docs: moved quickstart (#54)

Makefile

@@ -8,14 +8,14 @@ format: ## Running code formatter: black and isort
 	@echo "(isort) Ordering imports..."
 	@isort .
 	@echo "(black) Formatting codebase..."
-	@black --config pyproject.toml src tests examples experiments
+	@black --config pyproject.toml src tests docs experiments
 	@echo "(black) Formatting stubs..."
 	@find src -name "*.pyi" ! -name "*_pb2*" -exec black --pyi --config pyproject.toml {} \;
 	@echo "(ruff) Running fix only..."
-	@ruff check src examples tests --fix-only
+	@ruff check src docs tests --fix-only
 lint: ## Running lint checker: ruff
 	@echo "(ruff) Linting development project..."
-	@ruff check src examples tests
+	@ruff check src docs tests
 type: ## Running type checker: pyright
 	@echo "(pyright) Typechecking codebase..."
 	@pyright src

README.md

@@ -16,7 +16,7 @@
     <a href="https://github.com/explodinggradients/ragas/blob/master/LICENSE">
         <img alt="License" src="https://img.shields.io/github/license/explodinggradients/ragas.svg?color=green">
     </a>
-    <a href="https://colab.research.google.com/github/explodinggradients/ragas/blob/main/examples/quickstart.ipynb">
+    <a href="https://colab.research.google.com/github/explodinggradients/ragas/blob/main/docs/quickstart.ipynb">
         <img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg">
     </a>
     <a href="https://discord.gg/5djav8GGNZ">
@@ -77,7 +77,7 @@ results = evaluate(dataset)
 # {'ragas_score': 0.860, 'context_relavency': 0.817, 
 # 'faithfulness': 0.892, 'answer_relevancy': 0.874}
 ```
-If you want a more in-depth explanation of core components, check out our [quick-start notebook](./examples/quickstart.ipynb)
+If you want a more in-depth explanation of core components, check out our [quick-start notebook](./docs/quickstart.ipynb)
 ## :luggage: Metrics
 
 Ragas measures your pipeline's performance against two dimensions

examples/quickstart.ipynb → docs/quickstart.ipynb

@@ -5,13 +5,29 @@
    "id": "2e63f667",
    "metadata": {},
    "source": [
+    "<a href=\"https://colab.research.google.com/github/explodinggradients/ragas/blob/main/docs/quickstart.ipynb\">\n",
+    "    <img alt=\"Open In Colab\" \n",
+    "         align=\"left\"\n",
+    "         src=\"https://colab.research.google.com/assets/colab-badge.svg\">\n",
+    "</a>\n",
     "# Quickstart\n",
     "\n",
     "welcome to the ragas quickstart. We're going to get you up and running with ragas as qickly as you can so that you can go back to improving your Retrieval Augmented Generation pipelines while this library makes sure your changes are improving your entire pipeline.\n",
     "\n",
     "to kick things of lets start with the data"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "18274e1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if using colab uncomment this\n",
+    "#!pip install ragas"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

experiments/assesments/metrics_assesments.ipynb

@@ -177,8 +177,8 @@
    "outputs": [],
    "source": [
     "def get_corr(targets, predictions):\n",
-    "    scores =  [kendalltau(x, y).correlation for x, y in zip(targets, predictions)]\n",
-    "    return [score if not np.isnan(score) else 0 for score in scores ]"
+    "    scores = [kendalltau(x, y).correlation for x, y in zip(targets, predictions)]\n",
+    "    return [score if not np.isnan(score) else 0 for score in scores]"
    ]
   },
   {
@@ -355,18 +355,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def gpt_faithfulness(question:list, context:list, answer:list):\n",
-    "    prompt = [faithfulness.format(c,q, a) for c,q,a in zip(question,context,answer)]\n",
-    "    output = [output for output in llm(prompt)['choices']]\n",
-    "    scores = [(out[\"text\"].strip()) for out in output ]\n",
-    "    scores = [int(score) if score in ['1','2','3','4','5'] else 1 for score in scores]\n",
+    "def gpt_faithfulness(question: list, context: list, answer: list):\n",
+    "    prompt = [\n",
+    "        faithfulness.format(c, q, a) for c, q, a in zip(question, context, answer)\n",
+    "    ]\n",
+    "    output = [output for output in llm(prompt)[\"choices\"]]\n",
+    "    scores = [(out[\"text\"].strip()) for out in output]\n",
+    "    scores = [\n",
+    "        int(score) if score in [\"1\", \"2\", \"3\", \"4\", \"5\"] else 1 for score in scores\n",
+    "    ]\n",
     "    return scores\n",
     "\n",
-    "def gpt_relevance(question:list, answer:list):\n",
-    "    prompt = [relevence.format(q,a) for q,a in zip(question,answer)]\n",
-    "    output = [output for output in llm(prompt)['choices']]\n",
-    "    scores = [(out[\"text\"].strip()) for out in output ]\n",
-    "    scores = [int(score) if score in ['1','2','3','4','5'] else 1 for score in scores]\n",
+    "\n",
+    "def gpt_relevance(question: list, answer: list):\n",
+    "    prompt = [relevence.format(q, a) for q, a in zip(question, answer)]\n",
+    "    output = [output for output in llm(prompt)[\"choices\"]]\n",
+    "    scores = [(out[\"text\"].strip()) for out in output]\n",
+    "    scores = [\n",
+    "        int(score) if score in [\"1\", \"2\", \"3\", \"4\", \"5\"] else 1 for score in scores\n",
+    "    ]\n",
     "    return scores"
    ]
   },
@@ -425,7 +432,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "q,a,c = wikiqa_ragas['train'][0]['question'],wikiqa_ragas['train'][0]['generated_without_rag'],wikiqa_ragas['train'][0]['context']"
+    "q, a, c = (\n",
+    "    wikiqa_ragas[\"train\"][0][\"question\"],\n",
+    "    wikiqa_ragas[\"train\"][0][\"generated_without_rag\"],\n",
+    "    wikiqa_ragas[\"train\"][0][\"context\"],\n",
+    ")"
    ]
   },
   {
@@ -446,7 +457,7 @@
     }
    ],
    "source": [
-    "gpt_faithfulness([q],[c], [a])"
+    "gpt_faithfulness([q], [c], [a])"
    ]
   },
   {
@@ -517,12 +528,12 @@
     "def predict_(examples):\n",
     "    scores = {}\n",
     "    questions = examples[\"question\"]\n",
-    "    context = examples['context']\n",
+    "    context = examples[\"context\"]\n",
     "    for col in COLUMNS:\n",
     "        passage = examples[col]\n",
     "        inputs = list(zip(questions, passage))\n",
-    "        #scores[f\"{col}_relevance\"] = t5_qgen.predict(inputs, show_progress=False)\n",
-    "        scores[f\"{col}_relevance\"] = gpt_faithfulness(questions,context,passage)\n",
+    "        # scores[f\"{col}_relevance\"] = t5_qgen.predict(inputs, show_progress=False)\n",
+    "        scores[f\"{col}_relevance\"] = gpt_faithfulness(questions, context, passage)\n",
     "    return scores"
    ]
   },
@@ -553,10 +564,7 @@
    },
    "outputs": [],
    "source": [
-    "output = (\n",
-    "    wikiqa_ragas[\"train\"]\n",
-    "    .map(predict_relevance, batched=True, batch_size=10)\n",
-    ")"
+    "output = wikiqa_ragas[\"train\"].map(predict_relevance, batched=True, batch_size=10)"
    ]
   },
   {
@@ -622,10 +630,7 @@
     }
    ],
    "source": [
-    "output = (\n",
-    "    wikiqa_ragas[\"train\"]\n",
-    "    .map(predict_relevance, batched=True, batch_size=10)\n",
-    ")"
+    "output = wikiqa_ragas[\"train\"].map(predict_relevance, batched=True, batch_size=10)"
    ]
   },
   {
@@ -877,7 +882,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def predict_faithfulness(examples,scoring_fun=NLI.score):\n",
+    "def predict_faithfulness(examples, scoring_fun=NLI.score):\n",
     "    scores = {}\n",
     "    questions = examples[\"question\"]\n",
     "    contexts = examples[\"answer_context\"]\n",