{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f97dd1e",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e974df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from typing import TypedDict, List, Optional, Annotated\n",
    "from IPython.display import Image, display\n",
    "\n",
    "from langchain_core.documents import Document\n",
    "from langchain_core.messages import BaseMessage, SystemMessage\n",
    "from langchain_core.tools import tool\n",
    "from langgraph.checkpoint.memory import InMemorySaver\n",
    "from langgraph.graph.message import add_messages\n",
    "from langchain_ollama import ChatOllama, OllamaEmbeddings\n",
    "from langchain_elasticsearch import ElasticsearchStore\n",
    "from langgraph.graph import StateGraph, END\n",
    "from langgraph.prebuilt import ToolNode\n",
    "from langfuse import get_client\n",
    "from langfuse.langchain import CallbackHandler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30edcecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "ES_URL = os.getenv(\"ELASTICSEARCH_LOCAL_URL\")\n",
    "INDEX_NAME = os.getenv(\"ELASTICSEARCH_INDEX\")\n",
    "BASE_URL = os.getenv(\"LLM_BASE_LOCAL_URL\")\n",
    "MODEL_NAME = os.getenv(\"OLLAMA_MODEL_NAME\")\n",
    "EMB_MODEL_NAME = os.getenv(\"OLLAMA_EMB_MODEL_NAME\")\n",
    "LANGFUSE_PUBLIC_KEY = os.getenv(\"LANGFUSE_PUBLIC_KEY\")\n",
    "LANGFUSE_SECRET_KEY = os.getenv(\"LANGFUSE_SECRET_KEY\")\n",
    "LANGFUSE_HOST = os.getenv(\"LANGFUSE_HOST\")\n",
    "\n",
    "# langfuse = get_client()\n",
    "# langfuse_handler = CallbackHandler()\n",
    "\n",
    "embeddings = OllamaEmbeddings(base_url=BASE_URL, model=EMB_MODEL_NAME)\n",
    "llm = ChatOllama(base_url=BASE_URL, model=MODEL_NAME)\n",
    "\n",
    "vector_store = ElasticsearchStore(\n",
    "    es_url=ES_URL,\n",
    "    index_name=INDEX_NAME,\n",
    "    embedding=embeddings,\n",
    "    query_field=\"text\",\n",
    "    vector_query_field=\"vector\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad98841b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if langfuse.auth_check():\n",
    "#     print(\"Langfuse client is authenticated and ready!\")\n",
    "# else:\n",
    "#     print(\"Authentication failed. Please check your credentials and host.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873ea2f6",
   "metadata": {},
   "source": [
    "### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f8c88cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AgentState(TypedDict):\n",
    "    messages: Annotated[list, add_messages]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d60c120",
   "metadata": {},
   "source": [
    "### Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0a21230",
   "metadata": {},
   "outputs": [],
   "source": [
    "retrieve_kwargs = {\"k\": 5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9359747",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_context(docs: List[Document]) -> str:\n",
    "    chunks: List[str] = []\n",
    "    for i, doc in enumerate(docs, 1):\n",
    "        source = (doc.metadata or {}).get(\"source\", \"Untitled\")\n",
    "        source_id = (doc.metadata or {}).get(\"id\", f\"chunk-{i}\")\n",
    "        text = doc.page_content or \"\"\n",
    "        chunks.append(f\"[{i}] id={source_id} source={source}\\n{text}\")\n",
    "    return \"\\n\\n\".join(chunks)\n",
    "\n",
    "\n",
    "@tool\n",
    "def retrieve(query: str) -> str:\n",
    "    \"\"\"This tool retrieves relevant documents from the vector store based on the input query and formats them for the agent's response.\n",
    "    Args:\n",
    "        query (str): The input query for which to retrieve relevant documents.\n",
    "    \"\"\"\n",
    "    retriever = vector_store.as_retriever(\n",
    "        search_type=\"similarity\",\n",
    "        search_kwargs=retrieve_kwargs,\n",
    "    )\n",
    "    docs = retriever.invoke(query)\n",
    "    return format_context(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5247ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def should_continue(state: AgentState) -> str:\n",
    "    last = state[\"messages\"][-1]\n",
    "    \n",
    "    if getattr(last, \"tool_calls\", None):\n",
    "        return \"tools\"\n",
    "    return \"end\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a644f6fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "tools = [retrieve]\n",
    "tool_node = ToolNode(tools)\n",
    "memory = InMemorySaver()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395966e2",
   "metadata": {},
   "source": [
    "### Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36d0f54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def agent(state: AgentState) -> AgentState:\n",
    "    messages: List[BaseMessage] = state[\"messages\"]\n",
    "\n",
    "    system = SystemMessage(\n",
    "        content=(\n",
    "            \"\"\" \n",
    "            You are a specialized agent whose job is to respond questions about a new language code called AVAP, which is a language designed to build APIs.\n",
    "            To responde to the user's question, you have access to a tool called \"retrieve\" that allows you to retrieve relevant documents about AVAP from a vector store.\n",
    "            You must respond in the language the user is assking you.\n",
    "            \"\"\"\n",
    "        )\n",
    "    )\n",
    "\n",
    "    model = llm.bind_tools(tools)\n",
    "    resp = model.invoke([system, *messages])\n",
    "\n",
    "    return {\"messages\": [*messages, resp]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef55bca3",
   "metadata": {},
   "source": [
    "### Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fae46a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = StateGraph(AgentState)\n",
    "graph.add_node(\"agent\", agent)\n",
    "graph.add_node(\"tools\", tool_node)\n",
    "\n",
    "graph.set_entry_point(\"agent\")\n",
    "graph.add_conditional_edges(\"agent\", should_continue, {\"tools\": \"tools\", \"end\": END})\n",
    "graph.add_edge(\"tools\", \"agent\")\n",
    "\n",
    "agent_graph = graph.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2fec3fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    display(Image(agent_graph.get_graph().draw_mermaid_png()))\n",
    "except Exception:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e9aff05",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8569cf39",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\"configurable\": {\"thread_id\": \"5\"}, \n",
    "          #\"callbacks\": [langfuse_handler],\n",
    "          #\"run_name\": \"rag-local-test\"differences between getDatetime() and getTimeStamp() functions in AVAP\n",
    "          }\n",
    "\n",
    "def stream_graph_updates(user_input: str):\n",
    "    for event in agent_graph.stream(\n",
    "        {\"messages\": [{\"role\": \"user\", \"content\": user_input}]},\n",
    "        #config=config,\n",
    "        stream_mode=\"values\",\n",
    "    ):\n",
    "        event[\"messages\"][-1].pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a1a1f3cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_input = \"Create a small snippet that adds 2 numbers using AVAP language. Use the provided tool.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53b89690",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = stream_graph_updates(user_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367b898a",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4119e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Any, Iterable\n",
    "\n",
    "import numpy as np\n",
    "\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)"
   ]
  },
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      "text/plain": [
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    {
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      "text/plain": [
       "ruby-qrels/train-00000-of-00001.parquet:   0%|          | 0.00/283k [00:00<?, ?B/s]"
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    {
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      "text/plain": [
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    {
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       "version_major": 2,
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      "text/plain": [
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      ]
     },
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    },
    {
     "data": {
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      ]
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    {
     "data": {
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      ]
     },
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    {
     "data": {
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      },
      "text/plain": [
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      ]
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       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
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    },
    {
     "data": {
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      ]
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
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    },
    {
     "data": {
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    {
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      },
      "text/plain": [
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      ]
     },
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    {
     "data": {
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      ]
     },
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    {
     "data": {
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    {
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    {
     "data": {
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    {
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    {
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    {
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      ]
     },
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    {
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    {
     "data": {
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    {
     "data": {
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      ]
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    {
     "data": {
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    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
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    {
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       "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": "66e03865675a4027a5c92186375066f8",
       "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": "00f6dfcba9a34d24827ff9929f23ac1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filtering queries by qrels:   0%|          | 0/14918 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb9181bb83ff4d0585390b3fad7742bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing queries for dataloading:   0%|          | 0/14918 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7139c9c103b42709940168130dd84de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting corpus dict:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dafb9d036ed94575985b01f4f1b59858",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting corpus dict:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "994d09e7a4454b81b64bab20716967f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting corpus dict:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m      2\u001b[39m search_model = SearchEncoderWrapper(encoder)\n\u001b[32m      4\u001b[39m tasks = mteb.get_tasks([\n\u001b[32m      5\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mCodeSearchNetRetrieval\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      6\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mCodeSearchNetCCRetrieval\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      7\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mAppsRetrieval\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      8\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mStackOverflowDupQuestions\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      9\u001b[39m ])\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m results = \u001b[43mmteb\u001b[49m\u001b[43m.\u001b[49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     11\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43msearch_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     12\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     13\u001b[39m \u001b[43m    \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbatch_size\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mshow_progress_bar\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\n\u001b[32m     14\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m     16\u001b[39m \u001b[38;5;28mprint\u001b[39m(results)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/evaluate.py:388\u001b[39m, in \u001b[36mevaluate\u001b[39m\u001b[34m(model, tasks, co2_tracker, raise_error, encode_kwargs, cache, overwrite_strategy, prediction_folder, show_progress_bar, public_only, num_proc)\u001b[39m\n\u001b[32m    386\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, task \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tasks_tqdm):\n\u001b[32m    387\u001b[39m     tasks_tqdm.set_description(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mEvaluating task \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtask.metadata.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m     _res = \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    389\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    390\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    391\u001b[39m \u001b[43m        \u001b[49m\u001b[43mco2_tracker\u001b[49m\u001b[43m=\u001b[49m\u001b[43mco2_tracker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    392\u001b[39m \u001b[43m        \u001b[49m\u001b[43mraise_error\u001b[49m\u001b[43m=\u001b[49m\u001b[43mraise_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    393\u001b[39m \u001b[43m        \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    394\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    395\u001b[39m \u001b[43m        \u001b[49m\u001b[43moverwrite_strategy\u001b[49m\u001b[43m=\u001b[49m\u001b[43moverwrite_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    396\u001b[39m \u001b[43m        \u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    397\u001b[39m \u001b[43m        \u001b[49m\u001b[43mshow_progress_bar\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    398\u001b[39m \u001b[43m        \u001b[49m\u001b[43mpublic_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpublic_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    399\u001b[39m \u001b[43m        \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    400\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    401\u001b[39m     evaluate_results.extend(_res.task_results)\n\u001b[32m    402\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m _res.exceptions:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/evaluate.py:487\u001b[39m, in \u001b[36mevaluate\u001b[39m\u001b[34m(model, tasks, co2_tracker, raise_error, encode_kwargs, cache, overwrite_strategy, prediction_folder, show_progress_bar, public_only, num_proc)\u001b[39m\n\u001b[32m    485\u001b[39m         result = TaskError(task_name=task.metadata.name, exception=\u001b[38;5;28mstr\u001b[39m(e))\n\u001b[32m    486\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m487\u001b[39m     result = \u001b[43m_evaluate_task\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    488\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    489\u001b[39m \u001b[43m        \u001b[49m\u001b[43msplits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmissing_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    490\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtask\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    491\u001b[39m \u001b[43m        \u001b[49m\u001b[43mco2_tracker\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    492\u001b[39m \u001b[43m        \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    493\u001b[39m \u001b[43m        \u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    494\u001b[39m \u001b[43m        \u001b[49m\u001b[43mpublic_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpublic_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    495\u001b[39m \u001b[43m        \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    496\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    497\u001b[39m logger.info(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m✓ Finished evaluation for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtask.metadata.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m    499\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, TaskError):\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/evaluate.py:161\u001b[39m, in \u001b[36m_evaluate_task\u001b[39m\u001b[34m(model, task, splits, co2_tracker, encode_kwargs, prediction_folder, public_only, num_proc)\u001b[39m\n\u001b[32m    159\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m split, hf_subsets \u001b[38;5;129;01min\u001b[39;00m splits.items():\n\u001b[32m    160\u001b[39m     tick = time()\n\u001b[32m--> \u001b[39m\u001b[32m161\u001b[39m     task_results[split] = \u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    162\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    163\u001b[39m \u001b[43m        \u001b[49m\u001b[43msplit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    164\u001b[39m \u001b[43m        \u001b[49m\u001b[43msubsets_to_run\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhf_subsets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    165\u001b[39m \u001b[43m        \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    166\u001b[39m \u001b[43m        \u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    167\u001b[39m \u001b[43m        \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    168\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    169\u001b[39m     tock = time()\n\u001b[32m    171\u001b[39m     logger.debug(\n\u001b[32m    172\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mEvaluation for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtask.metadata.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msplit\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m took \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtock\u001b[38;5;250m \u001b[39m-\u001b[38;5;250m \u001b[39mtick\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m seconds\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    173\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/abstasks/retrieval.py:342\u001b[39m, in \u001b[36mAbsTaskRetrieval.evaluate\u001b[39m\u001b[34m(self, model, split, subsets_to_run, encode_kwargs, prediction_folder, num_proc, **kwargs)\u001b[39m\n\u001b[32m    339\u001b[39m \u001b[38;5;66;03m# TODO: convert all tasks directly https://github.com/embeddings-benchmark/mteb/issues/2030\u001b[39;00m\n\u001b[32m    340\u001b[39m \u001b[38;5;28mself\u001b[39m.convert_v1_dataset_format_to_v2(num_proc=num_proc)\n\u001b[32m--> \u001b[39m\u001b[32m342\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    343\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    344\u001b[39m \u001b[43m    \u001b[49m\u001b[43msplit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    345\u001b[39m \u001b[43m    \u001b[49m\u001b[43msubsets_to_run\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    346\u001b[39m \u001b[43m    \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    347\u001b[39m \u001b[43m    \u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    348\u001b[39m \u001b[43m    \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    349\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    350\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/abstasks/abstask.py:199\u001b[39m, in \u001b[36mAbsTask.evaluate\u001b[39m\u001b[34m(self, model, split, subsets_to_run, encode_kwargs, prediction_folder, num_proc, **kwargs)\u001b[39m\n\u001b[32m    197\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    198\u001b[39m         data_split = \u001b[38;5;28mself\u001b[39m.dataset[hf_subset][split]\n\u001b[32m--> \u001b[39m\u001b[32m199\u001b[39m     scores[hf_subset] = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_evaluate_subset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    200\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    201\u001b[39m \u001b[43m        \u001b[49m\u001b[43mdata_split\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    202\u001b[39m \u001b[43m        \u001b[49m\u001b[43mhf_split\u001b[49m\u001b[43m=\u001b[49m\u001b[43msplit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    203\u001b[39m \u001b[43m        \u001b[49m\u001b[43mhf_subset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhf_subset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    204\u001b[39m \u001b[43m        \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    205\u001b[39m \u001b[43m        \u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprediction_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    206\u001b[39m \u001b[43m        \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    207\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    208\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    209\u001b[39m     \u001b[38;5;28mself\u001b[39m._add_main_score(scores[hf_subset])\n\u001b[32m    210\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m scores\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/abstasks/retrieval.py:409\u001b[39m, in \u001b[36mAbsTaskRetrieval._evaluate_subset\u001b[39m\u001b[34m(self, model, data_split, encode_kwargs, hf_split, hf_subset, prediction_folder, num_proc, **kwargs)\u001b[39m\n\u001b[32m    404\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m    405\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRetrievalEvaluator expects a SearchInterface, Encoder, or CrossEncoder, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(model)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    406\u001b[39m     )\n\u001b[32m    408\u001b[39m start_time = time()\n\u001b[32m--> \u001b[39m\u001b[32m409\u001b[39m results = \u001b[43mretriever\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    410\u001b[39m \u001b[43m    \u001b[49m\u001b[43msearch_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    411\u001b[39m \u001b[43m    \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    412\u001b[39m \u001b[43m    \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    413\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    414\u001b[39m end_time = time()\n\u001b[32m    415\u001b[39m logger.debug(\n\u001b[32m    416\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRunning retrieval task - Time taken to retrieve: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mend_time\u001b[38;5;250m \u001b[39m-\u001b[38;5;250m \u001b[39mstart_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m seconds\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    417\u001b[39m )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/_evaluators/retrieval_evaluator.py:70\u001b[39m, in \u001b[36mRetrievalEvaluator.__call__\u001b[39m\u001b[34m(self, search_model, encode_kwargs, num_proc)\u001b[39m\n\u001b[32m     61\u001b[39m search_model.index(\n\u001b[32m     62\u001b[39m     corpus=\u001b[38;5;28mself\u001b[39m.corpus,\n\u001b[32m     63\u001b[39m     task_metadata=\u001b[38;5;28mself\u001b[39m.task_metadata,\n\u001b[32m   (...)\u001b[39m\u001b[32m     67\u001b[39m     num_proc=num_proc,\n\u001b[32m     68\u001b[39m )\n\u001b[32m     69\u001b[39m logger.info(\u001b[33m\"\u001b[39m\u001b[33mRunning retrieval task - Searching queries...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m70\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msearch_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     71\u001b[39m \u001b[43m    \u001b[49m\u001b[43mqueries\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mqueries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     72\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     73\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtask_metadata\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtask_metadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     74\u001b[39m \u001b[43m    \u001b[49m\u001b[43mhf_split\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mhf_split\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     75\u001b[39m \u001b[43m    \u001b[49m\u001b[43mhf_subset\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mhf_subset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     76\u001b[39m \u001b[43m    \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     77\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtop_ranked\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtop_ranked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     78\u001b[39m \u001b[43m    \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     79\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/models/search_wrappers.py:183\u001b[39m, in \u001b[36mSearchEncoderWrapper.search\u001b[39m\u001b[34m(self, queries, task_metadata, hf_split, hf_subset, top_k, encode_kwargs, top_ranked, num_proc)\u001b[39m\n\u001b[32m    181\u001b[39m logger.info(\u001b[33m\"\u001b[39m\u001b[33mPerforming full corpus search...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m    182\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.index_backend \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m183\u001b[39m     result_heaps = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_full_corpus_search\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    184\u001b[39m \u001b[43m        \u001b[49m\u001b[43mquery_idx_to_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquery_idx_to_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    185\u001b[39m \u001b[43m        \u001b[49m\u001b[43mquery_embeddings\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquery_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    186\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtask_metadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtask_metadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    187\u001b[39m \u001b[43m        \u001b[49m\u001b[43mhf_subset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhf_subset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    188\u001b[39m \u001b[43m        \u001b[49m\u001b[43mhf_split\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhf_split\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    189\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    190\u001b[39m \u001b[43m        \u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    191\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    192\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    193\u001b[39m     cos_scores_top_k_values, cos_scores_top_k_idx = (\n\u001b[32m    194\u001b[39m         \u001b[38;5;28mself\u001b[39m.index_backend.search(\n\u001b[32m    195\u001b[39m             query_embeddings,\n\u001b[32m   (...)\u001b[39m\u001b[32m    200\u001b[39m         )\n\u001b[32m    201\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/mteb/models/search_wrappers.py:253\u001b[39m, in \u001b[36mSearchEncoderWrapper._full_corpus_search\u001b[39m\u001b[34m(self, query_idx_to_id, query_embeddings, task_metadata, hf_subset, hf_split, top_k, encode_kwargs)\u001b[39m\n\u001b[32m    249\u001b[39m sub_corpus = \u001b[38;5;28mself\u001b[39m.task_corpus.select(\n\u001b[32m    250\u001b[39m     \u001b[38;5;28mrange\u001b[39m(corpus_start_idx, corpus_end_idx)\n\u001b[32m    251\u001b[39m )\n\u001b[32m    252\u001b[39m sub_corpus_ids = sub_corpus[\u001b[33m\"\u001b[39m\u001b[33mid\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m253\u001b[39m sub_corpus_embeddings = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    254\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcreate_dataloader\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    255\u001b[39m \u001b[43m        \u001b[49m\u001b[43msub_corpus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    256\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtask_metadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    257\u001b[39m \u001b[43m        \u001b[49m\u001b[43mprompt_type\u001b[49m\u001b[43m=\u001b[49m\u001b[43mPromptType\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdocument\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    258\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    259\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    260\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtask_metadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtask_metadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    261\u001b[39m \u001b[43m    \u001b[49m\u001b[43mhf_split\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhf_split\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    262\u001b[39m \u001b[43m    \u001b[49m\u001b[43mhf_subset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhf_subset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    263\u001b[39m \u001b[43m    \u001b[49m\u001b[43mprompt_type\u001b[49m\u001b[43m=\u001b[49m\u001b[43mPromptType\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdocument\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    264\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mencode_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    265\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    267\u001b[39m \u001b[38;5;66;03m# Compute similarities using either cosine-similarity or dot product\u001b[39;00m\n\u001b[32m    268\u001b[39m logger.info(\u001b[33m\"\u001b[39m\u001b[33mComputing Similarities...\u001b[39m\u001b[33m\"\u001b[39m)\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 64\u001b[39m, in \u001b[36mOllamaLangChainEncoder.encode\u001b[39m\u001b[34m(self, inputs, task_metadata, hf_split, hf_subset, prompt_type, **kwargs)\u001b[39m\n\u001b[32m     62\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m inputs:\n\u001b[32m     63\u001b[39m     texts = _to_text_list(batch)\n\u001b[32m---> \u001b[39m\u001b[32m64\u001b[39m     vecs = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlc_embeddings\u001b[49m\u001b[43m.\u001b[49m\u001b[43membed_documents\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtexts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     65\u001b[39m     arr = np.asarray(vecs, dtype=np.float32)\n\u001b[32m     66\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.normalize:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/langchain_ollama/embeddings.py:301\u001b[39m, in \u001b[36mOllamaEmbeddings.embed_documents\u001b[39m\u001b[34m(self, texts)\u001b[39m\n\u001b[32m    296\u001b[39m     msg = (\n\u001b[32m    297\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mOllama client is not initialized. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    298\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mPlease ensure Ollama is running and the model is loaded.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    299\u001b[39m     )\n\u001b[32m    300\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m301\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43membed\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    302\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtexts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_default_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeep_alive\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeep_alive\u001b[49m\n\u001b[32m    303\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33membeddings\u001b[39m\u001b[33m\"\u001b[39m]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ollama/_client.py:393\u001b[39m, in \u001b[36mClient.embed\u001b[39m\u001b[34m(self, model, input, truncate, options, keep_alive, dimensions)\u001b[39m\n\u001b[32m    384\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34membed\u001b[39m(\n\u001b[32m    385\u001b[39m   \u001b[38;5;28mself\u001b[39m,\n\u001b[32m    386\u001b[39m   model: \u001b[38;5;28mstr\u001b[39m = \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    391\u001b[39m   dimensions: Optional[\u001b[38;5;28mint\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m    392\u001b[39m ) -> EmbedResponse:\n\u001b[32m--> \u001b[39m\u001b[32m393\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    394\u001b[39m \u001b[43m    \u001b[49m\u001b[43mEmbedResponse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    395\u001b[39m \u001b[43m    \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mPOST\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    396\u001b[39m \u001b[43m    \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m/api/embed\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    397\u001b[39m \u001b[43m    \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mEmbedRequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    398\u001b[39m \u001b[43m      \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    399\u001b[39m \u001b[43m      \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    400\u001b[39m \u001b[43m      \u001b[49m\u001b[43mtruncate\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtruncate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    401\u001b[39m \u001b[43m      \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    402\u001b[39m \u001b[43m      \u001b[49m\u001b[43mkeep_alive\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkeep_alive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    403\u001b[39m \u001b[43m      \u001b[49m\u001b[43mdimensions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdimensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    404\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmodel_dump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexclude_none\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    405\u001b[39m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ollama/_client.py:189\u001b[39m, in \u001b[36mClient._request\u001b[39m\u001b[34m(self, cls, stream, *args, **kwargs)\u001b[39m\n\u001b[32m    185\u001b[39m         \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(**part)\n\u001b[32m    187\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[32m--> \u001b[39m\u001b[32m189\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(**\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_request_raw\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.json())\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ollama/_client.py:129\u001b[39m, in \u001b[36mClient._request_raw\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m    127\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_request_raw\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m    128\u001b[39m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m129\u001b[39m     r = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\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    130\u001b[39m     r.raise_for_status()\n\u001b[32m    131\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m r\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpx/_client.py:825\u001b[39m, in \u001b[36mClient.request\u001b[39m\u001b[34m(self, method, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)\u001b[39m\n\u001b[32m    810\u001b[39m     warnings.warn(message, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel=\u001b[32m2\u001b[39m)\n\u001b[32m    812\u001b[39m request = \u001b[38;5;28mself\u001b[39m.build_request(\n\u001b[32m    813\u001b[39m     method=method,\n\u001b[32m    814\u001b[39m     url=url,\n\u001b[32m   (...)\u001b[39m\u001b[32m    823\u001b[39m     extensions=extensions,\n\u001b[32m    824\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m825\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpx/_client.py:914\u001b[39m, in \u001b[36mClient.send\u001b[39m\u001b[34m(self, request, stream, auth, follow_redirects)\u001b[39m\n\u001b[32m    910\u001b[39m \u001b[38;5;28mself\u001b[39m._set_timeout(request)\n\u001b[32m    912\u001b[39m auth = \u001b[38;5;28mself\u001b[39m._build_request_auth(request, auth)\n\u001b[32m--> \u001b[39m\u001b[32m914\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    915\u001b[39m \u001b[43m    \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    916\u001b[39m \u001b[43m    \u001b[49m\u001b[43mauth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    917\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    918\u001b[39m \u001b[43m    \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    919\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    920\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    921\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpx/_client.py:942\u001b[39m, in \u001b[36mClient._send_handling_auth\u001b[39m\u001b[34m(self, request, auth, follow_redirects, history)\u001b[39m\n\u001b[32m    939\u001b[39m request = \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[32m    941\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m942\u001b[39m     response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    943\u001b[39m \u001b[43m        \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    944\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    945\u001b[39m \u001b[43m        \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    946\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    947\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    948\u001b[39m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpx/_client.py:979\u001b[39m, in \u001b[36mClient._send_handling_redirects\u001b[39m\u001b[34m(self, request, follow_redirects, history)\u001b[39m\n\u001b[32m    976\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mrequest\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m    977\u001b[39m     hook(request)\n\u001b[32m--> \u001b[39m\u001b[32m979\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    980\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    981\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mresponse\u001b[39m\u001b[33m\"\u001b[39m]:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpx/_client.py:1014\u001b[39m, in \u001b[36mClient._send_single_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m   1009\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m   1010\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1011\u001b[39m     )\n\u001b[32m   1013\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request=request):\n\u001b[32m-> \u001b[39m\u001b[32m1014\u001b[39m     response = \u001b[43mtransport\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1016\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, SyncByteStream)\n\u001b[32m   1018\u001b[39m response.request = request\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpx/_transports/default.py:250\u001b[39m, in \u001b[36mHTTPTransport.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m    237\u001b[39m req = httpcore.Request(\n\u001b[32m    238\u001b[39m     method=request.method,\n\u001b[32m    239\u001b[39m     url=httpcore.URL(\n\u001b[32m   (...)\u001b[39m\u001b[32m    247\u001b[39m     extensions=request.extensions,\n\u001b[32m    248\u001b[39m )\n\u001b[32m    249\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[32m--> \u001b[39m\u001b[32m250\u001b[39m     resp = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    252\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp.stream, typing.Iterable)\n\u001b[32m    254\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[32m    255\u001b[39m     status_code=resp.status,\n\u001b[32m    256\u001b[39m     headers=resp.headers,\n\u001b[32m    257\u001b[39m     stream=ResponseStream(resp.stream),\n\u001b[32m    258\u001b[39m     extensions=resp.extensions,\n\u001b[32m    259\u001b[39m )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpcore/_sync/connection_pool.py:256\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m    253\u001b[39m         closing = \u001b[38;5;28mself\u001b[39m._assign_requests_to_connections()\n\u001b[32m    255\u001b[39m     \u001b[38;5;28mself\u001b[39m._close_connections(closing)\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    258\u001b[39m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[32m    259\u001b[39m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n\u001b[32m    260\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, typing.Iterable)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpcore/_sync/connection_pool.py:236\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m    232\u001b[39m connection = pool_request.wait_for_connection(timeout=timeout)\n\u001b[32m    234\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    235\u001b[39m     \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m236\u001b[39m     response = \u001b[43mconnection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    237\u001b[39m \u001b[43m        \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\n\u001b[32m    238\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    239\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[32m    240\u001b[39m     \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[32m    241\u001b[39m     \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[32m    242\u001b[39m     \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m    243\u001b[39m     \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n\u001b[32m    244\u001b[39m     pool_request.clear_connection()\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpcore/_sync/connection.py:103\u001b[39m, in \u001b[36mHTTPConnection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m    100\u001b[39m     \u001b[38;5;28mself\u001b[39m._connect_failed = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m    101\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_connection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpcore/_sync/http11.py:136\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m    134\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[33m\"\u001b[39m\u001b[33mresponse_closed\u001b[39m\u001b[33m\"\u001b[39m, logger, request) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m    135\u001b[39m         \u001b[38;5;28mself\u001b[39m._response_closed()\n\u001b[32m--> \u001b[39m\u001b[32m136\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpcore/_sync/http11.py:106\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m     95\u001b[39m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m     97\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\n\u001b[32m     98\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mreceive_response_headers\u001b[39m\u001b[33m\"\u001b[39m, logger, request, kwargs\n\u001b[32m     99\u001b[39m ) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m    100\u001b[39m     (\n\u001b[32m    101\u001b[39m         http_version,\n\u001b[32m    102\u001b[39m         status,\n\u001b[32m    103\u001b[39m         reason_phrase,\n\u001b[32m    104\u001b[39m         headers,\n\u001b[32m    105\u001b[39m         trailing_data,\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m     ) = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_response_headers\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    107\u001b[39m     trace.return_value = (\n\u001b[32m    108\u001b[39m         http_version,\n\u001b[32m    109\u001b[39m         status,\n\u001b[32m    110\u001b[39m         reason_phrase,\n\u001b[32m    111\u001b[39m         headers,\n\u001b[32m    112\u001b[39m     )\n\u001b[32m    114\u001b[39m network_stream = \u001b[38;5;28mself\u001b[39m._network_stream\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpcore/_sync/http11.py:177\u001b[39m, in \u001b[36mHTTP11Connection._receive_response_headers\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m    174\u001b[39m timeout = timeouts.get(\u001b[33m\"\u001b[39m\u001b[33mread\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m    176\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m177\u001b[39m     event = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    178\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(event, h11.Response):\n\u001b[32m    179\u001b[39m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpcore/_sync/http11.py:217\u001b[39m, in \u001b[36mHTTP11Connection._receive_event\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m    214\u001b[39m     event = \u001b[38;5;28mself\u001b[39m._h11_state.next_event()\n\u001b[32m    216\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m event \u001b[38;5;129;01mis\u001b[39;00m h11.NEED_DATA:\n\u001b[32m--> \u001b[39m\u001b[32m217\u001b[39m     data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_network_stream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    218\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mREAD_NUM_BYTES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m    219\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    221\u001b[39m     \u001b[38;5;66;03m# If we feed this case through h11 we'll raise an exception like:\u001b[39;00m\n\u001b[32m    222\u001b[39m     \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m    223\u001b[39m     \u001b[38;5;66;03m#     httpcore.RemoteProtocolError: can't handle event type\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    227\u001b[39m     \u001b[38;5;66;03m# perspective. Instead we handle this case distinctly and treat\u001b[39;00m\n\u001b[32m    228\u001b[39m     \u001b[38;5;66;03m# it as a ConnectError.\u001b[39;00m\n\u001b[32m    229\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m data == \u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._h11_state.their_state == h11.SEND_RESPONSE:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/httpcore/_backends/sync.py:128\u001b[39m, in \u001b[36mSyncStream.read\u001b[39m\u001b[34m(self, max_bytes, timeout)\u001b[39m\n\u001b[32m    126\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[32m    127\u001b[39m     \u001b[38;5;28mself\u001b[39m._sock.settimeout(timeout)\n\u001b[32m--> \u001b[39m\u001b[32m128\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sock\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrecv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_bytes\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[31mKeyboardInterrupt\u001b[39m: "
     ]
    }
   ],
   "source": [
    "encoder = OllamaLangChainEncoder(lc_embeddings=embeddings, normalize=True)\n",
    "search_model = SearchEncoderWrapper(encoder)\n",
    "\n",
    "tasks = mteb.get_tasks([\n",
    "    \"CodeSearchNetRetrieval\",\n",
    "    \"CodeSearchNetCCRetrieval\",\n",
    "    \"AppsRetrieval\",\n",
    "    \"StackOverflowDupQuestions\",\n",
    "])\n",
    "results = mteb.evaluate(\n",
    "    model=search_model,\n",
    "    tasks=tasks,\n",
    "    encode_kwargs={\"batch_size\": 32, \"show_progress_bar\": True}\n",
    ")\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2c5d9f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModelResult(model_name=no_model_name/available, model_revision=no_revision_available, task_results=[...](#1))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7364562f",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'scores'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mresults\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtask_results\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscores\u001b[49m\n",
      "\u001b[31mAttributeError\u001b[39m: 'list' object has no attribute 'scores'"
     ]
    }
   ],
   "source": [
    "results.task_results.scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a657676e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "assistance-engine",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}