fixing formatting errors

Author: CaesarGhazi

rag_and_distilled_model/Apollo11_rag&distilled.ipynb

@@ -51,6 +51,7 @@
     "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline\n",
     "import torch\n",
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "PROMPTS_FILE = \"data/test_data.json\"\n",
@@ -94,7 +95,9 @@
    "outputs": [],
    "source": [
     "embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)\n",
-    "splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)"
+    "splitter = RecursiveCharacterTextSplitter(\n",
+    "    chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP\n",
+    ")"
    ]
   },
   {
@@ -136,6 +139,8 @@
     "        device=device,\n",
     "    )\n",
     "    return HuggingFacePipeline(pipeline=pipe)\n",
+    "\n",
+    "\n",
     "llm = initialize_local_llm()"
    ]
   },
@@ -208,12 +213,10 @@
    "source": [
     "def build_chroma_store(docs, persist_dir=PERSIST_DIR):\n",
     "    db = Chroma.from_documents(\n",
-    "        documents=docs,\n",
-    "        embedding=embedder,\n",
-    "        persist_directory=persist_dir\n",
+    "        documents=docs, embedding=embedder, persist_directory=persist_dir\n",
     "    )\n",
     "    db.persist()\n",
-    "    return db\n"
+    "    return db"
    ]
   },
   {
@@ -283,12 +286,13 @@
    "source": [
     "def query_database(query_text, k=TOP_K_RESULTS, threshold=RELEVANCE_THRESHOLD):\n",
     "    results = db.similarity_search_with_relevance_scores(query_text, k=k)\n",
-    "    \n",
+    "\n",
     "    if len(results) == 0 or results[0][1] < threshold:\n",
     "        return []\n",
-    "    \n",
+    "\n",
     "    return results\n",
     "\n",
+    "\n",
     "def generate_rag_response(\n",
     "    query_text, k=TOP_K_RESULTS, threshold=RELEVANCE_THRESHOLD, verbose=False\n",
     "):\n",
@@ -329,7 +333,8 @@
     "        \"prompt\": prompt,\n",
     "        \"scores\": [score for _, score in results],\n",
     "    }\n",
-    "    \n",
+    "\n",
+    "\n",
     "def ask(query_text):\n",
     "    result = generate_rag_response(query_text, verbose=True)\n",
     "    return result[\"answer\"]"
@@ -407,7 +412,7 @@
     "\n",
     "    print(f\" Model Answer: {answer}\")\n",
     "    if expected:\n",
-    "        print(f\" Expected: {expected}\")\n"
+    "        print(f\" Expected: {expected}\")"
    ]
   }
  ],