LangChain / LlamaIndex pipelines
We build AI pipelines on the LangChain / LlamaIndex frameworks: we connect models, data sources, search (RAG), tools and steps into managed chains — the foundation for complex assistants and automations. Honestly upfront: frameworks speed up development, but they are tools, not magic — they add their own complexity and dependency; they are easy to over-engineer, they change fast (maintenance needed), and for simple tasks direct integration without a heavy framework is often enough — we will honestly say what you need.
LangChain / LlamaIndex pipelines — overview
LangChain / LlamaIndex pipelines is building AI applications with these frameworks: they provide ready 'building blocks' (model connection, document loading and indexing, RAG search, dialog memory, tool calling, agent chains) from which the logic of an assistant or automation is assembled. This speeds up developing complex AI systems. Honestly about 'a tool, not magic', this is key: a framework is convenience, not intelligence by itself. It helps assemble a pipeline faster, but the result quality still depends on models, data, retrieval and design — the same honesty limits (hallucinations, data dependence, token cost) do not go away. Honestly about over-engineering, this matters: LangChain/LlamaIndex are easy to over-complicate — piling on extra abstractions and chains where the task is solved more simply. For a simple chatbot or search, direct, more transparent integration without a heavy framework is often enough. We honestly assess whether a framework is needed at all and do not build a complex system for the sake of a complex system (the principle 'working simple > complex elegant'). Honestly about dependency and volatility: frameworks add a dependency on third-party code that develops and changes fast (breaking updates happen) — this requires maintenance, not 'build and forget'. Honestly about the effect: for complex, multi-step AI systems frameworks genuinely save time; for simple ones they can be excessive. Honestly about cost: besides development, a working pipeline uses models/resources (paid per token). Honestly about access: data, sources, a description of the logic are needed. An important boundary: this is AI-pipeline orchestration; RAG as an approach — 893; vector DB — 892; AI agents — 897; LLMOps/monitoring — 898. Picture this: instead of 'assembling a complex assistant from scratch' — a managed pipeline on proven blocks, but without excess complexity. The base price starts from 55,000 ₽ (depends on the logic's complexity).
Problems we solve
- A complex AI assistant is hard to assemble from scattered parts.
- RAG, memory, tools, agents are needed in one logic.
- Fear of over-complicating the system with a trendy framework.
- It is unclear whether a framework is needed or simple integration is enough.
What's included in the LangChain / LlamaIndex pipelines service
- AI pipelines on LangChain/LlamaIndex (models, data, RAG, tools)
- Assembling managed assistant/automation logic
- An honest assessment: a framework or simpler direct integration
- Protection from over-engineering (working simple > complex)
- Honest boundaries (a tool, not magic; the same AI limits)
- A maintenance plan (frameworks change fast)
- A link with RAG (893), vector DB (892), agents (897)
- Handover and review with you
What you get
- A managed AI pipeline for complex logic
- Time savings on complex multi-step systems
- Without excess complexity (a framework only where justified)
- Honest boundaries (dependency/volatility, maintenance needed)
How the work goes: steps
- We assess the logic and whether a framework vs direct integration is needed
- We assemble a pipeline from proven components without over-engineering
- We build in maintenance, honestly set boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will the framework make my assistant smart by itself?
No, honestly: LangChain/LlamaIndex are tools for assembly, not intelligence. They speed up development, but quality still depends on models, data, retrieval and design. The same AI limits (hallucinations, data dependence, token cost) remain. A framework is convenience, not magic, and we honestly state this.
Does my task definitely need LangChain/LlamaIndex?
Not always, and we will honestly assess. These frameworks are easy to over-complicate — piling on abstractions where the task is solved more simply. For a simple chatbot or search, direct, more transparent integration without a heavy framework is often enough. We build working-simple rather than complex-for-its-own-sake, and apply a framework only where it genuinely saves and is justified.
Pipeline assembled — no costs afterward?
Not quite, honestly: first, a working pipeline uses models/resources for a fee (per token). Second, frameworks develop and change fast — breaking updates happen, maintenance and updating are needed. 'Build and forget' will not work. We honestly factor in the cost of ownership and support rather than promise a one-off project without consequences.
About the provider
The «LangChain / LlamaIndex pipelines» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.