moorcheh-client 0.1.3

Moorcheh client and runtime launcher for on-prem server containers.

moorcheh-client

Python package for Moorcheh on-prem: start the runtime with one command and call the API from your application.

• PyPI name: moorcheh-client
• Import: from moorcheh import MoorchehApiClient, MoorchehApiError
• CLI: moorcheh (after install)

Requires Docker. Pulls moorcheh/server:latest from Docker Hub on first moorcheh up.

Install

pip install moorcheh-client

Local development:

pip install -e .

Quick start

1. Start Moorcheh

moorcheh up

API: http://localhost:8080
Data: ~/.moorcheh/data (e.g. C:\Users\<you>\.moorcheh\data on Windows)

2. Use from Python

from moorcheh import MoorchehApiClient

client = MoorchehApiClient("http://localhost:8080")
print(client.health())  # items, max_items, remaining

3. Stop (data is kept)

moorcheh down

---

Python API

Use MoorchehApiClient in your app. Moorcheh must already be running (moorcheh up or your own deployment on port 8080).

Connect

from moorcheh import MoorchehApiClient, MoorchehApiError

client = MoorchehApiClient("http://localhost:8080", timeout=30)

Health and quota

health = client.health()
# status, model, items, max_items, remaining

Namespaces

client.create_namespace({
    "namespace_name": "docs",
    "type": "text",
})

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

namespaces = client.list_namespaces()
client.delete_namespace("docs")  # async; returns job_id

Upload documents (async)

resp = client.upload_namespace_documents("docs", {
    "documents": [
        {
            "id": "doc-1",
            "text": "Moorcheh on-prem retrieval test",
            "team": "ai",
        }
    ],
})
job_id = resp["job_id"]

# Poll until status == "completed"
status = client.upload_job_status("docs", job_id)

Upload vectors (async)

resp = client.upload_namespace_vectors("products_vec", {
    "vectors": [
        {
            "id": "vec-1",
            "vector": [0.1, 0.2, 0.3],  # length must match namespace dimension
            "source": "demo",
        }
    ],
})
job_id = resp["job_id"]
client.upload_job_status("products_vec", job_id)

Search

# Text query (text namespaces)
results = client.search({
    "query": "on prem retrieval",
    "namespaces": ["docs"],
    "top_k": 5,
    "threshold": 0.0,
    "metadata": {"team": "ai"},  # optional filter
})

# Vector query (vector namespaces)
results = client.search({
    "query": [0.1, 0.2, 0.3],
    "namespaces": ["products_vec"],
    "top_k": 5,
})

Get and delete items

Item ids are unique per namespace (the same id string may exist in different namespaces).

client.get_namespace_items("docs", {"ids": ["doc-1"]})
client.delete_namespace_items("docs", {"ids": ["doc-1"]})

Errors (including 100k limit)

try:
    client.upload_namespace_documents("docs", {"documents": [...]})
except MoorchehApiError as e:
    if e.is_item_limit_exceeded:  # HTTP 409
        print(e.body)  # items, max_items, requested_new
    else:
        print(e.status_code, e)

Typical app flow

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

client.create_namespace({"namespace_name": "myapp", "type": "text"})
resp = client.upload_namespace_documents("myapp", {"documents": [...]})

import time
job_id = resp["job_id"]
while True:
    job = client.upload_job_status("myapp", job_id)
    if job.get("status") == "completed":
        break
    time.sleep(0.5)

hits = client.search({
    "query": "user question here",
    "namespaces": ["myapp"],
    "top_k": 10,
})

---

Data storage

Vectors and documents are stored on your machine at:

Path	Contents
~/.moorcheh/data/moorcheh_data_store.json	All items
~/.moorcheh/data/namespace_registry.json	Namespace definitions

• Created automatically on first moorcheh up
• Not inside your Python project folder
• Survives moorcheh down — back up ~/.moorcheh to save everything

---

Global item limit (100k)

• At most 100,000 items total (text + vectors, all namespaces)
• client.health() or moorcheh status → items, max_items, remaining
• New ids over the cap → 409 (whole batch rejected)
• Re-uploading an existing id in the same namespace = update (no extra quota)
• Delete items or a namespace to free quota

---

CLI reference

For local ops and testing. Most app code should use the Python API above.

Command	Description
moorcheh up	Start Moorcheh (moorcheh/server:latest)
moorcheh down	Stop containers; keeps ~/.moorcheh
moorcheh status	Health + quota
moorcheh namespace-create --name X --type text	Create text namespace
moorcheh namespace-create --name X --type vector --vector-dimension 768	Create vector namespace
moorcheh namespace-list	List namespaces
moorcheh namespace-delete --namespace-name X	Delete namespace (async)
moorcheh upload-documents --namespace-name X --documents-file file.json	Upload documents
moorcheh upload-vectors --namespace-name X --vectors-file file.json	Upload vectors
moorcheh upload-job-status --namespace-name X --job-id JOB	Poll upload job
moorcheh items-get --namespace-name X --ids-json '["id1"]'	Get items
moorcheh items-delete --namespace-name X --ids-json '["id1"]'	Delete items
moorcheh search --query "..." --namespaces docs --top-k 5	Semantic search

API commands accept --base-url http://localhost:8080 (default).

Embedding configuration

Text namespaces and text search use an embedding provider configured in ~/.moorcheh/config.json.

On first moorcheh up, the CLI prompts for provider (ollama, openai, cohere), then shows a numbered list of models for that provider, then API key (for cloud providers only). API base URLs are fixed per provider and injected via container env — users are not asked for URLs.

Where settings live

What	Where
Provider, model, API key, base URL	~/.moorcheh/config.json on your machine (mode 600)
Provider API URLs	Code defaults written into base_url on save; edit config to override (no prompt during setup)
Search/index data	~/.moorcheh/data/ (bind-mounted into the container)

At moorcheh up, the CLI reads config.json and passes EMBEDDING_PROVIDER, EMBEDDING_MODEL, and EMBEDDING_API_KEY into the Docker container environment. The API key is not written into the data directory.

moorcheh configure
moorcheh up --embedding-provider openai --embedding-model text-embedding-3-small --embedding-api-key "$OPENAI_API_KEY"

Provider	Example models (menu)	API key
ollama	nomic-embed-text, mxbai-embed-large, …	Not required
openai	text-embedding-3-small, text-embedding-3-large, …	Required
cohere	embed-english-v3.0, embed-multilingual-v3.0, …	Required

moorcheh up options

Flag	Default	Purpose
--server-port	8080	Host port for API
--server-image	moorcheh/server:latest	Docker image
--embedding-provider	from config	ollama, openai, or cohere
--embedding-model	from config	Model name for the provider
--embedding-api-key	from config	Cloud provider API key
--configure	off	Run interactive setup before start
--no-configure	off	Fail if ~/.moorcheh/config.json is missing
--ollama-port	11434	Host port to detect/use Ollama (ollama provider only)
--use-host-ollama	off	Never start moorcheh-ollama container
--bundled-ollama	off	Always start Ollama in Docker

When provider is ollama, if Ollama is already running on 127.0.0.1:11434, moorcheh up reuses it and does not start a second Ollama container. OpenAI/Cohere only start the Moorcheh server container.

CLI example (documents file)

docs-upload.json:

{
  "documents": [
    {
      "id": "doc-1",
      "text": "Moorcheh on-prem retrieval test",
      "team": "ai"
    }
  ]
}

moorcheh upload-documents --namespace-name docs --documents-file docs-upload.json
moorcheh upload-job-status --namespace-name docs --job-id <job_id from response>

---

Optional: endpoint tester (Flask)

Internal browser UI to hit all endpoints (not for production apps):

pip install .[web]
python app.py

Open http://localhost:5000. Requires moorcheh up on port 8080.

---

Requirements

• Python 3.10+
• Docker Desktop (or Docker Engine)
• Embedding provider: Ollama (host or --bundled-ollama), OpenAI, or Cohere (API key in ~/.moorcheh/config.json)