Picture by Creator | ideogram.ai# Introduction
With the surge of huge language fashions (LLMs) in recent times, 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 out to be accessible, every with its professionals and cons. Sadly, there’s nonetheless no normal method to entry all these fashions, as every firm can develop its personal framework. That’s the reason having an open-source device equivalent to LiteLLM is helpful while you want standardized entry to your LLM apps with none extra value.
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 device helps over 100 totally different LLM providers by way 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 have to work interchangeably.
A number of examples of the key mannequin suppliers that LiteLLM helps embrace:
- 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 without 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 have to get hold of the mannequin identify and the respective API keys from the mannequin supplier to entry them. This flexibility makes LiteLLM best for functions that use a number of fashions or for performing mannequin comparisons.
# Profit 2: Value Monitoring and Optimization
When working with LLM functions, you will need to observe token utilization and spending for every mannequin you implement and throughout all built-in suppliers, particularly in real-time eventualities.
LiteLLM permits customers to keep up an in depth log of mannequin API name utilization, offering all the mandatory data to manage prices successfully. For instance, the `completion` name above could 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’s quite a lot of data, however a very powerful 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 gives 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 enable customers to set a spending finances and restrict, whereas additionally connecting the LiteLLM value utilization data to an analytics dashboard to extra simply mixture data. It is also potential to offer 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 software prices and finances 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 atmosphere. 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 advanced extra configuration.
Talking of configuration, we are able to arrange LiteLLM extra effectively utilizing a YAML config file to listing all the mandatory data, such because the mannequin identify, API keys, and any important customized settings to your LLM Apps. You can even use a backend database equivalent to SQLite or PostgreSQL to retailer its state.
For information privateness, you might be answerable for your personal privateness as a consumer deploying LiteLLM your self, however this method is safer for the reason that information by no means leaves your managed atmosphere 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 software wants a safer atmosphere.
General, LiteLLM supplies versatile deployment choices and configuration whereas retaining the info safe.
# Profit 4: Resilience Options
Resilience is essential when constructing LLM Apps, as we wish our software to stay operational even within the face of surprising points. To advertise resilience, LiteLLM supplies 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 an identical requests do not incur repeated prices or latency. It’s a helpful function if our software ceaselessly receives the identical queries. The caching system is versatile, supporting each in-memory and distant caching, equivalent 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 routinely retry the request. It’s additionally potential to arrange extra fallback mechanisms, equivalent to utilizing one other mannequin if the request has already hit the retry restrict.
Lastly, we are able to set price 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 software infrastructure necessities.
# Conclusion
Within the period of LLM product development, it has turn out to be 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. That is why LiteLLM can assist us construct LLM Apps effectively.
I hope this has helped!
Cornellius Yudha Wijaya is an information science assistant supervisor and information author. Whereas working full-time at Allianz Indonesia, he likes to share Python and information suggestions by way of social media and writing media. Cornellius writes on quite a lot of AI and machine studying matters.