Web Scraper Extractor

Built-in extractor names are a deprecated alias — collections are now created by picking features. This pipeline is selected with features: ["web_crawl"]. Existing feature_extractor configs keep working; see the migration guide.

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Runnable reference for this extractor — inputs, parameters, output fields, embedding models, and copy-paste examples. Auto-generated from the live registry.

Web scraper extractor pipeline showing crawling, content extraction, chunking, and multimodal embeddings

The web scraper extractor recursively crawls websites to extract multimodal content with semantic embeddings. Automatically discovers and extracts text, code blocks, images, and asset links from web pages. Each extracted document receives E5-Large text embeddings (1024D) for semantic search, Jina Code embeddings (768D) for code snippets, and optional SigLIP visual embeddings (768D) for images. Supports JavaScript-rendered SPAs, includes resilience features like retry logic, proxy rotation, and captcha detection.

View extractor details at api.mixpeek.com/v1/collections/features/extractors/web_scraper_v1 or fetch programmatically with GET /v1/collections/features/extractors/{feature_extractor_id}.

Pipeline Steps

Filter Dataset (if collection_id provided)
- Filter to specified collection
Crawl Configuration & Setup
- Parse seed URL and configure crawl parameters
- Set up URL filtering rules, rendering strategy, resilience options
Recursive Web Crawling
- BFS-based link traversal with depth limit
- JavaScript rendering support (auto-detect or explicit)
- URL filtering (include/exclude patterns)
- Resilience: retry logic, proxy rotation, captcha detection
Content Extraction Per Page
- Extract text content, title, metadata
- Identify and extract code blocks with language detection
- Discover images with alt text, dimensions
- Find asset links (PDFs, documents, archives)
- Optional: Structured extraction via LLM (response_shape)
Content Chunking (optional)
- Split page content by strategy: sentences, paragraphs, words, characters
- Configurable chunk size and overlap
- Track chunk metadata for joined results
Document Expansion
- Create separate documents for page content, each code block, each image
- Preserve parent URL and crawl depth metadata
Multi-Modal Embedding Generation
- E5-Large (1024D) for page text content
- Jina Code (768D) for code blocks
- SigLIP (768D) for images (if generate_image_embeddings=true)
Output
- Documents with text content, code blocks, images
- Asset links discovered but not crawled
- Multiple embeddings per document for hybrid search

When to Use

Use Case	Description
API documentation	Index technical documentation with code examples and diagrams
Knowledge base crawling	Extract FAQs, guides, and tutorials from support sites
Job board scraping	Find job listings with parsed content and structured fields
News aggregation	Collect and index articles with multimodal content
Competitive analysis	Monitor competitor websites for content changes
Open source docs	Index project documentation from GitHub Pages, ReadTheDocs
Product research	Gather product information from multiple websites

When NOT to Use

Scenario	Recommended Alternative
Protected/authenticated content	Configure via `custom_headers` with auth tokens
PDF-only extraction	`document_graph_extractor` (better OCR, layout detection)
Social media scraping	Use platform-specific APIs (Twitter API, Instagram Graph API)
E-commerce product catalogs	Use platform APIs when available (better data structure)
Very large sites (10K+ pages)	Increase `max_pages`, implement crawl goal filtering

Input Schema

Field	Type	Required	Description
`url`	string	Yes	Seed URL to start crawling from. Example: `https://docs.example.com/api/`

{
  "url": "https://docs.example.com/getting-started"
}

Input Examples:

Type	Example
API documentation	`https://docs.openai.com/api/`
Knowledge base	`https://help.example.com/`
Blog	`https://blog.example.com/`
Job board	`https://jobs.example.com/listings`

Output Schema

Each crawled page produces one or more documents depending on content extraction and expansion settings:

Field	Type	Description
`content`	string	Extracted text content from page
`title`	string	Page title (from `<title>` tag or heading)
`page_url`	string	Full URL of crawled page
`code_blocks`	array	Code blocks found on page (structure: `[{language, code, line_start, line_end}]`)
`images`	array	Images found on page (structure: `[{src, alt, title, width, height}]`)
`asset_links`	array	Downloadable assets discovered (structure: `[{url, file_type, link_text, file_extension}]`)
`chunk_index`	integer	Position within page chunks (if chunking enabled)
`total_chunks`	integer	Total chunks from this page (if chunking enabled)
`crawl_depth`	integer	Depth from seed URL (0 = seed, 1 = links from seed, etc.)
`parent_url`	string	Referrer URL (previous page in crawl path)
`intfloat__multilingual_e5_large_instruct`	float[1024]	E5-Large text embedding, L2 normalized
`jinaai__jina_embeddings_v2_base_code`	float[768]	Jina Code embedding (if code blocks extracted)
`google__siglip_base_patch16_224`	float[768]	SigLIP visual embedding (if `generate_image_embeddings=true`)

{
  "content": "The REST API provides endpoints for creating, reading, updating, and deleting resources...",
  "title": "REST API Overview - Example Docs",
  "page_url": "https://docs.example.com/api/overview",
  "code_blocks": [
    {
      "language": "python",
      "code": "import requests\nresponse = requests.get('https://api.example.com/users')",
      "line_start": 1,
      "line_end": 2
    }
  ],
  "images": [
    {
      "src": "https://docs.example.com/images/api-flow.png",
      "alt": "API request flow diagram",
      "width": 800,
      "height": 600
    }
  ],
  "asset_links": [
    {
      "url": "https://docs.example.com/downloads/openapi.yaml",
      "file_type": "openapi",
      "link_text": "Download OpenAPI Spec",
      "file_extension": "yaml"
    }
  ],
  "crawl_depth": 2,
  "parent_url": "https://docs.example.com/api/",
  "intfloat__multilingual_e5_large_instruct": [0.023, -0.041, 0.018, ...],
  "jinaai__jina_embeddings_v2_base_code": [0.045, -0.023, ...],
  "google__siglip_base_patch16_224": [0.078, -0.091, ...]
}

Parameters

Crawl Configuration Parameters

Parameter	Type	Default	Range	Description
`max_depth`	integer	2	0-1000	Maximum link depth from seed (0 = seed URL only, higher = deeper crawl)
`max_pages`	integer	50	1-1000000	Maximum pages to crawl in single run
`crawl_timeout`	integer	300	10-3600	Maximum time for crawl in seconds (10s - 1h)
`crawl_mode`	enum	`"deterministic"`	deterministic, semantic	BFS deterministic or LLM-guided semantic crawling
`crawl_goal`	string	null	-	Goal for semantic crawling (e.g., “find all API endpoints”). Used with `crawl_mode: semantic`

Rendering Parameters

Parameter	Type	Default	Description
`render_strategy`	enum	`"auto"`	Rendering method: `static` (HTML only), `javascript` (Puppeteer), `auto` (auto-detect)

URL Filtering Parameters

Parameter	Type	Default	Description
`include_patterns`	array	null	Regex patterns for URLs to include (whitelist). Example: `["/docs/.", "/api/."]`
`exclude_patterns`	array	null	Regex patterns for URLs to exclude (blacklist). Example: `["/admin/.", ".logout.*"]`

Content Chunking Parameters

Parameter	Type	Default	Range	Description
`chunk_strategy`	enum	`"none"`	none, sentences, paragraphs, words, characters	How to split page content
`chunk_size`	integer	500	1-10000	Target size per chunk (units depend on strategy)
`chunk_overlap`	integer	50	0-5000	Overlap between consecutive chunks

Document Identity Parameters

Parameter	Type	Default	Description
`document_id_strategy`	enum	`"url"`	How to generate document IDs: `url` (unique per page), `position` (sequential), `content` (hash-based)

Embedding Parameters

Parameter	Type	Default	Description
`generate_text_embeddings`	boolean	true	Generate E5-Large text embeddings for page content
`generate_code_embeddings`	boolean	true	Generate Jina Code embeddings for code blocks
`generate_image_embeddings`	boolean	true	Generate SigLIP embeddings for discovered images

LLM Structured Extraction Parameters

Parameter	Type	Default	Description
`response_shape`	string or object	null	Define structured extraction: natural language description or JSON schema
`llm_provider`	string	null	LLM provider: `openai`, `google`, `anthropic` (required if using `response_shape`)
`llm_model`	string	null	Specific LLM model (e.g., `gpt-4o-mini`, `gemini-2.5-flash`)
`llm_api_key`	string	null	API key (supports secret vault references like `${vault:openai-key}`)

Resilience: Retry Parameters

Parameter	Type	Default	Range	Description
`max_retries`	integer	3	0-10	Maximum retry attempts on request failure
`retry_base_delay`	number	1.0	0.1-30.0	Base delay for exponential backoff (seconds)
`retry_max_delay`	number	30.0	1.0-300.0	Maximum delay between retries (seconds)
`respect_retry_after`	boolean	true	-	Respect `Retry-After` header from server

Resilience: Proxy Parameters

Parameter	Type	Default	Description
`proxies`	array	null	Proxy URLs for rotation. Example: `["http://proxy1:8080", "http://proxy2:8080"]`
`rotate_proxy_on_error`	boolean	true	Rotate proxy when request fails
`rotate_proxy_every_n_requests`	integer	0	Rotate proxy every N requests (0 = no periodic rotation)

Resilience: Captcha Parameters

Parameter	Type	Default	Description
`captcha_service_provider`	string	null	Captcha solving service: `2captcha`, `anti-captcha`, `capsolver`
`captcha_service_api_key`	string	null	API key for captcha service (supports secret vault references)
`detect_captcha`	boolean	true	Auto-detect captcha challenges and attempt to solve

Resilience: Session Parameters

Parameter	Type	Default	Description
`persist_cookies`	boolean	true	Persist cookies across requests within single crawl
`custom_headers`	object	null	Custom HTTP headers. Example: `{"Authorization": "Bearer token", "User-Agent": "Custom"}`

Politeness Parameters

Parameter	Type	Default	Range	Description
`delay_between_requests`	number	0.0	0.0-60.0	Delay between consecutive requests (seconds)

Configuration Examples

{
  "feature_extractor": {
    "feature_extractor_name": "web_scraper",
    "version": "v1",
    "input_mappings": {
      "url": "docs_url"
    },
    "field_passthrough": [
      { "source_path": "metadata.vendor" },
      { "source_path": "metadata.product" }
    ],
    "parameters": {
      "max_depth": 2,
      "max_pages": 50,
      "crawl_timeout": 300,
      "render_strategy": "auto",
      "generate_text_embeddings": true,
      "generate_code_embeddings": true,
      "generate_image_embeddings": false,
      "delay_between_requests": 0.5
    }
  }
}

{
  "feature_extractor": {
    "feature_extractor_name": "web_scraper",
    "version": "v1",
    "input_mappings": {
      "url": "api_docs_url"
    },
    "parameters": {
      "max_depth": 3,
      "max_pages": 100,
      "include_patterns": ["/api/.*", "/reference/.*"],
      "exclude_patterns": ["/changelog/.*", "/deprecated/.*"],
      "render_strategy": "javascript",
      "response_shape": {
        "type": "object",
        "properties": {
          "endpoints": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "method": { "type": "string" },
                "path": { "type": "string" },
                "description": { "type": "string" },
                "parameters": { "type": "array", "items": { "type": "string" } }
              }
            }
          },
          "authentication": { "type": "string" },
          "rate_limits": { "type": "string" }
        }
      },
      "llm_provider": "openai",
      "llm_model": "gpt-4o-mini",
      "generate_text_embeddings": true,
      "generate_code_embeddings": true,
      "generate_image_embeddings": true
    }
  }
}

{
  "feature_extractor": {
    "feature_extractor_name": "web_scraper",
    "version": "v1",
    "input_mappings": {
      "url": "kb_url"
    },
    "parameters": {
      "max_depth": 4,
      "max_pages": 200,
      "crawl_mode": "semantic",
      "crawl_goal": "Find all articles related to troubleshooting and error resolution",
      "chunk_strategy": "paragraphs",
      "chunk_size": 1000,
      "chunk_overlap": 100,
      "generate_text_embeddings": true,
      "generate_code_embeddings": false,
      "generate_image_embeddings": false
    }
  }
}

{
  "feature_extractor": {
    "feature_extractor_name": "web_scraper",
    "version": "v1",
    "input_mappings": {
      "url": "job_board_url"
    },
    "parameters": {
      "max_depth": 2,
      "max_pages": 500,
      "render_strategy": "javascript",
      "max_retries": 5,
      "retry_base_delay": 2.0,
      "retry_max_delay": 60.0,
      "proxies": ["http://proxy1:8080", "http://proxy2:8080"],
      "rotate_proxy_on_error": true,
      "rotate_proxy_every_n_requests": 10,
      "persist_cookies": true,
      "delay_between_requests": 1.0,
      "captcha_service_provider": "2captcha",
      "captcha_service_api_key": "${vault:captcha-api-key}",
      "generate_text_embeddings": true,
      "generate_code_embeddings": false
    }
  }
}

{
  "feature_extractor": {
    "feature_extractor_name": "web_scraper",
    "version": "v1",
    "input_mappings": {
      "url": "website_url"
    },
    "parameters": {
      "max_depth": 5,
      "max_pages": 1000,
      "crawl_timeout": 3600,
      "include_patterns": ["^https://example\\.com/.*"],
      "exclude_patterns": [".*login.*", ".*admin.*", ".*/search\\?.*"],
      "chunk_strategy": "sentences",
      "chunk_size": 500,
      "chunk_overlap": 50,
      "document_id_strategy": "url",
      "generate_text_embeddings": true,
      "generate_code_embeddings": true,
      "generate_image_embeddings": false,
      "max_retries": 3,
      "delay_between_requests": 0.2
    }
  }
}

{
  "feature_extractor": {
    "feature_extractor_name": "web_scraper",
    "version": "v1",
    "input_mappings": {
      "url": "target_url"
    },
    "parameters": {
      "max_depth": 3,
      "max_pages": 250,
      "crawl_timeout": 1800,
      "crawl_mode": "semantic",
      "crawl_goal": "Find all technical content, code examples, and API documentation",
      "render_strategy": "javascript",
      "include_patterns": ["/docs/.*", "/api/.*", "/guide/.*"],
      "exclude_patterns": ["/legacy/.*"],
      "chunk_strategy": "paragraphs",
      "chunk_size": 1000,
      "chunk_overlap": 100,
      "response_shape": "Extract key topics, code language, and frameworks mentioned",
      "llm_provider": "anthropic",
      "llm_model": "claude-3-5-haiku-20241022",
      "generate_text_embeddings": true,
      "generate_code_embeddings": true,
      "generate_image_embeddings": true,
      "max_retries": 5,
      "retry_base_delay": 1.5,
      "proxies": ["http://proxy1:8080"],
      "rotate_proxy_every_n_requests": 5,
      "persist_cookies": true,
      "custom_headers": {
        "User-Agent": "Mozilla/5.0 (compatible; MixpeekBot/1.0)"
      },
      "delay_between_requests": 0.5
    }
  }
}

Performance & Costs

Metric	Value
Average page load	2-5 seconds (depends on page complexity and rendering)
Pages per minute	12-30 pages (with delays and retries)
Code block extraction	~10ms per 1KB of code
Image extraction	~50ms per 10 images
Embedding latency	~5ms per text page (E5), ~10ms per code block (Jina), ~50ms per image (SigLIP)
Cost	Billed per crawled page — see Billing & Pricing; rates come from `GET /v1/billing/pricing`
Memory usage	~100MB base + ~1MB per 100 pages in crawl queue

Vector Indexes

All three embeddings are stored as MVS named vectors for hybrid search:

Property	Value
Index 1 name	`intfloat__multilingual_e5_large_instruct`
Dimensions	1024
Type	Dense
Distance metric	Cosine
Datatype	float32
Normalization	L2 normalized

Property	Value
Index 2 name	`jinaai__jina_embeddings_v2_base_code`
Dimensions	768
Type	Dense
Distance metric	Cosine
Datatype	float32
Normalization	L2 normalized

Property	Value
Index 3 name	`google__siglip_base_patch16_224`
Dimensions	768
Type	Dense
Distance metric	Cosine
Datatype	float32
Status	Optional (if `generate_image_embeddings=true`)

Comparison with Other Extractors

Feature	web_scraper	text_extractor	multimodal_extractor	document_graph_extractor
Input types	URLs (crawling)	Text only	Video, Image, Text	PDF only
Recursive crawling	✅ Yes	✗	✗	✗
Code extraction	✅ Yes	✗	✗	✗
Image extraction	✅ Yes	✗	✅ Yes	✗
Multimodal embeddings	✅ Yes	Text only	✅ Yes	Text only
LLM extraction	✅ Yes	✅ Yes	✗	✗
Resilience features	✅ Yes	✗	✗	✗
Best for	Web crawling	Text search	Video/image/text	PDF analysis
Cost	Per crawled page — see Billing & Pricing	Per token	Per video minute	Per page

Resilience & Robustness

The web scraper includes enterprise-grade resilience features:

Retry Strategy

Exponential backoff with configurable base and max delays
Respects server Retry-After headers
Retries on network errors, timeouts, and temporary failures (5xx)

Proxy Rotation

Support for multiple proxies with automatic rotation
Rotate on error or periodic rotation every N requests
Helps avoid rate limiting and IP bans

Captcha Detection & Solving

Auto-detect common captcha types (reCAPTCHA, hCaptcha)
Integration with 2captcha, Anti-Captcha, CapSolver services
Fallback to manual review if solving fails

Session Management

Persistent cookies across requests within a single crawl
Custom HTTP headers for authentication
Support for API key and bearer token injection

URL Filtering

Include patterns (whitelist): Only crawl matching URLs
Exclude patterns (blacklist): Skip URLs matching patterns
Prevent crawling auth/admin pages, search results, etc.

Limitations

Content-only crawling: Does not execute custom JavaScript actions (clicking, form submission, scrolling)
Authentication: Limited to HTTP headers (Bearer tokens, API keys). No interactive login flows.
Dynamic content: JavaScript-rendering adds 2-3x latency per page
Large sites: 10K+ page sites may require high max_pages and long timeouts
Robots.txt: Does not parse robots.txt; respect via delay_between_requests and max_pages
Rate limiting: May be blocked by aggressive rate limiting; use proxies and delays

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Resources

Web Scraper Extractor

View on GitHub

Pipeline Steps

When to Use

When NOT to Use

Input Schema

Output Schema

Parameters

Crawl Configuration Parameters

Rendering Parameters

URL Filtering Parameters

Content Chunking Parameters

Document Identity Parameters

Embedding Parameters

LLM Structured Extraction Parameters

Resilience: Retry Parameters

Resilience: Proxy Parameters

Resilience: Captcha Parameters

Resilience: Session Parameters

Politeness Parameters

Configuration Examples

Performance & Costs

Vector Indexes

Comparison with Other Extractors

Resilience & Robustness

Retry Strategy

Proxy Rotation

Captcha Detection & Solving

Session Management

URL Filtering

Limitations

View on GitHub

​Pipeline Steps

​When to Use

​When NOT to Use

​Input Schema

​Output Schema

​Parameters

​Crawl Configuration Parameters

​Rendering Parameters

​URL Filtering Parameters

​Content Chunking Parameters

​Document Identity Parameters

​Embedding Parameters

​LLM Structured Extraction Parameters

​Resilience: Retry Parameters

​Resilience: Proxy Parameters

​Resilience: Captcha Parameters

​Resilience: Session Parameters

​Politeness Parameters

​Configuration Examples

​Performance & Costs

​Vector Indexes

​Comparison with Other Extractors

​Resilience & Robustness

​Retry Strategy

​Proxy Rotation

​Captcha Detection & Solving

​Session Management

​URL Filtering

​Limitations

​Related

Pipeline Steps

When to Use

When NOT to Use

Input Schema

Output Schema

Parameters

Crawl Configuration Parameters

Rendering Parameters

URL Filtering Parameters

Content Chunking Parameters

Document Identity Parameters

Embedding Parameters

LLM Structured Extraction Parameters

Resilience: Retry Parameters

Resilience: Proxy Parameters

Resilience: Captcha Parameters

Resilience: Session Parameters

Politeness Parameters

Configuration Examples

Performance & Costs

Vector Indexes

Comparison with Other Extractors

Resilience & Robustness

Retry Strategy

Proxy Rotation

Captcha Detection & Solving

Session Management

URL Filtering

Limitations

Related