柚子快報邀請碼778899分享：前端 Llama

http://yzkb.51969.com/

基于ollama的本地大模型的LlamaIndex示例代碼

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings

from llama_index.core.embeddings import resolve_embed_model

from llama_index.llms.ollama import Ollama

import logging

import sys

logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)

logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

documents = SimpleDirectoryReader("data").load_data()

# bge embedding model

Settings.embed_model = resolve_embed_model("local:/Users/leicq/ai_pychat/BAAI_bge-base-zh-v1.5")

# ollama

Settings.llm = Ollama(model="llama2-chinese", request_timeout=30.0)

import os.path

from llama_index.core import (

VectorStoreIndex,

SimpleDirectoryReader,

StorageContext,

load_index_from_storage,

)

# check if storage already exists

PERSIST_DIR = "./storage"

if not os.path.exists(PERSIST_DIR):

# load the documents and create the index

documents = SimpleDirectoryReader("data").load_data()

index = VectorStoreIndex.from_documents(documents)

# store it for later

index.storage_context.persist(persist_dir=PERSIST_DIR)

else:

# load the existing index

storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)

index = load_index_from_storage(storage_context)

# Either way we can now query the index

query_engine = index.as_query_engine()

response = query_engine.query("What did the author do growing up?")

print(response)

柚子快報邀請碼778899分享：前端 Llama

http://yzkb.51969.com/

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