
Sarvadnya
This repo is a collection of various PoCs (Proof-of-Concepts) to interface custom data using LLMs.
Stars: 52

Sarvadnya is a repository focused on interfacing custom data using Large Language Models (LLMs) through Proof-of-Concepts (PoCs) like Retrieval Augmented Generation (RAG) and Fine-Tuning. It aims to enable domain adaptation for LLMs to answer on user-specific corpora. The repository also covers topics such as Indic-languages models, 3D World Simulations, Knowledge Graphs Generation, Signal Processing, Drones, UAV Image Processing, and Floor Plan Segmentation. It provides insights into building chatbots of various modalities, preparing videos, and creating content for different platforms like Medium, LinkedIn, and YouTube. The tech stacks involved range from enterprise solutions like Google Doc AI and Microsoft Azure Language AI Services to open-source tools like Langchain and HuggingFace.
README:
Chatbots can be real WoW!! The recent evidence is: ChatGPT. Now that they are more human-like with the latest LLMs (Large Language Models). But these LLMs are Pretrained on their own (HUGE) data. Mere mortals don't have any ways ($$, time, expertise) to train own LLMs. RAG and/or Fine-tuning is the way out for Domain Adaptation ie. LLMs answering on your corpus. This repo is a collection of various PoCs (Proof-of-Concepts) to interface custom data using LLMs.
A few other topics are (or can be) part of this repo is to build
- Indic-languages models, some notes here
- 3D World Simulations, Agents, some notes here
- Knowledge Graphs Generation, some notes here
- Signal Processing, some notes here
- Drones, UAV Image Processing, Shynakshi here
- Floor Plan Segmentation here
- Prep chatbots of various modalities, use cases and domains, diff datasets
- Prep videos, write Medium Posts (GDE/TH), LinkedIn posts, Youtube channel
- Retrieval Augmented Generation (RAG) on own data
- Fine-tuning LLMs with own data using LoRA etc
- When?: {less, streaming, private} data and less {compute, money, expertise}
- What?:
- on knowledge graphs, more grounding
- tabular financial data, representation and similarity
- midcurveNN Geometric serialization and retrieval
- active loop idea of fine-tuning your data
- Langchain and Llamaindex with any new LLM
-
When? Sufficient curated date is available, not a whole lot though, in a batch (not running) state
-
What: Instead of unstructured text (input prompts) to unstructured text (output response), more value is in prompt to structured output, such as :
- text2json: many enterprises such as financial companies.
- text2cypher: for graph databases, from Neo4j, like Langchain implementation by Tomaz Britanic
- text2SQL: classical case, many pro solutions available, study them, follow them, for other QLs
- text2Manim: Maths Animation, dataset available, see if generated video can be shown in the same streamlit page
- text23DJS: Good for 3D+LLM+Agents like Metamorph from Nvidia, Geometry or shape representation as text, is the key
- textGraph2textGraph: MidcurveNN if we get Graph representation as text, right.
-
Here, key would be robust post-processing and evaluation as the response needs to be near perfect, no scope of relaxation even in syntax or format.
- Enterprise: Google Doc AI, Vertex AI, Microsoft Azure Language AI Services
- Open Source: Langchain (Serve/Smith/Graph), HuggingFace, Streamlit for UI
Not looking for Success, but Wonder!!
- तमसो मा ज्योतिर्गमय : From Dark (hidden in text data) to Light (insights)
- Abhinav Kimothi, RAG Expert: LinkedIn, Projects Portfolio, Website, Medium, LinkedIn Articles, LinekdIn Posts, Company
- Pradip Nichite, Freelancing Expert: LinkedIn, Projects Portfolio, Blog, Youtube, LinekdIn Posts, Company
- Sahar Mor: LinkedIn, Blogs
- LangChain How to and guides
- Building the Future with LLMs, LangChain, & Pinecone
- LangChain for Gen AI and LLMs - James Briggs
- Finetuning GPT-3 David Shapiro ~ AI
- Build overpowered AI apps with the OP stack (OpenAI + Pinecone)
- Learn about AI Language Models and Reinforcement Learning Kamalraj M M
- GPT-4 & LangChain Tutorial: How to Chat With A 56-Page PDF Document (w/Pinecone)
- LangChain - Data Independent
- Retrieval-Augmented Generation for Large Language Models: A Survey
Author ([email protected]) gives no guarantee of the results of the program. It is just a fun script. Lot of improvements are still to be made. So, don’t depend on it at all.
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