Research
Retrieval-Augmented Generation (RAG) is an advanced approach in natural language processing that combines retrieval-based and generation-based methods to enhance the performance of language models.
In a RAG model, there are two main components:
An agent with RAG owns enhanced accuracy, up-to-date knowledge, and improved generalization.
In this tutorial we will cover how to use customized RAG and auto RAG with CAMEL framework.
4 main parts included:
- Customized RAG
- Auto RAG
- Single Agent with Auto RAG
- Role-playing with Auto RAG
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 requestsos.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)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_keyImport 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 (go to https://milvus.io/ to obtain Mulvus URL and Token):
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)We use integrated Unstructured Module to splite the content into small chunks, and 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, that will take some time.
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 string list includes:
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)In this section we will run the AutoRetriever with default settings. It uses OpenAIEmbedding as default embedding model and Milvus as default vector storage.
What you need to do is:
The Auto RAG pipeline would create collections for given content input paths. The collection name will be set automaticlly 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 projec, 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)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 = 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(
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 infomation to agent
user_msg = BaseMessage.make_user_message(role_name="User", content=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 projec, what should I do?"))
Enjoy playing with it using different queries!
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 (
MATH_FUNCS,
RETRIEVAL_FUNCS,
)
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_MINI,
chat_turn_limit=5,
) -> 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=ModelFactory.create(
model_platform=model_platform,
model_type=model_type,
model_config_dict=assistant_model_config.as_dict(),
),
tools=function_list,
),
user_agent_kwargs=dict(
model=ModelFactory.create(
model_platform=model_platform,
model_type=model_type,
model_config_dict=user_model_config.as_dict(),
),
),
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.msgMore tutorials for CAMEL will come out soon and you will find out what CAMEL can do may be beyond what you would expect.
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