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94 lines
2.2 KiB
Plaintext
94 lines
2.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fc0db1bc",
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"metadata": {},
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"source": [
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"# VectorStore Retriever\n",
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"\n",
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"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",
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"\n",
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"Once you construct a VectorStore, its very easy to construct a retriever. Let's walk through an example."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5831703b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"loader = TextLoader('../../state_of_the_union.txt')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "9fbcc58f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running Chroma using direct local API.\n",
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"Using DuckDB in-memory for database. Data will be transient.\n"
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]
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}
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],
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"source": [
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Chroma\n",
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"from langchain.embeddings import OpenAIEmbeddings\n",
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"\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"texts = text_splitter.split_documents(documents)\n",
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"embeddings = OpenAIEmbeddings()\n",
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"db = Chroma.from_documents(texts, embeddings)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "0cbfb1af",
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"metadata": {},
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"outputs": [],
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"source": [
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"retriever = db.as_retriever()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fc12700b",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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