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
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When? Sufficient curated date is available, not a whole lot though, in a batch (not running) state
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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.
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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 (yogeshkulkarni@yahoo.com) 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.