28 Feb Components/ Modules of LangChain

LangChain provides a modular and extensible architecture for building complex workflows involving LLMs. The following are the components of LangChain:

Preprocessing

• Prepares raw data (e.g., documents, text) for use in LangChain workflows.
• Includes tasks like splitting text into chunks, cleaning data, and generating embeddings.

Models

• Refers to the large language models (LLMs) or embedding models used in the application.
• Examples include OpenAI’s GPT, Hugging Face models, or custom fine-tuned models.

Prompts

• Input queries or instructions given to the LLM to generate responses.
• Can be static or dynamically generated based on context or user input.

Memory

• Stores and retrieves context or state across interactions (e.g., chat history).
• Enables applications to maintain continuity and context-awareness.

Chains

• Combines multiple components (e.g., retrieval + generation) into a sequence of steps.
• Allows for complex workflows like question answering or summarization.

Indexes

• Tools for organizing and retrieving data efficiently (e.g., vector stores like FAISS).
• Enables fast and context-aware retrieval of relevant information.

Agents

• Systems that use LLMs to decide actions and interact with external tools or APIs.
• Can perform tasks like querying databases, calling APIs, or solving problems dynamically.

In the next lesson, let us understand the components and modules of LangChain one-on-one before moving toward a live-running example.

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