langchain-chromadb-tiny-llama.ipynb

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  {
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   "execution_count": 30,
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   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from langchain_community.llms import Ollama\n",
    "from langchain_openai.chat_models import ChatOpenAI\n",
    "from langchain_community.embeddings import OllamaEmbeddings\n",
    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
    "\n",
    "load_dotenv()\n",
    "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
    "#MODEL = \"gpt-3.5-turbo\"\n",
    "#MODEL = \"mixtral:8x7b\"\n",
    "#MODEL = \"tinyllama\"\n",
    "MODEL = \"my-custom-tinyllama\"\n",
    "\n",
    "##langsmith tracking\n",
    "os.environ[\"LANGCHAIN_TRACING_V2\"]=\"true\"\n",
    "os.environ[\"LANGCHAIN_API_KEY\"]=os.getenv(\"LANGCHAIN_API_KEY\")\n",
    "\n",
    "if MODEL.startswith(\"gpt\"):\n",
    "    model = ChatOpenAI(openai_api_key=OPENAI_API_KEY, model=MODEL)\n",
    "    embeddings = OpenAIEmbeddings()\n",
    "else:\n",
    "    model = Ollama(model=MODEL)\n",
    "    embeddings = OllamaEmbeddings(model=MODEL)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#langchain to load chroma db data\n",
    "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
    "embed_model = HuggingFaceEmbeddings(model_name=\"BAAI/bge-base-en-v1.5\")\n",
    "from langchain.vectorstores import Chroma\n",
    "chroma_db = Chroma(persist_directory=\"./chroma_db\", \n",
    "                   embedding_function=embed_model,\n",
    "                   collection_name=\"DB_collection\")\n",
    "retriever = chroma_db.as_retriever(search_kwargs={\"k\": 5})\n",
    "retriever.invoke(input=\"who is Naresh Dulam?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
   
    }
   ],
   "source": [
    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
    "from langchain.chains import create_retrieval_chain\n",
    "#from langchain_core.globals import set_debug\n",
    "from langchain.prompts import PromptTemplate\n",
    "#set_debug(True)\n",
    "\n",
    "template = \"\"\"\n",
    "Answer the question based on the context below. If you can't \n",
    "answer the question, reply \"I don't know\".\n",
    "Context: {context}\n",
    "Question: {input}\n",
    "\"\"\"\n",
    "prompt = PromptTemplate.from_template(template)\n",
    "\n",
    "\n",
    "combine_docs_chain = create_stuff_documents_chain(model, prompt)\n",
    "retrieval_chain = create_retrieval_chain(retriever, combine_docs_chain)\n",
    "\n",
    "#Invoke the retrieval chain\n",
    "inputs = [\"How may memebers in naresh's family?\"]\n",
    "#inputs = [\"Where is naresh living currently?\",\"What are the certification naresh have?\",\"What is Naresh's wife name?\",\"who is aparna?\",\"How may kids naresh have?\"]\n",
    "for input in inputs:\n",
    "    response=retrieval_chain.invoke({\"input\":input})\n",
    "    print(input + \" ====> \"+ response[\"answer\"])\n",
    "\n",
    "#Print the answer to the question\n",
    "#print(response[\"answer\"])\n",
    "\n"
   ]
  }
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