langchain/docs/modules/indexes/retrievers/examples/vectorstore.ipynb
constDave 5f356b9993
Fixed typo missing "use" (#5991)
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Fixed a simple typo on
https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/vectorstore.html
where the word "use" was missing.

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2023-06-10 14:31:58 -07:00

213 lines
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{
"cells": [
{
"cell_type": "markdown",
"id": "fc0db1bc",
"metadata": {},
"source": [
"# VectorStore\n",
"\n",
"The index - and therefore the retriever - that LangChain has the most support for is the `VectorStoreRetriever`. As the name suggests, this retriever is backed heavily by a VectorStore.\n",
"\n",
"Once you construct a VectorStore, its very easy to construct a retriever. Let's walk through an example."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5831703b",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader('../../../state_of_the_union.txt')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9fbcc58f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exiting: Cleaning up .chroma directory\n"
]
}
],
"source": [
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores import FAISS\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(documents)\n",
"embeddings = OpenAIEmbeddings()\n",
"db = FAISS.from_documents(texts, embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0cbfb1af",
"metadata": {},
"outputs": [],
"source": [
"retriever = db.as_retriever()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fc12700b",
"metadata": {},
"outputs": [],
"source": [
"docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\")"
]
},
{
"cell_type": "markdown",
"id": "79b783de",
"metadata": {},
"source": [
"## Maximum Marginal Relevance Retrieval\n",
"By default, the vectorstore retriever uses similarity search. If the underlying vectorstore support maximum marginal relevance search, you can specify that as the search type."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "44c7303e",
"metadata": {},
"outputs": [],
"source": [
"retriever = db.as_retriever(search_type=\"mmr\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d16ceec6",
"metadata": {},
"outputs": [],
"source": [
"docs = retriever.get_relevant_documents(\"what did he say abotu ketanji brown jackson\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "2d958271",
"metadata": {},
"source": [
"## Similarity Score Threshold Retrieval\n",
"\n",
"You can also use a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d4272ad8",
"metadata": {},
"outputs": [],
"source": [
"retriever = db.as_retriever(search_type=\"similarity_score_threshold\", search_kwargs={\"score_threshold\": .5})"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "438e761d",
"metadata": {},
"outputs": [],
"source": [
"docs = retriever.get_relevant_documents(\"what did he say abotu ketanji brown jackson\")"
]
},
{
"cell_type": "markdown",
"id": "c23b7698",
"metadata": {},
"source": [
"## Specifying top k\n",
"You can also specify search kwargs like `k` to use when doing retrieval."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b5f44cdf",
"metadata": {},
"outputs": [],
"source": [
"retriever = db.as_retriever(search_kwargs={\"k\": 1})"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "56b6a545",
"metadata": {},
"outputs": [],
"source": [
"docs = retriever.get_relevant_documents(\"what did he say abotu ketanji brown jackson\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b5416858",
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(docs)"
]
},
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"id": "9a658023",
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