Upload 3 files

Llm_local.py

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+import ollama
+import time
+import  streamlit as st 
+import fitz  
+from docx import Document
+from io import BytesIO
+from fpdf import FPDF
+
+def get_response_from_mistral(query, context):
+    prompt_text = f"""
+    "Tú eres un asistente para tareas de respuesta a preguntas. "
+    "Usa los siguientes fragmentos de contexto recuperado para responder la pregunta. "
+    "Si el contexto está vacío o no contiene información relevante, responde: 'Disculpa, no tengo información para responder esa pregunta'. "
+    "Si el contexto es válido, responde la pregunta usando un mínimo de 2 oraciones y un máximo de 4, manteniendo la respuesta clara y concisa. "
+    "No inventes ni asumas nada que no esté explícitamente en el contexto."
+    "\n\n"
+
+    Usa solo este contexto:
+    {context}
+    **IMPORTANTE***
+    "Ojo siempre que tu contexto es vacio, tu respuesta debe ser : Disculpa, no tengo información para responder esa pregunta"
+    **
+    Y Responde esta pregunta:
+    {query}
+    """
+
+    respuesta = ollama.chat(
+        model="mistral",
+        messages=[
+            {"role": "system", "content": "Eres un asistente especializado en análisis de datos."},
+            {"role": "user", "content": prompt_text}
+        ]
+    )
+
+    respuesta_texto = respuesta["message"]["content"] 
+    
+    for word in respuesta_texto.split():  
+        yield word + " "
+        time.sleep(0.05)
+
+def generarPages():
+    with st.sidebar:
+        st.page_link("chatbox_v1.py", label="Inicio", icon="🏠")
+        st.page_link("pages/resumen_word.py", label="Informe de PDF y Word", icon="📄")
+        st.page_link("pages/insertardocumentos.py", label="Documentos a vector", icon="🛢️")
+
+def informes_mistral(context):
+    prompt_text = f"""
+        **Atención**: No generes una historia o narrativa, tu tarea es realizar un análisis detallado y preciso del documento legal. No se requiere creatividad, solo precisión.
+        Eres un asistente experto en procesamiento y análisis de documentos. Tu tarea es leer y comprender el contenido proporcionado y generar un informe extenso, detallado y bien estructurado. 
+        El informe debe incluir las siguientes secciones:
+
+        1. **Resumen General**: Proporciona un resumen completo y detallado de todo el contenido del documento. Incluye los aspectos más relevantes, pero sin dejar de lado detalles importantes.
+        
+        2. **Puntos Clave**: Enumera los puntos más importantes del documento, resaltando las ideas principales y los aspectos críticos que se abordan.
+
+        3. **Análisis Crítico**: Realiza un análisis en profundidad sobre el contenido del documento. Comenta sobre su calidad, lógica, coherencia, posibles fallos, aspectos positivos, y cualquier otro elemento que pueda ser relevante. 
+
+        4. **Recomendaciones**: Proporciona sugerencias o recomendaciones para mejorar el contenido. Si el documento se trata de un informe técnico, análisis de datos, o investigación, incluye sugerencias de cómo se podría mejorar la interpretación de los datos, el análisis o la presentación.
+
+        5. **Conclusiones**: Finaliza con una sección de conclusiones que recapitule los puntos clave del análisis y del documento en general, además de una visión global de las implicaciones del contenido.
+
+        6: **En caso de**: En caso de que el contenido sea acerca de un decreto legislativo o algo acera de una ley incluye un seccion donde hables lo mas importante de todos los articulos y menciones cuales son los mas relevantes.
+
+        7: **Documentos Analizados**: Menciona el nombre de todos los documentos que componen el contenido analizado. Si hay más de uno, asegúrate de listarlos todos y dejar claro que el análisis se basa en todos ellos en conjunto.
+
+        Siempre deberás comenzar el informe con los nombres de los archivos que componen el contenido.
+        Recuerda que siempre debes mantener la estructura que te mande
+
+        Contenido del documento:
+        {context}
+
+        Utiliza un estilo claro y profesional en todo momento, y asegúrate de que cada sección esté claramente diferenciada. Tu informe debe ser extenso y abarcativo, no debe ser corto ni vago.
+        recuerda siempre reponder en español
+    """
+    respuesta = ollama.chat(
+        model="mistral",
+        messages=[
+            {"role": "system", "content": "Eres un asistente especializado en análisis detallado de documentos."},
+            {"role": "user", "content": prompt_text}
+        ]
+    )
+
+    respuesta_texto = respuesta["message"]["content"] 
+    
+    for word in respuesta_texto.split():  
+        yield word + " "
+        time.sleep(0.05)
+
+def extraer_texto(archivo):
+    if archivo.name.endswith(".pdf"):
+        texto = ""
+        with fitz.open(stream=archivo.read(), filetype="pdf") as doc:
+            for page in doc:
+                texto += page.get_text()
+        return texto
+    elif archivo.name.endswith(".txt"):
+        return archivo.read().decode("utf-8")
+    else:
+        return ""
+
+def extraer_texto_word(file):
+    texto = ""
+    doc = Document(file)
+    for para in doc.paragraphs:
+        texto += para.text + "\n"
+    return texto
+
+def generar_docx(texto):
+    doc = Document()
+    doc.add_heading("Resumen generado por IA", 0)
+    for parrafo in texto.split("\n"):
+        doc.add_paragraph(parrafo)
+    buffer = BytesIO()
+    doc.save(buffer)
+    buffer.seek(0)
+    return buffer
+
+def generar_pdf(texto):
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.set_font("Arial", size=12)
+    for linea in texto.split("\n"):
+        pdf.multi_cell(0, 10, linea)
+    return bytes(pdf.output(dest='S').encode('latin-1'))

Rag_milvus.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bf597549",
+   "metadata": {},
+   "source": [
+    "PRIMERO PREPARAMOS TODAS NUESTRAS FUNCIONES PARA PODER SER CONVOCADAS LUEGO."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7968949c",
+   "metadata": {},
+   "source": [
+    "Instalamos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9a192af6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting qdrant-client\n",
+      "  Obtaining dependency information for qdrant-client from https://files.pythonhosted.org/packages/e4/52/f49b0aa96253010f57cf80315edecec4f469e7a39c1ed92bf727fa290e57/qdrant_client-1.14.2-py3-none-any.whl.metadata\n",
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+      "Collecting protobuf>=3.20.0 (from qdrant-client)\n",
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+      "Successfully installed grpcio-1.71.0 h2-4.2.0 hpack-4.1.0 hyperframe-6.1.0 portalocker-2.10.1 protobuf-6.30.2 pymupdf-1.25.5 qdrant-client-1.14.2\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
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+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 23.2.1 -> 25.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install qdrant-client transformers torch langchain pymupdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4833977",
+   "metadata": {},
+   "source": [
+    "Importamos librerias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1684b4de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sentence_transformers import SentenceTransformer\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.schema import Document\n",
+    "from qdrant_client import QdrantClient\n",
+    "from qdrant_client.models import PointStruct, Distance, VectorParams\n",
+    "import fitz  # PyMuPDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e612e674",
+   "metadata": {},
+   "source": [
+    "Definimos funciones\n",
+    "1) Cargar los pdf por sus bloques de paginas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5a594ed8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pdfachunk(path, chunk_size_pages=20):\n",
+    "    doc = fitz.open(path)\n",
+    "    chunks = []\n",
+    "    for i in range(0, len(doc), chunk_size_pages):\n",
+    "        text = \"\"\n",
+    "        for page_num in range(i, min(i + chunk_size_pages, len(doc))):\n",
+    "            text += doc[page_num].get_text()\n",
+    "        chunks.append(text)\n",
+    "    doc.close()\n",
+    "    return chunks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59d048b7",
+   "metadata": {},
+   "source": [
+    "2) Dividir texto en chunks más pequeños con solapamiento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bffac6eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_chunks(raw_chunks, chunk_size=1024, chunk_overlap=100):\n",
+    "    docs = [Document(page_content=chunk) for chunk in raw_chunks]\n",
+    "    splitter = RecursiveCharacterTextSplitter(\n",
+    "        chunk_size=chunk_size,\n",
+    "        chunk_overlap=chunk_overlap,\n",
+    "        separators=[\"\\n\\n\", \"\\n\", \".\", \" \"]\n",
+    "    )\n",
+    "    return splitter.split_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8664bf6f",
+   "metadata": {},
+   "source": [
+    "3) Generar embeddings en batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "35a4df0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generaremben(model, texts):\n",
+    "    texts = [t for t in texts if t.strip()]  # filtra vacíos\n",
+    "    if not texts:\n",
+    "        raise ValueError(\"No hay textos válidos para generar embeddings.\")\n",
+    "    return model.encode(texts, batch_size=16, show_progress_bar=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c28a5724",
+   "metadata": {},
+   "source": [
+    "4) Insertar los docemtos en QDRANT localemente"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3c61ddca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def insertarenqdra(embeddings, texts, collection_name=\"pdf_chunks\"):\n",
+    "    client = QdrantClient(path=\"./qdrant_data\")  # persistente\n",
+    "\n",
+    "    dim = len(embeddings[0])\n",
+    "    client.recreate_collection(\n",
+    "        collection_name=collection_name,\n",
+    "        vectors_config=VectorParams(size=dim, distance=Distance.COSINE)\n",
+    "    )\n",
+    "\n",
+    "    points = [\n",
+    "        PointStruct(id=i, vector=embeddings[i].tolist(), payload={\"text\": texts[i]})\n",
+    "        for i in range(len(embeddings))\n",
+    "    ]\n",
+    "\n",
+    "    client.upsert(collection_name=collection_name, points=points)\n",
+    "    print(f\"✅ Insertados {len(points)} vectores en Qdrant.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "566e06c8",
+   "metadata": {},
+   "source": [
+    "5) Funcion modelo para no cargarlo siempre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "eec86477",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_nv_model():\n",
+    "    return AutoModel.from_pretrained(\"nvidia/NV-Embed-v2\", trust_remote_code=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08510d36",
+   "metadata": {},
+   "source": [
+    "Probamos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "2735d1a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pdf_path=\"./DOCS/Decreto-Supremo-N_-018-2019-JUS.pdf\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6ede8122",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pdf_chunks = pdfachunk(pdf_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8f33af13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_docs = split_chunks(pdf_chunks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "b0fb3761",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts = [doc.page_content for doc in split_docs]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85f2ee3f",
+   "metadata": {},
+   "source": [
+    "Definimos nuestro modelo de embbending"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5eb18c36",
+   "metadata": {},
+   "source": [
+    "NECESITAMOS DATASETS Y EINOPS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74262eaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install datasets einops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "93bbbcde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f9f4d5bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'transformers_modules.nvidia.NV-Embed-v2.c50d55f43bde7e6a18e0eaa15a62fd63a930f1a1.modeling_nvembed.NVEmbedModel'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(model))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "e594d6af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 12/12 [00:03<00:00,  3.29it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "embeddings = generaremben(model, texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "beba3991",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\adm\\AppData\\Local\\Temp\\ipykernel_26272\\752761030.py:5: DeprecationWarning: `recreate_collection` method is deprecated and will be removed in the future. Use `collection_exists` to check collection existence and `create_collection` instead.\n",
+      "  client.recreate_collection(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Insertados 181 vectores en Qdrant.\n"
+     ]
+    }
+   ],
+   "source": [
+    "insertarenqdra(embeddings, texts, collection_name=\"jus_decreto_018\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c241dc6",
+   "metadata": {},
+   "source": [
+    "Funcion para consultar con qdrant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "86beaf73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qdrant_client import QdrantClient\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b81de586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def query_qdrant(query, model, collection_name, top_k=5):\n",
+    "    # Generar embedding de la consulta\n",
+    "    query_embedding = model.encode([query])[0]\n",
+    "    \n",
+    "    # Conexión al cliente Qdrant\n",
+    "    client = QdrantClient(path=\"./qdrant_data\")\n",
+    "\n",
+    "    # Realizar búsqueda en la colección\n",
+    "    results = client.search(\n",
+    "        collection_name=collection_name,\n",
+    "        query_vector=query_embedding.tolist(),\n",
+    "        limit=top_k,  # Limitar a los primeros K resultados más similares\n",
+    "        with_payload=True  # Incluir el texto en los resultados\n",
+    "    )\n",
+    "\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "9d448736",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Storage folder ./qdrant_data is already accessed by another instance of Qdrant client. If you require concurrent access, use Qdrant server instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31merror\u001b[39m                                     Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\adm\\Documents\\rag_MILVUS\\.venv\\Lib\\site-packages\\portalocker\\portalocker.py:49\u001b[39m, in \u001b[36mlock\u001b[39m\u001b[34m(file_, flags)\u001b[39m\n\u001b[32m     48\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m49\u001b[39m     \u001b[43mwin32file\u001b[49m\u001b[43m.\u001b[49m\u001b[43mLockFileEx\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos_fh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m-\u001b[49m\u001b[32;43m0x10000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m__overlapped\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     50\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m pywintypes.error \u001b[38;5;28;01mas\u001b[39;00m exc_value:\n\u001b[32m     51\u001b[39m     \u001b[38;5;66;03m# error: (33, 'LockFileEx', 'The process cannot access the file\u001b[39;00m\n\u001b[32m     52\u001b[39m     \u001b[38;5;66;03m# because another process has locked a portion of the file.')\u001b[39;00m\n",
+      "\u001b[31merror\u001b[39m: (33, 'LockFileEx', 'El proceso no tiene acceso al archivo porque otro proceso tiene bloqueada una parte del archivo.')",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[31mAlreadyLocked\u001b[39m                             Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\adm\\Documents\\rag_MILVUS\\.venv\\Lib\\site-packages\\qdrant_client\\local\\qdrant_local.py:133\u001b[39m, in \u001b[36mQdrantLocal._load\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    132\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m133\u001b[39m     \u001b[43mportalocker\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlock\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    134\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_flock_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    135\u001b[39m \u001b[43m        \u001b[49m\u001b[43mportalocker\u001b[49m\u001b[43m.\u001b[49m\u001b[43mLockFlags\u001b[49m\u001b[43m.\u001b[49m\u001b[43mEXCLUSIVE\u001b[49m\u001b[43m \u001b[49m\u001b[43m|\u001b[49m\u001b[43m \u001b[49m\u001b[43mportalocker\u001b[49m\u001b[43m.\u001b[49m\u001b[43mLockFlags\u001b[49m\u001b[43m.\u001b[49m\u001b[43mNON_BLOCKING\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    136\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    137\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m portalocker.exceptions.LockException:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\adm\\Documents\\rag_MILVUS\\.venv\\Lib\\site-packages\\portalocker\\portalocker.py:54\u001b[39m, in \u001b[36mlock\u001b[39m\u001b[34m(file_, flags)\u001b[39m\n\u001b[32m     53\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exc_value.winerror == winerror.ERROR_LOCK_VIOLATION:\n\u001b[32m---> \u001b[39m\u001b[32m54\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions.AlreadyLocked(\n\u001b[32m     55\u001b[39m         exceptions.LockException.LOCK_FAILED,\n\u001b[32m     56\u001b[39m         exc_value.strerror,\n\u001b[32m     57\u001b[39m         fh=file_,\n\u001b[32m     58\u001b[39m     ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexc_value\u001b[39;00m\n\u001b[32m     59\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m     60\u001b[39m     \u001b[38;5;66;03m# Q:  Are there exceptions/codes we should be dealing with\u001b[39;00m\n\u001b[32m     61\u001b[39m     \u001b[38;5;66;03m# here?\u001b[39;00m\n",
+      "\u001b[31mAlreadyLocked\u001b[39m: (1, 'El proceso no tiene acceso al archivo porque otro proceso tiene bloqueada una parte del archivo.')",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[31mRuntimeError\u001b[39m                              Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[36]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m query = \u001b[33m\"\u001b[39m\u001b[33m¿Cuál es el propósito de la Ley 018-2019?\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m results = \u001b[43mquery_qdrant\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mjus_decreto_018\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[34]\u001b[39m\u001b[32m, line 6\u001b[39m, in \u001b[36mquery_qdrant\u001b[39m\u001b[34m(query, model, collection_name, top_k)\u001b[39m\n\u001b[32m      3\u001b[39m query_embedding = model.encode([query])[\u001b[32m0\u001b[39m]\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# Conexión al cliente Qdrant\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m client = \u001b[43mQdrantClient\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m./qdrant_data\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m      8\u001b[39m \u001b[38;5;66;03m# Realizar búsqueda en la colección\u001b[39;00m\n\u001b[32m      9\u001b[39m results = client.search(\n\u001b[32m     10\u001b[39m     collection_name=collection_name,\n\u001b[32m     11\u001b[39m     query_vector=query_embedding.tolist(),\n\u001b[32m     12\u001b[39m     limit=top_k,  \u001b[38;5;66;03m# Limitar a los primeros K resultados más similares\u001b[39;00m\n\u001b[32m     13\u001b[39m     with_payload=\u001b[38;5;28;01mTrue\u001b[39;00m  \u001b[38;5;66;03m# Incluir el texto en los resultados\u001b[39;00m\n\u001b[32m     14\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\adm\\Documents\\rag_MILVUS\\.venv\\Lib\\site-packages\\qdrant_client\\qdrant_client.py:133\u001b[39m, in \u001b[36mQdrantClient.__init__\u001b[39m\u001b[34m(self, location, url, port, grpc_port, prefer_grpc, https, api_key, prefix, timeout, host, path, force_disable_check_same_thread, grpc_options, auth_token_provider, cloud_inference, local_inference_batch_size, check_compatibility, **kwargs)\u001b[39m\n\u001b[32m    128\u001b[39m     \u001b[38;5;28mself\u001b[39m._client = QdrantLocal(\n\u001b[32m    129\u001b[39m         location=location,\n\u001b[32m    130\u001b[39m         force_disable_check_same_thread=force_disable_check_same_thread,\n\u001b[32m    131\u001b[39m     )\n\u001b[32m    132\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m133\u001b[39m     \u001b[38;5;28mself\u001b[39m._client = \u001b[43mQdrantLocal\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    134\u001b[39m \u001b[43m        \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    135\u001b[39m \u001b[43m        \u001b[49m\u001b[43mforce_disable_check_same_thread\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_disable_check_same_thread\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    136\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    137\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    138\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m location \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m url \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\adm\\Documents\\rag_MILVUS\\.venv\\Lib\\site-packages\\qdrant_client\\local\\qdrant_local.py:66\u001b[39m, in \u001b[36mQdrantLocal.__init__\u001b[39m\u001b[34m(self, location, force_disable_check_same_thread)\u001b[39m\n\u001b[32m     64\u001b[39m \u001b[38;5;28mself\u001b[39m.aliases: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m] = {}\n\u001b[32m     65\u001b[39m \u001b[38;5;28mself\u001b[39m._flock_file: Optional[TextIOWrapper] = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m66\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     67\u001b[39m \u001b[38;5;28mself\u001b[39m._closed: \u001b[38;5;28mbool\u001b[39m = \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\adm\\Documents\\rag_MILVUS\\.venv\\Lib\\site-packages\\qdrant_client\\local\\qdrant_local.py:138\u001b[39m, in \u001b[36mQdrantLocal._load\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    133\u001b[39m     portalocker.lock(\n\u001b[32m    134\u001b[39m         \u001b[38;5;28mself\u001b[39m._flock_file,\n\u001b[32m    135\u001b[39m         portalocker.LockFlags.EXCLUSIVE | portalocker.LockFlags.NON_BLOCKING,\n\u001b[32m    136\u001b[39m     )\n\u001b[32m    137\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m portalocker.exceptions.LockException:\n\u001b[32m--> \u001b[39m\u001b[32m138\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m    139\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mStorage folder \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.location\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m is already accessed by another instance of Qdrant client.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    140\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m If you require concurrent access, use Qdrant server instead.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    141\u001b[39m     )\n",
+      "\u001b[31mRuntimeError\u001b[39m: Storage folder ./qdrant_data is already accessed by another instance of Qdrant client. If you require concurrent access, use Qdrant server instead."
+     ]
+    }
+   ],
+   "source": [
+    "query = \"¿Cuál es el propósito de la Ley 018-2019?\"\n",
+    "results = query_qdrant(query, model,\"jus_decreto_018\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61d76427",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

chatbox_v1.py

@@ -0,0 +1,60 @@
+import  streamlit as st 
+from Rag_milvus import query_qdrant, obtener_colecciones, query_qdrant_sinumbral
+from Llm_local import get_response_from_mistral, generarPages
+from sentence_transformers import SentenceTransformer
+
+col1, col2 = st.columns([1, 4])
+with col1:
+    st.image("Procuradurialogo.jpg", width=600)
+
+with col2:
+    st.markdown("""
+        <div style='display: flex; align-items: center; height: 100%;'>
+            <h1 style='margin: 0; text-align: center;'>ProcurIA</h1>
+        </div>
+    """, unsafe_allow_html=True)
+
+st.sidebar.title("Menú de Funciones")
+generarPages()
+#Inicializamos nuestro historial de chat
+if "messages" not in st.session_state:
+     st.session_state.messages = [{"role": "assistant", "content": "Hola!, en que puedo ayudarte?"}]
+
+#Definimos modelo
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+#Elegimos una coleccion
+colecciones = obtener_colecciones()
+coleccion_seleccionada = st.sidebar.selectbox("Selecciona una colección", colecciones)
+
+# Mostrar el historial de mensajes
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Entrada del usuario
+if prompt := st.chat_input("Escribe tus dudas"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        if coleccion_seleccionada == "Todas las colecciones":
+            colecciones_disponibles = obtener_colecciones()
+            results = []
+            umbral=1
+            for coleccion in colecciones_disponibles[1:]:  
+                coleccion_results = query_qdrant_sinumbral(prompt,model,coleccion)
+                results.extend(coleccion_results) 
+        else:
+            umbral=0.56
+            results = query_qdrant(prompt, model, coleccion_seleccionada,5,umbral)
+
+        if not results:
+            response = "Disculpa, no tengo información para responder esa pregunta."
+        else:
+            response = st.write_stream(get_response_from_mistral(prompt, results))
+        
+    st.session_state.messages.append({"role": "assistant", "content": response})
+    st.write(results)