LLMs/3. Chroma Implementation.ipynb

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    "# CHROMOA\n",
    "from langchain.vectorstores import Chroma\n",
    "from langchain.embeddings import SentenceTransformerEmbeddings\n",
    "from langchain.schema import Document\n",
    "from ollama import chat\n",
    "import os\n",
    "import re\n",
    "\n",
    "DOCUMENT_PATHS = [\n",
    "    r'/home/masih/rag_data/Hamrah.txt', #replace path\n",
    "    r'/home/masih/rag_data/vape.txt',\n",
    "    r'/home/masih/rag_data/Shah.txt',\n",
    "    r'/home/masih/rag_data/Khalife.txt',\n",
    "    r'/home/masih/rag_data/carbon.txt',\n",
    "    r'/home/masih/rag_data/takapoo.txt'\n",
    "]\n",
    "\n",
    "EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'\n",
    "LLM_MODEL = 'gemma2:9b'\n",
    "CHUNK_SIZE = 1000\n",
    "OVERLAP = 200\n",
    "CHROMA_PERSIST_DIR = r'\\home\\Masih\\chroma_db\\chroma_db'  \n",
    "\n",
    "class ChromaRAGSystem:\n",
    "    def __init__(self):\n",
    "        # Init embedding model\n",
    "        self.embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL)\n",
    "        # Vector store instance\n",
    "        self.vector_db = None\n",
    "        \n",
    "    def build_vector_store(self):\n",
    "        \"\"\"Process documents and create Chroma vector store\"\"\"\n",
    "        all_docs = []\n",
    "        \n",
    "\n",
    "        for doc_idx, path in enumerate(DOCUMENT_PATHS):\n",
    "            with open(path, 'r', encoding='utf-8') as f:\n",
    "                text = re.sub(r'\\s+', ' ', f.read()).strip()\n",
    "                # sliding window chunking\n",
    "                chunks = [\n",
    "                    text[i:i+CHUNK_SIZE] \n",
    "                    for i in range(0, len(text), CHUNK_SIZE - OVERLAP)\n",
    "                ]\n",
    "                # LangChain documents with metadata\n",
    "                for chunk in chunks:\n",
    "                    all_docs.append(Document(\n",
    "                        page_content=chunk,\n",
    "                        metadata={\"source_doc\": doc_idx}\n",
    "                    ))\n",
    "        \n",
    "        # Chroma vector store\n",
    "        self.vector_db = Chroma.from_documents(\n",
    "            documents=all_docs,\n",
    "            embedding=self.embeddings,\n",
    "            persist_directory=CHROMA_PERSIST_DIR\n",
    "        )\n",
    "        self.vector_db.persist()\n",
    "        \n",
    "    def load_vector_store(self):\n",
    "        \"\"\"Load existing Chroma vector store\"\"\"\n",
    "        self.vector_db = Chroma(\n",
    "            persist_directory=CHROMA_PERSIST_DIR,\n",
    "            embedding_function=self.embeddings\n",
    "        )\n",
    "        \n",
    "    def document_query(self, query, doc_index, top_k=5):\n",
    "        \"\"\"Retrieve context from specific document\"\"\"\n",
    "        # Chroma metadata filtering\n",
    "        results = self.vector_db.similarity_search(\n",
    "            query=query,\n",
    "            k=top_k,\n",
    "            filter={\"source_doc\": doc_index}\n",
    "        )\n",
    "        return [doc.page_content for doc in results]\n",
    "\n",
    "class AnswerGenerator:\n",
    "    def __init__(self, rag_system):\n",
    "        self.rag = rag_system\n",
    "        \n",
    "    def generate_response(self, question, doc_index):\n",
    "        \"\"\"Generate context-aware answer using LLM\"\"\"\n",
    "        # Retrieve relevant context\n",
    "        context_chunks = self.rag.document_query(question, doc_index)\n",
    "        context = \"\\n\".join(context_chunks)\n",
    "        \n",
    "        prompt = f\"\"\"با استفاده از متن زیر به سوال پاسخ دهید:\n",
    "{context}\n",
    "\n",
    "اگر پاسخ در متن وجود ندارد عبارت 'پاسخی یافت نشد' را برگردانید\n",
    "\n",
    "سوال: {question}\n",
    "پاسخ:\"\"\"\n",
    "        \n",
    "        response = chat(model=LLM_MODEL, messages=[{'role': 'user', 'content': prompt}])\n",
    "        return response['message']['content']\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    rag_system = ChromaRAGSystem()\n",
    "    \n",
    "    # Init vector store\n",
    "    if not os.path.exists(CHROMA_PERSIST_DIR):\n",
    "        print(\"Creating new vector store...\")\n",
    "        rag_system.build_vector_store()\n",
    "    else:\n",
    "        print(\"Loading existing vector store...\")\n",
    "        rag_system.load_vector_store()\n",
    "    \n",
    "    # Init answer generator\n",
    "    answer_engine = AnswerGenerator(rag_system)\n",
    "\n",
    "    queries = [\n",
    "        (\"چرا اینترنت همراه اول گوشی وصل نمیشود؟\", 0),\n",
    "        (\"چطوری ویپ مورد نظرمو پیدا کنم؟\", 1),\n",
    "        (\"شاه عباس که بود؟\", 2),\n",
    "        (\"خلیفه سلطان که بود و چه کرد؟\", 3),\n",
    "        (\"کربن اکتیو و کربن بلک چه هستند و چه تفاوتی دارند و برای چه استفاده میشن؟\", 4),\n",
    "        (\"شرکت تکاپو صنعت نامی چه محصولاتی ارایه میدهد؟ چه چیزی این شرکت را منحصر به فرد میسازد؟ سهام این شرکت صعودی است یا نزولی؟\", 5)\n",
    "    ]\n",
    "    \n",
    "    with open( r'/home/masih/rag_data/response.txt', 'w', encoding='utf-8') as output_file: #repalce path\n",
    "        for q_num, (query, doc_idx) in enumerate(queries):\n",
    "            answer = answer_engine.generate_response(query, doc_idx)\n",
    "            output_file.write(f\"سوال {q_num+1} ({doc_idx+1}):\\n{query}\\n\\nپاسخ:\\n{answer}\\n\\n{'='*50}\\n\\n\")\n",
    "            print(f\"پردازش سوال {q_num+1}/{len(queries)} تکمیل شد\")\n",
    "\n",
    "print(\"تمامی سوالات با موفقیت پردازش شدند!\")"
   ]
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