langchain/docs/modules/indexes/retriever_examples/vectorstore-retriever.ipynb

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2023-03-24 14:46:49 +00:00
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"# VectorStore Retriever\n",
"\n",
"The index - and therefor the retriever - that LangChain has the most support for is a 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": 3,
"id": "5831703b",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader('../../state_of_the_union.txt')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9fbcc58f",
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{
"name": "stdout",
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"text": [
"Running Chroma using direct local API.\n",
"Using DuckDB in-memory for database. Data will be transient.\n"
]
}
],
"source": [
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores import Chroma\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 = Chroma.from_documents(texts, embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "0cbfb1af",
"metadata": {},
"outputs": [],
"source": [
"retriever = db.as_retriever()"
]
},
{
"cell_type": "code",
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"id": "fc12700b",
"metadata": {},
"outputs": [],
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