Let’s consider four extensive NASA documents, each between 80 to 100 pages, containing visual descriptions, and pose a highly complex question. ChatGPT despite having full PDFs in context, failed after 11 minutes of reasoning. Now, imagine you have thousands of corporate documents that can’t be contained in a context.
In contrast, Activeloop-L0 provided the correct answer in 4 minutes and can scale to million documents. It is available starting today on chat.activeloop.ai.
Why can’t we reliably analyze corporate documents?
Enterprise IT teams often dive into retrieval-augmented generation (RAG) projects with high hopes, only to discover unexpected complexities after the initial prototype.
A vast majority of RAG pilots currently stall out due to architectural and technical hurdles – data integration woes, infrastructure and cost surprises, reliability and safety challenges, and the difficulty of scaling beyond a narrowly tuned solution.
- Commodity RAG is Insufficient: Basic Retrieval-Augmented Generation (RAG) falls short for enterprise-level, multimodal data (documents, images, audio), limiting deep insights.
- Infrastructure Complexity: Managing parsing, chunking, embeddings, indexing, vector databases and agentic loops slows innovation and burdens development teams.
- Last Mile Challenge: Merely retrieving information doesn’t bridge the gap—organizations need insights integrated seamlessly into decision-making workflows.
Over-engineering a pilot to perfection in a siloed context often undermines its broader usefulness, leading to brittle systems that break outside the lab.
What is Activeloop-L0?
Activeloop-L0 ingests your unstructured data and returns sourced answers with relevancy scores and visual reasoning. Behind the scenes, Deep Lake indexes neural representations at scale, then fuses “thinking tokens” with high-precision retrieval for fast multi-hop reasoning.
How is it different than a RAG?
RAG systems often rely on predefined loops, custom logic and rigid agent scaffolds, which can limit flexibility and efficiency. Instead of building 100s of “if-else” statements to handle edge cases which often fail across different use cases or enterprise-wide adoption, directly start with Activeloop-L0 to generalize on new data.
- Multimodal: Built-in support for images, PDFs, audio, and spreadsheets—no complex preprocessing.
- Integrated Reasoning & Retrieval: Seamlessly combines reasoning and retrieval, eliminating the need for loops.
- Affordable, Flexible Storage & Deep Indexing: Cost-effective multi-layer indexing for richer context early on.
- No Premature Optimization Needed: Automate ingestion and embeddings—focus on innovation, not infrastructure.
- Deploys on Your Cloud: Keep your data secure, private, and fully under your control.
- Grounded and Accurate: Clear citations and visual reasoning for trustworthy insights enriched with relevancy scores.
Quick Start
Get token
Sign up at chat.activeloop.ai → ⚙️ API tokens, then set the token as ACTIVELOOP_TOKEN environment variable.
Upload Large Documents
1import os, io, requests
2
3pdf_urls = ["https://www.nasa.gov/wp-content/uploads/2022/03/sls-reference-guide-2022-v2-508-0.pdf",
4 "https://www.nasa.gov/wp-content/uploads/2023/02/orion-reference-guide-111022.pdf",
5 "https://www.lpi.usra.edu/lunar/artemis/Artemis-I-Reference-Guide_NP-2022-03-3045-HQ.pdf",
6 "https://www.ulalaunch.com/docs/default-source/rockets/2023_vulcan_user_guide.pdf"]
7
8files = [('file', (os.path.basename(url), io.BytesIO(requests.get(url).content))) for url in pdf_urls]
9
10response = requests.post(
11 'https://api.activeloop.ai/files',
12 headers={"Authorization": f"Bearer {os.getenv('ACTIVELOOP_TOKEN')}"},
13 files=files
14)
15# Once uploaded, it would take a several minutes to index
16
Answer Complex Question
1import os
2from openai import OpenAI
3
4client = OpenAI(
5 base_url="https://api.activeloop.ai/",
6 api_key=os.getenv('ACTIVELOOP_TOKEN')
7)
8
9response = client.chat.completions.create(
10 model="activeloop-l0",
11 messages=[
12 {
13 "role": "user",
14 "content": "Using the side-view diagrams that annotate overall height, rank SLS Block 1, Orion (CM + SM), " +
15 "Falcon 9 (v1.2 FT), and Vulcan Centaur by height; which vehicle is the second tallest, " +
16 "and what is its annotated height (m, one decimal place)?"
17 }
18 ],
19 stream=True
20)
21
22chunks = [chunk.choices[0] for chunk in response]
23
24thinking = "".join([c.delta.reasoning_content for c in chunks if c.delta.reasoning_content is not None])
25answer = "".join([c.delta.content for c in chunks if c.delta.content is not None])
26citations = chunks[-1].metadata['relevant_docs']
27
Explore advanced features docs.activeloop.ai including filtering the dataset, enriching with metadata, streaming reasoning and more. You can learn more on upcoming data modalities here.
SOTA Accuracy on Multimodal Documents
Activeloop-L0 achieves overall 85.6% state-of-the-art accuracy on 1,142 multimodal questions (292 PDFs, 5.5K pages). It outperforms text only RAG by +20%, visual RAG by +10%, and Alibaba’s ViDoRAG by +6% on their own ViDoSeek benchmark.
Best performer across every sub-task including 2D layout, charts, tables, plain text, multi-hop reasoning, aside from being on par on text It works remarkably well on tables and multi-hop question that requires gathering information from different parts.
Pricing
Think of 1M input tokens roughly 1,000 pages of a document. Output tokens include thinking and visually analyzing search results.
Example Scenario
Financial: 2,100 pages of documents including quarterly reports, market news video transcripts, and earnings calls. Approximate total cost is $140/month | Input 2M tokens → $2, Output 8M tokens → $120, Free Storage under 1GB.
Use Cases
- Pharmaceutical Research: Quickly analyze clinical trials, medical imagery, and publications to accelerate drug discovery.
- Legal Document Automation: Automate clause extraction, precedent analysis, and improve legal research efficiency.
- Financial Data Analysis: Analyze extensive investment documentation, transaction histories, and market research reports to inform strategic private equity decisions.
Need Enterprise Deployment?
Activeloop-L0 is an advanced Knowledge Agent to transform how enterprises interact with their data. Move beyond over engineered RAGs, with adaptive, multimodal reasoning across all your critical information. Deploy securely within your own infrastructure and connect seamlessly to your private data sources.
- Your Cloud: Deploy on your cloud, ensuring data never leaves your infrastructure.
- Your Models: Integrate custom storage and LLMs.
- Your Data Secure: Enjoy SOC2 compliance, fine-grained access control, and SSO.
Activeloop is trusted by F500 including likes of Bayer, Flagship Pioneering, and cutting edge startups Arcee.
Book a call to discuss enterprise deployment.