File size: 3,122 Bytes
45562ea 0553d6a 45562ea 0553d6a 45562ea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import numpy as np
from lightrag import LightRAG, QueryParam
from lightrag.utils import EmbeddingFunc
from lightrag.llm.jina import jina_embed
from lightrag.llm.openai import openai_complete_if_cache
import os
import asyncio
async def embedding_func(texts: list[str]) -> np.ndarray:
return await jina_embed(texts, api_key="YourJinaAPIKey")
WORKING_DIR = "./dickens"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], **kwargs
) -> str:
return await openai_complete_if_cache(
"solar-mini",
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
api_key=os.getenv("UPSTAGE_API_KEY"),
base_url="https://api.upstage.ai/v1/solar",
**kwargs,
)
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=1024, max_token_size=8192, func=embedding_func
),
)
async def lightraginsert(file_path, semaphore):
async with semaphore:
try:
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
except UnicodeDecodeError:
# If UTF-8 decoding fails, try other encodings
with open(file_path, "r", encoding="gbk") as f:
content = f.read()
await rag.ainsert(content)
async def process_files(directory, concurrency_limit):
semaphore = asyncio.Semaphore(concurrency_limit)
tasks = []
for root, dirs, files in os.walk(directory):
for f in files:
file_path = os.path.join(root, f)
if f.startswith("."):
continue
tasks.append(lightraginsert(file_path, semaphore))
await asyncio.gather(*tasks)
async def main():
try:
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=1024,
max_token_size=8192,
func=embedding_func,
),
)
asyncio.run(process_files(WORKING_DIR, concurrency_limit=4))
# Perform naive search
print(
await rag.aquery(
"What are the top themes in this story?", param=QueryParam(mode="naive")
)
)
# Perform local search
print(
await rag.aquery(
"What are the top themes in this story?", param=QueryParam(mode="local")
)
)
# Perform global search
print(
await rag.aquery(
"What are the top themes in this story?",
param=QueryParam(mode="global"),
)
)
# Perform hybrid search
print(
await rag.aquery(
"What are the top themes in this story?",
param=QueryParam(mode="hybrid"),
)
)
except Exception as e:
print(f"An error occurred: {e}")
if __name__ == "__main__":
asyncio.run(main())
|