fix black in pre-commit (#1940)

examples/frontend_language/usage/rag_using_parea/trace_and_evaluate_rag_using_parea.ipynb

@@ -71,7 +71,7 @@
    "source": [
     "import json\n",
     "import os\n",
-	"from typing import List\n",
+    "from typing import List\n",
     "\n",
     "import chromadb\n",
     "\n",
@@ -80,7 +80,7 @@
     "if not os.path.exists(path_qca):\n",
     "    !wget https://virattt.github.io/datasets/abnb-2023-10k.json -O airbnb-2023-10k-qca.json\n",
     "\n",
-    "with open(path_qca, 'r') as f:\n",
+    "with open(path_qca, \"r\") as f:\n",
     "    question_context_answers = json.load(f)\n",
     "\n",
     "chroma_client = chromadb.PersistentClient()\n",
@@ -88,7 +88,7 @@
     "if collection.count() == 0:\n",
     "    collection.add(\n",
     "        documents=[qca[\"context\"] for qca in question_context_answers],\n",
-    "        ids=[str(i) for i in range(len(question_context_answers))]\n",
+    "        ids=[str(i) for i in range(len(question_context_answers))],\n",
     "    )"
    ],
    "metadata": {
@@ -123,7 +123,7 @@
     "\n",
     "load_dotenv()\n",
     "\n",
-    "os.environ['TOKENIZERS_PARALLELISM'] = \"false\"\n",
+    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
     "\n",
     "p = Parea(api_key=os.getenv(\"PAREA_API_KEY\"), project_name=\"rag_sglang\")\n",
     "p.integrate_with_sglang()\n",
@@ -150,10 +150,7 @@
    "source": [
     "@trace\n",
     "def retrieval(question: str) -> List[str]:\n",
-    "    return collection.query(\n",
-    "        query_texts=[question],\n",
-    "        n_results=1\n",
-    "    )['documents'][0]"
+    "    return collection.query(query_texts=[question], n_results=1)[\"documents\"][0]"
    ],
    "metadata": {
     "collapsed": false
@@ -176,7 +173,9 @@
     "@function\n",
     "def generation_sglang(s, question: str, *context: str):\n",
     "    context = \"\\n\".join(context)\n",
-    "    s += user(f'Given this question:\\n{question}\\n\\nAnd this context:\\n{context}\\n\\nAnswer the question.')\n",
+    "    s += user(\n",
+    "        f\"Given this question:\\n{question}\\n\\nAnd this context:\\n{context}\\n\\nAnswer the question.\"\n",
+    "    )\n",
     "    s += assistant(gen(\"answer\"))\n",
     "\n",
     "\n",
@@ -223,7 +222,9 @@
     "    return generation(question, *contexts)\n",
     "\n",
     "\n",
-    "rag_pipeline(\"When did the World Health Organization formally declare an end to the COVID-19 global health emergency?\")"
+    "rag_pipeline(\n",
+    "    \"When did the World Health Organization formally declare an end to the COVID-19 global health emergency?\"\n",
+    ")"
    ]
   },
   {
@@ -271,7 +272,10 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "from parea.evals.rag import context_query_relevancy_factory, percent_target_supported_by_context_factory\n",
+    "from parea.evals.rag import (\n",
+    "    context_query_relevancy_factory,\n",
+    "    percent_target_supported_by_context_factory,\n",
+    ")\n",
     "\n",
     "\n",
     "context_relevancy_eval = context_query_relevancy_factory()\n",
@@ -280,10 +284,7 @@
     "\n",
     "@trace(eval_funcs=[context_relevancy_eval, percent_target_supported_by_context])\n",
     "def retrieval(question: str) -> List[str]:\n",
-    "    return collection.query(\n",
-    "        query_texts=[question],\n",
-    "        n_results=1\n",
-    "    )['documents'][0]"
+    "    return collection.query(query_texts=[question], n_results=1)[\"documents\"][0]"
    ],
    "metadata": {
     "collapsed": false
@@ -310,10 +311,13 @@
     "answer_context_faithfulness = answer_context_faithfulness_statement_level_factory()\n",
     "answer_matches_target_llm_grader = answer_matches_target_llm_grader_factory()\n",
     "\n",
+    "\n",
     "@function\n",
     "def generation_sglang(s, question: str, *context: str):\n",
     "    context = \"\\n\".join(context)\n",
-    "    s += user(f'Given this question:\\n{question}\\n\\nAnd this context:\\n{context}\\n\\nAnswer the question.')\n",
+    "    s += user(\n",
+    "        f\"Given this question:\\n{question}\\n\\nAnd this context:\\n{context}\\n\\nAnswer the question.\"\n",
+    "    )\n",
     "    s += assistant(gen(\"answer\", max_tokens=1_000))\n",
     "\n",
     "\n",
@@ -357,7 +361,9 @@
     "    return generation(question, *contexts)\n",
     "\n",
     "\n",
-    "rag_pipeline(\"When did the World Health Organization formally declare an end to the COVID-19 global health emergency?\")"
+    "rag_pipeline(\n",
+    "    \"When did the World Health Organization formally declare an end to the COVID-19 global health emergency?\"\n",
+    ")"
    ],
    "metadata": {
     "collapsed": false
@@ -402,6 +408,7 @@
    "source": [
     "!pip install nest-asyncio\n",
     "import nest_asyncio\n",
+    "\n",
     "nest_asyncio.apply()"
    ],
    "metadata": {
@@ -461,7 +468,7 @@
    ],
    "source": [
     "e = p.experiment(\n",
-    "    'RAG',\n",
+    "    \"RAG\",\n",
     "    data=[\n",
     "        {\n",
     "            \"question\": qca[\"question\"],\n",
@@ -469,7 +476,7 @@
     "        }\n",
     "        for qca in question_context_answers\n",
     "    ],\n",
-    "    func=rag_pipeline\n",
+    "    func=rag_pipeline,\n",
     ").run()"
    ],
    "metadata": {

examples/runtime/engine/input_ids.py

@@ -7,6 +7,7 @@ from sglang.srt.hf_transformers_utils import get_tokenizer
 
 MODEL_PATH = "meta-llama/Llama-3.1-8B-Instruct"
 
+
 def main():
     # Sample prompts.
     prompts = [