docs: make links internal (langchain-ai#19063)

So they can be properly link checked

docs/docs/expression_language/streaming.ipynb

@@ -55,7 +55,7 @@
    "id": "9eb73e8b",
    "metadata": {},
    "source": [
-    "We will show examples of streaming using the chat model from [Anthropic](https://python.langchain.com/docs/integrations/platforms/anthropic). To use the model, you will need to install the `langchain-anthropic` package. You can do this with the following command:"
+    "We will show examples of streaming using the chat model from [Anthropic](/docs/integrations/platforms/anthropic). To use the model, you will need to install the `langchain-anthropic` package. You can do this with the following command:"
    ]
   },
   {

docs/docs/guides/evaluation/string/json.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# JSON Evaluators\n",
     "\n",
-    "Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following `JSON` validators provide functionality to check your model's output consistently.\n",
+    "Evaluating [extraction](/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following `JSON` validators provide functionality to check your model's output consistently.\n",
     "\n",
     "## JsonValidityEvaluator\n",
     "\n",

docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb

@@ -24,7 +24,7 @@
     "<img src=\"/img/qa_privacy_protection.png\" width=\"900\"/>\n",
     "\n",
     "\n",
-    "In the following notebook, we will not go into the details of how the anonymizer works. If you are interested, please visit [this part of the documentation](https://python.langchain.com/docs/guides/privacy/presidio_data_anonymization/).\n",
+    "In the following notebook, we will not go into the details of how the anonymizer works. If you are interested, please visit [this part of the documentation](/docs/guides/privacy/presidio_data_anonymization/).\n",
     "\n",
     "## Quickstart\n",
     "\n",

docs/docs/integrations/callbacks/promptlayer.ipynb

@@ -9,7 +9,7 @@
     "\n",
     ">[PromptLayer](https://docs.promptlayer.com/introduction) is a platform for prompt engineering. It also helps with the LLM observability to visualize requests, version prompts, and track usage.\n",
     ">\n",
-    ">While `PromptLayer` does have LLMs that integrate directly with LangChain (e.g. [`PromptLayerOpenAI`](https://python.langchain.com/docs/integrations/llms/promptlayer_openai)), using a callback is the recommended way to integrate `PromptLayer` with LangChain.\n",
+    ">While `PromptLayer` does have LLMs that integrate directly with LangChain (e.g. [`PromptLayerOpenAI`](/docs/integrations/llms/promptlayer_openai)), using a callback is the recommended way to integrate `PromptLayer` with LangChain.\n",
     "\n",
     "In this guide, we will go over how to setup the `PromptLayerCallbackHandler`. \n",
     "\n",

docs/docs/integrations/callbacks/trubrics.ipynb

@@ -124,7 +124,7 @@
     "tags": []
    },
    "source": [
-    "Here are two examples of how to use the `TrubricsCallbackHandler` with Langchain [LLMs](https://python.langchain.com/docs/modules/model_io/llms/) or [Chat Models](https://python.langchain.com/docs/modules/model_io/chat/). We will use OpenAI models, so set your `OPENAI_API_KEY` key here:"
+    "Here are two examples of how to use the `TrubricsCallbackHandler` with Langchain [LLMs](/docs/modules/model_io/llms/) or [Chat Models](/docs/modules/model_io/chat/). We will use OpenAI models, so set your `OPENAI_API_KEY` key here:"
    ]
   },
   {

docs/docs/integrations/chat/huggingface.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "In particular, we will:\n",
     "1. Utilize the [HuggingFaceTextGenInference](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_text_gen_inference.py), [HuggingFaceEndpoint](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_endpoint.py), or [HuggingFaceHub](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_hub.py) integrations to instantiate an `LLM`.\n",
-    "2. Utilize the `ChatHuggingFace` class to enable any of these LLMs to interface with LangChain's [Chat Messages](https://python.langchain.com/docs/modules/model_io/chat/#messages) abstraction.\n",
+    "2. Utilize the `ChatHuggingFace` class to enable any of these LLMs to interface with LangChain's [Chat Messages](/docs/modules/model_io/chat/#messages) abstraction.\n",
     "3. Demonstrate how to use an open-source LLM to power an `ChatAgent` pipeline\n",
     "\n",
     "\n",
@@ -280,7 +280,7 @@
    "source": [
     "## 3. Take it for a spin as an agent!\n",
     "\n",
-    "Here we'll test out `Zephyr-7B-beta` as a zero-shot `ReAct` Agent. The example below is taken from [here](https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models).\n",
+    "Here we'll test out `Zephyr-7B-beta` as a zero-shot `ReAct` Agent. The example below is taken from [here](/docs/modules/agents/agent_types/react#using-chat-models).\n",
     "\n",
     "> Note: To run this section, you'll need to have a [SerpAPI Token](https://serpapi.com/) saved as an environment variable: `SERPAPI_API_KEY`"
    ]

docs/docs/integrations/chat/llama2_chat.ipynb

@@ -17,9 +17,9 @@
    "source": [
     "# Llama2Chat\n",
     "\n",
-    "This notebook shows how to augment Llama-2 `LLM`s with the `Llama2Chat` wrapper to support the [Llama-2 chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). Several `LLM` implementations in LangChain can be used as interface to Llama-2 chat models. These include [HuggingFaceTextGenInference](https://python.langchain.com/docs/integrations/llms/huggingface_textgen_inference), [LlamaCpp](https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa), [GPT4All](https://python.langchain.com/docs/integrations/llms/gpt4all), ..., to mention a few examples. \n",
+    "This notebook shows how to augment Llama-2 `LLM`s with the `Llama2Chat` wrapper to support the [Llama-2 chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). Several `LLM` implementations in LangChain can be used as interface to Llama-2 chat models. These include [ChatHuggingFace](/docs/integrations/chat/huggingface), [LlamaCpp](/docs/use_cases/question_answering/local_retrieval_qa), [GPT4All](/docs/integrations/llms/gpt4all), ..., to mention a few examples. \n",
     "\n",
-    "`Llama2Chat` is a generic wrapper that implements `BaseChatModel` and can therefore be used in applications as [chat model](https://python.langchain.com/docs/modules/model_io/models/chat/). `Llama2Chat` converts a list of [chat messages](https://python.langchain.com/docs/modules/model_io/models/chat/#messages) into the [required chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and forwards the formatted prompt as `str` to the wrapped `LLM`."
+    "`Llama2Chat` is a generic wrapper that implements `BaseChatModel` and can therefore be used in applications as [chat model](/docs/modules/model_io/chat/). `Llama2Chat` converts a list of Messages into the [required chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and forwards the formatted prompt as `str` to the wrapped `LLM`."
    ]
   },
   {
@@ -77,7 +77,7 @@
    "id": "2ff99380",
    "metadata": {},
    "source": [
-    "A [HuggingFaceTextGenInference](https://python.langchain.com/docs/integrations/llms/huggingface_textgen_inference) LLM encapsulates access to a [text-generation-inference](https://github.com/huggingface/text-generation-inference) server. In the following example, the inference server serves a [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) model. It can be started locally with:\n",
+    "A HuggingFaceTextGenInference LLM encapsulates access to a [text-generation-inference](https://github.com/huggingface/text-generation-inference) server. In the following example, the inference server serves a [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) model. It can be started locally with:\n",
     "\n",
     "```bash\n",
     "docker run \\\n",
@@ -220,7 +220,7 @@
    "id": "52c1a0b9",
    "metadata": {},
    "source": [
-    "For using a Llama-2 chat model with a [LlamaCPP](https://python.langchain.com/docs/integrations/llms/llamacpp) `LMM`, install the `llama-cpp-python` library using [these installation instructions](https://python.langchain.com/docs/integrations/llms/llamacpp#installation). The following example uses a quantized [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_0.gguf) model stored locally at `~/Models/llama-2-7b-chat.Q4_0.gguf`. \n",
+    "For using a Llama-2 chat model with a [LlamaCPP](/docs/integrations/llms/llamacpp) `LMM`, install the `llama-cpp-python` library using [these installation instructions](/docs/integrations/llms/llamacpp#installation). The following example uses a quantized [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_0.gguf) model stored locally at `~/Models/llama-2-7b-chat.Q4_0.gguf`. \n",
     "\n",
     "After creating a `LlamaCpp` instance, the `llm` is again wrapped into `Llama2Chat`"
    ]
@@ -731,7 +731,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb

@@ -1005,7 +1005,7 @@
     "id": "79efa62d"
    },
    "source": [
-    "Like any other integration, ChatNVIDIA is fine to support chat utilities like conversation buffers by default. Below, we show the [LangChain ConversationBufferMemory](https://python.langchain.com/docs/modules/memory/types/buffer) example applied to the `mixtral_8x7b` model."
+    "Like any other integration, ChatNVIDIA is fine to support chat utilities like conversation buffers by default. Below, we show the [LangChain ConversationBufferMemory](/docs/modules/memory/types/buffer) example applied to the `mixtral_8x7b` model."
    ]
   },
   {

docs/docs/integrations/chat/ollama.ipynb

@@ -107,7 +107,7 @@
     "\n",
     "# using LangChain Expressive Language chain syntax\n",
     "# learn more about the LCEL on\n",
-    "# https://python.langchain.com/docs/expression_language/why\n",
+    "# /docs/expression_language/why\n",
     "chain = prompt | llm | StrOutputParser()\n",
     "\n",
     "# for brevity, response is printed in terminal\n",
@@ -235,7 +235,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Take a look at the [LangChain Expressive Language (LCEL) Interface](https://python.langchain.com/docs/expression_language/interface) for the other available interfaces for use when a chain is created.\n",
+    "Take a look at the [LangChain Expressive Language (LCEL) Interface](/docs/expression_language/interface) for the other available interfaces for use when a chain is created.\n",
     "\n",
     "## Building from source\n",
     "\n",
@@ -250,7 +250,7 @@
     " \n",
     "Use the latest version of Ollama and supply the [`format`](https://github.com/jmorganca/ollama/blob/main/docs/api.md#json-mode) flag. The `format` flag will force the model to produce the response in JSON.\n",
     "\n",
-    "> **Note:** You can also try out the experimental [OllamaFunctions](https://python.langchain.com/docs/integrations/chat/ollama_functions) wrapper for convenience."
+    "> **Note:** You can also try out the experimental [OllamaFunctions](/docs/integrations/chat/ollama_functions) wrapper for convenience."
    ]
   },
   {

docs/docs/integrations/document_loaders/docugami.ipynb

@@ -118,7 +118,7 @@
     "\n",
     "1. You can set min and max chunk size, which the system tries to adhere to with minimal truncation. You can set `loader.min_text_length` and `loader.max_text_length` to control these.\n",
     "2. By default, only the text for chunks is returned. However, Docugami's XML knowledge graph has additional rich information including semantic tags for entities inside the chunk. Set `loader.include_xml_tags = True` if you want the additional xml metadata on the returned chunks.\n",
-    "3. In addition, you can set `loader.parent_hierarchy_levels` if you want Docugami to return parent chunks in the chunks it returns. The child chunks point to the parent chunks via the `loader.parent_id_key` value. This is useful e.g. with the [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) for [small-to-big](https://www.youtube.com/watch?v=ihSiRrOUwmg) retrieval. See detailed example later in this notebook."
+    "3. In addition, you can set `loader.parent_hierarchy_levels` if you want Docugami to return parent chunks in the chunks it returns. The child chunks point to the parent chunks via the `loader.parent_id_key` value. This is useful e.g. with the [MultiVector Retriever](/docs/modules/data_connection/retrievers/multi_vector) for [small-to-big](https://www.youtube.com/watch?v=ihSiRrOUwmg) retrieval. See detailed example later in this notebook."
    ]
   },
   {
@@ -457,7 +457,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Documents are inherently semi-structured and the DocugamiLoader is able to navigate the semantic and structural contours of the document to provide parent chunk references on the chunks it returns. This is useful e.g. with the [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) for [small-to-big](https://www.youtube.com/watch?v=ihSiRrOUwmg) retrieval.\n",
+    "Documents are inherently semi-structured and the DocugamiLoader is able to navigate the semantic and structural contours of the document to provide parent chunk references on the chunks it returns. This is useful e.g. with the [MultiVector Retriever](/docs/modules/data_connection/retrievers/multi_vector) for [small-to-big](https://www.youtube.com/watch?v=ihSiRrOUwmg) retrieval.\n",
     "\n",
     "To get parent chunk references, you can set `loader.parent_hierarchy_levels` to a non-zero value."
    ]

docs/docs/integrations/document_loaders/geopandas.ipynb

@@ -47,7 +47,7 @@
    "id": "04981332",
    "metadata": {},
    "source": [
-    "Create a GeoPandas dataframe from [`Open City Data`](https://python.langchain.com/docs/integrations/document_loaders/open_city_data) as an example input."
+    "Create a GeoPandas dataframe from [`Open City Data`](/docs/integrations/document_loaders/open_city_data) as an example input."
    ]
   },
   {

docs/docs/integrations/document_loaders/google_bigtable.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "> [Bigtable](https://cloud.google.com/bigtable) is a key-value and wide-column store, ideal for fast access to structured, semi-structured, or unstructured data. Extend your database application to build AI-powered experiences leveraging Bigtable's Langchain integrations.\n",
     "\n",
-    "This notebook goes over how to use [Bigtable](https://cloud.google.com/bigtable) to [save, load and delete langchain documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/) with `BigtableLoader` and `BigtableSaver`.\n",
+    "This notebook goes over how to use [Bigtable](https://cloud.google.com/bigtable) to [save, load and delete langchain documents](/docs/modules/data_connection/document_loaders/) with `BigtableLoader` and `BigtableSaver`.\n",
     "\n",
     "Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-bigtable-python/).\n",
     "\n",

docs/docs/integrations/document_loaders/google_cloud_sql_mssql.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "> [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers [MySQL](https://cloud.google.com/sql/mysql), [PostgreSQL](https://cloud.google.com/sql/postgres), and [SQL Server](https://cloud.google.com/sql/sqlserver) database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
     "\n",
-    "This notebook goes over how to use [Cloud SQL for SQL server](https://cloud.google.com/sql/sqlserver) to [save, load and delete langchain documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/) with `MSSQLLoader` and `MSSQLDocumentSaver`.\n",
+    "This notebook goes over how to use [Cloud SQL for SQL server](https://cloud.google.com/sql/sqlserver) to [save, load and delete langchain documents](/docs/modules/data_connection/document_loaders/) with `MSSQLLoader` and `MSSQLDocumentSaver`.\n",
     "\n",
     "Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-cloud-sql-mssql-python/).\n",
     "\n",

docs/docs/integrations/document_loaders/google_cloud_sql_mysql.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "> [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers [MySQL](https://cloud.google.com/sql/mysql), [PostgreSQL](https://cloud.google.com/sql/postgresql), and [SQL Server](https://cloud.google.com/sql/sqlserver) database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
     "\n",
-    "This notebook goes over how to use [Cloud SQL for MySQL](https://cloud.google.com/sql/mysql) to [save, load and delete langchain documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/) with `MySQLLoader` and `MySQLDocumentSaver`.\n",
+    "This notebook goes over how to use [Cloud SQL for MySQL](https://cloud.google.com/sql/mysql) to [save, load and delete langchain documents](/docs/modules/data_connection/document_loaders/) with `MySQLLoader` and `MySQLDocumentSaver`.\n",
     "\n",
     "Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/).\n",
     "\n",

docs/docs/integrations/document_loaders/google_datastore.ipynb

@@ -8,7 +8,7 @@
         "\n",
         "> [Firestore in Datastore Mode](https://cloud.google.com/datastore) is a NoSQL document database built for automatic scaling, high performance and ease of application development. Extend your database application to build AI-powered experiences leveraging Datastore's Langchain integrations.\n",
         "\n",
-        "This notebook goes over how to use [Firestore in Datastore Mode](https://cloud.google.com/datastore) to [save, load and delete langchain documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/) with `DatastoreLoader` and `DatastoreSaver`.\n",
+        "This notebook goes over how to use [Firestore in Datastore Mode](https://cloud.google.com/datastore) to [save, load and delete langchain documents](/docs/modules/data_connection/document_loaders/) with `DatastoreLoader` and `DatastoreSaver`.\n",
         "\n",
         "Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-datastore-python/).\n",
         "\n",

docs/docs/integrations/document_loaders/google_el_carro.ipynb

@@ -18,7 +18,7 @@
         "by leveraging the El Carro Langchain integration.\n",
         "\n",
         "This guide goes over how to use El Carro Langchain integration to\n",
-        "[save, load and delete langchain documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/)\n",
+        "[save, load and delete langchain documents](/docs/modules/data_connection/document_loaders/)\n",
         "with `ElCarroLoader` and `ElCarroDocumentSaver`. This integration works for any Oracle database, regardless of where it is running.\n",
         "\n",
         "Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-el-carro-python/).\n",

docs/docs/integrations/document_loaders/google_firestore.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "> [Firestore](https://cloud.google.com/firestore) is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging Firestore's Langchain integrations.\n",
     "\n",
-    "This notebook goes over how to use [Firestore](https://cloud.google.com/firestore) to [save, load and delete langchain documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/) with `FirestoreLoader` and `FirestoreSaver`.\n",
+    "This notebook goes over how to use [Firestore](https://cloud.google.com/firestore) to [save, load and delete langchain documents](/docs/modules/data_connection/document_loaders/) with `FirestoreLoader` and `FirestoreSaver`.\n",
     "\n",
     "Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-firestore-python/).\n",
     "\n",