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Just a simple extension for LlamaIndex for better apply some llm such as DeepSeek.

Project description

LlamaIndex Extra LLM

Just a simple extension for LlamaIndex for better apply some llm such as DeepSeek.

Features

  • Support DeepSeek

Installation / Environment

Pytorch is needed, it is easier to install by conda if you are using local PC with GPU

conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

Quick Usage

Initialize

from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index import ServiceContext
from llama_index_extra_llm.deepseek import DeepSeekLLM
llm = DeepSeekLLM(
    model_name="deepseek-ai/deepseek-llm-7b-chat",
    tokenizer_name="deepseek-ai/deepseek-llm-7b-chat",
    context_window=3900,
    max_new_tokens=1024,
    generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95},
    device_map="auto",
)
service_context = ServiceContext.from_defaults(llm=llm)
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
query_engine = index.as_query_engine()

Predict

prompt=DeepSeekLLM.messages2prompt(messages=[{"role": "user", "content": "Hello"}])
assistant=query_engine.query(prompt)
messages.append({"role": "assistant", "content": assistant})

For stream output

query_engine = index.as_query_engine(streaming=True, similarity_top_k=1)
prompt=DeepSeekLLM.messages2prompt(messages=[{"role": "user", "content": "Hello"}])
streaming_response=query_engine.query(prompt)
streaming_response.print_response_stream()
assistant=handle_output(generator)
messages.append({"role": "assistant", "content": assistant})

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