{
  "title": "Multiturn RAG follow-up with previous_response_id",
  "turns": [
    {
      "request": {
        "model": "moonshotai/Kimi-K2.6:novita",
        "instructions": "This is turn 1 of a multiturn API example. Keep it concise. Do not launch jobs or broad research sub-agents. Use direct Hub/model knowledge or at most two direct Hub lookups.",
        "input": "For technical-document RAG, compare BAAI/bge-m3 and Qwen/Qwen3-Embedding-8B. Recommend one default embedding model for a startup that cares about good quality but low latency. Keep under 250 words.",
        "background": true,
        "max_cost_usd": 2.0,
        "metadata": {
          "example_slug": "multiturn-rag-followup",
          "turn": "1"
        }
      },
      "response": {
        "id": "resp_2768fb94ff614a3a90a1c455548d767f",
        "object": "response",
        "created_at": 1781315593,
        "completed_at": 1781315619,
        "status": "completed",
        "model": "moonshotai/Kimi-K2.6:novita",
        "background": true,
        "previous_response_id": null,
        "session_id": "3098e124-fd5a-4968-90d9-4c11d6dee78a",
        "max_cost_usd": 2.0,
        "instructions": "This is turn 1 of a multiturn API example. Keep it concise. Do not launch jobs or broad research sub-agents. Use direct Hub/model knowledge or at most two direct Hub lookups.",
        "output": [
          {
            "type": "custom_tool_call",
            "id": "hub_repo_details:0",
            "name": "hub_repo_details",
            "input": "{\"repo_ids\": [\"BAAI/bge-m3\", \"Qwen/Qwen3-Embedding-8B\"]}",
            "output": "**Type: Model**\n\n# BAAI/bge-m3\n\n## Overview\n- **Author:** BAAI\n- **Task:** sentence-similarity\n- **Library:** sentence-transformers\n- **Downloads:** 182.5M | **Likes:** 3104\n- **Updated:** 3 Jul, 2024\n\n## Technical Details\n- **Architecture:** xlm-roberta\n\n## Tags\n`sentence-transformers` `pytorch` `onnx` `xlm-roberta` `feature-extraction` `sentence-similarity` `arxiv:2402.03216` `arxiv:2004.04906` `arxiv:2106.14807` `arxiv:2107.05720` `arxiv:2004.12832` `license:mit` `eval-results` `text-embeddings-inference` `endpoints_compatible` `deploy:azure` `region:us`\n\n## Metadata\n- **License:** mit\n\n## Demo Spaces\n- [leaderboard](https://hf.co/spaces/mteb/leaderboard)\n- [DanbooruSearch](https://hf.co/spaces/SAkizuki/DanbooruSearch)\n- [WenQiuYue](https://hf.co/spaces/SAkizuki/WenQiuYue)\n- [ManthanAI](https://hf.co/spaces/anirudh594/ManthanAI)\n- [construction-rag](https://hf.co/spaces/Ashanasri/construction-rag)\n- [leaderboard](https://hf.co/spaces/mteb-pt/leaderboard)\n- [FinTech-FinalProject-Group7](https://hf.co/spaces/JasonRott/FinTech-FinalProject-Group7)\n- [rag2](https://hf.co/spaces/shreyasp11111111111111/rag2)\n- [foodeval-leaderboard](https://hf.co/spaces/latimal/foodeval-leaderboard)\n- [leaderboard_legacy](https://hf.co/spaces/mteb/leaderboard_legacy)\n- [thai_sentence_embedding_benchmark](https://hf.co/spaces/panuthept/thai_sentence_embedding_benchmark)\n- [mtem-pruner](https://hf.co/spaces/antoinelouis/mtem-pruner)\n- *... and 88 more spaces*\n\n## Inference Providers\n- **hf-inference** (live)\n\nTry this model in the [playground](https://hf.co/playground?modelId=BAAI/bge-m3)\n\n**Link:** [https://hf.co/BAAI/bge-m3](https://hf.co/BAAI/bge-m3)\n\n---\n\n**Type: Model**\n\n# Qwen/Qwen3-Embedding-8B\n\n## Overview\n- **Author:** Qwen\n- **Task:** feature-extraction\n- **Library:** sentence-transformers\n- **Downloads:** 13.8M | **Likes:** 705\n- **Updated:** 7 Jul, 2025\n\n## Technical Details\n- **Model Class:** AutoModel\n- **Parameters:** 7567.3M\n\n## Tags\n`sentence-transformers` `safetensors` `transformers` `sentence-similarity` `feature-extraction` `text-embeddings-inference` `arxiv:2506.05176` `base_model:Qwen/Qwen3-8B-Base` `base_model:finetune:Qwen/Qwen3-8B-Base` `license:apache-2.0` `endpoints_compatible` `deploy:azure` `region:us`\n\n## Metadata\n- **License:** apache-2.0\n\n## Demo Spaces\n- [leaderboard](https://hf.co/spaces/mteb/leaderboard)\n- [leaderboard](https://hf.co/spaces/mteb-pt/leaderboard)\n- [PhiRAG](https://hf.co/spaces/alx-d/PhiRAG)\n- [nomic-embeddings](https://hf.co/spaces/ipepe/nomic-embeddings)\n- [miian-Q3](https://hf.co/spaces/miiann/miian-Q3)\n- [rag_korean_manufacturing_docs](https://hf.co/spaces/Ahmed-El-Zainy/rag_korean_manufacturing_docs)\n- [germeval2025](https://hf.co/spaces/cortex359/germeval2025)\n- [Saudi-Law-AI-Assistant](https://hf.co/spaces/AldawsariNLP/Saudi-Law-AI-Assistant)\n- [AIDA](https://hf.co/spaces/destinyebuka/AIDA)\n- [legacy_code_modernizer](https://hf.co/spaces/MCP-1st-Birthday/legacy_code_modernizer)\n- [central-memory-agent](https://hf.co/spaces/MCP-1st-Birthday/central-memory-agent)\n- [leaderboard](https://hf.co/spaces/maxpar1/leaderboard)\n- *... and 49 more spaces*\n\n## Inference Providers\n- **scaleway** (live)\n- **novita** (error)\n- **nebius** (error)\n\nTry this model in the [playground](https://hf.co/playground?modelId=Qwen/Qwen3-Embedding-8B)\n\n**Link:** [https://hf.co/Qwen/Qwen3-Embedding-8B](https://hf.co/Qwen/Qwen3-Embedding-8B)",
            "status": "completed"
          },
          {
            "type": "message",
            "id": "msg_1",
            "role": "assistant",
            "status": "completed",
            "content": [
              {
                "type": "output_text",
                "text": "**Recommendation: BAAI/bge-m3**\n\n| | **BAAI/bge-m3** | **Qwen/Qwen3-Embedding-8B** |\n|---|---|---|\n| **Size** | ~568 M | 7.6 B |\n| **Architecture** | XLM-RoBERTa (encoder) | Qwen3 decoder-based |\n| **Downloads** | 182 M | 14 M |\n| **Embedding modes** | Dense, sparse, multi-vector | Dense |\n\nFor a startup optimizing quality **and** latency, **bge-m3 is the better default**.\n\nIt is roughly **13\u00d7 smaller** than Qwen3-Embedding-8B, so inference is significantly faster and cheaper to host\u2014even with `text-embeddings-inference` (TEI). Despite its smaller footprint, bge-m3 is a proven, production-grade retriever with strong MTEB scores, 100+ language support, and built-in sparse/multi-vector capabilities that improve technical-document recall without extra model calls.\n\nQwen3-Embedding-8B is newer and likely scores higher on some English-only MTEB tasks, but its 8 B parameter count introduces real latency and cost penalties at scale. For RAG, embedding latency directly impacts user-perceived response time, and the marginal retrieval gains rarely justify the slowdown for startup workloads.\n\n**Verdict:** Start with **BAAI/bge-m3**. It offers the best quality-per-latency trade-off for technical-document RAG and is trivial to deploy via TEI or sentence-transformers. Consider the Qwen model only if you later find specific retrieval gaps that smaller models cannot close."
              }
            ]
          }
        ],
        "error": null,
        "incomplete_details": null,
        "usage": {
          "session_id": "3098e124-fd5a-4968-90d9-4c11d6dee78a",
          "total_usd": 0.0,
          "inference_usd": 0.0,
          "hf_jobs_estimated_usd": 0.0,
          "sandbox_estimated_usd": 0.0,
          "llm_calls": 2,
          "hf_jobs_count": 0,
          "sandbox_count": 0,
          "prompt_tokens": 30620,
          "completion_tokens": 898,
          "cache_read_tokens": 28672,
          "cache_creation_tokens": 0,
          "total_tokens": 31518,
          "hf_jobs_billable_seconds_estimate": 0,
          "sandbox_billable_seconds_estimate": 0
        },
        "artifacts": [],
        "metadata": {
          "example_slug": "multiturn-rag-followup",
          "turn": "1"
        }
      }
    },
    {
      "request": {
        "model": "moonshotai/Kimi-K2.6:novita",
        "previous_response_id": "resp_2768fb94ff614a3a90a1c455548d767f",
        "instructions": "This is turn 2 of a multiturn API example. Reuse the prior recommendation; do not restate the comparison. Provide runnable minimal code only plus two setup notes. Do not launch jobs.",
        "input": "Using your recommended embedding model from the previous turn, write a minimal Python script that indexes 100 local Markdown files and retrieves the top 5 chunks for a query. Keep it compact.",
        "background": true,
        "max_cost_usd": 2.0,
        "metadata": {
          "example_slug": "multiturn-rag-followup",
          "turn": "2"
        }
      },
      "response": {
        "id": "resp_29eb917b2e2c4a0fbecdba4aa8303a21",
        "object": "response",
        "created_at": 1781315627,
        "completed_at": 1781315637,
        "status": "completed",
        "model": "moonshotai/Kimi-K2.6:novita",
        "background": true,
        "previous_response_id": "resp_2768fb94ff614a3a90a1c455548d767f",
        "session_id": "3098e124-fd5a-4968-90d9-4c11d6dee78a",
        "max_cost_usd": 2.0,
        "instructions": "This is turn 2 of a multiturn API example. Reuse the prior recommendation; do not restate the comparison. Provide runnable minimal code only plus two setup notes. Do not launch jobs.",
        "output": [
          {
            "type": "message",
            "id": "msg_0",
            "role": "assistant",
            "status": "completed",
            "content": [
              {
                "type": "output_text",
                "text": "```python\n# pip install sentence-transformers faiss-cpu markdown\nimport os, glob, faiss, numpy as np\nfrom sentence_transformers import SentenceTransformer\nfrom markdown import markdown\nfrom bs4 import BeautifulSoup\n\ndef md_to_text(path):\n    with open(path, encoding=\"utf-8\") as f:\n        html = markdown(f.read())\n    return BeautifulSoup(html, \"html.parser\").get_text()\n\nmodel = SentenceTransformer(\"BAAI/bge-m3\")\nfiles = glob.glob(\"docs/**/*.md\", recursive=True)[:100]\nchunks, metas = [], []\nfor f in files:\n    text = md_to_text(f)\n    for i in range(0, len(text), 500):\n        chunks.append(text[i:i+500])\n        metas.append((f, i))\n\nemb = model.encode(chunks, normalize_embeddings=True, show_progress_bar=False)\nindex = faiss.IndexFlatIP(emb.shape[1])\nindex.add(np.array(emb, dtype=\"float32\"))\n\ndef search(query, k=5):\n    q = model.encode([query], normalize_embeddings=True)\n    D, I = index.search(np.array(q, dtype=\"float32\"), k)\n    return [(metas[i], float(D[0][j])) for j, i in enumerate(I[0])]\n\n# Example\nfor (file, offset), score in search(\"how to configure the API\"):\n    print(f\"{score:.3f}  {file} @ {offset}\")\n```\n\n**Setup notes**\n1. Install dependencies: `pip install sentence-transformers faiss-cpu markdown beautifulsoup4`\n2. Place Markdown files under a `docs/` folder (or change the `glob.glob` path)."
              }
            ]
          }
        ],
        "error": null,
        "incomplete_details": null,
        "usage": {
          "session_id": "3098e124-fd5a-4968-90d9-4c11d6dee78a",
          "total_usd": 0.0,
          "inference_usd": 0.0,
          "hf_jobs_estimated_usd": 0.0,
          "sandbox_estimated_usd": 0.0,
          "llm_calls": 3,
          "hf_jobs_count": 0,
          "sandbox_count": 0,
          "prompt_tokens": 46984,
          "completion_tokens": 1317,
          "cache_read_tokens": 43008,
          "cache_creation_tokens": 0,
          "total_tokens": 48301,
          "hf_jobs_billable_seconds_estimate": 0,
          "sandbox_billable_seconds_estimate": 0
        },
        "artifacts": [],
        "metadata": {
          "example_slug": "multiturn-rag-followup",
          "turn": "2"
        }
      }
    }
  ]
}