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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0fc0309d-4d49-4bb5-bec0-bd92c6fddb28",
+   "metadata": {},
+   "source": [
+    "## 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://api.together.xyz/playground). 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d12fb75a-f707-48d5-82a5-efe2d041813c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install --quiet pypdf chromadb tiktoken openai 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_splitter 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_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import Chroma\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_community.llms 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",
+   "metadata": {},
+   "outputs": [
+    {
+     "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?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "755cf871-26b7-4e30-8b91-9ffd698470f4",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/935fd642-06a6-4b42-98e3-6074f93115cd/r"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}