🧿 [pre-commit.ci] auto fixes from pre-commit hooks

Author: pre-commit-ci[bot]

examples/basic_rag.ipynb

@@ -111,7 +111,7 @@
    "source": [
     "with open(\"../sample_data/documents/hotd.txt\") as f:\n",
     "    lines = f.read().splitlines()\n",
-    "    \n",
+    "\n",
     "lines[:3]"
    ]
   },
@@ -152,8 +152,7 @@
    "source": [
     "# create a new collection\n",
     "collection = lx.create_collection(\n",
-    "    collection_name=\"house_of_the_dragon\", \n",
-    "    description=\"House of the Dragon characters\"\n",
+    "    collection_name=\"house_of_the_dragon\", description=\"House of the Dragon characters\"\n",
     ")\n",
     "collection"
    ]
@@ -177,9 +176,7 @@
    },
    "outputs": [],
    "source": [
-    "collection.add_documents([\n",
-    "    {\"content\": line} for line in lines\n",
-    "])"
+    "collection.add_documents([{\"content\": line} for line in lines])"
    ]
   },
   {
@@ -205,12 +202,15 @@
    "source": [
     "# create an index\n",
     "index_fields = {\n",
-    "    \"embedding\": {\"type\": \"embedding\", \"extras\": {\"dims\": 1536, \"model\": \"text.embeddings.openai-3-small\"}}\n",
+    "    \"embedding\": {\n",
+    "        \"type\": \"embedding\",\n",
+    "        \"extras\": {\"dims\": 1536, \"model\": \"text.embeddings.openai-3-small\"},\n",
+    "    }\n",
     "}\n",
     "index = lx.create_index(\n",
-    "    index_id=\"hotd_embeddings\", \n",
+    "    index_id=\"hotd_embeddings\",\n",
     "    description=\"Text embeddings for House of the Dragon collection\",\n",
-    "    index_fields=index_fields\n",
+    "    index_fields=index_fields,\n",
     ")"
    ]
   },
@@ -260,7 +260,7 @@
     "binding = lx.create_binding(\n",
     "    collection_name=\"house_of_the_dragon\",\n",
     "    index_id=\"hotd_embeddings\",\n",
-    "    transformer_id=\"text.embeddings.openai-3-small\"\n",
+    "    transformer_id=\"text.embeddings.openai-3-small\",\n",
     ")\n",
     "binding"
    ]
@@ -409,9 +409,9 @@
     "results_ex = index.query(query_text=question_ex)\n",
     "\n",
     "# format results as context\n",
-    "context_ex = \"\\n\".join([\n",
-    "    f'[doc_id: {er[\"document_id\"]}] {er[\"document.content\"]}' for er in results_ex\n",
-    "])\n",
+    "context_ex = \"\\n\".join(\n",
+    "    [f'[doc_id: {er[\"document_id\"]}] {er[\"document.content\"]}' for er in results_ex]\n",
+    ")\n",
     "\n",
     "# construct prompt\n",
     "prompt_ex = question_template.format(question=question_ex, context=context_ex)\n",
@@ -457,8 +457,8 @@
     "    model=\"gpt-4\",\n",
     "    messages=[\n",
     "        {\"role\": \"system\", \"content\": system_prompt},\n",
-    "        {\"role\": \"user\", \"content\": prompt_ex}\n",
-    "    ]\n",
+    "        {\"role\": \"user\", \"content\": prompt_ex},\n",
+    "    ],\n",
     ")\n",
     "print(oai_response.choices[0].message.content)"
    ]
@@ -484,27 +484,26 @@
    },
    "outputs": [],
    "source": [
-    "def construct_prompt(question: str,  \n",
-    "                     result_template: str = \"[doc_id: {r[document_id]}] {r[document.content]}\",\n",
-    "                     **query_kwargs):\n",
+    "def construct_prompt(\n",
+    "    question: str,\n",
+    "    result_template: str = \"[doc_id: {r[document_id]}] {r[document.content]}\",\n",
+    "    **query_kwargs,\n",
+    "):\n",
     "    # retrieve most relevant results\n",
     "    results = index.query(query_text=question, **query_kwargs)\n",
     "    # format results for context\n",
-    "    context = \"\\n\".join([\n",
-    "        result_template.format(r=r) for r in results\n",
-    "    ])\n",
+    "    context = \"\\n\".join([result_template.format(r=r) for r in results])\n",
     "    # format prompt\n",
     "    return question_template.format(question=question, context=context)\n",
     "\n",
-    "def chat_completion(message: str,\n",
-    "                    system: str = system_prompt, \n",
-    "                    **chat_kwargs):\n",
+    "\n",
+    "def chat_completion(message: str, system: str = system_prompt, **chat_kwargs):\n",
     "    # generate response\n",
     "    return openai_client.chat.completions.create(\n",
     "        model=\"gpt-4\",\n",
     "        messages=[\n",
     "            {\"role\": \"system\", \"content\": system},\n",
-    "            {\"role\": \"user\", \"content\": message}\n",
+    "            {\"role\": \"user\", \"content\": message},\n",
     "        ],\n",
     "        **chat_kwargs,\n",
     "    )"
@@ -626,9 +625,13 @@
    "outputs": [],
    "source": [
     "# add a new document\n",
-    "collection.add_documents([\n",
-    "    {\"content\": \"Lexy was by far the largest of the Targaryen dragons, and was ridden by AGI the Conqueror.\"}\n",
-    "])"
+    "collection.add_documents(\n",
+    "    [\n",
+    "        {\n",
+    "            \"content\": \"Lexy was by far the largest of the Targaryen dragons, and was ridden by AGI the Conqueror.\"\n",
+    "        }\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
@@ -652,13 +655,12 @@
    },
    "outputs": [],
    "source": [
-    "new_result_template = \\\n",
-    "    \"[doc_id: {r[document_id]}, updated_at: {r[document.updated_at]}] {r[document.content]}\"\n",
+    "new_result_template = \"[doc_id: {r[document_id]}, updated_at: {r[document.updated_at]}] {r[document.content]}\"\n",
     "\n",
     "new_prompt = construct_prompt(\n",
-    "    question=\"which is the largest Targaryen dragon?\", \n",
-    "    result_template=new_result_template, \n",
-    "    return_fields=[\"document.content\", \"document.updated_at\"]\n",
+    "    question=\"which is the largest Targaryen dragon?\",\n",
+    "    result_template=new_result_template,\n",
+    "    return_fields=[\"document.content\", \"document.updated_at\"],\n",
     ")\n",
     "print(new_prompt)"
    ]
@@ -712,11 +714,11 @@
     "q = \"which is the largest Targaryen dragon?\"\n",
     "oai_response = chat_completion(\n",
     "    message=construct_prompt(\n",
-    "        question=q, \n",
-    "        result_template=new_result_template, \n",
-    "        return_fields=[\"document.content\", \"document.updated_at\"]\n",
+    "        question=q,\n",
+    "        result_template=new_result_template,\n",
+    "        return_fields=[\"document.content\", \"document.updated_at\"],\n",
     "    ),\n",
-    "    system=new_system_prompt\n",
+    "    system=new_system_prompt,\n",
     ")\n",
     "print(oai_response.choices[0].message.content)"
    ]