The document discusses using a vector database to enable question answering with custom data. Key points: - Data is converted to vector embeddings and stored in a vector database like Pinecone to allow for similarity searches. - When a user asks a question, it is converted to a vector and queried against the database to retrieve similar content to provide as input to a language model for generating an answer. - The OpenAI API can also be used to build an assistant using a language model, where custom data is loaded to enable answering questions about that data as a "support manager."

1. OPEN AI
query on your own data
Dori Waldman

2. RAG
• Retrieval and generation: the actual RAG chain, which takes
the user query at run time and retrieves the relevant data from
the index, then passes that to the model
• model is language model like OpenAI
• Store can be Pinecone vector DB
Data Query

3. Embedding:
Text to vector array

4. Example:

5. Before Assistant api
• Convert your data to vector presentation (embedding)
• Split your data to parts , you can add metadata per part
• Persist it in vector db
• Convert your question to vector
• Query database to find similar content/doc related to the question
• In vector database cat and mouse are similar (distance in space)
• On the result use language model to “understand” the document you
got from DB and return an answer to the user
• Return also the answer resource to the user (from which doc the
answer is from)
• Optional - get feedback how good is the answer

6. With assistant api
• Create assistant
• Select language model
• Add system instruction : “answer as support manager…”
• Load file (your custom data)
• Start chat

7. Resources
• https://www.youtube.com/watch?v=TLf90ipMzfE
• https://youtu.be/qItoyPzz01s?si=47pmtUMjZUsQlbzb
• https://www.youtube.com/watch?v=vQhEiR2bNY8
• https://www.youtube.com/watch?v=lNdpu6u9ZYM&t=2s
• https://www.youtube.com/watch?v=lTF43_-TjbQ
• https://www.youtube.com/watch?v=Vurdg6yrPL8&list=PLpdmBGJ6ELUIYHjmzYTuePlNRf
7yeCACz
• https://weaviate.io/blog/what-is-a-vector-database
• https://github.com/pinecone-io/canopy
• https://clickhouse.com/blog/vector-search-clickhouse-p1
• https://weaviate.io/blog/distance-metrics-in-vector-search
• https://python.langchain.com/docs/use_cases/question_answering/
• https://cobusgreyling.medium.com/knowledge-retrieval-via-the-openai-playground-
8b04682ebe37

8. Vector DB usage