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Create Text Embeddings for a Vector Store using LangChain
Google Cloud Training
Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn how to use LangChain to store documents as embeddings in a vector store. You will use the LangChain framework to split a set of documents into chunks, vectorize (embed) each chunk and then store the embeddings in a vector database.
Register for this course and see more details by visiting:
OpenCourser.com/course/wu7lu4/create
What's inside
Syllabus
Create Text Embeddings for a Vector Store using LangChain
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops a deep understanding of using LangChain and vector stores to create text embeddings in a vector store
Strong, industry-recognized instructors who hold vast experience in the topic of the course
Suitable for beginners seeking to build a foundation in using LangChain and vector stores to create text embeddings
Opportunity for hands-on practice in a Google Cloud environment, enhancing comprehension and retention
Taught by Google Cloud Training, a highly recognized provider who ensures the relevance and credibility of the course content
Provides practical lessons and interactive labs, fostering more effective learning through hands-on experience
Save this course
Save Create Text Embeddings for a Vector Store using LangChain to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Create Text Embeddings for a Vector Store using LangChain with these
activities:
Review your notes and assignments from previous courses related to vector vectorization and langchain
Show steps
Refresh your knowledge of relevant concepts covered in previous courses to strengthen your foundation before starting this course.
Browse courses on
LangChain
Show steps
-
Gather your notes and assignments from previous courses.
-
Review the materials and focus on key concepts related to vector vectorization and langchain.
Review vector vectorization and langchain in natural language processing
Show steps
Revisit the basics of vector vectorization and langchain to strengthen your foundation before starting the course.
Browse courses on
LangChain
Show steps
-
Review your notes on vector vectorization.
-
Revisit the documentation for langchain.
-
Complete a few practice exercises on vector vectorization and langchain.
Join a study group or participate in a discussion forum to connect with other learners
Show steps
Enhance your learning experience by connecting with other learners, sharing knowledge, and discussing the course material.
Show steps
-
Find a study group or discussion forum related to the course.
-
Participate in discussions and ask questions.
-
Share your knowledge and insights with other learners.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow a guided tutorial on using LangChain to store embeddings in a vector store
Show steps
Supplement your learning with a guided tutorial that provides step-by-step instructions on the specific topic of the course.
Show steps
-
Find a reputable tutorial on using LangChain.
-
Follow the tutorial step by step.
-
Experiment with the code and try different options.
Complete a series of practice drills on using LangChain to store embeddings in a vector store
Show steps
Reinforce your understanding by completing a series of practice drills that test your ability to apply the concepts of the course.
Show steps
-
Find a set of practice drills on using LangChain.
-
Complete the drills, checking your answers against the provided solutions.
-
Review the solutions to identify areas where you need improvement.
Build a prototype application that uses LangChain to store embeddings in a vector store
Show steps
Apply your learning by creating a tangible deliverable that demonstrates your ability to use the concepts of the course in a practical setting.
Show steps
-
Design the architecture of your application.
-
Implement the application using LangChain.
-
Test the application to ensure that it works as intended.
Review your notes and assignments from previous courses related to vector vectorization and langchain
Show steps
Refresh your knowledge of relevant concepts covered in previous courses to strengthen your foundation before starting this course.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Gather your notes and assignments from previous courses.
- Review the materials and focus on key concepts related to vector vectorization and langchain.
Review vector vectorization and langchain in natural language processing
Show steps
Revisit the basics of vector vectorization and langchain to strengthen your foundation before starting the course.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Review your notes on vector vectorization.
- Revisit the documentation for langchain.
- Complete a few practice exercises on vector vectorization and langchain.
Join a study group or participate in a discussion forum to connect with other learners
Show steps
Enhance your learning experience by connecting with other learners, sharing knowledge, and discussing the course material.
Show steps
Show steps
Show steps
- Find a study group or discussion forum related to the course.
- Participate in discussions and ask questions.
- Share your knowledge and insights with other learners.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow a guided tutorial on using LangChain to store embeddings in a vector store
Show steps
Supplement your learning with a guided tutorial that provides step-by-step instructions on the specific topic of the course.
Show steps
Show steps
Show steps
- Find a reputable tutorial on using LangChain.
- Follow the tutorial step by step.
- Experiment with the code and try different options.
Complete a series of practice drills on using LangChain to store embeddings in a vector store
Show steps
Reinforce your understanding by completing a series of practice drills that test your ability to apply the concepts of the course.
Show steps
Show steps
Show steps
- Find a set of practice drills on using LangChain.
- Complete the drills, checking your answers against the provided solutions.
- Review the solutions to identify areas where you need improvement.
Build a prototype application that uses LangChain to store embeddings in a vector store
Show steps
Apply your learning by creating a tangible deliverable that demonstrates your ability to use the concepts of the course in a practical setting.
Show steps
Show steps
Show steps
- Design the architecture of your application.
- Implement the application using LangChain.
- Test the application to ensure that it works as intended.
Career center
Learners who complete Create Text Embeddings for a Vector Store using LangChain will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/wu7lu4/create
Reading list
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For more information about how these books relate to this course, visit:
OpenCourser.com/course/wu7lu4/create
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Effort
90 minutes
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 26, 2024
- September 9, 2024
Language
English
Good to know
Develops a deep understanding of using LangChain and vector stores to create text embeddings in a vector store
Strong, industry-recognized instructors who hold vast experience in the topic of the course
Suitable for beginners seeking to build a foundation in using LangChain and vector stores to create text embeddings
Opportunity for hands-on practice in a Google Cloud environment, enhancing comprehension and retention
Taught by Google Cloud Training, a highly recognized provider who ensures the relevance and credibility of the course content
Provides practical lessons and interactive labs, fostering more effective learning through hands-on experience
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