How to conect several LLMs with LlamaIndex?

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To connect several LLMs (Language Learning Models) with LlamaIndex, you can follow these steps:

Instantiate each LLM you want to use. For example, if you're using OpenAI's GPT-4 model, you can instantiate it like this:

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from llama_index.llms import OpenAI
llm = OpenAI(temperature=0.1, model="gpt-4")

Create a ServiceContext and pass your LLM to it. This context will then be passed to other stages of the pipeline:

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from llama_index import ServiceContext
service_context = ServiceContext.from_defaults(llm=llm)

Repeat the above steps for each LLM you want to use. You can use more than one LLM in your application if you wish.

Use the LLMs at different stages of your pipeline. For example, during the indexing stage, you may use an LLM to determine the relevance of data or to summarize the raw data. During the querying stage, LLMs can be used for retrieval and response synthesis.

Here's an example of how you might use an LLM during the indexing stage:

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from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents, service_context=service_context)

Remember, LlamaIndex provides a single interface to a large number of different LLMs, allowing you to pass in any LLM you choose to any stage of the pipeline.

For more detailed information, you can refer to the LlamaIndex documentation ().
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How to conect several LLMs with LlamaIndex?