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Merge pull request #144 from tackhwa/lmdeploy_backend
[Feature] support lmdeploy backend
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import os | ||
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from lightrag import LightRAG, QueryParam | ||
from lightrag.llm import lmdeploy_model_if_cache, hf_embedding | ||
from lightrag.utils import EmbeddingFunc | ||
from transformers import AutoModel, AutoTokenizer | ||
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WORKING_DIR = "./dickens" | ||
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if not os.path.exists(WORKING_DIR): | ||
os.mkdir(WORKING_DIR) | ||
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async def lmdeploy_model_complete( | ||
prompt=None, system_prompt=None, history_messages=[], **kwargs | ||
) -> str: | ||
model_name = kwargs["hashing_kv"].global_config["llm_model_name"] | ||
return await lmdeploy_model_if_cache( | ||
model_name, | ||
prompt, | ||
system_prompt=system_prompt, | ||
history_messages=history_messages, | ||
## please specify chat_template if your local path does not follow original HF file name, | ||
## or model_name is a pytorch model on huggingface.co, | ||
## you can refer to https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/model.py | ||
## for a list of chat_template available in lmdeploy. | ||
chat_template="llama3", | ||
# model_format ='awq', # if you are using awq quantization model. | ||
# quant_policy=8, # if you want to use online kv cache, 4=kv int4, 8=kv int8. | ||
**kwargs, | ||
) | ||
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rag = LightRAG( | ||
working_dir=WORKING_DIR, | ||
llm_model_func=lmdeploy_model_complete, | ||
llm_model_name="meta-llama/Llama-3.1-8B-Instruct", # please use definite path for local model | ||
embedding_func=EmbeddingFunc( | ||
embedding_dim=384, | ||
max_token_size=5000, | ||
func=lambda texts: hf_embedding( | ||
texts, | ||
tokenizer=AutoTokenizer.from_pretrained( | ||
"sentence-transformers/all-MiniLM-L6-v2" | ||
), | ||
embed_model=AutoModel.from_pretrained( | ||
"sentence-transformers/all-MiniLM-L6-v2" | ||
), | ||
), | ||
), | ||
) | ||
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with open("./book.txt", "r", encoding="utf-8") as f: | ||
rag.insert(f.read()) | ||
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# Perform naive search | ||
print( | ||
rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")) | ||
) | ||
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# Perform local search | ||
print( | ||
rag.query("What are the top themes in this story?", param=QueryParam(mode="local")) | ||
) | ||
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# Perform global search | ||
print( | ||
rag.query("What are the top themes in this story?", param=QueryParam(mode="global")) | ||
) | ||
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# Perform hybrid search | ||
print( | ||
rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")) | ||
) |
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Original file line number | Diff line number | Diff line change |
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@@ -13,3 +13,4 @@ tiktoken | |
torch | ||
transformers | ||
xxhash | ||
# lmdeploy[all] |