wauldo 0.7.0

Official Python SDK for Wauldo — Verified AI answers from your documents

Wauldo Python SDK

Verified AI answers from your documents — or no answer at all.

Most RAG APIs guess. Wauldo verifies.

0% hallucination  |  83% accuracy  |  61 eval tasks  |  14 LLMs tested

     

Demo • Docs • Free API Key • Benchmarks

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Quickstart (30 seconds)

pip install wauldo

Try it locally (no API key needed)

from wauldo import MockHttpClient

client = MockHttpClient(chat_response="Returns are accepted within 60 days.")

client.rag_upload(content="Our refund policy allows returns within 60 days.", filename="policy.txt")
result = client.rag_query("What is the refund policy?")
print(result.answer)    # Returns are accepted within 60 days.
print(result.sources)   # [RagSource(document_id='mock-doc-001', ...)]

Run examples/quickstart.py for the full offline walkthrough.

With a real server

from wauldo import HttpClient

client = HttpClient(base_url="https://api.wauldo.com", api_key="YOUR_API_KEY")

client.rag_upload(content="Our refund policy allows returns within 60 days...", filename="policy.txt")
result = client.rag_query("What is the refund policy?")
print(result.answer)
print(result.sources)

Output:
Answer: Returns are accepted within 60 days of purchase.
Sources: policy.txt — "Our refund policy allows returns within 60 days"
Grounded: true | Confidence: 0.92

Try the demo | Get a free API key

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Why Wauldo (and not standard RAG)

Typical RAG pipeline

retrieve → generate → hope it's correct

Wauldo pipeline

retrieve → extract facts → generate → verify → return or refuse

If the answer can't be verified, it returns "insufficient evidence" instead of guessing.

See the difference

Document: "Refunds are processed within 60 days"

Typical RAG:  "Refunds are processed within 30 days"     ← wrong
Wauldo:       "Refunds are processed within 60 days"     ← verified
              or "insufficient evidence" if unclear       ← safe

---

Examples

Upload a PDF and ask questions

result = client.upload_file("contract.pdf", title="Q3 Contract")
print(f"Extracted {result.chunks_count} chunks, quality: {result.quality_label}")
# -> Extracted 12 chunks, quality: high

result = client.rag_query("What are the payment terms?")
print(f"Answer: {result.answer}")
# -> Answer: Net 30 from invoice date.
print(f"Confidence: {result.get_confidence():.0%}")
# -> Confidence: 94%
print(f"Grounded: {result.audit.grounded}")
# -> Grounded: True

Fact-check any LLM output

result = client.fact_check(
    text="Returns are accepted within 60 days.",
    source_context="Our policy allows returns within 14 days.",
    mode="lexical",
)
print(result.verdict)           # "rejected"
print(result.action)            # "block"
print(result.claims[0].reason)  # "numerical_mismatch"

Verify citations

result = client.verify_citation(
    text="The policy covers damage [Source: Manual]. Warranty is unlimited.",
    sources=[{"name": "Manual", "content": "Coverage for accidental damage only."}],
)
print(result.citation_ratio)     # 0.5
print(result.uncited_sentences)  # ["Warranty is unlimited."]

Chat (OpenAI-compatible)

reply = client.chat_simple("auto", "Explain Python decorators")
print(reply)

Streaming

Sync — print tokens as they arrive:

import sys
from wauldo import ChatRequest, HttpChatMessage

request = ChatRequest(
    model="auto",
    messages=[
        HttpChatMessage.system("You are a helpful assistant."),
        HttpChatMessage.user("Explain Python decorators"),
    ],
)

for chunk in client.chat_stream(request):
    sys.stdout.write(chunk)
    sys.stdout.flush()
print()

Async streaming (requires pip install wauldo[async]):

import asyncio
from wauldo import AsyncHttpClient, ChatRequest, HttpChatMessage

async def main():
    async with AsyncHttpClient(base_url="https://api.wauldo.com", api_key="YOUR_API_KEY") as client:
        req = ChatRequest.quick("auto", "How does HTTP/2 multiplexing work?")
        async for token in client.chat_stream(req):
            print(token, end="", flush=True)
        print()

asyncio.run(main())

RAG query with streaming answer:

client.rag_upload(content="Our SLA guarantees 99.9% uptime...", filename="sla.txt")

req = ChatRequest(
    model="auto",
    messages=[HttpChatMessage.user("What uptime does the SLA guarantee?")],
)
for chunk in client.chat_stream(req):
    print(chunk, end="", flush=True)
print()

Error handling during streaming:

from wauldo import WauldoError, ServerError, AgentConnectionError

try:
    for chunk in client.chat_stream(request):
        print(chunk, end="", flush=True)
except AgentConnectionError:
    print("\n[connection lost]")
except ServerError as e:
    print(f"\n[server error: {e}]")
except WauldoError as e:
    print(f"\n[error: {e}]")

See examples/streaming_chat.py and examples/async_streaming.py for runnable scripts.

Real-world use cases

Example	Description
pdf_qa.py	Upload a product manual PDF and ask technical questions
support_chatbot.py	Build a verified support chatbot from FAQ docs
contract_analysis.py	Extract clauses from a contract + fact-check claims
multi_document.py	Cross-reference answers across multiple documents

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Async Support

pip install wauldo[async]

import asyncio
from wauldo import AsyncHttpClient

async def main():
    async with AsyncHttpClient(base_url="https://api.wauldo.com", api_key="YOUR_API_KEY") as client:
        result = await client.rag_query("What are the payment terms?")
        print(result.answer)

asyncio.run(main())

All sync methods have async equivalents. Contributed by @qorexdev.

---

Features

• Pre-generation fact extraction — numbers, dates, limits injected as constraints before the LLM call
• Post-generation grounding check — every answer verified against sources
• Citation validation — detects phantom references
• Analytics & Insights — track token savings, cache performance, cost per hour, and per-tenant traffic
• Fact-check API — verify any claim against any source (3 modes: lexical, hybrid, semantic)
• Native PDF/DOCX upload — server-side extraction with quality scoring
• Smart model routing — auto-selects cheapest model that meets quality
• OpenAI-compatible — swap your base_url, keep your existing code
• Sync + Async — full async/await support

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Built For

• Production RAG systems that need reliable answers
• Teams where "confidently wrong" is unacceptable
• Legal, finance, healthcare, support automation
• Anyone replacing "hope-based" RAG

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Benchmarks

Metric	Result
Hallucination rate	0%
Accuracy	83% (17% = correct refusals)
Eval tasks	61
LLMs tested	14 models, 3 runs each
Avg latency	~1.2s

---

Error Handling

from wauldo import WauldoError, ServerError, AgentTimeoutError

try:
    response = client.chat(ChatRequest.quick("auto", "Hello"))
except ServerError as e:
    print(f"Server error: {e}")
except AgentTimeoutError:
    print("Request timed out")
except WauldoError as e:
    print(f"SDK error: {e}")

---

RapidAPI

client = HttpClient(
    base_url="https://api.wauldo.com",
    headers={
        "X-RapidAPI-Key": "YOUR_RAPIDAPI_KEY",
        "X-RapidAPI-Host": "smart-rag-api.p.rapidapi.com",
    },
)

Free tier (300 req/month): RapidAPI

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Troubleshooting

Error	Cause	Fix
ConnectionError	Server unreachable	Check base_url, make sure the server is running
ServerError (401)	Bad API key	Verify your key at RapidAPI dashboard
AgentTimeoutError	Request took too long	Pass timeout_ms=30000 or try a smaller document
ModuleNotFoundError: aiohttp	Async extras not installed	Run pip install wauldo[async]
ImportError: MockHttpClient	Old SDK version	Run pip install --upgrade wauldo

Still stuck? Open an issue.

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Contributing

PRs welcome. Check the good first issues.

Contributors

• @qorexdev — async client, streaming, MockHttpClient, quickstart, CONTRIBUTING guide
• @dagangtj — analytics demo + MockHttpClient analytics methods

License

MIT — see LICENSE