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71 lines
1.7 KiB
Python
71 lines
1.7 KiB
Python
import asyncio
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import os
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import inspect
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import logging
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import ollama_model_complete, ollama_embedding
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from lightrag.utils import EmbeddingFunc
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WORKING_DIR = "./dickens"
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logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
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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=ollama_model_complete,
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llm_model_name="gemma2:2b",
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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={"host": "http://localhost:11434", "options": {"num_ctx": 32768}},
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embedding_func=EmbeddingFunc(
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embedding_dim=768,
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max_token_size=8192,
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func=lambda texts: ollama_embedding(
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texts, embed_model="nomic-embed-text", host="http://localhost:11434"
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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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# 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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