使用 Milvus 和 Camel 的检索增强生成（RAG）

本指南演示如何使用 CAMEL 和 Milvus 构建检索-增强生成（RAG）系统。

RAG 系统结合了检索系统和生成模型，可根据给定提示生成新文本。该系统首先使用 Milvus 从语料库中检索相关文档，然后使用生成模型根据检索到的文档生成新文本。

CAMEL是一个多代理框架。Milvus是世界上最先进的开源向量数据库，用于支持嵌入式相似性搜索和人工智能应用。

在本笔记本中，我们将展示 CAMEL 检索模块的自定义和自动两种使用方式。我们还将展示如何将AutoRetriever 与ChatAgent 结合起来，并通过使用Function Calling 进一步将AutoRetriever 与RolePlaying 结合起来。

包括 4 个主要部分：

• 自定义 RAG
• 自动 RAG
• 使用自动 RAG 的单一代理
• 使用自动 RAG 进行角色扮演

加载数据

首先，让我们从 https://arxiv.org/pdf/2303.17760.pdf 加载 CAMEL 文件。这将是我们的本地示例数据。

$ pip install -U "camel-ai[all]" pymilvus

import os
import requests

os.makedirs("local_data", exist_ok=True)

url = "https://arxiv.org/pdf/2303.17760.pdf"
response = requests.get(url)
with open("local_data/camel paper.pdf", "wb") as file:
file.write(response.content)

1.自定义 RAG

在本节中，我们将设置自定义 RAG 管道，以VectorRetriever 为例。我们将把OpenAIEmbedding 设置为嵌入模型，把MilvusStorage 设置为其存储空间。

要设置 OpenAI 嵌入，我们需要设置OPENAI_API_KEY 。

os.environ["OPENAI_API_KEY"] = "Your Key"

导入并设置嵌入实例：

from camel.embeddings import OpenAIEmbedding

embedding*instance = OpenAIEmbedding()

导入并设置向量存储实例：

from camel.storages import MilvusStorage

storage_instance = MilvusStorage(
vector_dim=embedding_instance.get_output_dim(),
url_and_api_key=(
"./milvus_demo.db", # Your Milvus connection URI
"", # Your Milvus token
),
collection_name="camel_paper",
)

导入并设置检索器实例：

默认情况下，similarity_threshold 设置为 0.75。您可以更改。

from camel.retrievers import VectorRetriever

vector*retriever = VectorRetriever(
embedding_model=embedding_instance, storage=storage_instance
)

我们使用集成的Unstructured Module 将内容分割成小块，内容将通过chunk_by_title 功能自动分割，每个小块的最大字符数为 500 字符，这是OpenAIEmbedding 的合适长度。分块中的所有文本都将嵌入并存储到向量存储实例中，这需要一些时间，请稍候。

vector_retriever.process(content_input_path="local_data/camel paper.pdf")

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /root/nltk_data...
[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.

现在，我们可以通过查询从向量存储中获取信息。默认情况下，它会返回余弦相似度得分最高的前 1 个块中的文本内容，相似度得分应高于 0.75，以确保检索到的内容与查询相关。您也可以更改top_k 值。

返回的字符串列表包括

• 相似度得分
• 内容路径
• 元数据
• 文本

retrieved_info = vector_retriever.query(query="What is CAMEL?", top_k=1)
print(retrieved_info)

[{'similarity score': '0.8321675658226013', 'content path': 'local_data/camel paper.pdf', 'metadata': {'last_modified': '2024-04-19T14:40:00', 'filetype': 'application/pdf', 'page_number': 45}, 'text': 'CAMEL Data and Code License The intended purpose and licensing of CAMEL is solely for research use. The source code is licensed under Apache 2.0. The datasets are licensed under CC BY NC 4.0, which permits only non-commercial usage. It is advised that any models trained using the dataset should not be utilized for anything other than research purposes.\n\n45'}]

让我们试试不相关的查询：

retrieved_info_irrelevant = vector_retriever.query(
    query="Compared with dumpling and rice, which should I take for dinner?", top_k=1
)

print(retrieved_info_irrelevant)

[{'text': 'No suitable information retrieved from local_data/camel paper.pdf                 with similarity_threshold = 0.75.'}]

2.自动 RAG

在本节中，我们将使用默认设置运行AutoRetriever 。它使用OpenAIEmbedding 作为默认嵌入模型，使用Milvus 作为默认向量存储。

您需要做的是

• 设置内容输入路径，可以是本地路径或远程网址
• 为 Milvus 设置远程 URL 和 api 密钥
• 提供查询

自动 RAG 管道将为给定的内容输入路径创建集合，集合名称将根据内容输入路径名称自动设置，如果集合存在，它将直接进行检索。

from camel.retrievers import AutoRetriever
from camel.types import StorageType

auto_retriever = AutoRetriever(
url_and_api_key=(
"./milvus_demo.db", # Your Milvus connection URI
"", # Your Milvus token
),
storage_type=StorageType.MILVUS,
embedding_model=embedding_instance,
)

retrieved_info = auto_retriever.run_vector_retriever(
query="What is CAMEL-AI",
content_input_paths=[
"local_data/camel paper.pdf", # example local path
"https://www.camel-ai.org/", # example remote url
],
top_k=1,
return_detailed_info=True,
)

print(retrieved_info)

Original Query:
{What is CAMEL-AI}
Retrieved Context:
{'similarity score': '0.8252888321876526', 'content path': 'local_data/camel paper.pdf', 'metadata': {'last_modified': '2024-04-19T14:40:00', 'filetype': 'application/pdf', 'page_number': 7}, 'text': ' Section 3.2, to simulate assistant-user cooperation. For our analysis, we set our attention on AI Society setting. We also gathered conversational data, named CAMEL AI Society and CAMEL Code datasets and problem-solution pairs data named CAMEL Math and CAMEL Science and analyzed and evaluated their quality. Moreover, we will discuss potential extensions of our framework and highlight both the risks and opportunities that future AI society might present.'}
{'similarity score': '0.8378663659095764', 'content path': 'https://www.camel-ai.org/', 'metadata': {'filetype': 'text/html', 'languages': ['eng'], 'page_number': 1, 'url': 'https://www.camel-ai.org/', 'link_urls': ['#h.3f4tphhd9pn8', 'https://join.slack.com/t/camel-ai/shared_invite/zt-2g7xc41gy-_7rcrNNAArIP6sLQqldkqQ', 'https://discord.gg/CNcNpquyDc'], 'link_texts': [None, None, None], 'emphasized_text_contents': ['Mission', 'CAMEL-AI.org', 'is an open-source community dedicated to the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we provide, implement, and support various types of agents, tasks, prompts, models, datasets, and simulated environments.', 'Join us via', 'Slack', 'Discord', 'or'], 'emphasized_text_tags': ['span', 'span', 'span', 'span', 'span', 'span', 'span']}, 'text': 'Mission\n\nCAMEL-AI.org is an open-source community dedicated to the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we provide, implement, and support various types of agents, tasks, prompts, models, datasets, and simulated environments.\n\nJoin us via\n\nSlack\n\nDiscord\n\nor'}

3.带有自动 RAG 的单一代理

在本节中，我们将展示如何将AutoRetriever 与一个ChatAgent 结合起来。

让我们设置一个代理函数，在该函数中，我们可以通过向该代理提供查询来获取响应。

from camel.agents import ChatAgent
from camel.messages import BaseMessage
from camel.types import RoleType
from camel.retrievers import AutoRetriever
from camel.types import StorageType

def single_agent(query: str) -> str:
# Set agent role
assistant_sys_msg = BaseMessage(
role_name="Assistant",
role_type=RoleType.ASSISTANT,
meta_dict=None,
content="""You are a helpful assistant to answer question,
I will give you the Original Query and Retrieved Context,
answer the Original Query based on the Retrieved Context,
if you can't answer the question just say I don't know.""",
)

    # Add auto retriever
    auto_retriever = AutoRetriever(
        url_and_api_key=(
            "./milvus_demo.db",  # Your Milvus connection URI
            "",  # Your Milvus token
        ),
        storage_type=StorageType.MILVUS,
        embedding_model=embedding_instance,
    )

    retrieved_info = auto_retriever.run_vector_retriever(
        query=query,
        content_input_paths=[
            "local_data/camel paper.pdf",  # example local path
            "https://www.camel-ai.org/",  # example remote url
        ],
        # vector_storage_local_path="storage_default_run",
        top_k=1,
        return_detailed_info=True,
    )

    # Pass the retrieved infomation to agent
    user_msg = BaseMessage.make_user_message(role_name="User", content=retrieved_info)
    agent = ChatAgent(assistant_sys_msg)

    # Get response
    assistant_response = agent.step(user_msg)
    return assistant_response.msg.content

print(single_agent("What is CAMEL-AI"))

CAMEL-AI is an open-source community dedicated to the study of autonomous and communicative agents. It provides, implements, and supports various types of agents, tasks, prompts, models, datasets, and simulated environments to facilitate research in this field.

4.使用自动 RAG 进行角色扮演

在本节中，我们将展示如何通过应用Function Calling 将RETRIEVAL_FUNCS 与RolePlaying 结合起来。

from typing import List
from colorama import Fore

from camel.agents.chat_agent import FunctionCallingRecord
from camel.configs import ChatGPTConfig
from camel.functions import (
MATH_FUNCS,
RETRIEVAL_FUNCS,
)
from camel.societies import RolePlaying
from camel.types import ModelType
from camel.utils import print_text_animated

def role_playing_with_rag(
task_prompt, model_type=ModelType.GPT_4O, chat_turn_limit=10
) -> None:
task_prompt = task_prompt

    user_model_config = ChatGPTConfig(temperature=0.0)

    function_list = [
        *MATH_FUNCS,
        *RETRIEVAL_FUNCS,
    ]
    assistant_model_config = ChatGPTConfig(
        tools=function_list,
        temperature=0.0,
    )

    role_play_session = RolePlaying(
        assistant_role_name="Searcher",
        user_role_name="Professor",
        assistant_agent_kwargs=dict(
            model_type=model_type,
            model_config=assistant_model_config,
            tools=function_list,
        ),
        user_agent_kwargs=dict(
            model_type=model_type,
            model_config=user_model_config,
        ),
        task_prompt=task_prompt,
        with_task_specify=False,
    )

    print(
        Fore.GREEN
        + f"AI Assistant sys message:\n{role_play_session.assistant_sys_msg}\n"
    )
    print(Fore.BLUE + f"AI User sys message:\n{role_play_session.user_sys_msg}\n")

    print(Fore.YELLOW + f"Original task prompt:\n{task_prompt}\n")
    print(
        Fore.CYAN
        + f"Specified task prompt:\n{role_play_session.specified_task_prompt}\n"
    )
    print(Fore.RED + f"Final task prompt:\n{role_play_session.task_prompt}\n")

    n = 0
    input_msg = role_play_session.init_chat()
    while n < chat_turn_limit:
        n += 1
        assistant_response, user_response = role_play_session.step(input_msg)

        if assistant_response.terminated:
            print(
                Fore.GREEN
                + (
                    "AI Assistant terminated. Reason: "
                    f"{assistant_response.info['termination_reasons']}."
                )
            )
            break
        if user_response.terminated:
            print(
                Fore.GREEN
                + (
                    "AI User terminated. "
                    f"Reason: {user_response.info['termination_reasons']}."
                )
            )
            break

        # Print output from the user
        print_text_animated(Fore.BLUE + f"AI User:\n\n{user_response.msg.content}\n")

        # Print output from the assistant, including any function
        # execution information
        print_text_animated(Fore.GREEN + "AI Assistant:")
        tool_calls: List[FunctionCallingRecord] = assistant_response.info["tool_calls"]
        for func_record in tool_calls:
            print_text_animated(f"{func_record}")
        print_text_animated(f"{assistant_response.msg.content}\n")

        if "CAMEL_TASK_DONE" in user_response.msg.content:
            break

        input_msg = assistant_response.msg

使用定义的检索器函数运行角色扮演：

role_playing_with_rag(
    task_prompt="""What is the main termination reasons for AI Society
                   dataset, how many number of messages did camel decided to
                   limit, what's the value plus 100? You should refer to the
                   content in path camel/local_data/camel paper.pdf"""
)

AI Assistant sys message:
BaseMessage(role_name='Searcher', role_type=<RoleType.ASSISTANT: 'assistant'>, meta_dict={'task': "What is the main termination reasons for AI Society dataset, how many number of messages did camel decided to limit, what's the value plus 100?", 'assistant_role': 'Searcher', 'user_role': 'Professor'}, content="===== RULES OF ASSISTANT =====\nNever forget you are a Searcher and I am a Professor. Never flip roles! Never instruct me!\nWe share a common interest in collaborating to successfully complete a task.\nYou must help me to complete the task.\nHere is the task: What is the main termination reasons for AI Society dataset, how many number of messages did camel decided to limit, what's the value plus 100?. Never forget our task!\nI must instruct you based on your expertise and my needs to complete the task.\n\nI must give you one instruction at a time.\nYou must write a specific solution that appropriately solves the requested instruction and explain your solutions.\nYou must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons.\nUnless I say the task is completed, you should always start with:\n\nSolution: <YOUR_SOLUTION>\n\n<YOUR_SOLUTION> should be very specific, include detailed explanations and provide preferable detailed implementations and examples and lists for task-solving.\nAlways end <YOUR_SOLUTION> with: Next request.")

AI User sys message:
BaseMessage(role_name='Professor', role_type=<RoleType.USER: 'user'>, meta_dict={'task': "What is the main termination reasons for AI Society dataset, how many number of messages did camel decided to limit, what's the value plus 100?", 'assistant_role': 'Searcher', 'user_role': 'Professor'}, content='===== RULES OF USER =====\nNever forget you are a Professor and I am a Searcher. Never flip roles! You will always instruct me.\nWe share a common interest in collaborating to successfully complete a task.\nI must help you to complete the task.\nHere is the task: What is the main termination reasons for AI Society dataset, how many number of messages did camel decided to limit, what\'s the value plus 100?. Never forget our task!\nYou must instruct me based on my expertise and your needs to solve the task ONLY in the following two ways:\n\n1. Instruct with a necessary input:\nInstruction: <YOUR_INSTRUCTION>\nInput: <YOUR_INPUT>\n\n2. Instruct without any input:\nInstruction: <YOUR_INSTRUCTION>\nInput: None\n\nThe "Instruction" describes a task or question. The paired "Input" provides further context or information for the requested "Instruction".\n\nYou must give me one instruction at a time.\nI must write a response that appropriately solves the requested instruction.\nI must decline your instruction honestly if I cannot perform the instruction due to physical, moral, legal reasons or my capability and explain the reasons.\nYou should instruct me not ask me questions.\nNow you must start to instruct me using the two ways described above.\nDo not add anything else other than your instruction and the optional corresponding input!\nKeep giving me instructions and necessary inputs until you think the task is completed.\nWhen the task is completed, you must only reply with a single word <CAMEL_TASK_DONE>.\nNever say <CAMEL_TASK_DONE> unless my responses have solved your task.')

Original task prompt:
What is the main termination reasons for AI Society dataset, how many number of messages did camel decided to limit, what's the value plus 100?

Specified task prompt:
None

Final task prompt:
What is the main termination reasons for AI Society dataset, how many number of messages did camel decided to limit, what's the value plus 100?

AI User:

Instruction: Provide a summary of the main termination reasons in the AI Society dataset.
Input: None

AI Assistant:

Function Execution: local_retriever
Args: {'query': 'main termination reasons for AI Society dataset'}
Result: Original Query:
{main termination reasons for AI Society dataset}
Retrieved Context:
Next we examine the conversation termination reasons for both AI Society and Code datasets. As can be seen in Figure 8, the main termination reasons for AI Society dataset is Assistant Instruct whereas for Code it is Token Limit. The latter is expected as the since responses that contain code tend to be long. It is also interesting to note that in both datasets, the termination due to Maximum Number of Messages is low indicating that the limit of 40 maximum messages is reasonable. Our decision t

Solution: The main termination reason for the AI Society dataset is "Assistant Instruct." This indicates that conversations in the AI Society dataset typically end when the assistant is instructed to terminate the conversation.

Next request.

AI User:

Instruction: Identify the number of messages that the camel decided to limit in the context provided.
Input: None

AI Assistant:

Solution: The context provided from the local retriever indicates that the camel decided to limit the number of messages to a maximum of 40 messages. This is mentioned in the retrieved context where it states that the termination due to Maximum Number of Messages is low, indicating that the limit of 40 maximum messages is reasonable.

Next request.

AI User:

Instruction: Calculate the value of the message limit plus 100.
Input: None

AI Assistant:

Function Execution: add
Args: {'a': 40, 'b': 100}
Result: 140

Solution: The value of the message limit plus 100 is 140.

Next request.

AI User:

CAMEL_TASK_DONE

AI Assistant:

Solution: Understood, the task is completed.

Next request.