Modules of LangChain

LangChain is a modular framework designed to build applications powered by large language models (LLMs). Its architecture allows developers to integrate LLMs with external data, prompt engineering, retrieval-augmented generation (RAG), semantic search, and agent workflows. Below is a detailed walkthrough of LangChain’s main modules, their roles, and code examples, following the latest practices and your provided context.

LangChain’s modularity enables:

• Flexible prompt creation and chaining.
• Integration with various LLMs (OpenAI, Anthropic, Gemini etc).
• Parsing and structuring LLM outputs.
• Retrieval-augmented generation (RAG) for grounding responses in external data.
• Semantic search using embeddings and vector databases.
• Agentic workflows for multi-step reasoning and tool use.

1. Prompt Module

Purpose : Structure and format user input into prompts that LLMs can interpret, supporting both plain text and chat-based roles.

Types:

• PromptTemplate: For single-turn, string-based prompts.
• ChatPromptTemplate: For multi-turn, role-based conversations.

Output

2. Chat Model and LLM Module

Purpose : Interface with LLMs for text or chat completion. Supports providers like OpenAI, Anthropic, Gemini, etc.

Example (Gemini 2.0 Flash):

Output

3. Output Parser Module

Purpose : Parse and structure the raw output from LLMs into usable formats (e.g., extracting values, JSON, or structured text).

Output

4. Retrieval-Augmented Generation (RAG) Module

Purpose : Augment LLM outputs by retrieving relevant data from external sources (HTML, DOC, S3, web buckets, etc.) and injecting it into prompts.

Components:

• Document loaders: Ingest data from HTML, DOC, S3, etc.
• Embeddings: Convert documents to semantic vectors.
• Vector database: Store vectors for similarity search (e.g., FAISS, Pinecone).
• Retriever: Finds relevant docs for a query.
• LLM Integration: Supplies retrieved content as context.

Output

5. Embedding & Vector Database Module

Purpose:

• Embeddings: Capture semantic meaning of text for similarity search.
• Vector DB: Store and index embeddings for fast retrieval.

6. Data Storage (HTML, DOC, S3, Web Buckets)

Purpose : Store and manage unstructured data for retrieval. LangChain supports loading documents from HTML, DOC, S3, and web buckets, which are then embedded and stored in a vector DB(database) for RAG.

7. Agent Module

Purpose : Enable autonomous, multi-step reasoning by chaining LLM calls with tool use (calculators, web search, database queries, etc).

Example:

Output

Colab link:Modules of LangChain