diff --git a/docs/docs/how_to/add_scores_retriever.ipynb b/docs/docs/how_to/add_scores_retriever.ipynb index 2d3851ae200..4ae139eb66a 100644 --- a/docs/docs/how_to/add_scores_retriever.ipynb +++ b/docs/docs/how_to/add_scores_retriever.ipynb @@ -11,11 +11,11 @@ "1. From [vectorstore retrievers](/docs/how_to/vectorstore_retriever);\n", "2. From higher-order LangChain retrievers, such as [SelfQueryRetriever](/docs/how_to/self_query) or [MultiVectorRetriever](/docs/how_to/multi_vector).\n", "\n", - "For (1), we will implement a short wrapper function around the corresponding vectorstore. For (2), we will update a method of the corresponding class.\n", + "For (1), we will implement a short wrapper function around the corresponding vector store. For (2), we will update a method of the corresponding class.\n", "\n", - "## Create vectorstore\n", + "## Create vector store\n", "\n", - "First we populate a vectorstore with some data. We will use a [PineconeVectorStore](https://api.python.langchain.com/en/latest/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html), but this guide is compatible with any LangChain vectorstore that implements a `.similarity_search_with_score` method." + "First we populate a vector store with some data. We will use a [PineconeVectorStore](https://api.python.langchain.com/en/latest/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html), but this guide is compatible with any LangChain vector store that implements a `.similarity_search_with_score` method." ] }, { @@ -73,7 +73,7 @@ "source": [ "## Retriever\n", "\n", - "To obtain scores from a vectorstore retriever, we wrap the underlying vectorstore's `.similarity_search_with_score` method in a short function that packages scores into the associated document's metadata.\n", + "To obtain scores from a vector store retriever, we wrap the underlying vector store's `.similarity_search_with_score` method in a short function that packages scores into the associated document's metadata.\n", "\n", "We add a `@chain` decorator to the function to create a [Runnable](/docs/concepts/#langchain-expression-language) that can be used similarly to a typical retriever." ] @@ -142,7 +142,7 @@ "\n", "`SelfQueryRetriever` will use a LLM to generate a query that is potentially structured-- for example, it can construct filters for the retrieval on top of the usual semantic-similarity driven selection. See [this guide](/docs/how_to/self_query) for more detail.\n", "\n", - "`SelfQueryRetriever` includes a short (1 - 2 line) method `_get_docs_with_query` that executes the vectorstore search. We can subclass `SelfQueryRetriever` and override this method to propagate similarity scores.\n", + "`SelfQueryRetriever` includes a short (1 - 2 line) method `_get_docs_with_query` that executes the `vectorstore` search. We can subclass `SelfQueryRetriever` and override this method to propagate similarity scores.\n", "\n", "First, following the [how-to guide](/docs/how_to/self_query), we will need to establish some metadata on which to filter:" ] @@ -187,7 +187,7 @@ "id": "0a6c6fa8-1e2f-45ee-83e9-a6cbd82292d2", "metadata": {}, "source": [ - "We then override the `_get_docs_with_query` to use the `similarity_search_with_score` method of the underlying vectorstore: " + "We then override the `_get_docs_with_query` to use the `similarity_search_with_score` method of the underlying vector store: " ] }, { @@ -259,7 +259,7 @@ "source": [ "## MultiVectorRetriever\n", "\n", - "`MultiVectorRetriever` allows you to associate multiple vectors with a single document. This can be useful in a number of applications. For example, we can index small chunks of a larger document and run the retrieval on the chunks, but return the larger \"parent\" document when invoking the retriever. [ParentDocumentRetriever](/docs/how_to/parent_document_retriever/), a subclass of `MultiVectorRetriever`, includes convenience methods for populating a vectorstore to support this. Further applications are detailed in this [how-to guide](/docs/how_to/multi_vector/).\n", + "`MultiVectorRetriever` allows you to associate multiple vectors with a single document. This can be useful in a number of applications. For example, we can index small chunks of a larger document and run the retrieval on the chunks, but return the larger \"parent\" document when invoking the retriever. [ParentDocumentRetriever](/docs/how_to/parent_document_retriever/), a subclass of `MultiVectorRetriever`, includes convenience methods for populating a vector store to support this. Further applications are detailed in this [how-to guide](/docs/how_to/multi_vector/).\n", "\n", "To propagate similarity scores through this retriever, we can again subclass `MultiVectorRetriever` and override a method. This time we will override `_get_relevant_documents`.\n", "\n", @@ -290,7 +290,7 @@ "id": "453b7415-4a6d-45d4-a329-9c1d7271d1b2", "metadata": {}, "source": [ - "Next we will add some fake \"sub-documents\" to our vectorstore. We can link these sub-documents to the parent documents by populating the `\"doc_id\"` key in its metadata." + "Next we will add some fake \"sub-documents\" to our vector store. We can link these sub-documents to the parent documents by populating the `\"doc_id\"` key in its metadata." ] }, { @@ -338,7 +338,7 @@ "source": [ "To propagate the scores, we subclass `MultiVectorRetriever` and override its `_get_relevant_documents` method. Here we will make two changes:\n", "\n", - "1. We will add similarity scores to the metadata of the corresponding \"sub-documents\" using the `similarity_search_with_score` method of the underlying vectorstore as above;\n", + "1. We will add similarity scores to the metadata of the corresponding \"sub-documents\" using the `similarity_search_with_score` method of the underlying vector store as above;\n", "2. We will include a list of these sub-documents in the metadata of the retrieved parent document. This surfaces what snippets of text were identified by the retrieval, together with their corresponding similarity scores." ] },