How many vector indexes do you need to implement an AI assistant?

[lancejpollard]

I am a JS/TS software developer with many years of experience, but new to AI.

I have read about vector databases, and learned about langchain and pinecone, but am unsure how many pinecone indexes I should be creating to implement an AI assistant. What are your suggestions?

Do I only need 1 index, or a hundred or how many roughly speaking? When do you need a new index?

My goal is to create an AI assistant for learning a fantasy language. It should be able to remember conversations with individual users, and have a central knowledge base about the fantasy language. I am still learning the pieces of the puzzle, so not sure yet if I should be feeding the fantasy language dictionary terms/definitions in the form of prompts and somehow store them into 1 index, or if each conversation needs its own index, along with a central index, etc.. If each conversation needs an index, then how do I communicate between indexes? That sort of stuff.

Just looking for some basic info and to be pointed in some helpful directions. Thank you so much!

[dosubot[bot]]

Answer generated by a 🤖

Answer

Hello,

Based on your requirements, you would likely only need one Pinecone vector index. This index would be used to store and retrieve the embeddings of the documents, where each document could represent a conversation or a piece of knowledge about the fantasy language. The AI assistant can then use the Pinecone vector index to retrieve relevant conversations or pieces of knowledge based on the user's input.

Here's a basic example of how you might create a Pinecone vector index with LangChain:

from langchain.schema import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone

embeddings = OpenAIEmbeddings()
# create new index
pinecone.create_index("langchain-self-retriever-demo", dimension=1536)

In this code, pinecone.create_index("langchain-self-retriever-demo", dimension=1536) is used to create a new Pinecone vector index with the name "langchain-self-retriever-demo" and a dimension of 1536. The dimension should match the dimension of the embeddings used.

LangChain uses the Pinecone vector database to store and retrieve conversations. The conversations are stored as text vectors in the Pinecone index. To store a conversation, LangChain uses the add_texts method. This method takes an iterable of strings (the conversations), optional metadata, and other vectorstore specific parameters. It embeds the texts using the provided embedding method, associates each text with an id (generating a new one if not provided), and upserts the resulting documents (id, embedding, metadata) to the Pinecone index.

To retrieve conversations, LangChain provides several methods, including similarity_search, similarity_search_with_score, and max_marginal_relevance_search. These methods take a query text, embed it, and use the Pinecone index to find the most similar texts (conversations) stored in the index.

I hope this helps! If you have any more questions, feel free to ask.

Sources

• docs/extras/modules/data_connection/retrievers/self_query/pinecone.ipynb
• libs/langchain/langchain/vectorstores/pinecone.py
• libs/langchain/langchain/vectorstores/base.py

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