# mlchain-client
A Mlchain App Service-API Client, using for build a webapp by request Service-API
## Usage
First, install `mlchain-client` python sdk package:
```
pip install mlchain-client
```
Write your code with sdk:
- completion generate with `blocking` response_mode
```python
from mlchain_client import CompletionClient
api_key = "your_api_key"
# Initialize CompletionClient
completion_client = CompletionClient(api_key)
# Create Completion Message using CompletionClient
completion_response = completion_client.create_completion_message(inputs={"query": "What's the weather like today?"},
response_mode="blocking", user="user_id")
completion_response.raise_for_status()
result = completion_response.json()
print(result.get('answer'))
```
- completion using vision model, like gpt-4-vision
```python
from mlchain_client import CompletionClient
api_key = "your_api_key"
# Initialize CompletionClient
completion_client = CompletionClient(api_key)
files = [{
"type": "image",
"transfer_method": "remote_url",
"url": "your_image_url"
}]
# files = [{
# "type": "image",
# "transfer_method": "local_file",
# "upload_file_id": "your_file_id"
# }]
# Create Completion Message using CompletionClient
completion_response = completion_client.create_completion_message(inputs={"query": "Describe the picture."},
response_mode="blocking", user="user_id", files=files)
completion_response.raise_for_status()
result = completion_response.json()
print(result.get('answer'))
```
- chat generate with `streaming` response_mode
```python
import json
from mlchain_client import ChatClient
api_key = "your_api_key"
# Initialize ChatClient
chat_client = ChatClient(api_key)
# Create Chat Message using ChatClient
chat_response = chat_client.create_chat_message(inputs={}, query="Hello", user="user_id", response_mode="streaming")
chat_response.raise_for_status()
for line in chat_response.iter_lines(decode_unicode=True):
line = line.split('data:', 1)[-1]
if line.strip():
line = json.loads(line.strip())
print(line.get('answer'))
```
- chat using vision model, like gpt-4-vision
```python
from mlchain_client import ChatClient
api_key = "your_api_key"
# Initialize ChatClient
chat_client = ChatClient(api_key)
files = [{
"type": "image",
"transfer_method": "remote_url",
"url": "your_image_url"
}]
# files = [{
# "type": "image",
# "transfer_method": "local_file",
# "upload_file_id": "your_file_id"
# }]
# Create Chat Message using ChatClient
chat_response = chat_client.create_chat_message(inputs={}, query="Describe the picture.", user="user_id",
response_mode="blocking", files=files)
chat_response.raise_for_status()
result = chat_response.json()
print(result.get("answer"))
```
- upload file when using vision model
```python
from mlchain_client import MlchainClient
api_key = "your_api_key"
# Initialize Client
mlchain_client = MlchainClient(api_key)
file_path = "your_image_file_path"
file_name = "panda.jpeg"
mime_type = "image/jpeg"
with open(file_path, "rb") as file:
files = {
"file": (file_name, file, mime_type)
}
response = mlchain_client.file_upload("user_id", files)
result = response.json()
print(f'upload_file_id: {result.get("id")}')
```
- Others
```python
from mlchain_client import ChatClient
api_key = "your_api_key"
# Initialize Client
client = ChatClient(api_key)
# Get App parameters
parameters = client.get_application_parameters(user="user_id")
parameters.raise_for_status()
print('[parameters]')
print(parameters.json())
# Get Conversation List (only for chat)
conversations = client.get_conversations(user="user_id")
conversations.raise_for_status()
print('[conversations]')
print(conversations.json())
# Get Message List (only for chat)
messages = client.get_conversation_messages(user="user_id", conversation_id="conversation_id")
messages.raise_for_status()
print('[messages]')
print(messages.json())
# Rename Conversation (only for chat)
rename_conversation_response = client.rename_conversation(conversation_id="conversation_id",
name="new_name", user="user_id")
rename_conversation_response.raise_for_status()
print('[rename result]')
print(rename_conversation_response.json())
```
Raw data
{
"_id": null,
"home_page": "https://github.com/mlchain/mlchain",
"name": "mlchain-client",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "mlchain nlp ai language-processing",
"author": "Mlchain",
"author_email": "hello@mlchain.khulnasoft.com",
"download_url": "https://files.pythonhosted.org/packages/b0/e6/f627d2655d8bf5f764f17f49fa1b75172628de1cc784697d31e234e1a8c3/mlchain_client-0.1.12.tar.gz",
"platform": null,
"description": "# mlchain-client\n\nA Mlchain App Service-API Client, using for build a webapp by request Service-API\n\n## Usage\n\nFirst, install `mlchain-client` python sdk package:\n\n```\npip install mlchain-client\n```\n\nWrite your code with sdk:\n\n- completion generate with `blocking` response_mode\n\n```python\nfrom mlchain_client import CompletionClient\n\napi_key = \"your_api_key\"\n\n# Initialize CompletionClient\ncompletion_client = CompletionClient(api_key)\n\n# Create Completion Message using CompletionClient\ncompletion_response = completion_client.create_completion_message(inputs={\"query\": \"What's the weather like today?\"},\n response_mode=\"blocking\", user=\"user_id\")\ncompletion_response.raise_for_status()\n\nresult = completion_response.json()\n\nprint(result.get('answer'))\n```\n\n- completion using vision model, like gpt-4-vision\n\n```python\nfrom mlchain_client import CompletionClient\n\napi_key = \"your_api_key\"\n\n# Initialize CompletionClient\ncompletion_client = CompletionClient(api_key)\n\nfiles = [{\n \"type\": \"image\",\n \"transfer_method\": \"remote_url\",\n \"url\": \"your_image_url\"\n}]\n\n# files = [{\n# \"type\": \"image\",\n# \"transfer_method\": \"local_file\",\n# \"upload_file_id\": \"your_file_id\"\n# }]\n\n# Create Completion Message using CompletionClient\ncompletion_response = completion_client.create_completion_message(inputs={\"query\": \"Describe the picture.\"},\n response_mode=\"blocking\", user=\"user_id\", files=files)\ncompletion_response.raise_for_status()\n\nresult = completion_response.json()\n\nprint(result.get('answer'))\n```\n\n- chat generate with `streaming` response_mode\n\n```python\nimport json\nfrom mlchain_client import ChatClient\n\napi_key = \"your_api_key\"\n\n# Initialize ChatClient\nchat_client = ChatClient(api_key)\n\n# Create Chat Message using ChatClient\nchat_response = chat_client.create_chat_message(inputs={}, query=\"Hello\", user=\"user_id\", response_mode=\"streaming\")\nchat_response.raise_for_status()\n\nfor line in chat_response.iter_lines(decode_unicode=True):\n line = line.split('data:', 1)[-1]\n if line.strip():\n line = json.loads(line.strip())\n print(line.get('answer'))\n```\n\n- chat using vision model, like gpt-4-vision\n\n```python\nfrom mlchain_client import ChatClient\n\napi_key = \"your_api_key\"\n\n# Initialize ChatClient\nchat_client = ChatClient(api_key)\n\nfiles = [{\n \"type\": \"image\",\n \"transfer_method\": \"remote_url\",\n \"url\": \"your_image_url\"\n}]\n\n# files = [{\n# \"type\": \"image\",\n# \"transfer_method\": \"local_file\",\n# \"upload_file_id\": \"your_file_id\"\n# }]\n\n# Create Chat Message using ChatClient\nchat_response = chat_client.create_chat_message(inputs={}, query=\"Describe the picture.\", user=\"user_id\",\n response_mode=\"blocking\", files=files)\nchat_response.raise_for_status()\n\nresult = chat_response.json()\n\nprint(result.get(\"answer\"))\n```\n\n- upload file when using vision model\n\n```python\nfrom mlchain_client import MlchainClient\n\napi_key = \"your_api_key\"\n\n# Initialize Client\nmlchain_client = MlchainClient(api_key)\n\nfile_path = \"your_image_file_path\"\nfile_name = \"panda.jpeg\"\nmime_type = \"image/jpeg\"\n\nwith open(file_path, \"rb\") as file:\n files = {\n \"file\": (file_name, file, mime_type)\n }\n response = mlchain_client.file_upload(\"user_id\", files)\n\n result = response.json()\n print(f'upload_file_id: {result.get(\"id\")}')\n```\n \n\n\n- Others\n\n```python\nfrom mlchain_client import ChatClient\n\napi_key = \"your_api_key\"\n\n# Initialize Client\nclient = ChatClient(api_key)\n\n# Get App parameters\nparameters = client.get_application_parameters(user=\"user_id\")\nparameters.raise_for_status()\n\nprint('[parameters]')\nprint(parameters.json())\n\n# Get Conversation List (only for chat)\nconversations = client.get_conversations(user=\"user_id\")\nconversations.raise_for_status()\n\nprint('[conversations]')\nprint(conversations.json())\n\n# Get Message List (only for chat)\nmessages = client.get_conversation_messages(user=\"user_id\", conversation_id=\"conversation_id\")\nmessages.raise_for_status()\n\nprint('[messages]')\nprint(messages.json())\n\n# Rename Conversation (only for chat)\nrename_conversation_response = client.rename_conversation(conversation_id=\"conversation_id\",\n name=\"new_name\", user=\"user_id\")\nrename_conversation_response.raise_for_status()\n\nprint('[rename result]')\nprint(rename_conversation_response.json())\n```\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A package for interacting with the Mlchain Service-API",
"version": "0.1.12",
"project_urls": {
"Homepage": "https://github.com/mlchain/mlchain"
},
"split_keywords": [
"mlchain",
"nlp",
"ai",
"language-processing"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "b982a14a018e44d04fd5ff471b9d6d09e86ce4787abc839fe788911910a249e5",
"md5": "bb0dfbf5773a9d7a28a1bb92deceac25",
"sha256": "3a22bb520e5123af00f3e0dbfbc7cdeaceb6990305fe1843c0f94577d76d2663"
},
"downloads": -1,
"filename": "mlchain_client-0.1.12-py3-none-any.whl",
"has_sig": false,
"md5_digest": "bb0dfbf5773a9d7a28a1bb92deceac25",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 6362,
"upload_time": "2024-11-26T11:51:06",
"upload_time_iso_8601": "2024-11-26T11:51:06.138446Z",
"url": "https://files.pythonhosted.org/packages/b9/82/a14a018e44d04fd5ff471b9d6d09e86ce4787abc839fe788911910a249e5/mlchain_client-0.1.12-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b0e6f627d2655d8bf5f764f17f49fa1b75172628de1cc784697d31e234e1a8c3",
"md5": "693f06a090f0cc0a06558765a1a523ba",
"sha256": "b3bd41eebf8f99a19507f537ed8b57b00023a6dda7f0097f956f9b5681c564db"
},
"downloads": -1,
"filename": "mlchain_client-0.1.12.tar.gz",
"has_sig": false,
"md5_digest": "693f06a090f0cc0a06558765a1a523ba",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 7669,
"upload_time": "2024-11-26T11:51:07",
"upload_time_iso_8601": "2024-11-26T11:51:07.951738Z",
"url": "https://files.pythonhosted.org/packages/b0/e6/f627d2655d8bf5f764f17f49fa1b75172628de1cc784697d31e234e1a8c3/mlchain_client-0.1.12.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-26 11:51:07",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "mlchain",
"github_project": "mlchain",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "mlchain-client"
}
JSON
