Conversational Retrieval QA Chain

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Node Details

• Name: conversationalRetrievalQAChain
• Type: ConversationalRetrievalQAChain
• Category: [[Chains]]
• Version: 3.0

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Parameters

1. Chat Model (Required)

   • Type: BaseChatModel
   • Description: The language model used for generating responses.

2. Vector Store Retriever (Required)

   • Type: BaseRetriever
   • Description: The retriever used to fetch relevant documents from a vector store.

3. Memory (Optional)

   • Type: BaseMemory
   • Description: The memory component to store conversation history. If not provided, a default BufferMemory will be used.

4. Return Source Documents (Optional)

   • Type: boolean
   • Description: Whether to return the source documents used to generate the answer.

5. Rephrase Prompt (Optional)

   • Type: string
   • Description: Custom prompt for rephrasing the question based on chat history.
   • Default: A predefined template (REPHRASE_TEMPLATE)
   • Additional Params: true

6. Response Prompt (Optional)

   • Type: string
   • Description: Custom prompt for generating the final response.
   • Default: A predefined template (RESPONSE_TEMPLATE)
   • Additional Params: true

7. Input Moderation (Optional)

   • Type: Moderation[]
   • Description: Moderation tools to detect and prevent harmful input.
   • Additional Params: true

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Input

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

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Output

• If Return Source Documents is false:
  • A string containing the answer to the user’s question.
• If Return Source Documents is true:
  • An object containing:
    • text: The answer to the user’s question.
    • sourceDocuments: An array of documents used to generate the answer.

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How It Works

1. The chain receives a user question.
2. It uses the chat history and the rephrase prompt to generate a standalone question.
3. The vector store retriever fetches relevant documents based on the rephrased question.
4. The response prompt is used along with the retrieved documents to generate a final answer.
5. The answer (and optionally source documents) is returned as output.
6. The conversation history is updated with the new question-answer pair.

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Use Cases

• Building conversational AI systems with access to large document repositories
• Creating chatbots that can answer questions based on specific knowledge bases
• Implementing virtual assistants for customer support or information retrieval
• Developing educational tools that can answer follow-up questions and maintain context
• Enhancing search functionality with conversational capabilities

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Special Features

• Conversational Context: Maintains and utilizes chat history for more coherent interactions.
• Dynamic Question Rephrasing: Reformulates questions based on conversation context.
• Flexible Retrieval: Uses vector store for efficient and relevant document retrieval.
• Customizable Prompts: Allows fine-tuning of question rephrasing and answer generation.
• Source Attribution: Option to return source documents for transparency.
• Input Moderation: Can implement safeguards against inappropriate or harmful inputs.
• Memory Management: Supports various memory types for different use cases.

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Notes

• The quality of answers depends on both the underlying language model and the relevance of retrieved documents.
• Custom prompts can significantly impact the chain’s behavior and should be carefully crafted.
• The chain supports streaming responses for real-time interaction in compatible environments.
• Proper error handling and input validation should be implemented for production use