mirror of
https://github.com/hwchase17/langchain
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c104d507bf
Co-authored-by: cameronccohen <cameron.c.cohen@gmail.com> Co-authored-by: Cameron Cohen <cameron.cohen@quantco.com>
105 lines
2.6 KiB
Plaintext
105 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "07c1e3b9",
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"metadata": {},
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"source": [
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"# Vector DB Question/Answering\n",
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"\n",
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"This example showcases question answering over a vector database."
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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": 1,
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"id": "82525493",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.vectorstores.faiss import FAISS\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain import OpenAI, VectorDBQA"
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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": "5c7049db",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('../state_of_the_union.txt') as f:\n",
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" state_of_the_union = f.read()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"texts = text_splitter.split_text(state_of_the_union)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"docsearch = FAISS.from_texts(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": 5,
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"id": "3018f865",
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"metadata": {},
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"outputs": [],
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"source": [
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"qa = VectorDBQA.from_llm(llm=OpenAI(), vectorstore=docsearch)"
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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": 4,
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"id": "032a47f8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator and federal public defender, and from a family of public school educators and police officers. He also said that she has received a broad range of support since she was nominated, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"qa.run(query)"
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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": "f056f6fd",
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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": "3.10.8"
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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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