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    "## Together AI + RAG\n",
    " \n",
    "[Together AI](https://python.langchain.com/docs/integrations/llms/together) has a broad set of OSS LLMs via inference API.\n",
    "\n",
    "See [here](https://docs.together.ai/docs/inference-models). We use `\"mistralai/Mixtral-8x7B-Instruct-v0.1` for RAG on the Mixtral paper.\n",
    "\n",
    "Download the paper:\n",
    "https://arxiv.org/pdf/2401.04088.pdf"
   ]
  },
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   "execution_count": null,
   "id": "d12fb75a-f707-48d5-82a5-efe2d041813c",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install --quiet pypdf tiktoken openai langchain-chroma langchain-together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ab49327-0532-4480-804c-d066c302a322",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load\n",
    "from langchain_community.document_loaders import PyPDFLoader\n",
    "\n",
    "loader = PyPDFLoader(\"~/Desktop/mixtral.pdf\")\n",
    "data = loader.load()\n",
    "\n",
    "# Split\n",
    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)\n",
    "all_splits = text_splitter.split_documents(data)\n",
    "\n",
    "# Add to vectorDB\n",
    "from langchain_chroma import Chroma\n",
    "from langchain_community.embeddings import OpenAIEmbeddings\n",
    "\n",
    "\"\"\"\n",
    "from langchain_together.embeddings import TogetherEmbeddings\n",
    "embeddings = TogetherEmbeddings(model=\"togethercomputer/m2-bert-80M-8k-retrieval\")\n",
    "\"\"\"\n",
    "vectorstore = Chroma.from_documents(\n",
    "    documents=all_splits,\n",
    "    collection_name=\"rag-chroma\",\n",
    "    embedding=OpenAIEmbeddings(),\n",
    ")\n",
    "\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4efaddd9-3dbb-455c-ba54-0ad7f2d2ce0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.pydantic_v1 import BaseModel\n",
    "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
    "\n",
    "# RAG prompt\n",
    "template = \"\"\"Answer the question based only on the following context:\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\"\"\"\n",
    "prompt = ChatPromptTemplate.from_template(template)\n",
    "\n",
    "# LLM\n",
    "from langchain_together import Together\n",
    "\n",
    "llm = Together(\n",
    "    model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
    "    temperature=0.0,\n",
    "    max_tokens=2000,\n",
    "    top_k=1,\n",
    ")\n",
    "\n",
    "# RAG chain\n",
    "chain = (\n",
    "    RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
    "    | prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88b1ee51-1b0f-4ebf-bb32-e50e843f0eeb",
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    {
     "data": {
      "text/plain": [
       "'\\nAnswer: The architectural details of Mixtral are as follows:\\n- Dimension (dim): 4096\\n- Number of layers (n\\\\_layers): 32\\n- Dimension of each head (head\\\\_dim): 128\\n- Hidden dimension (hidden\\\\_dim): 14336\\n- Number of heads (n\\\\_heads): 32\\n- Number of kv heads (n\\\\_kv\\\\_heads): 8\\n- Context length (context\\\\_len): 32768\\n- Vocabulary size (vocab\\\\_size): 32000\\n- Number of experts (num\\\\_experts): 8\\n- Number of top k experts (top\\\\_k\\\\_experts): 2\\n\\nMixtral is based on a transformer architecture and uses the same modifications as described in [18], with the notable exceptions that Mixtral supports a fully dense context length of 32k tokens, and the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. This technique increases the number of parameters of a model while controlling cost and latency, as the model only uses a fraction of the total set of parameters per token. Mixtral is pretrained with multilingual data using a context size of 32k tokens. It either matches or exceeds the performance of Llama 2 70B and GPT-3.5, over several benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke(\"What are the Architectural details of Mixtral?\")"
   ]
  },
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   "id": "755cf871-26b7-4e30-8b91-9ffd698470f4",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/935fd642-06a6-4b42-98e3-6074f93115cd/r"
   ]
  }
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