Should I Use LangChain and a Vector Database for a Fine-Tuned Domain Chatbot?

When fine-tuning the response style of your chatbot, you can indeed focus on the LLM prompt or even fine-tune the model itself to get the desired tone. However, there are some reasons to consider keeping a vector database like Weaviate (or another supported vector store) as part of your LangChain setup:

• Efficient Retrieval:
  Even with hundreds of documents, embedding them and storing the embeddings in a vector store lets you efficiently search for the relevant context rather than feeding all documents into the prompt. This keeps your prompt size manageable and helps the model focus on the most pertinent information for each query.

• Scalability:
  As your document collection grows, retrieving only the relevant chunks becomes critical. A vector store makes this scalable. Even if your current corpus is small, using a vector retrieval approach now can ease future expansion.

• Targeted Context:
  Fine-tuning the response style is a separate concern from ensuring the chatbot retrieves accurate domain-specific information. Combining a fine-tuned prompt/system message with a retrieval chain that fetches the most relevant documents can lead to more precise and cohesive answers.

In summary, while you could technically supply all document text directly to the model if the corpus and token limits allow, integrating LangChain with a vector database offers significant benefits in retrieval efficiency, scalability, and overall performance. Therefore, it is generally recommended to maintain a retrieval mechanism (like with Weaviate) even if you are focusing on fine-tuning the chatbot’s style.