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Extraction basics

Dendrite supports structured data extraction for any website using natural language, making it ideal for LLMs and AI agents. This is based on our agents that write scripts for extracting data based on the prompt it’s given. There are different methods that are specialized on different use cases:
  • extract is ideal for high volume extraction from predictable page structures where content remains static
  • ask is ideal for dynamic content or context-sensitive data extraction where the page structure might be unpredictable

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Extract method

The extract method is made for predictable extraction where one or several subpages (like blog articles) will be mapped on a regular basis. It utilizes caching of successful scripts, making it ideal for repeat use. More about caching.

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Simple example

from dendrite import Dendrite

# Initiate Dendrite instance
browser = Dendrite()

# Navigate and interact with search field
browser.goto("https://www.ycombinator.com/companies")
browser.fill("Search field", value="AI agent")
browser.press("Enter")

# Extract startups with natural language description
startups = browser.extract(
  "All companies. Return a list of dicts with name, location, description and url"
)

print(startups)

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Example with typing

In this example, we extract all AI agent companies in the YC startup directory. We use a Pydantic model to get a typed response. 
from dendrite import Dendrite
from pydantic import BaseModel

# Define Pydantic models
class Startup(BaseModel):
  name: str
  location: str
  description: str
  url: str

class Startups(BaseModel):
  items: list[Startup]

# Initiate Dendrite instance
browser = Dendrite()

# Navigate and interact with search field
browser.goto("https://www.ycombinator.com/companies")
browser.fill("Search field", value="AI agent")
browser.press("Enter")

# Extract startups with structured output with Pydantic model
startups = browser.extract("", Startups)

print(startups.items)
The extract method exists on the Page and Dendrite classes. If invoked from a Dendrite instance, as above, it is performed on the active page

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Asking quesions about the page

The ask method allows you to ask questions about a page and get a structured output. It doesn’t write any scripts, it’s an agent that always uses the full context of the page to decide the return value. It has access to the page both through its source code and through computer vision. So you can ask any question that a human would be able to answer after taking a look at the page, and more. For certain tasks where the page is more dynamic, the ask is more helpful for structured data extraction that the extract method. For those use cases, ask support Pydantic models and JsonSchemas as type specification.

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Example with Page Validation

In this example, we’re asking the agent if there are any unread emails in the inbox and proceeding differently depending on the outcome. 
from dendrite import Dendrite

# Initiate Dendrite instance
browser = Dendrite(auth="mail.google.com")

# Navigate and wait for loading
browser.goto("https://mail.google.com/", expected_page="Email inbox")

# Ask agent about the state of the page
has_new_emails = browser.ask(
    "Do I have any unread emails in my inbox?", type_spec=bool
)

# Handle unread emails if there are any
if has_new_emails:
  print("There are unread emails in inbox")
else:
  print("No unread emails in inbox")

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Example with Data Extraction

In this example, we’re extracting the latest posts sent as DM to the user through Instagram. This is a perfect use case for ask, since the conversation will be dynamic with:
  1. messages sent to and from the current user
  2. a mix of plain text messages and shared posts
from dendrite import Dendrite
from pydantic import BaseModel

# Define Pydantic models
class Post(BaseModel):
  title: str
  url: str
  date_sent: str

class Posts(BaseModel):
  items: list[Post]

# Initiate Dendrite instance
browser = Dendrite(auth=["instagram.com"])

# Navigate to latest conversation
browser.goto(
  "https://www.instagram.com/direct/inbox/",
  expected_page="Instagram DM inbox"
)
browser.click("Conversation at the top of the the sidebar")

# Get shared posts
posts = browser.ask(
  "Extract the posts sent to me in this conversation",
  type_spec: Posts
)

print(posts.items)
The ask method exists on the Page and Dendrite classes. If invoked from a Dendrite instance, as above, it is performed on the active page