mirror of
https://github.com/HKUDS/LightRAG.git
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114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import gpt_4o_mini_complete, openai_embedding
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from lightrag.utils import EmbeddingFunc
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import numpy as np
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#########
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# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
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# import nest_asyncio
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# nest_asyncio.apply()
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#########
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WORKING_DIR = "./chromadb_test_dir"
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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# ChromaDB Configuration
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CHROMADB_HOST = os.environ.get("CHROMADB_HOST", "localhost")
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CHROMADB_PORT = int(os.environ.get("CHROMADB_PORT", 8000))
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CHROMADB_AUTH_TOKEN = os.environ.get("CHROMADB_AUTH_TOKEN", "secret-token")
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CHROMADB_AUTH_PROVIDER = os.environ.get(
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"CHROMADB_AUTH_PROVIDER", "chromadb.auth.token_authn.TokenAuthClientProvider"
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)
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CHROMADB_AUTH_HEADER = os.environ.get("CHROMADB_AUTH_HEADER", "X-Chroma-Token")
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# Embedding Configuration and Functions
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EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
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EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
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# ChromaDB requires knowing the dimension of embeddings upfront when
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# creating a collection. The embedding dimension is model-specific
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# (e.g. text-embedding-3-large uses 3072 dimensions)
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# we dynamically determine it by running a test embedding
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# and then pass it to the ChromaDBStorage class
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await openai_embedding(
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texts,
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model=EMBEDDING_MODEL,
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)
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async def get_embedding_dimension():
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test_text = ["This is a test sentence."]
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embedding = await embedding_func(test_text)
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return embedding.shape[1]
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async def create_embedding_function_instance():
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# Get embedding dimension
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embedding_dimension = await get_embedding_dimension()
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# Create embedding function instance
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return EmbeddingFunc(
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embedding_dim=embedding_dimension,
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max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
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func=embedding_func,
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)
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async def initialize_rag():
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embedding_func_instance = await create_embedding_function_instance()
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return LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=gpt_4o_mini_complete,
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embedding_func=embedding_func_instance,
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vector_storage="ChromaVectorDBStorage",
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log_level="DEBUG",
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embedding_batch_num=32,
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vector_db_storage_cls_kwargs={
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"host": CHROMADB_HOST,
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"port": CHROMADB_PORT,
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"auth_token": CHROMADB_AUTH_TOKEN,
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"auth_provider": CHROMADB_AUTH_PROVIDER,
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"auth_header_name": CHROMADB_AUTH_HEADER,
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"collection_settings": {
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"hnsw:space": "cosine",
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"hnsw:construction_ef": 128,
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"hnsw:search_ef": 128,
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"hnsw:M": 16,
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"hnsw:batch_size": 100,
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"hnsw:sync_threshold": 1000,
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},
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},
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)
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# Run the initialization
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rag = asyncio.run(initialize_rag())
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# with open("./dickens/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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