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from meshapi import MeshAPI
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client = MeshAPI(base_url="https://api.meshapi.ai", token="rsk_...")
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with open("handbook.pdf", "rb") as f:
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    upload = client.rag.upload_file(
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        file_name="handbook.pdf",
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        mime_type="application/pdf",
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        content=f.read(),
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    )
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print("File ID:", upload.file_id)

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from meshapi import AsyncMeshAPI
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async with AsyncMeshAPI(base_url="https://api.meshapi.ai", token="rsk_...") as client:
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    with open("handbook.pdf", "rb") as f:
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        upload = await client.rag.upload_file(
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            file_name="handbook.pdf",
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            mime_type="application/pdf",
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            content=f.read(),
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        )

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from meshapi._types import InitUploadRequest
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import httpx
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4
upload = client.rag.init_upload(
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    InitUploadRequest(file_name="handbook.pdf", mime_type="application/pdf", embed=False)
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)
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# PUT bytes directly to the signed URL (no auth header needed)
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httpx.put(upload.signed_url, content=pdf_bytes, headers={"Content-Type": "application/pdf"}).raise_for_status()

1
from meshapi._types import BulkEmbedRequest
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resp = client.rag.embed(BulkEmbedRequest(file_ids=[upload.file_id]))
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for r in resp.results:
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    print(r.file_id, r.embedding_status)

1
import time
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while True:
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    status = client.rag.get(upload.file_id)
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    if status.embedding_status == "ready":
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        break
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    if status.embedding_status == "failed":
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        raise RuntimeError(f"Embedding failed: {status.last_error_code}")
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    time.sleep(3)

1
from meshapi._types import SearchRequest
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results = client.rag.search(
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    SearchRequest(
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        query="What is the refund policy?",
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        top_k=5,
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        file_ids=[upload.file_id],  # omit to search all files
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    )
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)
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for r in results.results:
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    print(f"[{r.score:.3f}] {r.text}")

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page = client.rag.list(limit=50)
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print(f"{page.total} total files")
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for f in page.files:
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    print(f.file_id, f.upload_status, f.embedding_status)

1
from meshapi import ChatCompletionParams, ChatMessage
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results = client.rag.search(SearchRequest(query="What is the refund policy?", top_k=3))
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context = "\n\n".join(r.text for r in results.results)
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reply = client.chat.completions.create(
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    ChatCompletionParams(
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        model="openai/gpt-4o-mini",
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        messages=[
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            ChatMessage(role="system", content=f"Answer using only the context below.\n\n{context}"),
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            ChatMessage(role="user",   content="What is the refund policy?"),
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        ],
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    )
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)
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print(reply.choices[0].message.content)