How to Build a Fully Local RAG System with Semantic Kernel, Ollama, and Qdrant

In modern AI applications, combining structured data retrieval with generative reasoning has become a critical capability. Retrieval-Augmented Generation (RAG) is a method that blends vector-based semantic search with Large Language Model (LLM) reasoning, producing responses that are not only contextually relevant but also grounded in reliable sources. This approach is particularly valuable in knowledge-intensive domains, such as legal, compliance, and technical workflows, where accuracy, transparency, and traceability are essential.

This article presents a fully local RAG system implemented in C#, demonstrating how Semantic Kernel can orchestrate embeddings, vector search, and LLM reasoning while maintaining modularity, privacy, and extensibility. The system leverages Ollama for hosting LLMs and embeddings locally and QDrant for high-performance vector search. Developers following this guide can create a production-ready, fully local RAG pipeline capable of delivering precise, context-aware answers.

Semantic Kernel provides centralised orchestration for AI and memory components. The KernelMemoryBuilder class allows developers to integrate embeddings, vector storage, and LLM configurations consistently. A typical setup looks like this:

var memoryBuilder = new KernelMemoryBuilder()
    .WithOllamaTextGeneration(ollamaConfig)
    .WithOllamaTextEmbeddingGeneration(ollamaConfig)
    .WithQdrantMemoryDb(LegalDocConfig.QdrantUrl)
    .WithSearchClientConfig(
        new SearchClientConfig
        {
            AnswerTokens = 4096
        }
    );
var memory = memoryBuilder.Build(
    new KernelMemoryBuilderBuildOptions
    {
      AllowMixingVolatileAndPersistentData = true
    }
);

This configuration ensures all AI calls, embeddings, and retrieval operations are coordinated through a single Kernel instance. The result is a maintainable, modular system that allows both deterministic retrieval and AI reasoning to work seamlessly together.

In this system, legal documents such as NDAs, employment contracts, SLAs, and DPAs are transformed into embeddings and stored in QDrant with associated metadata. This enables precise semantic retrieval during query processing. A typical import process looks like this:

var docText = $ "Title: {doc.Title}Content: {doc.Content}";
var safeTitle = Regex.Replace(doc.Title.ToLower(), @ "[^a-z0-9._-]", "-");
var documentId = $ "legal-{safeTitle}";
await memory.ImportTextAsync(text: docText, documentId: documentId, index: LegalDocConfig.IndexName, tags: new TagCollection
{
    {
        "title",
        doc.Title
    },
    {
        "type",
        "legal-document"
    },
    {
        "source",
        "sample-legal-database"
    }
});

Each document is assigned a unique identifier and enriched with metadata tags, allowing fast and accurate retrieval during query processing. By storing metadata alongside embeddings, the system preserves context, source attribution, and document type information for every query result.

The system provides an interactive console interface for developers or end users. Queries are processed in the context of prior interactions to maintain conversational coherence. An example of how queries are handled is shown below:

var fullQuery = chatHistory.GetHistoryAsContext() + "User: " + userInput;
var answer = await memory.AskAsync(
    fullQuery,
    index: LegalDocConfig.IndexName,
    minRelevance: 0.3f
);

Chat history is preserved to provide contextual grounding across multiple interactions:

public string GetHistoryAsContext(int maxMessages = 6) => string.Join("", _messages.TakeLast(maxMessages));

This approach ensures that responses are not only contextually aware but also grounded in specific, retrievable document references, improving both accuracy and trustworthiness.

With this setup, users can pose detailed, domain-specific questions such as:

• “What are the key confidentiality clauses in the NDA?”
• “How can I terminate the employment contract?”
• “What is the defined uptime in the SLA?”

When a query is submitted, the system executes the following steps:

1. The query is embedded using nomic-embed-text.
2. Semantic search is performed in QDrant to retrieve the most relevant document sections.
3. The retrieved content, along with conversation context, is passed to the local LLM running in Ollama (gemma3:1b).
4. A context-aware response is generated, including references to the source documents.

This combination of deterministic retrieval and AI reasoning ensures responses are reliable, verifiable, and actionable.

The architecture separates deterministic and AI-driven components:

• Deterministic Retrieval: QDrant offers fast and reliable semantic search.

• AI Reasoning: Semantic Kernel orchestrates embeddings, memory access, and LLM reasoning.
  
  

This separation improves modularity, testability, and predictability. Developers can extend or modify one component without affecting others, allowing experimentation and adaptation for other knowledge-intensive domains.

Why This Architecture Works

• Separation of concerns: Deterministic retrieval and generative AI reasoning operate independently while remaining coordinated.
• Modularity: KernelMemory centralises embeddings, prompts, and model calls.
• Local execution: All models and vector storage run locally, ensuring data privacy and low latency.
• Extensibility: New document sources, models, or interfaces can be added with minimal changes to the core system.

The system is designed for local deployment with the following setup:

• Runtime: .NET 9.0 SDK for development and execution.
• Vector storage: QDrant running locally for semantic vector search.
• AI runtime: Ollama hosting local models, including nomic-embed-text:latest for embeddings and gemma3:1b for language model inference.
• Code structure: A modular codebase organised into separate components, including LegalDocumentImporter.cs for document import, LegalDocConfig.cs for configuration, LegalDocRagApp.cs for orchestration, and ChatService.cs for the console interface.

To enhance this system further, you can incorporate additional document sources to expand coverage, implement web or desktop interfaces for improved accessibility, introduce long-term memory to preserve context across sessions, apply advanced natural language query parsing and dynamic indexing pipelines, and adapt the RAG workflow for other knowledge-intensive domains such as healthcare, finance, or engineering.

This article demonstrated how to build a fully local RAG system combining Semantic Kernel, Ollama, and QDrant. By integrating deterministic vector search with local LLM reasoning, the system produces accurate, context-aware answers while maintaining transparency through source document references. Its modular architecture, local execution, and extensibility make it suitable for professional, production-ready applications in legal, compliance, and technical workflows.

You can explore the full source code for this local RAG system on GitHub and experiment with your own datasets to build fully local, context-aware AI solutions. By combining deterministic computation with generative reasoning, you can create AI systems that are both powerful and trustworthy.