127 lines
3.2 KiB
Python
127 lines
3.2 KiB
Python
import time
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import nest_asyncio
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import uuid
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from firecrawl.firecrawl import FirecrawlApp
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app = FirecrawlApp(api_key="fc-")
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# Scrape a website:
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scrape_result = app.scrape_url('firecrawl.dev')
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print(scrape_result['markdown'])
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# Crawl a website:
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idempotency_key = str(uuid.uuid4()) # optional idempotency key
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crawl_result = app.crawl_url('firecrawl.dev', {'excludePaths': ['blog/*']}, 2, idempotency_key)
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print(crawl_result)
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# Asynchronous Crawl a website:
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async_result = app.async_crawl_url('firecrawl.dev', {'excludePaths': ['blog/*']}, "")
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print(async_result)
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crawl_status = app.check_crawl_status(async_result['id'])
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print(crawl_status)
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attempts = 15
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while attempts > 0 and crawl_status['status'] != 'completed':
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print(crawl_status)
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crawl_status = app.check_crawl_status(async_result['id'])
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attempts -= 1
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time.sleep(1)
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crawl_status = app.get_crawl_status(async_result['id'])
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print(crawl_status)
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# LLM Extraction:
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# Define schema to extract contents into using pydantic
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from pydantic import BaseModel, Field
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from typing import List
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class ArticleSchema(BaseModel):
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title: str
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points: int
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by: str
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commentsURL: str
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class TopArticlesSchema(BaseModel):
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top: List[ArticleSchema] = Field(..., max_items=5, description="Top 5 stories")
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llm_extraction_result = app.scrape_url('https://news.ycombinator.com', {
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'formats': ['extract'],
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'extract': {
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'schema': TopArticlesSchema.model_json_schema()
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}
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})
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print(llm_extraction_result['extract'])
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# # Define schema to extract contents into using json schema
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json_schema = {
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"type": "object",
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"properties": {
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"top": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"points": {"type": "number"},
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"by": {"type": "string"},
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"commentsURL": {"type": "string"}
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},
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"required": ["title", "points", "by", "commentsURL"]
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},
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"minItems": 5,
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"maxItems": 5,
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"description": "Top 5 stories on Hacker News"
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}
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},
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"required": ["top"]
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}
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app2 = FirecrawlApp(api_key="fc-", version="v0")
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llm_extraction_result = app2.scrape_url('https://news.ycombinator.com', {
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'extractorOptions': {
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'extractionSchema': json_schema,
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'mode': 'llm-extraction'
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},
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'pageOptions':{
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'onlyMainContent': True
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}
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})
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# print(llm_extraction_result['llm_extraction'])
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# Map a website:
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map_result = app.map_url('https://firecrawl.dev', { 'search': 'blog' })
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print(map_result)
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# Crawl a website with WebSockets:
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# inside an async function...
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nest_asyncio.apply()
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# Define event handlers
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def on_document(detail):
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print("DOC", detail)
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def on_error(detail):
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print("ERR", detail['error'])
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def on_done(detail):
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print("DONE", detail['status'])
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# Function to start the crawl and watch process
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async def start_crawl_and_watch():
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# Initiate the crawl job and get the watcher
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watcher = app.crawl_url_and_watch('firecrawl.dev', { 'excludePaths': ['blog/*'], 'limit': 5 })
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# Add event listeners
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watcher.add_event_listener("document", on_document)
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watcher.add_event_listener("error", on_error)
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watcher.add_event_listener("done", on_done)
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# Start the watcher
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await watcher.connect()
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