【人工智能】langchain + qwen2.5 使用示例

ins_lizhiming

已于 2024-11-15 15:06:32 修改

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文章标签： langchain python

于 2024-11-15 15:04:39 首次发布

本文链接：https://blog.youkuaiyun.com/qq_39146974/article/details/143799569

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1. qwen2.5 + langchain + text2vec-large-chinese

实现本地知识库向量化后保存，然后langchain进行qa数据流

from transformers import AutoModelForCausalLM, AutoTokenizer
from abc import ABC
from langchain.llms.base import LLM
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
import os
import re
import torch
import numpy as np
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA

model_name = "/home/sky/model_data/Qwen/Qwen2.5-7B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

class Qwen(LLM, ABC):
    max_token: int = 10000
    temperature: float = 0.01
    top_p = 0.9
    history_len: int = 3

    def __init__(self):
        super().__init__()

    @property
    def _llm_type(self) -> str:
        return "Qwe