视频中所出现的代码 Tavily Search+RAG

微调Llama3实现在线搜索引擎和RAG检索增强生成功能！打造自己的perplexity和GPTs！用PDF实现本地知识库_哔哩哔哩_bilibili

一.准备工作

1.安装环境

conda create --name unsloth_env python=3.10
conda activate unsloth_env

conda install pytorch-cuda=12.1 pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

pip install --no-deps trl peft accelerate bitsandbytes

 2.微调代码（要先登录一下）

huggingface-cli login

点击提示的网页获取token（注意要选择可写的）

#dataset https://huggingface.co/datasets/shibing624/alpaca-zh/viewer

from unsloth import FastLanguageModel
import torch

from trl import SFTTrainer
from transformers import TrainingArguments




max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
    "unsloth/mistral-7b-bnb-4bit",
    "unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
    "unsloth/llama-2-7b-bnb-4bit",
    "unsloth/gemma-7b-bnb-4bit",
    "unsloth/gemma-7b-it-bnb-4bit", # Instruct version of Gemma 7b
    "unsloth/gemma-2b-bnb-4bit",
    "unsloth/gemma-2b-it-bnb-4bit", # Instruct version of Gemma 2b
    "unsloth/llama-3-8b-bnb-4bit", # [NEW] 15 Trillion token Llama-3
] # More models at https://huggingface.co/unsloth

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/llama-3-8b-bnb-4bit",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 16,
    lora_dropout = 0, # Supports any, but = 0 is optimized
    bias = "none",    # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
    random_state = 3407,
    use_rslora = False,  # We support rank stabilized LoRA
    loftq_config = None, # And LoftQ
)

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
def formatting_prompts_func(examples):
    instructions = examples["instruction"]
    inputs       = examples["input"]
    outputs      = examples["output"]
    texts = []
    for instruction, input, output in zip(instructions, inputs, outputs):
        # Must add EOS_TOKEN, otherwise your generation will go on forever!
        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
        texts.append(text)
    return { "text" : texts, }
pass

from datasets import load_dataset

#file_path = "/home/Ubuntu/alpaca_gpt4_data_zh.json"

#dataset = load_dataset("json", data_files={"train": file_path}, split="train")

dataset = load_dataset("yahma/alpaca-cleaned", split = "train")

dataset = dataset.map(formatting_prompts_func, batched = True,)




trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 2,
    packing = False, # Can make training 5x faster for short sequences.
    args = TrainingArguments(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 5,
        max_steps = 60,
        learning_rate = 2e-4,
        fp16 = not torch.cuda.is_bf16_supported(),
        bf16 = torch.cuda.is_bf16_supported(),
        logging_steps = 1,
        optim = "adamw_8bit",
        weight_decay = 0.01,
        lr_scheduler_type = "linear",
        seed = 3407,
        output_dir = "outputs",
    ),
)

trainer_stats = trainer.train()

model.save_pretrained_gguf("llama3", tokenizer, quantization_method = "q4_k_m")
model.save_pretrained_gguf("llama3", tokenizer, quantization_method = "q8_0")
model.save_pretrained_gguf("llama3", tokenizer, quantization_method = "f16")


#to hugging face
model.push_to_hub_gguf("leo009/llama3", tokenizer, quantization_method = "q4_k_m")
model.push_to_hub_gguf("leo009/llama3", tokenizer, quantization_method = "q8_0")
model.push_to_hub_gguf("leo009/llama3", tokenizer, quantization_method = "f16")

3.我们选择将hugging face上微调好的模型下载下来（https://huggingface.co/leo009/llama3/tree/main）

4.模型导入ollama

下载ollama

 导入ollama

FROM ./downloads/mistrallite.Q4_K_M.gguf
ollama create example -f Modelfile

二.实现在线搜索

1.获取Tavily AI API 

Tavily AI

export TAVILY_API_KEY=tvly-xxxxxxxxxxx

 2.install tavily-python

pip install tavily-python

3.运行app.py

#app.py
import warnings

# Suppress only the specific NotOpenSSLWarning
warnings.filterwarnings("ignore", message="urllib3 v2 only supports OpenSSL 1.1.1+")

from phi.assistant import Assistant
from phi.llm.ollama import OllamaTools
from phi.tools.tavily import TavilyTools


# 创建一个Assistant实例，配置其使用OllamaTools中的llama3模型，并整合Tavily工具
assistant = Assistant(
    llm=OllamaTools(model="mymodel3"),  # 使用OllamaTools的llama3模型
    tools=[TavilyTools()],
    show_tool_calls=True,  # 设置为True以展示工具调用信息
)

# 使用助手实例输出请求的响应，并以Markdown格式展示结果
assistant.print_response("Search tavily for 'GPT-5'", markdown=True)

 三.实现RAG

1.git clone https://github.com/phidatahq/phidata.git

2.phidata---->cookbook---->llms--->ollama--->rag里面 有示例和教程

修改assigant.py中的14行代码，将llama3改为自己微调好的模型

另外需要注意的是！！！

要将自己的模型名称加入到app.py里面的数组里