VertexAI, PrivateGPT, Linux, Python Langchain, ChromaDB and Modal Client. All these systems and frameworks are integrated in a single coherent discussion. Following will be the key points Steps discusssed : Langchain + VertexAI qna create cloud image and install necessary libraries Enable local file sharing for service accounts credentials …

LLMs will mis-understand and create completely random output when you prompt it with seemingly correct prompt. The reason for this can be many. LLMS model architecture is not advanced enough Not trained with the type of instructions and output you are providing Limitation like the not having access to file…

AI Ideas are increasing every hour. These ideas are no longer just code. They contain Language/ Vision/ Audio models that range from few 100 MBs to couple of GBs. Whether it is open source or paid models, the code that implements the Ideas with AI needs to be deployed. Modal helps in doing just that by abstracting away the complexity of allocating the hardware resources, setting up the environment, loading the models and finally making the product to face the customer.

SelfQueryRetriever queries itself, as the Langchain Docs say. In this discussion we dive into how it is doing, and how to build one. Given a VectorStore (Chroma / Weaviat / Pinecone at the moment) retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it’s underlying VectorStore. SQ Retriever uses user-input query for semantic similarity search and extracts filters, limits from the user query. Uses them on the metadata of stored documents and to execute those filters.

Langchain Devs in Open Source community have released the LLM class primitive for VertexAI API. This video discusses the GCP Service Account creation, Text-Bison-01 model LLM instantiation and using that LLM instance to integrate with LLMchain, RetreivalQA, Spark DataFrame Agent and Spark SQL Agent The code can be found at the below link https://github.com/insightbuilder/python_de_learners_data/blob/main/code_script_notebooks/projects/exploring_bard/Intro_vertexAI%26LC_final.ipynb

GUI applications are comfortable for new users, since the available commands are provided as menu, text boxes and Select options. Integration LLM/ AI application with GUI can allow majority of the users to enjoy the benefits of AI. Video introduces PySimpleGUI, and the integration with Langchain and connect with OpenAI models. The code and supporting documentation can be found below. https://github.com/Kamalabot/streamlit_deploys/blob/main/pysimplegui/retriever_doc.py

Self AI clones that are created using the data about one’s self and feeding it to large language model is possible. The video discusses about the places from where to collect the data, inject it into vector stores. It shows how to create a prompt that creates engaging AI chat bot, that contains the context from your data.

Leaderboards have a specific purpose, and this video goes in details about the ways in which large language models are compared for their performance. Details of individual benchmarks like arc, hellaswag, truthfulqa and multi-task efficiency is explained. Following that the tasks on which the embedding models are evaluated is explained along with the significance. The leader boards discussed are located at https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard https://huggingface.co/spaces/mteb/leaderboard https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=-any-&only_verified=0&task=-any- https://huggingface.co/spaces/ought/raft-leaderboard

Introducing Google Bard. In three modes, GUI, Free API and the Paid API. The free API mode is redirection of the Bard GUI output to the Python Execution Environment, using the reverse engineering done by the developers. Get more details about them below GitHub - acheong08/Bard: Reverse engineering of Google's Bard API Reverse engineering of Google's Bard API. Contribute to acheong08/Bard development by creating an account on GitHub.github.com