langchain/docs/docs/integrations/text_embedding/llamafile.ipynb

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    "# llamafile\n",
    "\n",
    "Let's load the [llamafile](https://github.com/Mozilla-Ocho/llamafile) Embeddings class.\n",
    "\n",
    "## Setup\n",
    "\n",
    "First, the are 3 setup steps:\n",
    "\n",
    "1. Download a llamafile. In this notebook, we use `TinyLlama-1.1B-Chat-v1.0.Q5_K_M` but there are many others available on [HuggingFace](https://huggingface.co/models?other=llamafile).\n",
    "2. Make the llamafile executable.\n",
    "3. Start the llamafile in server mode.\n",
    "\n",
    "You can run the following bash script to do all this:"
   ]
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    "%%bash\n",
    "# llamafile setup\n",
    "\n",
    "# Step 1: Download a llamafile. The download may take several minutes.\n",
    "wget -nv -nc https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile\n",
    "\n",
    "# Step 2: Make the llamafile executable. Note: if you're on Windows, just append '.exe' to the filename.\n",
    "chmod +x TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile\n",
    "\n",
    "# Step 3: Start llamafile server in background. All the server logs will be written to 'tinyllama.log'.\n",
    "# Alternatively, you can just open a separate terminal outside this notebook and run: \n",
    "#   ./TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile --server --nobrowser --embedding\n",
    "./TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile --server --nobrowser --embedding > tinyllama.log 2>&1 &\n",
    "pid=$!\n",
    "echo \"${pid}\" > .llamafile_pid  # write the process pid to a file so we can terminate the server later"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Embedding texts using LlamafileEmbeddings\n",
    "\n",
    "Now, we can use the `LlamafileEmbeddings` class to interact with the llamafile server that's currently serving our TinyLlama model at http://localhost:8080."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0be1af71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import LlamafileEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c66e5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedder = LlamafileEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01370375",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42e4035",
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   "source": [
    "To generate embeddings, you can either query an invidivual text, or you can query a list of texts."
   ]
  },
  {
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   "id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
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   "source": [
    "query_result = embedder.embed_query(text)\n",
    "query_result[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embedder.embed_documents([text])\n",
    "doc_result[0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ccc78fc-03ae-411d-ae73-74a4ee91c725",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "# cleanup: kill the llamafile server process\n",
    "kill $(cat .llamafile_pid)\n",
    "rm .llamafile_pid"
   ]
  }
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