Understanding LLMs in LangChain
LangChain allows developers to easily integrate and work with powerful Large Language Models (LLMs) — both cloud-based (like OpenAI) and open-source models that can run locally.
OpenAI models (text-davinci, gpt-4)
LangChain supports multiple models from OpenAI such as:
• text-davinci-003 – Great for tasks like text generation, summarization, and answering complex queries.
• gpt-4 – OpenAI’s most advanced model with superior reasoning, memory, and multi-turn conversation handling.
With LangChain, you can quickly connect to these models using your API key and start building:
Gemini models (Gemini Pro, Gemini Pro Vision)
LangChain supports powerful Gemini models from Google’s Generative AI suite. These models are great for both text-based and multimodal tasks.
Gemini Pro – Great for tasks like advanced reasoning, summarization, chatbots, and content creation.
Gemini Pro Vision – Designed to handle both text + image input, ideal for visual tasks.
With LangChain, you can quickly connect to Gemini models using your API key and start building:
LangChain-gemini
✅ This sets up a basic Gemini integration using LangChain — fast, simple, and ready to use!
Local LLMs (LLaMA, Mistral, Deepseek)
If you prefer to avoid cloud APIs or need more control, you can use local LLMs that run on your own machine or server:
• LLaMA (Meta) – Efficient, high-performance model for private use.
• Mistral – Lightweight, open-source model with strong performance.
• DeepSeek – Great for tasks like code generation, math problems, and content creation.
LangChain allows you to work with these models using backends like Hugging Face Transformers, Ollama, or LM Studio.
