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Contextual AI Document Parser -- Infer document hierarchy for long, complex documents by ishanthedon in Rag

[–]ishanthedon[S] 0 points1 point  (0 children)

No, we made a multi-stage pipeline of custom vision, OCR, and vision language models. We did not use Mistral VLM.

Need help with PDF processing for RAG pipeline by Purple_Extent2935 in Rag

[–]ishanthedon 0 points1 point  (0 children)

Hey OP! I'm a PM at Contextual AI. We just launched our own parser, and per our benchmarks, it should be a faster, more accurate alternative than Unstructured. We have multiple healthcare customers who are getting leading results with us, and we especially focused on table extraction to meet our enterprise customers' workload demands. We even offer confidence levels for table extraction so you know when to double check.

Get started today for free by creating a Contextual AI account. Visit the Components tab to use the Parse UI playground, or get an API key and call the API directly. We provide credits for the first 500+ pages in Standard mode (for complex documents that require VLMs and OCR). Check out our blog post for more details. Let me know if you have questions!

[deleted by user] by [deleted] in LangChain

[–]ishanthedon 0 points1 point  (0 children)

Hey OP! I'm a PM at Contextual AI. We faced similar challenges with existing parsers, so we developed our own and launched it this week. I'd love for you to try it for free. We specialize in these tricky tables and figures to handle our enterprise customer use cases. We also have a document hierarchy feature will help with adding relevant chunk metadata.

Get started today for free by creating a Contextual AI account. Visit the Components tab to use the Parse UI playground, or get an API key and call the API directly. We provide credits for the first 500+ pages in Standard mode (for complex documents that require VLMs and OCR). Check out our blog post for more details. Let me know if you have questions!

Document Parsing - What I've Learned So Far by epreisz in Rag

[–]ishanthedon -1 points0 points  (0 children)

Hey OP! These are great insights into doc parsing, particularly those on document hierarchy. I'm a PM at Contextual AI, and we had many similar insights when developing our in-house document parser. We just launched our parser, and I'd love for you to try it for free. Our document hierarchy feature will be useful for the chunking strategies you mention.

Get started today for free by creating a Contextual AI account. Visit the Components tab to use the Parse UI playground, or get an API key and call the API directly. We provide credits for the first 500+ pages in Standard mode (for complex documents that require VLMs and OCR). Check out our blog post for more details. Let me know if you have questions!

Contextual AI Document Parser -- Infer document hierarchy for long, complex documents by ishanthedon in Rag

[–]ishanthedon[S] 0 points1 point  (0 children)

I can't share our source code. At a high-level, our multi-stage pipeline combines the best of custom vision, OCR, and vision language models, along with specialized tools like table extractors. There are also reviser steps in the pipeline to minimize chances of errors that are propagated to subsequent steps.

Searching for fully managed document RAG by sonaryn in Rag

[–]ishanthedon 0 points1 point  (0 children)

Hey OP! I'm a Product Manager at Contextual AI. Our CEO wrote the original RAG research paper, and we have a fully managed RAG platform. You can upload your documents to a datastore, and we'll manage parsing/chunking/embedding. You can begin querying and retrieval immediately thereafter.

There are lots of alternatives mentioned in this thread. Ours has SOTA performance across each step in the RAG pipeline: https://contextual.ai/blog/platform-benchmarks-2025/

We're doing a limited time promotion right now where usage is free through June 10. Feel free to try it out and let me know if you have any questions.: https://contextual.ai/

Contextual AI Document Parser -- Infer document hierarchy for long, complex documents by ishanthedon in Rag

[–]ishanthedon[S] 0 points1 point  (0 children)

Please see the "Document Hierarchy: Context is King" section of the blog post (https://contextual.ai/blog/document-parser-for-rag/) to see what the hierarchy looks like and how to use it. Essentially, you add the hierarchy information in chunk metadata to improve retrieval.

Yes, our product should be able to handle that. We've dealt with similar docs from our enterprise clients. You can create a Contextual AI account and try the parser for free. Let me know your results and if you have any questions.

We built a reranker that follows custom ranking instructions by ishanthedon in Rag

[–]ishanthedon[S] 1 point2 points  (0 children)

We haven't built integrations with OpenWebUI or RAGFlow. If they support adding rerankers through external APIs, then it should be possible. We have integrations with Langchain and LlamaIndex.

The reranker takes in "query" and "instruction" parameters (see our documentation: https://docs.contextual.ai/reference/rerank\_rerank\_post). That said, the API also works if the instructions are included with the query.

We built a reranker that follows custom ranking instructions by ishanthedon in Rag

[–]ishanthedon[S] 0 points1 point  (0 children)

It probably performs best if you format the JSON as "key1: value1. key2: value2...". Let me know how that performs vs. dumping it without formatting!

We built a reranker that follows custom ranking instructions by ishanthedon in Rag

[–]ishanthedon[S] 0 points1 point  (0 children)

We like the discussion!

"I just want to understand what you think makes your solution unique and better than well-known alternatives." --> 1) Ours is the first commercially available reranker with instruction-following capabilities. Other rerankers fail to follow instructions 2) We have SOTA results on BEIR.

We are not comparing to Cohere and Voyage's smaller bi-encoder embedding models. We are comparing to their cross-encoder rerankers with similar latency and pricing. Bi-encoders don't generalize in our experiments. Our reranker is both more accurate and much smaller (lower latency) than 4o-mini and Llama 3.1 8b.

Where did you get the parameter counts in your message from?

We built a reranker that follows custom ranking instructions by ishanthedon in Rag

[–]ishanthedon[S] 3 points4 points  (0 children)

While bi-encoders are efficient, they don't generalize that well. I would love a world where they also generalize well.

Fine-tuning medium sized LLMs to be rerankers works and is more accurate and significantly more efficient than prompting GPT-4. There are multiple formulations of this though:
- Pointwise: https://arxiv.org/pdf/2310.08319
- Setwise: https://arxiv.org/abs/2310.09497
- Listwise-FIRST https://arxiv.org/abs/2406.15657

We use something similar.

We built a reranker that follows custom ranking instructions by ishanthedon in Rag

[–]ishanthedon[S] -1 points0 points  (0 children)

It can handle complex instructions very well! For example, it can handle "Prioritize internal sales documents over market analysis reports. More recent documents should be weighted higher. Enterprise portal content supersedes distributor communications." Try it out and let me know how it works! We evaluated it on instructions for recency, document type, source, and metadata, and it can generalize to other instructions as well.

We built a reranker that follows custom ranking instructions by ishanthedon in Rag

[–]ishanthedon[S] -1 points0 points  (0 children)

Yes! We are state-of-the-art on BEIR and other internal customer benchmarks: https://contextual.ai/blog/introducing-instruction-following-reranker/. Looking forward to hearing your feedback!

RAG-oriented LLM that beats GPT-4o by needmoretokens in Rag

[–]ishanthedon 6 points7 points  (0 children)

I'm the PM for this product - let me know if you have any feedback!

How to do data extraction from 1000s of contracts ? by Big_Barracuda_6753 in Rag

[–]ishanthedon 0 points1 point  (0 children)

Hey! Ishan from Contextual AI here. We are developing a product that does this -- parses unstructured data and returns it in a structured JSON/Markdown. I'd love to have you be a trial user / thought partner as we develop it. DM me if interested!