wip

docs/.local_build.sh

@@ -9,11 +9,10 @@ SCRIPT_DIR="$(cd "$(dirname "$0")"; pwd)"
 cd "${SCRIPT_DIR}"
 
 mkdir -p ../_dist
-rsync -ruv . ../_dist
+rsync -ruv --exclude ./node_modules . ../_dist
 cd ../_dist
 poetry run python scripts/model_feat_table.py
-find . -name "*.ipynb" -exec sed -i '' 's/```/\\`\\`\\`/g' {} \;
-poetry run nbdoc_build --srcdir docs --pause 0
+quarto render docs/
 cp ../cookbook/README.md src/pages/cookbook.mdx
 cp ../.github/CONTRIBUTING.md docs/contributing.md
 mkdir -p docs/templates

docs/docs/guides/evaluation/string/embedding_distance.ipynb

@@ -196,7 +196,13 @@
             "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 [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>"
+                "<a name=\"cite_note-1\"></a>\n",
+                "\n",
+                "<i>\n",
+                "\n",
+                "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))\n",
+                "\n",
+                "</i>"
             ]
         }
     ],

docs/vercel_build.sh

@@ -53,5 +53,5 @@ python3.11 scripts/copy_templates.py
 cp ../cookbook/README.md src/pages/cookbook.mdx
 cp ../.github/CONTRIBUTING.md docs/contributing.md
 wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md
-nbdoc_build --srcdir docs --pause 0
+quarto render docs/
 python3.11 scripts/generate_api_reference_links.py

docs/vercel_requirements.txt

@@ -1,3 +1,2 @@
 -e ../libs/langchain
 -e ../libs/core
-nbdoc

pyproject.toml

@@ -26,7 +26,6 @@ toml = "^0.10.2"
 myst-nb = "^0.17.1"
 linkchecker = "^10.2.1"
 sphinx-copybutton = "^0.5.1"
-nbdoc = "^0.0.82"
 
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.1.5"