davidmezzetti

import pandas as pd

from txtai import Embeddings

# Load Embeddings index
embeddings = Embeddings()
embeddings.load(provider="huggingface-hub", container="neuml/txtai-arxiv")

# Results as a DataFrame
pd.DataFrame(embeddings.search("Intelligent life beyond our planet", 5))

davidmezzetti

Original URL: https://dev.to/neuml/how-rag-with-txtai-works-4lkh

from txtai.pipeline import Textractor

textractor = Textractor(headers={"user-agent": "Mozilla/5.0"})
textractor("https://dev.to/neuml/how-rag-with-txtai-works-4lkh")

How RAG with txtai works - DEV Community

davidmezzetti

# CAUTION: PyMuPDF is AGPL-3
# pip install pymupdf4llm
import pymupdf4llm

from glob import glob

from txtai import Embeddings
from txtai.pipeline import Textractor

def stream(dir):

davidmezzetti

from txtai.pipeline import Textractor

textractor = Textractor(sections=True)

# Install [pipeline-data] extra to support extracting text from docx/pdf/xlsx
for section in textractor("https://github.com/neuml/txtai"):
    print(f"\n[SECTION]\n{section}")

davidmezzetti

import time

from datetime import timedelta

from datasets import load_dataset
from txtai import LLM
from txtai.pipeline import Labels, HFTrainer

def prompt(text):
    text = f"""

davidmezzetti

from txtai.pipeline import Translation

# Load pipeline
translate = Translation()

# Run translations
languages = ["fr", "es", "de", "hi", "ja"]
for language in languages:
  text = translate("The sky is blue, the stars are far", language)
  english = translate(text, "en")

davidmezzetti

# Install txtai with torch cpu
pip install txtai torch==2.3.1+cpu \
 -f https://download.pytorch.org/whl/torch_stable.html

# Install llama.cpp
CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python

davidmezzetti

import time

from txtai.pipeline import LLM, Summary, Textractor
from txtai.workflow import Task, Workflow

# Extract text from HTML, ignore boilerplate text
textractor = Textractor(lines=True, join=True, minlength=100)
text = textractor("https://github.com/neuml/txtai")

# Summarization with standard models

davidmezzetti

##################################
# Data functions
##################################

import re

from datasets import load_dataset

def clean(text):
    text = text.replace("\n", " ").strip()

davidmezzetti

from transformers import AutoTokenizer
from txtai.pipeline import Tokenizer

# Split using built-in Python method
print("Create embeddings for text".split())
print("🚀Create embeddings for text⭐".split())
print("为文本创建嵌入".split())

# Remove stop words
tokenizer = Tokenizer(stopwords=True)