Custom models

Run with custom model

You can instantiate Parsera with any chat model supported by LangChain, for example, to run the model from Azure:

import os
from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.getenv("AZURE_GPT_BASE_URL"),
    openai_api_version="2023-05-15",
    deployment_name=os.getenv("AZURE_GPT_DEPLOYMENT_NAME"),
    openai_api_key=os.getenv("AZURE_GPT_API_KEY"),
    openai_api_type="azure",
    temperature=0.0,
)

url = "https://news.ycombinator.com/"
elements = {
    "Title": "News title",
    "Points": "Number of points",
    "Comments": "Number of comments",
}
scrapper = Parsera(model=llm)
result = scrapper.run(url=url, elements=elements)

Run local model with Trasformers

Currently, we only support models that include a system token

You should install Transformers with either pytorch (recommended) or TensorFlow 2.0

Transformers Installation Guide

Example:

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from parsera.engine.model import HuggingFaceModel
from parsera import Parsera

# Define the URL and elements to scrape
url = "https://news.ycombinator.com/"
elements = {
"Title": "News title",
"Points": "Number of points",
"Comments": "Number of comments",
}

# Initialize model with transformers pipeline
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-128k-instruct", trust_remote_code=True)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=5000)

# Initialize HuggingFaceModel
llm = HuggingFaceModel(pipeline=pipe)

# Scrapper with HuggingFace model
scrapper = Parsera(model=llm)
result = scrapper.run(url=url, elements=elements)