Help: Example on how to use the assistant with Vector DB ?

[pedroresende]

It would be great to have an example on how we can integrate an assistant with a RAG.
Even though the documentation mentions that "Assistants can be configured with an LLM of your choice (currently only OpenAI), any vector search database and easily extended with additional tools." (https://github.com/andreibondarev/langchainrb/blob/main/lib/langchain/assistants/assistant.rb#L5) there is no simple example on how we can do it

[andreibondarev]

@pedroresende I created a tool that wraps any vector search database. Take a glance at the diff here: https://github.com/andreibondarev/langchainrb/compare/add-vectorsearch-wrapper-tool?expand=1.

This is how I tested it out:

# This could be any LLM. It'll be used to embed documents and query.
llm = Langchain::LLM::Ollama.new url: ENV['OLLAMA_URL']

# Initialize the vectorsearch db
chroma = Langchain::Vectorsearch::Chroma.new(url: ENV["CHROMA_URL"], index_name: "docs", llm: llm)

# Add documents to it
chroma.create_default_schema
chroma.add_data paths: [
  # I imported this file: https://www.coned.com/-/media/files/coned/documents/small-medium-large-businesses/gasyellowbook.pdf
  Langchain.root.join("./file1.pdf"),
  Langchain.root.join("./file2.pdf")
]

# Initialize the tool that will be passed to the Assistant
vectorsearch_tool = Langchain::Tool::Vectorsearch.new(vectorsearch: chroma)

# Initialize the Assistant
assistant = Langchain::Assistant.new(
  llm: Langchain::LLM::OpenAI.new(api_key: ENV['OPENAI_API_KEY']),
  thread: Langchain::Thread.new,
  # It's up to you to explain the Assistant when it should be accessing the vectorsearch DB. You could even tell it to access it every single time before answering the question.
  instructions: "You are a chat bot that helps users find information from the Con Edison Yellow Book that you have stored in your vector search database. Feel free to refer to it when answering questions.",
  tools: [
    vectorsearch_tool
  ]
)

# Ask away!
assistant.add_message_and_run content: "...", auto_tool_execution: true

I would really really love your feedback on the approach.

[pedroresende]

@andreibondarev it worked perfectly fine, thanks for the help. The only strange thing that occurs, sometimes it's the following error

[andreibondarev]

@andreibondarev it worked perfectly fine, thanks for the help. The only strange thing that occurs, sometimes it's the following error

I called the tool Langchain::Tool::Vectorsearch.

[pedroresende]

I know you did, I've renamed it to try to debug if it was clashing for some reason but I'm getting exactly the same error

[andreibondarev]

I know you did, I've renamed it to try to debug if it was clashing for some reason but I'm getting exactly the same error

Make sure to modify the NAME constant as well:

module Langchain::Tool
  class Vectorsearchtool < Base
    NAME = "vectorsearchtool"

[pedroresende]

I know you did, I've renamed it to try to debug if it was clashing for some reason but I'm getting exactly the same error

Make sure to modify the NAME constant as well:

module Langchain::Tool
  class Vectorsearchtool < Base
    NAME = "vectorsearchtool"

Sure thing

[andreibondarev]

Resolved.

[Jellyfishboy]

This currently does not work.

If I use .ask it will return a relevant response.

If I use the Vectorsearch tool inside an assistant, it just tells me it can't find the relevant information.

I ended up creating my own tool and directly using .ask method, which resulted in relevant information consistently being provided.

[andreibondarev]

@Jellyfishboy Could you please show me how you were using the Langchain::Tool::Vectorsearch tool?