Get Agent Response

{
    "success": true,
    "message": "Agent Response Found Successfully.",
    "data": {
        "id": 10932,
        "comments": [
            {
                "comment": "Testing comment",
                "created_by": "[email protected]",
                "created_on": "2025-01-28T21:30:17.368939Z"
            }
        ],
        "feedback": [
            {
                "is_upvote": true,
                "is_downvote": false
            }
        ],
        "question": "Can you tell me how RAG works from the sources given?",
        "response": {
            "query": "Explain how Retrieval-Augmented Generation (RAG) works.",
            "answer": "Retrieval-Augmented Generation (RAG) is a technique in natural language processing that enhances the capabilities of generative language models by integrating real-time data retrieval. This approach addresses the limitations of traditional generative models, which rely solely on their training data and may produce outdated or incorrect information if the data is not current. RAG ensures that the responses generated are both contextually appropriate and grounded in factual information by incorporating real-time data from external sources.\n",
            "run_id": "4c0c5eb6-1902-4b26-97f3-448bcb7ba31f",
            "history": [
                {
                    "bot": "Retrieval-Augmented Generation (RAG) is a technique in natural language processing that enhances the capabilities of generative language models by integrating real-time data retrieval.\n",
                    "user": "Can you tell me how RAG works from the sources given?"
                }
            ],
            "indexes": [],
            "sources": "[0]",
            "success": true,
            "cache_hit": false,
            "agent_mode": "",
            "corpus_ids": [
                645
            ],
            "references": [
                {
                    "url": "Introduction_to_RAG1738098993.txt",
                    "order": 1,
                    "number": 0
                }
            ],
            "instructions": [],
            "total_tokens": 4757,
            "prompt_prefix": "",
            "prompt_tokens": 4277,
            "completion_tokens": 480,
            "followup_questions": [
                "How does RAG ensure responses are contextually appropriate?",
                "What role does the retriever play in the RAG process?",
                "Why is RAG beneficial for real-time information synthesis?"
            ]
        },
        "created_by": "[email protected]",
        "created_on": "2025-01-28T21:18:06.614958Z",
        "is_deleted": false,
        "total_tokens": 4757,
        "prompt_tokens": 4277,
        "completion_tokens": 480,
        "cache_hit": false,
        "query_source": "web",
        "message_id": null,
        "cost": "0.02379",
        "chat_thread": 4354,
        "agent": 952,
        "user": 1,
        "llm_config": 8
    }
}