Restructure docs (#11620)

.github/workflows/doc_lint.yml

@@ -19,4 +19,4 @@ jobs:
       run: |
         # We should not encourage imports directly from main init file
         # Expect for hub
-        git grep 'from langchain import' docs/{extras,docs_skeleton,snippets} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
+        git grep 'from langchain import' docs/{docs,snippets} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0

.gitignore

@@ -174,6 +174,6 @@ docs/api_reference/*/
 !docs/api_reference/_static/
 !docs/api_reference/templates/
 !docs/api_reference/themes/
-docs/docs_skeleton/build
-docs/docs_skeleton/node_modules
-docs/docs_skeleton/yarn.lock
+docs/docs/build
+docs/docs/node_modules
+docs/docs/yarn.lock

.gitmodules

@@ -1,4 +0,0 @@
-[submodule "docs/_docs_skeleton"]
-	path = docs/_docs_skeleton
-	url = https://github.com/langchain-ai/langchain-shared-docs
-	branch = main

Makefile

@@ -18,7 +18,7 @@ docs_clean:
 	rm -r docs/_dist
 
 docs_linkcheck:
-	poetry run linkchecker docs/_dist/docs_skeleton/ --ignore-url node_modules
+	poetry run linkchecker docs/_dist/docs/ --ignore-url node_modules
 
 api_docs_build:
 	poetry run python docs/api_reference/create_api_rst.py

docs/.local_build.sh

@@ -8,10 +8,10 @@ set -o xtrace
 SCRIPT_DIR="$(cd "$(dirname "$0")"; pwd)"
 cd "${SCRIPT_DIR}"
 
-mkdir -p _dist/docs_skeleton
-cp -r {docs_skeleton,snippets} _dist
-cd _dist/docs_skeleton
-poetry run nbdoc_build
-poetry run python generate_api_reference_links.py
+mkdir -p ../_dist
+cp -r . ../_dist
+cd ../_dist
+poetry run nbdoc_build --srcdir docs
+poetry run python scripts/generate_api_reference_links.py
 yarn install
 yarn start

docs/docs/guides/evaluation/comparison/custom.ipynb

@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Custom Pairwise Evaluator\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/comparison/custom.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/custom.ipynb)\n",
                 "\n",
                 "You can make your own pairwise string evaluators by inheriting from `PairwiseStringEvaluator` class and overwriting the `_evaluate_string_pairs` method (and the `_aevaluate_string_pairs` method if you want to use the evaluator asynchronously).\n",
                 "\n",

docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb

@@ -8,7 +8,7 @@
             },
             "source": [
                 "# Pairwise Embedding Distance \n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb)\n",
                 "\n",
                 "One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
                 "\n",

docs/docs/guides/evaluation/comparison/pairwise_string.ipynb

@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Pairwise String Comparison\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/comparison/pairwise_string.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb)\n",
                 "\n",
                 "Often you will want to compare predictions of an LLM, Chain, or Agent for a given input. The `StringComparison` evaluators facilitate this so you can answer questions like:\n",
                 "\n",

docs/docs/guides/evaluation/examples/comparisons.ipynb

@@ -5,7 +5,7 @@
             "metadata": {},
             "source": [
                 "# Comparing Chain Outputs\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/examples/comparisons.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/examples/comparisons.ipynb)\n",
                 "\n",
                 "Suppose you have two different prompts (or LLMs). How do you know which will generate \"better\" results?\n",
                 "\n",

docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb

@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Criteria Evaluation\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/string/criteria_eval_chain.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb)\n",
                 "\n",
                 "In scenarios where you wish to assess a model's output using a specific rubric or criteria set, the `criteria` evaluator proves to be a handy tool. It allows you to verify if an LLM or Chain's output complies with a defined set of criteria.\n",
                 "\n",

docs/docs/guides/evaluation/string/custom.ipynb

@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Custom String Evaluator\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/string/custom.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/custom.ipynb)\n",
                 "\n",
                 "You can make your own custom string evaluators by inheriting from the `StringEvaluator` class and implementing the `_evaluate_strings` (and `_aevaluate_strings` for async support) methods.\n",
                 "\n",

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

@@ -7,7 +7,7 @@
             },
             "source": [
                 "# Embedding Distance\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/string/embedding_distance.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/embedding_distance.ipynb)\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 `embedding_distance` evaluator.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
                 "\n",

docs/docs/guides/evaluation/string/exact_match.ipynb

@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Exact Match\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/string/exact_match.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/exact_match.ipynb)\n",
                 "\n",
                 "Probably the simplest ways to evaluate an LLM or runnable's string output against a reference label is by a simple string equivalence.\n",
                 "\n",

docs/docs/guides/evaluation/string/regex_match.ipynb

@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Regex Match\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/string/regex_match.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/regex_match.ipynb)\n",
                 "\n",
                 "To evaluate chain or runnable string predictions against a custom regex, you can use the `regex_match` evaluator."
             ]

docs/docs/guides/evaluation/string/string_distance.ipynb

@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# String Distance\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/string/string_distance.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
                 "\n",
                 "One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
                 "\n",

docs/docs/guides/evaluation/trajectory/custom.ipynb

@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Custom Trajectory Evaluator\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/trajectory/custom.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/custom.ipynb)\n",
                 "\n",
                 "You can make your own custom trajectory evaluators by inheriting from the [AgentTrajectoryEvaluator](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.AgentTrajectoryEvaluator.html#langchain.evaluation.schema.AgentTrajectoryEvaluator) class and overwriting the `_evaluate_agent_trajectory` (and `_aevaluate_agent_action`) method.\n",
                 "\n",

docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb

@@ -8,7 +8,7 @@
             },
             "source": [
                 "# Agent Trajectory\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/evaluation/trajectory/trajectory_eval.ipynb)\n",
+                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb)\n",
                 "\n",
                 "Agents can be difficult to holistically evaluate due to the breadth of actions and generation they can make. We recommend using multiple evaluation techniques appropriate to your use case. One way to evaluate an agent is to look at the whole trajectory of actions taken along with their responses.\n",
                 "\n",

docs/docs/guides/langsmith/walkthrough.ipynb

@@ -8,7 +8,7 @@
    },
    "source": [
     "# LangSmith Walkthrough\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/langsmith/walkthrough.ipynb)\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/langsmith/walkthrough.ipynb)\n",
     "\n",
     "LangChain makes it easy to prototype LLM applications and Agents. However, delivering LLM applications to production can be deceptively difficult. You will likely have to heavily customize and iterate on your prompts, chains, and other components to create a high-quality product.\n",
     "\n",

docs/docs/guides/privacy/presidio_data_anonymization/index.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Data anonymization with Microsoft Presidio\n",
     "\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/index.ipynb)\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/privacy/presidio_data_anonymization/index.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",

docs/docs/guides/privacy/presidio_data_anonymization/multi_language.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Mutli-language data anonymization with Microsoft Presidio\n",
     "\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/multi_language.ipynb)\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/privacy/presidio_data_anonymization/multi_language.ipynb)\n",
     "\n",
     "\n",
     "## Use case\n",

docs/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb

@@ -6,7 +6,7 @@
          "source": [
             "# Reversible data anonymization with Microsoft Presidio\n",
             "\n",
-            "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb)\n",
+            "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb)\n",
             "\n",
             "\n",
             "## Use case\n",