docs: added a quickstart nb (#41)

Author: jjmachan

.gitignore

@@ -166,3 +166,4 @@ experiments/**/storage
 **/fil-result/
 experiments/baselines/fiqa/datasets
 src/ragas/_version.py
+.python-version

README.md

@@ -55,18 +55,23 @@ This is a small example program you can run to see ragas in action!
 ```python
 
 from ragas import evaluate
+from datasets import Dataset
 import os
 
 os.environ["OPENAI_API_KEY"] = "your-openai-key"
 
-ds = Dataset({
-    features: ['question','context','answer'],
-    num_rows: 25
-})
-results = evaluate(ds)
+# prepare your huggingface dataset in the format
+# Dataset({
+#     features: ['question','contexts','answer'],
+#     num_rows: 25
+# })
+
+dataset: Dataset
+
+results = evaluate(dataset)
 
 ```
-If you want a more in-depth explanation of core components, check out our quick-start notebook
+If you want a more in-depth explanation of core components, check out our [quick-start notebook](./examples/quickstart.ipynb)
 ## :luggage: Metrics
 
 Ragas measures your pipeline's performance against two dimensions

examples/quickstart.ipynb

@@ -64,7 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "os.chdir('/Users/shahules/belar/src/')"
+    "os.chdir(\"/Users/shahules/belar/src/\")"
    ]
   },
   {

experiments/assesments/metrics_assesments.ipynb

@@ -48,7 +48,11 @@
     "from beir.datasets.data_loader import GenericDataLoader\n",
     "\n",
     "dataset = \"fiqa\"\n",
-    "url = \"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip\".format(dataset)\n",
+    "url = (\n",
+    "    \"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip\".format(\n",
+    "        dataset\n",
+    "    )\n",
+    ")\n",
     "data_path = util.download_and_unzip(url, \"datasets\")"
    ]
   },
@@ -218,7 +222,7 @@
    "source": [
     "with open(os.path.join(data_path, \"corpus.jsonl\")) as f:\n",
     "    cs = [pd.Series(json.loads(l)) for l in f.readlines()]\n",
-    "    \n",
+    "\n",
     "corpus_df = pd.DataFrame(cs)\n",
     "corpus_df"
    ]
@@ -299,9 +303,7 @@
     }
    ],
    "source": [
-    "corpus_df = corpus_df.rename(columns={\n",
-    "    \"_id\": \"corpus-id\", \"text\": \"ground_truth\"\n",
-    "})\n",
+    "corpus_df = corpus_df.rename(columns={\"_id\": \"corpus-id\", \"text\": \"ground_truth\"})\n",
     "corpus_df = corpus_df.drop(columns=[\"title\", \"metadata\"])\n",
     "corpus_df[\"corpus-id\"] = corpus_df[\"corpus-id\"].astype(int)\n",
     "corpus_df.head()"
@@ -387,9 +389,7 @@
     "    qs = [pd.Series(json.loads(l)) for l in f.readlines()]\n",
     "\n",
     "queries_df = pd.DataFrame(qs)\n",
-    "queries_df = queries_df.rename(columns={\n",
-    "    \"_id\": \"query-id\", \"text\": \"question\"\n",
-    "})\n",
+    "queries_df = queries_df.rename(columns={\"_id\": \"query-id\", \"text\": \"question\"})\n",
     "queries_df = queries_df.drop(columns=[\"metadata\"])\n",
     "queries_df[\"query-id\"] = queries_df[\"query-id\"].astype(int)\n",
     "queries_df.head()"
@@ -474,10 +474,10 @@
     "splits = [\"dev\", \"test\", \"train\"]\n",
     "split_df = {}\n",
     "for s in splits:\n",
-    "    split_df[s] = pd.read_csv(\n",
-    "        os.path.join(data_path, f\"qrels/{s}.tsv\"), sep=\"\\t\"\n",
-    "    ).drop(columns=[\"score\"])\n",
-    "    \n",
+    "    split_df[s] = pd.read_csv(os.path.join(data_path, f\"qrels/{s}.tsv\"), sep=\"\\t\").drop(\n",
+    "        columns=[\"score\"]\n",
+    "    )\n",
+    "\n",
     "split_df[\"dev\"].head()"
    ]
   },
@@ -515,10 +515,14 @@
     "    df = queries_df.merge(split_df[split], on=\"query-id\")\n",
     "    df = df.merge(corpus_df, on=\"corpus-id\")\n",
     "    df = df.drop(columns=[\"corpus-id\"])\n",
-    "    grouped = df.groupby('query-id').apply(lambda x: pd.Series({\n",
-    "        'question': x['question'].sample().values[0],\n",
-    "        'ground_truths': x['ground_truth'].tolist()\n",
-    "    }))\n",
+    "    grouped = df.groupby(\"query-id\").apply(\n",
+    "        lambda x: pd.Series(\n",
+    "            {\n",
+    "                \"question\": x[\"question\"].sample().values[0],\n",
+    "                \"ground_truths\": x[\"ground_truth\"].tolist(),\n",
+    "            }\n",
+    "        )\n",
+    "    )\n",
     "\n",
     "    grouped = grouped.reset_index()\n",
     "    grouped = grouped.drop(columns=\"query-id\")\n",
@@ -797,11 +801,8 @@
     "assert os.path.exists(path_to_ds_repo), f\"{path_to_ds_repo} doesnot exist!\"\n",
     "\n",
     "for s in final_split_df:\n",
-    "    final_split_df[s].to_csv(\n",
-    "        os.path.join(path_to_ds_repo, f\"{s}.csv\"),\n",
-    "        index=False\n",
-    "    )\n",
-    "    \n",
+    "    final_split_df[s].to_csv(os.path.join(path_to_ds_repo, f\"{s}.csv\"), index=False)\n",
+    "\n",
     "corpus_df.to_csv(os.path.join(path_to_ds_repo, \"corpus.csv\"), index=False)"
    ]
   },
@@ -1009,18 +1010,11 @@
     "from llama_index.node_parser import SimpleNodeParser\n",
     "from langchain.text_splitter import TokenTextSplitter\n",
     "\n",
-    "spliter = TokenTextSplitter(\n",
-    "    chunk_size = 100,\n",
-    "    chunk_overlap = 50\n",
-    ")\n",
+    "spliter = TokenTextSplitter(chunk_size=100, chunk_overlap=50)\n",
     "\n",
-    "parser = SimpleNodeParser(\n",
-    "    text_splitter=spliter\n",
-    ")\n",
+    "parser = SimpleNodeParser(text_splitter=spliter)\n",
     "\n",
-    "nodes = parser.get_nodes_from_documents(\n",
-    "    documents=docs\n",
-    ")"
+    "nodes = parser.get_nodes_from_documents(documents=docs)"
    ]
   },
   {
@@ -1088,16 +1082,12 @@
    "source": [
     "# create index\n",
     "index = GPTVectorStoreIndex.from_documents(\n",
-    "    documents=docs, \n",
+    "    documents=docs,\n",
     "    service_context=openai_sc,\n",
     ")\n",
     "\n",
     "# query with embed_model specified\n",
-    "qe = index.as_query_engine(\n",
-    "    mode=\"embedding\", \n",
-    "    verbose=True, \n",
-    "    service_context=openai_sc\n",
-    ")"
+    "qe = index.as_query_engine(mode=\"embedding\", verbose=True, service_context=openai_sc)"
    ]
   },
   {
@@ -1171,10 +1161,7 @@
     "\n",
     "# query with embed_model specified\n",
     "qe = index.as_query_engine(\n",
-    "    mode=\"embedding\", \n",
-    "    verbose=True, \n",
-    "    service_context=openai_sc,\n",
-    "    use_async = False\n",
+    "    mode=\"embedding\", verbose=True, service_context=openai_sc, use_async=False\n",
     ")"
    ]
   },
@@ -1195,15 +1182,13 @@
     "\n",
     "# configure retriever\n",
     "retriever = VectorIndexRetriever(\n",
-    "    index=index, \n",
+    "    index=index,\n",
     "    similarity_top_k=3,\n",
     ")\n",
     "\n",
     "# configure response synthesizer\n",
     "response_synthesizer = ResponseSynthesizer.from_args(\n",
-    "    node_postprocessors=[\n",
-    "        SimilarityPostprocessor(similarity_cutoff=0.7)\n",
-    "    ]\n",
+    "    node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=0.7)]\n",
     ")\n",
     "\n",
     "# assemble query engine\n",
@@ -1257,9 +1242,10 @@
     "    r = qe.query(row[\"question\"])\n",
     "    row[\"answer\"] = r.response\n",
     "    row[\"contexts\"] = [sn.node.text for sn in r.source_nodes]\n",
-    "    \n",
+    "\n",
     "    return row\n",
     "\n",
+    "\n",
     "# generate_response(test_ds[0])"
    ]
   },
@@ -1530,10 +1516,7 @@
     "from ragas.metrics import factuality, answer_relevancy, context_relevancy\n",
     "from ragas import evaluate\n",
     "\n",
-    "evaluate(\n",
-    "    gen_ds, \n",
-    "    metrics=[factuality, answer_relevancy, context_relevancy]\n",
-    ")"
+    "evaluate(gen_ds, metrics=[factuality, answer_relevancy, context_relevancy])"
    ]
   },
   {