Loaders#
1. Concept#
CAMEL introduced two IO modules, Base IO and Unstructured IO which are designed for handling various file types and unstructured data processing.
Additionally, four new data readers were added, Apify Reader,Chunkr Reader, Firecrawl Reader, and Jina_url Reader, which enable retrieval of external data for improved data integration and analysis.
2. Types#
2.1. Base IO#
Base IO module is focused on fundamental input/output operations related to files. It includes functionalities for representing, reading, and processing different file formats.
2.2. Unstructured IO#
Unstructured IO module deals with the handling, parsing, and processing of unstructured data. It provides tools for parsing files or URLs, cleaning data, extracting specific information, staging elements for different platforms, and chunking elements. The core of this module lies in its advanced ETL capabilities to manipulate unstructured data to make it usable for various applications like Retrieval-Augmented Generation(RAG).
2.3. Apify Reader#
Apify Reader provides a Python interface to interact with the Apify platform for automating web workflows. It allows users to authenticate via an API key and offers methods to execute and manage actors (automated web tasks) and datasets on the platform. It includes functionalities for client initialization, actor management, dataset operation.
2.4. Chunkr Reader#
Chunkr Reader is a Python client for interacting with the Chunkr API, which processes documents and returns content in various formats. It includes functionalities for client intialization, task management, formating response.
2.5. Firecrawl Reader#
Firecrawl Reader provides a Python interface to interact with the Firecrawl API, allowing users to turn websites into large language model (LLM)-ready markdown format.
2.6. Jina_url Reader#
JinaURL Reader is a Python client for Jina AI’s URL reading service, optimized to provide cleaner, LLM-friendly content from URLs.
3. Get Started#
3.1. Using Base IO#
This module is designed to read files of various formats, extract their contents, and represent them as File objects, each tailored to handle a specific file type.
from io import BytesIO
from camel.loaders import create_file_from_raw_bytes
# Read a pdf file from disk
with open("test.pdf", "rb") as file:
file_content = file.read()
# Use the create_file function to create an object based on the file extension
file_obj = create_file_from_raw_bytes(file_content, "test.pdf")
# Once you have the File object, you can access its content
print(file_obj.docs[0]["page_content"])
3.2. Using Unstructured IO#
To get started with the Unstructured IO module, you first need to import the module and initialize an instance of it. Once initialized, you can utilize this module to handle a variety of functionalities such as parsing, cleaning, extracting data, and integrating with cloud services like AWS S3 and Azure. Here’s a basic guide to help you begin:
Utilize parse_file_or_url to load and parse unstructured data from a file or URL
from camel.loaders import UnstructuredIO
uio = UnstructuredIO()
# Set example url
example_url = (
"https://www.cnn.com/2023/01/30/sport/empire-state-building-green-"
"philadelphia-eagles-spt-intl/index.html")
elements = uio.parse_file_or_url(example_url)
print(("\n\n".join([str(el) for el in elements])))
>>> The Empire State Building was lit in green and white to celebrate the Philadelphia Eagles’ victory in the NFC Championship game on Sunday – a decision that’s sparked a bit of a backlash in the Big Apple.
>>> The Eagles advanced to the Super Bowl for the first time since 2018 after defeating the San Francisco 49ers 31-7, and the Empire State Building later tweeted how it was marking the occasion.
>>> Fly @Eagles Fly! We’re going Green and White in honor of the Eagles NFC Championship Victory. pic.twitter.com/RNiwbCIkt7— Empire State Building (@EmpireStateBldg)
>>> January 29, 2023...
Utilize clean_text_data to do various text cleaning operations
# Set example dirty text
example_dirty_text = ("\x93Some dirty text ’ with extra spaces and – dashes.")
# Set clean options
options = [
('replace_unicode_quotes', {}),
('clean_dashes', {}),
('clean_non_ascii_chars', {}),
('clean_extra_whitespace', {}),
]
cleaned_text = uio.clean_text_data(text=example_dirty_text,clean_options=options)
print(cleaned_text)
>>> Some dirty text with extra spaces and dashes.
Utilize extract_data_from_text to do text extraction operation
# Set example email to extract
example_email_text = "Contact me at example@email.com."
extracted_text = uio.extract_data_from_text(text=example_email_text,
extract_type="extract_email_address")
print(extracted_text)
>>> ['example@email.com']
Utilize chunk_elements to chunk the content
chunks = uio.chunk_elements(elements=elements,chunk_type="chunk_by_title")
for chunk in chunks:
print(chunk)
print("\n" + "-" * 80)
>>> The Empire State Building was lit in green and white to celebrate the Philadelphia Eagles’ victory in the NFC Championship game on Sunday – a decision that’s sparked a bit of a backlash in the Big Apple.
>>> The Eagles advanced to the Super Bowl for the first time since 2018 after defeating the San Francisco 49ers 31-7, and the Empire State Building later tweeted how it was marking the occasion.
>>> --------------------------------------------------------------------------------
>>> Fly @Eagles Fly! We’re going Green and White in honor of the Eagles NFC Championship Victory. pic.twitter.com/RNiwbCIkt7— Empire State Building (@EmpireStateBldg)
>>> --------------------------------------------------------------------------------
>>> January 29, 2023
Utilize stage_elements to do element staging
staged_element = uio.stage_elements(elements=elements,stage_type="stage_for_baseplate")
print(staged_element)
>>> {'rows': [{'data': {'type': 'UncategorizedText', 'element_id': 'e78902d05b0cb1e4c38fc7a79db450d5', 'text': 'CNN\n \xa0—'}, 'metadata': {'filetype': 'text/html', 'languages': ['eng'], 'page_number': 1, 'url': 'https://www.cnn.com/2023/01/30/sport/empire-state-building-green-philadelphia-eagles-spt-intl/index.html', 'emphasized_text_contents': ['CNN'], 'emphasized_text_tags': ['span']}}, ...
This is a basic guide to get you started with the Unstructured IO module. For more advanced usage, refer to the specific method documentation and the Unstructured IO Documentation.
3.3. Using Apify Reader#
Initialize the client, set up the required actors and parameters.
from camel.loaders import Apify
apify = Apify()
run_input = {
"startUrls": [{"url": "https://www.camel-ai.org/"}],
"maxCrawlDepth": 0,
"maxCrawlPages": 1,
}
actor_result = apify.run_actor(
actor_id="apify/website-content-crawler", run_input=run_input
)
Retrieve the result database ID and access it using the get_dataset_items method.
dataset_result = apify.get_dataset_items(
dataset_id=actor_result["defaultDatasetId"]
)
>>>[{'url': 'https://www.camel-ai.org/', 'crawl': {'loadedUrl': 'https://www.camel
-ai.org/', 'loadedTime': '2024-10-27T04:51:16.651Z', 'referrerUrl': 'https://ww
w.camel-ai.org/', 'depth': 0, 'httpStatusCode': 200}, 'metadata': {'canonicalUr
l': 'https://www.camel-ai.org/', 'title': 'CAMEL-AI', 'description': 'CAMEL-AI.
org is the 1st LLM multi-agent framework and an open-source community dedicated
to finding the scaling law of agents.', 'author': None, 'keywords': None, 'lan
guageCode': 'en', 'openGraph': [{'property': 'og:title', 'content': 'CAMEL-AI'
}, {'property': 'og:description', 'content': 'CAMEL-AI.org is the 1st LLM mult
i-agent framework and an open-source community dedicated to finding the scalin
g law of agents.'}, {'property': 'twitter:title', 'content': 'CAMEL-AI'}, {'pr
operty': 'twitter:description', 'content': 'CAMEL-AI.org is the 1st LLM multi-
agent framework and an open-source community dedicated to finding the scaling
law of agents.'}, {'property': 'og:type', 'content': 'website'}], 'jsonLd': No
ne, 'headers': {'date': 'Sun, 27 Oct 2024 04:50:18 GMT', 'content-type': 'text
/html', 'cf-ray': '8d901082dae7efbe-PDX', 'cf-cache-status': 'HIT', 'age': '10
81', 'content-encoding': 'gzip', 'last-modified': 'Sat, 26 Oct 2024 11:51:32 G
MT', 'strict-transport-security': 'max-age=31536000', 'surrogate-control': 'ma
x-age=432000', 'surrogate-key': 'www.camel-ai.org 6659a154491a54a40551bc78 pag
eId:6686a2bcb7ece5fb40457491 668181a0a818ade34e653b24 6659a155491a54a40551bd7e
', 'x-lambda-id': 'd6c4424b-ac67-4c54-b52a-cb2a22ca09f0', 'vary': 'Accept-Enco
ding', 'set-cookie': '__cf_bm=oX5EmZ2SNJDOBQXI8dL_reCYlCpp1FMzu40qCNxiopU-1730
004618-1.0.1.1-3teEeqUoemzHWAeGCtlPJVqdmAbiFkyu3JxopKfQFFndSCi_Z56RR.UDjLGZiq.
L_4LvAZYmNKxQ.k6VRhbA7g; path=/; expires=Sun, 27-Oct-24 05:20:18 GMT; domain=.
cdn.webflow.com; HttpOnly; Secure; SameSite=None\n_cfuvid=om_8lj9jNMIh.HEIxEAq
gszhEWaKlyS2kdXKwqGedSM-1730004618924-0.0.1.1-604800000; path=/; domain=.cdn.w
ebflow.com; HttpOnly; Secure; SameSite=None', 'alt-svc': 'h3=":443"; ma=86400'
, 'x-cluster-name': 'us-west-2-prod-hosting-red', 'x-firefox-spdy': 'h2'}}, 's
creenshotUrl': None, 'text': 'Build Multi-Agent Systems for _\nFEATURES & Inte
grations\nSeamless integrations with\npopular platforms \nScroll to explore ou
r features & integrations.', 'markdown': '# Build Multi-Agent Systems for \\_
\n\nFEATURES & Integrations\n\n## Seamless integrations with \npopular platfo
rms\n\nScroll to explore our features & integrations.'}]
3.4. Using Firecrawl Reader#
Initialize the client and set the URL from which we want to retrieve information. When the status is “completed,” the information retrieval is finished and ready for reading.
firecrawl = Firecrawl()
response = firecrawl.crawl(url="https://www.camel-ai.org/about")
print(response["status"])
>>>completed
Directly retrieve information from the returned results.
print(response["data"][0]["markdown"])
>>>Camel-AI Team
We are finding the
scaling law of agent
🐫 CAMEL is an open-source library designed for 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 implement and support various
types of agents, tasks, prompts, models, and simulated environments.
**We are** always looking for more **contributors** and **collaborators**.
Contact us to join forces via [Slack](https://join.slack.com/t/camel-kwr1314/
shared_invite/zt-1vy8u9lbo-ZQmhIAyWSEfSwLCl2r2eKA)
or [Discord](https://discord.gg/CNcNpquyDc)...
3.5. Using Chunkr Reader#
Initialize the client and set the local PDF file path, then use the generated task id to featch the output.
from camel.loaders import ChunkrReader
chunkr = ChunkrReader()
task_id = chunkr.submit_task(
file_path="/Users/enrei/Large Language Model based Multi-Agents- A Survey of Progress and Challenges.pdf",
model="Fast",
ocr_strategy="Auto",
target_chunk_length=512,
)
print(task_id)
>>>"7becf001-6f07-4f63-bddf-5633df363bbb"
Featch the output.
task_output = chunkr.get_task_output(
task_id="7becf001-6f07-4f63-bddf-5633df363bbb"
)
print(task_output)
>>>{
"task_id": "7becf001-6f07-4f63-bddf-5633df363bbb",
"status": "Succeeded",
"created_at": "2024-11-08T12:45:04.260765Z",
"finished_at": "2024-11-08T12:45:48.942365Z",
"expires_at": null,
"message": "Task succeeded",
"output": {
"chunks": [
{
"segments": [
{
"segment_id": "d53ec931-3779-41be-a220-3fe4da2770c5",
"bbox": {
"left": 224.16666,
"top": 370.0,
"width": 2101.6665,
"height": 64.166664
},
"page_number": 1,
"page_width": 2550.0,
"page_height": 3300.0,
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"html": "<h1>Large Language Model based Multi-Agents: A Survey of Progress and Challenges</h1>",
"markdown": "# Large Language Model based Multi-Agents: A Survey of Progress and Challenges\n\n"
}
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"chunk_length": 11
},
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"content": "Taicheng Guo 1 , Xiuying Chen 2 , Yaqi Wang 3 \u2217 , Ruidi Chang , Shichao Pei 4 , Nitesh V. Chawla 1 , Olaf Wiest 1 , Xiangliang Zhang 1 \u2020",
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3.6. Using Jina Reader#
Initialize the client and set the URL from which we want to retrieve information, then print the response.
from camel.loaders import JinaURLReader
from camel.types.enums import JinaReturnFormat
jina_reader = JinaURLReader(return_format=JinaReturnFormat.MARKDOWN)
response = jina_reader.read_content("https://docs.camel-ai.org/")
print(response)
>>>Welcome to CAMEL’s documentation! — CAMEL 0.2.17 documentation
===============
[Skip to main content](https://docs.camel-ai.org/#main-content)
Back to top Ctrl+K
[ CAMEL 0.2.17](https://docs.camel-ai.org/#)
Search Ctrl+K
Get Started
* [Installation](https://docs.camel-ai.org/get_started/installation.html)
* [API Setup](https://docs.camel-ai.org/get_started/setup.html)
Agents
* [Creating Your First Agent](https://docs.camel-ai.org/cookbooks/create_your_first_agent.html)
* [Creating Your First Agent Society](https://docs.camel-ai.org/cookbooks/create_your_first_agents_society.html)
* [Embodied Agents](https://docs.camel-ai.org/cookbooks/embodied_agents.html)
* [Critic Agents and Tree Search](https://docs.camel-ai.org/cookbooks/critic_agents_and_tree_search.html)
Key Modules
* [Models](https://docs.camel-ai...)