Skip to main content

Welcome to vishwa.ai 👋

vishwa-ml-sdk

Twitter Follow Discord

PyPI version GitHub version

Roadmap 🚀

Framework Status
Langchain ✅
LLamaIndex Planned
PyTorch Planned
SKLearn Planned
Transformers Planned
Stable Diffusion Next

💡 If support of any framework/feature is useful for you, please feel free to reach out to us via Discord or Github Discussions

🔗 Installation

  1. Install from PyPI
pip install vishwa-ml-sdk

🧩 Usage Example

from vishwa.mlmonitor.langchain.instrument import LangchainTelemetry
import os
import vishwa
from vishwa.prompt_hub import PromptClient

# Enable this for advance tracking with our vishwa-ai platform
vishwa.host_url = "https://api.vishwa.ai"
vishwa.api_key = "********************"  # Get from https://platform.vishwa.ai
vishwa.adv_tracing_enabled = "true" # Enable this for automated insights and log tracing via xpulsAI platform
# Add default labels that will be added to all captured metrics
default_labels = {"service": "ml-project-service", "k8s_cluster": "app0", "namespace": "dev", "agent_name": "fallback_value"}

# Enable the auto-telemetry
LangchainTelemetry(default_labels=default_labels,).auto_instrument()
prompt_client = PromptClient(   
    prompt_id="clrfm4v70jnlb1kph240",  # Get prompt_id from the platform
    environment_name="dev"  # Deployed environment name
)

## [Optional] Override labels for scope of decorator [Useful if you have multiple scopes where you need to override the default label values]
@TelemetryOverrideLabels(agent_name="chat_agent_alpha")
@TagToProject(project_slug="defaultoPIt9USSR")  # Get Project Slug from platform
def get_response_using_agent_alpha(prompt, query):
    agent = initialize_agent(llm=chat_model,
                             verbose=True,
                             agent=CONVERSATIONAL_REACT_DESCRIPTION,
                             memory=memory)
    
    data = prompt_client.get_prompt({"variable-1": "I'm the first variable"})  # Substitute any variables in prompt

    res = agent.run(data) # Pass the entire `XPPrompt` object to run or invoke method

ℹ️ Complete Usage Guides

🧾 License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.

📢 Contributing

We welcome contributions to xpuls-ml-sdk! If you're interested in contributing.

If you encounter any issues or have feature requests, please file an issue on our GitHub repository.

💬 Get in touch

👉 Join our Discord community!

🐦 Follow the latest from vishwa.ai team on Twitter @vishwa_ai

📮 Write to us at hello@vishwa.ai

Metadata

Release files for vishwa-ml-sdk 0.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for vishwa-ml-sdk 0.4.0
File Size Uploaded Provenance Actions
vishwa-ml-sdk-0.4.0.tar.gz 22.5 kB Without provenance Details for vishwa-ml-sdk-0.4.0.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for vishwa-ml-sdk 0.4.0
File Interpreter ABI Platform Actions
vishwa_ml_sdk-0.4.0-py3-none-any.whl Python 3 none any Details for vishwa_ml_sdk-0.4.0-py3-none-any.whl

Total release size: 52.8 kB

Release files / vishwa-ml-sdk-0.4.0.tar.gz

Download URL vishwa-ml-sdk-0.4.0.tar.gz
Size 22.5 kB
Tags Source
SHA-256 checksum
How to use checksums
c13915b859e4a89b77b12e291847913c04f3053f7f5357146117f10795f72a22
BLAKE2b-256 checksum
How to use checksums
0f79987ceb597488f6d2f6fc1b309b42590c1ae9768ef22d9e58e5a510b8f39b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.7

Release files / vishwa_ml_sdk-0.4.0-py3-none-any.whl

Download URL vishwa_ml_sdk-0.4.0-py3-none-any.whl
Size 30.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
6e99cc461cbc1d2f1c4abbabd6fb892b29605f3bc7e11f22f33e065b4c3b9ab4
BLAKE2b-256 checksum
How to use checksums
46a7ee8c5f82c74efed4d1f04ae2c7fb9a95011ce1465681efa78439ca7b6ccf
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.7

Security for vishwa-ml-sdk 0.4.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.4.0 This release

2 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page