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docs: Update broken vectorstore urls in retrievers.ipynb (#27838)

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**Description**: Update outdated `VectorStore` api reference urls in
`retrievers.ipynb`

Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
sifatj
2024-11-04 20:26:03 +00:00
committed by GitHub
parent dfa83531ad
commit 50563400fb
1 changed files with 5 additions and 5 deletions
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10
docs/docs/tutorials/retrievers.ipynb
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@@ -151,13 +151,13 @@
"id": "ff0f0b43-e5b8-4c79-b782-a02f17345487",
"metadata": {},
"source": [
"Calling `.from_documents` here will add the documents to the vector store. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) implements methods for adding documents that can also be called after the object is instantiated. Most implementations will allow you to connect to an existing vector store-- e.g., by providing a client, index name, or other information. See the documentation for a specific [integration](/docs/integrations/vectorstores) for more detail.\n",
"Calling `.from_documents` here will add the documents to the vector store. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) implements methods for adding documents that can also be called after the object is instantiated. Most implementations will allow you to connect to an existing vector store-- e.g., by providing a client, index name, or other information. See the documentation for a specific [integration](/docs/integrations/vectorstores) for more detail.\n",
"\n",
"Once we've instantiated a `VectorStore` that contains documents, we can query it. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) includes methods for querying:\n",
"Once we've instantiated a `VectorStore` that contains documents, we can query it. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) includes methods for querying:\n",
"- Synchronously and asynchronously;\n",
"- By string query and by vector;\n",
"- With and without returning similarity scores;\n",
"- By similarity and [maximum marginal relevance](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search) (to balance similarity with query to diversity in retrieved results).\n",
"- By similarity and [maximum marginal relevance](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html#langchain_core.vectorstores.base.VectorStore.max_marginal_relevance_search) (to balance similarity with query to diversity in retrieved results).\n",
"\n",
"The methods will generally include a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects in their outputs.\n",
"\n",
@@ -303,7 +303,7 @@
"source": [
"Learn more:\n",
"\n",
"- [API reference](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html)\n",
"- [API reference](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html)\n",
"- [How-to guide](/docs/how_to/vectorstores)\n",
"- [Integration-specific docs](/docs/integrations/vectorstores)\n",
"\n",
@@ -348,7 +348,7 @@
"id": "a36d3f64-a8bc-4baa-b2ea-07e324a0143e",
"metadata": {},
"source": [
"Vectorstores implement an `as_retriever` method that will generate a Retriever, specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html). These retrievers include specific `search_type` and `search_kwargs` attributes that identify what methods of the underlying vector store to call, and how to parameterize them. For instance, we can replicate the above with the following:"
"Vectorstores implement an `as_retriever` method that will generate a Retriever, specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStoreRetriever.html). These retrievers include specific `search_type` and `search_kwargs` attributes that identify what methods of the underlying vector store to call, and how to parameterize them. For instance, we can replicate the above with the following:"
]
},
{