working on dual index
This commit is contained in:
pseco
parent
c2e43c030a
commit
9575af3ff0
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,572 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"id": "0a8abbfa",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import re\n",
|
||||
"import uuid\n",
|
||||
"from pathlib import Path\n",
|
||||
"\n",
|
||||
"from langchain_core.documents import Document\n",
|
||||
"from langchain_elasticsearch import ElasticsearchStore\n",
|
||||
"from langchain_ollama import OllamaEmbeddings\n",
|
||||
"from transformers import AutoConfig\n",
|
||||
"from elasticsearch import Elasticsearch\n",
|
||||
"import nltk\n",
|
||||
"\n",
|
||||
"from lark import Lark, Transformer\n",
|
||||
"from src.config import PROJ_ROOT, DATA_DIR\n",
|
||||
"from datetime import datetime\n",
|
||||
"nltk.download(\"punkt\", quiet=True)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "5c9d292b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ELASTICSEARCH_URL = os.getenv(\"ELASTICSEARCH_LOCAL_URL\")\n",
|
||||
"ELASTICSEARCH_DOCS_INDEX = os.getenv(\"ELASTICSEARCH_DOCS_INDEX\")\n",
|
||||
"ELASTICSEARCH_CODE_INDEX = os.getenv(\"ELASTICSEARCH_CODE_INDEX\")\n",
|
||||
"HF_EMB_MODEL_NAME = os.getenv(\"HF_EMB_MODEL_NAME\")\n",
|
||||
"OLLAMA_URL = os.getenv(\"OLLAMA_URL\")\n",
|
||||
"OLLAMA_LOCAL_URL = os.getenv(\"OLLAMA_LOCAL_URL\")\n",
|
||||
"OLLAMA_MODEL_NAME = os.getenv(\"OLLAMA_MODEL_NAME\")\n",
|
||||
"OLLAMA_EMB_MODEL_NAME = os.getenv(\"OLLAMA_EMB_MODEL_NAME\")\n",
|
||||
"\n",
|
||||
"config = AutoConfig.from_pretrained(HF_EMB_MODEL_NAME)\n",
|
||||
"embedding_dim = config.hidden_size"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "0e1cd9b9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"grammar = (DATA_DIR / \"raw\" / \"code\" / \"BNF_v3.txt\").read_text(\n",
|
||||
" encoding=\"utf-8\"\n",
|
||||
")\n",
|
||||
"code = (DATA_DIR / \"raw\" / \"code\" / \"Code_Snippets_v1.txt\").read_text(\n",
|
||||
" encoding=\"utf-8\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "baa779f3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Functions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "ca43bd67",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class BnfToDict(Transformer):\n",
|
||||
" def NONTERMINAL(self, token):\n",
|
||||
" return str(token)[1:-1].strip()\n",
|
||||
"\n",
|
||||
" def QUOTED(self, token):\n",
|
||||
" return str(token)[1:-1]\n",
|
||||
"\n",
|
||||
" def IDENT(self, token):\n",
|
||||
" return str(token)\n",
|
||||
"\n",
|
||||
" def SYMBOL(self, token):\n",
|
||||
" return str(token)\n",
|
||||
"\n",
|
||||
" def alternative(self, items):\n",
|
||||
" return [str(item) for item in items]\n",
|
||||
"\n",
|
||||
" def expansion(self, items):\n",
|
||||
" return items\n",
|
||||
"\n",
|
||||
" def rule(self, items):\n",
|
||||
" lhs = items[0]\n",
|
||||
" rhs = items[1]\n",
|
||||
" return {\"lhs\": lhs, \"alternatives\": rhs}\n",
|
||||
"\n",
|
||||
" def start(self, items):\n",
|
||||
" return items\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def load_bnf_rules_for_rag(grammar, code_path: Path) -> list[dict]:\n",
|
||||
" parser = Lark(grammar, parser=\"lalr\")\n",
|
||||
" transformer = BnfToDict()\n",
|
||||
" rules: list[dict] = []\n",
|
||||
" skipped: list[str] = []\n",
|
||||
"\n",
|
||||
" for raw_line in code_path.read_text(encoding=\"utf-8\").splitlines():\n",
|
||||
" line = raw_line.strip()\n",
|
||||
" if not line or line.startswith(\"#\") or \"::=\" not in line:\n",
|
||||
" continue\n",
|
||||
" try:\n",
|
||||
" tree = parser.parse(line)\n",
|
||||
" parsed = transformer.transform(tree)\n",
|
||||
" rules.extend(parsed)\n",
|
||||
" except Exception:\n",
|
||||
" skipped.append(line)\n",
|
||||
"\n",
|
||||
" print(f\"Parsed rules: {len(rules)}\")\n",
|
||||
" print(f\"Skipped lines: {len(skipped)}\")\n",
|
||||
" return rules"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "7969500e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def bnf_rules_to_documents(rules: list[dict]) -> list[Document]:\n",
|
||||
" docs: list[Document] = []\n",
|
||||
" for rule in rules:\n",
|
||||
" lhs = rule[\"lhs\"]\n",
|
||||
" alternatives = rule[\"alternatives\"]\n",
|
||||
" rendered_alts = [\" \".join(alt) if alt else \"<empty>\" for alt in alternatives]\n",
|
||||
" content = f\"{lhs} ::= \" + \" | \".join(rendered_alts)\n",
|
||||
" docs.append(\n",
|
||||
" Document(\n",
|
||||
" id=str(uuid.uuid4()),\n",
|
||||
" page_content=content,\n",
|
||||
" metadata={\n",
|
||||
" \"source\": \"BNF.txt\",\n",
|
||||
" \"type\": \"grammar_rule\",\n",
|
||||
" \"non_terminal\": lhs,\n",
|
||||
" \"alternatives\": len(alternatives),\n",
|
||||
" },\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" return docs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "7c67fa0b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def extract_rag_chunks(source_code, parser):\n",
|
||||
" # Limpiamos comentarios y líneas de texto del ejemplo para que el parser no falle\n",
|
||||
" lines = [l for l in source_code.split('\\n') if not l.startswith('###') and l.strip()]\n",
|
||||
" \n",
|
||||
" chunks = []\n",
|
||||
" for line in lines:\n",
|
||||
" try:\n",
|
||||
" # Quitamos el número del ejemplo (ej: \"1. \")\n",
|
||||
" clean_line = line.split(':', 1)[-1] if ':' in line else line\n",
|
||||
" tree = parser.parse(clean_line.strip())\n",
|
||||
" \n",
|
||||
" chunks.append({\n",
|
||||
" \"code\": clean_line.strip(),\n",
|
||||
" \"type\": \"statement\",\n",
|
||||
" \"length\": len(clean_line)\n",
|
||||
" })\n",
|
||||
" except Exception as e:\n",
|
||||
" continue # Si la línea no cumple el BNF, la saltamos\n",
|
||||
" return chunks"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "ab8c2b9b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def extraer_codigo_puro(code, parser):\n",
|
||||
" # Extraemos el contenido dentro de bloques de código Markdown ``` ... ```\n",
|
||||
" bloques_codigo = re.findall(r'```(.*?)```', code, re.DOTALL)\n",
|
||||
" \n",
|
||||
" chunks_validados = []\n",
|
||||
" \n",
|
||||
" for bloque in bloques_codigo:\n",
|
||||
" lineas = bloque.strip().split('\\n')\n",
|
||||
" lineas_limpias = []\n",
|
||||
" \n",
|
||||
" for linea in lineas:\n",
|
||||
" linea = linea.strip()\n",
|
||||
" if not linea: continue\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" # Validamos cada línea con tu BNF\n",
|
||||
" parser.parse(linea)\n",
|
||||
" lineas_limpias.append(linea)\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"⚠️ Línea saltada (no cumple BNF): {linea}\")\n",
|
||||
" \n",
|
||||
" if lineas_limpias:\n",
|
||||
" chunks_validados.append(\"\\n\".join(lineas_limpias))\n",
|
||||
" \n",
|
||||
" return chunks_validados"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"id": "8ed54f3f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def procesar_snippet_con_metadata(code, parser):\n",
|
||||
" # 1. Separar por los bloques numerados (ej: 1. **Nombre**)\n",
|
||||
" patron_bloque = re.compile(r'\\d+\\.\\s+\\*\\*(.*?)\\*\\*')\n",
|
||||
" bloques = patron_bloque.split(code)[1:] \n",
|
||||
" \n",
|
||||
" documentos_elastic = []\n",
|
||||
" \n",
|
||||
" for i in range(0, len(bloques), 2):\n",
|
||||
" titulo = bloques[i].strip()\n",
|
||||
" contenido_bruto = bloques[i+1]\n",
|
||||
" \n",
|
||||
" # 2. Extraer el código dentro de las triple comillas ```\n",
|
||||
" codigo_match = re.search(r'```(.*?)```', contenido_bruto, re.DOTALL)\n",
|
||||
" if codigo_match:\n",
|
||||
" codigo_bloque = codigo_match.group(1).strip()\n",
|
||||
" \n",
|
||||
" # 3. Validar con Lark cada línea\n",
|
||||
" lineas_validas = []\n",
|
||||
" for linea in codigo_bloque.split('\\n'):\n",
|
||||
" linea_clean = linea.strip()\n",
|
||||
" if linea_clean:\n",
|
||||
" try:\n",
|
||||
" parser.parse(linea_clean)\n",
|
||||
" lineas_validas.append(linea_clean)\n",
|
||||
" except:\n",
|
||||
" print(f\"⚠️ Error sintáctico (BNF) en: {linea_clean}\")\n",
|
||||
" \n",
|
||||
" # 4. Crear el formato DOCUMENTO para Elasticsearch\n",
|
||||
" if lineas_validas:\n",
|
||||
" codigo_final = \"\\n\".join(lineas_validas)\n",
|
||||
" \n",
|
||||
" doc = {\n",
|
||||
" \"_id\": str(uuid.uuid4()), # ID único para evitar colisiones\n",
|
||||
" \"title\": titulo,\n",
|
||||
" \"content\": codigo_final, # El campo principal para el RAG\n",
|
||||
" \"metadata\": {\n",
|
||||
" \"tipo_bloque\": \"ejemplo_fundamentos\",\n",
|
||||
" \"lenguaje\": \"AVAP\",\n",
|
||||
" \"line_count\": len(lineas_validas),\n",
|
||||
" \"char_count\": len(codigo_final),\n",
|
||||
" # \"ingested_at\": datetime.now().isoformat()\n",
|
||||
" }\n",
|
||||
" }\n",
|
||||
" documentos_elastic.append(doc)\n",
|
||||
" \n",
|
||||
" return documentos_elastic"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "560f9f86",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Ensure every item is a langchain `Document` before indexing\n",
|
||||
"def ensure_documents(items):\n",
|
||||
" out: list[Document] = []\n",
|
||||
" for x in items:\n",
|
||||
" if isinstance(x, Document):\n",
|
||||
" out.append(x)\n",
|
||||
" continue\n",
|
||||
" if isinstance(x, dict):\n",
|
||||
" content = x.get(\"content\") or x.get(\"page_content\") or x.get(\"text\") or x.get(\"code\") or str(x)\n",
|
||||
" id_ = x.get(\"_id\") or x.get(\"id\") or str(uuid.uuid4())\n",
|
||||
" metadata = x.get(\"metadata\") or {k: v for k, v in x.items() if k not in (\"_id\", \"id\", \"content\", \"page_content\", \"text\", \"code\")}\n",
|
||||
" out.append(Document(id=id_, page_content=content, metadata=metadata))\n",
|
||||
" continue\n",
|
||||
" if isinstance(x, str):\n",
|
||||
" out.append(Document(id=str(uuid.uuid4()), page_content=x, metadata={\"source\": \"chunked_code\"}))\n",
|
||||
" continue\n",
|
||||
" # handle objects from chunkers with `.text` attribute\n",
|
||||
" if hasattr(x, \"text\") or hasattr(x, \"token_count\"):\n",
|
||||
" txt = getattr(x, \"text\", str(x))\n",
|
||||
" out.append(Document(id=str(uuid.uuid4()), page_content=txt, metadata={\"source\": \"chunker\", \"token_count\": getattr(x, \"token_count\", None)}))\n",
|
||||
" continue\n",
|
||||
" # fallback: stringify the object\n",
|
||||
" out.append(Document(id=str(uuid.uuid4()), page_content=str(x), metadata={\"source\": \"unknown\", \"orig_type\": type(x).__name__}))\n",
|
||||
" return out"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "23a92e13",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## BNF "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "6e777f53",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"parser = Lark(grammar)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "2981a944",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"code_chunks = extract_rag_chunks(source_code=code, parser=parser)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"id": "950b5789",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chunked_code = extraer_codigo_puro(code=code, parser=parser)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"id": "3be3c168",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"code_snippets=procesar_snippet_con_metadata(code=code, parser=parser)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "77f6c552",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Elastic Search"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "09ce3e29",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"es = Elasticsearch(\n",
|
||||
" ELASTICSEARCH_URL,\n",
|
||||
" request_timeout=120,\n",
|
||||
" max_retries=5,\n",
|
||||
" retry_on_timeout=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "d575c386",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if es.indices.exists(index=ELASTICSEARCH_DOCS_INDEX):\n",
|
||||
" es.indices.delete(index=ELASTICSEARCH_DOCS_INDEX)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "40ea0af8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"avap-docs-test\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for index in es.indices.get(index=\"*\"):\n",
|
||||
" print(index)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"id": "4e091b39",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"OllamaEmbeddings(model='qwen3-0.6B-emb:latest', validate_model_on_init=False, base_url='http://localhost:11434', client_kwargs={}, async_client_kwargs={}, sync_client_kwargs={}, mirostat=None, mirostat_eta=None, mirostat_tau=None, num_ctx=None, num_gpu=None, keep_alive=None, num_thread=None, repeat_last_n=None, repeat_penalty=None, temperature=None, stop=None, tfs_z=None, top_k=None, top_p=None)"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"embeddings = OllamaEmbeddings(base_url=OLLAMA_LOCAL_URL, model=OLLAMA_EMB_MODEL_NAME)\n",
|
||||
"embeddings"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1ed4c817",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Prepared docs: 50\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n",
|
||||
"# convert current list to Documents\n",
|
||||
"docs_to_index = ensure_documents(chunked_code)\n",
|
||||
"print(\"Prepared docs:\", len(docs_to_index))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"id": "5aff21c0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# index into Elasticsearch\n",
|
||||
"db = ElasticsearchStore.from_documents(\n",
|
||||
" docs_to_index,\n",
|
||||
" embeddings,\n",
|
||||
" client=es,\n",
|
||||
" index_name=ELASTICSEARCH_DOCS_INDEX,\n",
|
||||
" distance_strategy=\"COSINE\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "74c0a377",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"response = es.search(\n",
|
||||
" index=ELASTICSEARCH_DOCS_INDEX,\n",
|
||||
" body={\n",
|
||||
" \"query\": {\"match_all\": {}},\n",
|
||||
" \"size\": 10 \n",
|
||||
" }\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"for hit in response[\"hits\"][\"hits\"]:\n",
|
||||
" print(\"ID:\", hit[\"_id\"])\n",
|
||||
" print(\"Source:\", hit[\"_source\"])\n",
|
||||
" print(\"-\" * 40)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d823650e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Retrive"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5732a27d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"base_retriever = db.as_retriever(\n",
|
||||
" search_type=\"similarity\",\n",
|
||||
" search_kwargs={\"k\": 5}\n",
|
||||
" ) \n",
|
||||
"\n",
|
||||
"docs = base_retriever.invoke(\"What reserved words does AVAP have?\")\n",
|
||||
"docs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8706506f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = OllamaEmbeddings(base_url=OLLAMA_URL, model=OLLAMA_EMB_MODEL_NAME)\n",
|
||||
"\n",
|
||||
"vector_store = ElasticsearchStore(\n",
|
||||
" client=es,\n",
|
||||
" index_name=ELASTICSEARCH_DOCS_INDEX,\n",
|
||||
" embedding=embeddings,\n",
|
||||
" query_field=\"text\",\n",
|
||||
" vector_query_field=\"vector\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"results = vector_store.similarity_search_with_score(\n",
|
||||
" query=\"What data types does AVAP have?\",\n",
|
||||
" k=50\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"results"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
|
|
@ -22,9 +22,9 @@ if [ ! -f "$KUBECONFIG_PATH" ]; then
|
|||
fi
|
||||
|
||||
# 1. AI Model Tunnel (Ollama)
|
||||
# echo -e "${YELLOW}[1/3]${NC} Starting Ollama Light Service tunnel (localhost:11434)..."
|
||||
# kubectl port-forward --address 0.0.0.0 svc/ollama-light-service 11434:11434 -n brunix --kubeconfig "$KUBECONFIG_PATH" &
|
||||
# OLLAMA_PID=$!
|
||||
echo -e "${YELLOW}[1/3]${NC} Starting Ollama Light Service tunnel (localhost:11434)..."
|
||||
kubectl port-forward --address 0.0.0.0 svc/ollama-light-service 11434:11434 -n brunix --kubeconfig "$KUBECONFIG_PATH" &
|
||||
OLLAMA_PID=$!
|
||||
|
||||
# 2. Knowledge Base Tunnel (Elasticsearch)
|
||||
echo -e "${YELLOW}[2/3]${NC} Starting Elasticsearch Vector DB tunnel (localhost:9200)..."
|
||||
|
|
|
|||
Loading…
Reference in New Issue