para_base_de_datos.avap'},\n",
|
||||
" {'content': 'function suma(a, b){\\n total = a + b\\n return(total)\\n }\\nresultado = suma(10, 20)\\naddResult(resultado)',\n",
|
||||
" 'title': 'funcion_de_suma.avap'},\n",
|
||||
" {'content': 'function es_valido(token){\\n response = False\\n if(token, \"SECRET\", \"=\")\\n response = True\\n end()\\n return(response)\\n }\\nautorizado = es_valido(\"SECRET\")\\naddResult(autorizado)',\n",
|
||||
" 'title': 'funcion_validacion_acceso.avap'},\n",
|
||||
" {'content': 'randomString(\"[A-Z]\\\\d\", 32, token_seguridad)\\naddResult(token_seguridad)',\n",
|
||||
" 'title': 'generador_de_tokens_aleatorios.avap'},\n",
|
||||
" {'content': 'encodeSHA256(\"payload_data\", checksum)\\naddResult(checksum)',\n",
|
||||
" 'title': 'hash_SHA256_para_integridad.avap'},\n",
|
||||
" {'content': 'addVar(mensaje, \"Hola mundo desde AVAP\")\\naddResult(mensaje)',\n",
|
||||
" 'title': 'hola_mundo.avap'},\n",
|
||||
" {'content': 'addParam(password,pass_nueva)\\npass_antigua = \"password\"\\nif(pass_nueva, pass_antigua, \"!=\")\\n addVar(cambio, \"Contraseña actualizada\")\\nend()\\naddResult(cambio)',\n",
|
||||
" 'title': 'if_desigualdad.avap'},\n",
|
||||
" {'content': 'replace(\"REF_1234_OLD\",\"OLD\", \"NEW\", ref_actualizada)\\naddResult(ref_actualizada)',\n",
|
||||
" 'title': 'limpieza_de_strings.avap'},\n",
|
||||
" {'content': 'try()\\n ormDirect(\"UPDATE table_inexistente SET a=1\", res)\\nexception(e)\\n addVar(_status,500)\\n addResult(\"Error de base de datos\")',\n",
|
||||
" 'title': 'manejo_error_sql_critico.avap'},\n",
|
||||
" {'content': 'getDateTime(\"\", 0, \"UTC\", ahora)\\naddResult(ahora)',\n",
|
||||
" 'title': 'obtencion_timestamp.avap'},\n",
|
||||
" {'content': 'ormCheckTable(tabla_pruebas,resultado_comprobacion)\\nif(resultado_comprobacion,False,\\'==\\')\\n ormCreateTable(\"username,age\",\\'VARCHAR,INTEGER\\',tabla_pruebas,resultado_creacion)\\nend()\\naddResult(resultado_comprobacion)\\naddResult(resultado_creacion)',\n",
|
||||
" 'title': 'ormAccessCreate.avap'},\n",
|
||||
" {'content': 'addParam(\"page\", p)\\naddParam(\"size\", s)\\nregistros = [\"u1\", \"u2\", \"u3\", \"u4\", \"u5\", \"u6\"]\\noffset = int(p) * int(s)\\nlimite = offset + int(s)\\ncontador = 0\\naddResult(offset)\\naddResult(limite)\\nstartLoop(i, 2, limite)\\n actual = registros[int(i)]\\n titulo = \"reg_%s\" % i\\n AddvariableToJSON(titulo, actual, pagina_json)\\nendLoop()\\naddResult(pagina_json)',\n",
|
||||
" 'title': 'paginacion_dinamica_recursos.avap'},\n",
|
||||
" {'content': 'addVar(base, 1000)\\naddVar(copia, $base)\\naddResult(copia)',\n",
|
||||
" 'title': 'referencia_por_valor.avap'},\n",
|
||||
" {'content': 'addVar(code, 200)\\naddVar(status, \"Success\")\\naddResult(code)\\naddResult(status)',\n",
|
||||
" 'title': 'respuesta_multiple.avap'},\n",
|
||||
" {'content': 'encontrado = False\\nstartLoop(i, 1, 10)\\n if(i, 5, \"==\")\\n encontrado = True\\n i = 11 \\n end()\\nendLoop()\\naddResult(encontrado)',\n",
|
||||
" 'title': 'salida_bucle_correcta.avap'},\n",
|
||||
" {'content': 'try()\\n RequestGet(\"https://api.test.com/data\", 0, 0, respuesta)\\nexception(e)\\n addVar(error_trace, \"Fallo de conexión: %s\" % e)\\n addResult(error_trace)',\n",
|
||||
" 'title': 'try_catch_request.avap'},\n",
|
||||
" {'content': 'addParam(\"api_key\", key)\\nif(key, None, \"==\")\\n addVar(_status, 403)\\n addVar(error, \"Acceso denegado: falta API KEY\")\\n addResult(error)\\nend()',\n",
|
||||
" 'title': 'validacion_de_nulo.avap'},\n",
|
||||
" {'content': 'addParam(\"rol\", r)\\nif(r, [\"admin\", \"editor\", \"root\"], \"in\")\\n acceso = True\\nend()\\naddResult(acceso)',\n",
|
||||
" 'title': 'validacion_in_pertenece_a_lista.avap'}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"avap_examples_docs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "16a9e8ce",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "679e5f8c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "27e5774d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[32m2026-03-10 13:58:34.531\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m1\u001b[0m - \u001b[1mChunking documents...\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"logger.info(\"Chunking documents...\")\n",
|
||||
"chunker = SemanticChunker(\n",
|
||||
" embedding_model=os.getenv(\"HF_EMB_MODEL_NAME\"),\n",
|
||||
" chunk_size=2048,\n",
|
||||
" threshold=0.5,\n",
|
||||
" skip_window=1\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "a5ce984e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[32m2026-03-10 13:58:51.740\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m1\u001b[0m - \u001b[1mChunking AVAP GitHub docs...\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:01:00.535\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking AVAP.md\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:01:00.536\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mChunking AVAP web docs chapters...\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:01:09.128\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter1_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:01:12.763\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter2_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:01:42.995\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter3_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:01:48.772\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter4_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:01:48.772\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter5_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:01:48.773\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter6_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:02:06.408\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter7_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:02:21.501\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter8_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:07:27.158\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter9_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:07:48.389\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter10_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:08:10.823\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter11_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:08:27.335\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter12_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:08:55.010\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter13_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:09:10.211\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mscripts.pipelines.tasks.chunk\u001b[0m:\u001b[36mchunk_docs\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mFinished chunking chapter14_\u001b[0m\n",
|
||||
"\u001b[32m2026-03-10 14:09:10.211\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m7\u001b[0m - \u001b[1mCreating Langchain Document to index...\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"logger.info(\"Chunking AVAP GitHub docs...\")\n",
|
||||
"avap_github_docs_chunks = chunk_docs(avap_github_docs, chunker)\n",
|
||||
"\n",
|
||||
"logger.info(\"Chunking AVAP web docs chapters...\")\n",
|
||||
"avap_web_docs_chapters_chunks = chunk_docs(avap_web_docs_chapters, chunker)\n",
|
||||
"\n",
|
||||
"logger.info(\"Creating Langchain Document to index...\")\n",
|
||||
"avap_github_langchain_docs = chunks_to_document(avap_github_docs_chunks)\n",
|
||||
"avap_web_chapters_langchain_docs = chunks_to_document(avap_web_docs_chapters_chunks)\n",
|
||||
"avap_web_intro_langchain_docs = chunks_to_document(avap_web_docs_intro)\n",
|
||||
"avap_web_appendices_langchain_docs = chunks_to_document(avap_web_docs_appendices)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "dd1f4d79",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"avap_github_langchain_docs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c24e8a8f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"avap_web_chapters_langchain_docs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f6782a34",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"avap_web_intro_langchain_docs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "78c1190e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"avap_web_appendices_langchain_docs"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "assistance-engine",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -1,5 +1,10 @@
|
|||
import os
|
||||
import re
|
||||
import uuid
|
||||
|
||||
from loguru import logger
|
||||
from chonkie import Chunk, SemanticChunker
|
||||
from langchain_core.documents import Document
|
||||
|
||||
|
||||
def replace_javascript_with_avap(text: str) -> str:
|
||||
|
|
@ -29,33 +34,103 @@ def replace_javascript_with_avap(text: str) -> str:
|
|||
return text
|
||||
|
||||
|
||||
def read_concat_files(folder_path: str, file_prefix: str, concatenate: bool = True) -> str | list[str]:
|
||||
def read_files(
|
||||
folder_path: str, file_prefix: str | None = None, concatenate: bool = True
|
||||
) -> list[dict]:
|
||||
"""
|
||||
Read and concatenate all files in a folder whose names start with a given prefix.
|
||||
Read files in a folder whose names start with a given prefix.
|
||||
Replaces javascript language markers with avap.
|
||||
|
||||
Args:
|
||||
folder_path: Path to the folder to search in.
|
||||
file_prefix: The prefix that file names must start with.
|
||||
If None, all files in the folder are included.
|
||||
concatenate: Whether to concatenate the contents of the files.
|
||||
|
||||
Returns:
|
||||
A single string with the concatenated contents of all matching files,
|
||||
with javascript markers replaced with avap, or a list of strings if concatenate is False.
|
||||
A list of dictionaries, each containing 'content' and 'title' keys.
|
||||
If concatenate is True, returns a single dict with concatenated content and title as 'appendix'.
|
||||
If concatenate is False, returns one dict per file with filename as title.
|
||||
"""
|
||||
contents = []
|
||||
filenames = []
|
||||
|
||||
for filename in sorted(os.listdir(folder_path)):
|
||||
if filename.startswith(file_prefix):
|
||||
include_file = file_prefix is None or filename.startswith(file_prefix)
|
||||
if include_file:
|
||||
file_path = os.path.join(folder_path, filename)
|
||||
if os.path.isfile(file_path):
|
||||
with open(file_path, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
if content.strip():
|
||||
print(f"Reading file: {filename}") # Skip empty files
|
||||
contents.append(content)
|
||||
cleaned_content = content.strip()
|
||||
if cleaned_content:
|
||||
contents.append(cleaned_content)
|
||||
filenames.append(filename)
|
||||
|
||||
if concatenate:
|
||||
concatenated = "\n".join(contents)
|
||||
return replace_javascript_with_avap(concatenated)
|
||||
processed_content = replace_javascript_with_avap(concatenated)
|
||||
title = file_prefix if file_prefix is not None else "all_files"
|
||||
return [{"content": processed_content, "title": title}]
|
||||
else:
|
||||
return [replace_javascript_with_avap(content) for content in contents]
|
||||
return [
|
||||
{"content": replace_javascript_with_avap(content), "title": filename}
|
||||
for content, filename in zip(contents, filenames)
|
||||
]
|
||||
|
||||
|
||||
def get_chunk_docs(docs: list[dict], chunker: SemanticChunker) -> list[list[Chunk]]:
|
||||
"""
|
||||
Chunk the content of the documents using the provided chunker.
|
||||
|
||||
Args:
|
||||
docs: A list of dictionaries, each containing 'content' and 'title' keys.
|
||||
chunker: An instance of SemanticChunker to use for chunking the content.
|
||||
|
||||
Returns:
|
||||
A list of lists of Chunk objects, where each inner list corresponds to the chunks of a
|
||||
single document.
|
||||
"""
|
||||
list_chunks = []
|
||||
|
||||
for doc in docs:
|
||||
content = doc["content"]
|
||||
chunks = chunker.chunk(content)
|
||||
for chunk in chunks:
|
||||
chunk.context = {"source": doc["title"]}
|
||||
list_chunks.append(chunks)
|
||||
logger.info(f"Finished chunking {doc['title']}")
|
||||
|
||||
return list_chunks
|
||||
|
||||
|
||||
def convert_chunks_to_document(chunks: list[dict] | list[list[Chunk]]) -> list[Document]:
|
||||
"""
|
||||
Convert the chunked content into a list of Document objects.
|
||||
|
||||
Args:
|
||||
chunks: A list of dictionaries containing 'content' and 'title' keys.
|
||||
|
||||
Returns:
|
||||
A list of Document objects created from the chunked content.
|
||||
"""
|
||||
documents = []
|
||||
|
||||
if isinstance(chunks[0], dict):
|
||||
for chunk in chunks:
|
||||
content = chunk["content"]
|
||||
title = chunk["title"]
|
||||
documents.append(Document(id=str(uuid.uuid4()),
|
||||
page_content=content,
|
||||
metadata={"source": title}))
|
||||
|
||||
else:
|
||||
for chunk_list in chunks:
|
||||
for chunk in chunk_list:
|
||||
content = chunk.text
|
||||
title = chunk.context.get("source", "unknown")
|
||||
documents.append(Document(id=str(uuid.uuid4()),
|
||||
page_content=content,
|
||||
metadata={"source": title}))
|
||||
|
||||
return documents
|
||||
174
uv.lock
174
uv.lock
|
|
@ -250,6 +250,7 @@ name = "assistance-engine"
|
|||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "chonkie", extra = ["semantic"] },
|
||||
{ name = "grpcio" },
|
||||
{ name = "grpcio-reflection" },
|
||||
{ name = "grpcio-tools" },
|
||||
|
|
@ -281,11 +282,13 @@ dev = [
|
|||
{ name = "polars" },
|
||||
{ name = "ragas" },
|
||||
{ name = "ruff" },
|
||||
{ name = "selenium" },
|
||||
{ name = "tree-sitter-language-pack" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "chonkie", extras = ["semantic"], specifier = ">=1.5.6" },
|
||||
{ name = "grpcio", specifier = ">=1.78.0" },
|
||||
{ name = "grpcio-reflection", specifier = ">=1.78.0" },
|
||||
{ name = "grpcio-tools", specifier = ">=1.78.0" },
|
||||
|
|
@ -317,6 +320,7 @@ dev = [
|
|||
{ name = "polars", specifier = ">=1.38.1" },
|
||||
{ name = "ragas", specifier = ">=0.4.3" },
|
||||
{ name = "ruff", specifier = ">=0.15.1" },
|
||||
{ name = "selenium", specifier = ">=4.41.0" },
|
||||
{ name = "tree-sitter-language-pack", specifier = ">=0.13.0" },
|
||||
]
|
||||
|
||||
|
|
@ -589,6 +593,62 @@ wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "chonkie"
|
||||
version = "1.5.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "chonkie-core" },
|
||||
{ name = "numpy" },
|
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{ name = "tenacity" },
|
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{ name = "tqdm" },
|
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]
|
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sdist = { url = "https://files.pythonhosted.org/packages/a4/16/e51295955f5a627ebb7867dc2e7fa48d4c6dc2a5f3cde3690de84812e929/chonkie-1.5.6.tar.gz", hash = "sha256:282a24c20b88c4c28d8cae893ac78bcbee531a87d28ec86b419897a9eea2ecf3", size = 172066, upload-time = "2026-02-16T21:44:01.336Z" }
|
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wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/18/3a/24cf4cb377f4d44126231d55a19b48a645a0f78f891288a8d4300c95160d/chonkie-1.5.6-py3-none-any.whl", hash = "sha256:4c3be39a0f97315eb3c5efe6dc5d7933d3d27a1918b55c39ab211b403bb03df7", size = 210065, upload-time = "2026-02-16T21:43:59.926Z" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
semantic = [
|
||||
{ name = "model2vec" },
|
||||
{ name = "tokenizers" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "chonkie-core"
|
||||
version = "0.9.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/55/f5/547c836f488dc74116ea42a2b2355365f4829fe6d925564f4db7775e6d34/chonkie_core-0.9.2.tar.gz", hash = "sha256:a34f457016fb4bedf9d0a62e55afc334670d88f8316d50ba9af8df83be78b56a", size = 49480, upload-time = "2026-01-21T09:09:46.265Z" }
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wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/c3/27/8a1f0efc87ef5d99760a462d6c3b17e4e765c77f52a944d56b676a83adfd/chonkie_core-0.9.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:9a1bfe98b00e6f70fe97bd342759d67856677056f0cb4193e95f79e561ca35d8", size = 350021, upload-time = "2026-01-21T09:09:12.614Z" },
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{ url = "https://files.pythonhosted.org/packages/41/0e/bdf2863380373efb3f6c43e3361616d99c12cabd9e37f67949803809068f/chonkie_core-0.9.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0b40a3b33b381dc3543483b8ade4e4176280fa858f8690bdeb28acc082e1dc7e", size = 369363, upload-time = "2026-01-21T09:09:14.379Z" },
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{ url = "https://files.pythonhosted.org/packages/34/d2/7316952edf5d7a7788659bb6dba23438e04a0268f93b21fb731204ceee58/chonkie_core-0.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cac5919f384e38792c742c51979c2dac57ba1ce079361a1d782bdf7d43b66f30", size = 388981, upload-time = "2026-01-21T09:09:15.295Z" },
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{ url = "https://files.pythonhosted.org/packages/6c/1d/8265df0af95651cdca7a64dc6e57f3ad6c562966c53ccf03915a60440eb0/chonkie_core-0.9.2-cp311-cp311-win_amd64.whl", hash = "sha256:67cdf9aceef31ce4e02ecd82c4491c2a36bb70cc9230466f32c7d6cdb039285a", size = 224450, upload-time = "2026-01-21T09:09:16.522Z" },
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{ url = "https://files.pythonhosted.org/packages/52/55/8825b059e70a3c757c90efa319e35312a2650431aef1cec11b476ee8699b/chonkie_core-0.9.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:d6d11337842ca90713d8b48d42ce823bcc82874437d4071a8aced9d47b66ec76", size = 347854, upload-time = "2026-01-21T09:09:17.49Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/11/51/abac8676470c7e7a7967964eb9066e2efc346339c338da7190a41f412bba/chonkie_core-0.9.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:77fe2d6b9a709245408b53923dd4ebe1b79e09fdcdc5916df9c97e90c8e13eda", size = 333582, upload-time = "2026-01-21T09:09:18.863Z" },
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{ url = "https://files.pythonhosted.org/packages/d4/8c/f62d4ff0efbc08d8c281051ce1752cd6bcb6a7f3e816f8b3c143741d1b86/chonkie_core-0.9.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c0151a74791445985f30bc34cbe7d679e9a716d36e9acf67ed5dc3408be6a426", size = 365189, upload-time = "2026-01-21T09:09:19.884Z" },
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{ url = "https://files.pythonhosted.org/packages/7a/f2/cae3bf4174e7d2b8f0c9fe76a341bed8dc48e30069683854ca536fbed5bd/chonkie_core-0.9.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb5ba84745a7daa32749fe8cbdf691428c2bd4cef14e6555db4ce382b2edef05", size = 385232, upload-time = "2026-01-21T09:09:21.088Z" },
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{ url = "https://files.pythonhosted.org/packages/fe/1b/18323d5a7fa3638e9c0aaf00cb1fb1b678546466debb3ad57a6adea9d686/chonkie_core-0.9.2-cp312-cp312-win_amd64.whl", hash = "sha256:ee5093fd6a3f78163445bab5907cc6fd883ccea0514f8866abead0f059683d45", size = 222786, upload-time = "2026-01-21T09:09:21.919Z" },
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{ url = "https://files.pythonhosted.org/packages/28/ee/f45c8cb237e5a55eac366c9ac7a4a831329f6cf6f33401609063c1ed660d/chonkie_core-0.9.2-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:6a658cd4fc5cb7c12bc6587246eb545f84d6aa25b86001a92fdcb191cea632c8", size = 347713, upload-time = "2026-01-21T09:09:22.809Z" },
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{ url = "https://files.pythonhosted.org/packages/f9/31/0049eb4366cef2171404166e8ff1f39ffe350d7d8921247d262dbb3d4d6c/chonkie_core-0.9.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:ebf47e86be6603cbb940529aed6880655ac7f0bac232952565160fdbea5283d0", size = 333290, upload-time = "2026-01-21T09:09:23.786Z" },
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{ url = "https://files.pythonhosted.org/packages/03/d9/3a082faa359e3b24826547bdc725dc9af92b4180b262d3ca6872724cbfbb/chonkie_core-0.9.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:64645ff2c299b953b4a1ff951d1492b4a2b461c624b20604ced5612a8622b030", size = 364600, upload-time = "2026-01-21T09:09:24.64Z" },
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{ url = "https://files.pythonhosted.org/packages/04/0b/b89aa90c4f44ce4d82effc064031016bb791979cfd6147c155548e706ef7/chonkie_core-0.9.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:418748eeea1e09c539fd1d7f7f91c1611765c9d82a523e4c4ae0ed9e30a16b2d", size = 384806, upload-time = "2026-01-21T09:09:25.566Z" },
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{ url = "https://files.pythonhosted.org/packages/3d/d9/a7f8577b5550a4323aa9eda16336669b8ad6e8a5ea0c176c9baa25738436/chonkie_core-0.9.2-cp313-cp313-win_amd64.whl", hash = "sha256:f3718af3037480023423125e3b4a490c8f4cbf6de38d652169a97dd8ba391953", size = 222393, upload-time = "2026-01-21T09:09:26.897Z" },
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{ url = "https://files.pythonhosted.org/packages/72/73/cf6a32cfa9238f19a1d539a1d8371b7d90e21e42458a43fbc949c6476871/chonkie_core-0.9.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:66dc990ac58471fbd12845402726ca69b510602abab7c1c3e52cf8e21f9552e3", size = 363201, upload-time = "2026-01-21T09:09:27.764Z" },
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{ url = "https://files.pythonhosted.org/packages/1f/1f/56029d9a557e983cf71d22365b4229c4cfaf09401faa6cbc7e912cef2213/chonkie_core-0.9.2-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:325c0c853268fbdc37f4a65c3cde68fa56e3a25d164eee9512ceb41edc819902", size = 347292, upload-time = "2026-01-21T09:09:28.676Z" },
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{ url = "https://files.pythonhosted.org/packages/21/5b/08b8230d9264007cc7920cf1b1576f2ee1a1ef20d3cd5f8adb5e043e0908/chonkie_core-0.9.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:be9ea3bee05564a721f5c6c8699e1ad5996cf353b2faea2217a08ddee29e2de7", size = 332693, upload-time = "2026-01-21T09:09:29.541Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/eb/72/255c918da43a96c90b2bb96f1951a6ea1c513c18b36caecb6e9192275b83/chonkie_core-0.9.2-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:926db6c26e78b2d84dfb8422073a3f0f20478160ab48204f306fa35f3e1e95d7", size = 364578, upload-time = "2026-01-21T09:09:30.673Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/60/80/1710844b9706cd44324446eb368e813ebb4a085e96f469f54b61ddff67dc/chonkie_core-0.9.2-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c861f147e5932e659dcbe770ca0779a470acc62e9242bff87f36d03eae29644d", size = 384173, upload-time = "2026-01-21T09:09:32.015Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/5c/5c/a31b259ea94620ea4e4100ed4cc952ad770f2c7af36293bbc9154efb5c9e/chonkie_core-0.9.2-cp314-cp314-win_amd64.whl", hash = "sha256:83473c708a23652d6dc70142b2e586f965af3031b1d2a5c6336f1fc78614b452", size = 222275, upload-time = "2026-01-21T09:09:34.148Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/48/8a/c15c88f59bc9cf6f7ac994689d048fd60fcb72247f6b67ca31dc4eadf2f8/chonkie_core-0.9.2-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d6c0b89ee5e21d255bcf7d11f17b07ef811a71292717f50889244474cfab8bc", size = 363813, upload-time = "2026-01-21T09:09:35.175Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/1b/c3/844844bbadbfb6727d0f9b286fcb1398fbf2984c1348e8a8238dee335113/chonkie_core-0.9.2-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c6cf9a691cbbf32ef00c22eec66dd9f59b92067f6153c62b766ce24948a2ffd", size = 390277, upload-time = "2026-01-21T09:09:44.364Z" },
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.3.1"
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|
|
@ -2434,6 +2494,25 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/9b/f7/4a5e785ec9fbd65146a27b6b70b6cdc161a66f2024e4b04ac06a67f5578b/mistune-3.2.0-py3-none-any.whl", hash = "sha256:febdc629a3c78616b94393c6580551e0e34cc289987ec6c35ed3f4be42d0eee1", size = 53598, upload-time = "2025-12-23T11:36:33.211Z" },
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]
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|
||||
[[package]]
|
||||
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|
||||
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||||
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||||
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||||
{ name = "jinja2" },
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{ name = "setuptools" },
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{ name = "tokenizers" },
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sdist = { url = "https://files.pythonhosted.org/packages/2a/73/03badf0a639cdad59db887928f17a187f4240f021f2d3656ef39795058b7/model2vec-0.7.0.tar.gz", hash = "sha256:bf5d0420615e356dd8046794a057bb4f13c50253c44f0d0d1f4441bb489a6ed3", size = 2785837, upload-time = "2025-10-05T06:28:02.482Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/bd/3d/473b1d960a2e27b4115c7599b5d47adf3a487c44798468375d408b5fb825/model2vec-0.7.0-py3-none-any.whl", hash = "sha256:f7a6ebf1b9ca384ba1158c4ecc9a2d450407f63526c0a26e268711071e280c27", size = 53038, upload-time = "2025-10-05T06:28:00.533Z" },
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]
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[[package]]
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name = "mpmath"
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@ -3159,6 +3238,18 @@ wheels = [
|
|||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
|
|
@ -3809,6 +3900,15 @@ wheels = [
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|||
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||||
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|
||||
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||||
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|
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||||
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|
@ -4631,6 +4731,23 @@ wheels = [
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wheels = [
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[[package]]
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|
@ -4787,6 +4904,15 @@ wheels = [
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[[package]]
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[[package]]
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@ -5257,6 +5383,37 @@ wheels = [
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{ name = "idna" },
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{ name = "outcome" },
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wheels = [
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[[package]]
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wheels = [
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[[package]]
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[package.optional-dependencies]
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[[package]]
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[[package]]
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version = "3.6.0"
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|
|
|
|||
Loading…
Reference in New Issue