Generative artificial intelligence has revolutionized corporate productivity, but it has brought a major challenge: data privacy. By sending internal documents, source code, or customer data to third-party APIs, companies run serious risks of leaking sensitive information and violating regulations like GDPR or LGPD.
The solution to this dilemma is the implementation of Local Generative AI integrated with RAG (Retrieval-Augmented Generation). In this article, we detail how our team at the Data & Generative AI consultancy makes this architecture viable, secure, and high-performing within your enterprise infrastructure.
1. What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that extends the capability of a Large Language Model (LLM) by providing it with direct access to an updated and restricted knowledge base, without the need to retrain the language model (fine-tuning).
How RAG works in 3 simple steps:
- Indexing: Corporate documents (PDFs, spreadsheets, databases) are converted into mathematical vectors (embeddings) and stored in a corporate vector database (Vector DB).
- Retrieval: When a user asks a question, the system searches the Vector DB for the most relevant document snippets for that context.
- Generation: The user's query and the retrieved snippets are sent together (as a context) to the local LLM, which generates a precise, grounded response free of hallucinations.
2. A 100% Local and Secure Architecture
To ensure full compliance with governance and security rules, the entire technology stack is provisioned in isolation (On-Premises or within a private VPC in the cloud).
Key Components:
- Open-Source LLMs: Models like Llama 3 (Meta), Mistral (Mistral AI), or Qwen (Alibaba) are deployed locally. With the right hardware (dedicated GPUs), they deliver performance comparable to proprietary closed APIs.
- Local Vector DB: Databases like pgvector (PostgreSQL), Qdrant, or Milvus are configured locally to ensure fast and secure indexing.
- Orchestration: Frameworks like LangChain or LlamaIndex manage the connection between the user interface, the Vector DB, and the LLM transparently.
3. Strategic Business Benefits
By adopting this approach instead of relying on external solutions, the company gains absolute control over the technology.
- Zero Leakage: No data ever leaves your private infrastructure.
- Financial Efficiency: No recurring subscription costs per token of commercial APIs.
- Accurate Responses: The LLM only responds based on actual, auditable data from your company.
4. Run the model and the vector table
Ollama runs on the machine. The official script installs the daemon. The model stays on local disk. llama3.1 is the example. The vector size has to match the embedding model, not the chat LLM.
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.1
ollama pull nomic-embed-text
curl -sS http://127.0.0.1:11434/api/embeddings \
-d '{"model":"nomic-embed-text","prompt":"vacation policy"}'The JSON response carries the embedding array. In PostgreSQL, with pgvector, the table uses that size. nomic-embed-text produces 768 dimensions:
CREATE EXTENSION IF NOT EXISTS vector;
CREATE TABLE documents (
id bigserial PRIMARY KEY,
content text NOT NULL,
embedding vector(768)
);
CREATE INDEX documents_embedding_hnsw
ON documents USING hnsw (embedding vector_cosine_ops);The search returns the nearest chunks. That result goes into the llama3.1 prompt, together with the question. None of this calls an external API when Ollama and Postgres are on the same private network.
SELECT id, left(content, 80)
FROM documents
ORDER BY embedding <=> '[0.01, 0.02]'::vector
LIMIT 5;Replace the literal with the vector nomic-embed-text returned for the user's question.
Conclusion
Implementing Artificial Intelligence does not have to mean giving up corporate security. A local AI architecture combined with RAG offers the best of both worlds: the analytical and generation power of LLMs with the strict security control required for highly critical operations.