Picture by Creator | ideogram.ai# Introduction
With the surge of enormous language fashions (LLMs) lately, many LLM-powered functions are rising. LLM implementation has launched options that had been beforehand non-existent.
As time goes on, many LLM fashions and merchandise have turn into out there, every with its professionals and cons. Sadly, there’s nonetheless no commonplace strategy to entry all these fashions, as every firm can develop its personal framework. That’s the reason having an open-source software comparable to LiteLLM is beneficial once you want standardized entry to your LLM apps with none extra price.
On this article, we are going to discover why LiteLLM is helpful for constructing LLM functions.
Let’s get into it.
# Profit 1: Unified Entry
LiteLLM’s greatest benefit is its compatibility with totally different mannequin suppliers. The software helps over 100 totally different LLM companies by means of standardized interfaces, permitting us to entry them whatever the mannequin supplier we use. It’s particularly helpful in case your functions make the most of a number of totally 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 that we will change fashions simply without having to know 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 receive the mannequin identify and the respective API keys from the mannequin supplier to entry them. This flexibility makes LiteLLM splendid for functions that use a number of fashions or for performing mannequin comparisons.
# Profit 2: Price Monitoring and Optimization
When working with LLM functions, it is very important monitor token utilization and spending for every mannequin you implement and throughout all built-in suppliers, particularly in real-time situations.
LiteLLM allows customers to keep up an in depth log of mannequin API name utilization, offering all the mandatory data to regulate 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 will even present extra detailed data, together with the associated fee.
With the output just 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 may be quite a lot of data, however an important piece is `response_cost`, because it estimates the precise cost you’ll incur throughout that decision, though it might nonetheless be offset if the mannequin supplier presents free entry. Customers can even outline customized pricing for fashions (per token or per second) to calculate prices precisely.
A extra superior cost-tracking implementation will even permit customers to set a spending price range and restrict, whereas additionally connecting the LiteLLM price utilization data to an analytics dashboard to extra simply mixture data. It is also doable to supply customized label tags to assist attribute prices to sure utilization or departments.
By offering detailed price utilization information, LiteLLM helps customers and organizations optimize their LLM software prices and price range extra successfully.
# Profit 3: Ease of Deployment
LiteLLM is designed for straightforward deployment, whether or not you employ it for native improvement or a manufacturing surroundings. With modest assets required for Python library set up, we will run LiteLLM on our native laptop computer or host it in a containerized deployment with Docker with out a want for advanced extra configuration.
Talking of configuration, we will arrange LiteLLM extra effectively utilizing a YAML config file to record all the mandatory data, such because the mannequin identify, API keys, and any important customized settings on your LLM Apps. You too can use a backend database comparable to SQLite or PostgreSQL to retailer its state.
For information privateness, you’re answerable for your individual privateness as a person deploying LiteLLM your self, however this strategy is safer because the information by no means leaves your managed surroundings besides when despatched to the LLM suppliers. One function LiteLLM offers for enterprise customers is Single Signal-On (SSO), role-based entry management, and audit logs in case your software wants a safer surroundings.
Total, LiteLLM offers versatile deployment choices and configuration whereas protecting the info safe.
# Profit 4: Resilience Options
Resilience is essential when constructing LLM Apps, as we would like our software to stay operational even within the face of sudden points. To advertise resilience, LiteLLM offers many options which might be helpful in software improvement.
One function that LiteLLM has is built-in caching, the place customers can cache LLM prompts and responses in order that similar requests do not incur repeated prices or latency. It’s a helpful function if our software incessantly receives the identical queries. The caching system is versatile, supporting each in-memory and distant caching, comparable to with a vector database.
One other function of LiteLLM is automated retries, permitting customers to configure a mechanism when requests fail attributable to errors like timeouts or rate-limit errors to mechanically retry the request. It’s additionally doable to arrange extra fallback mechanisms, comparable to utilizing one other mannequin if the request has already hit the retry restrict.
Lastly, we will set fee limiting for outlined requests per minute (RPM) or tokens per minute (TPM) to restrict the utilization stage. It’s a good way to cap particular mannequin integrations to forestall failures and respect software infrastructure necessities.
# Conclusion
Within the period of LLM product progress, it has turn into a lot simpler to construct LLM functions. Nonetheless, with so many mannequin suppliers on the market, it turns into exhausting to determine a normal for LLM implementation, particularly within the case of multi-model system architectures. For this reason 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 by way of social media and writing media. Cornellius writes on quite a lot of AI and machine studying matters.
