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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