Sign up at . Navigate to ⚙️ → API tokens, and create a token. Set the token as ACTIVELOOP_TOKEN environment variable.
Upload Documents
We will fetch 4 reference guides from NASA each more than 90 pages and ask a highly complex question.
import os, io, requests
pdf_urls = ["https://www.nasa.gov/wp-content/uploads/2022/03/sls-reference-guide-2022-v2-508-0.pdf",
"https://www.nasa.gov/wp-content/uploads/2023/02/orion-reference-guide-111022.pdf",
"https://www.lpi.usra.edu/lunar/artemis/Artemis-I-Reference-Guide_NP-2022-03-3045-HQ.pdf",
"https://www.ulalaunch.com/docs/default-source/rockets/2023_vulcan_user_guide.pdf"]
files = [('file', (os.path.basename(url), io.BytesIO(requests.get(url).content))) for url in pdf_urls]
response = requests.post(
'https://api.activeloop.ai/files',
headers={"Authorization": f"Bearer {os.getenv('ACTIVELOOP_TOKEN')}"},
files=files
)
# Onced uploaded, it would take few minutes to index
You can learn about the state by .
Query Data
Once the data is indexed, you can run a query against it as if you are calling your LLM .
from openai import OpenAI
client = OpenAI(
base_url="https://api.activeloop.ai/",
api_key=os.getenv('ACTIVELOOP_TOKEN')
)
response = client.chat.completions.create(
model="activeloop-l0",
messages=[
{
"role": "user",
"content": "Using the side-view diagrams that annotate overall height, rank SLS Block 1, Orion (CM + SM), " +
"Falcon 9 (v1.2 FT), and Vulcan Centaur by height; which vehicle is the second tallest, " +
"and what is its annotated height (m, one decimal place)?"
}
],
stream=True
)
chunks = [chunk.choices[0] for chunk in response]
thinking = "".join([c.delta.reasoning_content for c in chunks if c.delta.reasoning_content is not None])
answer = "".join([c.delta.content for c in chunks if c.delta.content is not None])
citations = chunks[-1].metadata['relevant_docs']
