LLMWare: The Swiss Army Knife for Building Enterprise-Grade LLM Applications

https://github.com/llmware-ai/llmware

www.huggingface.co/llmware.

LLMWare is an open-source powerhouse enabling anyone — from coding newbies to sophisticated AI developers — to rapidly construct real-world large language model (LLM) apps. With its batteries-included toolchain combining intuitive abstractions, optimized models and configurable data pipelines, LLMWare streamlines even the most complex industrial-strength retrieval-augmented generation (RAG) systems.

Taming the Complexity of Scalable LLM Apps

Behind its simplicity, LLMWare tackles the multifaceted challenge of truly productionizing LLMs:

Multi-Step Logic Orchestration

LLMWare introduces Agent workflows to orchestrate conditional multi-LLM inferences as automated multi-step processes. Agents can integrate specialized function-calling SLIM models and query persisted knowledge to execute complex business logic flows.

Handling Structured Outputs

While foundation chat LLMs produce free-form text, LLMWare’s SLIM classification models emit structured JSON/Python/SQL outputs to enable programmatic evaluation and tracing of each step.

Secured Enterprise Data Access

With configurable pipelines and scaling persistent storage like MongoDB and Postgres, LLMWare enables tight yet secured integration with sensitive organizational data sources — key for private cloud deployment.

LLMWare’s Secret Sauce: Curated Building Blocks

Under the hood, LLMWare combines purpose-built capabilities that uniquely unlock real-world LLM adoption:

Producion-Grade Specialized Models

With 40+ optimized models spanning accurate 1B-7B parameter QA systems (DRAGON, BLING) to classification SLIMs, LLMWare delivers high performance without brute-force scale.

Unified Model Access and Finetuning

LLMWare establishes consistent model usage and training patterns, enabling seamless mixing-and-matching of stacked inferences:

from llmware.models import ModelCatalog 

sentiment_model = ModelCatalog().get("slim-sentiment-tool")

sentiment = sentiment_slim(text)
rating = rating_slim(text)  
question = bling_qa(text + f"The rating was {rating} and sentiment {sentiment}."

def agent(text):
    sentiment = sentiment_model(text) 
    rating = rating_model(text)
    intent = intent_model(text)
    
    return {
        "sentiment": sentiment,
        "rating": rating,
        "intent": intent
    }

Enterprise Data Integrations

With turnkey pipelines, vector database integrations and relational support, securely unlocking organizational knowledge for LLMs is simplified.

Hitting an LLM Innovation Inflection Point

LLMWare brings to bear an unprecedented consolidation of capabilities to overcome real-world blocking factors in enterprise LLM adoption. Its open toolkit fosters an ecosystem poised to accelerate innovation by orders of magnitude.