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Merge branch 'mrh-online-dev' of github.com:BRUNIX-AI/assistance-engine into mrh-online-dev

This commit is contained in:
pseco 2026-02-24 12:09:56 +01:00
parent 397dc7602b a386982722
commit 8e852c5417
3 changed files with 550 additions and 231 deletions
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3
pyproject.toml
View File

@ -27,6 +27,7 @@ dependencies = [
dev = [ dev = [
"jupyter>=1.1.1", "jupyter>=1.1.1",
"langfuse>=3.14.4", "langfuse>=3.14.4",
"ragas>=0.4.3", "mteb>=2.8.8",
"polars>=1.38.1",
"ruff>=0.15.1", "ruff>=0.15.1",
] ]

465
scratches/acano/langgraph_agent_simple.ipynb
View File

@ -34,7 +34,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "30edcecc", "id": "30edcecc",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -805,92 +805,423 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 64, "execution_count": 11,
"id": "f4119e2c", "id": "f4119e2c",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_125108/3827010405.py:4: DeprecationWarning: Importing answer_relevancy from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import answer_relevancy\n",
" from ragas.metrics import answer_relevancy, answer_similarity\n",
"/tmp/ipykernel_125108/3827010405.py:4: DeprecationWarning: Importing answer_similarity from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import answer_similarity\n",
" from ragas.metrics import answer_relevancy, answer_similarity\n",
"/tmp/ipykernel_125108/3827010405.py:6: DeprecationWarning: LangchainLLMWrapper is deprecated and will be removed in a future version. Use llm_factory instead: from openai import OpenAI; from ragas.llms import llm_factory; llm = llm_factory('gpt-4o-mini', client=OpenAI(api_key='...'))\n",
" ragas_llm = LangchainLLMWrapper(llm)\n",
"/tmp/ipykernel_125108/3827010405.py:7: DeprecationWarning: LangchainEmbeddingsWrapper is deprecated and will be removed in a future version. Use the modern embedding providers instead: embedding_factory('openai', model='text-embedding-3-small', client=openai_client) or from ragas.embeddings import OpenAIEmbeddings, GoogleEmbeddings, HuggingFaceEmbeddings\n",
" raggas_emb = LangchainEmbeddingsWrapper(embeddings)\n"
]
}
],
"source": [ "source": [
"from ragas.llms import LangchainLLMWrapper\n", "from dataclasses import dataclass\n",
"from ragas.embeddings import LangchainEmbeddingsWrapper\n", "from typing import Any, Iterable\n",
"from ragas import evaluate, EvaluationDataset\n",
"from ragas.metrics import answer_relevancy, answer_similarity\n",
"\n", "\n",
"ragas_llm = LangchainLLMWrapper(llm)\n", "import numpy as np\n",
"raggas_emb = LangchainEmbeddingsWrapper(embeddings)" "\n",
"import mteb\n",
"from mteb.types import Array\n",
"from mteb.models import SearchEncoderWrapper\n",
"\n",
"\n",
"def _l2_normalize(x: np.ndarray, eps: float = 1e-12) -> np.ndarray:\n",
" norms = np.linalg.norm(x, axis=1, keepdims=True)\n",
" return x / np.clip(norms, eps, None)\n",
"\n",
"\n",
"def _to_text_list(batch: dict[str, Any]) -> list[str]:\n",
" \"\"\"\n",
" MTEB batched inputs can be:\n",
" - TextInput: {\"text\": [..]}\n",
" - CorpusInput: {\"title\": [..], \"body\": [..], \"text\": [..]}\n",
" - QueryInput: {\"query\": [..], \"instruction\": [..], \"text\": [..]}\n",
" We prefer \"text\" if present; otherwise compose from title/body or query/instruction.\n",
" \"\"\"\n",
" if \"text\" in batch and batch[\"text\"] is not None:\n",
" return list(batch[\"text\"])\n",
"\n",
" if \"title\" in batch and \"body\" in batch:\n",
" titles = batch[\"title\"] or [\"\"] * len(batch[\"body\"])\n",
" bodies = batch[\"body\"] or [\"\"] * len(batch[\"title\"])\n",
" return [f\"{t} {b}\".strip() for t, b in zip(titles, bodies)]\n",
"\n",
" if \"query\" in batch:\n",
" queries = list(batch[\"query\"])\n",
" instructions = batch.get(\"instruction\")\n",
" if instructions:\n",
" return [f\"{i} {q}\".strip() for q, i in zip(queries, instructions)]\n",
" return queries\n",
"\n",
" raise ValueError(f\"Unsupported batch keys: {sorted(batch.keys())}\")\n",
"\n",
"\n",
"@dataclass\n",
"class OllamaLangChainEncoder:\n",
" lc_embeddings: Any # OllamaEmbeddings implements embed_documents()\n",
" normalize: bool = True\n",
"\n",
" # Optional metadata hook used by some wrappers; safe to keep as None for local runs\n",
" mteb_model_meta: Any = None\n",
"\n",
" def encode(\n",
" self,\n",
" inputs: Iterable[dict[str, Any]],\n",
" *,\n",
" task_metadata: Any,\n",
" hf_split: str,\n",
" hf_subset: str,\n",
" prompt_type: Any = None,\n",
" **kwargs: Any,\n",
" ) -> Array:\n",
" all_vecs: list[np.ndarray] = []\n",
"\n",
" for batch in inputs:\n",
" texts = _to_text_list(batch)\n",
" vecs = self.lc_embeddings.embed_documents(texts)\n",
" arr = np.asarray(vecs, dtype=np.float32)\n",
" if self.normalize:\n",
" arr = _l2_normalize(arr)\n",
" all_vecs.append(arr)\n",
"\n",
" if not all_vecs:\n",
" return np.zeros((0, 0), dtype=np.float32)\n",
"\n",
" return np.vstack(all_vecs)\n",
"\n",
" def similarity(self, embeddings1: Array, embeddings2: Array) -> Array:\n",
" a = np.asarray(embeddings1, dtype=np.float32)\n",
" b = np.asarray(embeddings2, dtype=np.float32)\n",
" if self.normalize:\n",
" # dot == cosine if already normalized\n",
" return a @ b.T\n",
" a = _l2_normalize(a)\n",
" b = _l2_normalize(b)\n",
" return a @ b.T\n",
"\n",
" def similarity_pairwise(self, embeddings1: Array, embeddings2: Array) -> Array:\n",
" a = np.asarray(embeddings1, dtype=np.float32)\n",
" b = np.asarray(embeddings2, dtype=np.float32)\n",
" if not self.normalize:\n",
" a = _l2_normalize(a)\n",
" b = _l2_normalize(b)\n",
" return np.sum(a * b, axis=1)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "8f3f2d9f", "id": "f99b0712",
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
" \n",
"dataset = load_dataset(\"sentence-transformers/natural-questions\")\n",
"dataset = dataset[\"train\"]"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "af4efe6b",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "ValueError", "data": {
"evalue": "The metric [answer_similarity] that is used requires the following additional columns ['reference'] to be present in the dataset.", "application/vnd.jupyter.widget-view+json": {
"output_type": "error", "model_id": "f6c5e1e6bbe846feae863f026990d9b0",
"traceback": [ "version_major": 2,
"\u001b[31m---------------------------------------------------------------------------\u001b[39m", "version_minor": 0
"\u001b[31mValueError\u001b[39m Traceback (most recent call last)", },
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[61]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m results = \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43manswer_similarity\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mllm\u001b[49m\u001b[43m=\u001b[49m\u001b[43mragas_llm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m=\u001b[49m\u001b[43mraggas_emb\u001b[49m\u001b[43m)\u001b[49m\n", "text/plain": [
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/_analytics.py:278\u001b[39m, in \u001b[36mtrack_was_completed.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 275\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 276\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m(*args: P.args, **kwargs: P.kwargs) -> T:\n\u001b[32m 277\u001b[39m track(IsCompleteEvent(event_type=func.\u001b[34m__name__\u001b[39m, is_completed=\u001b[38;5;28;01mFalse\u001b[39;00m))\n\u001b[32m--> \u001b[39m\u001b[32m278\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 279\u001b[39m track(IsCompleteEvent(event_type=func.\u001b[34m__name__\u001b[39m, is_completed=\u001b[38;5;28;01mTrue\u001b[39;00m))\n\u001b[32m 281\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "Evaluating tasks: 0%| | 0/2 [00:00<?, ?it/s]"
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/evaluation.py:484\u001b[39m, in \u001b[36mevaluate\u001b[39m\u001b[34m(dataset, metrics, llm, embeddings, experiment_name, callbacks, run_config, token_usage_parser, raise_exceptions, column_map, show_progress, batch_size, _run_id, _pbar, return_executor, allow_nest_asyncio)\u001b[39m\n\u001b[32m 480\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 481\u001b[39m \u001b[38;5;66;03m# Default behavior: use nest_asyncio for backward compatibility (Jupyter notebooks)\u001b[39;00m\n\u001b[32m 482\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mragas\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01masync_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m run\n\u001b[32m--> \u001b[39m\u001b[32m484\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_async_wrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", ]
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/async_utils.py:156\u001b[39m, in \u001b[36mrun\u001b[39m\u001b[34m(async_func, allow_nest_asyncio)\u001b[39m\n\u001b[32m 148\u001b[39m loop_type = \u001b[38;5;28mtype\u001b[39m(loop).\u001b[34m__name__\u001b[39m\n\u001b[32m 149\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 150\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCannot execute nested async code with \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloop_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 151\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33muvloop does not support nested event loop execution. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 152\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mPlease use asyncio\u001b[39m\u001b[33m'\u001b[39m\u001b[33ms standard event loop in Jupyter environments, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 153\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mor refactor your code to avoid nested async calls.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 154\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m156\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43masyncio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoro\u001b[49m\u001b[43m)\u001b[49m\n", },
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/nest_asyncio.py:30\u001b[39m, in \u001b[36m_patch_asyncio.<locals>.run\u001b[39m\u001b[34m(main, debug)\u001b[39m\n\u001b[32m 28\u001b[39m task = asyncio.ensure_future(main)\n\u001b[32m 29\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 31\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 32\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m task.done():\n", "metadata": {},
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/nest_asyncio.py:98\u001b[39m, in \u001b[36m_patch_loop.<locals>.run_until_complete\u001b[39m\u001b[34m(self, future)\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f.done():\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 97\u001b[39m \u001b[33m'\u001b[39m\u001b[33mEvent loop stopped before Future completed.\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m98\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "output_type": "display_data"
"\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.11.13-linux-x86_64-gnu/lib/python3.11/asyncio/futures.py:203\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 201\u001b[39m \u001b[38;5;28mself\u001b[39m.__log_traceback = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 202\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m203\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception.with_traceback(\u001b[38;5;28mself\u001b[39m._exception_tb)\n\u001b[32m 204\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._result\n", },
"\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.11.13-linux-x86_64-gnu/lib/python3.11/asyncio/tasks.py:277\u001b[39m, in \u001b[36mTask.__step\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 273\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 274\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 275\u001b[39m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[32m 276\u001b[39m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m277\u001b[39m result = coro.send(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 278\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 279\u001b[39m result = coro.throw(exc)\n", {
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/evaluation.py:457\u001b[39m, in \u001b[36mevaluate.<locals>._async_wrapper\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 456\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_async_wrapper\u001b[39m():\n\u001b[32m--> \u001b[39m\u001b[32m457\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m aevaluate(\n\u001b[32m 458\u001b[39m dataset=dataset,\n\u001b[32m 459\u001b[39m metrics=metrics,\n\u001b[32m 460\u001b[39m llm=llm,\n\u001b[32m 461\u001b[39m embeddings=embeddings,\n\u001b[32m 462\u001b[39m experiment_name=experiment_name,\n\u001b[32m 463\u001b[39m callbacks=callbacks,\n\u001b[32m 464\u001b[39m run_config=run_config,\n\u001b[32m 465\u001b[39m token_usage_parser=token_usage_parser,\n\u001b[32m 466\u001b[39m raise_exceptions=raise_exceptions,\n\u001b[32m 467\u001b[39m column_map=column_map,\n\u001b[32m 468\u001b[39m show_progress=show_progress,\n\u001b[32m 469\u001b[39m batch_size=batch_size,\n\u001b[32m 470\u001b[39m _run_id=_run_id,\n\u001b[32m 471\u001b[39m _pbar=_pbar,\n\u001b[32m 472\u001b[39m return_executor=return_executor,\n\u001b[32m 473\u001b[39m )\n", "data": {
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/evaluation.py:154\u001b[39m, in \u001b[36maevaluate\u001b[39m\u001b[34m(dataset, metrics, llm, embeddings, experiment_name, callbacks, run_config, token_usage_parser, raise_exceptions, column_map, show_progress, batch_size, _run_id, _pbar, return_executor)\u001b[39m\n\u001b[32m 151\u001b[39m dataset = EvaluationDataset.from_list(dataset.to_list())\n\u001b[32m 153\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dataset, EvaluationDataset):\n\u001b[32m--> \u001b[39m\u001b[32m154\u001b[39m \u001b[43mvalidate_required_columns\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 155\u001b[39m validate_supported_metrics(dataset, metrics)\n\u001b[32m 157\u001b[39m \u001b[38;5;66;03m# set the llm and embeddings\u001b[39;00m\n", "application/vnd.jupyter.widget-view+json": {
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/validation.py:43\u001b[39m, in \u001b[36mvalidate_required_columns\u001b[39m\u001b[34m(ds, metrics)\u001b[39m\n\u001b[32m 41\u001b[39m available_columns = \u001b[38;5;28mset\u001b[39m(ds.features())\n\u001b[32m 42\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m required_columns.issubset(available_columns):\n\u001b[32m---> \u001b[39m\u001b[32m43\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 44\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe metric [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mm.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m] that is used requires the following \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 45\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33madditional columns \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(required_columns\u001b[38;5;250m \u001b[39m-\u001b[38;5;250m \u001b[39mavailable_columns)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 46\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mto be present in the dataset.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 47\u001b[39m )\n", "model_id": "76da47370fb54500839924b2e03b3de3",
"\u001b[31mValueError\u001b[39m: The metric [answer_similarity] that is used requires the following additional columns ['reference'] to be present in the dataset." "version_major": 2,
] "version_minor": 0
},
"text/plain": [
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]
},
"metadata": {},
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{
"data": {
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]
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"text/plain": [
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]
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{
"data": {
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{
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{
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{
"data": {
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},
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{
"data": {
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"metadata": {},
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{
"data": {
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},
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},
"metadata": {},
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},
{
"data": {
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"model_id": "59cf3d3a8764454895f8c233f9c22964",
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},
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "731e1496062c460bbcca3de23f2321b1",
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},
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"model_id": "5c9afbf59f4148359c1c52a2c32751b5",
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b4d3c9fefcc4df9a1a738848a8b441c",
"version_major": 2,
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},
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f2598e789f94ad4900a23bd2975458b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bfdb133e17584645bcabff8ad859b617",
"version_major": 2,
"version_minor": 0
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]
},
"metadata": {},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"results = evaluate(dataset=dataset, metrics=[answer_similarity], llm=ragas_llm, embeddings=raggas_emb)" "encoder = OllamaLangChainEncoder(lc_embeddings=embeddings, normalize=True)\n",
"search_model = SearchEncoderWrapper(encoder)\n",
"\n",
"tasks = mteb.get_tasks(tasks=[\"SciFact\", \"FiQA2018\"])\n",
"results = mteb.evaluate(\n",
" search_model,\n",
" tasks=tasks,\n",
" encode_kwargs={\"batch_size\": 32, \"show_progress_bar\": True},\n",
")\n",
"\n",
"print(results)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "4ac6ce4b", "id": "d8cf11ac",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8687ef1b",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []

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