mirror of
https://github.com/HKUDS/LightRAG.git
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123 lines
3.2 KiB
Python
123 lines
3.2 KiB
Python
##############################################
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# Gremlin storage implementation is deprecated
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##############################################
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import asyncio
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import inspect
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import os
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# Uncomment these lines below to filter out somewhat verbose INFO level
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# logging prints (the default loglevel is INFO).
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# This has to go before the lightrag imports to work,
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# which triggers linting errors, so we keep it commented out:
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# import logging
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# logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.WARN)
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.ollama import ollama_embed, ollama_model_complete
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from lightrag.utils import EmbeddingFunc
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from lightrag.kg.shared_storage import initialize_pipeline_status
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WORKING_DIR = "./dickens_gremlin"
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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# Gremlin
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os.environ["GREMLIN_HOST"] = "localhost"
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os.environ["GREMLIN_PORT"] = "8182"
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os.environ["GREMLIN_GRAPH"] = "dickens"
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# Creating a non-default source requires manual
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# configuration and a restart on the server: use the dafault "g"
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os.environ["GREMLIN_TRAVERSE_SOURCE"] = "g"
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# No authorization by default on docker tinkerpop/gremlin-server
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os.environ["GREMLIN_USER"] = ""
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os.environ["GREMLIN_PASSWORD"] = ""
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async def initialize_rag():
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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="llama3.1:8b",
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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={
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"host": "http://localhost:11434",
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"options": {"num_ctx": 32768},
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},
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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_embed(
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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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graph_storage="GremlinStorage",
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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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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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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# Insert example text
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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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# Test different query modes
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print("\nNaive Search:")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="naive")
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)
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)
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print("\nLocal Search:")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="local")
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)
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)
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print("\nGlobal Search:")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="global")
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)
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)
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print("\nHybrid Search:")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="hybrid")
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)
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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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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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if __name__ == "__main__":
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main()
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