An Overview of LangChain: Core Concepts and Practical Implementations

LangChain is a programming framework built on top of large language models (LLMs) that bridges the gap between business logic and underlying LLM services, enabling rapid development of AI‑driven applications.

Core Concepts

Model I/O : Provides a unified interface for switching between different LLM providers (e.g., ChatGPT, ChatGLM, TongYi). It also offers caching to avoid redundant model calls.

Retriever : Connects LLMs with external data sources such as vector databases, allowing the model to answer questions based on user‑specific knowledge.

Chain : Represents composable workflows that orchestrate multiple components (retrieval, prompting, post‑processing) similar to a workflow engine.

Memory : Persists conversation history using databases or caches so that LLMs can maintain context across turns.

Agent : Enables the LLM to invoke external tools or services (e.g., API log query) when the task requires capabilities beyond pure language generation.

Practical Implementation (Java)

Embedding a document using TongYi embeddings:

TongYiEmbeddings embeddings = new TongYiEmbeddings();
embeddings.setServerAccessId(ALINLP_EMBEDDINGS_ACCESSID);
embeddings.setServerUrl(ALINLP_EMBEDDINGS_SERVER_URL);
embeddings.setServerUuid(ALINLP_EMBEDDINGS_UUID);
Document document = new Document();
document.setPageContent(rawText);
List<Document> documents = embeddings.embedDocument(Arrays.asList(document));
Document vecDocument = documents.get(0);
String embeddingString = JSON.toJSONString(vecDocument.getEmbedding())
    .replaceAll("\\[", "{")
    .replaceAll("\\]", "}");
return embeddingString;

Building a RetrievalQA chain with ChatGLM‑6B:

// Initialize ChatGLM parameters
ChatGLMV2Internal chatGLMV2Internal = new ChatGLMV2Internal();
chatGLMV2Internal.setTemperature(0.01d);
chatGLMV2Internal.setMaxLength(2048);

// Prompt template
PromptTemplate prompt = new PromptTemplate();
String template = "已知信息：
{context}
根据上述已知信息，简洁专业地回答用户的问题。若无法回答，请说明。问题是：{question}";
prompt.setTemplate(template);

// Retrieval configuration
RetrievalQA qa = new RetrievalQA();
qa.setRecommend(5);
qa.setMaxDistanceValue(10000.0d);
qa.setLlm(chatGLMV2Internal);
qa.setPrompt(prompt);
qa.setRetriever(holoRetriever.asRetriever());
qa.init();

// Run the chain
Map<String, Object> inputs = new HashMap<>();
inputs.put("question", question);
Map<String, Object> outputs = qa.run(inputs);
llmKnowledgeDO.setContent(String.valueOf(outputs.get("text")));
return llmKnowledgeDO;

The article also discusses agent registration, tool execution logic, and future directions such as micro‑service‑based agent factories.