Picture by Creator | ideogram.ai# Introduction
With the surge of huge language fashions (LLMs) lately, many LLM-powered purposes are rising. LLM implementation has launched options that had been beforehand non-existent.
As time goes on, many LLM fashions and merchandise have grow to be obtainable, every with its professionals and cons. Sadly, there’s nonetheless no customary option to entry all these fashions, as every firm can develop its personal framework. That’s the reason having an open-source device reminiscent of LiteLLM is beneficial while you want standardized entry to your LLM apps with none extra value.
On this article, we are going to discover why LiteLLM is useful for constructing LLM purposes.
Let’s get into it.
# Profit 1: Unified Entry
LiteLLM’s largest benefit is its compatibility with completely different mannequin suppliers. The device helps over 100 completely different LLM companies by way of standardized interfaces, permitting us to entry them whatever the mannequin supplier we use. It’s particularly helpful in case your purposes make the most of a number of completely different fashions that must work interchangeably.
A number of examples of the most important mannequin suppliers that LiteLLM helps embody:
- OpenAI and Azure OpenAI, like GPT-4.
- Anthropic, like Claude.
- AWS Bedrock & SageMaker, supporting fashions like Amazon Titan and Claude.
- Google Vertex AI, like Gemini.
- Hugging Face Hub and Ollama for open-source fashions like LLaMA and Mistral.
The standardized format follows OpenAI’s framework, utilizing its chat/completions schema. Which means we are able to swap fashions simply while not having to grasp the unique mannequin supplier’s schema.
For instance, right here is the Python code to make use of Google’s Gemini mannequin with LiteLLM.
from litellm import completion
immediate = "YOUR-PROMPT-FOR-LITELLM"
api_key = "YOUR-API-KEY-FOR-LLM"
response = completion(
mannequin="gemini/gemini-1.5-flash-latest",
messages=[{"content": prompt, "role": "user"}],
api_key=api_key)
response['choices'][0]['message']['content']
You solely must acquire the mannequin identify and the respective API keys from the mannequin supplier to entry them. This flexibility makes LiteLLM ultimate for purposes that use a number of fashions or for performing mannequin comparisons.
# Profit 2: Price Monitoring and Optimization
When working with LLM purposes, you will need to monitor token utilization and spending for every mannequin you implement and throughout all built-in suppliers, particularly in real-time eventualities.
LiteLLM allows customers to take care of an in depth log of mannequin API name utilization, offering all the mandatory info to manage prices successfully. For instance, the `completion` name above can have details about the token utilization, as proven under.
utilization=Utilization(completion_tokens=10, prompt_tokens=8, total_tokens=18, completion_tokens_details=None, prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=None, text_tokens=8, image_tokens=None))
Accessing the response’s hidden parameters may also present extra detailed info, together with the associated fee.
With the output much like under:
{'custom_llm_provider': 'gemini',
'region_name': None,
'vertex_ai_grounding_metadata': [],
'vertex_ai_url_context_metadata': [],
'vertex_ai_safety_results': [],
'vertex_ai_citation_metadata': [],
'optional_params': {},
'litellm_call_id': '558e4b42-95c3-46de-beb7-9086d6a954c1',
'api_base': 'https://generativelanguage.googleapis.com/v1beta/fashions/gemini-1.5-flash-latest:generateContent',
'model_id': None,
'response_cost': 4.8e-06,
'additional_headers': {},
'litellm_model_name': 'gemini/gemini-1.5-flash-latest'}
There’s a number of info, however crucial piece is `response_cost`, because it estimates the precise cost you’ll incur throughout that decision, though it may nonetheless be offset if the mannequin supplier presents free entry. Customers also can outline customized pricing for fashions (per token or per second) to calculate prices precisely.
A extra superior cost-tracking implementation may also permit customers to set a spending funds and restrict, whereas additionally connecting the LiteLLM value utilization info to an analytics dashboard to extra simply combination info. It is also doable to supply customized label tags to assist attribute prices to sure utilization or departments.
By offering detailed value utilization information, LiteLLM helps customers and organizations optimize their LLM utility prices and funds extra successfully.
# Profit 3: Ease of Deployment
LiteLLM is designed for straightforward deployment, whether or not you utilize it for native growth or a manufacturing surroundings. With modest assets required for Python library set up, we are able to run LiteLLM on our native laptop computer or host it in a containerized deployment with Docker with out a want for complicated extra configuration.
Talking of configuration, we are able to arrange LiteLLM extra effectively utilizing a YAML config file to checklist all the mandatory info, such because the mannequin identify, API keys, and any important customized settings in your LLM Apps. You can too use a backend database reminiscent of SQLite or PostgreSQL to retailer its state.
For information privateness, you’re chargeable for your personal privateness as a person deploying LiteLLM your self, however this method is safer for the reason that information by no means leaves your managed surroundings besides when despatched to the LLM suppliers. One function LiteLLM supplies for enterprise customers is Single Signal-On (SSO), role-based entry management, and audit logs in case your utility wants a safer surroundings.
Total, LiteLLM supplies versatile deployment choices and configuration whereas retaining the information safe.
# Profit 4: Resilience Options
Resilience is essential when constructing LLM Apps, as we wish our utility to stay operational even within the face of sudden points. To advertise resilience, LiteLLM supplies many options which might be helpful in utility growth.
One function that LiteLLM has is built-in caching, the place customers can cache LLM prompts and responses in order that an identical requests do not incur repeated prices or latency. It’s a helpful function if our utility continuously receives the identical queries. The caching system is versatile, supporting each in-memory and distant caching, reminiscent of with a vector database.
One other function of LiteLLM is automated retries, permitting customers to configure a mechanism when requests fail as a consequence of errors like timeouts or rate-limit errors to mechanically retry the request. It’s additionally doable to arrange extra fallback mechanisms, reminiscent of utilizing one other mannequin if the request has already hit the retry restrict.
Lastly, we are able to set fee limiting for outlined requests per minute (RPM) or tokens per minute (TPM) to restrict the utilization degree. It’s a good way to cap particular mannequin integrations to stop failures and respect utility infrastructure necessities.
# Conclusion
Within the period of LLM product progress, it has grow to be a lot simpler to construct LLM purposes. Nonetheless, with so many mannequin suppliers on the market, it turns into onerous to ascertain a normal for LLM implementation, particularly within the case of multi-model system architectures. That is why LiteLLM may also help us construct LLM Apps effectively.
I hope this has helped!
Cornellius Yudha Wijaya is a knowledge science assistant supervisor and information author. Whereas working full-time at Allianz Indonesia, he likes to share Python and information ideas through social media and writing media. Cornellius writes on quite a lot of AI and machine studying subjects.
