Merge pull request #79 from speedwagon1299/FixServiceContext

Modified from Service Context to LLaMa Settings

all_rag_techniques/choose_chunk_size.ipynb

@@ -19,7 +19,7 @@
     "nest_asyncio.apply()\n",
     "from dotenv import load_dotenv\n",
     "\n",
-    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
+    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
     "from llama_index.core.prompts import PromptTemplate\n",
     "\n",
     "from llama_index.core.evaluation import (\n",
@@ -28,6 +28,7 @@
     "    RelevancyEvaluator\n",
     ")\n",
     "from llama_index.llms.openai import OpenAI\n",
+    "from llama_index.core import Settings\n",
     "\n",
     "import openai\n",
     "import time\n",
@@ -90,11 +91,11 @@
     "# We will use GPT-4 for evaluating the responses\n",
     "gpt4 = OpenAI(temperature=0, model=\"gpt-4o\")\n",
     "\n",
-    "# Define service context for GPT-4 for evaluation\n",
-    "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)\n",
+    "# Set appropriate settings for the LLM\n",
+    "Settings.llm = gpt4\n",
     "\n",
-    "# Define Faithfulness and Relevancy Evaluators which are based on GPT-4\n",
-    "faithfulness_gpt4 = FaithfulnessEvaluator(service_context=service_context_gpt4)\n",
+    "# Define Faithfulness Evaluators which are based on GPT-4\n",
+    "faithfulness_gpt4 = FaithfulnessEvaluator()\n",
     "\n",
     "faithfulness_new_prompt_template = PromptTemplate(\"\"\" Please tell if a given piece of information is directly supported by the context.\n",
     "    You need to answer with either YES or NO.\n",
@@ -123,7 +124,9 @@
     "    \"\"\")\n",
     "\n",
     "faithfulness_gpt4.update_prompts({\"your_prompt_key\": faithfulness_new_prompt_template}) # Update the prompts dictionary with the new prompt template\n",
-    "relevancy_gpt4 = RelevancyEvaluator(service_context=service_context_gpt4)"
+    "\n",
+    "# Define Relevancy Evaluators which are based on GPT-4\n",
+    "relevancy_gpt4 = RelevancyEvaluator()"
    ]
   },
   {
@@ -159,10 +162,12 @@
     "    # create vector index\n",
     "    llm = OpenAI(model=\"gpt-3.5-turbo\")\n",
     "\n",
-    "    service_context = ServiceContext.from_defaults(llm=llm, chunk_size=chunk_size, chunk_overlap=chunk_size//5)  \n",
-    "    vector_index = VectorStoreIndex.from_documents(\n",
-    "        eval_documents, service_context=service_context\n",
-    "    )\n",
+    "    Settings.llm = llm\n",
+    "    Settings.chunk_size = chunk_size\n",
+    "    Settings.chunk_overlap = chunk_size // 5 \n",
+    "\n",
+    "    vector_index = VectorStoreIndex.from_documents(eval_documents)\n",
+    "    \n",
     "    # build query engine\n",
     "    query_engine = vector_index.as_query_engine(similarity_top_k=5)\n",
     "    num_questions = len(eval_questions)\n",
@@ -234,7 +239,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -248,7 +253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,