Integration

AI Framework

LlamaIndex

Use AlterLab as a data connector in LlamaIndex to build RAG pipelines with live web content. Create a custom reader, index pages into vector stores, and query with natural language.

Why AlterLab + LlamaIndex?

LlamaIndex excels at indexing and querying data. AlterLab handles the hard part — getting clean content from any website, including JavaScript-heavy SPAs and anti-bot-protected pages. Together, they make web-powered RAG pipelines simple.

Installation

Bash

pip install alterlab llama-index llama-index-llms-openai llama-index-embeddings-openai

You need the AlterLab Python SDK and LlamaIndex core. The examples below use OpenAI for LLM and embeddings, but you can substitute any LlamaIndex-compatible provider.

Custom Reader

Create an AlterLab reader that implements the LlamaIndex BaseReader interface. This reader scrapes URLs and converts them into LlamaIndex Document objects.

Python

from typing import List, Optional
from alterlab import AlterLab
from llama_index.core import Document
from llama_index.core.readers.base import BaseReader


class AlterLabReader(BaseReader):
    """LlamaIndex reader that loads web pages via AlterLab API."""

    def __init__(
        self,
        api_key: str,
        mode: str = "auto",
        output_format: str = "markdown",
    ):
        self.client = AlterLab(api_key=api_key)
        self.mode = mode
        self.output_format = output_format

    def load_data(
        self,
        urls: List[str],
        mode: Optional[str] = None,
        **kwargs,
    ) -> List[Document]:
        """Load web pages as LlamaIndex Documents."""
        documents = []
        scrape_mode = mode or self.mode

        for url in urls:
            result = self.client.scrape(
                url,
                formats=[self.output_format],
                mode=scrape_mode,
                **kwargs,
            )

            content = result.get(
                self.output_format,
                result.get("text", ""),
            )

            metadata = {
                "source": url,
                "title": result.get("metadata", {}).get("title", ""),
                "status_code": result.get("metadata", {}).get("status_code"),
                "credits_used": result.get("cost", {}).get("credits_charged"),
            }

            documents.append(
                Document(text=content, metadata=metadata)
            )

        return documents

Basic Usage

Python

from llama_index.core import Document

# Initialize the reader
reader = AlterLabReader(api_key="your_alterlab_key")

# Load a single page
documents = reader.load_data(["https://example.com/docs"])
print(f"Loaded {len(documents)} documents")
print(f"Content length: {len(documents[0].text)} chars")
print(f"Source: {documents[0].metadata['source']}")

# Load multiple pages
urls = [
    "https://example.com/docs/getting-started",
    "https://example.com/docs/api-reference",
    "https://example.com/docs/tutorials",
]
documents = reader.load_data(urls)
print(f"Loaded {len(documents)} documents")

RAG Pipeline

Step 1: Index Web Content

Python

from llama_index.core import VectorStoreIndex, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Configure LlamaIndex defaults
Settings.llm = OpenAI(model="gpt-4o-mini", temperature=0)
Settings.embed_model = OpenAIEmbedding()

# Load pages
reader = AlterLabReader(api_key="your_alterlab_key")
documents = reader.load_data([
    "https://docs.stripe.com/api/charges",
    "https://docs.stripe.com/api/customers",
    "https://docs.stripe.com/api/refunds",
])

# Build index
index = VectorStoreIndex.from_documents(documents)
print(f"Indexed {len(documents)} documents")

Step 2: Query

Python

# Create a query engine
query_engine = index.as_query_engine(similarity_top_k=3)

# Ask questions about the indexed content
response = query_engine.query("How do I create a charge in Stripe?")
print(response)

# The response includes source nodes with metadata
for node in response.source_nodes:
    print(f"  Source: {node.metadata.get('source')}")
    print(f"  Score: {node.score:.3f}")

Full Example

Complete end-to-end RAG pipeline that scrapes documentation and answers questions:

Python

from alterlab import AlterLab
from llama_index.core import Document, VectorStoreIndex, Settings
from llama_index.core.readers.base import BaseReader
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from typing import List, Optional


class AlterLabReader(BaseReader):
    def __init__(self, api_key: str, mode: str = "auto"):
        self.client = AlterLab(api_key=api_key)
        self.mode = mode

    def load_data(self, urls: List[str], **kwargs) -> List[Document]:
        documents = []
        for url in urls:
            result = self.client.scrape(url, formats=["markdown"], mode=self.mode)
            documents.append(
                Document(
                    text=result.get("markdown", ""),
                    metadata={"source": url},
                )
            )
        return documents


# 1. Configure
Settings.llm = OpenAI(model="gpt-4o-mini", temperature=0)
Settings.embed_model = OpenAIEmbedding()

# 2. Load web pages
reader = AlterLabReader(api_key="your_alterlab_key")
documents = reader.load_data([
    "https://docs.stripe.com/api/charges",
    "https://docs.stripe.com/api/customers",
    "https://docs.stripe.com/api/refunds",
])

# 3. Index
index = VectorStoreIndex.from_documents(documents)

# 4. Query
engine = index.as_query_engine(similarity_top_k=3)

questions = [
    "How do I create a charge?",
    "What parameters does the refund endpoint accept?",
    "How do I list all customers?",
]

for q in questions:
    response = engine.query(q)
    print(f"Q: {q}")
    print(f"A: {response}\n")

Batch Loading

For larger document sets, extend the reader to use AlterLab's batch API:

Python

import time
from alterlab import AlterLab
from llama_index.core import Document
from typing import List


def batch_load(
    api_key: str,
    urls: List[str],
    output_format: str = "markdown",
) -> List[Document]:
    """Load many URLs efficiently via batch scraping."""
    client = AlterLab(api_key=api_key)

    # Submit batch
    batch = client.batch_scrape(
        urls=[{"url": u, "formats": [output_format]} for u in urls],
    )

    # Poll until done
    while True:
        status = client.get_batch_status(batch["batch_id"])
        if status["status"] != "processing":
            break
        time.sleep(2)

    # Convert to Documents
    documents = []
    for item in status["items"]:
        if item["status"] == "succeeded":
            documents.append(
                Document(
                    text=item["result"].get(output_format, ""),
                    metadata={"source": item["url"]},
                )
            )

    return documents


# Use it
docs = batch_load("your_api_key", [
    "https://example.com/page-1",
    "https://example.com/page-2",
    "https://example.com/page-3",
])
print(f"Loaded {len(docs)} documents via batch")

LlamaIndex vs LangChain

Both frameworks work well with AlterLab. Here is when to use each:

Aspect	LlamaIndex	LangChain
Best for	Index-first RAG, knowledge bases, document QA	Multi-step chains, agents, complex orchestration
Index types	Vector, list, tree, keyword — built-in	Vector stores via integrations
Learning curve	Simpler for RAG-focused work	More flexible but more concepts to learn
AlterLab setup	Custom reader (shown above)	Document loader (see LangChain guide)

Tips & Best Practices

Use markdown format for the best RAG results. Markdown preserves document structure (headings, lists, code blocks) which improves chunking quality.
Set chunk overlap in your node parser. LlamaIndex defaults work well, but 200-token overlap helps with context continuity across chunks.
Use metadata filtering — store the source URL in document metadata so you can filter queries by domain or page type.
Persist your index to avoid re-scraping. Use index.storage_context.persist() to save locally, or connect to a persistent vector store like Pinecone or Weaviate.
Batch load for 10+ pages — the batch API is faster and handles concurrency. See the batch loading example above.

Last updated: June 2026

Integration

AI Framework

LlamaIndex

Use AlterLab as a data connector in LlamaIndex to build RAG pipelines with live web content. Create a custom reader, index pages into vector stores, and query with natural language.

Why AlterLab + LlamaIndex?

Installation

Bash

pip install alterlab llama-index llama-index-llms-openai llama-index-embeddings-openai

You need the AlterLab Python SDK and LlamaIndex core. The examples below use OpenAI for LLM and embeddings, but you can substitute any LlamaIndex-compatible provider.

Custom Reader

Create an AlterLab reader that implements the LlamaIndex BaseReader interface. This reader scrapes URLs and converts them into LlamaIndex Document objects.

Python

from typing import List, Optional
from alterlab import AlterLab
from llama_index.core import Document
from llama_index.core.readers.base import BaseReader


class AlterLabReader(BaseReader):
    """LlamaIndex reader that loads web pages via AlterLab API."""

    def __init__(
        self,
        api_key: str,
        mode: str = "auto",
        output_format: str = "markdown",
    ):
        self.client = AlterLab(api_key=api_key)
        self.mode = mode
        self.output_format = output_format

    def load_data(
        self,
        urls: List[str],
        mode: Optional[str] = None,
        **kwargs,
    ) -> List[Document]:
        """Load web pages as LlamaIndex Documents."""
        documents = []
        scrape_mode = mode or self.mode

        for url in urls:
            result = self.client.scrape(
                url,
                formats=[self.output_format],
                mode=scrape_mode,
                **kwargs,
            )

            content = result.get(
                self.output_format,
                result.get("text", ""),
            )

            metadata = {
                "source": url,
                "title": result.get("metadata", {}).get("title", ""),
                "status_code": result.get("metadata", {}).get("status_code"),
                "credits_used": result.get("cost", {}).get("credits_charged"),
            }

            documents.append(
                Document(text=content, metadata=metadata)
            )

        return documents

Basic Usage

Python

from llama_index.core import Document

# Initialize the reader
reader = AlterLabReader(api_key="your_alterlab_key")

# Load a single page
documents = reader.load_data(["https://example.com/docs"])
print(f"Loaded {len(documents)} documents")
print(f"Content length: {len(documents[0].text)} chars")
print(f"Source: {documents[0].metadata['source']}")

# Load multiple pages
urls = [
    "https://example.com/docs/getting-started",
    "https://example.com/docs/api-reference",
    "https://example.com/docs/tutorials",
]
documents = reader.load_data(urls)
print(f"Loaded {len(documents)} documents")

RAG Pipeline

Step 1: Index Web Content

Python

from llama_index.core import VectorStoreIndex, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Configure LlamaIndex defaults
Settings.llm = OpenAI(model="gpt-4o-mini", temperature=0)
Settings.embed_model = OpenAIEmbedding()

# Load pages
reader = AlterLabReader(api_key="your_alterlab_key")
documents = reader.load_data([
    "https://docs.stripe.com/api/charges",
    "https://docs.stripe.com/api/customers",
    "https://docs.stripe.com/api/refunds",
])

# Build index
index = VectorStoreIndex.from_documents(documents)
print(f"Indexed {len(documents)} documents")

Step 2: Query

Python

# Create a query engine
query_engine = index.as_query_engine(similarity_top_k=3)

# Ask questions about the indexed content
response = query_engine.query("How do I create a charge in Stripe?")
print(response)

# The response includes source nodes with metadata
for node in response.source_nodes:
    print(f"  Source: {node.metadata.get('source')}")
    print(f"  Score: {node.score:.3f}")

Full Example

Complete end-to-end RAG pipeline that scrapes documentation and answers questions:

Python

from alterlab import AlterLab
from llama_index.core import Document, VectorStoreIndex, Settings
from llama_index.core.readers.base import BaseReader
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from typing import List, Optional


class AlterLabReader(BaseReader):
    def __init__(self, api_key: str, mode: str = "auto"):
        self.client = AlterLab(api_key=api_key)
        self.mode = mode

    def load_data(self, urls: List[str], **kwargs) -> List[Document]:
        documents = []
        for url in urls:
            result = self.client.scrape(url, formats=["markdown"], mode=self.mode)
            documents.append(
                Document(
                    text=result.get("markdown", ""),
                    metadata={"source": url},
                )
            )
        return documents


# 1. Configure
Settings.llm = OpenAI(model="gpt-4o-mini", temperature=0)
Settings.embed_model = OpenAIEmbedding()

# 2. Load web pages
reader = AlterLabReader(api_key="your_alterlab_key")
documents = reader.load_data([
    "https://docs.stripe.com/api/charges",
    "https://docs.stripe.com/api/customers",
    "https://docs.stripe.com/api/refunds",
])

# 3. Index
index = VectorStoreIndex.from_documents(documents)

# 4. Query
engine = index.as_query_engine(similarity_top_k=3)

questions = [
    "How do I create a charge?",
    "What parameters does the refund endpoint accept?",
    "How do I list all customers?",
]

for q in questions:
    response = engine.query(q)
    print(f"Q: {q}")
    print(f"A: {response}\n")

Batch Loading

For larger document sets, extend the reader to use AlterLab's batch API:

Python

import time
from alterlab import AlterLab
from llama_index.core import Document
from typing import List


def batch_load(
    api_key: str,
    urls: List[str],
    output_format: str = "markdown",
) -> List[Document]:
    """Load many URLs efficiently via batch scraping."""
    client = AlterLab(api_key=api_key)

    # Submit batch
    batch = client.batch_scrape(
        urls=[{"url": u, "formats": [output_format]} for u in urls],
    )

    # Poll until done
    while True:
        status = client.get_batch_status(batch["batch_id"])
        if status["status"] != "processing":
            break
        time.sleep(2)

    # Convert to Documents
    documents = []
    for item in status["items"]:
        if item["status"] == "succeeded":
            documents.append(
                Document(
                    text=item["result"].get(output_format, ""),
                    metadata={"source": item["url"]},
                )
            )

    return documents


# Use it
docs = batch_load("your_api_key", [
    "https://example.com/page-1",
    "https://example.com/page-2",
    "https://example.com/page-3",
])
print(f"Loaded {len(docs)} documents via batch")

LlamaIndex vs LangChain

Both frameworks work well with AlterLab. Here is when to use each:

Aspect	LlamaIndex	LangChain
Best for	Index-first RAG, knowledge bases, document QA	Multi-step chains, agents, complex orchestration
Index types	Vector, list, tree, keyword — built-in	Vector stores via integrations
Learning curve	Simpler for RAG-focused work	More flexible but more concepts to learn
AlterLab setup	Custom reader (shown above)	Document loader (see LangChain guide)

Tips & Best Practices

Use markdown format for the best RAG results. Markdown preserves document structure (headings, lists, code blocks) which improves chunking quality.
Set chunk overlap in your node parser. LlamaIndex defaults work well, but 200-token overlap helps with context continuity across chunks.
Use metadata filtering — store the source URL in document metadata so you can filter queries by domain or page type.
Persist your index to avoid re-scraping. Use index.storage_context.persist() to save locally, or connect to a persistent vector store like Pinecone or Weaviate.
Batch load for 10+ pages — the batch API is faster and handles concurrency. See the batch loading example above.

Last updated: June 2026