scrapedatshi 0.1.1

Official Python SDK for the scrapedatshi RAG pipeline API

scrapedatshi-py

Official Python SDK for the scrapedatshi RAG pipeline API.

Scrape URLs, chunk documents, embed content, and inject into vector databases — all from a clean, typed Python interface.

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Installation

pip install scrapedatshi

Requires Python 3.10+.

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Quick Start

from scrapedatshi import ScrapedatshiClient

client = ScrapedatshiClient(api_key="sds_...")

# Chunk a URL to JSON (all tiers — no embedding required)
result = client.pipeline.chunk_url("https://docs.example.com")

print(f"Got {result.total_chunks} chunks")
for chunk in result.chunks:
    print(chunk.content[:80])

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Authentication

Pass your API key directly or set the SCRAPEDATSHI_API_KEY environment variable:

export SCRAPEDATSHI_API_KEY="sds_..."

# Explicit key
client = ScrapedatshiClient(api_key="sds_...")

# From environment variable
client = ScrapedatshiClient()

Get your API key at scrapedatshi.com/portal/register.

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Pipeline Methods

Chunk to JSON (all tiers)

No embedding or vector DB required. Returns structured JSON chunks from any source.

Chunk a URL

result = client.pipeline.chunk_url("https://docs.example.com")

# result.chunks       → list[Chunk]
# result.total_chunks → int
# result.source       → str (the URL)

Chunk a local file

Supports PDF, DOCX, TXT, MD, and HTML.

result = client.pipeline.chunk_file("./docs/manual.pdf")

print(f"Got {result.total_chunks} chunks from {result.source}")

Crawl a website (Basic tier+)

Crawls via sitemap and chunks all pages.

result = client.pipeline.crawl("https://example.com", max_pages=10)

print(f"Crawled {result.pages_crawled} pages → {result.total_chunks} chunks")

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Full Pipeline — Embed + Inject (Pro/Enterprise)

Scrape, embed, and inject directly into your vector database in one call.

Sync a URL

result = client.pipeline.sync(
    url="https://docs.example.com",
    embedding_provider="openai",
    embedding_api_key="sk-...",
    vector_db="pinecone",
    vector_db_api_key="pc-...",
    index_name="my-docs",
)

print(f"Upserted {result.vectors_upserted} vectors ({result.total_tokens} tokens)")

Ingest a local file

result = client.pipeline.ingest(
    file_path="./docs/manual.pdf",
    embedding_provider="openai",
    embedding_api_key="sk-...",
    vector_db="pinecone",
    vector_db_api_key="pc-...",
    index_name="my-docs",
)

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Contextual Retrieval (RAG 2.0) — Basic tier+

Prepend an LLM-generated document summary to every chunk before embedding, dramatically improving retrieval accuracy.

result = client.pipeline.chunk_url(
    "https://docs.example.com",
    contextual_retrieval=True,
    llm_provider="openai",
    llm_api_key="sk-...",
    llm_model="gpt-4o-mini",
)

Supported LLM providers: openai, anthropic, gemini

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Async Support

All methods have an _async variant for use with asyncio.

import asyncio
from scrapedatshi import ScrapedatshiClient

async def main():
    async with ScrapedatshiClient(api_key="sds_...") as client:
        result = await client.pipeline.chunk_url_async("https://docs.example.com")
        print(f"Got {result.total_chunks} chunks")

asyncio.run(main())

Parallel processing with asyncio.gather

async def main():
    async with ScrapedatshiClient(api_key="sds_...") as client:
        urls = [
            "https://docs.example.com/page1",
            "https://docs.example.com/page2",
            "https://docs.example.com/page3",
        ]
        results = await asyncio.gather(
            *[client.pipeline.chunk_url_async(url) for url in urls]
        )
        total = sum(r.total_chunks for r in results)
        print(f"Processed {len(urls)} URLs → {total} total chunks")

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Response Models

All methods return typed Pydantic models with full IDE autocomplete support.

ChunkResult

result.chunks              # list[Chunk]
result.total_chunks        # int
result.source              # str
result.contextual_retrieval_used  # bool

Chunk

chunk.content              # str — the chunk text
chunk.token_estimate       # int — estimated token count
chunk.metadata             # dict — source URL, page number, etc.

CrawlChunkResult

result.chunks              # list[Chunk]
result.total_chunks        # int
result.pages_crawled       # int
result.source_url          # str

SyncResult / IngestResult

result.status              # "success" | "error"
result.chunks_created      # int
result.vectors_upserted    # int
result.total_tokens        # int
result.embedding_provider  # str
result.vector_db_provider  # str

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Error Handling

from scrapedatshi.exceptions import (
    AuthError,        # Invalid or missing API key (401/403)
    TierError,        # Feature not available on your plan (403)
    RateLimitError,   # Monthly or per-minute limit exceeded (429)
    ValidationError,  # Bad request payload (422)
    ServerError,      # API server error (5xx)
    TimeoutError,     # Request timed out
    ScrapedatshiError # Base exception — catch-all
)

try:
    result = client.pipeline.sync(
        url="https://docs.example.com",
        embedding_provider="openai",
        embedding_api_key="sk-...",
        vector_db="pinecone",
        vector_db_api_key="pc-...",
        index_name="my-docs",
    )
except TierError as e:
    print(f"Upgrade required: {e.message}")
except RateLimitError as e:
    print(f"Rate limit hit: {e.message}")
except ScrapedatshiError as e:
    print(f"API error {e.status_code}: {e.message}")

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Tier Limits

Feature	Free	Basic	Pro	Enterprise
Price	$0/mo	$9/mo	$29/mo	$49/mo + usage
Chunk to JSON	✓	✓	✓	✓
Sitemap Crawl	—	✓	✓	✓
Contextual Retrieval	—	✓	✓	✓
Full Pipeline	—	—	✓	✓
Deep Spider Crawl	—	—	✓	✓
Max pages / crawl	5	10	25	50
Max chunks / request	500	2,000	10,000	Unlimited
Concurrent requests	1	3	10	25

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Development

git clone https://github.com/mxchris18/scrapedatshi-py
cd scrapedatshi-py
pip install -e ".[dev]"
pytest

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License

MIT — see LICENSE.