Getting Started with LangChain and Llama 2 in 15 Minutes | Beginner's Guide to LangChain

LangChain’s core value is turning large language models like Llama 2 into systems that can pull in outside information and take actions—by chaining together model calls, retrieval from documents, and tool-based reasoning. The most common pattern highlighted is retrieval-augmented generation: embed external data (such as PDFs or text files), search it for relevant chunks, and feed the matched context back into the model so answers stay grounded in a specific source rather than relying only on the model’s internal knowledge.

The walkthrough breaks LangChain into a set of building blocks. “Foundational” components include model wrappers (to run models such as GPT-style or Llama 2-style chat models), prompt templates (so prompts can be parameterized with variables instead of hard-coded strings), vector stores and indexes (to store embeddings of external documents and support similarity search), and memory modules (to retain conversation state across turns). Chains are the main orchestration unit: a retrieval chain can fetch relevant text from a vector store and then pass that retrieved context into the language model. Chains can also be composed—one chain’s output can become another chain’s input—using sequential chaining.

Agents are positioned as a step beyond chains. Where chains mainly connect steps in a fixed workflow, agents can decide when to use tools. The transcript points to typical tools such as online search, API calls, and code interpreters (e.g., a Python execution tool). This enables interactive applications like “ask a question, fetch data, compute results, then respond,” with the agent selecting the appropriate tool calls.

On the practical side, the setup uses Python with LangChain installed via pip, plus supporting libraries: Transformers (to run Llama 2 through a pipeline), and Unstructured (to load and parse external PDFs). The model is initialized using a Transformers pipeline compatible with LangChain, then wrapped so LangChain can call it with plain text inputs. Prompt templates are demonstrated with a parameterized system message and a variable “text” field, showing how formatting replaces placeholders before the model call.

Several concrete examples follow. A simple LLM chain runs a prompt to produce an answer. A sequential chain combines two steps: first summarizing input text, then generating three practical applications based on that summary. A chat-bot example uses message objects (system and human messages) and a specialized call that accepts structured messages, then reads the model’s response content.

The retrieval example grounds answers in the Bitcoin whitepaper. The workflow loads a markdown version of the paper, splits it into 1,024-character chunks (with 1,024-character chunking described), embeds the chunks using open-source embeddings, and stores them in a Chroma vector store. A retrieval QA chain then performs similarity search (returning the top two chunks) and uses a prompt template with the retrieved context to answer questions like how proof of work solves the “majority decision making problem,” with the response formatted to match the template. Timing is also observed: the first query takes several seconds on a T4 GPU, with subsequent queries taking longer.

Finally, an agent example creates a Python agent with a code interpreter tool. Given a math-and-division task, the agent executes Python code to compute an answer, with a warning that tool execution can run arbitrary code—so caution is required. Overall, the transcript frames LangChain as a practical toolkit for building grounded Q&A and tool-using assistants by combining prompt templates, retrieval pipelines, sequential chains, and agents.