docs: fix more links (#27598)

Fix more links

docs/docs/how_to/agent_executor.ipynb

@@ -35,7 +35,7 @@
     "- Creating a [Retriever](/docs/concepts/retrievers) to expose specific information to our agent\n",
     "- Using a Search [Tool](/docs/concepts/tools) to look up things online\n",
     "- [`Chat History`](/docs/concepts/chat_history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to follow-up questions. \n",
-    "- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
+    "- Debugging and tracing your application using [LangSmith](https://docs.smith.langchain.com/)\n",
     "\n",
     "## Setup\n",
     "\n",

docs/docs/how_to/example_selectors_langsmith.ipynb

@@ -13,7 +13,7 @@
     "<Prerequisites titlesAndLinks={[\n",
     "  [\"Chat models\", \"/docs/concepts/chat_models\"],\n",
     "  [\"Few-shot-prompting\", \"/docs/concepts/few-shot-prompting\"],\n",
-    "  [\"LangSmith\", \"/docs/concepts/#langsmith\"],\n",
+    "  [\"LangSmith\", \"https://docs.smith.langchain.com/\"],\n",
     "]} />\n",
     "\n",
     "\n",

docs/docs/how_to/few_shot_examples.ipynb

@@ -23,7 +23,7 @@
     "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "- [Example selectors](/docs/concepts/example_selectors)\n",
     "- [LLMs](/docs/concepts/text_llms)\n",
-    "- [Vectorstores](/docs/concepts/#vector-stores)\n",
+    "- [Vectorstores](/docs/concepts/vectorstores)\n",
     "\n",
     ":::\n",
     "\n",

docs/docs/how_to/few_shot_examples_chat.ipynb

@@ -23,7 +23,7 @@
     "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "- [Example selectors](/docs/concepts/example_selectors)\n",
     "- [Chat models](/docs/concepts/chat_models)\n",
-    "- [Vectorstores](/docs/concepts/#vector-stores)\n",
+    "- [Vectorstores](/docs/concepts/vectorstores)\n",
     "\n",
     ":::\n",
     "\n",

docs/docs/how_to/index.mdx

@@ -159,7 +159,7 @@ What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of languag
 
 ### Vector stores
 
-[Vector stores](/docs/concepts/#vector-stores) are databases that can efficiently store and retrieve embeddings.
+[Vector stores](/docs/concepts/vectorstores) are databases that can efficiently store and retrieve embeddings.
 
 - [How to: use a vector store to retrieve data](/docs/how_to/vectorstores)
 

docs/docs/integrations/retrievers/index.mdx

@@ -16,7 +16,7 @@ Retrievers accept a string query as input and return a list of [Documents](https
 
 For specifics on how to use retrievers, see the [relevant how-to guides here](/docs/how_to/#retrievers).
 
-Note that all [vector stores](/docs/concepts/#vector-stores) can be [cast to retrievers](/docs/how_to/vectorstore_retriever/).
+Note that all [vector stores](/docs/concepts/vectorstores) can be [cast to retrievers](/docs/how_to/vectorstore_retriever/).
 Refer to the vector store [integration docs](/docs/integrations/vectorstores/) for available vector stores.
 This page lists custom retrievers, implemented via subclassing [BaseRetriever](/docs/how_to/custom_retriever/).
 

docs/docs/integrations/vectorstores/index.mdx

@@ -7,7 +7,7 @@ sidebar_class_name: hidden
 
 import { CategoryTable, IndexTable } from "@theme/FeatureTables";
 
-A [vector store](/docs/concepts/#vector-stores) stores [embedded](/docs/concepts/embedding_models) data and performs similarity search.
+A [vector store](/docs/concepts/vectorstores) stores [embedded](/docs/concepts/embedding_models) data and performs similarity search.
 
 <CategoryTable category="vectorstores" />
 

docs/docs/tutorials/llm_chain.ipynb

@@ -27,7 +27,7 @@
     "\n",
     "- Using [LangChain Expression Language (LCEL)](/docs/concepts/lcel) to chain components together\n",
     "\n",
-    "- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
+    "- Debugging and tracing your application using [LangSmith](https://docs.smith.langchain.com/)\n",
     "\n",
     "- Deploying your application with [LangServe](/docs/concepts/#langserve)\n",
     "\n",

docs/docs/tutorials/local_rag.ipynb

@@ -14,7 +14,7 @@
     "- [Chat Models](/docs/concepts/chat_models)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Embeddings](/docs/concepts/embedding_models)\n",
-    "- [Vector stores](/docs/concepts/#vector-stores)\n",
+    "- [Vector stores](/docs/concepts/vectorstores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
     "\n",
     ":::\n",

docs/docs/tutorials/pdf_qa.ipynb

@@ -26,7 +26,7 @@
     "- [Document loaders](/docs/concepts/document_loaders)\n",
     "- [Chat models](/docs/concepts/chat_models)\n",
     "- [Embeddings](/docs/concepts/embedding_models)\n",
-    "- [Vector stores](/docs/concepts/#vector-stores)\n",
+    "- [Vector stores](/docs/concepts/vectorstores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
     "\n",
     ":::\n",
@@ -117,7 +117,7 @@
     "\n",
     "## Question answering with RAG\n",
     "\n",
-    "Next, you'll prepare the loaded documents for later retrieval. Using a [text splitter](/docs/concepts/text_splitters), you'll split your loaded documents into smaller documents that can more easily fit into an LLM's context window, then load them into a [vector store](/docs/concepts/#vector-stores). You can then create a [retriever](/docs/concepts/retrievers) from the vector store for use in our RAG chain:\n",
+    "Next, you'll prepare the loaded documents for later retrieval. Using a [text splitter](/docs/concepts/text_splitters), you'll split your loaded documents into smaller documents that can more easily fit into an LLM's context window, then load them into a [vector store](/docs/concepts/vectorstores). You can then create a [retriever](/docs/concepts/retrievers) from the vector store for use in our RAG chain:\n",
     "\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",

docs/docs/tutorials/qa_chat_history.ipynb

@@ -24,7 +24,7 @@
     "- [Chat history](/docs/concepts/chat_history)\n",
     "- [Chat models](/docs/concepts/chat_models)\n",
     "- [Embeddings](/docs/concepts/embedding_models)\n",
-    "- [Vector stores](/docs/concepts/#vector-stores)\n",
+    "- [Vector stores](/docs/concepts/vectorstores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
     "- [Tools](/docs/concepts/tools)\n",
     "- [Agents](/docs/concepts/agents)\n",

docs/docs/tutorials/query_analysis.ipynb

@@ -24,7 +24,7 @@
     "- [Document loaders](/docs/concepts/document_loaders)\n",
     "- [Chat models](/docs/concepts/chat_models)\n",
     "- [Embeddings](/docs/concepts/embedding_models)\n",
-    "- [Vector stores](/docs/concepts/#vector-stores)\n",
+    "- [Vector stores](/docs/concepts/vectorstores)\n",
     "- [Retrieval](/docs/concepts/retrieval)\n",
     "\n",
     ":::\n",

docs/docs/tutorials/rag.ipynb

@@ -41,7 +41,7 @@
     "### Indexing\n",
     "1. **Load**: First we need to load our data. This is done with [Document Loaders](/docs/concepts/document_loaders).\n",
     "2. **Split**: [Text splitters](/docs/concepts/text_splitters) break large `Documents` into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won't fit in a model's finite context window.\n",
-    "3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/#vector-stores) and [Embeddings](/docs/concepts/embedding_models) model.\n",
+    "3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/vectorstores) and [Embeddings](/docs/concepts/embedding_models) model.\n",
     "\n",
     "![index_diagram](../../static/img/rag_indexing.png)\n",
     "\n",

docs/scripts/append_related_links.py

@@ -3,47 +3,59 @@ import multiprocessing
 import re
 import sys
 from pathlib import Path
+from typing import Optional
 
+# List of 4-tuples (integration_name, display_name, concept_page, how_to_fragment)
+INTEGRATION_INFO = [
+    ("chat", "Chat model", "chat_models", "chat-models"),
+    ("llms", "LLM", "text_llms", "llms"),
+    ("text_embedding", "Embedding model", "embedding_models", "embedding-models"),
+    ("document_loaders", "Document loader", "document_loaders", "document-loaders"),
+    ("vectorstores", "Vector store", "vectorstores", "vector-stores"),
+    ("retrievers", "Retriever", "retrievers", "retrievers"),
+    ("tools", "Tool", "tools", "tools"),
+    # stores is a special case because there are no key-value store how-tos yet
+    # this is a placeholder for when we do have them
+    # for now the related links section will only contain the conceptual guide.
+    ("stores", "Key-value store", "key_value_stores", "key-value-stores"),
+]
 
-def _generate_related_links_section(integration_type: str, notebook_name: str):
-    concept_display_name = None
-    concept_heading = None
-    if integration_type == "chat":
-        concept_display_name = "Chat model"
-        concept_heading = "chat-models"
-    elif integration_type == "llms":
-        concept_display_name = "LLM"
-        concept_heading = "llms"
-    elif integration_type == "text_embedding":
-        concept_display_name = "Embedding model"
-        concept_heading = "embedding-models"
-    elif integration_type == "document_loaders":
-        concept_display_name = "Document loader"
-        concept_heading = "document-loaders"
-    elif integration_type == "vectorstores":
-        concept_display_name = "Vector store"
-        concept_heading = "vector-stores"
-    elif integration_type == "retrievers":
-        concept_display_name = "Retriever"
-        concept_heading = "retrievers"
-    elif integration_type == "tools":
-        concept_display_name = "Tool"
-        concept_heading = "tools"
-    elif integration_type == "stores":
-        concept_display_name = "Key-value store"
-        concept_heading = "key-value-stores"
-        # Special case because there are no key-value store how-tos yet
-        return f"""## Related
+# Create a dictionary with key being the first element (integration_name) and value being the rest of the tuple
+INTEGRATION_INFO_DICT = {
+    integration_name: rest for integration_name, *rest in INTEGRATION_INFO
+}
 
-- [{concept_display_name} conceptual guide](/docs/concepts/#{concept_heading})
+RELATED_LINKS_SECTION = """## Related
+- {concept_display_name} [conceptual guide](/docs/concepts/{concept_page})
+- {concept_display_name} [how-to guides](/docs/how_to/#{how_to_fragment})
 """
-    else:
+
+RELATED_LINKS_WITHOUT_HOW_TO_SECTION = """## Related
+- {concept_display_name} [conceptual guide](/docs/concepts/{concept_page})
+"""
+
+
+def _generate_related_links_section(
+    integration_type: str, notebook_name: str
+) -> Optional[str]:
+    if integration_type not in INTEGRATION_INFO_DICT:
         return None
-    return f"""## Related
+    concept_display_name, concept_page, how_to_fragment = INTEGRATION_INFO_DICT[
+        integration_type
+    ]
 
-- {concept_display_name} [conceptual guide](/docs/concepts/#{concept_heading})
-- {concept_display_name} [how-to guides](/docs/how_to/#{concept_heading})
-"""
+    # Special case because there are no key-value store how-tos yet
+    if integration_type == "stores":
+        return RELATED_LINKS_WITHOUT_HOW_TO_SECTION.format(
+            concept_display_name=concept_display_name,
+            concept_page=concept_page,
+        )
+
+    return RELATED_LINKS_SECTION.format(
+        concept_display_name=concept_display_name,
+        concept_page=concept_page,
+        how_to_fragment=how_to_fragment,
+    )
 
 
 def _process_path(doc_path: Path):