0

Updated docs

This commit is contained in:
rafaelsideguide 2024-05-09 10:36:56 -03:00
parent d6b4904ef1
commit f4d8b2c89a
5 changed files with 232 additions and 14 deletions

View File

@ -1,7 +1,13 @@
import FirecrawlApp from '@mendable/firecrawl-js';
import { z } from "zod";
const app = new FirecrawlApp({apiKey: "YOUR_API_KEY"});
const app = new FirecrawlApp({apiKey: "fc-YOUR_API_KEY"});
// Scrape a website:
const scrapeResult = await app.scrapeUrl('firecrawl.dev');
console.log(scrapeResult.data.content)
// Crawl a website:
const crawlResult = await app.crawlUrl('mendable.ai', {crawlerOptions: {excludes: ['blog/*'], limit: 5}}, false);
console.log(crawlResult)
@ -17,4 +23,61 @@ while (true) {
await new Promise(resolve => setTimeout(resolve, 1000)); // wait 1 second
}
console.log(job.data[0].content);
console.log(job.data[0].content);
// Search for a query:
const query = 'what is mendable?'
const searchResult = await app.search(query)
console.log(searchResult)
// LLM Extraction:
// Define schema to extract contents into using zod schema
const zodSchema = z.object({
top: z
.array(
z.object({
title: z.string(),
points: z.number(),
by: z.string(),
commentsURL: z.string(),
})
)
.length(5)
.describe("Top 5 stories on Hacker News"),
});
let llmExtractionResult = await app.scrapeUrl("https://news.ycombinator.com", {
extractorOptions: { extractionSchema: zodSchema },
});
console.log(llmExtractionResult.data.llm_extraction);
// Define schema to extract contents into using json schema
const jsonSchema = {
"type": "object",
"properties": {
"top": {
"type": "array",
"items": {
"type": "object",
"properties": {
"title": {"type": "string"},
"points": {"type": "number"},
"by": {"type": "string"},
"commentsURL": {"type": "string"}
},
"required": ["title", "points", "by", "commentsURL"]
},
"minItems": 5,
"maxItems": 5,
"description": "Top 5 stories on Hacker News"
}
},
"required": ["top"]
}
llmExtractionResult = await app.scrapeUrl("https://news.ycombinator.com", {
extractorOptions: { extractionSchema: jsonSchema },
});
console.log(llmExtractionResult.data.llm_extraction);

83
apps/js-sdk/example.ts Normal file
View File

@ -0,0 +1,83 @@
import FirecrawlApp, { JobStatusResponse } from '@mendable/firecrawl-js';
import { z } from "zod";
const app = new FirecrawlApp({apiKey: "fc-YOUR_API_KEY"});
// Scrape a website:
const scrapeResult = await app.scrapeUrl('firecrawl.dev');
console.log(scrapeResult.data.content)
// Crawl a website:
const crawlResult = await app.crawlUrl('mendable.ai', {crawlerOptions: {excludes: ['blog/*'], limit: 5}}, false);
console.log(crawlResult)
const jobId: string = await crawlResult['jobId'];
console.log(jobId);
let job: JobStatusResponse;
while (true) {
job = await app.checkCrawlStatus(jobId);
if (job.status === 'completed') {
break;
}
await new Promise(resolve => setTimeout(resolve, 1000)); // wait 1 second
}
console.log(job.data[0].content);
// Search for a query:
const query = 'what is mendable?'
const searchResult = await app.search(query)
console.log(searchResult)
// LLM Extraction:
// Define schema to extract contents into using zod schema
const zodSchema = z.object({
top: z
.array(
z.object({
title: z.string(),
points: z.number(),
by: z.string(),
commentsURL: z.string(),
})
)
.length(5)
.describe("Top 5 stories on Hacker News"),
});
let llmExtractionResult = await app.scrapeUrl("https://news.ycombinator.com", {
extractorOptions: { extractionSchema: zodSchema },
});
console.log(llmExtractionResult.data.llm_extraction);
// Define schema to extract contents into using json schema
const jsonSchema = {
"type": "object",
"properties": {
"top": {
"type": "array",
"items": {
"type": "object",
"properties": {
"title": {"type": "string"},
"points": {"type": "number"},
"by": {"type": "string"},
"commentsURL": {"type": "string"}
},
"required": ["title", "points", "by", "commentsURL"]
},
"minItems": 5,
"maxItems": 5,
"description": "Top 5 stories on Hacker News"
}
},
"required": ["top"]
}
llmExtractionResult = await app.scrapeUrl("https://news.ycombinator.com", {
extractorOptions: { extractionSchema: jsonSchema },
});
console.log(llmExtractionResult.data.llm_extraction);

View File

@ -77,6 +77,42 @@ To scrape a single URL with error handling, use the `scrapeUrl` method. It takes
scrapeExample();
```
### Extracting structured data from a URL
With LLM extraction, you can easily extract structured data from any URL. We support zod schemas to make it easier for you too. Here is how you to use it:
```js
import { z } from "zod";
const zodSchema = z.object({
top: z
.array(
z.object({
title: z.string(),
points: z.number(),
by: z.string(),
commentsURL: z.string(),
})
)
.length(5)
.describe("Top 5 stories on Hacker News"),
});
let llmExtractionResult = await app.scrapeUrl("https://news.ycombinator.com", {
extractorOptions: { extractionSchema: zodSchema },
});
console.log(llmExtractionResult.data.llm_extraction);
```
### Search for a query
Used to search the web, get the most relevant results, scrap each page and return the markdown.
```js
query = 'what is mendable?'
searchResult = app.search(query)
```
### Crawling a Website

View File

@ -1,6 +1,6 @@
{
"name": "@mendable/firecrawl-js",
"version": "0.0.19",
"version": "0.0.20",
"description": "JavaScript SDK for Firecrawl API",
"main": "build/index.js",
"types": "types/index.d.ts",

View File

@ -1,20 +1,19 @@
from firecrawl import FirecrawlApp
app = FirecrawlApp(api_key="fc-YOUR_API_KEY")
# Scrape a website:
scrape_result = app.scrape_url('firecrawl.dev')
print(scrape_result['markdown'])
# Crawl a website:
crawl_result = app.crawl_url('mendable.ai', {'crawlerOptions': {'excludes': ['blog/*']}})
print(crawl_result)
print(crawl_result[0]['markdown'])
job_id = crawl_result['jobId']
print(job_id)
status = app.check_crawl_status(job_id)
print(status)
# LLM Extraction:
# Define schema to extract contents into using pydantic
from pydantic import BaseModel, Field
from typing import List, Optional
from typing import List
class ArticleSchema(BaseModel):
title: str
@ -25,7 +24,7 @@ class ArticleSchema(BaseModel):
class TopArticlesSchema(BaseModel):
top: List[ArticleSchema] = Field(..., max_items=5, description="Top 5 stories")
a = app.scrape_url('https://news.ycombinator.com', {
llm_extraction_result = app.scrape_url('https://news.ycombinator.com', {
'extractorOptions': {
'extractionSchema': TopArticlesSchema.model_json_schema(),
'mode': 'llm-extraction'
@ -35,3 +34,40 @@ a = app.scrape_url('https://news.ycombinator.com', {
}
})
print(llm_extraction_result['llm_extraction'])
# Define schema to extract contents into using json schema
json_schema = {
"type": "object",
"properties": {
"top": {
"type": "array",
"items": {
"type": "object",
"properties": {
"title": {"type": "string"},
"points": {"type": "number"},
"by": {"type": "string"},
"commentsURL": {"type": "string"}
},
"required": ["title", "points", "by", "commentsURL"]
},
"minItems": 5,
"maxItems": 5,
"description": "Top 5 stories on Hacker News"
}
},
"required": ["top"]
}
llm_extraction_result = app.scrape_url('https://news.ycombinator.com', {
'extractorOptions': {
'extractionSchema': json_schema,
'mode': 'llm-extraction'
},
'pageOptions':{
'onlyMainContent': True
}
})
print(llm_extraction_result['llm_extraction'])