scrapedatshi 0.1.3

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 (no embedding required)
result = client.pipeline.chunk_url("https://docs.example.com")

print(f"Got {result.total_chunks} chunks")
print(f"Cost: ${result.credits_used:.4f} | Remaining: ${result.credits_remaining:.4f}")
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. New accounts receive $1.00 free credits — no credit card required.

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Pricing

scrapedatshi uses a pay-per-use credit wallet — no subscriptions, no monthly fees. Credits are deducted after each successful API call. Failed requests are never charged.

Operation	Rate	Applies To
URL Fetch	$0.0020 / URL	/v1/rag-chunk, /v1/crawl, /v1/crawl-chunk, /v1/sync, /v1/ingest
Spider Fetch	$0.0050 / URL	/v1/spider (replaces URL fetch)
Chunk Fee	$0.0005 / chunk	All routes — per chunk generated
Injection Fee	$0.0030 / chunk	/v1/sync, /v1/ingest — per chunk upserted to vector DB
Contextual Retrieval	$0.0030 / URL	When contextual_retrieval=True

Example: sync() on 1 URL → 10 chunks = $0.0020 + (10 × $0.0005) + (10 × $0.0030) = $0.0370

Top up your balance at scrapedatshi.com/portal/billing.

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Pipeline Methods

Chunk to JSON

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)
# result.credits_used        → float
# result.credits_remaining   → float
# result.content_truncated   → bool (True if content exceeded ~75,000 words)

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}")
print(f"Cost: ${result.credits_used:.4f}")

Crawl a website

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")
print(f"Cost: ${result.credits_used:.4f}")

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Full Pipeline — Embed + Inject

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)")
print(f"Cost: ${result.credits_used:.4f}")

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)

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

Additional cost: $0.0030 per URL when contextual_retrieval=True.

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 — cost ${result.credits_used:.4f}")

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)
        total_cost = sum(r.credits_used for r in results)
        print(f"Processed {len(urls)} URLs → {total} total chunks — total cost ${total_cost:.4f}")

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Response Models

All methods return typed Pydantic models with full IDE autocomplete support. Every response includes credits_used and credits_remaining for programmatic spend tracking.

ChunkResult

result.chunks                  # list[Chunk]
result.total_chunks            # int
result.source                  # str
result.contextual_retrieval_used  # bool
result.content_truncated       # bool — True if content exceeded ~75,000 words
result.credits_used            # float — credits deducted for this request
result.credits_remaining       # float — account balance after this request

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
result.credits_used        # float
result.credits_remaining   # float

SyncResult / IngestResult

result.status              # "success" | "partial" | "error"
result.chunks_created      # int
result.vectors_upserted    # int
result.total_tokens        # int
result.embedding_provider  # str
result.vector_db_provider  # str
result.credits_used        # float
result.credits_remaining   # float

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Error Handling

from scrapedatshi.exceptions import (
    AuthError,              # Invalid or missing API key (401/403)
    InsufficientCreditsError,  # Balance too low — top up at portal/billing (402)
    RateLimitError,         # Per-request hard cap or rate 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 InsufficientCreditsError:
    print("Balance too low — top up at scrapedatshi.com/portal/billing")
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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Hard Caps

Per-request hard caps protect server stability and apply to all accounts:

Cap	Limit
Max pages / crawl	35
Max pages / spider	35
Max chunks / request	35,000
Max content size	~75,000 words (auto-truncated)

Exceeding a hard cap returns HTTP 400. Content exceeding the size limit is automatically truncated — check result.content_truncated to detect this.

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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.