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

Docker/config.py

@@ -0,0 +1,81 @@
+from pathlib import Path
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from dotenv import load_dotenv
+from datetime import timedelta
+import warnings
+import os
+
+load_dotenv()
+
+MODEL_NAME=os.getenv("MODEL_NAME", "gpt-3.5-turbo")
+OPENAI_API_KEY=os.getenv("OPENAI_API_KEY", "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")
+ 
+OLLAMA_URL=os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
+OLLAMA_LOCAL_URL=os.getenv("OLLAMA_LOCAL_URL", "http://localhost:11434")
+OLLAMA_MODEL_NAME=os.getenv("OLLAMA_MODEL_NAME", "qwen3-0.6B:latest")
+OLLAMA_EMB_MODEL_NAME=os.getenv("OLLAMA_EMB_MODEL_NAME", "qwen3-0.6B-emb:latest")
+ 
+LANGFUSE_HOST=os.getenv("LANGFUSE_HOST", "http://45.77.119.180")
+LANGFUSE_PUBLIC_KEY=os.getenv("LANGFUSE_PUBLIC_KEY", "pk-lf-0e6db694-3e95-4dd4-aedf-5a2694267058")
+LANGFUSE_SECRET_KEY=os.getenv("LANGFUSE_SECRET_KEY", "sk-lf-dbf28bb9-15bb-4d03-a8c3-05caa3e3905f")
+ 
+ELASTICSEARCH_URL=os.getenv("ELASTICSEARCH_URL", "http://host.docker.internal:9200")
+ELASTICSEARCH_LOCAL_URL=os.getenv("ELASTICSEARCH_LOCAL_URL", "http://localhost:9200")
+ELASTICSEARCH_INDEX=os.getenv("ELASTICSEARCH_INDEX", "avap-docs-test")
+ 
+DATABASE_URL=os.getenv("DATABASE_URL", "postgresql://postgres:brunix_pass@host.docker.internal:5432/postgres")
+ 
+KUBECONFIG_PATH=os.getenv("KUBECONFIG_PATH", "kubernetes/kubeconfig.yaml")
+ 
+HF_TOKEN=os.getenv("HF_TOKEN", "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")
+HF_EMB_MODEL_NAME=os.getenv("HF_EMB_MODEL_NAME", "Qwen/Qwen3-Embedding-0.6B")
+
+MRH_AVAP_DATA_PATH_=os.getenv("MRH_AVAP_DATA_PATH_", "/home/pseco/VsCodeProjects/assistance-engine/data/")
+
+MRH_AVAP_MODELS_PATH_=os.getenv("MRH_AVAP_MODELS_PATH_", "/home/pseco/VsCodeProjects/assistance-engine/data/models")
+MRH_AVAP_RAW_PATH_=os.getenv("MRH_AVAP_RAW_PATH_", "/home/pseco/VsCodeProjects/assistance-engine/data/raw")
+MRH_AVAP_PROCESSED_PATH_=os.getenv("MRH_AVAP_PROCESSED_PATH_", "/home/pseco/VsCodeProjects/assistance-engine/data/processed")
+MRH_AVAP_INTERIM_PATH_=os.getenv("MRH_AVAP_INTERIM_PATH_", "/home/pseco/VsCodeProjects/assistance-engine/data/interim")
+MRH_AVAP_EXTERNAL_PATH_=os.getenv("MRH_AVAP_EXTERNAL_PATH_", "/home/pseco/VsCodeProjects/assistance-engine/data/external")
+
+
+class Settings(BaseSettings):
+    raw_path_: str
+    processed_path_: str
+    models_path_:str
+    interim_path_:str
+    external_path_:str
+
+    model_config = SettingsConfigDict(
+        env_prefix="mrh_avap_",
+        env_file=".env",
+        env_file_encoding="utf-8",
+        case_sensitive=False,
+        extra="ignore",
+    )
+
+    @property
+    def raw_path(self) -> Path:
+        return Path(self.raw_path_)
+
+    @property
+    def processed_path(self) -> Path:
+        return Path(self.processed_path_)
+
+    @property
+    def proj_root(self) -> Path:
+        return Path(__file__).resolve().parents[1]
+
+    @property
+    def interim_path(self) -> Path:
+        return Path(self.interim_path_)
+    
+    @property
+    def external_path(self) -> Path:
+        return Path(self.external_path_)
+
+    @property
+    def models_path(self) -> Path:
+        return Path(self.models_path_)
+
+settings = Settings()

Makefile

@@ -40,4 +40,8 @@ sync_data_down:
 .PHONY: sync_data_up
 sync_data_up:
 	aws s3 sync --exclude "*.gitkeep" data/ \
-		s3://mrh-avap/data 
+		s3://mrh-avap/data 
+
+.PHONY: ollama_local
+ollama_local:
+	ssh -i ~/.ssh/mrh-transformers.pem -L 11434:localhost:11434 ubuntu@172.18.14.34

pyproject.toml

@@ -5,12 +5,14 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
+    "accelerate>=1.12.0",
     "grpcio>=1.78.0",
     "grpcio-reflection>=1.78.0",
     "grpcio-tools>=1.78.0",
     "langchain>=1.2.10",
     "langchain-community>=0.4.1",
     "langchain-elasticsearch>=1.0.0",
+    "langchain-huggingface>=1.2.0",
     "langchain-ollama>=1.0.1",
     "loguru>=0.7.3",
     "nltk>=3.9.2",
@@ -20,15 +22,16 @@ dependencies = [
     "torch>=2.10.0",
     "torchvision>=0.25.0",
     "tqdm>=4.67.3",
-    "transformers>=5.2.0",
 ]
 
 [dependency-groups]
 dev = [
     "beir>=2.2.0",
     "jupyter>=1.1.1",
-    "langfuse>=3.14.4",
+    "langfuse<=3",
+    "markdown>=3.10.2",
     "mteb>=2.8.8",
     "polars>=1.38.1",
+    "ragas>=0.4.3",
     "ruff>=0.15.1",
 ]

scratches/acano/langgraph_agent_simple.ipynb

@@ -611,12 +611,146 @@
    ]
   },
   {
+<<<<<<< HEAD
    "cell_type": "code",
    "execution_count": null,
    "id": "4052f229",
    "metadata": {},
    "outputs": [],
    "source": []
+=======
+   "cell_type": "markdown",
+   "id": "07f9f5e5",
+   "metadata": {},
+   "source": [
+    "# Evaluate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec2362c4",
+   "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db6fa201",
+   "metadata": {},
+   "outputs": [],
+   "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)"
+   ]
+>>>>>>> 6480c77edb061d556280a688e9f3e8c5c7f5f054
   }
  ],
  "metadata": {

scratches/pseco/agent/n00 langgraph_agent_simple.ipynb

@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "5f8c88cf",
    "metadata": {},
    "outputs": [],
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "f9359747",
    "metadata": {},
    "outputs": [],
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "e5247ab9",
    "metadata": {},
    "outputs": [],
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "a644f6fa",
    "metadata": {},
    "outputs": [],
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "36d0f54e",
    "metadata": {},
    "outputs": [],
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "fae46a58",
    "metadata": {},
    "outputs": [],
@@ -260,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "2fec3fdb",
    "metadata": {},
    "outputs": [
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "a7f4fbf6",
    "metadata": {},
    "outputs": [],
@@ -302,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "8569cf39",
    "metadata": {},
    "outputs": [],
@@ -324,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "53b89690",
    "metadata": {},
    "outputs": [
@@ -342,6 +342,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "Failed to export span batch code: 404, reason: <!DOCTYPE html><html lang=\"en\"><head><meta charSet=\"utf-8\"/><meta name=\"viewport\" content=\"width=device-width\"/><meta name=\"next-head-count\" content=\"2\"/><link rel=\"preload\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" as=\"style\"/><link rel=\"stylesheet\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" data-n-g=\"\"/><noscript data-n-css=\"\"></noscript><script defer=\"\" nomodule=\"\" src=\"/_next/static/chunks/polyfills-42372ed130431b0a.js\"></script><script src=\"/_next/static/chunks/webpack-fafe7122277f5772.js\" defer=\"\"></script><script src=\"/_next/static/chunks/framework-d2dcb18632b956b2.js\" defer=\"\"></script><script src=\"/_next/static/chunks/main-19694d19b6f6c228.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_app-9e6c56ce79ebaf0a.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_error-881d11b9e6da73d1.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_buildManifest.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_ssgManifest.js\" defer=\"\"></script></head><body><div id=\"__next\"><script>!function(){try{var d=document.documentElement,c=d.classList;c.remove('light','dark');var e=localStorage.getItem('theme');if('system'===e||(!e&&true)){var t='(prefers-color-scheme: dark)',m=window.matchMedia(t);if(m.media!==t||m.matches){d.style.colorScheme = 'dark';c.add('dark')}else{d.style.colorScheme = 'light';c.add('light')}}else if(e){c.add(e|| '')}if(e==='light'||e==='dark')d.style.colorScheme=e}catch(e){}}()</script><div class=\"flex min-h-full flex-1 flex-col justify-center py-12 sm:px-6 lg:px-8\" data-sentry-component=\"Spinner\" data-sentry-source-file=\"spinner.tsx\"><div class=\"sm:mx-auto sm:w-full sm:max-w-md\"><img src=\"/icon.svg\" width=\"42\" height=\"42\" alt=\"Langfuse Icon\" class=\"mx-auto motion-safe:animate-spin\" data-sentry-component=\"LangfuseIcon\" data-sentry-source-file=\"LangfuseLogo.tsx\"/><h2 class=\"mt-5 text-center text-2xl font-bold leading-9 tracking-tight text-primary\">Loading<!-- --> ...</h2></div></div></div><script id=\"__NEXT_DATA__\" type=\"application/json\">{\"props\":{\"pageProps\":{\"statusCode\":404}},\"page\":\"/_error\",\"query\":{},\"buildId\":\"Eu5QEyLpa3Avvo-CHkMDG\",\"nextExport\":true,\"isFallback\":false,\"gip\":true,\"locale\":\"en\",\"locales\":[\"en\"],\"defaultLocale\":\"en\",\"scriptLoader\":[]}</script></body></html>\n",
       "Failed to export span batch code: 404, reason: <!DOCTYPE html><html lang=\"en\"><head><meta charSet=\"utf-8\"/><meta name=\"viewport\" content=\"width=device-width\"/><meta name=\"next-head-count\" content=\"2\"/><link rel=\"preload\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" as=\"style\"/><link rel=\"stylesheet\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" data-n-g=\"\"/><noscript data-n-css=\"\"></noscript><script defer=\"\" nomodule=\"\" src=\"/_next/static/chunks/polyfills-42372ed130431b0a.js\"></script><script src=\"/_next/static/chunks/webpack-fafe7122277f5772.js\" defer=\"\"></script><script src=\"/_next/static/chunks/framework-d2dcb18632b956b2.js\" defer=\"\"></script><script src=\"/_next/static/chunks/main-19694d19b6f6c228.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_app-9e6c56ce79ebaf0a.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_error-881d11b9e6da73d1.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_buildManifest.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_ssgManifest.js\" defer=\"\"></script></head><body><div id=\"__next\"><script>!function(){try{var d=document.documentElement,c=d.classList;c.remove('light','dark');var e=localStorage.getItem('theme');if('system'===e||(!e&&true)){var t='(prefers-color-scheme: dark)',m=window.matchMedia(t);if(m.media!==t||m.matches){d.style.colorScheme = 'dark';c.add('dark')}else{d.style.colorScheme = 'light';c.add('light')}}else if(e){c.add(e|| '')}if(e==='light'||e==='dark')d.style.colorScheme=e}catch(e){}}()</script><div class=\"flex min-h-full flex-1 flex-col justify-center py-12 sm:px-6 lg:px-8\" data-sentry-component=\"Spinner\" data-sentry-source-file=\"spinner.tsx\"><div class=\"sm:mx-auto sm:w-full sm:max-w-md\"><img src=\"/icon.svg\" width=\"42\" height=\"42\" alt=\"Langfuse Icon\" class=\"mx-auto motion-safe:animate-spin\" data-sentry-component=\"LangfuseIcon\" data-sentry-source-file=\"LangfuseLogo.tsx\"/><h2 class=\"mt-5 text-center text-2xl font-bold leading-9 tracking-tight text-primary\">Loading<!-- --> ...</h2></div></div></div><script id=\"__NEXT_DATA__\" type=\"application/json\">{\"props\":{\"pageProps\":{\"statusCode\":404}},\"page\":\"/_error\",\"query\":{},\"buildId\":\"Eu5QEyLpa3Avvo-CHkMDG\",\"nextExport\":true,\"isFallback\":false,\"gip\":true,\"locale\":\"en\",\"locales\":[\"en\"],\"defaultLocale\":\"en\",\"scriptLoader\":[]}</script></body></html>\n"
      ]
     },
@@ -351,11 +352,15 @@
      "text": [
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "To provide you with an accurate answer about what vertical farming proposes, I would need to call a specific function that retrieves context from Elasticsearch. However, without access to actual data points or recent developments in the field of hydroponic agriculture, it's difficult to give an exhaustive and up-to-date response.\n",
+      "<think>\n",
+      "Okay, the user is asking what vertical farming proposes. First, I need to recall what vertical farming is. From what I remember, vertical farming is a method of growing crops in stacked layers, often in controlled environments. It's a form of hydroponics or aquaponics. The idea is to maximize space efficiency by using vertical structures. \n",
       "\n",
-      "For precise information on current practices and innovations within vertical farming, you might want to refer to specialized agricultural news outlets, industry reports, or directly contact experts in this area. This could include websites focusing on indoor agriculture, research institutions working with hydroponics, or the latest publications from agritech companies that specialize in vertical farming technology.\n",
+      "But wait, maybe I should check if there's any specific information I'm missing. The user might be looking for details about the technology involved, like using LED lights, hydroponics, or aquaponics. Also, vertical farms can reduce the need for land, water, and pesticides. \n",
       "\n",
-      "If you're interested in learning about current trends and advancements in vertical farming, I can try to provide a more comprehensive answer when I do have access to relevant data.\n",
+      "I should present this information clearly. Let me make sure to mention the key aspects: stacked layers, controlled environment, space efficiency, and sustainability. That should answer the question comprehensively.\n",
+      "</think>\n",
+      "\n",
+      "Vertical farming is a method of growing crops in stacked layers (like vertical gardens) in controlled environments, often using hydroponics or aquaponics. It maximizes space efficiency, reduces water and land use, and promotes sustainability by minimizing resource consumption.\n",
       "\n",
       "Flushing traces to Langfuse...\n",
       "✓ Traces flushed (check your Langfuse dashboard at http://45.77.119.180)\n"
@@ -364,12 +369,7 @@
    ],
    "source": [
     "print(\"Starting agent...\")\n",
-    "stream_graph_updates(user_input)\n",
-    "\n",
-    "# Ensure all spans are flushed to Langfuse\n",
-    "print(\"\\nFlushing traces to Langfuse...\")\n",
-    "langfuse.flush()\n",
-    "print(\"✓ Traces flushed (check your Langfuse dashboard at \" + LANGFUSE_HOST + \")\")"
+    "stream_graph_updates(user_input)"
    ]
   },
   {

scratches/pseco/evaluation/embeddings/n00 Beir Analysis CodeXGlue.ipynb

@@ -174,11 +174,33 @@
     "# Evaluar métricas (NDCG, MAP, Recall, Precision)\n",
     "ndcg, _map, recall, precision = evaluator.evaluate(\n",
     "    qrels, results, [1, 3, 5, 10]\n",
-    ")\n",
-    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5c0f9845",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resultados para CodeXGLUE:\n",
+      "NDCG: {'NDCG@1': 0.94971, 'NDCG@3': 0.96956, 'NDCG@5': 0.97166, 'NDCG@10': 0.97342}\n",
+      "MAP: {'MAP@1': 0.94971, 'MAP@3': 0.96504, 'MAP@5': 0.9662, 'MAP@10': 0.96694}\n",
+      "Recall: {'Recall@1': 0.94971, 'Recall@3': 0.98251, 'Recall@5': 0.98761, 'Recall@10': 0.99297}\n",
+      "Precision: {'P@1': 0.94971, 'P@3': 0.3275, 'P@5': 0.19752, 'P@10': 0.0993}\n"
+     ]
+    }
+   ],
+   "source": [
     "print(f\"Resultados para CodeXGLUE:\")\n",
-    "print(\"NDCG@10:\", ndcg[\"NDCG@10\"])\n",
-    "print(\"Recall@10:\", recall[\"Recall@10\"])"
+    "print(\"NDCG:\", ndcg)\n",
+    "print(\"MAP:\", _map)\n",
+    "print(\"Recall:\", recall)\n",
+    "print(\"Precision:\", precision)"
    ]
   },
   {
@@ -191,43 +213,99 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "5ced1c25",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NDCG: {'NDCG@1': 0.02333, 'NDCG@3': 0.03498, 'NDCG@5': 0.0404, 'NDCG@10': 0.04619, 'NDCG@100': 0.07768}\n",
-      "MAP: {'MAP@1': 0.02083, 'MAP@3': 0.03083, 'MAP@5': 0.03375, 'MAP@10': 0.03632, 'MAP@100': 0.04123}\n",
-      "Recall: {'Recall@1': 0.02083, 'Recall@3': 0.04417, 'Recall@5': 0.0575, 'Recall@10': 0.07417, 'Recall@100': 0.23144}\n",
-      "Precision: {'P@1': 0.02333, 'P@3': 0.01556, 'P@5': 0.01267, 'P@10': 0.00833, 'P@100': 0.00277}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "model = BEIROllamaEmbeddings(\n",
+    "model_q2 = BEIROllamaEmbeddings(\n",
     "    base_url=\"http://localhost:11434\",\n",
     "    model=\"qwen2.5:1.5b\",\n",
     "    batch_size=64,\n",
     ")\n",
     "\n",
     "# Inicializar buscador y evaluador\n",
-    "retriever = DenseRetrievalExactSearch(model, batch_size=64)\n",
-    "evaluator = EvaluateRetrieval(retriever, score_function=\"cos_sim\")\n",
+    "retriever_q2 = DenseRetrievalExactSearch(model_q2, batch_size=64)\n",
+    "evaluator_q2 = EvaluateRetrieval(retriever_q2, score_function=\"cos_sim\")\n",
     "\n",
     "# Ejecutar recuperación\n",
-    "results = evaluator.retrieve(corpus, queries)\n",
+    "results_q2 = evaluator_q2.retrieve(corpus, queries)\n",
     "\n",
     "# Evaluar métricas (NDCG, MAP, Recall, Precision)\n",
-    "ndcg, _map, recall, precision = evaluator.evaluate(\n",
-    "    qrels, results, [1, 3, 5, 10]\n",
-    ")\n",
-    "\n",
+    "ndcg_qwen_2, _map_qwen_2, recall_qwen_2, precision_qwen_2 = evaluator_q2.evaluate(\n",
+    "    qrels, results_q2, [1, 3, 5, 10]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6a95189e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resultados para CodeXGLUE:\n",
+      "NDCG: {'NDCG@1': 0.00031, 'NDCG@3': 0.00061, 'NDCG@5': 0.00086, 'NDCG@10': 0.00118}\n",
+      "MAP: {'MAP@1': 0.00031, 'MAP@3': 0.00051, 'MAP@5': 0.00065, 'MAP@10': 0.00078}\n",
+      "Recall: {'Recall@1': 0.00031, 'Recall@3': 0.00088, 'Recall@5': 0.00151, 'Recall@10': 0.0025}\n",
+      "Precision: {'P@1': 0.00031, 'P@3': 0.00029, 'P@5': 0.0003, 'P@10': 0.00025}\n"
+     ]
+    }
+   ],
+   "source": [
     "print(f\"Resultados para CodeXGLUE:\")\n",
-    "print(\"NDCG@10:\", ndcg[\"NDCG@10\"])\n",
-    "print(\"Recall@10:\", recall[\"Recall@10\"])"
+    "print(\"NDCG:\", ndcg_qwen_2)\n",
+    "print(\"MAP:\", _map_qwen_2)\n",
+    "print(\"Recall:\", recall_qwen_2)\n",
+    "print(\"Precision:\", precision_qwen_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3dad9811",
+   "metadata": {},
+   "source": [
+    "# Save data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f875dd8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resultados guardados en /home/pseco/VsCodeProjects/assistance-engine/data/interim/beir_CodeXGlue_results.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_data = {\n",
+    "    \"qwen3-0.6B-emb:latest\": {\n",
+    "        \"NDCG\": ndcg,\n",
+    "        \"MAP\": _map,\n",
+    "        \"Recall\": recall,\n",
+    "        \"Precision\": precision,\n",
+    "    },\n",
+    "    \"qwen2.5:1.5b\": {\n",
+    "        \"NDCG\": ndcg_qwen_2,\n",
+    "        \"MAP\": _map_qwen_2,\n",
+    "        \"Recall\": recall_qwen_2,\n",
+    "        \"Precision\": precision_qwen_2,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "output_file = \"/home/pseco/VsCodeProjects/assistance-engine/data/interim/beir_CodeXGlue_results.json\"\n",
+    "with open(output_file, \"w\") as f:\n",
+    "    json.dump(results_data, f, indent=2)\n",
+    "\n",
+    "print(f\"Resultados guardados en {output_file}\")"
    ]
   }
  ],

scratches/pseco/evaluation/embeddings/n00 Beir Analysis.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "c01c19dc",
    "metadata": {},
    "outputs": [],
@@ -252,6 +252,51 @@
     "print(\"Recall:\", recall_qwen_2)\n",
     "print(\"Precision:\", precision_qwen_2)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9402837",
+   "metadata": {},
+   "source": [
+    "# Save Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c281d5e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resultados guardados en /home/pseco/VsCodeProjects/assistance-engine/data/interim/beir_Scifact_results.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_data = {\n",
+    "    \"qwen3-0.6B-emb:latest\": {\n",
+    "        \"NDCG\": ndcg,\n",
+    "        \"MAP\": _map,\n",
+    "        \"Recall\": recall,\n",
+    "        \"Precision\": precision,\n",
+    "    },\n",
+    "    \"qwen2.5:1.5b\": {\n",
+    "        \"NDCG\": ndcg_qwen_2,\n",
+    "        \"MAP\": _map_qwen_2,\n",
+    "        \"Recall\": recall_qwen_2,\n",
+    "        \"Precision\": precision_qwen_2,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "output_file = \"/home/pseco/VsCodeProjects/assistance-engine/data/interim/beir_Scifact_results.json\"\n",
+    "with open(output_file, \"w\") as f:\n",
+    "    json.dump(results_data, f, indent=2)\n",
+    "\n",
+    "print(f\"Resultados guardados en {output_file}\")"
+   ]
   }
  ],
  "metadata": {

scratches/pseco/evaluation/embeddings/n00 Beir Analysis_cosqa.ipynb

@@ -261,6 +261,8 @@
     "print(\"Recall:\", recall_qwen_2)\n",
     "print(\"Precision:\", precision_qwen_2)"
    ]
+<<<<<<< HEAD
+=======
   },
   {
    "cell_type": "code",
@@ -306,6 +308,7 @@
    "metadata": {},
    "outputs": [],
    "source": []
+>>>>>>> 4b5352d93cf89b7562895b550fb5bd62160586c5
   }
  ],
  "metadata": {

scratches/pseco/ingestion/n00 Count tokens.ipynb

@@ -0,0 +1,297 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ed60d28c",
+   "metadata": {},
+   "source": [
+    "# Libreries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "95cf533e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "from Docker.config import settings\n",
+    "from pathlib import Path\n",
+    "from transformers import AutoConfig\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9b7265a",
+   "metadata": {},
+   "source": [
+    "# Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6fd7de78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_text_from_file(file_path: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Load text content from a specified file.\n",
+    "\n",
+    "    Args:\n",
+    "        file_path: Path to the .txt file to load.\n",
+    "\n",
+    "    Returns:\n",
+    "        The text content of the file.\n",
+    "\n",
+    "    Raises:\n",
+    "        FileNotFoundError: If the file does not exist.\n",
+    "        IOError: If the file cannot be read.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n",
+    "            return file.read()\n",
+    "    except FileNotFoundError:\n",
+    "        raise FileNotFoundError(f\"El archivo '{file_path}' no existe.\")\n",
+    "    except IOError as error:\n",
+    "        raise IOError(f\"Error al leer '{file_path}': {error}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6389092e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def infer_context_window(model_id: str, tokenizer_obj) -> int:\n",
+    "    \"\"\"Infer context window from tokenizer/model config.\"\"\"\n",
+    "    large_sentinel = int(1e9)\n",
+    "\n",
+    "    tokenizer_limit = getattr(tokenizer_obj, \"model_max_length\", None)\n",
+    "    if isinstance(tokenizer_limit, int) and 0 < tokenizer_limit < large_sentinel:\n",
+    "        return tokenizer_limit\n",
+    "\n",
+    "    config = AutoConfig.from_pretrained(model_id)\n",
+    "\n",
+    "    for field_name in (\n",
+    "        \"max_position_embeddings\",\n",
+    "        \"n_positions\",\n",
+    "        \"seq_length\",\n",
+    "        \"model_max_length\",\n",
+    "    ):\n",
+    "        value = getattr(config, field_name, None)\n",
+    "        if isinstance(value, int) and value > 0:\n",
+    "            return value\n",
+    "\n",
+    "    raise ValueError(\n",
+    "        \"No se pudo inferir la ventana de contexto del modelo. \"\n",
+    "        \"Define context_window manualmente.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7190080b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_token_count_test() -> dict[str, int | bool]:\n",
+    "    \"\"\"Run token count test across all raw .txt files.\"\"\"\n",
+    "    raw_dir = Path(settings.raw_path)\n",
+    "    txt_files = sorted(raw_dir.glob(\"*.txt\"))\n",
+    "\n",
+    "    if not txt_files:\n",
+    "        print(f\"No se encontraron .txt en: {raw_dir}\")\n",
+    "        return {\n",
+    "            \"total_tokens_without_special\": 0,\n",
+    "            \"total_tokens_with_special\": 0,\n",
+    "            \"fits_without\": True,\n",
+    "            \"fits_with\": True,\n",
+    "        }\n",
+    "\n",
+    "    total_tokens_without_special = 0\n",
+    "    total_tokens_with_special = 0\n",
+    "\n",
+    "    print(f\"Modelo: {model_name}\")\n",
+    "    print(f\"Ventana de contexto detectada: {context_window}\")\n",
+    "    print(f\"Archivos analizados: {len(txt_files)}\")\n",
+    "    print(\"-\" * 80)\n",
+    "\n",
+    "    for file_path in txt_files:\n",
+    "        content = load_text_from_file(str(file_path))\n",
+    "        token_ids_without_special = tokenizer.encode(\n",
+    "            content, add_special_tokens=False\n",
+    "        )\n",
+    "        token_ids_with_special = tokenizer.encode(content)\n",
+    "\n",
+    "        count_without_special = len(token_ids_without_special)\n",
+    "        count_with_special = len(token_ids_with_special)\n",
+    "\n",
+    "        total_tokens_without_special += count_without_special\n",
+    "        total_tokens_with_special += count_with_special\n",
+    "\n",
+    "        print(\n",
+    "            f\"{file_path.name:<35} \"\n",
+    "            f\"sin especiales: {count_without_special:>8} | \"\n",
+    "            f\"con especiales: {count_with_special:>8}\"\n",
+    "        )\n",
+    "\n",
+    "    print(\"-\" * 80)\n",
+    "    print(\n",
+    "        f\"TOTAL sin especiales: {total_tokens_without_special} tokens\"\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"TOTAL con especiales: {total_tokens_with_special} tokens\"\n",
+    "    )\n",
+    "\n",
+    "    fits_without = total_tokens_without_special <= context_window\n",
+    "    fits_with = total_tokens_with_special <= context_window\n",
+    "\n",
+    "    print(\n",
+    "        f\"¿Cabe en ventana ({context_window}) sin especiales? \"\n",
+    "        f\"{'Sí' if fits_without else 'No'}\"\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"¿Cabe en ventana ({context_window}) con especiales? \"\n",
+    "        f\"{'Sí' if fits_with else 'No'}\"\n",
+    "    )\n",
+    "\n",
+    "    if not fits_with:\n",
+    "        overflow = total_tokens_with_special - context_window\n",
+    "        print(\n",
+    "            f\"Exceso aproximado: {overflow} tokens\"\n",
+    "        )\n",
+    "\n",
+    "    return {\n",
+    "        \"total_tokens_without_special\": total_tokens_without_special,\n",
+    "        \"total_tokens_with_special\": total_tokens_with_special,\n",
+    "        \"fits_without\": fits_without,\n",
+    "        \"fits_with\": fits_with,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04e0f72f",
+   "metadata": {},
+   "source": [
+    "# Model Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "19c815e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name = os.getenv(\"HF_EMB_MODEL_NAME\")\n",
+    "if not model_name:\n",
+    "    raise ValueError(\n",
+    "        \"No se encontró HF_EMB_MODEL_NAME en variables de entorno.\"\n",
+    "    )\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "context_window = infer_context_window(model_name, tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22bcc0fe",
+   "metadata": {},
+   "source": [
+    "# Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f6517705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Modelo: Qwen/Qwen3-Embedding-0.6B\n",
+      "Ventana de contexto detectada: 131072\n",
+      "Archivos analizados: 24\n",
+      "--------------------------------------------------------------------------------\n",
+      "10_Execution_model_in_avap.txt      sin especiales:    10349 | con especiales:    10350\n",
+      "11_Conditional_statements.txt       sin especiales:      524 | con especiales:      525\n",
+      "12_Loop_statement.txt               sin especiales:      594 | con especiales:      595\n",
+      "13_Api_inbound_interface.txt        sin especiales:      415 | con especiales:      416\n",
+      "14_Working_with_libraries.txt       sin especiales:      873 | con especiales:      874\n",
+      "15_Function_declaration.txt         sin especiales:      394 | con especiales:      395\n",
+      "16_Appendix.txt                     sin especiales:     9209 | con especiales:     9210\n",
+      "17_Architecture_memory_foundations.txt sin especiales:     1086 | con especiales:     1087\n",
+      "18_Input_output_management.txt      sin especiales:     1104 | con especiales:     1105\n",
+      "19_Control_logic_decision_structures.txt sin especiales:     1166 | con especiales:     1167\n",
+      "1_Introduction.txt                  sin especiales:     1072 | con especiales:     1073\n",
+      "20_Concurrency_asynchrony.txt       sin especiales:     1049 | con especiales:     1050\n",
+      "21_Persistance_connectors_orm.txt   sin especiales:     1135 | con especiales:     1136\n",
+      "22_System_utilities_transformation.txt sin especiales:      882 | con especiales:      883\n",
+      "23_Function_architecture_scopes.txt sin especiales:      604 | con especiales:      605\n",
+      "24_Master_example.txt               sin especiales:      241 | con especiales:      242\n",
+      "2_Dynamic_Programming_Language.txt  sin especiales:      707 | con especiales:      708\n",
+      "3_Notation.txt                      sin especiales:     1368 | con especiales:     1369\n",
+      "4_Lexics.txt                        sin especiales:      750 | con especiales:      751\n",
+      "5_Data_Model.txt                    sin especiales:      605 | con especiales:      606\n",
+      "6_Data_Types.txt                    sin especiales:      611 | con especiales:      612\n",
+      "7_Working_With_Variables.txt        sin especiales:      601 | con especiales:      602\n",
+      "8_How_to_work_with_comments.txt     sin especiales:      726 | con especiales:      727\n",
+      "9_Expressions_in_avap.txt           sin especiales:      646 | con especiales:      647\n",
+      "--------------------------------------------------------------------------------\n",
+      "TOTAL sin especiales: 36711 tokens\n",
+      "TOTAL con especiales: 36735 tokens\n",
+      "¿Cabe en ventana (131072) sin especiales? Sí\n",
+      "¿Cabe en ventana (131072) con especiales? Sí\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'total_tokens_without_special': 36711,\n",
+       " 'total_tokens_with_special': 36735,\n",
+       " 'fits_without': True,\n",
+       " 'fits_with': True}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_result = run_token_count_test()\n",
+    "test_result"
+   ]
+  }
+ ],
+ "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": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

scratches/pseco/synthetic_dataset/n00 Ragas TestSet Generation.ipynb

@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "efbabfec",
+   "metadata": {},
+   "source": [
+    "# Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e06aaf8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.config import ELASTICSEARCH_INDEX, OPENAI_API_KEY, OPENAI_MODEL, BASE_URL, MODEL_NAME"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8488db9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'ragas.testset.generator'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_core\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdocuments\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Document\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_ollama\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ChatOllama, OllamaEmbeddings\n\u001b[32m----> \u001b[39m\u001b[32m6\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;01mtestset\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgenerator\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TestsetGenerator\n\u001b[32m      7\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;01mllms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m LangchainLLMWrapper\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'ragas.testset.generator'"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.embeddings import Embeddings  \n",
+    "from ragas.testset import TestsetGenerator  \n",
+    "from langchain_community.document_loaders import DirectoryLoader  \n",
+    "from openai import OpenAI  \n",
+    "from ragas.llms import llm_factory  \n",
+    "from langchain_core.documents import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "816afb58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ElasticsearchEmbeddings(Embeddings):  \n",
+    "    \"\"\"Fetch precomputed embeddings from Elasticsearch.\"\"\"  \n",
+    "    def __init__(self, es_client, index_name, id_field=\"_id\", embedding_field=\"embedding_vector\"):  \n",
+    "        self.es = es_client  \n",
+    "        self.index = index_name  \n",
+    "        self.id_field = id_field  \n",
+    "        self.embedding_field = embedding_field  \n",
+    "  \n",
+    "    def embed_query(self, text: str):  \n",
+    "        # Example: hash text to a doc ID or perform a text lookup in ES  \n",
+    "        doc_id = self._text_to_doc_id(text)  # implement your mapping  \n",
+    "        resp = self.es.get(index=self.index, id=doc_id)  \n",
+    "        return resp[\"_source\"][self.embedding_field]  \n",
+    "  \n",
+    "    def embed_documents(self, texts):  \n",
+    "        return [self.embed_query(t) for t in texts]  \n",
+    "  \n",
+    "    # Optional: implement async variants if Ragas calls them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65a7c504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = DirectoryLoader(\"./my_code_repo\", glob=\"**/*.py\", recursive=True)  \n",
+    "docs = loader.load()  \n",
+    "  \n",
+    "# 2) Ollama LLM via OpenAI-compatible client  \n",
+    "ollama_client = OpenAI(base_url=\"http://localhost:11434/v1\", api_key=\"ollama\")  \n",
+    "generator_llm = llm_factory(\"codellama\", provider=\"openai\", client=ollama_client)  \n",
+    "  \n",
+    "# 3) Custom ES embeddings wrapper  \n",
+    "from elasticsearch import Elasticsearch  \n",
+    "es_client = Elasticsearch(\"http://localhost:9200\")  \n",
+    "es_embeddings = ElasticsearchEmbeddings(es_client, index_name=\"my_docs_embeddings\")  \n",
+    "  \n",
+    "# 4) Ragas TestsetGenerator using the ES embeddings  \n",
+    "generator = TestsetGenerator.from_langchain(generator_llm, es_embeddings)  \n",
+    "  \n",
+    "# 5) Generate testset  \n",
+    "dataset = generator.generate_with_langchain_docs(docs, testset_size=20)  \n",
+    "  \n",
+    "# 6) Export  \n",
+    "df = dataset.to_pandas()  \n",
+    "print(df[[\"user_input\", \"reference\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06f41852",
+   "metadata": {},
+   "source": [
+    "# Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d24262b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fetch_documents_from_elasticsearch(\n",
+    "    es_url: str, \n",
+    "    index_name: str, \n",
+    "    size: int = 100\n",
+    ") -> List[Document]:\n",
+    "    \"\"\"\n",
+    "    Retrieve documents from Elasticsearch and convert to LangChain Document objects.\n",
+    "    \n",
+    "    Args:\n",
+    "        es_url: Elasticsearch URL\n",
+    "        index_name: Index name to query\n",
+    "        size: Number of documents to retrieve\n",
+    "        \n",
+    "    Returns:\n",
+    "        List of LangChain Document objects with metadata\n",
+    "    \"\"\"\n",
+    "    es = Elasticsearch(es_url)\n",
+    "    \n",
+    "    # Query all documents from the index\n",
+    "    response = es.search(\n",
+    "        index=index_name,\n",
+    "        body={\"query\": {\"match_all\": {}}, \"size\": size}\n",
+    "    )\n",
+    "    \n",
+    "    documents = []\n",
+    "    for hit in response[\"hits\"][\"hits\"]:\n",
+    "        source = hit.get(\"_source\", {})\n",
+    "        \n",
+    "        # Extract text content (field name is 'text' based on your ES setup)\n",
+    "        page_content = source.get(\"text\", \"\")\n",
+    "        \n",
+    "        if not page_content:\n",
+    "            continue\n",
+    "        \n",
+    "        # Extract metadata from the document\n",
+    "        metadata = {\n",
+    "            \"source\": source.get(\"source\", \"unknown\"),\n",
+    "            \"doc_id\": source.get(\"doc_id\", hit.get(\"_id\", \"\")),\n",
+    "            \"chunk_id\": source.get(\"chunk_id\", \"\"),\n",
+    "            \"title\": source.get(\"title\", \"Untitled\"),\n",
+    "            \"es_id\": hit.get(\"_id\")  # Store the ES document ID for reference\n",
+    "        }\n",
+    "        \n",
+    "        # Create LangChain Document object\n",
+    "        doc = Document(page_content=page_content, metadata=metadata)\n",
+    "        documents.append(doc)\n",
+    "    \n",
+    "    print(f\"✓ Retrieved {len(documents)} documents from Elasticsearch\")\n",
+    "    return documents\n",
+    "\n",
+    "\n",
+    "# Fetch documents from Elasticsearch\n",
+    "documents = fetch_documents_from_elasticsearch(ELASTICSEARCH_URL, ELASTICSEARCH_INDEX, size=100)\n",
+    "\n",
+    "if documents:\n",
+    "    print(f\"\\nFirst document sample:\")\n",
+    "    print(f\"  Content length: {len(documents[0].page_content)} chars\")\n",
+    "    print(f\"  Metadata: {documents[0].metadata}\")\n",
+    "else:\n",
+    "    print(\"⚠️  No documents found in Elasticsearch. Check index name and ES connection.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf8797ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not documents:\n",
+    "    print(\"❌ Cannot proceed: No documents to generate testset from.\")\n",
+    "    print(\"Ensure Elasticsearch is running and the index contains documents.\")\n",
+    "else:\n",
+    "    print(\"\\n\" + \"=\" * 60)\n",
+    "    print(\"Initializing Ragas TestSetGenerator\")\n",
+    "    print(\"=\" * 60)\n",
+    "    \n",
+    "    # Initialize LLMs for test generation\n",
+    "    generator_llm = LangchainLLMWrapper(\n",
+    "        ChatOllama(base_url=BASE_URL, model=MODEL_NAME)\n",
+    "    )\n",
+    "    \n",
+    "    # Initialize embeddings for semantic similarity evaluation\n",
+    "    embeddings = OllamaEmbeddings(base_url=BASE_URL, model=MODEL_NAME)\n",
+    "    \n",
+    "    # Create the TestsetGenerator\n",
+    "    generator = TestsetGenerator.from_langchain(\n",
+    "        llm=generator_llm,\n",
+    "        embedding_model=embeddings\n",
+    "    )\n",
+    "    \n",
+    "    print(\"✓ TestsetGenerator initialized successfully\")\n",
+    "    print(f\"  - LLM Model: {MODEL_NAME}\")\n",
+    "    print(f\"  - Using {len(documents)} documents for generation\")\n",
+    "    print(\"\\n⏳ Generating synthetic test set (this may take a few minutes)...\")\n",
+    "    \n",
+    "    # Generate synthetic test set\n",
+    "    # Adjust testset_size based on your needs and token budget\n",
+    "    testset_size = min(50, len(documents) * 2)  # Generate up to 50 test cases or 2x the docs\n",
+    "    \n",
+    "    testset = generator.generate_with_langchain_docs(\n",
+    "        documents=documents,\n",
+    "        testset_size=testset_size,\n",
+    "        raise_exceptions=False\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"✓ Test set generated with {len(testset)} examples\")\n",
+    "    \n",
+    "    # Convert to DataFrame for easy viewing and exporting\n",
+    "    df = testset.to_pandas()\n",
+    "    \n",
+    "    print(\"\\n\" + \"=\" * 60)\n",
+    "    print(\"Test Set Summary\")\n",
+    "    print(\"=\" * 60)\n",
+    "    print(f\"Total test cases: {len(df)}\")\n",
+    "    print(f\"\\nColumns: {list(df.columns)}\")\n",
+    "    print(f\"\\nFirst few rows:\")\n",
+    "    print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8dfd3472",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Define output directory\n",
+    "output_dir = Path(\"data/interim/synthetic_testsets\")\n",
+    "output_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Generate filename with timestamp\n",
+    "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
+    "output_file = output_dir / f\"testset_{timestamp}.csv\"\n",
+    "\n",
+    "# Save to CSV\n",
+    "df.to_csv(output_file, index=False)\n",
+    "print(f\"✓ Test set saved to: {output_file}\")\n",
+    "\n",
+    "# Optional: Save as JSON for better structure preservation\n",
+    "json_file = output_dir / f\"testset_{timestamp}.json\"\n",
+    "testset.save_as_json(str(json_file))\n",
+    "print(f\"✓ Test set saved (JSON) to: {json_file}\")\n",
+    "\n",
+    "# Display statistics\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\"Test Set Distribution\")\n",
+    "print(\"=\" * 60)\n",
+    "if \"question_type\" in df.columns:\n",
+    "    print(\"\\nQuestion types:\")\n",
+    "    print(df[\"question_type\"].value_counts())\n",
+    "\n",
+    "if \"retrieval_context\" in df.columns:\n",
+    "    avg_context_len = df[\"retrieval_context\"].apply(lambda x: len(x) if x else 0).mean()\n",
+    "    print(f\"\\nAverage context length: {avg_context_len:.0f} characters\")"
+   ]
+  }
+ ],
+ "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": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/config.py

@@ -0,0 +1,36 @@
+from pathlib import Path
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+OPENAI_API_KEY=os.getenv("OPENAI_API_KEY", "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")
+ 
+OLLAMA_URL=os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
+OLLAMA_LOCAL_URL=os.getenv("OLLAMA_LOCAL_URL", "http://localhost:11434")
+OLLAMA_MODEL_NAME=os.getenv("OLLAMA_MODEL_NAME", "qwen3-0.6B:latest")
+OLLAMA_EMB_MODEL_NAME=os.getenv("OLLAMA_EMB_MODEL_NAME", "qwen3-0.6B-emb:latest")
+ 
+LANGFUSE_HOST=os.getenv("LANGFUSE_HOST", "http://45.77.119.180")
+LANGFUSE_PUBLIC_KEY=os.getenv("LANGFUSE_PUBLIC_KEY", "pk-lf-0e6db694-3e95-4dd4-aedf-5a2694267058")
+LANGFUSE_SECRET_KEY=os.getenv("LANGFUSE_SECRET_KEY", "sk-lf-dbf28bb9-15bb-4d03-a8c3-05caa3e3905f")
+ 
+ELASTICSEARCH_URL=os.getenv("ELASTICSEARCH_URL", "http://host.docker.internal:9200")
+ELASTICSEARCH_LOCAL_URL=os.getenv("ELASTICSEARCH_LOCAL_URL", "http://localhost:9200")
+ELASTICSEARCH_INDEX=os.getenv("ELASTICSEARCH_INDEX", "avap-docs-test")
+ 
+DATABASE_URL=os.getenv("DATABASE_URL", "postgresql://postgres:brunix_pass@host.docker.internal:5432/postgres")
+ 
+KUBECONFIG_PATH=os.getenv("KUBECONFIG_PATH", "kubernetes/kubeconfig.yaml")
+ 
+HF_TOKEN=os.getenv("HF_TOKEN", "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")
+HF_EMB_MODEL_NAME=os.getenv("HF_EMB_MODEL_NAME", "Qwen/Qwen3-Embedding-0.6B")
+
+PROJ_ROOT = Path(__file__).resolve().parents[1]
+
+DATA_DIR=PROJ_ROOT / "data"
+MODELS_DIR=DATA_DIR / "models"
+RAW_DIR=DATA_DIR / "raw"
+PROCESSED_DIR=DATA_DIR / "processed"
+INTERIM_DIR=DATA_DIR / "interim"
+EXTERNAL_DIR=DATA_DIR / "external"

src/llm_factory v1.py

@@ -0,0 +1,152 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import StrEnum
+from typing import Optional
+
+from langchain_ollama import ChatOllama, OllamaEmbeddings
+
+
+class Provider(StrEnum):
+    OLLAMA = "ollama"
+    OPENAI = "openai"
+    ANTHROPIC = "anthropic"
+    AWS_BEDROCK = "aws_bedrock"
+    HUGGINGFACE = "huggingface"
+
+
+@dataclass(frozen=True)
+class ChatModelConfig:
+    provider: Provider
+    model: str
+    temperature: float = 0.0
+
+    # Ollama
+    ollama_base_url: Optional[str] = None
+    validate_model_on_init: bool = True
+
+    # OpenAI / Anthropic / Azure
+    api_key: Optional[str] = None
+    azure_endpoint: Optional[str] = None
+    azure_deployment: Optional[str] = None
+    api_version: Optional[str] = None
+
+
+@dataclass(frozen=True)
+class EmbeddingsConfig:
+    provider: Provider
+    model: str
+
+    # Ollama
+    ollama_base_url: Optional[str] = None
+
+    # OpenAI / Azure
+    api_key: Optional[str] = None
+    azure_endpoint: Optional[str] = None
+    azure_deployment: Optional[str] = None
+    api_version: Optional[str] = None
+
+
+def build_chat_model(cfg: ChatModelConfig):
+    match cfg.provider:
+        case Provider.OLLAMA:
+            return ChatOllama(
+                model=cfg.model,
+                temperature=cfg.temperature,
+                validate_model_on_init=cfg.validate_model_on_init,
+                base_url=cfg.ollama_base_url,
+            )
+
+        case Provider.OPENAI:
+            from langchain_openai import ChatOpenAI  # pip install langchain-openai
+
+            if not cfg.api_key:
+                raise ValueError("Missing api_key for OpenAI provider.")
+            return ChatOpenAI(
+                model=cfg.model,
+                temperature=cfg.temperature,
+                api_key=cfg.api_key,
+            )
+
+        case Provider.ANTHROPIC:
+            from langchain_anthropic import ChatAnthropic  # pip install langchain-anthropic
+
+            if not cfg.api_key:
+                raise ValueError("Missing api_key for Anthropic provider.")
+            return ChatAnthropic(
+                model=cfg.model,
+                temperature=cfg.temperature,
+                api_key=cfg.api_key,
+            )
+
+        case Provider.AZURE_OPENAI:
+            from langchain_openai import AzureChatOpenAI  # pip install langchain-openai
+
+            missing = [
+                name
+                for name, value in {
+                    "api_key": cfg.api_key,
+                    "azure_endpoint": cfg.azure_endpoint,
+                    "azure_deployment": cfg.azure_deployment,
+                    "api_version": cfg.api_version,
+                }.items()
+                if not value
+            ]
+            if missing:
+                raise ValueError(f"Missing Azure settings: {', '.join(missing)}")
+
+            return AzureChatOpenAI(
+                api_key=cfg.api_key,
+                azure_endpoint=cfg.azure_endpoint,
+                azure_deployment=cfg.azure_deployment,
+                api_version=cfg.api_version,
+                temperature=cfg.temperature,
+            )
+
+        case _:
+            raise ValueError(f"Unsupported provider: {cfg.provider}")
+
+
+def build_embeddings(cfg: EmbeddingsConfig):
+    match cfg.provider:
+        case Provider.OLLAMA:
+            return OllamaEmbeddings(
+                model=cfg.model,
+                base_url=cfg.ollama_base_url,
+            )
+
+        case Provider.OPENAI:
+            from langchain_openai import OpenAIEmbeddings  # pip install langchain-openai
+
+            if not cfg.api_key:
+                raise ValueError("Missing api_key for OpenAI embeddings provider.")
+            return OpenAIEmbeddings(
+                model=cfg.model,
+                api_key=cfg.api_key,
+            )
+
+        case Provider.AZURE_OPENAI:
+            from langchain_openai import AzureOpenAIEmbeddings  # pip install langchain-openai
+
+            missing = [
+                name
+                for name, value in {
+                    "api_key": cfg.api_key,
+                    "azure_endpoint": cfg.azure_endpoint,
+                    "azure_deployment": cfg.azure_deployment,
+                    "api_version": cfg.api_version,
+                }.items()
+                if not value
+            ]
+            if missing:
+                raise ValueError(f"Missing Azure settings: {', '.join(missing)}")
+
+            return AzureOpenAIEmbeddings(
+                api_key=cfg.api_key,
+                azure_endpoint=cfg.azure_endpoint,
+                azure_deployment=cfg.azure_deployment,
+                api_version=cfg.api_version,
+            )
+
+        case _:
+            raise ValueError(f"Unsupported embeddings provider: {cfg.provider}")

src/llm_factory v2.py

@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import StrEnum
+from typing import Optional
+
+
+# ---------- Providers ----------
+class Provider(StrEnum):
+    OLLAMA = "ollama"
+    OPENAI = "openai"
+    ANTHROPIC = "anthropic"
+    AWS_BEDROCK = "aws_bedrock"
+    HUGGINGFACE = "huggingface"
+
+
+# ---------- Provider-specific configs ----------
+@dataclass(frozen=True)
+class OllamaCfg:
+    base_url: Optional[str] = None
+    validate_model_on_init: bool = True
+
+
+@dataclass(frozen=True)
+class OpenAICfg:
+    api_key: str
+
+
+@dataclass(frozen=True)
+class AnthropicCfg:
+    api_key: str
+
+
+@dataclass(frozen=True)
+class BedrockCfg:
+    # depende de cómo autentiques: env vars, perfil AWS, role, etc.
+    region_name: Optional[str] = None
+    # model_kwargs típicos: temperature, max_tokens, etc. (según wrapper)
+    # lo dejamos mínimo para no acoplar
+    pass
+
+
+@dataclass(frozen=True)
+class HuggingFaceCfg:
+    # puede ser token HF o endpoint, según uses Inference API o local
+    api_key: Optional[str] = None
+    endpoint_url: Optional[str] = None
+
+
+# ---------- Base configs ----------
+@dataclass(frozen=True)
+class ChatModelConfig:
+    provider: Provider
+    model: str
+    temperature: float = 0.0
+
+    # EXACTAMENTE una de estas debería venir informada según provider:
+    ollama: Optional[OllamaCfg] = None
+    openai: Optional[OpenAICfg] = None
+    anthropic: Optional[AnthropicCfg] = None
+    bedrock: Optional[BedrockCfg] = None
+    huggingface: Optional[HuggingFaceCfg] = None
+
+
+@dataclass(frozen=True)
+class EmbeddingsConfig:
+    provider: Provider
+    model: str
+
+    ollama: Optional[OllamaCfg] = None
+    openai: Optional[OpenAICfg] = None
+    bedrock: Optional[BedrockCfg] = None
+    huggingface: Optional[HuggingFaceCfg] = None
+
+
+# ---------- Helpers ----------
+def _require(value, msg: str):
+    if value is None:
+        raise ValueError(msg)
+    return value
+
+
+def _require_cfg(cfg_obj, msg: str):
+    if cfg_obj is None:
+        raise ValueError(msg)
+    return cfg_obj
+
+
+# ---------- Builders ----------
+def build_chat_model(cfg: ChatModelConfig):
+    match cfg.provider:
+        case Provider.OLLAMA:
+            from langchain_ollama import ChatOllama
+
+            ocfg = cfg.ollama or OllamaCfg()
+            return ChatOllama(
+                model=cfg.model,
+                temperature=cfg.temperature,
+                validate_model_on_init=ocfg.validate_model_on_init,
+                base_url=ocfg.base_url,
+            )
+
+        case Provider.OPENAI:
+            from langchain_openai import ChatOpenAI  # pip install langchain-openai
+
+            ocfg = _require_cfg(cfg.openai, "Missing cfg.openai for OpenAI provider.")
+            return ChatOpenAI(
+                model=cfg.model,
+                temperature=cfg.temperature,
+                api_key=ocfg.api_key,
+            )
+
+        case Provider.ANTHROPIC:
+            from langchain_anthropic import ChatAnthropic  # pip install langchain-anthropic
+
+            acfg = _require_cfg(cfg.anthropic, "Missing cfg.anthropic for Anthropic provider.")
+            return ChatAnthropic(
+                model=cfg.model,
+                temperature=cfg.temperature,
+                api_key=acfg.api_key,
+            )
+
+        case Provider.AWS_BEDROCK:
+            # wrapper típico: langchain-aws (según versión) o langchain-community en algunos setups
+            # aquí lo dejo como ejemplo con guardrail claro
+            try:
+                from langchain_aws import ChatBedrock  # pip install langchain-aws
+            except Exception as e:
+                raise ImportError(
+                    "To use AWS Bedrock, install `langchain-aws` and configure AWS credentials."
+                ) from e
+
+            bcfg = cfg.bedrock or BedrockCfg()
+            # OJO: ChatBedrock suele usar model_id en vez de model, depende del wrapper/versión.
+            return ChatBedrock(
+                model_id=cfg.model,
+                region_name=bcfg.region_name,
+                model_kwargs={"temperature": cfg.temperature},
+            )
+
+        case Provider.HUGGINGFACE:
+            # depende MUCHO: endpoint, local pipeline, inference API...
+            raise NotImplementedError(
+                "HUGGINGFACE provider not implemented here (depends on whether you use Inference API, TGI, or local pipeline)."
+            )
+
+        case _:
+            raise ValueError(f"Unsupported provider: {cfg.provider}")
+
+
+def build_embeddings(cfg: EmbeddingsConfig):
+    match cfg.provider:
+        case Provider.OLLAMA:
+            from langchain_ollama import OllamaEmbeddings
+
+            ocfg = cfg.ollama or OllamaCfg()
+            return OllamaEmbeddings(
+                model=cfg.model,
+                base_url=ocfg.base_url,
+            )
+
+        case Provider.OPENAI:
+            from langchain_openai import OpenAIEmbeddings  # pip install langchain-openai
+
+            ocfg = _require_cfg(cfg.openai, "Missing cfg.openai for OpenAI embeddings provider.")
+            return OpenAIEmbeddings(
+                model=cfg.model,
+                api_key=ocfg.api_key,
+            )
+
+        case Provider.AWS_BEDROCK:
+            # Igual: depende del wrapper
+            raise NotImplementedError("Bedrock embeddings: añade el wrapper que uses y mapea aquí.")
+
+        case Provider.HUGGINGFACE:
+            raise NotImplementedError("HuggingFace embeddings: depende del wrapper (endpoint/local).")
+
+        case _:
+            raise ValueError(f"Unsupported embeddings provider: {cfg.provider}")

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
@@ -16,6 +16,24 @@ resolution-markers = [
     "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
+[[package]]
+name = "accelerate"
+version = "1.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9f/d2/c581486aa6c4fbd7394c23c47b83fa1a919d34194e16944241daf9e762dd/accelerate-1.12.0-py3-none-any.whl", hash = "sha256:3e2091cd341423207e2f084a6654b1efcd250dc326f2a37d6dde446e07cabb11", size = 380935, upload-time = "2025-11-21T11:27:44.522Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -171,6 +189,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
 ]
 
+[[package]]
+name = "appdirs"
+version = "1.4.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d7/d8/05696357e0311f5b5c316d7b95f46c669dd9c15aaeecbb48c7d0aeb88c40/appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41", size = 13470, upload-time = "2020-05-11T07:59:51.037Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3b/00/2344469e2084fb287c2e0b57b72910309874c3245463acd6cf5e3db69324/appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128", size = 9566, upload-time = "2020-05-11T07:59:49.499Z" },
+]
+
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -241,12 +268,14 @@ name = "assistance-engine"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "accelerate" },
     { name = "grpcio" },
     { name = "grpcio-reflection" },
     { name = "grpcio-tools" },
     { name = "langchain" },
     { name = "langchain-community" },
     { name = "langchain-elasticsearch" },
+    { name = "langchain-huggingface" },
     { name = "langchain-ollama" },
     { name = "loguru" },
     { name = "nltk" },
@@ -256,7 +285,6 @@ dependencies = [
     { name = "torch" },
     { name = "torchvision" },
     { name = "tqdm" },
-    { name = "transformers" },
 ]
 
 [package.dev-dependencies]
@@ -264,19 +292,23 @@ dev = [
     { name = "beir" },
     { name = "jupyter" },
     { name = "langfuse" },
+    { name = "markdown" },
     { name = "mteb" },
     { name = "polars" },
+    { name = "ragas" },
     { name = "ruff" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "accelerate", specifier = ">=1.12.0" },
     { name = "grpcio", specifier = ">=1.78.0" },
     { name = "grpcio-reflection", specifier = ">=1.78.0" },
     { name = "grpcio-tools", specifier = ">=1.78.0" },
     { name = "langchain", specifier = ">=1.2.10" },
     { name = "langchain-community", specifier = ">=0.4.1" },
     { name = "langchain-elasticsearch", specifier = ">=1.0.0" },
+    { name = "langchain-huggingface", specifier = ">=1.2.0" },
     { name = "langchain-ollama", specifier = ">=1.0.1" },
     { name = "loguru", specifier = ">=0.7.3" },
     { name = "nltk", specifier = ">=3.9.2" },
@@ -286,16 +318,17 @@ requires-dist = [
     { name = "torch", specifier = ">=2.10.0" },
     { name = "torchvision", specifier = ">=0.25.0" },
     { name = "tqdm", specifier = ">=4.67.3" },
-    { name = "transformers", specifier = ">=5.2.0" },
 ]
 
 [package.metadata.requires-dev]
 dev = [
     { name = "beir", specifier = ">=2.2.0" },
     { name = "jupyter", specifier = ">=1.1.1" },
-    { name = "langfuse", specifier = ">=3.14.4" },
+    { name = "langfuse", specifier = "<=3" },
+    { name = "markdown", specifier = ">=3.10.2" },
     { name = "mteb", specifier = ">=2.8.8" },
     { name = "polars", specifier = ">=1.38.1" },
+    { name = "ragas", specifier = ">=0.4.3" },
     { name = "ruff", specifier = ">=0.15.1" },
 ]
 
@@ -684,6 +717,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "diskcache"
+version = "5.6.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3f/21/1c1ffc1a039ddcc459db43cc108658f32c57d271d7289a2794e401d0fdb6/diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc", size = 67916, upload-time = "2023-08-31T06:12:00.316Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/27/4570e78fc0bf5ea0ca45eb1de3818a23787af9b390c0b0a0033a1b8236f9/diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19", size = 45550, upload-time = "2023-08-31T06:11:58.822Z" },
+]
+
 [[package]]
 name = "distro"
 version = "1.9.0"
@@ -693,6 +735,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
 ]
 
+[[package]]
+name = "docstring-parser"
+version = "0.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b2/9d/c3b43da9515bd270df0f80548d9944e389870713cc1fe2b8fb35fe2bcefd/docstring_parser-0.17.0.tar.gz", hash = "sha256:583de4a309722b3315439bb31d64ba3eebada841f2e2cee23b99df001434c912", size = 27442, upload-time = "2025-07-21T07:35:01.868Z" }
+wheels = [
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+]
+
 [[package]]
 name = "elastic-transport"
 version = "8.17.1"
@@ -1139,23 +1190,21 @@ wheels = [
 
 [[package]]
 name = "huggingface-hub"
-version = "1.4.1"
+version = "0.36.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock" },
     { name = "fsspec" },
-    { name = "hf-xet", marker = "platform_machine == 'AMD64' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" },
-    { name = "httpx" },
+    { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" },
     { name = "packaging" },
     { name = "pyyaml" },
-    { name = "shellingham" },
+    { name = "requests" },
     { name = "tqdm" },
-    { name = "typer-slim" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/c4/fc/eb9bc06130e8bbda6a616e1b80a7aa127681c448d6b49806f61db2670b61/huggingface_hub-1.4.1.tar.gz", hash = "sha256:b41131ec35e631e7383ab26d6146b8d8972abc8b6309b963b306fbcca87f5ed5", size = 642156, upload-time = "2026-02-06T09:20:03.013Z" }
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+    { name = "diskcache" },
+    { name = "docstring-parser" },
+    { name = "jinja2" },
+    { name = "jiter" },
+    { name = "openai" },
+    { name = "pydantic" },
+    { name = "pydantic-core" },
+    { name = "requests" },
+    { name = "rich" },
+    { name = "tenacity" },
+    { name = "typer" },
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 version = "7.2.0"
@@ -1291,87 +1363,87 @@ wheels = [
 
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