Summarize private documents using RAG, LangChain, and LLMs
Use IBM watsonx.ai, LangChain, and RAG to enable LLMs to retrieve information from your own private document. Learn to split, embed, and summarize vast amounts of texts with advanced LLMs, crafting a smart agent that not only retrieves and condenses information, but also remembers your interactions. If you're looking to revolutionize data handling, this tutorial offers hands-on experience in AI-driven document management, setting a new standard in efficiency.
4.4 (14 Reviews)
Language
- English
Topic
- Artificial Intelligence
Enrollment Count
- 113
Skills You Will Learn
- Artificial Intelligence, Generative AI, LLM, NLP, RAG, LangChain
Offered By
- IBMSkillsNetwork
Estimated Effort
- 45 minutes
Platform
- SkillsNetwork
Last Update
- May 17, 2024
What you'll learn and achieve:
- Master document processing techniques: Learn to split and embed documents in formats that LLMs can efficiently process, making large volumes of text easily manageable.
- Utilize advanced LLMs: Gain hands-on experience with IBM watsonx.ai, choosing the best LLMs for your document processing needs to achieve high-quality outcomes.
- Customize information retrieval: Implement various retrieval chains from LangChain, adapting your document retrieval process for different purposes, and enhancing the precision of information extraction using promote templates.
- Build a smart agent: Develop an agent that integrates LLMs, LangChain, and RAG technologies for an interactive experience and is capable of efficiently retrieving and summarizing documents based on user queries.
What you'll need
Language
- English
Topic
- Artificial Intelligence
Enrollment Count
- 113
Skills You Will Learn
- Artificial Intelligence, Generative AI, LLM, NLP, RAG, LangChain
Offered By
- IBMSkillsNetwork
Estimated Effort
- 45 minutes
Platform
- SkillsNetwork
Last Update
- May 17, 2024
Instructors
Kang Wang
Data Scientist
I am a Data Scientist in the IBM. I am also a PhD Candidate in the University of Waterloo.
Read moreContributors
Sina Nazeri
Data Scientist at IBM
I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.
Read moreWojciech "Victor" Fulmyk
Data Scientist at IBM
As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. From modeling to storytelling, I bring a holistic approach to data science. Leveraging machine learning algorithms, I construct predictive models tailored to both real-world challenges as well as old, well-understood problems. My knack for data-driven storytelling ensures that the insights uncovered resonate with both technical and non-technical audiences. Open to collaboration, I'm eager to take on new challenges and contribute to transformative data-driven endeavors. Whether you seek to extract insights, enhance predictive models, or explore untapped potential within your datasets, I'm here to help. Feel free to connect to me via my LinkedIn profile. Let's learn from each other!
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