Getting started

pip install moorcheh-client
moorcheh up

from moorcheh import MoorchehApiClient

client = MoorchehApiClient("http://localhost:8080")
health = client.health()
assert health["status"] == "ok"

import time
from moorcheh import MoorchehApiClient, MoorchehApiError

client = MoorchehApiClient("http://localhost:8080")

# 1. Check quota
health = client.health()
print(f"Items: {health['items']} / {health['max_items']}")

# 2. Create a text namespace
try:
    client.create_namespace({
        "namespace_name": "docs",
        "type": "text",
    })
except MoorchehApiError as e:
    if e.status_code != 409:  # 409 = namespace already exists
        raise

# 3. Upload documents (async job)
resp = client.upload_namespace_documents("docs", {
    "documents": [
        {"id": "doc-1", "text": "Hello Moorcheh", "team": "ai"},
    ],
})

# 4. Poll until upload completes
job_id = resp["job_id"]
while True:
    job = client.upload_job_status("docs", job_id)
    if job["status"] == "completed":
        if job["failed"]:
            print("Upload errors:", job.get("last_error"))
        break
    time.sleep(0.5)

# 5. Search
hits = client.search({
    "query": "hello moorcheh",
    "namespaces": ["docs"],
    "top_k": 5,
    "metadata": {"team": "ai"},
})

for r in hits["results"]:
    print(r["id"], r["score"], r["label"], r.get("text"))

client.create_namespace({
    "namespace_name": "embeddings",
    "type": "vector",
    "vector_dimension": 768,
})

resp = client.upload_namespace_vectors("embeddings", {
    "vectors": [
        {"id": "vec-1", "vector": [0.1] * 768, "source": "demo"},
    ],
})

# Poll upload job, then search with a vector query
hits = client.search({
    "query": [0.1] * 768,
    "namespaces": ["embeddings"],
    "top_k": 5,
})

moorcheh status
moorcheh namespace-create --name docs --type text
moorcheh upload-documents --namespace-name docs --documents-file docs.json
moorcheh upload-job-status --namespace-name docs --job-id <job_id>
moorcheh search --query "hello moorcheh" --namespaces docs --top-k 5

{
  "documents": [
    {"id": "doc-1", "text": "Hello Moorcheh", "team": "ai"}
  ]
}