mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-11-04 03:39:35 +00:00
80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
import os
|
|
|
|
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
|
|
|
|
WORKING_DIR = "./dickens"
|
|
|
|
if not os.path.exists(WORKING_DIR):
|
|
os.mkdir(WORKING_DIR)
|
|
|
|
|
|
async def lmdeploy_model_complete(
|
|
prompt=None,
|
|
system_prompt=None,
|
|
history_messages=[],
|
|
keyword_extraction=False,
|
|
**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,
|
|
)
|
|
|
|
|
|
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"
|
|
),
|
|
),
|
|
),
|
|
)
|
|
|
|
|
|
with open("./book.txt", "r", encoding="utf-8") as f:
|
|
rag.insert(f.read())
|
|
|
|
# Perform naive search
|
|
print(
|
|
rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
|
|
)
|
|
|
|
# Perform local search
|
|
print(
|
|
rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
|
|
)
|
|
|
|
# Perform global search
|
|
print(
|
|
rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
|
|
)
|
|
|
|
# Perform hybrid search
|
|
print(
|
|
rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
|
|
)
|