gardel/lightrag
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

delete private test file

Browse Source
This commit is contained in:
yangdx
2025-01-16 20:14:43 +08:00
parent 95ff048a9e
commit 68de8f6d04
1 changed files with 0 additions and 82 deletions
Download Patch File Download Diff File Expand all files Collapse all files

82
examples/lightrag_yangdx.py
View File

@@ -1,82 +0,0 @@
import os
# import asyncio
# import inspect
import logging
from dotenv import load_dotenv
from lightrag import LightRAG, QueryParam
from lightrag.llm import openai_complete_if_cache, ollama_embedding
from lightrag.utils import EmbeddingFunc
load_dotenv()
WORKING_DIR = "./examples/output"
logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
return await openai_complete_if_cache(
"deepseek-chat",
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
api_key=os.getenv("DEEPSEEK_API_KEY"),
base_url=os.getenv("DEEPSEEK_ENDPOINT"),
**kwargs,
)
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=1024,
max_token_size=8192,
func=lambda texts: ollama_embedding(
texts, embed_model="bge-m3:latest", host="http://m4.lan.znipower.com:11434"
),
),
)
with open("./examples/input/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"))
)
# # stream response
# resp = rag.query(
# "What are the top themes in this story?",
# param=QueryParam(mode="hybrid", stream=True),
# )
# async def print_stream(stream):
# async for chunk in stream:
# print(chunk, end="", flush=True)
# if inspect.isasyncgen(resp):
# asyncio.run(print_stream(resp))
# else:
# print(resp)