Retrieval Augmented Generation
Bridging LLMs and Information Retrieval
Don’t wait for the future to happen – be a part of shaping it with Invenci.
Enhancing Generative AI with Your Knowledge.
Your data stands as the bedrock for augmenting AI systems, providing the rich, contextual insights needed to tailor solutions that are not only innovative but also deeply aligned with specific business objectives.
Prepare Your Data
Retrieval augmented generation often begins with selecting an algorithm for embedding your data into a vector database, making it available for semantic search. Invenci has deployed custom ingestion and embedding pipelines for our customers to elevate their semantic retrieval beyond linear chunking to enable document summarization across their structured and unstructured documents.
Choose Your LLM
Invenci deploys large language models that fit our customer’s use case, which can be either a private LLM hosted internally, an LLM as a cloud service managed by Invenci, or a commercial instance. Whether it’s open source or closed source, we partner with our customers to deploy the best model.
The User Interface
Selecting the appropriate user interface (UI) for an augmented retrieval generation AI application is critical, as it directly impacts the usability and effectiveness of the technology. A well-designed UI should intuitively guide users through the process of information retrieval and content generation, ensuring a seamless integration of AI capabilities into their workflow.
Invenci has deployed AI UI solutions including data science tailored platforms such as Streamlit and custom web UI solutions where a user experience tailored to a highly specific use case is required.
Orchestration
Orchestration is the glue that binds the semantic search, large language models and user interfaces into an end-to-end AI solution that unlocks the mountains of structured and unstructured data within our customer’s data domains.
Invenci has deployed the leading open source orchestration frameworks including LangChain and LlamaIndex to provide rich multimodal experiences.
Discover more you can do with retrieval augmented generation.
Why Retrieval Augmented Generation?
Retrieval augmented generation, or RAG, addresses all of these limitations by augmenting the data used used to train LLMs with proprietary customer data while maintaining the privacy our customer’s demand. RAG offers contextualization by enhancing LLM responses with incorporated real-time, external data semantic retrieval. It clears up ambiguity in user queries and mitigates LLM hallucination.
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The Invenci Difference
Invenci has the full spectrum of RAG covered. Including but not limited to:
- Rapid prototyping and proof of concept with Streamlit
- Vector database tuning with our RAG explorer
- RAG orchestration with LlamaIndex, LangChain and LangFlow
- Observability
- Private LLM buildout to maintain privacy and IP
- LLM tuning for our customer data
- LLM benchmarking and reporting
- Managed services
Pioneers in building the AI community.
Invenci strongly believes that the future of AI is open source. We move ourselves and our clients forward by giving back, whether that’s in the form of being active with contributions to open source software, or mentoring ambitious students at top schools.
