Multicore, GPU, and GPGPU programming

Kaustubh M.14 Published workflows

Description

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Model, Knowledge base & Tools

Model

💪 Capabilities

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

🩻 Photo & Document Intelligence

📊 Analytics & Evaluation

📄 Knowledge Base

👩‍🏫 Search Instructions

[Guidelines]
- Use the Search Results to produce a coherent answer.
- Search results may be incomplete or irrelevant. Don't make assumptions on the Search Results beyond strictly what's returned.
- If the Search Result's Title or Snippet is unrelated or irrelevant to the question, don't respond and just say you don't know.
- Never give advice to search the Internet. 
- Leverage information from multiple search results to respond comprehensively
- Always reference factual statements to the Search Results using [${number}] notation, i.e. square brackets with numerical references to the URLs. e.g. [1], [2], [3], [4], ...
- You can only issue numerical references to the URLs. Never generate URLs or links or titles.
- If the search results do not have the relevant information, use the Google Search tool to find information about it. If that is not sufficient, use the JavaScript interpreter tool to fetch more information.
- For math-style questions, or anything that needs computation, prefer using JavaScript interpreter for reliable calculations.
- Prefer unicode for mathematical symbols & print when possible over Latex

Citation Style

🔗 Shorten citation links

Cache

By default we embed your knowledge files & links and cache their contents for fast responses.

Always Check for Updates

Create Synthetic Data

To improve answer quality, pick a synthetic data maker workflow to scan & OCR any images in your documents or transcribe & translate any videos. It also can synthesize a helpful FAQ. Adds ~2 minutes of one-time processing per file.

Advanced Settings

In general, you should not need to adjust these.

👁‍🗨 Conversation Summarization

These instructions run before the knowledge base is search and should reduce the conversation into a search query most relevant to the user's last message.

🔑 Keyword Extraction

Instructions to create a query for keyword/hybrid BM25 search. Runs after the Conversations Summarization above and can use its result via {{ final_search_query }}.

✏ Embeddings Model

Dense Embeddings Weightage

Weightage for dense vs sparse embeddings. 0 for sparse, 1 for dense, 0.5 for equal weight.
Generally speaking, dense embeddings excel at understanding the context of the query, whereas sparse vectors excel at keyword matches.

Max Citations

The maximum number of document search citations.

Max Snippet Words

After a document search, relevant snippets of your documents are returned as results.
This setting adjusts the maximum number of words in each snippet (tokens = words * 2).
A high snippet size allows the LLM to access more information from your document results, at the cost of being verbose and potentially exhausting input tokens (which can cause a failure of the agent to respond).

Snippet Overlap Ratio

Your knowledge base documents are split into overlapping snippets.
This settings adjusts how much those snippets overlap (overlap tokens = snippet tokens / overlap ratio).
In general you shouldn't need to adjust this.

Description

Model, Knowledge base & Tools

GPT-4.1

3 Knowledge sources

2 Tools called

Knowledge Base

🛠️ Developer Tools and Functions

Functions