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 accessible, every with its execs and cons. Sadly, there may be nonetheless no customary technique to entry all these fashions, as every firm can develop its personal framework. That’s the reason having an open-source instrument equivalent to LiteLLM is helpful whenever you want standardized entry to your LLM apps with none further value.
On this article, we’ll discover why LiteLLM is helpful for constructing LLM functions.
Let’s get into it.
# Profit 1: Unified Entry
LiteLLM’s largest benefit is its compatibility with totally different mannequin suppliers. The instrument helps over 100 totally different LLM companies by 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 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 have to receive the mannequin title and the respective API keys from the mannequin supplier to entry them. This flexibility makes LiteLLM superb 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 situations.
LiteLLM permits customers to take care of an in depth log of mannequin API name utilization, offering all the required info to regulate prices successfully. For instance, the `completion` name above may have details about the token utilization, as proven beneath.
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 info, together with the price.
With the output much like beneath:
{'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 whole lot of info, 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 affords 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 will even permit customers to set a spending funds and restrict, whereas additionally connecting the LiteLLM value utilization info to an analytics dashboard to extra simply mixture info. It is also attainable 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 funds extra successfully.
# Profit 3: Ease of Deployment
LiteLLM is designed for simple deployment, whether or not you utilize it for native growth 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 no want for advanced further configuration.
Talking of configuration, we will arrange LiteLLM extra effectively utilizing a YAML config file to record all the required info, such because the mannequin title, API keys, and any important customized settings in your LLM Apps. You can even use a backend database equivalent to SQLite or PostgreSQL to retailer its state.
For information privateness, you’re answerable 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 gives 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 gives versatile deployment choices and configuration whereas holding 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 surprising points. To advertise resilience, LiteLLM gives many options which are helpful in software growth.
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, equivalent to with a vector database.
One other function of LiteLLM is computerized retries, permitting customers to configure a mechanism when requests fail as a consequence of errors like timeouts or rate-limit errors to robotically retry the request. It’s additionally attainable to arrange further fallback mechanisms, equivalent 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 degree. It’s an effective 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 laborious to determine a regular for LLM implementation, particularly within the case of multi-model system architectures. For this reason LiteLLM will 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 suggestions through social media and writing media. Cornellius writes on quite a lot of AI and machine studying matters.