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docs: fix more links (#27598)
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Fix more links
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eyurtsev authored Oct 24, 2024
1 parent f203229 commit d081a54
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2 changes: 1 addition & 1 deletion docs/docs/how_to/agent_executor.ipynb
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"- 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",
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2 changes: 1 addition & 1 deletion docs/docs/how_to/example_selectors_langsmith.ipynb
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"<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",
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2 changes: 1 addition & 1 deletion docs/docs/how_to/few_shot_examples.ipynb
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"- [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",
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2 changes: 1 addition & 1 deletion docs/docs/how_to/few_shot_examples_chat.ipynb
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"- [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",
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2 changes: 1 addition & 1 deletion docs/docs/how_to/index.mdx
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Expand Up @@ -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)

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2 changes: 1 addition & 1 deletion docs/docs/integrations/retrievers/index.mdx
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Expand Up @@ -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/).

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2 changes: 1 addition & 1 deletion docs/docs/integrations/vectorstores/index.mdx
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Expand Up @@ -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" />

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2 changes: 1 addition & 1 deletion docs/docs/tutorials/llm_chain.ipynb
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"\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",
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2 changes: 1 addition & 1 deletion docs/docs/tutorials/local_rag.ipynb
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"- [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",
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4 changes: 2 additions & 2 deletions docs/docs/tutorials/pdf_qa.ipynb
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"- [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",
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"\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",
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2 changes: 1 addition & 1 deletion docs/docs/tutorials/qa_chat_history.ipynb
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Expand Up @@ -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",
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2 changes: 1 addition & 1 deletion docs/docs/tutorials/query_analysis.ipynb
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"- [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",
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2 changes: 1 addition & 1 deletion docs/docs/tutorials/rag.ipynb
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Expand Up @@ -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",
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82 changes: 47 additions & 35 deletions docs/scripts/append_related_links.py
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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:
return None
return f"""## Related

- {concept_display_name} [conceptual guide](/docs/concepts/#{concept_heading})
- {concept_display_name} [how-to guides](/docs/how_to/#{concept_heading})
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
concept_display_name, concept_page, how_to_fragment = INTEGRATION_INFO_DICT[
integration_type
]

# 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):
content = doc_path.read_text()
pattern = r"/docs/integrations/([^/]+)/([^/]+).mdx?"
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