From 88ab69c2885c4ee63b85729bbec06fd7588eaeb9 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Fri, 6 Oct 2023 10:09:41 -0700
Subject: [PATCH] mv docs extras (#11399)

---
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diff --git a/docs/.local_build.sh b/docs/.local_build.sh
index 8b6de50bad..e3d0b6c697 100755
--- a/docs/.local_build.sh
+++ b/docs/.local_build.sh
@@ -10,7 +10,6 @@ cd "${SCRIPT_DIR}"
 
 mkdir -p _dist/docs_skeleton
 cp -r {docs_skeleton,snippets} _dist
-cp -r extras/* _dist/docs_skeleton/docs
 cd _dist/docs_skeleton
 poetry run nbdoc_build
 poetry run python generate_api_reference_links.py
diff --git a/docs/extras/_templates/integration.mdx b/docs/docs_skeleton/docs/_templates/integration.mdx
similarity index 100%
rename from docs/extras/_templates/integration.mdx
rename to docs/docs_skeleton/docs/_templates/integration.mdx
diff --git a/docs/extras/additional_resources/dependents.mdx b/docs/docs_skeleton/docs/additional_resources/dependents.mdx
similarity index 100%
rename from docs/extras/additional_resources/dependents.mdx
rename to docs/docs_skeleton/docs/additional_resources/dependents.mdx
diff --git a/docs/extras/additional_resources/tutorials.mdx b/docs/docs_skeleton/docs/additional_resources/tutorials.mdx
similarity index 100%
rename from docs/extras/additional_resources/tutorials.mdx
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diff --git a/docs/extras/additional_resources/youtube.mdx b/docs/docs_skeleton/docs/additional_resources/youtube.mdx
similarity index 100%
rename from docs/extras/additional_resources/youtube.mdx
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diff --git a/docs/extras/expression_language/cookbook/agent.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/agent.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/agent.ipynb
rename to docs/docs_skeleton/docs/expression_language/cookbook/agent.ipynb
diff --git a/docs/extras/expression_language/cookbook/code_writing.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/code_writing.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/code_writing.ipynb
rename to docs/docs_skeleton/docs/expression_language/cookbook/code_writing.ipynb
diff --git a/docs/extras/expression_language/cookbook/index.mdx b/docs/docs_skeleton/docs/expression_language/cookbook/index.mdx
similarity index 100%
rename from docs/extras/expression_language/cookbook/index.mdx
rename to docs/docs_skeleton/docs/expression_language/cookbook/index.mdx
diff --git a/docs/extras/expression_language/cookbook/memory.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/memory.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/memory.ipynb
rename to docs/docs_skeleton/docs/expression_language/cookbook/memory.ipynb
diff --git a/docs/extras/expression_language/cookbook/moderation.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/moderation.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/moderation.ipynb
rename to docs/docs_skeleton/docs/expression_language/cookbook/moderation.ipynb
diff --git a/docs/extras/expression_language/cookbook/multiple_chains.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/multiple_chains.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/multiple_chains.ipynb
rename to docs/docs_skeleton/docs/expression_language/cookbook/multiple_chains.ipynb
diff --git a/docs/extras/expression_language/cookbook/prompt_llm_parser.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/prompt_llm_parser.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/prompt_llm_parser.ipynb
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diff --git a/docs/extras/expression_language/cookbook/retrieval.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/retrieval.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/retrieval.ipynb
rename to docs/docs_skeleton/docs/expression_language/cookbook/retrieval.ipynb
diff --git a/docs/extras/expression_language/cookbook/sql_db.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/sql_db.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/sql_db.ipynb
rename to docs/docs_skeleton/docs/expression_language/cookbook/sql_db.ipynb
diff --git a/docs/extras/expression_language/cookbook/tools.ipynb b/docs/docs_skeleton/docs/expression_language/cookbook/tools.ipynb
similarity index 100%
rename from docs/extras/expression_language/cookbook/tools.ipynb
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diff --git a/docs/extras/expression_language/how_to/binding.ipynb b/docs/docs_skeleton/docs/expression_language/how_to/binding.ipynb
similarity index 100%
rename from docs/extras/expression_language/how_to/binding.ipynb
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diff --git a/docs/extras/expression_language/how_to/fallbacks.ipynb b/docs/docs_skeleton/docs/expression_language/how_to/fallbacks.ipynb
similarity index 100%
rename from docs/extras/expression_language/how_to/fallbacks.ipynb
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diff --git a/docs/extras/expression_language/how_to/functions.ipynb b/docs/docs_skeleton/docs/expression_language/how_to/functions.ipynb
similarity index 100%
rename from docs/extras/expression_language/how_to/functions.ipynb
rename to docs/docs_skeleton/docs/expression_language/how_to/functions.ipynb
diff --git a/docs/extras/expression_language/how_to/index.mdx b/docs/docs_skeleton/docs/expression_language/how_to/index.mdx
similarity index 100%
rename from docs/extras/expression_language/how_to/index.mdx
rename to docs/docs_skeleton/docs/expression_language/how_to/index.mdx
diff --git a/docs/extras/expression_language/how_to/map.ipynb b/docs/docs_skeleton/docs/expression_language/how_to/map.ipynb
similarity index 100%
rename from docs/extras/expression_language/how_to/map.ipynb
rename to docs/docs_skeleton/docs/expression_language/how_to/map.ipynb
diff --git a/docs/extras/expression_language/how_to/routing.ipynb b/docs/docs_skeleton/docs/expression_language/how_to/routing.ipynb
similarity index 100%
rename from docs/extras/expression_language/how_to/routing.ipynb
rename to docs/docs_skeleton/docs/expression_language/how_to/routing.ipynb
diff --git a/docs/extras/expression_language/interface.ipynb b/docs/docs_skeleton/docs/expression_language/interface.ipynb
similarity index 100%
rename from docs/extras/expression_language/interface.ipynb
rename to docs/docs_skeleton/docs/expression_language/interface.ipynb
diff --git a/docs/extras/guides/adapters/_category_.yml b/docs/docs_skeleton/docs/guides/adapters/_category_.yml
similarity index 100%
rename from docs/extras/guides/adapters/_category_.yml
rename to docs/docs_skeleton/docs/guides/adapters/_category_.yml
diff --git a/docs/extras/guides/adapters/openai.ipynb b/docs/docs_skeleton/docs/guides/adapters/openai.ipynb
similarity index 100%
rename from docs/extras/guides/adapters/openai.ipynb
rename to docs/docs_skeleton/docs/guides/adapters/openai.ipynb
diff --git a/docs/extras/guides/debugging.md b/docs/docs_skeleton/docs/guides/debugging.md
similarity index 100%
rename from docs/extras/guides/debugging.md
rename to docs/docs_skeleton/docs/guides/debugging.md
diff --git a/docs/extras/guides/deployments/index.mdx b/docs/docs_skeleton/docs/guides/deployments/index.mdx
similarity index 100%
rename from docs/extras/guides/deployments/index.mdx
rename to docs/docs_skeleton/docs/guides/deployments/index.mdx
diff --git a/docs/extras/guides/deployments/template_repos.mdx b/docs/docs_skeleton/docs/guides/deployments/template_repos.mdx
similarity index 100%
rename from docs/extras/guides/deployments/template_repos.mdx
rename to docs/docs_skeleton/docs/guides/deployments/template_repos.mdx
diff --git a/docs/extras/guides/evaluation/comparison/custom.ipynb b/docs/docs_skeleton/docs/guides/evaluation/comparison/custom.ipynb
similarity index 99%
rename from docs/extras/guides/evaluation/comparison/custom.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/comparison/custom.ipynb
index d017f7c287..525b90a0c2 100644
--- a/docs/extras/guides/evaluation/comparison/custom.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/comparison/pairwise_embedding_distance.ipynb b/docs/docs_skeleton/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb
similarity index 98%
rename from docs/extras/guides/evaluation/comparison/pairwise_embedding_distance.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb
index b16a15a0ce..16b026b9f7 100644
--- a/docs/extras/guides/evaluation/comparison/pairwise_embedding_distance.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/comparison/pairwise_string.ipynb b/docs/docs_skeleton/docs/guides/evaluation/comparison/pairwise_string.ipynb
similarity index 99%
rename from docs/extras/guides/evaluation/comparison/pairwise_string.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/comparison/pairwise_string.ipynb
index 20c5d1345b..b10c6fc917 100644
--- a/docs/extras/guides/evaluation/comparison/pairwise_string.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/examples/comparisons.ipynb b/docs/docs_skeleton/docs/guides/evaluation/examples/comparisons.ipynb
similarity index 99%
rename from docs/extras/guides/evaluation/examples/comparisons.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/examples/comparisons.ipynb
index 6a68654974..3e73b031de 100644
--- a/docs/extras/guides/evaluation/examples/comparisons.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/string/criteria_eval_chain.ipynb b/docs/docs_skeleton/docs/guides/evaluation/string/criteria_eval_chain.ipynb
similarity index 99%
rename from docs/extras/guides/evaluation/string/criteria_eval_chain.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/string/criteria_eval_chain.ipynb
index 0f66ca2a10..489439ff0d 100644
--- a/docs/extras/guides/evaluation/string/criteria_eval_chain.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/string/custom.ipynb b/docs/docs_skeleton/docs/guides/evaluation/string/custom.ipynb
similarity index 98%
rename from docs/extras/guides/evaluation/string/custom.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/string/custom.ipynb
index 4ce73c3862..a5d4330056 100644
--- a/docs/extras/guides/evaluation/string/custom.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/string/embedding_distance.ipynb b/docs/docs_skeleton/docs/guides/evaluation/string/embedding_distance.ipynb
similarity index 98%
rename from docs/extras/guides/evaluation/string/embedding_distance.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/string/embedding_distance.ipynb
index 63fe22809f..3cff88facb 100644
--- a/docs/extras/guides/evaluation/string/embedding_distance.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/string/exact_match.ipynb b/docs/docs_skeleton/docs/guides/evaluation/string/exact_match.ipynb
similarity index 97%
rename from docs/extras/guides/evaluation/string/exact_match.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/string/exact_match.ipynb
index 6673de8a6a..befbb0c45b 100644
--- a/docs/extras/guides/evaluation/string/exact_match.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/string/regex_match.ipynb b/docs/docs_skeleton/docs/guides/evaluation/string/regex_match.ipynb
similarity index 98%
rename from docs/extras/guides/evaluation/string/regex_match.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/string/regex_match.ipynb
index 96793ba596..2c4b80bb4a 100644
--- a/docs/extras/guides/evaluation/string/regex_match.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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."
             ]
diff --git a/docs/extras/guides/evaluation/string/scoring_eval_chain.ipynb b/docs/docs_skeleton/docs/guides/evaluation/string/scoring_eval_chain.ipynb
similarity index 100%
rename from docs/extras/guides/evaluation/string/scoring_eval_chain.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/string/scoring_eval_chain.ipynb
diff --git a/docs/extras/guides/evaluation/string/string_distance.ipynb b/docs/docs_skeleton/docs/guides/evaluation/string/string_distance.ipynb
similarity index 98%
rename from docs/extras/guides/evaluation/string/string_distance.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/string/string_distance.ipynb
index 9e8d08b3e0..641b6115f4 100644
--- a/docs/extras/guides/evaluation/string/string_distance.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/trajectory/custom.ipynb b/docs/docs_skeleton/docs/guides/evaluation/trajectory/custom.ipynb
similarity index 98%
rename from docs/extras/guides/evaluation/trajectory/custom.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/trajectory/custom.ipynb
index 75eb12197a..352e01cc35 100644
--- a/docs/extras/guides/evaluation/trajectory/custom.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/evaluation/trajectory/trajectory_eval.ipynb b/docs/docs_skeleton/docs/guides/evaluation/trajectory/trajectory_eval.ipynb
similarity index 99%
rename from docs/extras/guides/evaluation/trajectory/trajectory_eval.ipynb
rename to docs/docs_skeleton/docs/guides/evaluation/trajectory/trajectory_eval.ipynb
index 57cceb8aa8..237598ede4 100644
--- a/docs/extras/guides/evaluation/trajectory/trajectory_eval.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/fallbacks.ipynb b/docs/docs_skeleton/docs/guides/fallbacks.ipynb
similarity index 100%
rename from docs/extras/guides/fallbacks.ipynb
rename to docs/docs_skeleton/docs/guides/fallbacks.ipynb
diff --git a/docs/extras/guides/langsmith/img/log_traces.png b/docs/docs_skeleton/docs/guides/langsmith/img/log_traces.png
similarity index 100%
rename from docs/extras/guides/langsmith/img/log_traces.png
rename to docs/docs_skeleton/docs/guides/langsmith/img/log_traces.png
diff --git a/docs/extras/guides/langsmith/img/test_results.png b/docs/docs_skeleton/docs/guides/langsmith/img/test_results.png
similarity index 100%
rename from docs/extras/guides/langsmith/img/test_results.png
rename to docs/docs_skeleton/docs/guides/langsmith/img/test_results.png
diff --git a/docs/extras/guides/langsmith/walkthrough.ipynb b/docs/docs_skeleton/docs/guides/langsmith/walkthrough.ipynb
similarity index 99%
rename from docs/extras/guides/langsmith/walkthrough.ipynb
rename to docs/docs_skeleton/docs/guides/langsmith/walkthrough.ipynb
index 7312a15688..6a687731f6 100644
--- a/docs/extras/guides/langsmith/walkthrough.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/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",
diff --git a/docs/extras/guides/local_llms.ipynb b/docs/docs_skeleton/docs/guides/local_llms.ipynb
similarity index 100%
rename from docs/extras/guides/local_llms.ipynb
rename to docs/docs_skeleton/docs/guides/local_llms.ipynb
diff --git a/docs/extras/guides/model_laboratory.ipynb b/docs/docs_skeleton/docs/guides/model_laboratory.ipynb
similarity index 100%
rename from docs/extras/guides/model_laboratory.ipynb
rename to docs/docs_skeleton/docs/guides/model_laboratory.ipynb
diff --git a/docs/extras/guides/privacy/_category_.yml b/docs/docs_skeleton/docs/guides/privacy/_category_.yml
similarity index 100%
rename from docs/extras/guides/privacy/_category_.yml
rename to docs/docs_skeleton/docs/guides/privacy/_category_.yml
diff --git a/docs/extras/guides/privacy/presidio_data_anonymization/index.ipynb b/docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/index.ipynb
similarity index 99%
rename from docs/extras/guides/privacy/presidio_data_anonymization/index.ipynb
rename to docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/index.ipynb
index 617809d489..b5a0099270 100644
--- a/docs/extras/guides/privacy/presidio_data_anonymization/index.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/docs/guides/privacy/presidio_data_anonymization/index.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
diff --git a/docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb b/docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/multi_language.ipynb
similarity index 99%
rename from docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb
rename to docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/multi_language.ipynb
index ed91304e36..64c47d5b97 100644
--- a/docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/docs/guides/privacy/presidio_data_anonymization/multi_language.ipynb)\n",
     "\n",
     "\n",
     "## Use case\n",
diff --git a/docs/extras/guides/privacy/presidio_data_anonymization/reversible.ipynb b/docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb
similarity index 99%
rename from docs/extras/guides/privacy/presidio_data_anonymization/reversible.ipynb
rename to docs/docs_skeleton/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb
index a61f7894d3..6da03deb8b 100644
--- a/docs/extras/guides/privacy/presidio_data_anonymization/reversible.ipynb
+++ b/docs/docs_skeleton/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/extras/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_skeleton/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb)\n",
     "\n",
     "\n",
     "## Use case\n",
diff --git a/docs/extras/guides/pydantic_compatibility.md b/docs/docs_skeleton/docs/guides/pydantic_compatibility.md
similarity index 100%
rename from docs/extras/guides/pydantic_compatibility.md
rename to docs/docs_skeleton/docs/guides/pydantic_compatibility.md
diff --git a/docs/extras/guides/safety/_category_.yml b/docs/docs_skeleton/docs/guides/safety/_category_.yml
similarity index 100%
rename from docs/extras/guides/safety/_category_.yml
rename to docs/docs_skeleton/docs/guides/safety/_category_.yml
diff --git a/docs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynb b/docs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynb
index 49f3d6f41e..25eb4f2505 100644
--- a/docs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynb
+++ b/docs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynb
@@ -22,16 +22,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b39ac41a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -U langchain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
    "id": "3f8518ad-c762-413c-b8c9-f1c211fc311d",
    "metadata": {
     "tags": []
@@ -53,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "74550d74-3c01-4ba7-ad32-ca66d955d001",
    "metadata": {
     "tags": []
@@ -117,8 +107,7 @@
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n",
-    "    # replace with your own expletive\n",
-    "    \"Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.\"\n",
+    "    \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n",
     "]\n",
     "llm = FakeListLLM(responses=responses)\n",
     "\n",
@@ -134,9 +123,9 @@
     ")\n",
     "\n",
     "try:\n",
-    "    response = chain.invoke({\"question\": \"A sample SSN number looks like this . Can you give me some more samples?\"})\n",
+    "    response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-456-7890. Can you give me some more samples?\"})\n",
     "except ModerationPiiError as e:\n",
-    "    print(str(e))\n",
+    "    print(e.message)\n",
     "else:\n",
     "    print(response['output'])\n"
    ]
@@ -166,36 +155,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "d6e8900a-44ef-4967-bde8-b88af282139d",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "from langchain_experimental.comprehend_moderation import (BaseModerationConfig, \n",
-    "                                 ModerationIntentConfig, \n",
-    "                                 ModerationPiiConfig, \n",
-    "                                 ModerationToxicityConfig\n",
-    ")\n",
-    "\n",
-    "pii_config = ModerationPiiConfig(\n",
-    "    labels=[\"SSN\"],\n",
-    "    redact=True,\n",
-    "    mask_character=\"X\"\n",
-    ")\n",
-    "\n",
-    "toxicity_config = ModerationToxicityConfig(\n",
-    "    threshold=0.5\n",
-    ")\n",
-    "\n",
-    "intent_config = ModerationIntentConfig(\n",
-    "    threshold=0.5\n",
-    ")\n",
-    "\n",
-    "moderation_config = BaseModerationConfig(\n",
-    "    filters=[pii_config, toxicity_config, intent_config]\n",
-    ")"
+    "from langchain_experimental.comprehend_moderation import BaseModerationActions, BaseModerationFilters\n",
+    "\n",
+    "moderation_config = { \n",
+    "        \"filters\":[ \n",
+    "                BaseModerationFilters.PII, \n",
+    "                BaseModerationFilters.TOXICITY,\n",
+    "                BaseModerationFilters.INTENT\n",
+    "        ],\n",
+    "        \"pii\":{ \n",
+    "                \"action\": BaseModerationActions.ALLOW, \n",
+    "                \"threshold\":0.5, \n",
+    "                \"labels\":[\"SSN\"],\n",
+    "                \"mask_character\": \"X\"\n",
+    "        },\n",
+    "        \"toxicity\":{ \n",
+    "                \"action\": BaseModerationActions.STOP, \n",
+    "                \"threshold\":0.5\n",
+    "        },\n",
+    "        \"intent\":{ \n",
+    "                \"action\": BaseModerationActions.STOP, \n",
+    "                \"threshold\":0.5\n",
+    "        }\n",
+    "}"
    ]
   },
   {
@@ -203,20 +192,16 @@
    "id": "3634376b-5938-43df-9ed6-70ca7e99290f",
    "metadata": {},
    "source": [
-    "At the core of the the configuration there are three configuration models to be used\n",
+    "At the core of the configuration you have three filters specified in the `filters` key:\n",
+    "\n",
+    "1. `BaseModerationFilters.PII`\n",
+    "2. `BaseModerationFilters.TOXICITY`\n",
+    "3. `BaseModerationFilters.INTENT`\n",
     "\n",
-    "- `ModerationPiiConfig` used for configuring the behavior of the PII validations. Following are the parameters it can be initialized with\n",
-    "  - `labels` the PII entity labels. Defaults to an empty list which means that the PII validation will consider all PII entities.\n",
-    "  - `threshold` the confidence threshold for the detected entities, defaults to 0.5 or 50%\n",
-    "  - `redact` a boolean flag to enforce whether redaction should be performed on the text, defaults to `False`. When `False`, the PII validation will error out when it detects any PII entity, when set to `True` it simply redacts the PII values in the text.\n",
-    "  - `mask_character` the character used for masking, defaults to asterisk (*)\n",
-    "- `ModerationToxicityConfig` used for configuring the behavior of the toxicity validations. Following are the parameters it can be initialized with\n",
-    "  - `labels` the Toxic entity labels. Defaults to an empty list which means that the toxicity validation will consider all toxic entities. all\n",
-    "  - `threshold` the confidence threshold for the detected entities, defaults to 0.5 or 50% \n",
-    "- `ModerationIntentConfig` used for configuring the behavior of the intent validation\n",
-    "  - `threshold` the confidence threshold for the the intent classification, defaults to 0.5 or 50% \n",
+    "And an `action` key that defines two possible actions for each moderation function:\n",
     "\n",
-    "Finally, you use the `BaseModerationConfig` to define the order in which each of these checks are to be performed. The `BaseModerationConfig` takes an optional `filters` parameter which can be a list of one or more than one of the above validation checks, as seen in the previous code block. The  `BaseModerationConfig` can also be initialized with any `filters` in which case it will use all the checks with default configuration (more on this explained later).\n",
+    "1. `BaseModerationActions.ALLOW` - `allows` the prompt to pass through but masks detected PII in case of PII check. The default behavior is to run and redact all PII entities. If there is an entity specified in the `labels` field, then only those entities will go through the PII check and masked.\n",
+    "2. `BaseModerationActions.STOP` - `stops` the prompt from passing through to the next step in case any PII, Toxicity, or incorrect Intent is detected. The action of `BaseModerationActions.STOP` will raise a Python `Exception` essentially stopping the chain in progress.\n",
     "\n",
     "Using the configuration in the previous cell will perform PII checks and will allow the prompt to pass through however it will mask any SSN numbers present in either the prompt or the LLM output.\n"
    ]
@@ -254,8 +239,7 @@
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n",
-    "    # replace with your own expletive\n",
-    "    \"Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.\"\n",
+    "    \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n",
     "]\n",
     "llm = FakeListLLM(responses=responses)\n",
     "\n",
@@ -380,19 +364,22 @@
    },
    "outputs": [],
    "source": [
-    "pii_config = ModerationPiiConfig(\n",
-    "    labels=[\"SSN\"],\n",
-    "    redact=True,\n",
-    "    mask_character=\"X\"\n",
-    ")\n",
-    "\n",
-    "toxicity_config = ModerationToxicityConfig(\n",
-    "    threshold=0.5\n",
-    ")\n",
-    "\n",
-    "moderation_config = BaseModerationConfig(\n",
-    "    filters=[pii_config, toxicity_config]\n",
-    ")\n",
+    "moderation_config = { \n",
+    "        \"filters\": [ \n",
+    "                BaseModerationFilters.PII, \n",
+    "                BaseModerationFilters.TOXICITY\n",
+    "        ],\n",
+    "        \"pii\":{ \n",
+    "                \"action\": BaseModerationActions.STOP, \n",
+    "                \"threshold\":0.5, \n",
+    "                \"labels\":[\"SSN\"], \n",
+    "                \"mask_character\": \"X\" \n",
+    "        },\n",
+    "        \"toxicity\":{ \n",
+    "                \"action\": BaseModerationActions.STOP, \n",
+    "                \"threshold\":0.5 \n",
+    "        }\n",
+    "}\n",
     "\n",
     "comp_moderation_with_config = AmazonComprehendModerationChain(\n",
     "        moderation_config=moderation_config, # specify the configuration\n",
@@ -423,8 +410,7 @@
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n",
-    "    # replace with your own expletive\n",
-    "    \"Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.\"\n",
+    "    \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n",
     "]\n",
     "\n",
     "llm = FakeListLLM(responses=responses)\n",
@@ -458,7 +444,7 @@
     "## `moderation_config` and moderation execution order\n",
     "---\n",
     "\n",
-    "If `AmazonComprehendModerationChain` is not initialized with any `moderation_config` then it is initialized with the default values of `BaseModerationConfig`. If no `filters` are used then the sequence of moderation check is as follows.\n",
+    "If `AmazonComprehendModerationChain` is not initialized with any `moderation_config` then the default action is `STOP` and default order of moderation check is as follows.\n",
     "\n",
     "```\n",
     "AmazonComprehendModerationChain\n",
@@ -478,25 +464,32 @@
     "                        └── Return Prompt\n",
     "```\n",
     "\n",
-    "If any of the check raises a validation exception then the subsequent checks will not be performed. If a `callback` is provided in this case, then it will be called for each of the checks that have been performed. For example, in the case above, if the Chain fails due to presence of PII then the Toxicity and Intent checks will not be performed.\n",
+    "If any of the check raises exception then the subsequent checks will not be performed. If a `callback` is provided in this case, then it will be called for each of the checks that have been performed. For example, in the case above, if the Chain fails due to presence of PII then the Toxicity and Intent checks will not be performed.\n",
     "\n",
-    "You can override the execution order by passing `moderation_config` and simply specifying the desired order in the `filters` parameter of the `BaseModerationConfig`. In case you specify the filters, then the order of the checks as specified in the `filters` parameter will be maintained. For example, in the configuration below, first Toxicity check will be performed, then PII, and finally Intent validation will be performed. In this case, `AmazonComprehendModerationChain` will perform the desired checks in the specified order with default values of each model `kwargs`.\n",
+    "You can override the execution order by passing `moderation_config` and simply specifying the desired order in the `filters` key of the configuration. In case you use `moderation_config` then the order of the checks as specified in the `filters` key will be maintained. For example, in the configuration below, first Toxicity check will be performed, then PII, and finally Intent validation will be performed. In this case, `AmazonComprehendModerationChain` will perform the desired checks in the specified order with default values of each model `kwargs`.\n",
     "\n",
     "```python\n",
-    "pii_check = ModerationPiiConfig()\n",
-    "toxicity_check = ModerationToxicityConfig()\n",
-    "intent_check = ModerationIntentConfig()\n",
-    "\n",
-    "moderation_config = BaseModerationConfig(filters=[toxicity_check, pii_check, intent_check])\n",
+    "moderation_config = { \n",
+    "        \"filters\":[ BaseModerationFilters.TOXICITY, \n",
+    "                    BaseModerationFilters.PII, \n",
+    "                    BaseModerationFilters.INTENT]\n",
+    "   }\n",
     "```\n",
     "\n",
-    "You can have also use more than one configuration for a specific moderation check, for example in the sample below, two consecutive PII checks are performed. First the configuration checks for any SSN, if found it would raise an error. If any SSN isn't found then it will next check if any NAME and CREDIT_DEBIT_NUMBER is present in the prompt and will mask it.\n",
+    "Model `kwargs` are specified by the `pii`, `toxicity`, and `intent` keys within the `moderation_config` dictionary. For example, in the `moderation_config` below, the default order of moderation is overriden and the `pii` & `toxicity` model `kwargs` have been overriden. For `intent` the chain's default `kwargs` will be used.\n",
     "\n",
     "```python\n",
-    "pii_check_1 = ModerationPiiConfig(labels=[\"SSN\"])\n",
-    "pii_check_2 = ModerationPiiConfig(labels=[\"NAME\", \"CREDIT_DEBIT_NUMBER\"], redact=True)\n",
-    "\n",
-    "moderation_config = BaseModerationConfig(filters=[pii_check_1, pii_check_2])\n",
+    " moderation_config = { \n",
+    "        \"filters\":[ BaseModerationFilters.TOXICITY, \n",
+    "                    BaseModerationFilters.PII, \n",
+    "                    BaseModerationFilters.INTENT],\n",
+    "        \"pii\":{ \"action\": BaseModerationActions.ALLOW, \n",
+    "                \"threshold\":0.5, \n",
+    "                \"labels\":[\"SSN\"], \n",
+    "                \"mask_character\": \"X\" },\n",
+    "        \"toxicity\":{ \"action\": BaseModerationActions.STOP, \n",
+    "                     \"threshold\":0.5 }\n",
+    "   }\n",
     "```\n",
     "\n",
     "1. For a list of PII labels see Amazon Comprehend Universal PII entity types - https://docs.aws.amazon.com/comprehend/latest/dg/how-pii.html#how-pii-types\n",
@@ -519,9 +512,9 @@
     "# Examples\n",
     "---\n",
     "\n",
-    "## With HuggingFace Hub Models\n",
+    "## With Hugging Face Hub Models\n",
     "\n",
-    "Get your API Key from Huggingface hub - https://huggingface.co/docs/api-inference/quicktour#get-your-api-token"
+    "Get your API Key from Hugging Face hub - https://huggingface.co/docs/api-inference/quicktour#get-your-api-token"
    ]
   },
   {
@@ -546,8 +539,7 @@
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = \"<YOUR HF TOKEN HERE>\""
+    "%env HUGGINGFACEHUB_API_TOKEN=\"<HUGGINGFACEHUB_API_TOKEN>\""
    ]
   },
   {
@@ -560,7 +552,7 @@
    "outputs": [],
    "source": [
     "# See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n",
-    "repo_id = \"google/flan-t5-xxl\"  "
+    "repo_id = \"google/flan-t5-xxl\"  \n"
    ]
   },
   {
@@ -575,9 +567,12 @@
     "from langchain.llms import HuggingFaceHub\n",
     "from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain\n",
     "\n",
-    "template = \"\"\"Question: {question}\"\"\"\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer:\"\"\"\n",
     "\n",
     "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "\n",
     "llm = HuggingFaceHub(\n",
     "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 256}\n",
     ")\n",
@@ -601,32 +596,22 @@
    },
    "outputs": [],
    "source": [
-    "pii_config = ModerationPiiConfig(\n",
-    "    labels=[\"SSN\", \"CREDIT_DEBIT_NUMBER\"],\n",
-    "    redact=True,\n",
-    "    mask_character=\"X\"\n",
-    ")\n",
-    "\n",
-    "toxicity_config = ModerationToxicityConfig(\n",
-    "    threshold=0.5\n",
-    ")\n",
-    "\n",
-    "intent_config = ModerationIntentConfig(\n",
-    "    threshold=0.8\n",
-    ")\n",
-    "\n",
-    "moderation_config = BaseModerationConfig(\n",
-    "    filters=[pii_config, toxicity_config, intent_config]\n",
-    ")\n",
-    "# with callback\n",
+    "moderation_config = { \n",
+    "        \"filters\":[ BaseModerationFilters.PII, BaseModerationFilters.TOXICITY, BaseModerationFilters.INTENT ],\n",
+    "        \"pii\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5, \"labels\":[\"SSN\",\"CREDIT_DEBIT_NUMBER\"], \"mask_character\": \"X\"},\n",
+    "        \"toxicity\":{\"action\": BaseModerationActions.STOP, \"threshold\":0.5},\n",
+    "        \"intent\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5,},\n",
+    "   }\n",
+    "\n",
+    "# without any callback\n",
     "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config, \n",
     "                                                         client=comprehend_client,\n",
-    "                                                         moderation_callback=my_callback,\n",
     "                                                         verbose=True)\n",
     "\n",
-    "# without callback\n",
+    "# with callback\n",
     "amazon_comp_moderation_out = AmazonComprehendModerationChain(moderation_config=moderation_config, \n",
     "                                                         client=comprehend_client,\n",
+    "                                                         moderation_callback=my_callback,\n",
     "                                                         verbose=True)"
    ]
   },
@@ -657,10 +642,7 @@
     ")\n",
     "\n",
     "try:\n",
-    "    response = chain.invoke({\"question\": \"\"\"What is John Doe's address, phone number and SSN from the following text?\n",
-    "\n",
-    "John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.\n",
-    "\"\"\"})\n",
+    "    response = chain.invoke({\"question\": \"My AnyCompany Financial Services, LLC credit card account 1111-0000-1111-0008 has 24$ due by July 31st. Can you give me some more credit car number samples?\"})\n",
     "except Exception as e:\n",
     "    print(str(e))\n",
     "else:\n",
@@ -753,26 +735,15 @@
    },
    "outputs": [],
    "source": [
-    "pii_config = ModerationPiiConfig(\n",
-    "    labels=[\"SSN\"],\n",
-    "    redact=True,\n",
-    "    mask_character=\"X\"\n",
-    ")\n",
-    "\n",
-    "toxicity_config = ModerationToxicityConfig(\n",
-    "    threshold=0.5\n",
-    ")\n",
-    "\n",
-    "intent_config = ModerationIntentConfig(\n",
-    "    threshold=0.8\n",
-    ")\n",
-    "\n",
-    "moderation_config = BaseModerationConfig(\n",
-    "    filters=[pii_config, toxicity_config, intent_config]\n",
-    ")\n",
+    "moderation_config = { \n",
+    "        \"filters\":[ BaseModerationFilters.PII, BaseModerationFilters.TOXICITY ],\n",
+    "        \"pii\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5, \"labels\":[\"SSN\"], \"mask_character\": \"X\"},\n",
+    "        \"toxicity\":{\"action\": BaseModerationActions.STOP, \"threshold\":0.5},\n",
+    "        \"intent\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5,},\n",
+    "   }\n",
     "\n",
     "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config, \n",
-    "                                                         client=comprehend_client,\n",
+    "                                                         client=comprehend_client ,\n",
     "                                                         verbose=True)"
    ]
   },
@@ -803,10 +774,7 @@
     ")\n",
     "\n",
     "try:\n",
-    "    response = chain.invoke({\"question\": \"\"\"What is John Doe's address, phone number and SSN from the following text?\n",
-    "\n",
-    "John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.\n",
-    "\"\"\"})\n",
+    "    response = chain.invoke({\"question\": \"My AnyCompany Financial Services, LLC credit card account 1111-0000-1111-0008 has 24$ due by July 31st. Can you give me some more samples?\"})\n",
     "except Exception as e:\n",
     "    print(str(e))\n",
     "else:\n",
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index 83ffffcc29..7f17ec8dcd 100644
--- a/docs/extras/integrations/document_loaders/grobid.ipynb
+++ b/docs/docs_skeleton/docs/integrations/document_loaders/grobid.ipynb
@@ -16,7 +16,7 @@
     "---\n",
     "The best approach is to install Grobid via docker, see https://grobid.readthedocs.io/en/latest/Grobid-docker/. \n",
     "\n",
-    "(Note: additional instructions can be found [here](https://python.langchain.com/docs/extras/integrations/providers/grobid.mdx).)\n",
+    "(Note: additional instructions can be found [here](https://python.langchain.com/docs/docs_skeleton/docs/integrations/providers/grobid.mdx).)\n",
     "\n",
     "Once grobid is up-and-running you can interact as described below. \n"
    ]
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new file mode 100644
index 0000000000..70c16295f0
--- /dev/null
+++ b/docs/docs_skeleton/docs/integrations/providers/aws_dynamodb.mdx
@@ -0,0 +1,23 @@
+# AWS DynamoDB
+
+>[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html) 
+> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.
+ 
+## Installation and Setup
+
+We have to configur the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html). 
+
+We need to install the `boto3` library.
+
+```bash
+pip install boto3
+```
+
+
+## Memory
+
+See a [usage example](/docs/integrations/memory/aws_dynamodb).
+
+```python
+from langchain.memory import DynamoDBChatMessageHistory
+```
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index 0000000000..431cc4ac5f
--- /dev/null
+++ b/docs/docs_skeleton/docs/integrations/providers/google_document_ai.mdx
@@ -0,0 +1,28 @@
+# Google Document AI
+
+>[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform` 
+> service to transform unstructured data from documents into structured data, making it easier 
+> to understand, analyze, and consume.  
+
+
+## Installation and Setup
+
+You need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)  
+The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`) 
+and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`.
+You can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details`
+tab in the Google Cloud Console.
+
+```bash
+pip install google-cloud-documentai
+pip install google-cloud-documentai-toolbox
+```
+
+## Document Transformer
+
+See a [usage example](/docs/integrations/document_transformers/docai).
+
+```python
+from langchain.document_loaders.blob_loaders import Blob
+from langchain.document_loaders.parsers import DocAIParser
+```
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new file mode 100644
index 0000000000..f801207b82
--- /dev/null
+++ b/docs/docs_skeleton/docs/integrations/providers/scann.mdx
@@ -0,0 +1,29 @@
+# ScaNN
+
+>[Google ScaNN](https://github.com/google-research/google-research/tree/master/scann)
+> (Scalable Nearest Neighbors) is a python package.
+> 
+>`ScaNN` is a method for efficient vector similarity search at scale.
+
+>ScaNN includes search space pruning and quantization for Maximum Inner
+> Product Search and also supports other distance functions such as
+> Euclidean distance. The implementation is optimized for x86 processors
+> with AVX2 support. See its [Google Research github](https://github.com/google-research/google-research/tree/master/scann)
+> for more details.
+
+## Installation and Setup
+
+We need to install `scann` python package.
+
+```bash
+pip install scann
+```
+
+## Vector Store
+
+See a [usage example](/docs/integrations/vectorstores/scann).
+
+```python
+from langchain.vectorstores import ScaNN
+```
+
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Google Cloud Enterprise Search\n",
+    "\n",
+    "\n",
+    "[Enterprise Search](https://cloud.google.com/enterprise-search) is a part of the Generative AI App Builder suite of tools offered by Google Cloud.\n",
+    "\n",
+    "Gen AI App Builder lets developers, even those with limited machine learning skills, quickly and easily tap into the power of Google’s foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications. \n",
+    "\n",
+    "Enterprise Search lets organizations quickly build generative AI powered search engines for customers and employees.Enterprise Search is underpinned by a variety of Google Search technologies, including semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user’s query input. Enterprise Search also benefits from Google’s expertise in understanding how users search and factors in content relevance to order displayed results. \n",
+    "\n",
+    "Google Cloud offers Enterprise Search via Gen App Builder in Google Cloud Console and via an API for enterprise workflow integration. \n",
+    "\n",
+    "This notebook demonstrates how to configure Enterprise Search and use the Enterprise Search retriever. The Enterprise Search retriever encapsulates the [Generative AI App Builder Python client library](https://cloud.google.com/generative-ai-app-builder/docs/libraries#client-libraries-install-python) and uses it to access the Enterprise Search [Search Service API](https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1beta.services.search_service)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install pre-requisites\n",
+    "\n",
+    "You need to install the `google-cloud-discoverengine` package to use the Enterprise Search retriever."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install google-cloud-discoveryengine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure access to Google Cloud and Google Cloud Enterprise Search\n",
+    "\n",
+    "Enterprise Search is generally available for the allowlist (which means customers need to be approved for access) as of June 6, 2023. Contact your Google Cloud sales team for access and pricing details. We are previewing additional features that are coming soon to the generally available offering as part of our [Trusted Tester](https://cloud.google.com/ai/earlyaccess/join?hl=en) program. Sign up for [Trusted Tester](https://cloud.google.com/ai/earlyaccess/join?hl=en) and contact your Google Cloud sales team for an expedited trial.\n",
+    "\n",
+    "Before you can run this notebook you need to:\n",
+    "- Set or create a Google Cloud project and turn on Gen App Builder\n",
+    "- Create and populate an unstructured data store\n",
+    "- Set credentials to access `Enterprise Search API`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set or create a Google Cloud poject and turn on Gen App Builder\n",
+    "\n",
+    "Follow the instructions in the [Enterprise Search Getting Started guide](https://cloud.google.com/generative-ai-app-builder/docs/before-you-begin) to set/create a GCP project and enable Gen App Builder.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create and populate an unstructured data store\n",
+    "\n",
+    "[Use Google Cloud Console to create an unstructured data store](https://cloud.google.com/generative-ai-app-builder/docs/create-engine-es#unstructured-data) and populate it with the example PDF documents from the  `gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs` Cloud Storage folder. Make sure to use the `Cloud Storage (without metadata)` option."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set credentials to access Enterprise Search API\n",
+    "\n",
+    "The [Gen App Builder client libraries](https://cloud.google.com/generative-ai-app-builder/docs/libraries) used by the Enterprise Search retriever provide high-level language support for authenticating to Gen App Builder programmatically. Client libraries support [Application Default Credentials (ADC)](https://cloud.google.com/docs/authentication/application-default-credentials); the libraries look for credentials in a set of defined locations and use those credentials to authenticate requests to the API. With ADC, you can make credentials available to your application in a variety of environments, such as local development or production, without needing to modify your application code.\n",
+    "\n",
+    "If running in [Google Colab](https://colab.google) authenticate with `google.colab.google.auth` otherwise follow one of the [supported methods](https://cloud.google.com/docs/authentication/application-default-credentials) to make sure that you Application Default Credentials are properly set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "if \"google.colab\" in sys.modules:\n",
+    "    from google.colab import auth as google_auth\n",
+    "\n",
+    "    google_auth.authenticate_user()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure and use the Enterprise Search retriever\n",
+    "\n",
+    "The Enterprise Search retriever is implemented in the `langchain.retriever.GoogleCloudEntepriseSearchRetriever` class. The `get_relevant_documents` method returns a list of `langchain.schema.Document` documents where the `page_content` field of each document is populated the document content.\n",
+    "Depending on the data type used in Enterprise search (structured or unstructured) the `page_content` field is populated as follows:\n",
+    "- Structured data source: either an `extractive segment` or an `extractive answer` that matches a query. The `metadata` field is populated with metadata (if any) of the document from which the segments or answers were extracted.\n",
+    "- Unstructured data source: a string json containing all the fields returned from the structured data source. The `metadata` field is populated with metadata (if any) of the document  \n",
+    "\n",
+    "### Only for Unstructured data sources:\n",
+    "An extractive answer is verbatim text that is returned with each search result. It is extracted directly from the original document. Extractive answers are typically displayed near the top of web pages to provide an end user with a brief answer that is contextually relevant to their query. Extractive answers are available for website and unstructured search.\n",
+    "\n",
+    "An extractive segment is verbatim text that is returned with each search result. An extractive segment is usually more verbose than an extractive answer. Extractive segments can be displayed as an answer to a query, and can be used to perform post-processing tasks and as input for large language models to generate answers or new text. Extractive segments are available for unstructured search.\n",
+    "\n",
+    "For more information about extractive segments and extractive answers refer to [product documentation](https://cloud.google.com/generative-ai-app-builder/docs/snippets).\n",
+    "\n",
+    "When creating an instance of the retriever you can specify a number of parameters that control which Enterprise data store to access and how a natural language query is processed, including configurations for extractive answers and segments.\n",
+    "\n",
+    "\n",
+    "### The mandatory parameters are:\n",
+    "\n",
+    "- `project_id` - Your Google Cloud PROJECT_ID\n",
+    "- `search_engine_id` - The ID of the data store you want to use. \n",
+    "\n",
+    "The `project_id` and `search_engine_id` parameters can be provided explicitly in the retriever's constructor or through the environment variables - `PROJECT_ID` and `SEARCH_ENGINE_ID`.\n",
+    "\n",
+    "You can also configure a number of optional parameters, including:\n",
+    "\n",
+    "- `max_documents` - The maximum number of documents used to provide extractive segments or extractive answers\n",
+    "- `get_extractive_answers` - By default, the retriever is configured to return extractive segments. Set this field to `True` to return extractive answers. This is used only when `engine_data_type` set to 0 (unstructured) \n",
+    "- `max_extractive_answer_count` - The maximum number of extractive answers returned in each search result.\n",
+    "    At most 5 answers will be returned. This is used only when `engine_data_type` set to 0 (unstructured) \n",
+    "- `max_extractive_segment_count` - The maximum number of extractive segments returned in each search result.\n",
+    "    Currently one segment will be returned. This is used only when `engine_data_type` set to 0 (unstructured) \n",
+    "- `filter` - The filter expression that allows you filter the search results based on the metadata associated with the documents in the searched data store. \n",
+    "- `query_expansion_condition` - Specification to determine under which conditions query expansion should occur.\n",
+    "    0 - Unspecified query expansion condition. In this case, server behavior defaults to disabled.\n",
+    "    1 - Disabled query expansion. Only the exact search query is used, even if SearchResponse.total_size is zero.\n",
+    "    2 - Automatic query expansion built by the Search API.\n",
+    "- `engine_data_type` -  Defines the enterprise search data type\n",
+    "    0 - Unstructured data \n",
+    "    1 - Structured data\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure and use the retriever for **unstructured** data with extractve segments "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.retrievers import GoogleCloudEnterpriseSearchRetriever\n",
+    "\n",
+    "PROJECT_ID = \"<YOUR PROJECT ID>\"  # Set to your Project ID\n",
+    "SEARCH_ENGINE_ID = \"<YOUR SEARCH ENGINE ID>\"  # Set to your data store ID"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = GoogleCloudEnterpriseSearchRetriever(\n",
+    "    project_id=PROJECT_ID,\n",
+    "    search_engine_id=SEARCH_ENGINE_ID,\n",
+    "    max_documents=3,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What are Alphabet's Other Bets?\"\n",
+    "\n",
+    "result = retriever.get_relevant_documents(query)\n",
+    "for doc in result:\n",
+    "    print(doc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure and use the retriever for **unstructured** data with extractve answers "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = GoogleCloudEnterpriseSearchRetriever(\n",
+    "    project_id=PROJECT_ID,\n",
+    "    search_engine_id=SEARCH_ENGINE_ID,\n",
+    "    max_documents=3,\n",
+    "    max_extractive_answer_count=3,\n",
+    "    get_extractive_answers=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What are Alphabet's Other Bets?\"\n",
+    "\n",
+    "result = retriever.get_relevant_documents(query)\n",
+    "for doc in result:\n",
+    "    print(doc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure and use the retriever for **structured** data with extractve answers "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = GoogleCloudEnterpriseSearchRetriever(\n",
+    "    project_id=PROJECT_ID,\n",
+    "    search_engine_id=SEARCH_ENGINE_ID,\n",
+    "    max_documents=3,\n",
+    "    engine_data_type=1\n",
+    ")\n",
+    "\n",
+    "result = retriever.get_relevant_documents(query)\n",
+    "for doc in result:\n",
+    "    print(doc)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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similarity index 100%
rename from docs/extras/integrations/vectorstores/scann.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/scann.ipynb
diff --git a/docs/extras/integrations/vectorstores/singlestoredb.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/singlestoredb.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/singlestoredb.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/singlestoredb.ipynb
diff --git a/docs/extras/integrations/vectorstores/sklearn.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/sklearn.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/sklearn.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/sklearn.ipynb
diff --git a/docs/extras/integrations/vectorstores/sqlitevss.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/sqlitevss.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/sqlitevss.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/sqlitevss.ipynb
diff --git a/docs/extras/integrations/vectorstores/starrocks.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/starrocks.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/starrocks.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/starrocks.ipynb
diff --git a/docs/extras/integrations/vectorstores/supabase.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/supabase.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/supabase.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/supabase.ipynb
diff --git a/docs/extras/integrations/vectorstores/tair.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/tair.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/tair.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/tair.ipynb
diff --git a/docs/extras/integrations/vectorstores/tencentvectordb.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/tencentvectordb.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/tencentvectordb.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/tencentvectordb.ipynb
diff --git a/docs/extras/integrations/vectorstores/tigris.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/tigris.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/tigris.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/tigris.ipynb
diff --git a/docs/extras/integrations/vectorstores/timescalevector.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/timescalevector.ipynb
similarity index 95%
rename from docs/extras/integrations/vectorstores/timescalevector.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/timescalevector.ipynb
index 02f318dd67..a6d9f4bf64 100644
--- a/docs/extras/integrations/vectorstores/timescalevector.ipynb
+++ b/docs/docs_skeleton/docs/integrations/vectorstores/timescalevector.ipynb
@@ -1312,10 +1312,10 @@
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-       " Document(page_content='{\"commit\": \" 3749953e9704e45df8f621607989ada0714ce28d\", \"author\": \"Fabr\\\\u00edzio de Royes Mello<fabriziomello@gmail.com>\", \"date\": \"Wed Oct 5 18:45:40 2022 -0300\", \"change summary\": \"Hierarchical Continuous Aggregates\", \"change details\": \"Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates).  With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster.  A problem with this feature can be in upper levels we can end up with the \\\\\"average of averages\\\\\". But to get the \\\\\"real average\\\\\" we can rely on \\\\\"stats_aggs\\\\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\\\\"average\\\\\" and \\\\\"sum\\\\\".  Closes #1400 \"}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/extras/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': 'fabriziomello@gmail.com'}),\n",
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+       " Document(page_content='{\"commit\": \" 3749953e9704e45df8f621607989ada0714ce28d\", \"author\": \"Fabr\\\\u00edzio de Royes Mello<fabriziomello@gmail.com>\", \"date\": \"Wed Oct 5 18:45:40 2022 -0300\", \"change summary\": \"Hierarchical Continuous Aggregates\", \"change details\": \"Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates).  With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster.  A problem with this feature can be in upper levels we can end up with the \\\\\"average of averages\\\\\". But to get the \\\\\"real average\\\\\" we can rely on \\\\\"stats_aggs\\\\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\\\\"average\\\\\" and \\\\\"sum\\\\\".  Closes #1400 \"}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': 'fabriziomello@gmail.com'}),\n",
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@@ -1343,10 +1343,10 @@
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+       " Document(page_content='{\"commit\": \" b1314e63f2ff6151ab5becfb105afa3682286a4d\", \"author\": \"Sven Klemm<sven@timescale.com>\", \"date\": \"Thu Dec 22 12:03:35 2022 +0100\", \"change summary\": \"Fix RPM package test for PG15 on centos 7\", \"change details\": \"Installing PG15 on Centos 7 requires the EPEL repository to satisfy the dependencies. \"}', metadata={'id': '477b1d80-81e8-11ed-9c8c-9b5abbd67c98', 'date': '2022-12-22 12:03:35+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 408, 'author_name': 'Sven Klemm', 'commit_hash': ' b1314e63f2ff6151ab5becfb105afa3682286a4d', 'author_email': 'sven@timescale.com'})]"
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@@ -1374,10 +1374,10 @@
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-       " Document(page_content='{\"commit\": \" feef9206facc5c5f506661de4a81d96ef059b095\", \"author\": \"Sven Klemm<sven@timescale.com>\", \"date\": \"Fri Mar 31 08:22:57 2023 +0200\", \"change summary\": \"Add _timescaledb_functions schema\", \"change details\": \"Currently internal user objects like chunks and our functions live in the same schema making locking down that schema hard. This patch adds a new schema _timescaledb_functions that is meant to be the schema used for timescaledb internal functions to allow separation of code and chunks or other user objects. \"}', metadata={'id': '7a257680-cf8c-11ed-848c-a515e8687479', 'date': '2023-03-31 08:22:57+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/extras/modules/ts_git_log.json', 'seq_num': 239, 'author_name': 'Sven Klemm', 'commit_hash': ' feef9206facc5c5f506661de4a81d96ef059b095', 'author_email': 'sven@timescale.com'}),\n",
-       " Document(page_content='{\"commit\": \" 0a66bdb8d36a1879246bd652e4c28500c4b951ab\", \"author\": \"Sven Klemm<sven@timescale.com>\", \"date\": \"Sun Aug 20 22:47:10 2023 +0200\", \"change summary\": \"Move functions to _timescaledb_functions schema\", \"change details\": \"To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) \"}', metadata={'id': 'bb99db00-3f9a-11ee-a8dc-0b9c1a5a37c4', 'date': '2023-08-20 22:47:10+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/extras/modules/ts_git_log.json', 'seq_num': 41, 'author_name': 'Sven Klemm', 'commit_hash': ' 0a66bdb8d36a1879246bd652e4c28500c4b951ab', 'author_email': 'sven@timescale.com'}),\n",
-       " Document(page_content='{\"commit\": \" 56ea8b4de93cefc38e002202d8ac96947dcbaa77\", \"author\": \"Sven Klemm<sven@timescale.com>\", \"date\": \"Thu Apr 13 13:16:14 2023 +0200\", \"change summary\": \"Move trigger functions to _timescaledb_functions schema\", \"change details\": \"To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for our trigger functions. \"}', metadata={'id': '9a255300-d9ec-11ed-988f-7086c8ca463a', 'date': '2023-04-13 13:16:14+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/extras/modules/ts_git_log.json', 'seq_num': 44, 'author_name': 'Sven Klemm', 'commit_hash': ' 56ea8b4de93cefc38e002202d8ac96947dcbaa77', 'author_email': 'sven@timescale.com'})]"
+       "[Document(page_content='{\"commit\": \" 04f43335dea11e9c467ee558ad8edfc00c1a45ed\", \"author\": \"Sven Klemm<sven@timescale.com>\", \"date\": \"Thu Apr 6 13:00:00 2023 +0200\", \"change summary\": \"Move aggregate support function into _timescaledb_functions\", \"change details\": \"This patch moves the support functions for histogram, first and last into the _timescaledb_functions schema. Since we alter the schema of the existing functions in upgrade scripts and do not change the aggregates this should work completely transparently for any user objects using those aggregates. \"}', metadata={'id': '2cb47800-d46a-11ed-8f0e-2b624245c561', 'date': '2023-04-6 13:00:00+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 233, 'author_name': 'Sven Klemm', 'commit_hash': ' 04f43335dea11e9c467ee558ad8edfc00c1a45ed', 'author_email': 'sven@timescale.com'}),\n",
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+       " Document(page_content='{\"commit\": \" 0a66bdb8d36a1879246bd652e4c28500c4b951ab\", \"author\": \"Sven Klemm<sven@timescale.com>\", \"date\": \"Sun Aug 20 22:47:10 2023 +0200\", \"change summary\": \"Move functions to _timescaledb_functions schema\", \"change details\": \"To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) \"}', metadata={'id': 'bb99db00-3f9a-11ee-a8dc-0b9c1a5a37c4', 'date': '2023-08-20 22:47:10+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 41, 'author_name': 'Sven Klemm', 'commit_hash': ' 0a66bdb8d36a1879246bd652e4c28500c4b951ab', 'author_email': 'sven@timescale.com'}),\n",
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@@ -1405,10 +1405,10 @@
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-       " Document(page_content='{\"commit\": \" 88aaf23ae37fe7f47252b87325eb570aa417c607\", \"author\": \"noctarius aka Christoph Engelbert<me@noctarius.com>\", \"date\": \"Wed Jul 12 14:53:40 2023 +0200\", \"change summary\": \"Allow Replica Identity (Alter Table) on CAGGs (#5868)\", \"change details\": \"This commit is a follow up of #5515, which added support for ALTER TABLE\\\\r ... REPLICA IDENTITY (FULL | INDEX) on hypertables.\\\\r \\\\r This commit allows the execution against materialized hypertables to\\\\r enable update / delete operations on continuous aggregates when logical\\\\r replication in enabled for them.\"}', metadata={'id': '1fcfa200-20b3-11ee-9a18-370561c7cb1a', 'date': '2023-07-12 14:53:40+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/extras/modules/ts_git_log.json', 'seq_num': 96, 'author_name': 'noctarius aka Christoph Engelbert', 'commit_hash': ' 88aaf23ae37fe7f47252b87325eb570aa417c607', 'author_email': 'me@noctarius.com'}),\n",
-       " Document(page_content='{\"commit\": \" d5268c36fbd23fa2a93c0371998286e8688247bb\", \"author\": \"Alexander Kuzmenkov<36882414+akuzm@users.noreply.github.com>\", \"date\": \"Fri Jul 28 13:35:05 2023 +0200\", \"change summary\": \"Fix SQLSmith workflow\", \"change details\": \"The build was failing because it was picking up the wrong version of Postgres. Remove it. \"}', metadata={'id': 'cc0fba80-2d3a-11ee-ae7d-36dc25cad3b8', 'date': '2023-07-28 13:35:05+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/extras/modules/ts_git_log.json', 'seq_num': 82, 'author_name': 'Alexander Kuzmenkov', 'commit_hash': ' d5268c36fbd23fa2a93c0371998286e8688247bb', 'author_email': '36882414+akuzm@users.noreply.github.com'}),\n",
-       " Document(page_content='{\"commit\": \" 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9\", \"author\": \"Lakshmi Narayanan Sreethar<lakshmi@timescale.com>\", \"date\": \"Tue Jul 25 16:11:35 2023 +0530\", \"change summary\": \"Fix broken CI after PG12 removal\", \"change details\": \"The commit cdea343cc updated the gh_matrix_builder.py script but failed to import PG_LATEST variable into the script thus breaking the CI. Import that variable to fix the CI tests. \"}', metadata={'id': 'd3835980-2ad7-11ee-b98d-c4e3092e076e', 'date': '2023-07-25 16:11:35+0850', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/extras/modules/ts_git_log.json', 'seq_num': 84, 'author_name': 'Lakshmi Narayanan Sreethar', 'commit_hash': ' 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9', 'author_email': 'lakshmi@timescale.com'})]"
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+       " Document(page_content='{\"commit\": \" 88aaf23ae37fe7f47252b87325eb570aa417c607\", \"author\": \"noctarius aka Christoph Engelbert<me@noctarius.com>\", \"date\": \"Wed Jul 12 14:53:40 2023 +0200\", \"change summary\": \"Allow Replica Identity (Alter Table) on CAGGs (#5868)\", \"change details\": \"This commit is a follow up of #5515, which added support for ALTER TABLE\\\\r ... REPLICA IDENTITY (FULL | INDEX) on hypertables.\\\\r \\\\r This commit allows the execution against materialized hypertables to\\\\r enable update / delete operations on continuous aggregates when logical\\\\r replication in enabled for them.\"}', metadata={'id': '1fcfa200-20b3-11ee-9a18-370561c7cb1a', 'date': '2023-07-12 14:53:40+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 96, 'author_name': 'noctarius aka Christoph Engelbert', 'commit_hash': ' 88aaf23ae37fe7f47252b87325eb570aa417c607', 'author_email': 'me@noctarius.com'}),\n",
+       " Document(page_content='{\"commit\": \" d5268c36fbd23fa2a93c0371998286e8688247bb\", \"author\": \"Alexander Kuzmenkov<36882414+akuzm@users.noreply.github.com>\", \"date\": \"Fri Jul 28 13:35:05 2023 +0200\", \"change summary\": \"Fix SQLSmith workflow\", \"change details\": \"The build was failing because it was picking up the wrong version of Postgres. Remove it. \"}', metadata={'id': 'cc0fba80-2d3a-11ee-ae7d-36dc25cad3b8', 'date': '2023-07-28 13:35:05+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 82, 'author_name': 'Alexander Kuzmenkov', 'commit_hash': ' d5268c36fbd23fa2a93c0371998286e8688247bb', 'author_email': '36882414+akuzm@users.noreply.github.com'}),\n",
+       " Document(page_content='{\"commit\": \" 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9\", \"author\": \"Lakshmi Narayanan Sreethar<lakshmi@timescale.com>\", \"date\": \"Tue Jul 25 16:11:35 2023 +0530\", \"change summary\": \"Fix broken CI after PG12 removal\", \"change details\": \"The commit cdea343cc updated the gh_matrix_builder.py script but failed to import PG_LATEST variable into the script thus breaking the CI. Import that variable to fix the CI tests. \"}', metadata={'id': 'd3835980-2ad7-11ee-b98d-c4e3092e076e', 'date': '2023-07-25 16:11:35+0850', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 84, 'author_name': 'Lakshmi Narayanan Sreethar', 'commit_hash': ' 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9', 'author_email': 'lakshmi@timescale.com'})]"
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@@ -1436,8 +1436,8 @@
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-       "[Document(page_content='{\"commit\": \" 35c91204987ccb0161d745af1a39b7eb91bc65a5\", \"author\": \"Fabr\\\\u00edzio de Royes Mello<fabriziomello@gmail.com>\", \"date\": \"Thu Nov 24 13:19:36 2022 -0300\", \"change summary\": \"Add Hierarchical Continuous Aggregates validations\", \"change details\": \"Commit 3749953e introduce Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate) but it lacks of some basic validations.  Validations added during the creation of a Hierarchical Continuous Aggregate:  * Forbid create a continuous aggregate with fixed-width bucket on top of   a continuous aggregate with variable-width bucket.  * Forbid incompatible bucket widths:   - should not be equal;   - bucket width of the new continuous aggregate should be greater than     the source continuous aggregate;   - bucket width of the new continuous aggregate should be multiple of     the source continuous aggregate. \"}', metadata={'id': 'c98d1c00-6c13-11ed-9bbe-23925ce74d13', 'date': '2022-11-24 13:19:36+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/extras/modules/ts_git_log.json', 'seq_num': 446, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 35c91204987ccb0161d745af1a39b7eb91bc65a5', 'author_email': 'fabriziomello@gmail.com'}),\n",
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+       "[Document(page_content='{\"commit\": \" 35c91204987ccb0161d745af1a39b7eb91bc65a5\", \"author\": \"Fabr\\\\u00edzio de Royes Mello<fabriziomello@gmail.com>\", \"date\": \"Thu Nov 24 13:19:36 2022 -0300\", \"change summary\": \"Add Hierarchical Continuous Aggregates validations\", \"change details\": \"Commit 3749953e introduce Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate) but it lacks of some basic validations.  Validations added during the creation of a Hierarchical Continuous Aggregate:  * Forbid create a continuous aggregate with fixed-width bucket on top of   a continuous aggregate with variable-width bucket.  * Forbid incompatible bucket widths:   - should not be equal;   - bucket width of the new continuous aggregate should be greater than     the source continuous aggregate;   - bucket width of the new continuous aggregate should be multiple of     the source continuous aggregate. \"}', metadata={'id': 'c98d1c00-6c13-11ed-9bbe-23925ce74d13', 'date': '2022-11-24 13:19:36+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 446, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 35c91204987ccb0161d745af1a39b7eb91bc65a5', 'author_email': 'fabriziomello@gmail.com'}),\n",
+       " Document(page_content='{\"commit\": \" 3749953e9704e45df8f621607989ada0714ce28d\", \"author\": \"Fabr\\\\u00edzio de Royes Mello<fabriziomello@gmail.com>\", \"date\": \"Wed Oct 5 18:45:40 2022 -0300\", \"change summary\": \"Hierarchical Continuous Aggregates\", \"change details\": \"Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates).  With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster.  A problem with this feature can be in upper levels we can end up with the \\\\\"average of averages\\\\\". But to get the \\\\\"real average\\\\\" we can rely on \\\\\"stats_aggs\\\\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\\\\"average\\\\\" and \\\\\"sum\\\\\".  Closes #1400 \"}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': 'fabriziomello@gmail.com'})]"
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@@ -1550,7 +1550,7 @@
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+       "(Document(page_content='{\"commit\": \" 00b566dfe478c11134bcf1e7bcf38943e7fafe8f\", \"author\": \"Fabr\\\\u00edzio de Royes Mello<fabriziomello@gmail.com>\", \"date\": \"Mon Mar 6 15:51:03 2023 -0300\", \"change summary\": \"Remove unused functions\", \"change details\": \"We don\\'t use `ts_catalog_delete[_only]` functions anywhere and instead we rely on `ts_catalog_delete_tid[_only]` functions so removing it from our code base. \"}', metadata={'id': 'd7f5c580-bc4f-11ed-9712-ffa0126a201a', 'date': '2023-03-6 15:51:03+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs_skeleton/docs/modules/ts_git_log.json', 'seq_num': 285, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 00b566dfe478c11134bcf1e7bcf38943e7fafe8f', 'author_email': 'fabriziomello@gmail.com'}),\n",
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diff --git a/docs/extras/integrations/vectorstores/typesense.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/typesense.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/typesense.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/typesense.ipynb
diff --git a/docs/extras/integrations/vectorstores/usearch.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/usearch.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/usearch.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/usearch.ipynb
diff --git a/docs/extras/integrations/vectorstores/vald.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/vald.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/vald.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/vald.ipynb
diff --git a/docs/extras/integrations/vectorstores/vearch.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/vearch.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/vearch.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/vearch.ipynb
diff --git a/docs/extras/integrations/vectorstores/vectara.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/vectara.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/vectara.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/vectara.ipynb
diff --git a/docs/extras/integrations/vectorstores/vespa.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/vespa.ipynb
similarity index 100%
rename from docs/extras/integrations/vectorstores/vespa.ipynb
rename to docs/docs_skeleton/docs/integrations/vectorstores/vespa.ipynb
diff --git a/docs/extras/integrations/vectorstores/weaviate.ipynb b/docs/docs_skeleton/docs/integrations/vectorstores/weaviate.ipynb
similarity index 100%
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diff --git a/docs/extras/use_cases/apis.ipynb b/docs/docs_skeleton/docs/use_cases/apis.ipynb
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index 8d9259c3ca..ecbb7924ce 100644
--- a/docs/extras/use_cases/apis.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/apis.ipynb
@@ -16,7 +16,7 @@
    "id": "a15e6a18",
    "metadata": {},
    "source": [
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/apis.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_skeleton/docs/use_cases/apis.ipynb)\n",
     "\n",
     "## Use case \n",
     "\n",
diff --git a/docs/extras/use_cases/chatbots.ipynb b/docs/docs_skeleton/docs/use_cases/chatbots.ipynb
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index c67d595c9f..ca808708de 100644
--- a/docs/extras/use_cases/chatbots.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/chatbots.ipynb
@@ -16,7 +16,7 @@
    "id": "ee7f95e4",
    "metadata": {},
    "source": [
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/chatbots.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_skeleton/docs/use_cases/chatbots.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
diff --git a/docs/extras/use_cases/code_understanding.ipynb b/docs/docs_skeleton/docs/use_cases/code_understanding.ipynb
similarity index 99%
rename from docs/extras/use_cases/code_understanding.ipynb
rename to docs/docs_skeleton/docs/use_cases/code_understanding.ipynb
index 0f6933c5d9..a69ade0c18 100644
--- a/docs/extras/use_cases/code_understanding.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/code_understanding.ipynb
@@ -14,7 +14,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/code_understanding.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_skeleton/docs/use_cases/code_understanding.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
@@ -28,7 +28,7 @@
     "\n",
     "## Overview\n",
     "\n",
-    "The pipeline for QA over code follows [the steps we do for document question answering](/docs/extras/use_cases/question_answering), with some differences:\n",
+    "The pipeline for QA over code follows [the steps we do for document question answering](/docs/docs_skeleton/docs/use_cases/question_answering), with some differences:\n",
     "\n",
     "In particular, we can employ a [splitting strategy](https://python.langchain.com/docs/integrations/document_loaders/source_code) that does a few things:\n",
     "\n",
@@ -182,7 +182,7 @@
     "\n",
     "When setting up the vectorstore retriever:\n",
     "\n",
-    "* We test [max marginal relevance](/docs/extras/use_cases/question_answering) for retrieval\n",
+    "* We test [max marginal relevance](/docs/docs_skeleton/docs/use_cases/question_answering) for retrieval\n",
     "* And 8 documents returned\n",
     "\n",
     "#### Go deeper\n",
@@ -214,7 +214,7 @@
    "source": [
     "### Chat\n",
     "\n",
-    "Test chat, just as we do for [chatbots](/docs/extras/use_cases/chatbots).\n",
+    "Test chat, just as we do for [chatbots](/docs/docs_skeleton/docs/use_cases/chatbots).\n",
     "\n",
     "#### Go deeper\n",
     "\n",
diff --git a/docs/extras/use_cases/extraction.ipynb b/docs/docs_skeleton/docs/use_cases/extraction.ipynb
similarity index 99%
rename from docs/extras/use_cases/extraction.ipynb
rename to docs/docs_skeleton/docs/use_cases/extraction.ipynb
index b607029751..0d8360268f 100644
--- a/docs/extras/use_cases/extraction.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/extraction.ipynb
@@ -16,7 +16,7 @@
    "id": "b84edb4e",
    "metadata": {},
    "source": [
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/extraction.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_skeleton/docs/use_cases/extraction.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
diff --git a/docs/extras/use_cases/more/_category_.yml b/docs/docs_skeleton/docs/use_cases/more/_category_.yml
similarity index 100%
rename from docs/extras/use_cases/more/_category_.yml
rename to docs/docs_skeleton/docs/use_cases/more/_category_.yml
diff --git a/docs/extras/use_cases/more/agents/agent_simulations/camel_role_playing.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/camel_role_playing.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/agents/agent_simulations/camel_role_playing.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/camel_role_playing.ipynb
diff --git a/docs/extras/use_cases/more/agents/agent_simulations/characters.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/characters.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/agents/agent_simulations/characters.ipynb
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diff --git a/docs/extras/use_cases/more/agents/agent_simulations/gymnasium.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/gymnasium.ipynb
similarity index 100%
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diff --git a/docs/extras/use_cases/more/agents/agent_simulations/index.mdx b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/index.mdx
similarity index 100%
rename from docs/extras/use_cases/more/agents/agent_simulations/index.mdx
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diff --git a/docs/extras/use_cases/more/agents/agent_simulations/multi_player_dnd.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/multi_player_dnd.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/agents/agent_simulations/multi_player_dnd.ipynb
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diff --git a/docs/extras/use_cases/more/agents/agent_simulations/multiagent_authoritarian.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/multiagent_authoritarian.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/agents/agent_simulations/multiagent_authoritarian.ipynb
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diff --git a/docs/extras/use_cases/more/agents/agent_simulations/multiagent_bidding.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/multiagent_bidding.ipynb
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rename from docs/extras/use_cases/more/agents/agent_simulations/multiagent_bidding.ipynb
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diff --git a/docs/extras/use_cases/more/agents/agent_simulations/petting_zoo.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/petting_zoo.ipynb
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diff --git a/docs/extras/use_cases/more/agents/agent_simulations/two_agent_debate_tools.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/two_agent_debate_tools.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/agents/agent_simulations/two_agent_debate_tools.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/two_agent_debate_tools.ipynb
diff --git a/docs/extras/use_cases/more/agents/agent_simulations/two_player_dnd.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/two_player_dnd.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/agents/agent_simulations/two_player_dnd.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/agents/agent_simulations/two_player_dnd.ipynb
diff --git a/docs/extras/use_cases/more/agents/agents.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agents.ipynb
similarity index 99%
rename from docs/extras/use_cases/more/agents/agents.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/agents/agents.ipynb
index 3cee468486..f248ad32d4 100644
--- a/docs/extras/use_cases/more/agents/agents.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/more/agents/agents.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Agents\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/extras/use_cases/more/agents/agents.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_skeleton/docs/use_cases/more/agents/agents.ipynb)\n",
     "\n",
     "## Use case \n",
     "\n",
diff --git a/docs/extras/use_cases/more/agents/agents/camel_role_playing.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agents/camel_role_playing.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/agents/agents/camel_role_playing.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/agents/agents/camel_role_playing.ipynb
diff --git a/docs/extras/use_cases/more/agents/agents/custom_agent_with_plugin_retrieval.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agents/custom_agent_with_plugin_retrieval.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/agents/agents/custom_agent_with_plugin_retrieval.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/agents/agents/custom_agent_with_plugin_retrieval.ipynb
diff --git a/docs/extras/use_cases/more/agents/agents/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agents/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb
similarity index 100%
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rename to docs/docs_skeleton/docs/use_cases/more/agents/agents/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb
diff --git a/docs/extras/use_cases/more/agents/agents/index.mdx b/docs/docs_skeleton/docs/use_cases/more/agents/agents/index.mdx
similarity index 100%
rename from docs/extras/use_cases/more/agents/agents/index.mdx
rename to docs/docs_skeleton/docs/use_cases/more/agents/agents/index.mdx
diff --git a/docs/extras/use_cases/more/agents/agents/sales_agent_with_context.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/agents/sales_agent_with_context.ipynb
similarity index 100%
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diff --git a/docs/extras/use_cases/more/agents/autonomous_agents/baby_agi_with_agent.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/autonomous_agents/baby_agi_with_agent.ipynb
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diff --git a/docs/extras/use_cases/more/agents/autonomous_agents/hugginggpt.ipynb b/docs/docs_skeleton/docs/use_cases/more/agents/autonomous_agents/hugginggpt.ipynb
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rename from docs/extras/use_cases/more/code_writing/llm_math.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/code_writing/llm_math.ipynb
diff --git a/docs/extras/use_cases/more/code_writing/llm_symbolic_math.ipynb b/docs/docs_skeleton/docs/use_cases/more/code_writing/llm_symbolic_math.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/code_writing/llm_symbolic_math.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/code_writing/llm_symbolic_math.ipynb
diff --git a/docs/extras/use_cases/more/code_writing/pal.ipynb b/docs/docs_skeleton/docs/use_cases/more/code_writing/pal.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/code_writing/pal.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/code_writing/pal.ipynb
diff --git a/docs/extras/use_cases/more/data_generation.ipynb b/docs/docs_skeleton/docs/use_cases/more/data_generation.ipynb
similarity index 99%
rename from docs/extras/use_cases/more/data_generation.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/data_generation.ipynb
index 969f37cbe2..ddd86329a0 100644
--- a/docs/extras/use_cases/more/data_generation.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/more/data_generation.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "# Synthetic Data generation\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/extras/use_cases/data_generation.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_skeleton/docs/use_cases/data_generation.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
diff --git a/docs/extras/use_cases/more/graph/diffbot_graphtransformer.ipynb b/docs/docs_skeleton/docs/use_cases/more/graph/diffbot_graphtransformer.ipynb
similarity index 98%
rename from docs/extras/use_cases/more/graph/diffbot_graphtransformer.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/graph/diffbot_graphtransformer.ipynb
index f8961174ce..275ecd7833 100644
--- a/docs/extras/use_cases/more/graph/diffbot_graphtransformer.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/more/graph/diffbot_graphtransformer.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Diffbot Graph Transformer\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/extras/use_cases/more/graph/diffbot_graphtransformer.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_skeleton/docs/use_cases/more/graph/diffbot_graphtransformer.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
diff --git a/docs/extras/use_cases/more/graph/graph_arangodb_qa.ipynb b/docs/docs_skeleton/docs/use_cases/more/graph/graph_arangodb_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/graph/graph_arangodb_qa.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/graph/graph_arangodb_qa.ipynb
diff --git a/docs/extras/use_cases/more/graph/graph_cypher_qa.ipynb b/docs/docs_skeleton/docs/use_cases/more/graph/graph_cypher_qa.ipynb
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rename from docs/extras/use_cases/more/graph/graph_cypher_qa.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/graph/graph_cypher_qa.ipynb
diff --git a/docs/extras/use_cases/more/graph/graph_falkordb_qa.ipynb b/docs/docs_skeleton/docs/use_cases/more/graph/graph_falkordb_qa.ipynb
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similarity index 100%
rename from docs/extras/use_cases/more/graph/graph_sparql_qa.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/graph/graph_sparql_qa.ipynb
diff --git a/docs/extras/use_cases/more/graph/index.mdx b/docs/docs_skeleton/docs/use_cases/more/graph/index.mdx
similarity index 100%
rename from docs/extras/use_cases/more/graph/index.mdx
rename to docs/docs_skeleton/docs/use_cases/more/graph/index.mdx
diff --git a/docs/extras/use_cases/more/graph/neptune_cypher_qa.ipynb b/docs/docs_skeleton/docs/use_cases/more/graph/neptune_cypher_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/graph/neptune_cypher_qa.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/graph/neptune_cypher_qa.ipynb
diff --git a/docs/extras/use_cases/more/graph/tot.ipynb b/docs/docs_skeleton/docs/use_cases/more/graph/tot.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/graph/tot.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/graph/tot.ipynb
diff --git a/docs/extras/use_cases/more/learned_prompt_optimization.ipynb b/docs/docs_skeleton/docs/use_cases/more/learned_prompt_optimization.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/learned_prompt_optimization.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/learned_prompt_optimization.ipynb
diff --git a/docs/extras/use_cases/more/self_check/index.mdx b/docs/docs_skeleton/docs/use_cases/more/self_check/index.mdx
similarity index 100%
rename from docs/extras/use_cases/more/self_check/index.mdx
rename to docs/docs_skeleton/docs/use_cases/more/self_check/index.mdx
diff --git a/docs/extras/use_cases/more/self_check/llm_checker.ipynb b/docs/docs_skeleton/docs/use_cases/more/self_check/llm_checker.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/self_check/llm_checker.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/self_check/llm_checker.ipynb
diff --git a/docs/extras/use_cases/more/self_check/llm_summarization_checker.ipynb b/docs/docs_skeleton/docs/use_cases/more/self_check/llm_summarization_checker.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/self_check/llm_summarization_checker.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/self_check/llm_summarization_checker.ipynb
diff --git a/docs/extras/use_cases/more/self_check/smart_llm.ipynb b/docs/docs_skeleton/docs/use_cases/more/self_check/smart_llm.ipynb
similarity index 100%
rename from docs/extras/use_cases/more/self_check/smart_llm.ipynb
rename to docs/docs_skeleton/docs/use_cases/more/self_check/smart_llm.ipynb
diff --git a/docs/extras/use_cases/qa_structured/_category_.yml b/docs/docs_skeleton/docs/use_cases/qa_structured/_category_.yml
similarity index 100%
rename from docs/extras/use_cases/qa_structured/_category_.yml
rename to docs/docs_skeleton/docs/use_cases/qa_structured/_category_.yml
diff --git a/docs/extras/use_cases/qa_structured/integrations/_category_.yml b/docs/docs_skeleton/docs/use_cases/qa_structured/integrations/_category_.yml
similarity index 100%
rename from docs/extras/use_cases/qa_structured/integrations/_category_.yml
rename to docs/docs_skeleton/docs/use_cases/qa_structured/integrations/_category_.yml
diff --git a/docs/extras/use_cases/qa_structured/integrations/databricks.ipynb b/docs/docs_skeleton/docs/use_cases/qa_structured/integrations/databricks.ipynb
similarity index 100%
rename from docs/extras/use_cases/qa_structured/integrations/databricks.ipynb
rename to docs/docs_skeleton/docs/use_cases/qa_structured/integrations/databricks.ipynb
diff --git a/docs/extras/use_cases/qa_structured/integrations/elasticsearch.ipynb b/docs/docs_skeleton/docs/use_cases/qa_structured/integrations/elasticsearch.ipynb
similarity index 97%
rename from docs/extras/use_cases/qa_structured/integrations/elasticsearch.ipynb
rename to docs/docs_skeleton/docs/use_cases/qa_structured/integrations/elasticsearch.ipynb
index e28bc6bf61..dabee6ccc3 100644
--- a/docs/extras/use_cases/qa_structured/integrations/elasticsearch.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/qa_structured/integrations/elasticsearch.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Elasticsearch\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/extras/use_cases/qa_structured/integrations/elasticsearch.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_skeleton/docs/use_cases/qa_structured/integrations/elasticsearch.ipynb)\n",
     "\n",
     "We can use LLMs to interact with Elasticsearch analytics databases in natural language.\n",
     "\n",
diff --git a/docs/extras/use_cases/qa_structured/integrations/myscale_vector_sql.ipynb b/docs/docs_skeleton/docs/use_cases/qa_structured/integrations/myscale_vector_sql.ipynb
similarity index 100%
rename from docs/extras/use_cases/qa_structured/integrations/myscale_vector_sql.ipynb
rename to docs/docs_skeleton/docs/use_cases/qa_structured/integrations/myscale_vector_sql.ipynb
diff --git a/docs/extras/use_cases/qa_structured/integrations/sqlite.mdx b/docs/docs_skeleton/docs/use_cases/qa_structured/integrations/sqlite.mdx
similarity index 100%
rename from docs/extras/use_cases/qa_structured/integrations/sqlite.mdx
rename to docs/docs_skeleton/docs/use_cases/qa_structured/integrations/sqlite.mdx
diff --git a/docs/extras/use_cases/qa_structured/sql.ipynb b/docs/docs_skeleton/docs/use_cases/qa_structured/sql.ipynb
similarity index 99%
rename from docs/extras/use_cases/qa_structured/sql.ipynb
rename to docs/docs_skeleton/docs/use_cases/qa_structured/sql.ipynb
index 23bde6a2a5..41e8db80f9 100644
--- a/docs/extras/use_cases/qa_structured/sql.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/qa_structured/sql.ipynb
@@ -14,7 +14,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/qa_structured/sql.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_skeleton/docs/use_cases/qa_structured/sql.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
diff --git a/docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb
diff --git a/docs/extras/use_cases/question_answering/how_to/code/index.mdx b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/code/index.mdx
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/code/index.mdx
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/code/index.mdx
diff --git a/docs/extras/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake.ipynb
diff --git a/docs/extras/use_cases/question_answering/how_to/conversational_retrieval_agents.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/conversational_retrieval_agents.ipynb
similarity index 79%
rename from docs/extras/use_cases/question_answering/how_to/conversational_retrieval_agents.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/conversational_retrieval_agents.ipynb
index 5a407ba1af..ebe12b2116 100644
--- a/docs/extras/use_cases/question_answering/how_to/conversational_retrieval_agents.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/conversational_retrieval_agents.ipynb
@@ -30,7 +30,7 @@
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../../../../docs/extras/modules/state_of_the_union.txt')"
+    "loader = TextLoader('../../../../../docs/docs_skeleton/docs/modules/state_of_the_union.txt')"
    ]
   },
   {
@@ -269,7 +269,7 @@
       "Invoking: `search_state_of_union` with `{'query': 'Kentaji Brown Jackson'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../../../docs/extras/modules/state_of_the_union.txt'}), Document(page_content='One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \\n\\nWhen they came home, many of the world’s fittest and best trained warriors were never the same. \\n\\nHeadaches. Numbness. Dizziness. \\n\\nA cancer that would put them in a flag-draped coffin. \\n\\nI know. \\n\\nOne of those soldiers was my son Major Beau Biden. \\n\\nWe don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \\n\\nBut I’m committed to finding out everything we can. \\n\\nCommitted to military families like Danielle Robinson from Ohio. \\n\\nThe widow of Sergeant First Class Heath Robinson.  \\n\\nHe was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \\n\\nStationed near Baghdad, just yards from burn pits the size of football fields. \\n\\nHeath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.', metadata={'source': '../../../../../docs/extras/modules/state_of_the_union.txt'}), Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../../../docs/extras/modules/state_of_the_union.txt'}), Document(page_content='We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nI’ve worked on these issues a long time. \\n\\nI know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../../../docs/extras/modules/state_of_the_union.txt'})]\u001b[0m\u001b[32;1m\u001b[1;3mIn the most recent state of the union, the President mentioned Kentaji Brown Jackson. The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. The President described Judge Ketanji Brown Jackson as one of our nation's top legal minds who will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../../../docs/docs_skeleton/docs/modules/state_of_the_union.txt'}), Document(page_content='One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \\n\\nWhen they came home, many of the world’s fittest and best trained warriors were never the same. \\n\\nHeadaches. Numbness. Dizziness. \\n\\nA cancer that would put them in a flag-draped coffin. \\n\\nI know. \\n\\nOne of those soldiers was my son Major Beau Biden. \\n\\nWe don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \\n\\nBut I’m committed to finding out everything we can. \\n\\nCommitted to military families like Danielle Robinson from Ohio. \\n\\nThe widow of Sergeant First Class Heath Robinson.  \\n\\nHe was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \\n\\nStationed near Baghdad, just yards from burn pits the size of football fields. \\n\\nHeath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.', metadata={'source': '../../../../../docs/docs_skeleton/docs/modules/state_of_the_union.txt'}), Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../../../docs/docs_skeleton/docs/modules/state_of_the_union.txt'}), Document(page_content='We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nI’ve worked on these issues a long time. \\n\\nI know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../../../docs/docs_skeleton/docs/modules/state_of_the_union.txt'})]\u001b[0m\u001b[32;1m\u001b[1;3mIn the most recent state of the union, the President mentioned Kentaji Brown Jackson. The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. The President described Judge Ketanji Brown Jackson as one of our nation's top legal minds who will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
diff --git a/docs/extras/use_cases/question_answering/how_to/document-context-aware-QA.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/document-context-aware-QA.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/document-context-aware-QA.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/document-context-aware-QA.ipynb
diff --git a/docs/extras/use_cases/question_answering/how_to/flare.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/flare.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/flare.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/flare.ipynb
diff --git a/docs/extras/use_cases/question_answering/how_to/hyde.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/hyde.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/hyde.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/hyde.ipynb
diff --git a/docs/extras/use_cases/question_answering/how_to/local_retrieval_qa.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/local_retrieval_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/local_retrieval_qa.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/local_retrieval_qa.ipynb
diff --git a/docs/extras/use_cases/question_answering/how_to/multiple_retrieval.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/multiple_retrieval.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/multiple_retrieval.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/multiple_retrieval.ipynb
diff --git a/docs/extras/use_cases/question_answering/how_to/qa_citations.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/qa_citations.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/qa_citations.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/qa_citations.ipynb
diff --git a/docs/extras/use_cases/question_answering/how_to/vector_db_text_generation.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/vector_db_text_generation.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/how_to/vector_db_text_generation.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/how_to/vector_db_text_generation.ipynb
diff --git a/docs/extras/use_cases/question_answering/integrations/_category_.yml b/docs/docs_skeleton/docs/use_cases/question_answering/integrations/_category_.yml
similarity index 100%
rename from docs/extras/use_cases/question_answering/integrations/_category_.yml
rename to docs/docs_skeleton/docs/use_cases/question_answering/integrations/_category_.yml
diff --git a/docs/extras/use_cases/question_answering/integrations/openai_functions_retrieval_qa.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/integrations/openai_functions_retrieval_qa.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa.ipynb
diff --git a/docs/extras/use_cases/question_answering/integrations/semantic-search-over-chat.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/integrations/semantic-search-over-chat.ipynb
similarity index 100%
rename from docs/extras/use_cases/question_answering/integrations/semantic-search-over-chat.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/integrations/semantic-search-over-chat.ipynb
diff --git a/docs/extras/use_cases/question_answering/question_answering.ipynb b/docs/docs_skeleton/docs/use_cases/question_answering/question_answering.ipynb
similarity index 99%
rename from docs/extras/use_cases/question_answering/question_answering.ipynb
rename to docs/docs_skeleton/docs/use_cases/question_answering/question_answering.ipynb
index d7c9d1b1a5..d83d98e57b 100644
--- a/docs/extras/use_cases/question_answering/question_answering.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/question_answering/question_answering.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Question Answering\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/extras/use_cases/question_answering/qa.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_skeleton/docs/use_cases/question_answering/qa.ipynb)\n",
     "\n",
     "## Use case\n",
     "Suppose you have some text documents (PDF, blog, Notion pages, etc.) and want to ask questions related to the contents of those documents. \n",
diff --git a/docs/extras/use_cases/summarization.ipynb b/docs/docs_skeleton/docs/use_cases/summarization.ipynb
similarity index 99%
rename from docs/extras/use_cases/summarization.ipynb
rename to docs/docs_skeleton/docs/use_cases/summarization.ipynb
index 6d7e118ab7..366a84bb42 100644
--- a/docs/extras/use_cases/summarization.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/summarization.ipynb
@@ -16,7 +16,7 @@
    "id": "cf13f702",
    "metadata": {},
    "source": [
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/summarization.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_skeleton/docs/use_cases/summarization.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
diff --git a/docs/extras/use_cases/tagging.ipynb b/docs/docs_skeleton/docs/use_cases/tagging.ipynb
similarity index 99%
rename from docs/extras/use_cases/tagging.ipynb
rename to docs/docs_skeleton/docs/use_cases/tagging.ipynb
index 37242a84f5..97f5e94c7c 100644
--- a/docs/extras/use_cases/tagging.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/tagging.ipynb
@@ -16,7 +16,7 @@
    "id": "a0507a4b",
    "metadata": {},
    "source": [
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/tagging.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_skeleton/docs/use_cases/tagging.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",
diff --git a/docs/extras/use_cases/web_scraping.ipynb b/docs/docs_skeleton/docs/use_cases/web_scraping.ipynb
similarity index 99%
rename from docs/extras/use_cases/web_scraping.ipynb
rename to docs/docs_skeleton/docs/use_cases/web_scraping.ipynb
index a9aa4a32b1..fb3e94fb5d 100644
--- a/docs/extras/use_cases/web_scraping.ipynb
+++ b/docs/docs_skeleton/docs/use_cases/web_scraping.ipynb
@@ -16,7 +16,7 @@
       "id": "6605e7f7",
       "metadata": {},
       "source": [
-        "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/web_scraping.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_skeleton/docs/use_cases/web_scraping.ipynb)\n",
         "\n",
         "## Use case\n",
         "\n",
diff --git a/docs/docs_skeleton/vercel_build.sh b/docs/docs_skeleton/vercel_build.sh
index 1c7e3799d7..73ba144a45 100755
--- a/docs/docs_skeleton/vercel_build.sh
+++ b/docs/docs_skeleton/vercel_build.sh
@@ -47,7 +47,7 @@ python3.11 -m venv .venv
 source .venv/bin/activate
 python3.11 -m pip install --upgrade pip
 python3.11 -m pip install -r vercel_requirements.txt
-cp -r extras/* docs_skeleton/docs
+python3.11 scripts/model_feat_table.py
 cd docs_skeleton
 nbdoc_build
-python3.11 generate_api_reference_links.py
+python3.11 ../scripts/generate_api_reference_links.py
diff --git a/docs/extras/guides/safety/amazon_comprehend_chain.ipynb b/docs/extras/guides/safety/amazon_comprehend_chain.ipynb
deleted file mode 100644
index 25eb4f2505..0000000000
--- a/docs/extras/guides/safety/amazon_comprehend_chain.ipynb
+++ /dev/null
@@ -1,1390 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "25a3f834-60b7-4c21-bfb4-ad16d30fd3f7",
-   "metadata": {},
-   "source": [
-    "# Amazon Comprehend Moderation Chain\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2c4236d8-4054-473d-84a4-87a4db278a62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install boto3 nltk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f8518ad-c762-413c-b8c9-f1c211fc311d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import boto3\n",
-    "\n",
-    "comprehend_client = boto3.client('comprehend', region_name='us-east-1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1f0ba28",
-   "metadata": {},
-   "source": [
-    "Import `AmazonComprehendModerationChain`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74550d74-3c01-4ba7-ad32-ca66d955d001",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f00c338b-de9f-40e5-9295-93c9e26058e3",
-   "metadata": {},
-   "source": [
-    "Initialize an instance of the Amazon Comprehend Moderation Chain to be used with your LLM chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cde58cc6-ff83-493a-9aed-93d755f984a7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "comprehend_moderation = AmazonComprehendModerationChain(\n",
-    "    client=comprehend_client, #optional\n",
-    "    verbose=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad646d01-82d2-435a-939b-c450693857ab",
-   "metadata": {},
-   "source": [
-    "Using it with your LLM chain. \n",
-    "\n",
-    "**Note**: The example below uses the _Fake LLM_ from LangChain, but same concept could be applied to other LLMs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0efa1946-d4a9-467a-920a-a8fb78720fc2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain\n",
-    "from langchain.llms.fake import FakeListLLM\n",
-    "from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ModerationPiiError\n",
-    "\n",
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer:\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "\n",
-    "responses = [\n",
-    "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n",
-    "    \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n",
-    "]\n",
-    "llm = FakeListLLM(responses=responses)\n",
-    "\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "chain = (\n",
-    "    prompt \n",
-    "    | comprehend_moderation \n",
-    "    | {llm_chain.input_keys[0]: lambda x: x['output'] }  \n",
-    "    | llm_chain \n",
-    "    | { \"input\": lambda x: x['text'] } \n",
-    "    | comprehend_moderation \n",
-    ")\n",
-    "\n",
-    "try:\n",
-    "    response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-456-7890. Can you give me some more samples?\"})\n",
-    "except ModerationPiiError as e:\n",
-    "    print(e.message)\n",
-    "else:\n",
-    "    print(response['output'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6da25d96-0d96-4c01-94ae-a2ead17f10aa",
-   "metadata": {},
-   "source": [
-    "## Using `moderation_config` to customize your moderation\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfd550e7-5012-41fa-9546-8b78ddf1c673",
-   "metadata": {},
-   "source": [
-    "Use Amazon Comprehend Moderation with a configuration to control what moderations you wish to perform and what actions should be taken for each of them. There are three different moderations that happen when no configuration is passed as demonstrated above. These moderations are:\n",
-    "\n",
-    "- PII (Personally Identifiable Information) checks \n",
-    "- Toxicity content detection\n",
-    "- Intention detection\n",
-    "\n",
-    "Here is an example of a moderation config."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d6e8900a-44ef-4967-bde8-b88af282139d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_experimental.comprehend_moderation import BaseModerationActions, BaseModerationFilters\n",
-    "\n",
-    "moderation_config = { \n",
-    "        \"filters\":[ \n",
-    "                BaseModerationFilters.PII, \n",
-    "                BaseModerationFilters.TOXICITY,\n",
-    "                BaseModerationFilters.INTENT\n",
-    "        ],\n",
-    "        \"pii\":{ \n",
-    "                \"action\": BaseModerationActions.ALLOW, \n",
-    "                \"threshold\":0.5, \n",
-    "                \"labels\":[\"SSN\"],\n",
-    "                \"mask_character\": \"X\"\n",
-    "        },\n",
-    "        \"toxicity\":{ \n",
-    "                \"action\": BaseModerationActions.STOP, \n",
-    "                \"threshold\":0.5\n",
-    "        },\n",
-    "        \"intent\":{ \n",
-    "                \"action\": BaseModerationActions.STOP, \n",
-    "                \"threshold\":0.5\n",
-    "        }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3634376b-5938-43df-9ed6-70ca7e99290f",
-   "metadata": {},
-   "source": [
-    "At the core of the configuration you have three filters specified in the `filters` key:\n",
-    "\n",
-    "1. `BaseModerationFilters.PII`\n",
-    "2. `BaseModerationFilters.TOXICITY`\n",
-    "3. `BaseModerationFilters.INTENT`\n",
-    "\n",
-    "And an `action` key that defines two possible actions for each moderation function:\n",
-    "\n",
-    "1. `BaseModerationActions.ALLOW` - `allows` the prompt to pass through but masks detected PII in case of PII check. The default behavior is to run and redact all PII entities. If there is an entity specified in the `labels` field, then only those entities will go through the PII check and masked.\n",
-    "2. `BaseModerationActions.STOP` - `stops` the prompt from passing through to the next step in case any PII, Toxicity, or incorrect Intent is detected. The action of `BaseModerationActions.STOP` will raise a Python `Exception` essentially stopping the chain in progress.\n",
-    "\n",
-    "Using the configuration in the previous cell will perform PII checks and will allow the prompt to pass through however it will mask any SSN numbers present in either the prompt or the LLM output.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3a4f7e65-f733-4863-ae6d-34c9faffd849",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "comp_moderation_with_config = AmazonComprehendModerationChain(\n",
-    "    moderation_config=moderation_config, #specify the configuration\n",
-    "    client=comprehend_client,            #optionally pass the Boto3 Client\n",
-    "    verbose=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a25e6f93-765b-4f99-8c1c-929157dbd4aa",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer:\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "\n",
-    "responses = [\n",
-    "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n",
-    "    \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n",
-    "]\n",
-    "llm = FakeListLLM(responses=responses)\n",
-    "\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "chain = ( \n",
-    "    prompt \n",
-    "    | comp_moderation_with_config \n",
-    "    | {llm_chain.input_keys[0]: lambda x: x['output'] }  \n",
-    "    | llm_chain \n",
-    "    | { \"input\": lambda x: x['text'] } \n",
-    "    | comp_moderation_with_config \n",
-    ")\n",
-    "\n",
-    "try:\n",
-    "    response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-456-7890. Can you give me some more samples?\"})\n",
-    "except Exception as e:\n",
-    "    print(str(e))\n",
-    "else:\n",
-    "    print(response['output'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba890681-feeb-43ca-a0d5-9c11d2d9de3e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Unique ID, and Moderation Callbacks\n",
-    "---\n",
-    "\n",
-    "When Amazon Comprehend moderation action is specified as `STOP`, the chain will raise one of the following exceptions-\n",
-    "    - `ModerationPiiError`, for PII checks\n",
-    "    - `ModerationToxicityError`, for Toxicity checks \n",
-    "    - `ModerationIntentionError` for Intent checks\n",
-    "\n",
-    "In addition to the moderation configuration, the `AmazonComprehendModerationChain` can also be initialized with the following parameters\n",
-    "\n",
-    "- `unique_id` [Optional] a string parameter. This parameter can be used to pass any string value or ID. For example, in a chat application you may want to keep track of abusive users, in this case you can pass the user's username/email id etc. This defaults to `None`.\n",
-    "\n",
-    "- `moderation_callback` [Optional] the `BaseModerationCallbackHandler` that will be called asynchronously (non-blocking to the chain). Callback functions are useful when you want to perform additional actions when the moderation functions are executed, for example logging into a database, or writing a log file. You can override three functions by subclassing `BaseModerationCallbackHandler` - `on_after_pii()`, `on_after_toxicity()`, and `on_after_intent()`. Note that all three functions must be `async` functions. These callback functions receive two arguments:\n",
-    "    - `moderation_beacon` a dictionary that will contain information about the moderation function, the full response from Amazon Comprehend model, a unique chain id, the moderation status, and the input string which was validated. The dictionary is of the following schema-\n",
-    "    \n",
-    "    ```\n",
-    "    { \n",
-    "        'moderation_chain_id': 'xxx-xxx-xxx', # Unique chain ID\n",
-    "        'moderation_type': 'Toxicity' | 'PII' | 'Intent', \n",
-    "        'moderation_status': 'LABELS_FOUND' | 'LABELS_NOT_FOUND',\n",
-    "        'moderation_input': 'A sample SSN number looks like this 123-456-7890. Can you give me some more samples?',\n",
-    "        'moderation_output': {...} #Full Amazon Comprehend PII, Toxicity, or Intent Model Output\n",
-    "    }\n",
-    "    ```\n",
-    "    \n",
-    "    - `unique_id` if passed to the `AmazonComprehendModerationChain`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c178835-0264-4ac6-aef4-091d2993d06c",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\"> <b>NOTE:</b> <code>moderation_callback</code> is different from LangChain Chain Callbacks. You can still use LangChain Chain callbacks with <code>AmazonComprehendModerationChain</code> via the callbacks parameter. Example: <br/>\n",
-    "<pre>\n",
-    "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
-    "comp_moderation_with_config = AmazonComprehendModerationChain(verbose=True, callbacks=[StdOutCallbackHandler()])\n",
-    "</pre>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ec38536-8cc9-408e-860b-e4a439283643",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_experimental.comprehend_moderation import BaseModerationCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1be744c7-3f99-4165-bf7f-9c5c249bbb53",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Define callback handlers by subclassing BaseModerationCallbackHandler\n",
-    "\n",
-    "class MyModCallback(BaseModerationCallbackHandler):\n",
-    "    \n",
-    "    async def on_after_pii(self, output_beacon, unique_id):\n",
-    "        import json\n",
-    "        moderation_type = output_beacon['moderation_type']\n",
-    "        chain_id = output_beacon['moderation_chain_id']\n",
-    "        with open(f'output-{moderation_type}-{chain_id}.json', 'w') as file:\n",
-    "            data = { 'beacon_data': output_beacon, 'unique_id': unique_id }\n",
-    "            json.dump(data, file)\n",
-    "    \n",
-    "    '''\n",
-    "    async def on_after_toxicity(self, output_beacon, unique_id):\n",
-    "        pass\n",
-    "    \n",
-    "    async def on_after_intent(self, output_beacon, unique_id):\n",
-    "        pass\n",
-    "    '''\n",
-    "    \n",
-    "\n",
-    "my_callback = MyModCallback()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "362a3fe0-f09f-411e-9df1-d79b3e87510c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "moderation_config = { \n",
-    "        \"filters\": [ \n",
-    "                BaseModerationFilters.PII, \n",
-    "                BaseModerationFilters.TOXICITY\n",
-    "        ],\n",
-    "        \"pii\":{ \n",
-    "                \"action\": BaseModerationActions.STOP, \n",
-    "                \"threshold\":0.5, \n",
-    "                \"labels\":[\"SSN\"], \n",
-    "                \"mask_character\": \"X\" \n",
-    "        },\n",
-    "        \"toxicity\":{ \n",
-    "                \"action\": BaseModerationActions.STOP, \n",
-    "                \"threshold\":0.5 \n",
-    "        }\n",
-    "}\n",
-    "\n",
-    "comp_moderation_with_config = AmazonComprehendModerationChain(\n",
-    "        moderation_config=moderation_config, # specify the configuration\n",
-    "        client=comprehend_client,            # optionally pass the Boto3 Client\n",
-    "        unique_id='john.doe@email.com',      # A unique ID\n",
-    "        moderation_callback=my_callback,     # BaseModerationCallbackHandler\n",
-    "        verbose=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2af07937-67ea-4738-8343-c73d4d28c2cc",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain\n",
-    "from langchain.llms.fake import FakeListLLM\n",
-    "\n",
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer:\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "\n",
-    "responses = [\n",
-    "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n",
-    "    \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n",
-    "]\n",
-    "\n",
-    "llm = FakeListLLM(responses=responses)\n",
-    "\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "chain = (\n",
-    "    prompt \n",
-    "    | comp_moderation_with_config \n",
-    "    | {llm_chain.input_keys[0]: lambda x: x['output'] }  \n",
-    "    | llm_chain \n",
-    "    | { \"input\": lambda x: x['text'] } \n",
-    "    | comp_moderation_with_config \n",
-    ") \n",
-    "\n",
-    "try:\n",
-    "    response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-456-7890. Can you give me some more samples?\"})\n",
-    "except Exception as e:\n",
-    "    print(str(e))\n",
-    "else:\n",
-    "    print(response['output'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "706454b2-2efa-4d41-abc8-ccf2b4e87822",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## `moderation_config` and moderation execution order\n",
-    "---\n",
-    "\n",
-    "If `AmazonComprehendModerationChain` is not initialized with any `moderation_config` then the default action is `STOP` and default order of moderation check is as follows.\n",
-    "\n",
-    "```\n",
-    "AmazonComprehendModerationChain\n",
-    "│\n",
-    "└──Check PII with Stop Action\n",
-    "    ├── Callback (if available)\n",
-    "    ├── Label Found ⟶ [Error Stop]\n",
-    "    └── No Label Found \n",
-    "        └──Check Toxicity with Stop Action\n",
-    "            ├── Callback (if available)\n",
-    "            ├── Label Found ⟶ [Error Stop]\n",
-    "            └── No Label Found\n",
-    "                └──Check Intent with Stop Action\n",
-    "                    ├── Callback (if available)\n",
-    "                    ├── Label Found ⟶ [Error Stop]\n",
-    "                    └── No Label Found\n",
-    "                        └── Return Prompt\n",
-    "```\n",
-    "\n",
-    "If any of the check raises exception then the subsequent checks will not be performed. If a `callback` is provided in this case, then it will be called for each of the checks that have been performed. For example, in the case above, if the Chain fails due to presence of PII then the Toxicity and Intent checks will not be performed.\n",
-    "\n",
-    "You can override the execution order by passing `moderation_config` and simply specifying the desired order in the `filters` key of the configuration. In case you use `moderation_config` then the order of the checks as specified in the `filters` key will be maintained. For example, in the configuration below, first Toxicity check will be performed, then PII, and finally Intent validation will be performed. In this case, `AmazonComprehendModerationChain` will perform the desired checks in the specified order with default values of each model `kwargs`.\n",
-    "\n",
-    "```python\n",
-    "moderation_config = { \n",
-    "        \"filters\":[ BaseModerationFilters.TOXICITY, \n",
-    "                    BaseModerationFilters.PII, \n",
-    "                    BaseModerationFilters.INTENT]\n",
-    "   }\n",
-    "```\n",
-    "\n",
-    "Model `kwargs` are specified by the `pii`, `toxicity`, and `intent` keys within the `moderation_config` dictionary. For example, in the `moderation_config` below, the default order of moderation is overriden and the `pii` & `toxicity` model `kwargs` have been overriden. For `intent` the chain's default `kwargs` will be used.\n",
-    "\n",
-    "```python\n",
-    " moderation_config = { \n",
-    "        \"filters\":[ BaseModerationFilters.TOXICITY, \n",
-    "                    BaseModerationFilters.PII, \n",
-    "                    BaseModerationFilters.INTENT],\n",
-    "        \"pii\":{ \"action\": BaseModerationActions.ALLOW, \n",
-    "                \"threshold\":0.5, \n",
-    "                \"labels\":[\"SSN\"], \n",
-    "                \"mask_character\": \"X\" },\n",
-    "        \"toxicity\":{ \"action\": BaseModerationActions.STOP, \n",
-    "                     \"threshold\":0.5 }\n",
-    "   }\n",
-    "```\n",
-    "\n",
-    "1. For a list of PII labels see Amazon Comprehend Universal PII entity types - https://docs.aws.amazon.com/comprehend/latest/dg/how-pii.html#how-pii-types\n",
-    "2. Following are the list of available Toxicity labels-\n",
-    "    - `HATE_SPEECH`: Speech that criticizes, insults, denounces or dehumanizes a person or a group on the basis of an identity, be it race, ethnicity, gender identity, religion, sexual orientation, ability, national origin, or another identity-group.\n",
-    "    - `GRAPHIC`: Speech that uses visually descriptive, detailed and unpleasantly vivid imagery is considered as graphic. Such language is often made verbose so as to amplify an insult, discomfort or harm to the recipient.\n",
-    "    - `HARASSMENT_OR_ABUSE`: Speech that imposes disruptive power dynamics between the speaker and hearer, regardless of intent, seeks to affect the psychological well-being of the recipient, or objectifies a person should be classified as Harassment.\n",
-    "    - `SEXUAL`: Speech that indicates sexual interest, activity or arousal by using direct or indirect references to body parts or physical traits or sex is considered as toxic with toxicityType \"sexual\". \n",
-    "    - `VIOLENCE_OR_THREAT`: Speech that includes threats which seek to inflict pain, injury or hostility towards a person or group.\n",
-    "    - `INSULT`: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language.\n",
-    "    - `PROFANITY`: Speech that contains words, phrases or acronyms that are impolite, vulgar, or offensive is considered as profane.\n",
-    "3. For a list of Intent labels refer to documentation [link here]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78905aec-55ae-4fc3-a23b-8a69bd1e33f2",
-   "metadata": {},
-   "source": [
-    "# Examples\n",
-    "---\n",
-    "\n",
-    "## With Hugging Face Hub Models\n",
-    "\n",
-    "Get your API Key from Hugging Face hub - https://huggingface.co/docs/api-inference/quicktour#get-your-api-token"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "359b9627-769b-46ce-8be2-c8a5cf7728ba",
-   "metadata": {
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%pip install huggingface_hub"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "41b7ea98-ad16-4454-8f12-c03c17113a86",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%env HUGGINGFACEHUB_API_TOKEN=\"<HUGGINGFACEHUB_API_TOKEN>\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3b235427-cc06-4c07-874b-1f67c2d1f924",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n",
-    "repo_id = \"google/flan-t5-xxl\"  \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d86e256-34fb-4c8e-8092-1a4f863a5c96",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.llms import HuggingFaceHub\n",
-    "from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain\n",
-    "\n",
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer:\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "\n",
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 256}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad603796-ad8b-4599-9022-a486f1c1b89a",
-   "metadata": {},
-   "source": [
-    "Create a configuration and initialize an Amazon Comprehend Moderation chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "decc3409-5be5-433d-b6da-38b9e5c5ee3f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "moderation_config = { \n",
-    "        \"filters\":[ BaseModerationFilters.PII, BaseModerationFilters.TOXICITY, BaseModerationFilters.INTENT ],\n",
-    "        \"pii\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5, \"labels\":[\"SSN\",\"CREDIT_DEBIT_NUMBER\"], \"mask_character\": \"X\"},\n",
-    "        \"toxicity\":{\"action\": BaseModerationActions.STOP, \"threshold\":0.5},\n",
-    "        \"intent\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5,},\n",
-    "   }\n",
-    "\n",
-    "# without any callback\n",
-    "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config, \n",
-    "                                                         client=comprehend_client,\n",
-    "                                                         verbose=True)\n",
-    "\n",
-    "# with callback\n",
-    "amazon_comp_moderation_out = AmazonComprehendModerationChain(moderation_config=moderation_config, \n",
-    "                                                         client=comprehend_client,\n",
-    "                                                         moderation_callback=my_callback,\n",
-    "                                                         verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1256bc8-1321-4624-9e8a-a2d4a8df59bf",
-   "metadata": {},
-   "source": [
-    "The `moderation_config` will now prevent any inputs and model outputs containing obscene words or sentences, bad intent, or PII with entities other than SSN with score above threshold or 0.5 or 50%. If it finds Pii entities - SSN - it will redact them before allowing the call to proceed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0337becc-7c3c-483e-a55c-a225226cb9ee",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chain = (\n",
-    "    prompt \n",
-    "    | amazon_comp_moderation \n",
-    "    | {llm_chain.input_keys[0]: lambda x: x['output'] }  \n",
-    "    | llm_chain \n",
-    "    | { \"input\": lambda x: x['text'] } \n",
-    "    | amazon_comp_moderation_out\n",
-    ")\n",
-    "\n",
-    "try:\n",
-    "    response = chain.invoke({\"question\": \"My AnyCompany Financial Services, LLC credit card account 1111-0000-1111-0008 has 24$ due by July 31st. Can you give me some more credit car number samples?\"})\n",
-    "except Exception as e:\n",
-    "    print(str(e))\n",
-    "else:\n",
-    "    print(response['output'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee52c7b8-6526-4f68-a2b3-b5ad3cf82489",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "---\n",
-    "## With Amazon SageMaker Jumpstart\n",
-    "\n",
-    "The exmaple below shows how to use Amazon Comprehend Moderation chain with an Amazon SageMaker Jumpstart hosted LLM. You should have an Amazon SageMaker Jumpstart hosted LLM endpoint within your AWS Account. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cd49d075-bc23-4ab8-a92c-0ddbbc436c30",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoint_name = \"<SAGEMAKER_ENDPOINT_NAME>\" # replace with your SageMaker Endpoint name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5978a5e6-667d-4926-842c-d965f88e5640",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import SagemakerEndpoint\n",
-    "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n",
-    "from langchain.chains import LLMChain\n",
-    "from langchain.prompts import load_prompt, PromptTemplate\n",
-    "import json\n",
-    "\n",
-    "class ContentHandler(LLMContentHandler):\n",
-    "    content_type = \"application/json\"\n",
-    "    accepts = \"application/json\"\n",
-    "\n",
-    "    def transform_input(self, prompt: str, model_kwargs: dict) -> bytes:\n",
-    "        input_str = json.dumps({\"text_inputs\": prompt,  **model_kwargs})\n",
-    "        return input_str.encode('utf-8')\n",
-    "    \n",
-    "    def transform_output(self, output: bytes) -> str:\n",
-    "        response_json = json.loads(output.read().decode(\"utf-8\"))\n",
-    "        return response_json['generated_texts'][0]\n",
-    "\n",
-    "content_handler = ContentHandler()\n",
-    "\n",
-    "#prompt template for input text\n",
-    "llm_prompt = PromptTemplate(input_variables=[\"input_text\"], template=\"{input_text}\")\n",
-    "\n",
-    "llm_chain = LLMChain(\n",
-    "    llm=SagemakerEndpoint(\n",
-    "        endpoint_name=endpoint_name, \n",
-    "        region_name='us-east-1',\n",
-    "        model_kwargs={\"temperature\":0.97,\n",
-    "                      \"max_length\": 200,\n",
-    "                      \"num_return_sequences\": 3,\n",
-    "                      \"top_k\": 50,\n",
-    "                      \"top_p\": 0.95,\n",
-    "                      \"do_sample\": True},\n",
-    "        content_handler=content_handler\n",
-    "    ),\n",
-    "    prompt=llm_prompt\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d577b036-99a4-47fe-9a8e-4a34aa4cd88d",
-   "metadata": {},
-   "source": [
-    "Create a configuration and initialize an Amazon Comprehend Moderation chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "859da135-94d3-4a9c-970e-a873913592e2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "moderation_config = { \n",
-    "        \"filters\":[ BaseModerationFilters.PII, BaseModerationFilters.TOXICITY ],\n",
-    "        \"pii\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5, \"labels\":[\"SSN\"], \"mask_character\": \"X\"},\n",
-    "        \"toxicity\":{\"action\": BaseModerationActions.STOP, \"threshold\":0.5},\n",
-    "        \"intent\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5,},\n",
-    "   }\n",
-    "\n",
-    "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config, \n",
-    "                                                         client=comprehend_client ,\n",
-    "                                                         verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9abb191f-7a96-4077-8c30-b9ddc225bd6b",
-   "metadata": {},
-   "source": [
-    "The `moderation_config` will now prevent any inputs and model outputs containing obscene words or sentences, bad intent, or Pii with entities other than SSN with score above threshold or 0.5 or 50%. If it finds Pii entities - SSN - it will redact them before allowing the call to proceed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6db5aa2a-9c00-42a0-8e24-c5ba39994f7d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chain = (\n",
-    "    prompt \n",
-    "    | amazon_comp_moderation \n",
-    "    | {llm_chain.input_keys[0]: lambda x: x['output'] }  \n",
-    "    | llm_chain \n",
-    "    | { \"input\": lambda x: x['text'] } \n",
-    "    | amazon_comp_moderation \n",
-    ")\n",
-    "\n",
-    "try:\n",
-    "    response = chain.invoke({\"question\": \"My AnyCompany Financial Services, LLC credit card account 1111-0000-1111-0008 has 24$ due by July 31st. Can you give me some more samples?\"})\n",
-    "except Exception as e:\n",
-    "    print(str(e))\n",
-    "else:\n",
-    "    print(response['output'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7fdfedf9-1a0a-4a9f-a6b0-d9ed2dbaa5ad",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "availableInstances": [
-   {
-    "_defaultOrder": 0,
-    "_isFastLaunch": true,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
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-    "vcpuNum": 0
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-   },
-   {
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-    "_isFastLaunch": true,
-    "category": "Accelerated computing",
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 16,
-    "name": "ml.g4dn.xlarge",
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-   },
-   {
-    "_defaultOrder": 30,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 32,
-    "name": "ml.g4dn.2xlarge",
-    "vcpuNum": 8
-   },
-   {
-    "_defaultOrder": 31,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 64,
-    "name": "ml.g4dn.4xlarge",
-    "vcpuNum": 16
-   },
-   {
-    "_defaultOrder": 32,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 128,
-    "name": "ml.g4dn.8xlarge",
-    "vcpuNum": 32
-   },
-   {
-    "_defaultOrder": 33,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 4,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 192,
-    "name": "ml.g4dn.12xlarge",
-    "vcpuNum": 48
-   },
-   {
-    "_defaultOrder": 34,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 256,
-    "name": "ml.g4dn.16xlarge",
-    "vcpuNum": 64
-   },
-   {
-    "_defaultOrder": 35,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 61,
-    "name": "ml.p3.2xlarge",
-    "vcpuNum": 8
-   },
-   {
-    "_defaultOrder": 36,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 4,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 244,
-    "name": "ml.p3.8xlarge",
-    "vcpuNum": 32
-   },
-   {
-    "_defaultOrder": 37,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 8,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 488,
-    "name": "ml.p3.16xlarge",
-    "vcpuNum": 64
-   },
-   {
-    "_defaultOrder": 38,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 8,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 768,
-    "name": "ml.p3dn.24xlarge",
-    "vcpuNum": 96
-   },
-   {
-    "_defaultOrder": 39,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 16,
-    "name": "ml.r5.large",
-    "vcpuNum": 2
-   },
-   {
-    "_defaultOrder": 40,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 32,
-    "name": "ml.r5.xlarge",
-    "vcpuNum": 4
-   },
-   {
-    "_defaultOrder": 41,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 64,
-    "name": "ml.r5.2xlarge",
-    "vcpuNum": 8
-   },
-   {
-    "_defaultOrder": 42,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 128,
-    "name": "ml.r5.4xlarge",
-    "vcpuNum": 16
-   },
-   {
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-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 256,
-    "name": "ml.r5.8xlarge",
-    "vcpuNum": 32
-   },
-   {
-    "_defaultOrder": 44,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 384,
-    "name": "ml.r5.12xlarge",
-    "vcpuNum": 48
-   },
-   {
-    "_defaultOrder": 45,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 512,
-    "name": "ml.r5.16xlarge",
-    "vcpuNum": 64
-   },
-   {
-    "_defaultOrder": 46,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 768,
-    "name": "ml.r5.24xlarge",
-    "vcpuNum": 96
-   },
-   {
-    "_defaultOrder": 47,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 16,
-    "name": "ml.g5.xlarge",
-    "vcpuNum": 4
-   },
-   {
-    "_defaultOrder": 48,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 32,
-    "name": "ml.g5.2xlarge",
-    "vcpuNum": 8
-   },
-   {
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-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 64,
-    "name": "ml.g5.4xlarge",
-    "vcpuNum": 16
-   },
-   {
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-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 128,
-    "name": "ml.g5.8xlarge",
-    "vcpuNum": 32
-   },
-   {
-    "_defaultOrder": 51,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 1,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 256,
-    "name": "ml.g5.16xlarge",
-    "vcpuNum": 64
-   },
-   {
-    "_defaultOrder": 52,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 4,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 192,
-    "name": "ml.g5.12xlarge",
-    "vcpuNum": 48
-   },
-   {
-    "_defaultOrder": 53,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 4,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 384,
-    "name": "ml.g5.24xlarge",
-    "vcpuNum": 96
-   },
-   {
-    "_defaultOrder": 54,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 8,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 768,
-    "name": "ml.g5.48xlarge",
-    "vcpuNum": 192
-   },
-   {
-    "_defaultOrder": 55,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 8,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 1152,
-    "name": "ml.p4d.24xlarge",
-    "vcpuNum": 96
-   },
-   {
-    "_defaultOrder": 56,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 8,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 1152,
-    "name": "ml.p4de.24xlarge",
-    "vcpuNum": 96
-   }
-  ],
-  "instance_type": "ml.t3.medium",
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/docs_skeleton/generate_api_reference_links.py b/docs/scripts/generate_api_reference_links.py
similarity index 98%
rename from docs/docs_skeleton/generate_api_reference_links.py
rename to docs/scripts/generate_api_reference_links.py
index 22b2dd3d16..6df19659ee 100644
--- a/docs/docs_skeleton/generate_api_reference_links.py
+++ b/docs/scripts/generate_api_reference_links.py
@@ -24,8 +24,8 @@ _IMPORT_RE = re.compile(
 
 _CURRENT_PATH = Path(__file__).parent.absolute()
 # Directory where generated markdown files are stored
-_DOCS_DIR = _CURRENT_PATH / "docs"
-_JSON_PATH = _CURRENT_PATH.parent / "api_reference" / "guide_imports.json"
+_DOCS_DIR = _CURRENT_PATH / "docs_skeleton" / "docs"
+_JSON_PATH = _CURRENT_PATH / "api_reference" / "guide_imports.json"
 
 
 def find_files(path):
diff --git a/docs/_scripts/model_feat_table.py b/docs/scripts/model_feat_table.py
similarity index 96%
rename from docs/_scripts/model_feat_table.py
rename to docs/scripts/model_feat_table.py
index 6099950844..c54f35f969 100644
--- a/docs/_scripts/model_feat_table.py
+++ b/docs/scripts/model_feat_table.py
@@ -6,7 +6,10 @@ from langchain.chat_models.base import BaseChatModel, SimpleChatModel
 from langchain.llms.base import BaseLLM, LLM
 
 INTEGRATIONS_DIR = (
-    Path(os.path.abspath(__file__)).parents[1] / "extras" / "integrations"
+    Path(os.path.abspath(__file__)).parents[1]
+    / "docs_skeleton"
+    / "docs"
+    / "integrations"
 )
 LLM_IGNORE = ("FakeListLLM", "OpenAIChat", "PromptLayerOpenAIChat")
 LLM_FEAT_TABLE_CORRECTION = {
@@ -110,7 +113,15 @@ def get_llm_table():
         "batch_generate",
         "batch_agenerate",
     ]
-    title = ["Model", "Invoke", "Async invoke", "Stream", "Async stream", "Batch", "Async batch"]
+    title = [
+        "Model",
+        "Invoke",
+        "Async invoke",
+        "Stream",
+        "Async stream",
+        "Batch",
+        "Async batch",
+    ]
     rows = [title, [":-"] + [":-:"] * (len(title) - 1)]
     for llm, feats in sorted(final_feats.items()):
         rows += [[llm, "✅"] + ["✅" if feats.get(h) else "❌" for h in header[1:]]]