RAG Cookbook

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Overview

In this notebook, we show the usage of CAMEL Retrieve Module in both customized way and auto way. We will also show how to combine AutoRetriever with ChatAgent, and further combine AutoRetriever with RolePlaying by using Function Calling. 4 main parts included:

Customized RAG
Auto RAG
Single Agent with Auto RAG
Role-playing with Auto RAG

Installation

Ensure you have CAMEL AI installed in your Python environment:

!pip install "camel-ai[all]==0.2.16"

Load Data

Let’s first load the CAMEL paper from https://arxiv.org/pdf/2303.17760.pdf. This will be our local example data.

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. Customized RAG

In this section we will set our customized RAG pipeline, we will take VectorRetriever as an example. Set embedding model, we will use OpenAIEmbedding as the embedding model, so we need to set the OPENAI_API_KEY in below.

from getpass import getpass
# Prompt for the OpenAI API key securely
openai_api_key = getpass('Enter your API key: ')
os.environ["OPENAI_API_KEY"] = openai_api_key

Alternatively, if running on Colab, you could save your API keys and tokens as Colab Secrets, and use them across notebooks. To do so, comment out the above manual API key prompt code block(s), and uncomment the following codeblock. ⚠️ Don’t forget granting access to the API key you would be using to the current notebook.

# import os
# from google.colab import userdata

# os.environ["OPENAI_API_KEY"] = userdata.get("OPENAI_API_KEY")

Import and set the embedding instance:

from camel.embeddings import OpenAIEmbedding
from camel.types import EmbeddingModelType

embedding_instance = OpenAIEmbedding(model_type=EmbeddingModelType.TEXT_EMBEDDING_3_LARGE)

Import and set the vector storage instance:

from camel.storages import QdrantStorage

storage_instance = QdrantStorage(
    vector_dim=embedding_instance.get_output_dim(),
    path="local_data",
    collection_name="camel_paper",
)

Import and set the retriever instance:

from camel.retrievers import VectorRetriever

vector_retriever = VectorRetriever(embedding_model=embedding_instance,
                                   storage=storage_instance)

We use integrated Unstructured Module to splite the content into small chunks, the content will be splited automacitlly with its chunk_by_title function, the max character for each chunk is 500 characters, which is a suitable length for OpenAIEmbedding. All the text in the chunks will be embed and stored to the vector storage instance, it will take some time, please wait..

vector_retriever.process(
    content="local_data/camel_paper.pdf",
)

Now we can retrieve information from the vector storage by giving a query. By default it will give you back the text content from top 1 chunk with highest Cosine similarity score, and the similarity score should be higher than 0.75 to ensure the retrieved content is relevant to the query. You can also change the top_k value and similarity_threshold value with your needs. The returned dictionary list includes:

similarity score
content path
metadata
text

retrieved_info = vector_retriever.query(
    query="To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing .",
    top_k=1
)
print(retrieved_info)

Let’s try an irrelevant query:

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

print(retrieved_info_irrevelant)

2. Auto RAG

In this section we will run the AutoRetriever with default settings. It uses OpenAIEmbedding as default embedding model and Qdrant as default vector storage. What you need to do is:

Set content input paths, which can be local paths or remote urls
Give a query

The Auto RAG pipeline would create collections for given content input paths, the collection name will be set automatically based on the content input path name, if the collection exists, it will do the retrieve directly.

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

auto_retriever = AutoRetriever(
        vector_storage_local_path="local_data2/",
        storage_type=StorageType.QDRANT,
        embedding_model=embedding_instance)

retrieved_info = auto_retriever.run_vector_retriever(
    query="If I'm interest in contributing to the CAMEL project, what should I do?",
    contents=[
        "local_data/camel_paper.pdf",  # example local path
        "https://github.com/camel-ai/camel/wiki/Contributing-Guidlines",  # example remote url
    ],
    top_k=1,
    return_detailed_info=True,
    similarity_threshold=0.5
)

print(retrieved_info)

3. Single Agent with Auto RAG

In this section we will show how to combine the AutoRetriever with one ChatAgent. Let’s set an agent function, in this function we can get the response by providing a query to this agent.

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 = """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(
            vector_storage_local_path="local_data2/",
            storage_type=StorageType.QDRANT,
            embedding_model=embedding_instance)

    retrieved_info = auto_retriever.run_vector_retriever(
        query=query,
        contents=[
            "local_data/camel_paper.pdf",  # example local path
            "https://github.com/camel-ai/camel/wiki/Contributing-Guidlines",  # example remote url
        ],
        top_k=1,
        return_detailed_info=False,
        similarity_threshold=0.5
    )

    # Pass the retrieved information to agent
    user_msg = str(retrieved_info)
    agent = ChatAgent(assistant_sys_msg)

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

print(single_agent("If I'm interest in contributing to the CAMEL project, what should I do?"))

4. Role-playing with Auto RAG

In this section we will show how to combine the RETRIEVAL_FUNCS with RolePlaying by applying Function Calling.

from typing import List
from colorama import Fore

from camel.agents.chat_agent import FunctionCallingRecord
from camel.configs import ChatGPTConfig
from camel.toolkits import (
    MathToolkit,
    RetrievalToolkit,
)
from camel.societies import RolePlaying
from camel.types import ModelType, ModelPlatformType
from camel.utils import print_text_animated
from camel.models import ModelFactory

def role_playing_with_rag(
    task_prompt,
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O,
    chat_turn_limit=5,
) -> None:
    task_prompt = task_prompt

    tools_list = [
        *MathToolkit().get_tools(),
        *RetrievalToolkit().get_tools(),
    ]


    role_play_session = RolePlaying(
        assistant_role_name="Searcher",
        user_role_name="Professor",
        assistant_agent_kwargs=dict(
            model=ModelFactory.create(
                model_platform=model_platform,
                model_type=model_type,
            ),
            tools=tools_list,
        ),
        user_agent_kwargs=dict(
            model=ModelFactory.create(
                model_platform=model_platform,
                model_type=model_type,
            ),
        ),
        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
        + "Specified task prompt:"
        + f"\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] = [
            FunctionCallingRecord(**call.as_dict())
            for call in 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

Run the role-playing with defined retriever function:

role_playing_with_rag(task_prompt = """If I'm interest in contributing to the CAMEL projec and I encounter some challenges during the setup process, what should I do? You should refer to the content in url https://github.com/camel-ai/camel/wiki/Contributing-Guidlines to answer my question, don't generate the answer by yourself, adjust the similarity threshold to lower value is necessary""")

Get Started

Key Modules

Awesome Toolkits

CAMEL with MCP

Cookbooks

Overview

Installation

Load Data

1. Customized RAG

2. Auto RAG

3. Single Agent with Auto RAG

4. Role-playing with Auto RAG

Get Started

Key Modules

Awesome Toolkits

CAMEL with MCP

Cookbooks

​Overview

​Installation

​Load Data

​1. Customized RAG

​2. Auto RAG

​3. Single Agent with Auto RAG

​4. Role-playing with Auto RAG

Overview

Installation

Load Data

1. Customized RAG

2. Auto RAG

3. Single Agent with Auto RAG

4. Role-playing with Auto RAG