Are you tired of relying on an internet connection to access the power of language models? Do you value privacy and security when it comes to your data? Look no further! Introducing privateGPT, a revolutionary solution that allows you to ask questions to your documents without an internet connection, leveraging the capabilities of Language Models (LLMs). In this blog post, we will dive into the technical details of how this groundbreaking technology works and explore specific examples of its usage.
How Does It Work?
privateGPT is designed to enable you to interact with your documents and ask questions without the need for an internet connection. It ensures complete privacy as no data ever leaves your execution environment. Now, let’s explore the technical details of how this innovative technology operates.
1. Document Ingestion and Embedding Creation
The first step in the privateGPT workflow is document ingestion and embedding creation, utilizing LangChain tools to parse the document and generate embeddings locally. To accomplish this, it leverages the HuggingFaceEmbeddings library, which is powered by SentenceTransformers.
The document is processed to extract its content, and the text is transformed into numerical representations called embeddings. These embeddings capture the semantic information of the document, allowing for efficient search and retrieval of relevant context during question answering.
To store the embeddings, privateGPT utilizes a local vector database implemented with Chroma vector store. This vector database enables fast and convenient access to the embeddings, ensuring optimal performance during subsequent operations.
2. Question Understanding and Answer Generation
privateGPT provides the capability to ask questions and receive accurate answers based on the ingested documents. This is made possible through utilizing a local Language Model (LLM) based on GPT4All-J or LlamaCpp.
When a question is posed, the local LLM processes the question to understand its context and generate relevant answers. The key to providing accurate answers lies in the ability to extract the appropriate context from the ingested documents.
To accomplish this, privateGPT performs a similarity search within the local vector store. By comparing the embeddings of the question to those of the document content, privateGPT identifies the most relevant piece of context to derive the answer. This ensures that the answer is contextually accurate and meaningful.
Conclusion
privateGPT revolutionizes the way we interact with documents by empowering us to ask questions without the need for an internet connection. With its local models and the power of LangChain, privateGPT ensures privacy and data security, as no information ever leaves your execution environment.
By ingesting documents, creating embeddings, and utilizing a local LLM for question understanding and answer generation, privateGPT delivers a seamless and efficient user experience. Whether you are a technical writer, researcher, or knowledge seeker, privateGPT enables you to access valuable information offline, anytime and anywhere
