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49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
import os
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from lightrag import LightRAG
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from lightrag.llm import openai_complete, openai_embed
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from lightrag.utils import EmbeddingFunc
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from lightrag import QueryParam
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# WorkingDir
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ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
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WORKING_DIR = os.path.join(ROOT_DIR, "dickens")
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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print(f"WorkingDir: {WORKING_DIR}")
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api_key = "empty"
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=openai_complete,
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llm_model_name="qwen2.5-14b-instruct@4bit",
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llm_model_max_async=4,
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llm_model_max_token_size=32768,
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llm_model_kwargs={"base_url": "http://127.0.0.1:1234/v1", "api_key": api_key},
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embedding_func=EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=8192,
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func=lambda texts: openai_embed(
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texts=texts,
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model="text-embedding-bge-m3",
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base_url="http://127.0.0.1:1234/v1",
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api_key=api_key,
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),
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),
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)
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with open("./book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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resp = rag.query(
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"What are the top themes in this story?",
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param=QueryParam(mode="hybrid", stream=True),
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)
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async def print_stream(stream):
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async for chunk in stream:
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if chunk:
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print(chunk, end="", flush=True)
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