Merge branch 'next' of https://github.com/unclecode/crawl4ai into next
This commit is contained in:
UncleCode
@@ -142,6 +142,9 @@ docker pull unclecode/crawl4ai:gpu # GPU-enabled version
|
||||
# Run the container
|
||||
docker run -p 11235:11235 unclecode/crawl4ai:basic # Replace 'basic' with your chosen version
|
||||
|
||||
# In case you want to set platform to arm64
|
||||
docker run --platform linux/arm64 -p 11235:11235 unclecode/crawl4ai:basic
|
||||
|
||||
# In case to allocate more shared memory for the container
|
||||
docker run --shm-size=2gb -p 11235:11235 unclecode/crawl4ai:basic
|
||||
```
|
||||
@@ -158,6 +161,12 @@ docker build -t crawl4ai:local \
|
||||
--build-arg INSTALL_TYPE=basic \ # Options: basic, all
|
||||
.
|
||||
|
||||
# In case you want to set platform to arm64
|
||||
docker build -t crawl4ai:local \
|
||||
--build-arg INSTALL_TYPE=basic \ # Options: basic, all
|
||||
--platform linux/arm64 \
|
||||
.
|
||||
|
||||
# Run your local build
|
||||
docker run -p 11235:11235 crawl4ai:local
|
||||
```
|
||||
|
||||
@@ -1,2 +1,2 @@
|
||||
# crawl4ai/_version.py
|
||||
__version__ = "0.3.743"
|
||||
__version__ = "0.3.743"
|
||||
|
||||
@@ -1,19 +0,0 @@
|
||||
# Railway Deployment
|
||||
|
||||
## Quick Deploy
|
||||
[](https://railway.app/template/crawl4ai)
|
||||
|
||||
## Manual Setup
|
||||
1. Fork this repository
|
||||
2. Create a new Railway project
|
||||
3. Configure environment variables:
|
||||
- `INSTALL_TYPE`: basic or all
|
||||
- `ENABLE_GPU`: true/false
|
||||
4. Deploy!
|
||||
|
||||
## Configuration
|
||||
See `railway.toml` for:
|
||||
- Memory limits
|
||||
- Health checks
|
||||
- Restart policies
|
||||
- Scaling options
|
||||
@@ -1,33 +0,0 @@
|
||||
{
|
||||
"name": "Crawl4AI",
|
||||
"description": "LLM Friendly Web Crawler & Scraper",
|
||||
"render": {
|
||||
"dockerfile": {
|
||||
"path": "Dockerfile"
|
||||
}
|
||||
},
|
||||
"env": [
|
||||
{
|
||||
"key": "INSTALL_TYPE",
|
||||
"description": "Installation type (basic/all)",
|
||||
"default": "basic",
|
||||
"required": true
|
||||
},
|
||||
{
|
||||
"key": "ENABLE_GPU",
|
||||
"description": "Enable GPU support",
|
||||
"default": "false",
|
||||
"required": false
|
||||
}
|
||||
],
|
||||
"services": [
|
||||
{
|
||||
"name": "web",
|
||||
"dockerfile": "./Dockerfile",
|
||||
"healthcheck": {
|
||||
"path": "/health",
|
||||
"port": 11235
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,18 +0,0 @@
|
||||
# railway.toml
|
||||
[build]
|
||||
builder = "DOCKERFILE"
|
||||
dockerfilePath = "Dockerfile"
|
||||
|
||||
[deploy]
|
||||
startCommand = "uvicorn main:app --host 0.0.0.0 --port $PORT"
|
||||
healthcheckPath = "/health"
|
||||
restartPolicyType = "ON_FAILURE"
|
||||
restartPolicyMaxRetries = 3
|
||||
|
||||
[deploy.memory]
|
||||
soft = 2048 # 2GB min for Playwright
|
||||
hard = 4096 # 4GB max
|
||||
|
||||
[deploy.scaling]
|
||||
min = 1
|
||||
max = 1
|
||||
@@ -1,27 +0,0 @@
|
||||
services:
|
||||
crawl4ai:
|
||||
image: unclecode/crawl4ai:basic # Pull image from Docker Hub
|
||||
ports:
|
||||
- "11235:11235" # FastAPI server
|
||||
- "8000:8000" # Alternative port
|
||||
- "9222:9222" # Browser debugging
|
||||
- "8080:8080" # Additional port
|
||||
environment:
|
||||
- CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-} # Optional API token
|
||||
- OPENAI_API_KEY=${OPENAI_API_KEY:-} # Optional OpenAI API key
|
||||
- CLAUDE_API_KEY=${CLAUDE_API_KEY:-} # Optional Claude API key
|
||||
volumes:
|
||||
- /dev/shm:/dev/shm # Shared memory for browser operations
|
||||
deploy:
|
||||
resources:
|
||||
limits:
|
||||
memory: 4G
|
||||
reservations:
|
||||
memory: 1G
|
||||
restart: unless-stopped
|
||||
healthcheck:
|
||||
test: ["CMD", "curl", "-f", "http://localhost:11235/health"]
|
||||
interval: 30s
|
||||
timeout: 10s
|
||||
retries: 3
|
||||
start_period: 40s
|
||||
@@ -1,33 +0,0 @@
|
||||
services:
|
||||
crawl4ai:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
args:
|
||||
PYTHON_VERSION: 3.10
|
||||
INSTALL_TYPE: all
|
||||
ENABLE_GPU: false
|
||||
ports:
|
||||
- "11235:11235" # FastAPI server
|
||||
- "8000:8000" # Alternative port
|
||||
- "9222:9222" # Browser debugging
|
||||
- "8080:8080" # Additional port
|
||||
environment:
|
||||
- CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-} # Optional API token
|
||||
- OPENAI_API_KEY=${OPENAI_API_KEY:-} # Optional OpenAI API key
|
||||
- CLAUDE_API_KEY=${CLAUDE_API_KEY:-} # Optional Claude API key
|
||||
volumes:
|
||||
- /dev/shm:/dev/shm # Shared memory for browser operations
|
||||
deploy:
|
||||
resources:
|
||||
limits:
|
||||
memory: 4G
|
||||
reservations:
|
||||
memory: 1G
|
||||
restart: unless-stopped
|
||||
healthcheck:
|
||||
test: ["CMD", "curl", "-f", "http://localhost:11235/health"]
|
||||
interval: 30s
|
||||
timeout: 10s
|
||||
retries: 3
|
||||
start_period: 40s
|
||||
@@ -4,8 +4,8 @@ services:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
args:
|
||||
PYTHON_VERSION: 3.10
|
||||
INSTALL_TYPE: all
|
||||
PYTHON_VERSION: "3.10"
|
||||
INSTALL_TYPE: ${INSTALL_TYPE:-basic}
|
||||
ENABLE_GPU: false
|
||||
profiles: ["local"]
|
||||
ports:
|
||||
|
||||
131
pages/app.css
131
pages/app.css
@@ -1,131 +0,0 @@
|
||||
:root {
|
||||
--ifm-font-size-base: 100%;
|
||||
--ifm-line-height-base: 1.65;
|
||||
--ifm-font-family-base: system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, Cantarell, Noto Sans, sans-serif,
|
||||
BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||||
"Segoe UI Symbol";
|
||||
}
|
||||
html {
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-webkit-text-size-adjust: 100%;
|
||||
text-size-adjust: 100%;
|
||||
font: var(--ifm-font-size-base) / var(--ifm-line-height-base) var(--ifm-font-family-base);
|
||||
}
|
||||
body {
|
||||
background-color: #1a202c;
|
||||
color: #fff;
|
||||
}
|
||||
.tab-content {
|
||||
max-height: 400px;
|
||||
overflow: auto;
|
||||
}
|
||||
pre {
|
||||
white-space: pre-wrap;
|
||||
font-size: 14px;
|
||||
}
|
||||
pre code {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
/* Custom styling for docs-item class and Markdown generated elements */
|
||||
.docs-item {
|
||||
background-color: #2d3748; /* bg-gray-800 */
|
||||
padding: 1rem; /* p-4 */
|
||||
border-radius: 0.375rem; /* rounded */
|
||||
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); /* shadow-md */
|
||||
margin-bottom: 1rem; /* space between items */
|
||||
line-height: 1.5; /* leading-normal */
|
||||
}
|
||||
|
||||
.docs-item h3,
|
||||
.docs-item h4 {
|
||||
color: #ffffff; /* text-white */
|
||||
font-size: 1.25rem; /* text-xl */
|
||||
font-weight: 700; /* font-bold */
|
||||
margin-bottom: 0.5rem; /* mb-2 */
|
||||
}
|
||||
.docs-item h4 {
|
||||
font-size: 1rem; /* text-xl */
|
||||
}
|
||||
|
||||
.docs-item p {
|
||||
color: #e2e8f0; /* text-gray-300 */
|
||||
margin-bottom: 0.5rem; /* mb-2 */
|
||||
}
|
||||
|
||||
.docs-item code {
|
||||
background-color: #1a202c; /* bg-gray-900 */
|
||||
color: #e2e8f0; /* text-gray-300 */
|
||||
padding: 0.25rem 0.5rem; /* px-2 py-1 */
|
||||
border-radius: 0.25rem; /* rounded */
|
||||
font-size: 0.875rem; /* text-sm */
|
||||
}
|
||||
|
||||
.docs-item pre {
|
||||
background-color: #1a202c; /* bg-gray-900 */
|
||||
color: #e2e8f0; /* text-gray-300 */
|
||||
padding: 0.5rem; /* p-2 */
|
||||
border-radius: 0.375rem; /* rounded */
|
||||
overflow: auto; /* overflow-auto */
|
||||
margin-bottom: 0.5rem; /* mb-2 */
|
||||
}
|
||||
|
||||
.docs-item div {
|
||||
color: #e2e8f0; /* text-gray-300 */
|
||||
font-size: 1rem; /* prose prose-sm */
|
||||
line-height: 1.25rem; /* line-height for readability */
|
||||
}
|
||||
|
||||
/* Adjustments to make prose class more suitable for dark mode */
|
||||
.prose {
|
||||
max-width: none; /* max-w-none */
|
||||
}
|
||||
|
||||
.prose p,
|
||||
.prose ul {
|
||||
margin-bottom: 1rem; /* mb-4 */
|
||||
}
|
||||
|
||||
.prose code {
|
||||
/* background-color: #4a5568; */ /* bg-gray-700 */
|
||||
color: #65a30d; /* text-white */
|
||||
padding: 0.25rem 0.5rem; /* px-1 py-0.5 */
|
||||
border-radius: 0.25rem; /* rounded */
|
||||
display: inline-block; /* inline-block */
|
||||
}
|
||||
|
||||
.prose pre {
|
||||
background-color: #1a202c; /* bg-gray-900 */
|
||||
color: #ffffff; /* text-white */
|
||||
padding: 0.5rem; /* p-2 */
|
||||
border-radius: 0.375rem; /* rounded */
|
||||
}
|
||||
|
||||
.prose h3 {
|
||||
color: #65a30d; /* text-white */
|
||||
font-size: 1.25rem; /* text-xl */
|
||||
font-weight: 700; /* font-bold */
|
||||
margin-bottom: 0.5rem; /* mb-2 */
|
||||
}
|
||||
|
||||
body {
|
||||
background-color: #1a1a1a;
|
||||
color: #b3ff00;
|
||||
}
|
||||
.sidebar {
|
||||
color: #b3ff00;
|
||||
border-right: 1px solid #333;
|
||||
}
|
||||
.sidebar a {
|
||||
color: #b3ff00;
|
||||
text-decoration: none;
|
||||
}
|
||||
.sidebar a:hover {
|
||||
background-color: #555;
|
||||
}
|
||||
.content-section {
|
||||
display: none;
|
||||
}
|
||||
.content-section.active {
|
||||
display: block;
|
||||
}
|
||||
356
pages/app.js
356
pages/app.js
@@ -1,356 +0,0 @@
|
||||
// JavaScript to manage dynamic form changes and logic
|
||||
document.getElementById("extraction-strategy-select").addEventListener("change", function () {
|
||||
const strategy = this.value;
|
||||
const providerModelSelect = document.getElementById("provider-model-select");
|
||||
const tokenInput = document.getElementById("token-input");
|
||||
const instruction = document.getElementById("instruction");
|
||||
const semantic_filter = document.getElementById("semantic_filter");
|
||||
const instruction_div = document.getElementById("instruction_div");
|
||||
const semantic_filter_div = document.getElementById("semantic_filter_div");
|
||||
const llm_settings = document.getElementById("llm_settings");
|
||||
|
||||
if (strategy === "LLMExtractionStrategy") {
|
||||
// providerModelSelect.disabled = false;
|
||||
// tokenInput.disabled = false;
|
||||
// semantic_filter.disabled = true;
|
||||
// instruction.disabled = false;
|
||||
llm_settings.classList.remove("hidden");
|
||||
instruction_div.classList.remove("hidden");
|
||||
semantic_filter_div.classList.add("hidden");
|
||||
} else if (strategy === "NoExtractionStrategy") {
|
||||
semantic_filter_div.classList.add("hidden");
|
||||
instruction_div.classList.add("hidden");
|
||||
llm_settings.classList.add("hidden");
|
||||
} else {
|
||||
// providerModelSelect.disabled = true;
|
||||
// tokenInput.disabled = true;
|
||||
// semantic_filter.disabled = false;
|
||||
// instruction.disabled = true;
|
||||
llm_settings.classList.add("hidden");
|
||||
instruction_div.classList.add("hidden");
|
||||
semantic_filter_div.classList.remove("hidden");
|
||||
}
|
||||
|
||||
|
||||
});
|
||||
|
||||
// Get the selected provider model and token from local storage
|
||||
const storedProviderModel = localStorage.getItem("provider_model");
|
||||
const storedToken = localStorage.getItem(storedProviderModel);
|
||||
|
||||
if (storedProviderModel) {
|
||||
document.getElementById("provider-model-select").value = storedProviderModel;
|
||||
}
|
||||
|
||||
if (storedToken) {
|
||||
document.getElementById("token-input").value = storedToken;
|
||||
}
|
||||
|
||||
// Handle provider model dropdown change
|
||||
document.getElementById("provider-model-select").addEventListener("change", () => {
|
||||
const selectedProviderModel = document.getElementById("provider-model-select").value;
|
||||
const storedToken = localStorage.getItem(selectedProviderModel);
|
||||
|
||||
if (storedToken) {
|
||||
document.getElementById("token-input").value = storedToken;
|
||||
} else {
|
||||
document.getElementById("token-input").value = "";
|
||||
}
|
||||
});
|
||||
|
||||
// Fetch total count from the database
|
||||
axios
|
||||
.get("/total-count")
|
||||
.then((response) => {
|
||||
document.getElementById("total-count").textContent = response.data.count;
|
||||
})
|
||||
.catch((error) => console.error(error));
|
||||
|
||||
// Handle crawl button click
|
||||
document.getElementById("crawl-btn").addEventListener("click", () => {
|
||||
// validate input to have both URL and API token
|
||||
// if selected extraction strategy is LLMExtractionStrategy, then API token is required
|
||||
if (document.getElementById("extraction-strategy-select").value === "LLMExtractionStrategy") {
|
||||
if (!document.getElementById("url-input").value || !document.getElementById("token-input").value) {
|
||||
alert("Please enter both URL(s) and API token.");
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
const selectedProviderModel = document.getElementById("provider-model-select").value;
|
||||
const apiToken = document.getElementById("token-input").value;
|
||||
const extractBlocks = document.getElementById("extract-blocks-checkbox").checked;
|
||||
const bypassCache = document.getElementById("bypass-cache-checkbox").checked;
|
||||
|
||||
// Save the selected provider model and token to local storage
|
||||
localStorage.setItem("provider_model", selectedProviderModel);
|
||||
localStorage.setItem(selectedProviderModel, apiToken);
|
||||
|
||||
const urlsInput = document.getElementById("url-input").value;
|
||||
const urls = urlsInput.split(",").map((url) => url.trim());
|
||||
const data = {
|
||||
urls: urls,
|
||||
include_raw_html: true,
|
||||
bypass_cache: bypassCache,
|
||||
extract_blocks: extractBlocks,
|
||||
word_count_threshold: parseInt(document.getElementById("threshold").value),
|
||||
extraction_strategy: document.getElementById("extraction-strategy-select").value,
|
||||
extraction_strategy_args: {
|
||||
provider: selectedProviderModel,
|
||||
api_token: apiToken,
|
||||
instruction: document.getElementById("instruction").value,
|
||||
semantic_filter: document.getElementById("semantic_filter").value,
|
||||
},
|
||||
chunking_strategy: document.getElementById("chunking-strategy-select").value,
|
||||
chunking_strategy_args: {},
|
||||
css_selector: document.getElementById("css-selector").value,
|
||||
screenshot: document.getElementById("screenshot-checkbox").checked,
|
||||
// instruction: document.getElementById("instruction").value,
|
||||
// semantic_filter: document.getElementById("semantic_filter").value,
|
||||
verbose: true,
|
||||
};
|
||||
|
||||
// import requests
|
||||
|
||||
// data = {
|
||||
// "urls": [
|
||||
// "https://www.nbcnews.com/business"
|
||||
// ],
|
||||
// "word_count_threshold": 10,
|
||||
// "extraction_strategy": "NoExtractionStrategy",
|
||||
// }
|
||||
|
||||
// response = requests.post("https://crawl4ai.com/crawl", json=data) # OR local host if your run locally
|
||||
// print(response.json())
|
||||
|
||||
// save api token to local storage
|
||||
localStorage.setItem("api_token", document.getElementById("token-input").value);
|
||||
|
||||
document.getElementById("loading").classList.remove("hidden");
|
||||
document.getElementById("result").style.visibility = "hidden";
|
||||
document.getElementById("code_help").style.visibility = "hidden";
|
||||
|
||||
axios
|
||||
.post("/crawl", data)
|
||||
.then((response) => {
|
||||
const result = response.data.results[0];
|
||||
const parsedJson = JSON.parse(result.extracted_content);
|
||||
document.getElementById("json-result").textContent = JSON.stringify(parsedJson, null, 2);
|
||||
document.getElementById("cleaned-html-result").textContent = result.cleaned_html;
|
||||
document.getElementById("markdown-result").textContent = result.markdown;
|
||||
document.getElementById("media-result").textContent = JSON.stringify( result.media, null, 2);
|
||||
if (result.screenshot){
|
||||
const imgElement = document.createElement("img");
|
||||
// Set the src attribute with the base64 data
|
||||
imgElement.src = `data:image/png;base64,${result.screenshot}`;
|
||||
document.getElementById("screenshot-result").innerHTML = "";
|
||||
document.getElementById("screenshot-result").appendChild(imgElement);
|
||||
}
|
||||
|
||||
// Update code examples dynamically
|
||||
const extractionStrategy = data.extraction_strategy;
|
||||
const isLLMExtraction = extractionStrategy === "LLMExtractionStrategy";
|
||||
|
||||
// REMOVE API TOKEN FROM CODE EXAMPLES
|
||||
data.extraction_strategy_args.api_token = "your_api_token";
|
||||
|
||||
if (data.extraction_strategy === "NoExtractionStrategy") {
|
||||
delete data.extraction_strategy_args;
|
||||
delete data.extrac_blocks;
|
||||
}
|
||||
|
||||
if (data.chunking_strategy === "RegexChunking") {
|
||||
delete data.chunking_strategy_args;
|
||||
}
|
||||
|
||||
delete data.verbose;
|
||||
|
||||
if (data.css_selector === "") {
|
||||
delete data.css_selector;
|
||||
}
|
||||
|
||||
if (!data.bypass_cache) {
|
||||
delete data.bypass_cache;
|
||||
}
|
||||
|
||||
if (!data.extract_blocks) {
|
||||
delete data.extract_blocks;
|
||||
}
|
||||
|
||||
if (!data.include_raw_html) {
|
||||
delete data.include_raw_html;
|
||||
}
|
||||
|
||||
document.getElementById(
|
||||
"curl-code"
|
||||
).textContent = `curl -X POST -H "Content-Type: application/json" -d '${JSON.stringify({
|
||||
...data,
|
||||
api_token: isLLMExtraction ? "your_api_token" : undefined,
|
||||
}, null, 2)}' https://crawl4ai.com/crawl`;
|
||||
|
||||
document.getElementById("python-code").textContent = `import requests\n\ndata = ${JSON.stringify(
|
||||
{ ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
|
||||
null,
|
||||
2
|
||||
)}\n\nresponse = requests.post("https://crawl4ai.com/crawl", json=data) # OR local host if your run locally \nprint(response.json())`;
|
||||
|
||||
document.getElementById(
|
||||
"nodejs-code"
|
||||
).textContent = `const axios = require('axios');\n\nconst data = ${JSON.stringify(
|
||||
{ ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
|
||||
null,
|
||||
2
|
||||
)};\n\naxios.post("https://crawl4ai.com/crawl", data) // OR local host if your run locally \n .then(response => console.log(response.data))\n .catch(error => console.error(error));`;
|
||||
|
||||
document.getElementById(
|
||||
"library-code"
|
||||
).textContent = `from crawl4ai.web_crawler import WebCrawler\nfrom crawl4ai.extraction_strategy import *\nfrom crawl4ai.chunking_strategy import *\n\ncrawler = WebCrawler()\ncrawler.warmup()\n\nresult = crawler.run(\n url='${
|
||||
urls[0]
|
||||
}',\n word_count_threshold=${data.word_count_threshold},\n extraction_strategy=${
|
||||
isLLMExtraction
|
||||
? `${extractionStrategy}(provider="${data.provider_model}", api_token="${data.api_token}")`
|
||||
: extractionStrategy + "()"
|
||||
},\n chunking_strategy=${data.chunking_strategy}(),\n bypass_cache=${
|
||||
data.bypass_cache
|
||||
},\n css_selector="${data.css_selector}"\n)\nprint(result)`;
|
||||
|
||||
// Highlight code syntax
|
||||
hljs.highlightAll();
|
||||
|
||||
// Select JSON tab by default
|
||||
document.querySelector('.tab-btn[data-tab="json"]').click();
|
||||
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
|
||||
document.getElementById("result").style.visibility = "visible";
|
||||
document.getElementById("code_help").style.visibility = "visible";
|
||||
|
||||
// increment the total count
|
||||
document.getElementById("total-count").textContent =
|
||||
parseInt(document.getElementById("total-count").textContent) + 1;
|
||||
})
|
||||
.catch((error) => {
|
||||
console.error(error);
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle tab clicks
|
||||
document.querySelectorAll(".tab-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const tab = btn.dataset.tab;
|
||||
document.querySelectorAll(".tab-btn").forEach((b) => b.classList.remove("bg-lime-700", "text-white"));
|
||||
btn.classList.add("bg-lime-700", "text-white");
|
||||
document.querySelectorAll(".tab-content.code pre").forEach((el) => el.classList.add("hidden"));
|
||||
document.getElementById(`${tab}-result`).parentElement.classList.remove("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle code tab clicks
|
||||
document.querySelectorAll(".code-tab-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const tab = btn.dataset.tab;
|
||||
document.querySelectorAll(".code-tab-btn").forEach((b) => b.classList.remove("bg-lime-700", "text-white"));
|
||||
btn.classList.add("bg-lime-700", "text-white");
|
||||
document.querySelectorAll(".tab-content.result pre").forEach((el) => el.classList.add("hidden"));
|
||||
document.getElementById(`${tab}-code`).parentElement.classList.remove("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle copy to clipboard button clicks
|
||||
|
||||
async function copyToClipboard(text) {
|
||||
if (navigator.clipboard && navigator.clipboard.writeText) {
|
||||
return navigator.clipboard.writeText(text);
|
||||
} else {
|
||||
return fallbackCopyTextToClipboard(text);
|
||||
}
|
||||
}
|
||||
|
||||
function fallbackCopyTextToClipboard(text) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const textArea = document.createElement("textarea");
|
||||
textArea.value = text;
|
||||
|
||||
// Avoid scrolling to bottom
|
||||
textArea.style.top = "0";
|
||||
textArea.style.left = "0";
|
||||
textArea.style.position = "fixed";
|
||||
|
||||
document.body.appendChild(textArea);
|
||||
textArea.focus();
|
||||
textArea.select();
|
||||
|
||||
try {
|
||||
const successful = document.execCommand("copy");
|
||||
if (successful) {
|
||||
resolve();
|
||||
} else {
|
||||
reject();
|
||||
}
|
||||
} catch (err) {
|
||||
reject(err);
|
||||
}
|
||||
|
||||
document.body.removeChild(textArea);
|
||||
});
|
||||
}
|
||||
|
||||
document.querySelectorAll(".copy-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const target = btn.dataset.target;
|
||||
const code = document.getElementById(target).textContent;
|
||||
//navigator.clipboard.writeText(code).then(() => {
|
||||
copyToClipboard(code).then(() => {
|
||||
btn.textContent = "Copied!";
|
||||
setTimeout(() => {
|
||||
btn.textContent = "Copy";
|
||||
}, 2000);
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
document.addEventListener("DOMContentLoaded", async () => {
|
||||
try {
|
||||
const extractionResponse = await fetch("/strategies/extraction");
|
||||
const extractionStrategies = await extractionResponse.json();
|
||||
|
||||
const chunkingResponse = await fetch("/strategies/chunking");
|
||||
const chunkingStrategies = await chunkingResponse.json();
|
||||
|
||||
renderStrategies("extraction-strategies", extractionStrategies);
|
||||
renderStrategies("chunking-strategies", chunkingStrategies);
|
||||
} catch (error) {
|
||||
console.error("Error fetching strategies:", error);
|
||||
}
|
||||
});
|
||||
|
||||
function renderStrategies(containerId, strategies) {
|
||||
const container = document.getElementById(containerId);
|
||||
container.innerHTML = ""; // Clear any existing content
|
||||
strategies = JSON.parse(strategies);
|
||||
Object.entries(strategies).forEach(([strategy, description]) => {
|
||||
const strategyElement = document.createElement("div");
|
||||
strategyElement.classList.add("bg-zinc-800", "p-4", "rounded", "shadow-md", "docs-item");
|
||||
|
||||
const strategyDescription = document.createElement("div");
|
||||
strategyDescription.classList.add("text-gray-300", "prose", "prose-sm");
|
||||
strategyDescription.innerHTML = marked.parse(description);
|
||||
|
||||
strategyElement.appendChild(strategyDescription);
|
||||
|
||||
container.appendChild(strategyElement);
|
||||
});
|
||||
}
|
||||
document.querySelectorAll(".sidebar a").forEach((link) => {
|
||||
link.addEventListener("click", function (event) {
|
||||
event.preventDefault();
|
||||
document.querySelectorAll(".content-section").forEach((section) => {
|
||||
section.classList.remove("active");
|
||||
});
|
||||
const target = event.target.getAttribute("data-target");
|
||||
document.getElementById(target).classList.add("active");
|
||||
});
|
||||
});
|
||||
// Highlight code syntax
|
||||
hljs.highlightAll();
|
||||
@@ -1,971 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Crawl4AI</title>
|
||||
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com" />
|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
|
||||
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@100..900&display=swap" rel="stylesheet" />
|
||||
|
||||
<!-- <link href="https://cdn.jsdelivr.net/npm/tailwindcss@3.4.3/dist/tailwind.min.css" rel="stylesheet" /> -->
|
||||
<script src="https://cdn.tailwindcss.com"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
|
||||
<link
|
||||
rel="stylesheet"
|
||||
href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/monokai.min.css"
|
||||
/>
|
||||
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
|
||||
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/highlight.min.js"></script>
|
||||
<style>
|
||||
:root {
|
||||
--ifm-font-size-base: 100%;
|
||||
--ifm-line-height-base: 1.65;
|
||||
--ifm-font-family-base: system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, Cantarell, Noto Sans,
|
||||
sans-serif, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji",
|
||||
"Segoe UI Emoji", "Segoe UI Symbol";
|
||||
}
|
||||
html {
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-webkit-text-size-adjust: 100%;
|
||||
text-size-adjust: 100%;
|
||||
font: var(--ifm-font-size-base) / var(--ifm-line-height-base) var(--ifm-font-family-base);
|
||||
}
|
||||
body {
|
||||
background-color: #1a202c;
|
||||
color: #fff;
|
||||
}
|
||||
.tab-content {
|
||||
max-height: 400px;
|
||||
overflow: auto;
|
||||
}
|
||||
pre {
|
||||
white-space: pre-wrap;
|
||||
font-size: 14px;
|
||||
}
|
||||
pre code {
|
||||
width: 100%;
|
||||
}
|
||||
</style>
|
||||
<style>
|
||||
/* Custom styling for docs-item class and Markdown generated elements */
|
||||
.docs-item {
|
||||
background-color: #2d3748; /* bg-gray-800 */
|
||||
padding: 1rem; /* p-4 */
|
||||
border-radius: 0.375rem; /* rounded */
|
||||
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); /* shadow-md */
|
||||
margin-bottom: 1rem; /* space between items */
|
||||
}
|
||||
|
||||
.docs-item h3,
|
||||
.docs-item h4 {
|
||||
color: #ffffff; /* text-white */
|
||||
font-size: 1.25rem; /* text-xl */
|
||||
font-weight: 700; /* font-bold */
|
||||
margin-bottom: 0.5rem; /* mb-2 */
|
||||
}
|
||||
|
||||
.docs-item p {
|
||||
color: #e2e8f0; /* text-gray-300 */
|
||||
margin-bottom: 0.5rem; /* mb-2 */
|
||||
}
|
||||
|
||||
.docs-item code {
|
||||
background-color: #1a202c; /* bg-gray-900 */
|
||||
color: #e2e8f0; /* text-gray-300 */
|
||||
padding: 0.25rem 0.5rem; /* px-2 py-1 */
|
||||
border-radius: 0.25rem; /* rounded */
|
||||
}
|
||||
|
||||
.docs-item pre {
|
||||
background-color: #1a202c; /* bg-gray-900 */
|
||||
color: #e2e8f0; /* text-gray-300 */
|
||||
padding: 0.5rem; /* p-2 */
|
||||
border-radius: 0.375rem; /* rounded */
|
||||
overflow: auto; /* overflow-auto */
|
||||
margin-bottom: 0.5rem; /* mb-2 */
|
||||
}
|
||||
|
||||
.docs-item div {
|
||||
color: #e2e8f0; /* text-gray-300 */
|
||||
font-size: 1rem; /* prose prose-sm */
|
||||
line-height: 1.25rem; /* line-height for readability */
|
||||
}
|
||||
|
||||
/* Adjustments to make prose class more suitable for dark mode */
|
||||
.prose {
|
||||
max-width: none; /* max-w-none */
|
||||
}
|
||||
|
||||
.prose p,
|
||||
.prose ul {
|
||||
margin-bottom: 1rem; /* mb-4 */
|
||||
}
|
||||
|
||||
.prose code {
|
||||
/* background-color: #4a5568; */ /* bg-gray-700 */
|
||||
color: #65a30d; /* text-white */
|
||||
padding: 0.25rem 0.5rem; /* px-1 py-0.5 */
|
||||
border-radius: 0.25rem; /* rounded */
|
||||
display: inline-block; /* inline-block */
|
||||
}
|
||||
|
||||
.prose pre {
|
||||
background-color: #1a202c; /* bg-gray-900 */
|
||||
color: #ffffff; /* text-white */
|
||||
padding: 0.5rem; /* p-2 */
|
||||
border-radius: 0.375rem; /* rounded */
|
||||
}
|
||||
|
||||
.prose h3 {
|
||||
color: #65a30d; /* text-white */
|
||||
font-size: 1.25rem; /* text-xl */
|
||||
font-weight: 700; /* font-bold */
|
||||
margin-bottom: 0.5rem; /* mb-2 */
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body class="bg-black text-gray-200">
|
||||
<header class="bg-zinc-950 text-white py-4 flex">
|
||||
<div class="mx-auto px-4">
|
||||
<h1 class="text-2xl font-bold">🔥🕷️ Crawl4AI: Web Data for your Thoughts</h1>
|
||||
</div>
|
||||
<div class="mx-auto px-4 flex font-bold text-xl gap-2">
|
||||
<span>📊 Total Website Processed</span>
|
||||
<span id="total-count" class="text-lime-400">2</span>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<section class="try-it py-8 px-16 pb-20">
|
||||
<div class="container mx-auto px-4">
|
||||
<h2 class="text-2xl font-bold mb-4">Try It Now</h2>
|
||||
<div class="grid grid-cols-1 lg:grid-cols-3 gap-4">
|
||||
<div class="space-y-4">
|
||||
<div class="flex flex-col">
|
||||
<label for="url-input" class="text-lime-500 font-bold text-xs">URL(s)</label>
|
||||
<input
|
||||
type="text"
|
||||
id="url-input"
|
||||
value="https://www.nbcnews.com/business"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
|
||||
placeholder="Enter URL(s) separated by commas"
|
||||
/>
|
||||
</div>
|
||||
<div class="flex flex-col">
|
||||
<label for="threshold" class="text-lime-500 font-bold text-xs">Min Words Threshold</label>
|
||||
<select
|
||||
id="threshold"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
|
||||
>
|
||||
<option value="5">5</option>
|
||||
<option value="10" selected>10</option>
|
||||
<option value="15">15</option>
|
||||
<option value="20">20</option>
|
||||
<option value="25">25</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="flex flex-col">
|
||||
<label for="css-selector" class="text-lime-500 font-bold text-xs">CSS Selector</label>
|
||||
<input
|
||||
type="text"
|
||||
id="css-selector"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
|
||||
placeholder="Enter CSS Selector"
|
||||
/>
|
||||
</div>
|
||||
<div class="flex flex-col">
|
||||
<label for="extraction-strategy-select" class="text-lime-500 font-bold text-xs"
|
||||
>Extraction Strategy</label
|
||||
>
|
||||
<select
|
||||
id="extraction-strategy-select"
|
||||
class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-lime-500"
|
||||
>
|
||||
<option value="CosineStrategy">CosineStrategy</option>
|
||||
<option value="LLMExtractionStrategy">LLMExtractionStrategy</option>
|
||||
<option value="NoExtractionStrategy">NoExtractionStrategy</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="flex flex-col">
|
||||
<label for="chunking-strategy-select" class="text-lime-500 font-bold text-xs"
|
||||
>Chunking Strategy</label
|
||||
>
|
||||
<select
|
||||
id="chunking-strategy-select"
|
||||
class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-lime-500"
|
||||
>
|
||||
<option value="RegexChunking">RegexChunking</option>
|
||||
<option value="NlpSentenceChunking">NlpSentenceChunking</option>
|
||||
<option value="TopicSegmentationChunking">TopicSegmentationChunking</option>
|
||||
<option value="FixedLengthWordChunking">FixedLengthWordChunking</option>
|
||||
<option value="SlidingWindowChunking">SlidingWindowChunking</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="flex flex-col">
|
||||
<label for="provider-model-select" class="text-lime-500 font-bold text-xs"
|
||||
>Provider Model</label
|
||||
>
|
||||
<select
|
||||
id="provider-model-select"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
|
||||
disabled
|
||||
>
|
||||
<option value="groq/llama3-70b-8192">groq/llama3-70b-8192</option>
|
||||
<option value="groq/llama3-8b-8192">groq/llama3-8b-8192</option>
|
||||
<option value="openai/gpt-4-turbo">gpt-4-turbo</option>
|
||||
<option value="openai/gpt-3.5-turbo">gpt-3.5-turbo</option>
|
||||
<option value="anthropic/claude-3-haiku-20240307">claude-3-haiku</option>
|
||||
<option value="anthropic/claude-3-opus-20240229">claude-3-opus</option>
|
||||
<option value="anthropic/claude-3-sonnet-20240229">claude-3-sonnet</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="flex flex-col">
|
||||
<label for="token-input" class="text-lime-500 font-bold text-xs">API Token</label>
|
||||
<input
|
||||
type="password"
|
||||
id="token-input"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-lime-500"
|
||||
placeholder="Enter Groq API token"
|
||||
disabled
|
||||
/>
|
||||
</div>
|
||||
<div class="flex gap-3">
|
||||
<div class="flex items-center gap-2">
|
||||
<input type="checkbox" id="bypass-cache-checkbox" />
|
||||
<label for="bypass-cache-checkbox" class="text-lime-500 font-bold">Bypass Cache</label>
|
||||
</div>
|
||||
<div class="flex items-center gap-2">
|
||||
<input type="checkbox" id="extract-blocks-checkbox" checked />
|
||||
<label for="extract-blocks-checkbox" class="text-lime-500 font-bold"
|
||||
>Extract Blocks</label
|
||||
>
|
||||
</div>
|
||||
<button id="crawl-btn" class="bg-lime-600 text-black font-bold px-4 py-0 rounded">
|
||||
Crawl
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="result" class=" ">
|
||||
<div id="loading" class="hidden">
|
||||
<p class="text-white">Loading... Please wait.</p>
|
||||
</div>
|
||||
<div class="tab-buttons flex gap-2">
|
||||
<button
|
||||
class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="json"
|
||||
>
|
||||
JSON
|
||||
</button>
|
||||
<button
|
||||
class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="cleaned-html"
|
||||
>
|
||||
Cleaned HTML
|
||||
</button>
|
||||
<button
|
||||
class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="markdown"
|
||||
>
|
||||
Markdown
|
||||
</button>
|
||||
</div>
|
||||
<div class="tab-content code bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
|
||||
<pre class="h-full flex"><code id="json-result" class="language-json"></code></pre>
|
||||
<pre
|
||||
class="hidden h-full flex"
|
||||
><code id="cleaned-html-result" class="language-html"></code></pre>
|
||||
<pre
|
||||
class="hidden h-full flex"
|
||||
><code id="markdown-result" class="language-markdown"></code></pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="code_help" class=" ">
|
||||
<div class="tab-buttons flex gap-2">
|
||||
<button
|
||||
class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="curl"
|
||||
>
|
||||
cURL
|
||||
</button>
|
||||
<button
|
||||
class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="library"
|
||||
>
|
||||
Python Library
|
||||
</button>
|
||||
<button
|
||||
class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="python"
|
||||
>
|
||||
Python (Request)
|
||||
</button>
|
||||
<button
|
||||
class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="nodejs"
|
||||
>
|
||||
Node.js
|
||||
</button>
|
||||
</div>
|
||||
<div class="tab-content result bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
|
||||
<pre class="h-full flex relative">
|
||||
<code id="curl-code" class="language-bash"></code>
|
||||
<button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="curl-code">Copy</button>
|
||||
</pre>
|
||||
<pre class="hidden h-full flex relative">
|
||||
<code id="python-code" class="language-python"></code>
|
||||
<button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="python-code">Copy</button>
|
||||
</pre>
|
||||
<pre class="hidden h-full flex relative">
|
||||
<code id="nodejs-code" class="language-javascript"></code>
|
||||
<button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="nodejs-code">Copy</button>
|
||||
</pre>
|
||||
<pre class="hidden h-full flex relative">
|
||||
<code id="library-code" class="language-python"></code>
|
||||
<button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="library-code">Copy</button>
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section class="bg-zinc-900 text-zinc-300 p-6 px-20">
|
||||
<div class="grid grid-cols-2 gap-4 p-4 bg-zinc-900 text-lime-500">
|
||||
<!-- Step 1 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
🌟 <strong>Welcome to the Crawl4ai Quickstart Guide! Let's dive into some web crawling fun!</strong>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">
|
||||
First Step: Create an instance of WebCrawler and call the <code>warmup()</code> function.
|
||||
</div>
|
||||
<div>
|
||||
<pre><code class="language-python">crawler = WebCrawler()
|
||||
crawler.warmup()</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 2 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
🧠 <strong>Understanding 'bypass_cache' and 'include_raw_html' parameters:</strong>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">First crawl (caches the result):</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business")</code></pre>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Second crawl (Force to crawl again):</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)</code></pre>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Crawl result without raw HTML content:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business", include_raw_html=False)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 3 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
📄
|
||||
<strong
|
||||
>The 'include_raw_html' parameter, when set to True, includes the raw HTML content in the
|
||||
response. By default, it is set to True.</strong
|
||||
>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Set <code>always_by_pass_cache</code> to True:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">crawler.always_by_pass_cache = True</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 4 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
🧩 <strong>Let's add a chunking strategy: RegexChunking!</strong>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Using RegexChunking:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
chunking_strategy=RegexChunking(patterns=["\n\n"])
|
||||
)</code></pre>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Using NlpSentenceChunking:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
chunking_strategy=NlpSentenceChunking()
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 5 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
🧠 <strong>Let's get smarter with an extraction strategy: CosineStrategy!</strong>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Using CosineStrategy:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
extraction_strategy=CosineStrategy(word_count_threshold=10, max_dist=0.2, linkage_method="ward", top_k=3)
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 6 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
🤖 <strong>Time to bring in the big guns: LLMExtractionStrategy without instructions!</strong>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Using LLMExtractionStrategy without instructions:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
extraction_strategy=LLMExtractionStrategy(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'))
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 7 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
📜 <strong>Let's make it even more interesting: LLMExtractionStrategy with instructions!</strong>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Using LLMExtractionStrategy with instructions:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
extraction_strategy=LLMExtractionStrategy(
|
||||
provider="openai/gpt-4o",
|
||||
api_token=os.getenv('OPENAI_API_KEY'),
|
||||
instruction="I am interested in only financial news"
|
||||
)
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 8 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
🎯 <strong>Targeted extraction: Let's use a CSS selector to extract only H2 tags!</strong>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Using CSS selector to extract H2 tags:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
css_selector="h2"
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 9 -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
🖱️ <strong>Let's get interactive: Passing JavaScript code to click 'Load More' button!</strong>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-2 rounded">Using JavaScript to click 'Load More' button:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">js_code = """
|
||||
const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
|
||||
loadMoreButton && loadMoreButton.click();
|
||||
"""
|
||||
crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
|
||||
crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
|
||||
result = crawler.run(url="https://www.nbcnews.com/business")</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Conclusion -->
|
||||
<div class="col-span-2 bg-yellow-500 p-2 rounded text-zinc-900">
|
||||
🎉
|
||||
<strong
|
||||
>Congratulations! You've made it through the Crawl4ai Quickstart Guide! Now go forth and crawl
|
||||
the web like a pro! 🕸️</strong
|
||||
>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section class="bg-zinc-900 text-zinc-300 p-6 px-20">
|
||||
<h1 class="text-3xl font-bold mb-4">Installation 💻</h1>
|
||||
<p class="mb-4">
|
||||
There are two ways to use Crawl4AI: as a library in your Python projects or as a standalone local
|
||||
server.
|
||||
</p>
|
||||
|
||||
<p class="mb-4">
|
||||
You can also try Crawl4AI in a Google Colab
|
||||
<a href="https://colab.research.google.com/drive/1wz8u30rvbq6Scodye9AGCw8Qg_Z8QGsk"
|
||||
><img
|
||||
src="https://colab.research.google.com/assets/colab-badge.svg"
|
||||
alt="Open In Colab"
|
||||
style="display: inline-block; width: 100px; height: 20px"
|
||||
/></a>
|
||||
</p>
|
||||
|
||||
<h2 class="text-2xl font-bold mb-2">Using Crawl4AI as a Library 📚</h2>
|
||||
<p class="mb-4">To install Crawl4AI as a library, follow these steps:</p>
|
||||
|
||||
<ol class="list-decimal list-inside mb-4">
|
||||
<li class="mb-2">
|
||||
Install the package from GitHub:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code>pip install git+https://github.com/unclecode/crawl4ai.git</code></pre>
|
||||
</li>
|
||||
<li class="mb-2">
|
||||
Alternatively, you can clone the repository and install the package locally:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code class = "language-python bash">virtualenv venv
|
||||
source venv/bin/activate
|
||||
git clone https://github.com/unclecode/crawl4ai.git
|
||||
cd crawl4ai
|
||||
pip install -e .
|
||||
</code></pre>
|
||||
</li>
|
||||
<li>
|
||||
Import the necessary modules in your Python script:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code class = "language-python hljs">from crawl4ai.web_crawler import WebCrawler
|
||||
from crawl4ai.chunking_strategy import *
|
||||
from crawl4ai.extraction_strategy import *
|
||||
import os
|
||||
|
||||
crawler = WebCrawler()
|
||||
|
||||
# Single page crawl
|
||||
single_url = UrlModel(url='https://www.nbcnews.com/business', forced=False)
|
||||
result = crawl4ai.fetch_page(
|
||||
url='https://www.nbcnews.com/business',
|
||||
word_count_threshold=5, # Minimum word count for a HTML tag to be considered as a worthy block
|
||||
chunking_strategy= RegexChunking( patterns = ["\\n\\n"]), # Default is RegexChunking
|
||||
extraction_strategy= CosineStrategy(word_count_threshold=10, max_dist=0.2, linkage_method='ward', top_k=3) # Default is CosineStrategy
|
||||
# extraction_strategy= LLMExtractionStrategy(provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY')),
|
||||
bypass_cache=False,
|
||||
extract_blocks =True, # Whether to extract semantical blocks of text from the HTML
|
||||
css_selector = "", # Eg: "div.article-body"
|
||||
verbose=True,
|
||||
include_raw_html=True, # Whether to include the raw HTML content in the response
|
||||
)
|
||||
print(result.model_dump())
|
||||
</code></pre>
|
||||
</li>
|
||||
</ol>
|
||||
<p class="mb-4">
|
||||
For more information about how to run Crawl4AI as a local server, please refer to the
|
||||
<a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
|
||||
</p>
|
||||
|
||||
</section>
|
||||
|
||||
<section class="bg-zinc-900 text-zinc-300 p-6 px-20">
|
||||
<h1 class="text-3xl font-bold mb-4">📖 Parameters</h1>
|
||||
<div class="overflow-x-auto">
|
||||
<table class="min-w-full bg-zinc-800 border border-zinc-700">
|
||||
<thead>
|
||||
<tr>
|
||||
<th class="py-2 px-4 border-b border-zinc-700">Parameter</th>
|
||||
<th class="py-2 px-4 border-b border-zinc-700">Description</th>
|
||||
<th class="py-2 px-4 border-b border-zinc-700">Required</th>
|
||||
<th class="py-2 px-4 border-b border-zinc-700">Default Value</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">urls</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">
|
||||
A list of URLs to crawl and extract data from.
|
||||
</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">Yes</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">-</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">include_raw_html</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">
|
||||
Whether to include the raw HTML content in the response.
|
||||
</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">No</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">false</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">bypass_cache</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">
|
||||
Whether to force a fresh crawl even if the URL has been previously crawled.
|
||||
</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">No</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">false</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">extract_blocks</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">
|
||||
Whether to extract semantical blocks of text from the HTML.
|
||||
</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">No</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">true</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">word_count_threshold</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">
|
||||
The minimum number of words a block must contain to be considered meaningful (minimum
|
||||
value is 5).
|
||||
</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">No</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">5</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">extraction_strategy</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">
|
||||
The strategy to use for extracting content from the HTML (e.g., "CosineStrategy").
|
||||
</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">No</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">CosineStrategy</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">chunking_strategy</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">
|
||||
The strategy to use for chunking the text before processing (e.g., "RegexChunking").
|
||||
</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">No</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">RegexChunking</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">css_selector</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">
|
||||
The CSS selector to target specific parts of the HTML for extraction.
|
||||
</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">No</td>
|
||||
<td class="py-2 px-4 border-b border-zinc-700">None</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="py-2 px-4">verbose</td>
|
||||
<td class="py-2 px-4">Whether to enable verbose logging.</td>
|
||||
<td class="py-2 px-4">No</td>
|
||||
<td class="py-2 px-4">true</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section id="extraction" class="py-8 px-20">
|
||||
<div class="overflow-x-auto mx-auto px-6">
|
||||
<h2 class="text-2xl font-bold mb-4">Extraction Strategies</h2>
|
||||
<div id="extraction-strategies" class="space-y-4"></div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section id="chunking" class="py-8 px-20">
|
||||
<div class="overflow-x-auto mx-auto px-6">
|
||||
<h2 class="text-2xl font-bold mb-4">Chunking Strategies</h2>
|
||||
<div id="chunking-strategies" class="space-y-4"></div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="hero bg-zinc-900 py-8 px-20">
|
||||
<div class="container mx-auto px-4">
|
||||
<h2 class="text-3xl font-bold mb-4">🤔 Why building this?</h2>
|
||||
<p class="text-lg mb-4">
|
||||
In recent times, we've witnessed a surge of startups emerging, riding the AI hype wave and charging
|
||||
for services that should rightfully be accessible to everyone. 🌍💸 One such example is scraping and
|
||||
crawling web pages and transforming them into a format suitable for Large Language Models (LLMs).
|
||||
🕸️🤖 We believe that building a business around this is not the right approach; instead, it should
|
||||
definitely be open-source. 🆓🌟 So, if you possess the skills to build such tools and share our
|
||||
philosophy, we invite you to join our "Robinhood" band and help set these products free for the
|
||||
benefit of all. 🤝💪
|
||||
</p>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="installation py-8 px-20">
|
||||
<div class="container mx-auto px-4">
|
||||
<h2 class="text-2xl font-bold mb-4">⚙️ Installation</h2>
|
||||
<p class="mb-4">
|
||||
To install and run Crawl4AI as a library or a local server, please refer to the 📚
|
||||
<a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
|
||||
</p>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<footer class="bg-zinc-900 text-white py-4">
|
||||
<div class="container mx-auto px-4">
|
||||
<div class="flex justify-between items-center">
|
||||
<p>© 2024 Crawl4AI. All rights reserved.</p>
|
||||
<div class="social-links">
|
||||
<a
|
||||
href="https://github.com/unclecode/crawl4ai"
|
||||
class="text-white hover:text-gray-300 mx-2"
|
||||
target="_blank"
|
||||
>😺 GitHub</a
|
||||
>
|
||||
<a
|
||||
href="https://twitter.com/unclecode"
|
||||
class="text-white hover:text-gray-300 mx-2"
|
||||
target="_blank"
|
||||
>🐦 Twitter</a
|
||||
>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</footer>
|
||||
|
||||
<script>
|
||||
// JavaScript to manage dynamic form changes and logic
|
||||
document.getElementById("extraction-strategy-select").addEventListener("change", function () {
|
||||
const strategy = this.value;
|
||||
const providerModelSelect = document.getElementById("provider-model-select");
|
||||
const tokenInput = document.getElementById("token-input");
|
||||
|
||||
if (strategy === "LLMExtractionStrategy") {
|
||||
providerModelSelect.disabled = false;
|
||||
tokenInput.disabled = false;
|
||||
} else {
|
||||
providerModelSelect.disabled = true;
|
||||
tokenInput.disabled = true;
|
||||
}
|
||||
});
|
||||
|
||||
// Get the selected provider model and token from local storage
|
||||
const storedProviderModel = localStorage.getItem("provider_model");
|
||||
const storedToken = localStorage.getItem(storedProviderModel);
|
||||
|
||||
if (storedProviderModel) {
|
||||
document.getElementById("provider-model-select").value = storedProviderModel;
|
||||
}
|
||||
|
||||
if (storedToken) {
|
||||
document.getElementById("token-input").value = storedToken;
|
||||
}
|
||||
|
||||
// Handle provider model dropdown change
|
||||
document.getElementById("provider-model-select").addEventListener("change", () => {
|
||||
const selectedProviderModel = document.getElementById("provider-model-select").value;
|
||||
const storedToken = localStorage.getItem(selectedProviderModel);
|
||||
|
||||
if (storedToken) {
|
||||
document.getElementById("token-input").value = storedToken;
|
||||
} else {
|
||||
document.getElementById("token-input").value = "";
|
||||
}
|
||||
});
|
||||
|
||||
// Fetch total count from the database
|
||||
axios
|
||||
.get("/total-count")
|
||||
.then((response) => {
|
||||
document.getElementById("total-count").textContent = response.data.count;
|
||||
})
|
||||
.catch((error) => console.error(error));
|
||||
|
||||
// Handle crawl button click
|
||||
document.getElementById("crawl-btn").addEventListener("click", () => {
|
||||
// validate input to have both URL and API token
|
||||
if (!document.getElementById("url-input").value || !document.getElementById("token-input").value) {
|
||||
alert("Please enter both URL(s) and API token.");
|
||||
return;
|
||||
}
|
||||
|
||||
const selectedProviderModel = document.getElementById("provider-model-select").value;
|
||||
const apiToken = document.getElementById("token-input").value;
|
||||
const extractBlocks = document.getElementById("extract-blocks-checkbox").checked;
|
||||
const bypassCache = document.getElementById("bypass-cache-checkbox").checked;
|
||||
|
||||
// Save the selected provider model and token to local storage
|
||||
localStorage.setItem("provider_model", selectedProviderModel);
|
||||
localStorage.setItem(selectedProviderModel, apiToken);
|
||||
|
||||
const urlsInput = document.getElementById("url-input").value;
|
||||
const urls = urlsInput.split(",").map((url) => url.trim());
|
||||
const data = {
|
||||
urls: urls,
|
||||
provider_model: selectedProviderModel,
|
||||
api_token: apiToken,
|
||||
include_raw_html: true,
|
||||
bypass_cache: bypassCache,
|
||||
extract_blocks: extractBlocks,
|
||||
word_count_threshold: parseInt(document.getElementById("threshold").value),
|
||||
extraction_strategy: document.getElementById("extraction-strategy-select").value,
|
||||
chunking_strategy: document.getElementById("chunking-strategy-select").value,
|
||||
css_selector: document.getElementById("css-selector").value,
|
||||
verbose: true,
|
||||
};
|
||||
|
||||
// save api token to local storage
|
||||
localStorage.setItem("api_token", document.getElementById("token-input").value);
|
||||
|
||||
document.getElementById("loading").classList.remove("hidden");
|
||||
//document.getElementById("result").classList.add("hidden");
|
||||
//document.getElementById("code_help").classList.add("hidden");
|
||||
|
||||
axios
|
||||
.post("/crawl", data)
|
||||
.then((response) => {
|
||||
const result = response.data.results[0];
|
||||
const parsedJson = JSON.parse(result.extracted_content);
|
||||
document.getElementById("json-result").textContent = JSON.stringify(parsedJson, null, 2);
|
||||
document.getElementById("cleaned-html-result").textContent = result.cleaned_html;
|
||||
document.getElementById("markdown-result").textContent = result.markdown;
|
||||
|
||||
// Update code examples dynamically
|
||||
const extractionStrategy = data.extraction_strategy;
|
||||
const isLLMExtraction = extractionStrategy === "LLMExtractionStrategy";
|
||||
|
||||
document.getElementById(
|
||||
"curl-code"
|
||||
).textContent = `curl -X POST -H "Content-Type: application/json" -d '${JSON.stringify({
|
||||
...data,
|
||||
api_token: isLLMExtraction ? "your_api_token" : undefined,
|
||||
})}' http://crawl4ai.uccode.io/crawl`;
|
||||
|
||||
document.getElementById(
|
||||
"python-code"
|
||||
).textContent = `import requests\n\ndata = ${JSON.stringify(
|
||||
{ ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
|
||||
null,
|
||||
2
|
||||
)}\n\nresponse = requests.post("http://crawl4ai.uccode.io/crawl", json=data) # OR local host if your run locally \nprint(response.json())`;
|
||||
|
||||
document.getElementById(
|
||||
"nodejs-code"
|
||||
).textContent = `const axios = require('axios');\n\nconst data = ${JSON.stringify(
|
||||
{ ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
|
||||
null,
|
||||
2
|
||||
)};\n\naxios.post("http://crawl4ai.uccode.io/crawl", data) // OR local host if your run locally \n .then(response => console.log(response.data))\n .catch(error => console.error(error));`;
|
||||
|
||||
document.getElementById(
|
||||
"library-code"
|
||||
).textContent = `from crawl4ai.web_crawler import WebCrawler\nfrom crawl4ai.extraction_strategy import *\nfrom crawl4ai.chunking_strategy import *\n\ncrawler = WebCrawler()\ncrawler.warmup()\n\nresult = crawler.run(\n url='${
|
||||
urls[0]
|
||||
}',\n word_count_threshold=${data.word_count_threshold},\n extraction_strategy=${
|
||||
isLLMExtraction
|
||||
? `${extractionStrategy}(provider="${data.provider_model}", api_token="${data.api_token}")`
|
||||
: extractionStrategy + "()"
|
||||
},\n chunking_strategy=${data.chunking_strategy}(),\n bypass_cache=${
|
||||
data.bypass_cache
|
||||
},\n css_selector="${data.css_selector}"\n)\nprint(result)`;
|
||||
|
||||
// Highlight code syntax
|
||||
hljs.highlightAll();
|
||||
|
||||
// Select JSON tab by default
|
||||
document.querySelector('.tab-btn[data-tab="json"]').click();
|
||||
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
document.getElementById("result").classList.remove("hidden");
|
||||
document.getElementById("code_help").classList.remove("hidden");
|
||||
|
||||
// increment the total count
|
||||
document.getElementById("total-count").textContent =
|
||||
parseInt(document.getElementById("total-count").textContent) + 1;
|
||||
})
|
||||
.catch((error) => {
|
||||
console.error(error);
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle tab clicks
|
||||
document.querySelectorAll(".tab-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const tab = btn.dataset.tab;
|
||||
document
|
||||
.querySelectorAll(".tab-btn")
|
||||
.forEach((b) => b.classList.remove("bg-lime-700", "text-white"));
|
||||
btn.classList.add("bg-lime-700", "text-white");
|
||||
document.querySelectorAll(".tab-content.code pre").forEach((el) => el.classList.add("hidden"));
|
||||
document.getElementById(`${tab}-result`).parentElement.classList.remove("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle code tab clicks
|
||||
document.querySelectorAll(".code-tab-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const tab = btn.dataset.tab;
|
||||
document
|
||||
.querySelectorAll(".code-tab-btn")
|
||||
.forEach((b) => b.classList.remove("bg-lime-700", "text-white"));
|
||||
btn.classList.add("bg-lime-700", "text-white");
|
||||
document.querySelectorAll(".tab-content.result pre").forEach((el) => el.classList.add("hidden"));
|
||||
document.getElementById(`${tab}-code`).parentElement.classList.remove("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle copy to clipboard button clicks
|
||||
|
||||
async function copyToClipboard(text) {
|
||||
if (navigator.clipboard && navigator.clipboard.writeText) {
|
||||
return navigator.clipboard.writeText(text);
|
||||
} else {
|
||||
return fallbackCopyTextToClipboard(text);
|
||||
}
|
||||
}
|
||||
|
||||
function fallbackCopyTextToClipboard(text) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const textArea = document.createElement("textarea");
|
||||
textArea.value = text;
|
||||
|
||||
// Avoid scrolling to bottom
|
||||
textArea.style.top = "0";
|
||||
textArea.style.left = "0";
|
||||
textArea.style.position = "fixed";
|
||||
|
||||
document.body.appendChild(textArea);
|
||||
textArea.focus();
|
||||
textArea.select();
|
||||
|
||||
try {
|
||||
const successful = document.execCommand("copy");
|
||||
if (successful) {
|
||||
resolve();
|
||||
} else {
|
||||
reject();
|
||||
}
|
||||
} catch (err) {
|
||||
reject(err);
|
||||
}
|
||||
|
||||
document.body.removeChild(textArea);
|
||||
});
|
||||
}
|
||||
|
||||
document.querySelectorAll(".copy-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const target = btn.dataset.target;
|
||||
const code = document.getElementById(target).textContent;
|
||||
//navigator.clipboard.writeText(code).then(() => {
|
||||
copyToClipboard(code).then(() => {
|
||||
btn.textContent = "Copied!";
|
||||
setTimeout(() => {
|
||||
btn.textContent = "Copy";
|
||||
}, 2000);
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
document.addEventListener("DOMContentLoaded", async () => {
|
||||
try {
|
||||
const extractionResponse = await fetch("/strategies/extraction");
|
||||
const extractionStrategies = await extractionResponse.json();
|
||||
|
||||
const chunkingResponse = await fetch("/strategies/chunking");
|
||||
const chunkingStrategies = await chunkingResponse.json();
|
||||
|
||||
renderStrategies("extraction-strategies", extractionStrategies);
|
||||
renderStrategies("chunking-strategies", chunkingStrategies);
|
||||
} catch (error) {
|
||||
console.error("Error fetching strategies:", error);
|
||||
}
|
||||
});
|
||||
|
||||
function renderStrategies(containerId, strategies) {
|
||||
const container = document.getElementById(containerId);
|
||||
container.innerHTML = ""; // Clear any existing content
|
||||
strategies = JSON.parse(strategies);
|
||||
Object.entries(strategies).forEach(([strategy, description]) => {
|
||||
const strategyElement = document.createElement("div");
|
||||
strategyElement.classList.add("bg-zinc-800", "p-4", "rounded", "shadow-md", "docs-item");
|
||||
|
||||
const strategyDescription = document.createElement("div");
|
||||
strategyDescription.classList.add("text-gray-300", "prose", "prose-sm");
|
||||
strategyDescription.innerHTML = marked.parse(description);
|
||||
|
||||
strategyElement.appendChild(strategyDescription);
|
||||
|
||||
container.appendChild(strategyElement);
|
||||
});
|
||||
}
|
||||
|
||||
// Highlight code syntax
|
||||
hljs.highlightAll();
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,73 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Crawl4AI</title>
|
||||
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com" />
|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
|
||||
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@100..900&display=swap" rel="stylesheet" />
|
||||
|
||||
<!-- <link href="https://cdn.jsdelivr.net/npm/tailwindcss@3.4.3/dist/tailwind.min.css" rel="stylesheet" /> -->
|
||||
<script src="https://cdn.tailwindcss.com"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
|
||||
<link rel="stylesheet" href="/pages/app.css" />
|
||||
<link
|
||||
rel="stylesheet"
|
||||
href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/monokai.min.css"
|
||||
/>
|
||||
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
|
||||
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/highlight.min.js"></script>
|
||||
</head>
|
||||
<body class="bg-black text-gray-200">
|
||||
<header class="bg-zinc-950 text-lime-500 py-4 flex">
|
||||
|
||||
<div class="mx-auto px-4">
|
||||
<h1 class="text-2xl font-bold">🔥🕷️ Crawl4AI: Web Data for your Thoughts</h1>
|
||||
</div>
|
||||
<div class="mx-auto px-4 flex font-bold text-xl gap-2">
|
||||
<span>📊 Total Website Processed</span>
|
||||
<span id="total-count" class="text-lime-400">2</span>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
{{ try_it | safe }}
|
||||
|
||||
<div class="mx-auto p-4 bg-zinc-950 text-lime-500 min-h-screen">
|
||||
<div class="container mx-auto">
|
||||
<div class="flex h-full px-20">
|
||||
<div class="sidebar w-1/4 p-4">
|
||||
<h2 class="text-lg font-bold mb-4">Outline</h2>
|
||||
<ul>
|
||||
<li class="mb-2"><a href="#" data-target="installation">Installation</a></li>
|
||||
<li class="mb-2"><a href="#" data-target="how-to-guide">How to Guide</a></li>
|
||||
<li class="mb-2"><a href="#" data-target="chunking-strategies">Chunking Strategies</a></li>
|
||||
<li class="mb-2">
|
||||
<a href="#" data-target="extraction-strategies">Extraction Strategies</a>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<!-- Main Content -->
|
||||
<div class="w-3/4 p-4">
|
||||
{{installation | safe}} {{how_to_guide | safe}}
|
||||
|
||||
<section id="chunking-strategies" class="content-section">
|
||||
<h1 class="text-2xl font-bold">Chunking Strategies</h1>
|
||||
<p>Content for chunking strategies...</p>
|
||||
</section>
|
||||
<section id="extraction-strategies" class="content-section">
|
||||
<h1 class="text-2xl font-bold">Extraction Strategies</h1>
|
||||
<p>Content for extraction strategies...</p>
|
||||
</section>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{{ footer | safe }}
|
||||
<script script src="/pages/app.js"></script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,425 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Crawl4AI</title>
|
||||
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com" />
|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
|
||||
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@100..900&display=swap" rel="stylesheet" />
|
||||
|
||||
<link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet" />
|
||||
<script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
|
||||
<link
|
||||
rel="stylesheet"
|
||||
href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/vs2015.min.css"
|
||||
/>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/highlight.min.js"></script>
|
||||
<style>
|
||||
:root {
|
||||
--ifm-font-size-base: 100%;
|
||||
--ifm-line-height-base: 1.65;
|
||||
--ifm-font-family-base: system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, Cantarell, Noto Sans,
|
||||
sans-serif, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji",
|
||||
"Segoe UI Emoji", "Segoe UI Symbol";
|
||||
}
|
||||
html {
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-webkit-text-size-adjust: 100%;
|
||||
text-size-adjust: 100%;
|
||||
font: var(--ifm-font-size-base) / var(--ifm-line-height-base) var(--ifm-font-family-base);
|
||||
}
|
||||
body {
|
||||
background-color: #1a202c;
|
||||
color: #fff;
|
||||
}
|
||||
.tab-content {
|
||||
max-height: 400px;
|
||||
overflow: auto;
|
||||
}
|
||||
pre {
|
||||
white-space: pre-wrap;
|
||||
font-size: 14px;
|
||||
}
|
||||
pre code {
|
||||
width: 100%;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<header class="bg-gray-900 text-white py-4">
|
||||
<div class="container mx-auto px-4">
|
||||
<h1 class="text-2xl font-bold">🔥🕷️ Crawl4AI: Open-source LLM Friendly Web scraper</h1>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<section class="try-it py-8 pb-20">
|
||||
<div class="container mx-auto px-4">
|
||||
<h2 class="text-2xl font-bold mb-4">Try It Now</h2>
|
||||
<div class="mb-4 flex w-full gap-2">
|
||||
<input
|
||||
type="text"
|
||||
id="url-input"
|
||||
value="https://kidocode.com"
|
||||
class="border border-gray-600 rounded px-4 py-2 flex-grow bg-gray-800 text-white"
|
||||
placeholder="Enter URL(s) separated by commas"
|
||||
/>
|
||||
<select
|
||||
id="provider-model-select"
|
||||
class="border border-gray-600 rounded px-4 py-2 bg-gray-800 text-white"
|
||||
>
|
||||
<!-- Add your option values here -->
|
||||
<option value="groq/llama3-70b-8192">groq/llama3-70b-8192</option>
|
||||
<option value="groq/llama3-8b-8192">groq/llama3-8b-8192</option>
|
||||
<option value="openai/gpt-4-turbo">gpt-4-turbo</option>
|
||||
<option value="openai/gpt-3.5-turbo">gpt-3.5-turbo</option>
|
||||
<option value="anthropic/claude-3-haiku-20240307">claude-3-haiku</option>
|
||||
<option value="anthropic/claude-3-opus-20240229">claude-3-opus</option>
|
||||
<option value="anthropic/claude-3-sonnet-20240229">claude-3-sonnet</option>
|
||||
</select>
|
||||
<input
|
||||
type="password"
|
||||
id="token-input"
|
||||
class="border border-gray-600 rounded px-4 py-2 flex-grow bg-gray-800 text-white"
|
||||
placeholder="Enter Groq API token"
|
||||
/>
|
||||
<div class="flex items-center justify-center">
|
||||
<input type="checkbox" id="extract-blocks-checkbox" class="mr-2" checked>
|
||||
<label for="extract-blocks-checkbox" class="text-white">Extract Blocks</label>
|
||||
</div>
|
||||
<button id="crawl-btn" class="bg-blue-600 text-white px-4 py-2 rounded">Crawl</button>
|
||||
</div>
|
||||
<div class="grid grid-cols-1 md:grid-cols-2 gap-8">
|
||||
<div id="loading" class="hidden mt-4">
|
||||
<p>Loading...</p>
|
||||
</div>
|
||||
<div id="result" class="tab-container flex-1 h-full flex-col">
|
||||
<div class="tab-buttons flex gap-2">
|
||||
<button class="tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="json">JSON</button>
|
||||
<button class="tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="cleaned-html">
|
||||
Cleaned HTML
|
||||
</button>
|
||||
<button class="tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="markdown">
|
||||
Markdown
|
||||
</button>
|
||||
</div>
|
||||
<div class="tab-content code bg-gray-800 p-2 rounded h-full flex-1 border border-gray-600">
|
||||
<pre class="h-full flex"><code id="json-result" class="language-json "></code></pre>
|
||||
<pre
|
||||
class="hidden h-full flex"
|
||||
><code id="cleaned-html-result" class="language-html "></code></pre>
|
||||
<pre
|
||||
class="hidden h-full flex"
|
||||
><code id="markdown-result" class="language-markdown "></code></pre>
|
||||
</div>
|
||||
</div>
|
||||
<div id="code_help" class="tab-container flex-1 h-full">
|
||||
<div class="tab-buttons flex gap-2">
|
||||
<button class="code-tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="curl">cURL</button>
|
||||
<button class="code-tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="python">
|
||||
Python
|
||||
</button>
|
||||
<button class="code-tab-btn px-4 py-2 bg-gray-700 rounded-t" data-tab="nodejs">
|
||||
Node.js
|
||||
</button>
|
||||
</div>
|
||||
<div class="tab-content result bg-gray-800 p-2 rounded h-full flex-1 border border-gray-600">
|
||||
<pre class="h-full flex relative">
|
||||
<code id="curl-code" class="language-bash"></code>
|
||||
<button class="absolute top-2 right-2 bg-gray-700 text-white px-2 py-1 rounded copy-btn" data-target="curl-code">Copy</button>
|
||||
</pre>
|
||||
<pre class="hidden h-full flex relative">
|
||||
<code id="python-code" class="language-python"></code>
|
||||
<button class="absolute top-2 right-2 bg-gray-700 text-white px-2 py-1 rounded copy-btn" data-target="python-code">Copy</button>
|
||||
</pre>
|
||||
<pre class="hidden h-full flex relative">
|
||||
<code id="nodejs-code" class="language-javascript"></code>
|
||||
<button class="absolute top-2 right-2 bg-gray-700 text-white px-2 py-1 rounded copy-btn" data-target="nodejs-code">Copy</button>
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="hero bg-gray-900 py-8">
|
||||
<div class="container mx-auto px-4">
|
||||
<h2 class="text-3xl font-bold mb-4">🤔 Why building this?</h2>
|
||||
<p class="text-lg mb-4">
|
||||
In recent times, we've seen numerous startups emerging, riding the AI hype wave and charging for
|
||||
services that should rightfully be accessible to everyone. 🌍💸 One for example is to scrap and crawl
|
||||
a web page, and transform it o a form suitable for LLM. We don't think one should build a business
|
||||
out of this, but definilty should be opened source. So if you possess the skills to build such things
|
||||
and you have such philosphy you should join our "Robinhood" band and help set
|
||||
these products free. 🆓🤝
|
||||
</p>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="installation py-8">
|
||||
<div class="container mx-auto px-4">
|
||||
<h2 class="text-2xl font-bold mb-4">⚙️ Installation</h2>
|
||||
<p class="mb-4">
|
||||
To install and run Crawl4AI locally or on your own service, the best way is to use Docker. 🐳 Follow
|
||||
these steps:
|
||||
</p>
|
||||
<ol class="list-decimal list-inside mb-4">
|
||||
<li>
|
||||
Clone the GitHub repository: 📥
|
||||
<code>git clone https://github.com/unclecode/crawl4ai.git</code>
|
||||
</li>
|
||||
<li>Navigate to the project directory: 📂 <code>cd crawl4ai</code></li>
|
||||
<li>
|
||||
Build the Docker image: 🛠️ <code>docker build -t crawl4ai .</code> On Mac, follow: 🍎
|
||||
<code>docker build --platform linux/amd64 -t crawl4ai .</code>
|
||||
</li>
|
||||
<li>Run the Docker container: ▶️ <code>docker run -p 8000:80 crawl4ai</code></li>
|
||||
</ol>
|
||||
<p>
|
||||
For more detailed instructions and advanced configuration options, please refer to the 📚
|
||||
<a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
|
||||
</p>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<footer class="bg-gray-900 text-white py-4">
|
||||
<div class="container mx-auto px-4">
|
||||
<div class="flex justify-between items-center">
|
||||
<p>© 2024 Crawl4AI. All rights reserved.</p>
|
||||
<div class="social-links">
|
||||
<a
|
||||
href="https://github.com/unclecode/crawl4ai"
|
||||
class="text-white hover:text-gray-300 mx-2"
|
||||
target="_blank"
|
||||
>😺 GitHub</a
|
||||
>
|
||||
<a
|
||||
href="https://twitter.com/unclecode"
|
||||
class="text-white hover:text-gray-300 mx-2"
|
||||
target="_blank"
|
||||
>🐦 Twitter</a
|
||||
>
|
||||
<a
|
||||
href="https://discord.gg/your-invite-link"
|
||||
class="text-white hover:text-gray-300 mx-2"
|
||||
target="_blank"
|
||||
>💬 Discord</a
|
||||
>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</footer>
|
||||
|
||||
<script>
|
||||
// Get the selected provider model and token from local storage
|
||||
const storedProviderModel = localStorage.getItem("provider_model");
|
||||
const storedToken = localStorage.getItem(storedProviderModel);
|
||||
|
||||
if (storedProviderModel) {
|
||||
document.getElementById("provider-model-select").value = storedProviderModel;
|
||||
}
|
||||
|
||||
if (storedToken) {
|
||||
document.getElementById("token-input").value = storedToken;
|
||||
}
|
||||
|
||||
// Handle provider model dropdown change
|
||||
document.getElementById("provider-model-select").addEventListener("change", () => {
|
||||
const selectedProviderModel = document.getElementById("provider-model-select").value;
|
||||
const storedToken = localStorage.getItem(selectedProviderModel);
|
||||
|
||||
if (storedToken) {
|
||||
document.getElementById("token-input").value = storedToken;
|
||||
} else {
|
||||
document.getElementById("token-input").value = "";
|
||||
}
|
||||
});
|
||||
|
||||
// Fetch total count from the database
|
||||
axios
|
||||
.get("/total-count")
|
||||
.then((response) => {
|
||||
document.getElementById("total-count").textContent = response.data.count;
|
||||
})
|
||||
.catch((error) => console.error(error));
|
||||
|
||||
// Handle crawl button click
|
||||
document.getElementById("crawl-btn").addEventListener("click", () => {
|
||||
// validate input to have both URL and API token
|
||||
if (!document.getElementById("url-input").value || !document.getElementById("token-input").value) {
|
||||
alert("Please enter both URL(s) and API token.");
|
||||
return;
|
||||
}
|
||||
|
||||
const selectedProviderModel = document.getElementById("provider-model-select").value;
|
||||
const apiToken = document.getElementById("token-input").value;
|
||||
const extractBlocks = document.getElementById("extract-blocks-checkbox").checked;
|
||||
|
||||
|
||||
// Save the selected provider model and token to local storage
|
||||
localStorage.setItem("provider_model", selectedProviderModel);
|
||||
localStorage.setItem(selectedProviderModel, apiToken);
|
||||
|
||||
const urlsInput = document.getElementById("url-input").value;
|
||||
const urls = urlsInput.split(",").map((url) => url.trim());
|
||||
const data = {
|
||||
urls: urls,
|
||||
provider_model: selectedProviderModel,
|
||||
api_token: apiToken,
|
||||
include_raw_html: true,
|
||||
forced: false,
|
||||
extract_blocks: extractBlocks,
|
||||
};
|
||||
|
||||
// save api token to local storage
|
||||
localStorage.setItem("api_token", document.getElementById("token-input").value);
|
||||
|
||||
document.getElementById("loading").classList.remove("hidden");
|
||||
document.getElementById("result").classList.add("hidden");
|
||||
document.getElementById("code_help").classList.add("hidden");
|
||||
|
||||
axios
|
||||
.post("/crawl", data)
|
||||
.then((response) => {
|
||||
const result = response.data.results[0];
|
||||
const parsedJson = JSON.parse(result.extracted_content);
|
||||
document.getElementById("json-result").textContent = JSON.stringify(parsedJson, null, 2);
|
||||
document.getElementById("cleaned-html-result").textContent = result.cleaned_html;
|
||||
document.getElementById("markdown-result").textContent = result.markdown;
|
||||
|
||||
// Update code examples dynamically
|
||||
// Update code examples dynamically
|
||||
document.getElementById(
|
||||
"curl-code"
|
||||
).textContent = `curl -X POST -H "Content-Type: application/json" -d '${JSON.stringify({
|
||||
...data,
|
||||
api_token: "your_api_token",
|
||||
})}' http://localhost:8000/crawl`;
|
||||
|
||||
document.getElementById(
|
||||
"python-code"
|
||||
).textContent = `import requests\n\ndata = ${JSON.stringify(
|
||||
{ ...data, api_token: "your_api_token" },
|
||||
null,
|
||||
2
|
||||
)}\n\nresponse = requests.post("http://localhost:8000/crawl", json=data)\nprint(response.json())`;
|
||||
|
||||
document.getElementById(
|
||||
"nodejs-code"
|
||||
).textContent = `const axios = require('axios');\n\nconst data = ${JSON.stringify(
|
||||
{ ...data, api_token: "your_api_token" },
|
||||
null,
|
||||
2
|
||||
)};\n\naxios.post("http://localhost:8000/crawl", data)\n .then(response => console.log(response.data))\n .catch(error => console.error(error));`;
|
||||
// Highlight code syntax
|
||||
hljs.highlightAll();
|
||||
|
||||
// Select JSON tab by default
|
||||
document.querySelector('.tab-btn[data-tab="json"]').click();
|
||||
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
document.getElementById("result").classList.remove("hidden");
|
||||
document.getElementById("code_help").classList.remove("hidden");
|
||||
})
|
||||
.catch((error) => {
|
||||
console.error(error);
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle tab clicks
|
||||
document.querySelectorAll(".tab-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const tab = btn.dataset.tab;
|
||||
document
|
||||
.querySelectorAll(".tab-btn")
|
||||
.forEach((b) => b.classList.remove("bg-blue-600", "text-white"));
|
||||
btn.classList.add("bg-blue-600", "text-white");
|
||||
document.querySelectorAll(".tab-content.code pre").forEach((el) => el.classList.add("hidden"));
|
||||
document.getElementById(`${tab}-result`).parentElement.classList.remove("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle code tab clicks
|
||||
document.querySelectorAll(".code-tab-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const tab = btn.dataset.tab;
|
||||
document
|
||||
.querySelectorAll(".code-tab-btn")
|
||||
.forEach((b) => b.classList.remove("bg-blue-600", "text-white"));
|
||||
btn.classList.add("bg-blue-600", "text-white");
|
||||
document.querySelectorAll(".tab-content.result pre").forEach((el) => el.classList.add("hidden"));
|
||||
document.getElementById(`${tab}-code`).parentElement.classList.remove("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
// Handle copy to clipboard button clicks
|
||||
document.querySelectorAll(".copy-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const target = btn.dataset.target;
|
||||
const code = document.getElementById(target).textContent;
|
||||
navigator.clipboard.writeText(code).then(() => {
|
||||
btn.textContent = "Copied!";
|
||||
setTimeout(() => {
|
||||
btn.textContent = "Copy";
|
||||
}, 2000);
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
|
||||
document.getElementById("crawl-btn").addEventListener("click", () => {
|
||||
const urlsInput = document.getElementById("url-input").value;
|
||||
const urls = urlsInput.split(",").map(url => url.trim());
|
||||
const apiToken = document.getElementById("token-input").value;
|
||||
const selectedProviderModel = document.getElementById("provider-model-select").value;
|
||||
const extractBlocks = document.getElementById("extract-blocks-checkbox").checked;
|
||||
|
||||
const data = {
|
||||
urls: urls,
|
||||
provider_model: selectedProviderModel,
|
||||
api_token: apiToken,
|
||||
include_raw_html: true,
|
||||
forced: false,
|
||||
extract_blocks: extractBlocks
|
||||
};
|
||||
|
||||
localStorage.setItem("api_token", apiToken);
|
||||
|
||||
document.getElementById("loading").classList.remove("hidden");
|
||||
document.getElementById("result").classList.add("hidden");
|
||||
document.getElementById("code_help").classList.add("hidden");
|
||||
|
||||
axios.post("/crawl", data)
|
||||
.then(response => {
|
||||
const taskId = response.data.task_id;
|
||||
pollTaskStatus(taskId);
|
||||
})
|
||||
.catch(error => {
|
||||
console.error('Error during fetch:', error);
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
});
|
||||
});
|
||||
|
||||
function pollTaskStatus(taskId) {
|
||||
axios.get(`/task/${taskId}`)
|
||||
.then(response => {
|
||||
const task = response.data;
|
||||
if (task.status === 'done') {
|
||||
displayResults(task.results[0]);
|
||||
} else if (task.status === 'pending') {
|
||||
setTimeout(() => pollTaskStatus(taskId), 2000); // Poll every 2 seconds
|
||||
} else {
|
||||
console.error('Task failed:', task.error);
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
}
|
||||
})
|
||||
.catch(error => {
|
||||
console.error('Error polling task status:', error);
|
||||
document.getElementById("loading").classList.add("hidden");
|
||||
});
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,36 +0,0 @@
|
||||
<section class="hero bg-zinc-900 py-8 px-20 text-zinc-400">
|
||||
<div class="container mx-auto px-4">
|
||||
<h2 class="text-3xl font-bold mb-4">🤔 Why building this?</h2>
|
||||
<p class="text-lg mb-4">
|
||||
In recent times, we've witnessed a surge of startups emerging, riding the AI hype wave and charging
|
||||
for services that should rightfully be accessible to everyone. 🌍💸 One such example is scraping and
|
||||
crawling web pages and transforming them into a format suitable for Large Language Models (LLMs).
|
||||
🕸️🤖 We believe that building a business around this is not the right approach; instead, it should
|
||||
definitely be open-source. 🆓🌟 So, if you possess the skills to build such tools and share our
|
||||
philosophy, we invite you to join our "Robinhood" band and help set these products free for the
|
||||
benefit of all. 🤝💪
|
||||
</p>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<footer class="bg-zinc-900 text-zinc-400 py-4">
|
||||
<div class="container mx-auto px-4">
|
||||
<div class="flex justify-between items-center">
|
||||
<p>© 2024 Crawl4AI. All rights reserved.</p>
|
||||
<div class="social-links">
|
||||
<a
|
||||
href="https://github.com/unclecode/crawl4ai"
|
||||
class="text-zinc-400 hover:text-gray-300 mx-2"
|
||||
target="_blank"
|
||||
>😺 GitHub</a
|
||||
>
|
||||
<a
|
||||
href="https://twitter.com/unclecode"
|
||||
class="text-zinc-400 hover:text-gray-300 mx-2"
|
||||
target="_blank"
|
||||
>🐦 Twitter</a
|
||||
>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</footer>
|
||||
@@ -1,174 +0,0 @@
|
||||
<section id="how-to-guide" class="content-section">
|
||||
<h1 class="text-2xl font-bold">How to Guide</h1>
|
||||
<div class="flex flex-col gap-4 p-4 bg-zinc-900 text-lime-500">
|
||||
<!-- Step 1 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🌟
|
||||
<strong
|
||||
>Welcome to the Crawl4ai Quickstart Guide! Let's dive into some web crawling
|
||||
fun!</strong
|
||||
>
|
||||
</div>
|
||||
<div class="">
|
||||
First Step: Create an instance of WebCrawler and call the
|
||||
<code>warmup()</code> function.
|
||||
</div>
|
||||
<div>
|
||||
<pre><code class="language-python">crawler = WebCrawler()
|
||||
crawler.warmup()</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 2 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🧠 <strong>Understanding 'bypass_cache' and 'include_raw_html' parameters:</strong>
|
||||
</div>
|
||||
<div class="">First crawl (caches the result):</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business")</code></pre>
|
||||
</div>
|
||||
<div class="">Second crawl (Force to crawl again):</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business", bypass_cache=True)</code></pre>
|
||||
<div class="bg-red-900 p-2 text-zinc-50">
|
||||
⚠️ Don't forget to set <code>`bypass_cache`</code> to True if you want to try different strategies for the same URL. Otherwise, the cached result will be returned. You can also set <code>`always_by_pass_cache`</code> in constructor to True to always bypass the cache.
|
||||
</div>
|
||||
</div>
|
||||
<div class="">Crawl result without raw HTML content:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(url="https://www.nbcnews.com/business", include_raw_html=False)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 3 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
📄
|
||||
<strong
|
||||
>The 'include_raw_html' parameter, when set to True, includes the raw HTML content
|
||||
in the response. By default, it is set to True.</strong
|
||||
>
|
||||
</div>
|
||||
<div class="">Set <code>always_by_pass_cache</code> to True:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">crawler.always_by_pass_cache = True</code></pre>
|
||||
</div>
|
||||
<!-- Step 3.5 Screenshot -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
📸
|
||||
<strong>Let's take a screenshot of the page!</strong>
|
||||
</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
screenshot=True
|
||||
)
|
||||
with open("screenshot.png", "wb") as f:
|
||||
f.write(base64.b64decode(result.screenshot))</code></pre>
|
||||
</div>
|
||||
|
||||
|
||||
<!-- Step 4 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🧩 <strong>Let's add a chunking strategy: RegexChunking!</strong>
|
||||
</div>
|
||||
<div class="">Using RegexChunking:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
chunking_strategy=RegexChunking(patterns=["\n\n"])
|
||||
)</code></pre>
|
||||
</div>
|
||||
<div class="">Using NlpSentenceChunking:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
chunking_strategy=NlpSentenceChunking()
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 5 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🧠 <strong>Let's get smarter with an extraction strategy: CosineStrategy!</strong>
|
||||
</div>
|
||||
<div class="">Using CosineStrategy:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
extraction_strategy=CosineStrategy(word_count_threshold=10, max_dist=0.2, linkage_method="ward", top_k=3)
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 6 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🤖
|
||||
<strong
|
||||
>Time to bring in the big guns: LLMExtractionStrategy without instructions!</strong
|
||||
>
|
||||
</div>
|
||||
<div class="">Using LLMExtractionStrategy without instructions:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
extraction_strategy=LLMExtractionStrategy(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'))
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 7 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
📜
|
||||
<strong
|
||||
>Let's make it even more interesting: LLMExtractionStrategy with
|
||||
instructions!</strong
|
||||
>
|
||||
</div>
|
||||
<div class="">Using LLMExtractionStrategy with instructions:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
extraction_strategy=LLMExtractionStrategy(
|
||||
provider="openai/gpt-4o",
|
||||
api_token=os.getenv('OPENAI_API_KEY'),
|
||||
instruction="I am interested in only financial news"
|
||||
)
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 8 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🎯
|
||||
<strong>Targeted extraction: Let's use a CSS selector to extract only H2 tags!</strong>
|
||||
</div>
|
||||
<div class="">Using CSS selector to extract H2 tags:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">result = crawler.run(
|
||||
url="https://www.nbcnews.com/business",
|
||||
css_selector="h2"
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 9 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🖱️
|
||||
<strong
|
||||
>Let's get interactive: Passing JavaScript code to click 'Load More' button!</strong
|
||||
>
|
||||
</div>
|
||||
<div class="">Using JavaScript to click 'Load More' button:</div>
|
||||
<div>
|
||||
<pre><code class="language-python">js_code = ["""
|
||||
const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
|
||||
loadMoreButton && loadMoreButton.click();
|
||||
"""]
|
||||
crawler = WebCrawler(verbos=crawler_strategy, always_by_pass_cache=True)
|
||||
result = crawler.run(url="https://www.nbcnews.com/business", js = js_code)</code></pre>
|
||||
<div class="">Remember that you can pass multiple JavaScript code snippets in the list. They all will be executed in the order they are passed.</div>
|
||||
</div>
|
||||
|
||||
<!-- Conclusion -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🎉
|
||||
<strong
|
||||
>Congratulations! You've made it through the Crawl4ai Quickstart Guide! Now go forth
|
||||
and crawl the web like a pro! 🕸️</strong
|
||||
>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
@@ -1,65 +0,0 @@
|
||||
<section id="installation" class="content-section active">
|
||||
<h1 class="text-2xl font-bold">Installation 💻</h1>
|
||||
<p class="mb-4">
|
||||
There are three ways to use Crawl4AI:
|
||||
<ol class="list-decimal list-inside mb-4">
|
||||
<li class="">
|
||||
As a library
|
||||
</li>
|
||||
<li class="">
|
||||
As a local server (Docker)
|
||||
</li>
|
||||
<li class="">
|
||||
As a Google Colab notebook. <a href="https://colab.research.google.com/drive/1wz8u30rvbq6Scodye9AGCw8Qg_Z8QGsk"
|
||||
><img
|
||||
src="https://colab.research.google.com/assets/colab-badge.svg"
|
||||
alt="Open In Colab"
|
||||
style="display: inline-block; width: 100px; height: 20px"
|
||||
/></a>
|
||||
</li>
|
||||
</p>
|
||||
|
||||
|
||||
<p class="my-4">To install Crawl4AI as a library, follow these steps:</p>
|
||||
|
||||
<ol class="list-decimal list-inside mb-4">
|
||||
<li class="mb-4">
|
||||
Install the package from GitHub:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code>virtualenv venv
|
||||
source venv/bin/activate
|
||||
pip install "crawl4ai[all] @ git+https://github.com/unclecode/crawl4ai.git"
|
||||
</code></pre>
|
||||
</li>
|
||||
<li class="mb-4">
|
||||
Run the following command to load the required models. This is optional, but it will boost the performance and speed of the crawler. You need to do this only once.
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code>crawl4ai-download-models</code></pre>
|
||||
</li>
|
||||
<li class="mb-4">
|
||||
Alternatively, you can clone the repository and install the package locally:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code class = "language-python bash">virtualenv venv
|
||||
source venv/bin/activate
|
||||
git clone https://github.com/unclecode/crawl4ai.git
|
||||
cd crawl4ai
|
||||
pip install -e .[all]
|
||||
</code></pre>
|
||||
</li>
|
||||
<li class="">
|
||||
Use docker to run the local server:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code class = "language-python bash">docker build -t crawl4ai .
|
||||
# docker build --platform linux/amd64 -t crawl4ai . For Mac users
|
||||
docker run -d -p 8000:80 crawl4ai</code></pre>
|
||||
</li>
|
||||
</ol>
|
||||
<p class="mb-4">
|
||||
For more information about how to run Crawl4AI as a local server, please refer to the
|
||||
<a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
|
||||
</p>
|
||||
</section>
|
||||
@@ -1,217 +0,0 @@
|
||||
<section class="try-it py-8 px-16 pb-20 bg-zinc-900 overflow-hidden">
|
||||
<div class="container mx-auto ">
|
||||
<h2 class="text-2xl font-bold mb-4 text-lime-500">Try It Now</h2>
|
||||
<div class="flex gap-4">
|
||||
<div class="flex flex-col flex-1 gap-2">
|
||||
<div class="flex flex-col">
|
||||
<label for="url-input" class="text-lime-500 font-bold text-xs">URL(s)</label>
|
||||
<input
|
||||
type="text"
|
||||
id="url-input"
|
||||
value="https://www.nbcnews.com/business"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300"
|
||||
placeholder="Enter URL(s) separated by commas"
|
||||
/>
|
||||
</div>
|
||||
<div class="flex gap-2">
|
||||
<div class="flex flex-col">
|
||||
<label for="threshold" class="text-lime-500 font-bold text-xs">Min Words Threshold</label>
|
||||
<select
|
||||
id="threshold"
|
||||
class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
|
||||
>
|
||||
<option value="1">1</option>
|
||||
<option value="5">5</option>
|
||||
<option value="10" selected>10</option>
|
||||
<option value="15">15</option>
|
||||
<option value="20">20</option>
|
||||
<option value="25">25</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="flex flex-col flex-1">
|
||||
<label for="css-selector" class="text-lime-500 font-bold text-xs">CSS Selector</label>
|
||||
<input
|
||||
type="text"
|
||||
id="css-selector"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300 placeholder-lime-700"
|
||||
placeholder="CSS Selector (e.g. .content, #main, article)"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
<div class="flex gap-2">
|
||||
<div class="flex flex-col">
|
||||
<label for="extraction-strategy-select" class="text-lime-500 font-bold text-xs"
|
||||
>Extraction Strategy</label
|
||||
>
|
||||
<select
|
||||
id="extraction-strategy-select"
|
||||
class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
|
||||
>
|
||||
<option value="NoExtractionStrategy" selected>NoExtractionStrategy</option>
|
||||
<option value="CosineStrategy">CosineStrategy</option>
|
||||
<option value="LLMExtractionStrategy">LLMExtractionStrategy</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="flex flex-col">
|
||||
<label for="chunking-strategy-select" class="text-lime-500 font-bold text-xs"
|
||||
>Chunking Strategy</label
|
||||
>
|
||||
<select
|
||||
id="chunking-strategy-select"
|
||||
class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
|
||||
>
|
||||
<option value="RegexChunking">RegexChunking</option>
|
||||
<option value="NlpSentenceChunking">NlpSentenceChunking</option>
|
||||
<option value="TopicSegmentationChunking">TopicSegmentationChunking</option>
|
||||
<option value="FixedLengthWordChunking">FixedLengthWordChunking</option>
|
||||
<option value="SlidingWindowChunking">SlidingWindowChunking</option>
|
||||
</select>
|
||||
</div>
|
||||
</div>
|
||||
<div id = "llm_settings" class="flex gap-2 hidden hidden">
|
||||
<div class="flex flex-col">
|
||||
<label for="provider-model-select" class="text-lime-500 font-bold text-xs"
|
||||
>Provider Model</label
|
||||
>
|
||||
<select
|
||||
id="provider-model-select"
|
||||
class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
|
||||
>
|
||||
<option value="groq/llama3-70b-8192">groq/llama3-70b-8192</option>
|
||||
<option value="groq/llama3-8b-8192">groq/llama3-8b-8192</option>
|
||||
<option value="groq/mixtral-8x7b-32768">groq/mixtral-8x7b-32768</option>
|
||||
<option value="openai/gpt-4-turbo">gpt-4-turbo</option>
|
||||
<option value="openai/gpt-3.5-turbo">gpt-3.5-turbo</option>
|
||||
<option value="openai/gpt-4o">gpt-4o</option>
|
||||
<option value="anthropic/claude-3-haiku-20240307">claude-3-haiku</option>
|
||||
<option value="anthropic/claude-3-opus-20240229">claude-3-opus</option>
|
||||
<option value="anthropic/claude-3-sonnet-20240229">claude-3-sonnet</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="flex flex-col flex-1">
|
||||
<label for="token-input" class="text-lime-500 font-bold text-xs">API Token</label>
|
||||
<input
|
||||
type="password"
|
||||
id="token-input"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300"
|
||||
placeholder="Enter Groq API token"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
<div class="flex gap-2">
|
||||
<!-- Add two textarea one for getting Keyword Filter and another one Instruction, make both grow whole with-->
|
||||
<div id = "semantic_filter_div" class="flex flex-col flex-1 hidden">
|
||||
<label for="keyword-filter" class="text-lime-500 font-bold text-xs">Keyword Filter</label>
|
||||
<textarea
|
||||
id="semantic_filter"
|
||||
rows="3"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300 placeholder-zinc-700"
|
||||
placeholder="Enter keywords for CosineStrategy to narrow down the content."
|
||||
></textarea>
|
||||
</div>
|
||||
<div id = "instruction_div" class="flex flex-col flex-1 hidden">
|
||||
<label for="instruction" class="text-lime-500 font-bold text-xs">Instruction</label>
|
||||
<textarea
|
||||
id="instruction"
|
||||
rows="3"
|
||||
class="border border-zinc-700 rounded px-4 py-0 bg-zinc-900 text-zinc-300 placeholder-zinc-700"
|
||||
placeholder="Enter instruction for the LLMEstrategy to instruct the model."
|
||||
></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="flex gap-3">
|
||||
<div class="flex items-center gap-2">
|
||||
<input type="checkbox" id="bypass-cache-checkbox" />
|
||||
<label for="bypass-cache-checkbox" class="text-lime-500 font-bold">Bypass Cache</label>
|
||||
</div>
|
||||
<div class="flex items-center gap-2">
|
||||
<input type="checkbox" id="screenshot-checkbox" checked />
|
||||
<label for="screenshot-checkbox" class="text-lime-500 font-bold">Screenshot</label>
|
||||
</div>
|
||||
<div class="flex items-center gap-2 hidden">
|
||||
<input type="checkbox" id="extract-blocks-checkbox" />
|
||||
<label for="extract-blocks-checkbox" class="text-lime-500 font-bold">Extract Blocks</label>
|
||||
</div>
|
||||
<button id="crawl-btn" class="bg-lime-600 text-black font-bold px-4 py-0 rounded">Crawl</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="loading" class="hidden">
|
||||
<p class="text-white">Loading... Please wait.</p>
|
||||
</div>
|
||||
<div id="result" class="flex-1 overflow-x-auto">
|
||||
<div class="tab-buttons flex gap-2">
|
||||
<button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="json">
|
||||
JSON
|
||||
</button>
|
||||
<button
|
||||
class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="cleaned-html"
|
||||
>
|
||||
Cleaned HTML
|
||||
</button>
|
||||
<button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="markdown">
|
||||
Markdown
|
||||
</button>
|
||||
<button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="media">
|
||||
Medias
|
||||
</button>
|
||||
<button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="screenshot">
|
||||
Screenshot
|
||||
</button>
|
||||
</div>
|
||||
<div class="tab-content code bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
|
||||
<pre class="h-full flex"><code id="json-result" class="language-json"></code></pre>
|
||||
<pre class="hidden h-full flex"><code id="cleaned-html-result" class="language-html"></code></pre>
|
||||
<pre class="hidden h-full flex"><code id="markdown-result" class="language-markdown"></code></pre>
|
||||
<pre class="hidden h-full flex"><code id="media-result" class="language-json"></code></pre>
|
||||
<pre class="hidden h-full flex"><code id="screenshot-result"></code></pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="code_help" class="flex-1 overflow-x-auto">
|
||||
<div class="tab-buttons flex gap-2">
|
||||
<button class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="curl">
|
||||
cURL
|
||||
</button>
|
||||
<button
|
||||
class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="library"
|
||||
>
|
||||
Python
|
||||
</button>
|
||||
<button
|
||||
class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="python"
|
||||
>
|
||||
REST API
|
||||
</button>
|
||||
<!-- <button
|
||||
class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500"
|
||||
data-tab="nodejs"
|
||||
>
|
||||
Node.js
|
||||
</button> -->
|
||||
</div>
|
||||
<div class="tab-content result bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
|
||||
<pre class="h-full flex relative overflow-x-auto">
|
||||
<code id="curl-code" class="language-bash"></code>
|
||||
<button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="curl-code">Copy</button>
|
||||
</pre>
|
||||
<pre class="hidden h-full flex relative overflow-x-auto">
|
||||
<code id="python-code" class="language-python"></code>
|
||||
<button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="python-code">Copy</button>
|
||||
</pre>
|
||||
<pre class="hidden h-full flex relative overflow-x-auto">
|
||||
<code id="nodejs-code" class="language-javascript"></code>
|
||||
<button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="nodejs-code">Copy</button>
|
||||
</pre>
|
||||
<pre class="hidden h-full flex relative overflow-x-auto">
|
||||
<code id="library-code" class="language-python"></code>
|
||||
<button class="absolute top-2 right-2 bg-zinc-700 text-white px-2 py-1 rounded copy-btn" data-target="library-code">Copy</button>
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
434
pages/tmp.html
434
pages/tmp.html
@@ -1,434 +0,0 @@
|
||||
<div class="w-3/4 p-4">
|
||||
<section id="installation" class="content-section active">
|
||||
<h1 class="text-2xl font-bold">Installation 💻</h1>
|
||||
<p class="mb-4">There are three ways to use Crawl4AI:</p>
|
||||
<ol class="list-decimal list-inside mb-4">
|
||||
<li class="">As a library</li>
|
||||
<li class="">As a local server (Docker)</li>
|
||||
<li class="">
|
||||
As a Google Colab notebook.
|
||||
<a href="https://colab.research.google.com/drive/1wz8u30rvbq6Scodye9AGCw8Qg_Z8QGsk"
|
||||
><img
|
||||
src="https://colab.research.google.com/assets/colab-badge.svg"
|
||||
alt="Open In Colab"
|
||||
style="display: inline-block; width: 100px; height: 20px"
|
||||
/></a>
|
||||
</li>
|
||||
<p></p>
|
||||
|
||||
<p class="my-4">To install Crawl4AI as a library, follow these steps:</p>
|
||||
|
||||
<ol class="list-decimal list-inside mb-4">
|
||||
<li class="mb-4">
|
||||
Install the package from GitHub:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code class="hljs language-bash">pip install git+https://github.com/unclecode/crawl4ai.git</code></pre>
|
||||
</li>
|
||||
<li class="mb-4">
|
||||
Alternatively, you can clone the repository and install the package locally:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code class="language-python bash hljs">virtualenv venv
|
||||
source venv/<span class="hljs-built_in">bin</span>/activate
|
||||
git clone https://github.com/unclecode/crawl4ai.git
|
||||
cd crawl4ai
|
||||
pip install -e .
|
||||
</code></pre>
|
||||
</li>
|
||||
<li class="">
|
||||
Use docker to run the local server:
|
||||
<pre
|
||||
class="bg-zinc-800 p-4 rounded mt-2 text-zinc-100"
|
||||
><code class="language-python bash hljs">docker build -t crawl4ai .
|
||||
<span class="hljs-comment"># docker build --platform linux/amd64 -t crawl4ai . For Mac users</span>
|
||||
docker run -d -p <span class="hljs-number">8000</span>:<span class="hljs-number">80</span> crawl4ai</code></pre>
|
||||
</li>
|
||||
</ol>
|
||||
<p class="mb-4">
|
||||
For more information about how to run Crawl4AI as a local server, please refer to the
|
||||
<a href="https://github.com/unclecode/crawl4ai" class="text-blue-400">GitHub repository</a>.
|
||||
</p>
|
||||
</ol>
|
||||
</section>
|
||||
<section id="how-to-guide" class="content-section">
|
||||
<h1 class="text-2xl font-bold">How to Guide</h1>
|
||||
<div class="flex flex-col gap-4 p-4 bg-zinc-900 text-lime-500">
|
||||
<!-- Step 1 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🌟
|
||||
<strong>Welcome to the Crawl4ai Quickstart Guide! Let's dive into some web crawling fun!</strong>
|
||||
</div>
|
||||
<div class="">
|
||||
First Step: Create an instance of WebCrawler and call the
|
||||
<code>warmup()</code> function.
|
||||
</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">crawler = WebCrawler()
|
||||
crawler.warmup()</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 2 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🧠 <strong>Understanding 'bypass_cache' and 'include_raw_html' parameters:</strong>
|
||||
</div>
|
||||
<div class="">First crawl (caches the result):</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>)</code></pre>
|
||||
</div>
|
||||
<div class="">Second crawl (Force to crawl again):</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>, bypass_cache=<span class="hljs-literal">True</span>)</code></pre>
|
||||
<div class="bg-red-900 p-2 text-zinc-50">
|
||||
⚠️ Don't forget to set <code>`bypass_cache`</code> to True if you want to try different strategies
|
||||
for the same URL. Otherwise, the cached result will be returned. You can also set
|
||||
<code>`always_by_pass_cache`</code> in constructor to True to always bypass the cache.
|
||||
</div>
|
||||
</div>
|
||||
<div class="">Crawl result without raw HTML content:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>, include_raw_html=<span class="hljs-literal">False</span>)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 3 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
📄
|
||||
<strong
|
||||
>The 'include_raw_html' parameter, when set to True, includes the raw HTML content in the response.
|
||||
By default, it is set to True.</strong
|
||||
>
|
||||
</div>
|
||||
<div class="">Set <code>always_by_pass_cache</code> to True:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">crawler.always_by_pass_cache = <span class="hljs-literal">True</span></code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 4 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🧩 <strong>Let's add a chunking strategy: RegexChunking!</strong>
|
||||
</div>
|
||||
<div class="">Using RegexChunking:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(
|
||||
url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
|
||||
chunking_strategy=RegexChunking(patterns=[<span class="hljs-string">"\n\n"</span>])
|
||||
)</code></pre>
|
||||
</div>
|
||||
<div class="">Using NlpSentenceChunking:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(
|
||||
url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
|
||||
chunking_strategy=NlpSentenceChunking()
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 5 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🧠 <strong>Let's get smarter with an extraction strategy: CosineStrategy!</strong>
|
||||
</div>
|
||||
<div class="">Using CosineStrategy:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(
|
||||
url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
|
||||
extraction_strategy=CosineStrategy(word_count_threshold=<span class="hljs-number">20</span>, max_dist=<span class="hljs-number">0.2</span>, linkage_method=<span class="hljs-string">"ward"</span>, top_k=<span class="hljs-number">3</span>)
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 6 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🤖
|
||||
<strong>Time to bring in the big guns: LLMExtractionStrategy without instructions!</strong>
|
||||
</div>
|
||||
<div class="">Using LLMExtractionStrategy without instructions:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(
|
||||
url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
|
||||
extraction_strategy=LLMExtractionStrategy(provider=<span class="hljs-string">"openai/gpt-4o"</span>, api_token=os.getenv(<span class="hljs-string">'OPENAI_API_KEY'</span>))
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 7 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
📜
|
||||
<strong>Let's make it even more interesting: LLMExtractionStrategy with instructions!</strong>
|
||||
</div>
|
||||
<div class="">Using LLMExtractionStrategy with instructions:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(
|
||||
url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
|
||||
extraction_strategy=LLMExtractionStrategy(
|
||||
provider=<span class="hljs-string">"openai/gpt-4o"</span>,
|
||||
api_token=os.getenv(<span class="hljs-string">'OPENAI_API_KEY'</span>),
|
||||
instruction=<span class="hljs-string">"I am interested in only financial news"</span>
|
||||
)
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 8 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🎯
|
||||
<strong>Targeted extraction: Let's use a CSS selector to extract only H2 tags!</strong>
|
||||
</div>
|
||||
<div class="">Using CSS selector to extract H2 tags:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">result = crawler.run(
|
||||
url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>,
|
||||
css_selector=<span class="hljs-string">"h2"</span>
|
||||
)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Step 9 -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🖱️
|
||||
<strong>Let's get interactive: Passing JavaScript code to click 'Load More' button!</strong>
|
||||
</div>
|
||||
<div class="">Using JavaScript to click 'Load More' button:</div>
|
||||
<div>
|
||||
<pre><code class="language-python hljs">js_code = <span class="hljs-string">"""
|
||||
const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
|
||||
loadMoreButton && loadMoreButton.click();
|
||||
"""</span>
|
||||
crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
|
||||
crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=<span class="hljs-literal">True</span>)
|
||||
result = crawler.run(url=<span class="hljs-string">"https://www.nbcnews.com/business"</span>)</code></pre>
|
||||
</div>
|
||||
|
||||
<!-- Conclusion -->
|
||||
<div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
|
||||
🎉
|
||||
<strong
|
||||
>Congratulations! You've made it through the Crawl4ai Quickstart Guide! Now go forth and crawl the
|
||||
web like a pro! 🕸️</strong
|
||||
>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section id="chunking-strategies" class="content-section">
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>RegexChunking</h3>
|
||||
<p>
|
||||
<code>RegexChunking</code> is a text chunking strategy that splits a given text into smaller parts
|
||||
using regular expressions. This is useful for preparing large texts for processing by language
|
||||
models, ensuring they are divided into manageable segments.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<ul>
|
||||
<li>
|
||||
<code>patterns</code> (list, optional): A list of regular expression patterns used to split the
|
||||
text. Default is to split by double newlines (<code>['\n\n']</code>).
|
||||
</li>
|
||||
</ul>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">chunker = RegexChunking(patterns=[r'\n\n', r'\. '])
|
||||
chunks = chunker.chunk("This is a sample text. It will be split into chunks.")
|
||||
</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>NlpSentenceChunking</h3>
|
||||
<p>
|
||||
<code>NlpSentenceChunking</code> uses a natural language processing model to chunk a given text into
|
||||
sentences. This approach leverages SpaCy to accurately split text based on sentence boundaries.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<ul>
|
||||
<li>
|
||||
None.
|
||||
</li>
|
||||
</ul>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">chunker = NlpSentenceChunking()
|
||||
chunks = chunker.chunk("This is a sample text. It will be split into sentences.")
|
||||
</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>TopicSegmentationChunking</h3>
|
||||
<p>
|
||||
<code>TopicSegmentationChunking</code> uses the TextTiling algorithm to segment a given text into
|
||||
topic-based chunks. This method identifies thematic boundaries in the text.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<ul>
|
||||
<li>
|
||||
<code>num_keywords</code> (int, optional): The number of keywords to extract for each topic
|
||||
segment. Default is <code>3</code>.
|
||||
</li>
|
||||
</ul>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">chunker = TopicSegmentationChunking(num_keywords=3)
|
||||
chunks = chunker.chunk("This is a sample text. It will be split into topic-based segments.")
|
||||
</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>FixedLengthWordChunking</h3>
|
||||
<p>
|
||||
<code>FixedLengthWordChunking</code> splits a given text into chunks of fixed length, based on the
|
||||
number of words.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<ul>
|
||||
<li>
|
||||
<code>chunk_size</code> (int, optional): The number of words in each chunk. Default is
|
||||
<code>100</code>.
|
||||
</li>
|
||||
</ul>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">chunker = FixedLengthWordChunking(chunk_size=100)
|
||||
chunks = chunker.chunk("This is a sample text. It will be split into fixed-length word chunks.")
|
||||
</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>SlidingWindowChunking</h3>
|
||||
<p>
|
||||
<code>SlidingWindowChunking</code> uses a sliding window approach to chunk a given text. Each chunk
|
||||
has a fixed length, and the window slides by a specified step size.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<ul>
|
||||
<li>
|
||||
<code>window_size</code> (int, optional): The number of words in each chunk. Default is
|
||||
<code>100</code>.
|
||||
</li>
|
||||
<li>
|
||||
<code>step</code> (int, optional): The number of words to slide the window. Default is
|
||||
<code>50</code>.
|
||||
</li>
|
||||
</ul>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">chunker = SlidingWindowChunking(window_size=100, step=50)
|
||||
chunks = chunker.chunk("This is a sample text. It will be split using a sliding window approach.")
|
||||
</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="extraction-strategies" class="content-section">
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>NoExtractionStrategy</h3>
|
||||
<p>
|
||||
<code>NoExtractionStrategy</code> is a basic extraction strategy that returns the entire HTML
|
||||
content without any modification. It is useful for cases where no specific extraction is required.
|
||||
Only clean html, and amrkdown.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<p>None.</p>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">extractor = NoExtractionStrategy()
|
||||
extracted_content = extractor.extract(url, html)
|
||||
</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>LLMExtractionStrategy</h3>
|
||||
<p>
|
||||
<code>LLMExtractionStrategy</code> uses a Language Model (LLM) to extract meaningful blocks or
|
||||
chunks from the given HTML content. This strategy leverages an external provider for language model
|
||||
completions.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<ul>
|
||||
<li>
|
||||
<code>provider</code> (str, optional): The provider to use for the language model completions.
|
||||
Default is <code>DEFAULT_PROVIDER</code> (e.g., openai/gpt-4).
|
||||
</li>
|
||||
<li>
|
||||
<code>api_token</code> (str, optional): The API token for the provider. If not provided, it will
|
||||
try to load from the environment variable <code>OPENAI_API_KEY</code>.
|
||||
</li>
|
||||
<li>
|
||||
<code>instruction</code> (str, optional): An instruction to guide the LLM on how to perform the
|
||||
extraction. This allows users to specify the type of data they are interested in or set the tone
|
||||
of the response. Default is <code>None</code>.
|
||||
</li>
|
||||
</ul>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">extractor = LLMExtractionStrategy(provider='openai', api_token='your_api_token', instruction='Extract only news about AI.')
|
||||
extracted_content = extractor.extract(url, html)
|
||||
</code></pre>
|
||||
<p>
|
||||
By providing clear instructions, users can tailor the extraction process to their specific needs,
|
||||
enhancing the relevance and utility of the extracted content.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>CosineStrategy</h3>
|
||||
<p>
|
||||
<code>CosineStrategy</code> uses hierarchical clustering based on cosine similarity to extract
|
||||
clusters of text from the given HTML content. This strategy is suitable for identifying related
|
||||
content sections.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<ul>
|
||||
<li>
|
||||
<code>semantic_filter</code> (str, optional): A string containing keywords for filtering relevant
|
||||
documents before clustering. If provided, documents are filtered based on their cosine
|
||||
similarity to the keyword filter embedding. Default is <code>None</code>.
|
||||
</li>
|
||||
<li>
|
||||
<code>word_count_threshold</code> (int, optional): Minimum number of words per cluster. Default
|
||||
is <code>20</code>.
|
||||
</li>
|
||||
<li>
|
||||
<code>max_dist</code> (float, optional): The maximum cophenetic distance on the dendrogram to
|
||||
form clusters. Default is <code>0.2</code>.
|
||||
</li>
|
||||
<li>
|
||||
<code>linkage_method</code> (str, optional): The linkage method for hierarchical clustering.
|
||||
Default is <code>'ward'</code>.
|
||||
</li>
|
||||
<li>
|
||||
<code>top_k</code> (int, optional): Number of top categories to extract. Default is
|
||||
<code>3</code>.
|
||||
</li>
|
||||
<li>
|
||||
<code>model_name</code> (str, optional): The model name for embedding generation. Default is
|
||||
<code>'BAAI/bge-small-en-v1.5'</code>.
|
||||
</li>
|
||||
</ul>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">extractor = CosineStrategy(semantic_filter='artificial intelligence', word_count_threshold=10, max_dist=0.2, linkage_method='ward', top_k=3, model_name='BAAI/bge-small-en-v1.5')
|
||||
extracted_content = extractor.extract(url, html)
|
||||
</code></pre>
|
||||
<h4>Cosine Similarity Filtering</h4>
|
||||
<p>
|
||||
When a <code>semantic_filter</code> is provided, the <code>CosineStrategy</code> applies an
|
||||
embedding-based filtering process to select relevant documents before performing hierarchical
|
||||
clustering.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="bg-zinc-800 p-4 rounded shadow-md docs-item">
|
||||
<div class="text-gray-300 prose prose-sm">
|
||||
<h3>TopicExtractionStrategy</h3>
|
||||
<p>
|
||||
<code>TopicExtractionStrategy</code> uses the TextTiling algorithm to segment the HTML content into
|
||||
topics and extracts keywords for each segment. This strategy is useful for identifying and
|
||||
summarizing thematic content.
|
||||
</p>
|
||||
<h4>Constructor Parameters:</h4>
|
||||
<ul>
|
||||
<li>
|
||||
<code>num_keywords</code> (int, optional): Number of keywords to represent each topic segment.
|
||||
Default is <code>3</code>.
|
||||
</li>
|
||||
</ul>
|
||||
<h4>Example usage:</h4>
|
||||
<pre><code class="language-python">extractor = TopicExtractionStrategy(num_keywords=3)
|
||||
extracted_content = extractor.extract(url, html)
|
||||
</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
</div>
|
||||
Reference in New Issue
Block a user