Docs Nits (#7874)

Add links to reference docs

docs/extras/modules/evaluation/string/embedding_distance.ipynb

@@ -8,12 +8,12 @@
    "source": [
     "# Embedding Distance\n",
     "\n",
-    "To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector vector distance metric the two embedded representations using the `embeding_distance` evaulator.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
+    "To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector vector distance metric the two embedded representations using the `embedding_distance` evaluator.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
     "\n",
     "\n",
     "**Note:** This returns a **distance** score, meaning that the lower the number, the **more** similar the prediction is to the reference, according to their embedded representation.\n",
     "\n",
-    "Check out the reference docs for the [PairwiseEmbeddingDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html#langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain) for more info."
+    "Check out the reference docs for the [EmbeddingDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html#langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain) for more info."
    ]
   },
   {
@@ -26,7 +26,7 @@
    "source": [
     "from langchain.evaluation import load_evaluator\n",
     "\n",
-    "evaluator = load_evaluator(\"pairwise_embedding_distance\")"
+    "evaluator = load_evaluator(\"embedding_distance\")"
    ]
   },
   {
@@ -48,7 +48,7 @@
     }
    ],
    "source": [
-    "evaluator.evaluate_string_pairs(prediction=\"I shall go\", prediction_b=\"I shan't go\")"
+    "evaluator.evaluate_strings(prediction=\"I shall go\", reference=\"I shan't go\")"
    ]
   },
   {
@@ -70,7 +70,7 @@
     }
    ],
    "source": [
-    "evaluator.evaluate_string_pairs(prediction=\"I shall go\", prediction_b=\"I will go\")"
+    "evaluator.evaluate_strings(prediction=\"I shall go\", reference=\"I will go\")"
    ]
   },
   {
@@ -118,8 +118,9 @@
    },
    "outputs": [],
    "source": [
+    "# You can load by enum or by raw python string\n",
     "evaluator = load_evaluator(\n",
-    "    \"pairwise_embedding_distance\", distance_metric=EmbeddingDistance.EUCLIDEAN\n",
+    "    \"embedding_distance\", distance_metric=EmbeddingDistance.EUCLIDEAN\n",
     ")"
    ]
   },
@@ -134,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "tags": []
    },
@@ -143,12 +144,12 @@
     "from langchain.embeddings import HuggingFaceEmbeddings\n",
     "\n",
     "embedding_model = HuggingFaceEmbeddings()\n",
-    "hf_evaluator = load_evaluator(\"pairwise_embedding_distance\", embeddings=embedding_model)"
+    "hf_evaluator = load_evaluator(\"embedding_distance\", embeddings=embedding_model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "tags": []
    },
@@ -159,18 +160,18 @@
        "{'score': 0.5486443280477362}"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hf_evaluator.evaluate_string_pairs(prediction=\"I shall go\", prediction_b=\"I shan't go\")"
+    "hf_evaluator.evaluate_strings(prediction=\"I shall go\", reference=\"I shan't go\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
@@ -181,20 +182,20 @@
        "{'score': 0.21018880025138598}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hf_evaluator.evaluate_string_pairs(prediction=\"I shall go\", prediction_b=\"I will go\")"
+    "hf_evaluator.evaluate_strings(prediction=\"I shall go\", reference=\"I will go\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a name=\"cite_note-1\"></a><i>1. Note: When it comes to semantic similarity, this often gives better results than older string distance metrics (such as those in the `PairwiseStringDistanceEvalChain`), though it tends to be less reliable than evaluators that use the LLM directly (such as the `PairwiseStringEvalChain`) </i>"
+    "<a name=\"cite_note-1\"></a><i>1. Note: When it comes to semantic similarity, this often gives better results than older string distance metrics (such as those in the [StringDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string_distance.base.StringDistanceEvalChain)), though it tends to be less reliable than evaluators that use the LLM directly (such as the [QAEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html#langchain.evaluation.qa.eval_chain.QAEvalChain) or [LabeledCriteriaEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html#langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain)) </i>"
    ]
   }
  ],