Picture by Creator | ideogram.ai# Introduction
With the surge of enormous language fashions (LLMs) in recent times, many LLM-powered purposes are rising. LLM implementation has launched options that have 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’s nonetheless no normal approach to entry all these fashions, as every firm can develop its personal framework. That’s the reason having an open-source software similar to LiteLLM is beneficial while you want standardized entry to your LLM apps with none extra value.
On this article, we’ll discover why LiteLLM is useful for constructing LLM purposes.
Let’s get into it.
# Profit 1: Unified Entry
LiteLLM’s greatest benefit is its compatibility with completely different mannequin suppliers. The software helps over 100 completely different LLM providers by 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.
Just a few examples of the key 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. Because of this we are able to change fashions simply with no need 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 receive the mannequin title and the respective API keys from the mannequin supplier to entry them. This flexibility makes LiteLLM very best 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 observe 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 required 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 even 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 may be 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 provides 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 even permit customers to set a spending price range 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 knowledge, 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 utilize it for native growth or a manufacturing surroundings. With modest sources 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 and not using a want for advanced extra configuration.
Talking of configuration, we are able to arrange LiteLLM extra effectively utilizing a YAML config file to checklist all the required info, such because the mannequin title, API keys, and any important customized settings to your LLM Apps. It’s also possible to use a backend database similar to SQLite or PostgreSQL to retailer its state.
For knowledge privateness, you’re chargeable for your personal privateness as a person deploying LiteLLM your self, however this method is safer because the knowledge by no means leaves your managed surroundings besides when despatched to the LLM suppliers. One characteristic LiteLLM supplies for enterprise customers is Single Signal-On (SSO), role-based entry management, and audit logs in case your software wants a safer surroundings.
General, LiteLLM supplies 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 surprising points. To advertise resilience, LiteLLM supplies many options which might be helpful in software growth.
One characteristic that LiteLLM has is built-in caching, the place customers can cache LLM prompts and responses in order that equivalent requests do not incur repeated prices or latency. It’s a helpful characteristic if our software regularly receives the identical queries. The caching system is versatile, supporting each in-memory and distant caching, similar to with a vector database.
One other characteristic 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 routinely retry the request. It’s additionally doable to arrange extra fallback mechanisms, similar to utilizing one other mannequin if the request has already hit the retry restrict.
Lastly, we are able to set charge 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 development, it has turn into a lot simpler to construct LLM purposes. Nonetheless, with so many mannequin suppliers on the market, it turns into onerous to determine a typical for LLM implementation, particularly within the case of multi-model system architectures. For this reason LiteLLM may help us construct LLM Apps effectively.
I hope this has helped!
Cornellius Yudha Wijaya is a knowledge science assistant supervisor and knowledge author. Whereas working full-time at Allianz Indonesia, he likes to share Python and knowledge ideas through social media and writing media. Cornellius writes on quite a lot of AI and machine studying matters.
