### Chat completions

Chat completion, as utilized in this application through OpenAI's API, refers to
the generation of conversational responses based on a given context or prompt.
In the application, it is used to provide intelligent, context-aware answers to
user queries by processing and integrating information from video transcriptions
and other inputs, enhancing the chatbot's interactive capabilities. For more
details, see OpenAI's
[Chat Completions API](https://platform.openai.com/docs/guides/text-generation) documentation.

### Pinecone

Pinecone is a vector database service optimized for similarity search, used for
building and deploying large-scale vector search applications. In this
application, Pinecone is employed to store and retrieve the embeddings of video
transcriptions, enabling efficient and relevant search functionality within the
application based on user queries. For more details, see
[pincone.io](https://www.pinecone.io/).

### Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is a technique that combines information
retrieval with a language model to generate responses based on retrieved
documents or data. In RAG, the system retrieves relevant information (in this
case, via embeddings from video transcriptions) and then uses a language model
to generate responses based on this retrieved data. For more details, see
OpenAI's cookbook for
[Retrieval Augmented Generative Question Answering with Pinecone](https://cookbook.openai.com/examples/vector_databases/pinecone/gen_qa).

## Next steps

Explore how to [create a PDF bot application](/guides/genai-pdf-bot/_index.md) using
generative AI, or view more GenAI samples in the
[GenAI Stack](https://github.com/docker/genai-stack) repository.