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83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
import os
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# import asyncio
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# import inspect
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import logging
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from dotenv import load_dotenv
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import openai_complete_if_cache, ollama_embedding
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from lightrag.utils import EmbeddingFunc
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load_dotenv()
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WORKING_DIR = "./examples/output"
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logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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return await openai_complete_if_cache(
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"deepseek-chat",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key=os.getenv("DEEPSEEK_API_KEY"),
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base_url=os.getenv("DEEPSEEK_ENDPOINT"),
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**kwargs,
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)
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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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: ollama_embedding(
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texts, embed_model="bge-m3:latest", host="http://m4.lan.znipower.com:11434"
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),
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),
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)
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with open("./examples/input/book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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# Perform naive search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
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)
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# Perform local search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
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)
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# Perform global search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
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)
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# Perform hybrid search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
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)
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# # stream response
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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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# print(chunk, end="", flush=True)
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# if inspect.isasyncgen(resp):
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# asyncio.run(print_stream(resp))
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# else:
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# print(resp)
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