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24 lines
1.6 KiB
Plaintext
---
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sidebar_position: 1
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---
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# Language models
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LangChain provides interfaces and integrations for two types of models:
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- [Chat models](/docs/modules/model_io/models/chat/): Models that are backed by a language model but take a list of Chat Messages as input and return a Chat Message
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- [LLMs](/docs/modules/model_io/models/llms/): Models that take a text string as input and return a text string
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## LLMs vs Chat models
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LLMs and chat models are subtly but importantly different. LLMs in LangChain refer to pure text completion models.
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The APIs they wrap take a string prompt as input and output a string completion. OpenAI's GPT-3 is implemented as an LLM.
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Chat models are often backed by LLMs but tuned specifically for having conversations.
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And, crucially, their provider APIs use a different interface than pure text completion models. Instead of a single string,
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they take a list of chat messages as input. Usually these messages are labeled with the speaker (usually one of "System",
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"AI", and "Human"). And they return an AI chat message as output. GPT-4 and Anthropic's Claude are both implemented as chat models.
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To make it possible to swap LLMs and chat models, both implement the Base Language Model interface. This includes common
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methods "predict", which takes a string and returns a string, and "predict messages", which takes messages and returns a message.
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If you are using a specific model it's recommended you use the methods specific to that model class (i.e., "predict" for LLMs and "predict messages" for chat models),
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but if you're creating an application that should work with different types of models the shared interface can be helpful.
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