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import os
import logging
logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.DEBUG)
from lightrag import LightRAG, QueryParam
from lightrag.llm import ollama_model_complete, ollama_embedding
from lightrag.utils import EmbeddingFunc
WORKING_DIR = "./dickens"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
rag = LightRAG(
working_dir=WORKING_DIR,
tiktoken_model_name="mistral:7b",
llm_model_func=ollama_model_complete,
llm_model_name="mistral:7b",
llm_model_max_async=2,
llm_model_kwargs={"host": "http://localhost:11434"},
embedding_func=EmbeddingFunc(
embedding_dim=768,
max_token_size=8192,
func=lambda texts: ollama_embedding(
texts, embed_model="nomic-embed-text", host="http://localhost:11434"
),
),
)
with open("./book.txt") as f:
rag.insert(f.read())
# Perform naive search
print(
rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
)
# Perform local search
print(
rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
)
# Perform global search
print(
rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
)
# Perform hybrid search
print(
rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
)
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