By fusing Large Language Models (LLMs) with outside data, LangChain makes it easier to develop sophisticated applications and increases the potential of LLM-driven ones. LangChain aims to increase the usefulness of LLMs by combining them with additional computational or informational resources in recognition of the transformative power of LLMs. Its products are divided into six main categories:
LLMs and Prompts: A common interface for all LLMs and tools for managing and optimising LLM prompts.
Chains: A collection of calls to LLMs or other utilities that streamline end-to-end operations.
LLMs can interact with external data sources for tasks like text summarization or Q&A thanks to data augmented generation.
Agents: Systems that allow LLMs to make decisions and take action based on observations.
Memory: Allows data or context to remain consistent throughout various calls in a sequence or agent operation.
Innovative techniques that are still in beta testing are used to evaluate the quality of generative models by using language models themselves.
- Data scientists
- AI and ML researchers
- Natural Language Processing (NLP) specialists
- Software engineers focused on AI integrations
- Knowledge management professionals
- Content creators leveraging AI
- AI-driven product developers
- Startups in the AI space
- Educators and trainers in AI and NLP.
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