Welcome to this tutorial video where we introduce an innovative approach to searching your PDF application using the power of Langchain, ChromaDB, and Open Source LLM, all running on your CPU.

Langchain is a powerful library designed for generative AI tasks, providing a range of capabilities that enhance language generation and understanding.
ChromaDB, on the other hand, acts as a vector store and database, enabling us to store and retrieve vectors efficiently. By integrating ChromaDB into our search tool, we can create a robust and scalable solution for managing the vector representations of PDF documents, allowing for faster and more accurate searches.
Finally, we utilize an Open Source LLM (Language Model) to enable question-answering capabilities within our search tool. With the power of LLM, we can process user queries and extract relevant information from PDF documents, providing precise and context-aware answers to the user's questions.
The unique aspect of this tutorial is that we do not rely on the OpenAI API, meaning you can run this entire system on your CPU without the need for external services. This ensures greater control, privacy, and accessibility for your PDF search needs.

Join us in this tutorial video as we guide you through the process of building your own PDF search tool using Langchain, ChromaDB, and Open Source LLM. Discover how to harness the potential of these technologies to create a powerful and efficient search system tailored to your requirements. Let's unlock the potential of your PDF application and revolutionize the way you search for information.

AI Anytime's GitHub: https://github.com/AIAnytime
LaMiNi LM Model: https://huggingface.co/MBZUAI/LaMini-...
ChromaDB: https://www.trychroma.com/
Langchain: https://python.langchain.com/docs/get...
LLM Playlist:    • Large Language Models  
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