VectorDB QA Chain

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Node Details

• Name: vectorDBQAChain
• Type: VectorDBQAChain
• Category: [[Chains]]
• Version: 2.0

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Parameters

1. Language Model (Required)
   • Type: BaseLanguageModel
   • Description: The language model used for generating answers.
2. Vector Store (Required)
   • Type: VectorStore
   • Description: The vector store used for storing and retrieving document embeddings.
3. Input Moderation (Optional)
   • Type: Moderation[]
   • Description: Moderation tools to detect and prevent harmful input.
   • List: true

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Input

• A string containing the user’s question or query.

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Output

• A string containing the answer to the user’s question.

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How It Works

1. The chain receives a user question.
2. If input moderation is enabled, it checks the input for potential harmful content.
3. The vector store retrieves relevant documents based on the similarity between the question and stored document embeddings.
4. The retrieved documents are combined with the original question to form a prompt for the language model.
5. The language model generates an answer based on the prompt and retrieved context.
6. The final answer is returned as output.

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Use Cases

• Building question-answering systems based on large document collections
• Creating AI assistants with access to specialized knowledge bases
• Implementing intelligent search functionality for databases or document repositories
• Developing automated customer support systems with access to product documentation
• Creating educational tools that can answer questions based on course materials

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Special Features

• Efficient Retrieval: Uses vector similarity search for fast and relevant document retrieval.
• Scalability: Can handle large document collections efficiently through vector database indexing.
• Flexibility: Compatible with various vector store implementations (e.g., FAISS, Pinecone, Chroma).
• Input Moderation: Can implement safeguards against inappropriate or harmful inputs.
• Customizable: The number of retrieved documents (k) can be adjusted for performance optimization.

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Notes

• The quality of answers depends on both the underlying language model and the relevance of retrieved documents.
• The effectiveness of the chain is influenced by the quality of document embeddings in the vector store.
• Proper error handling should be implemented, especially for potential API failures or retrieval issues.
• The chain’s performance can be optimized by tuning the number of retrieved documents (k) based on the specific use case.
• It’s important to ensure that the vector store is properly populated with relevant and up-to-date information.
• The chain does not inherently support returning source documents, focusing instead on providing concise answers.