RAG Systems

Where It's Applied

In my practice, I developed RAG (Retrieval-Augmented Generation) systems functioning as intelligent assistants for companies — for both internal employees and external clients. The system indexes the entire company knowledge base (documentation, policies, FAQs, technical materials), enables users to ask natural language questions, and provides accurate answers based on current information. This solution automates frequent question responses, reduces support burden, and enables employees to quickly find necessary information.

Who Will Benefit

I recommend this solution to large companies with extensive knowledge bases and high question volumes from employees and clients. SaaS companies building embedded assistants into products helping users. Consulting firms for internal assistants helping employees quickly find knowledge and best practices. Corporate support and training departments wanting to automate responses and improve service speed. Any companies with documentation quickly becoming outdated requiring frequent updates — RAG systems always use the freshest information.

Technologies

RAG Architecture and Core Workflow

RAG systems work in two stages: retrieval (finding relevant documents) and generation (answer generation). When users ask questions, the system first searches the knowledge base for the most relevant fragments, then sends these fragments with the question to a language model generating answers based on found information. This is critical for quality: models provide answers based on company facts, not training data.

Advantage: answers are always current (updating knowledge base means instant system updates), sources are known (I return original document references), LLM hallucination risk significantly decreases.

Qdrant as Primary Vector Storage

I use Qdrant — a specialized vector database for document indexing and search. Process: company knowledge base (documentation, articles, FAQs) splits into semantic blocks (chunks), each transforms into vector representation (embedding), all vectors load into Qdrant. Qdrant indexes vectors using HNSW algorithm for fast search.

Critical Qdrant advantages: runs locally (complete confidentiality), supports metadata filtering (search only specific document categories), has convenient web UI for collection management, scales to millions of vectors, excellent performance parameters (1-10ms search).

Local LLM with Qwen

For answer generation, I use Qwen — an open language model from Alibaba running locally on my infrastructure. Qwen comes in sizes: 7B parameters (light, fast, simple tasks), 14B (speed-quality balance), 72B (very quality, needs more GPU). I select size based on project requirements.

Local Qwen advantages: complete confidentiality (data doesn't reach cloud), no external API dependence, no request limits, no network delays. On GPU (NVIDIA CUDA), system generates answers in 1-5 seconds, acceptable for interactive mode.

OpenAI API as Alternative

For projects requiring maximum answer quality and willingness to pay for cloud service, I use OpenAI API (GPT-4 or GPT-4 Turbo). OpenAI models often provide better results due to larger size and parameters. My system supports switching between local Qwen and OpenAI API via configuration — users choose backends based on quality, speed, and cost requirements.

In practice: critical questions (legal consultation, financial calculations) use GPT-4, simple quick answers use local Qwen.

Embedding Models and Selection

For text-to-vector transformation, I use embedding models. Options: OpenAI embeddings (best quality, cloud), local models like BAAI/bge-base-en-v1.5 or multilingual versions (local operation, acceptable quality). I select based on supported languages. In practice: for Russian, I often use multilingual or Russian-specific embedding models.

Critical: embedding models must match those used when indexing in Qdrant. Switching embedding models requires re-indexing the entire knowledge base.

Knowledge Base Management and Updates

Knowledge bases must always stay current. I implemented automatic update systems: company documents (from Google Drive, GitHub, databases) sync with RAG systems automatically. Document updates trigger re-indexing in Qdrant. I use N8N for workflow orchestration.

For quality control, I added versioning: system tracks document versions used when answering, enabling quick verification if answers seem outdated.

Context and Multi-Turn Dialogue

System maintains conversation context — previous question and answer history includes in LLM requests. This enables follow-up questions ("what does that mean?", "more details on point three?"), with system considering previous answer context. In practice: conversations become more natural and users access information more efficiently.

Filtering and Access Control

Not all employees see all information. I implemented access control systems: documents in Qdrant get access metadata tags (e.g., "IT department only"), search returns only documents users can access. This enables single company-wide knowledge base with each user seeing only relevant information.

Source Highlighting and References

When generating answers, the system always indicates sources — document number, section, even specific text coordinates. Critical for trust: users can verify answers in original documents. Web interface shows relevant fragments alongside answers, enabling quick relevance assessment.

Confidence Assessment and Uncertainty Signals

System assesses answer confidence based on found document relevance. Low relevance triggers indicators ("I'm not very confident in this answer, recommend verification..."). Missing documents result in statements rather than hallucinations. Critical for UX — users must know when receiving reliable answers versus uncertain ones.

Scaling and Architecture

Architecture uses Python + FastAPI backend, Vue.js frontend. Asynchronous request processing enables simultaneous user service. I use caching (Redis) for frequent questions, accelerating responses. Qdrant and LLM models deploy on separate machines for performance — system scales horizontally easily.

In practice: system handles hundreds of questions daily without issues. Typical response time: 2-5 seconds (including Qdrant search and text generation).

Communication Channel Integration

RAG systems integrate into various channels: web interface (chat), Slack bots (internal employees), Telegram bots (clients), embedded website chat (visitors). In practice: I often create multiple interfaces for single RAG systems, customizing behavior by context (e.g., limiting client access to specific documents).

Monitoring and Improvement

System logs all questions and answers, enabling work quality analysis. I track metrics: useful answer frequency, follow-up question frequency (incomplete answer indicator), negative feedback frequency. Based on this, I iteratively improve: add new knowledge base documents, improve LLM prompts, optimize Qdrant search parameters.

Important Organizational Considerations

First — knowledge base quality and currency are critical. Outdated or error-containing documents cause incorrect answers. I organize processes: document owners maintain currency, periodic review schedules (typically quarterly), systems auto-detect stale information (year-old unupdated documents trigger owner alerts).

Second — answer testing and validation. Before deployment, I always test systems on critical question sets. Post-deployment, I monitor quality and collect user feedback. In practice: initial weeks may show odd answers, but after fine-tuning and adding missing documents, quality rapidly improves.

Third — confidential information handling. Sensitive company data (personal information, trade secrets) requires caution. I use document and request-level access control — systems must not expose sensitive data to unauthorized users.

Fourth — local vs. cloud solution choice. Local Qwen costs less long-term, ensures complete confidentiality, but requires own GPU and support. OpenAI API provides better quality, needs no hardware, but involves API costs and cloud data. I recommend hybrid approaches: local Qwen for non-critical questions, OpenAI API for critical ones.

Fifth — user training. People must understand proper RAG system use and question types it handles well. I create good question examples, documentation, and conduct periodic training. In practice: when users understand system capabilities, they ask better questions and receive better answers.

Sixth — company process integration. RAG systems are most useful when easily accessible. I integrate them where people already work: Slack, internal portals, company websites. Fewer user actions to ask questions means higher utilization.