mirror of
https://github.com/hwchase17/langchain
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f907b62526
# Scores in Vectorestores' Docs Are Explained Following vectorestores can return scores with similar documents by using `similarity_search_with_score`: - chroma - docarray_hnsw - docarray_in_memory - faiss - myscale - qdrant - supabase - vectara - weaviate However, in documents, these scores were either not explained at all or explained in a way that could lead to misunderstandings (e.g., FAISS). For instance in FAISS document: if we consider the score returned by the function as a similarity score, we understand that a document returning a higher score is more similar to the source document. However, since the scores returned by the function are distance scores, we should understand that smaller scores correspond to more similar documents. For the libraries other than Vectara, I wrote the scores they use by investigating from the source libraries. Since I couldn't be certain about the score metric used by Vectara, I didn't make any changes in its documentation. The links mentioned in Vectara's documentation became broken due to updates, so I replaced them with working ones. VectorStores / Retrievers / Memory - @dev2049 my twitter: [berkedilekoglu](https://twitter.com/berkedilekoglu) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
325 lines
9.3 KiB
Plaintext
325 lines
9.3 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "683953b3",
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"metadata": {},
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"source": [
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"# Vectara\n",
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"\n",
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">[Vectara](https://vectara.com/) is a API platform for building LLM-powered applications. It provides a simple to use API for document indexing and query that is managed by Vectara and is optimized for performance and accuracy. \n",
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"\n",
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"\n",
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"This notebook shows how to use functionality related to the `Vectara` vector database. \n",
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"\n",
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"See the [Vectara API documentation ](https://docs.vectara.com/docs/) for more information on how to use the API."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "7b2f111b-357a-4f42-9730-ef0603bdc1b5",
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"metadata": {},
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"source": [
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"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
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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": "082e7e8b-ac52-430c-98d6-8f0924457642",
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"metadata": {
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"tags": []
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},
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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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"OpenAI API Key:········\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
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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": 2,
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"id": "aac9563e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:22.282884Z",
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"start_time": "2023-04-04T10:51:21.408077Z"
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},
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"tags": []
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},
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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.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Vectara\n",
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"from langchain.document_loaders import TextLoader"
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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": "a3c3999a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:22.520144Z",
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"start_time": "2023-04-04T10:51:22.285826Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"loader = TextLoader('../../../state_of_the_union.txt')\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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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "eeead681",
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"metadata": {},
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"source": [
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"## Connecting to Vectara from LangChain\n",
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"\n",
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"The Vectara API provides simple API endpoints for indexing and querying."
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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": "8429667e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:22.525091Z",
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"start_time": "2023-04-04T10:51:22.522015Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"vectara = Vectara.from_documents(docs, embedding=None)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "1f9215c8",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T09:27:29.920258Z",
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"start_time": "2023-04-04T09:27:29.913714Z"
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}
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},
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"source": [
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"## Similarity search\n",
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"\n",
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"The simplest scenario for using Vectara is to perform a similarity search. "
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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": 6,
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"id": "a8c513ab",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.204469Z",
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"start_time": "2023-04-04T10:51:24.855618Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"found_docs = vectara.similarity_search(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": 7,
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"id": "fc516993",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.220984Z",
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"start_time": "2023-04-04T10:51:25.213943Z"
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},
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"tags": []
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},
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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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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender.\n"
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]
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}
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],
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"source": [
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"print(found_docs[0].page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "1bda9bf5",
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"metadata": {},
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"source": [
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"## Similarity search with score\n",
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"\n",
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"Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result."
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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": "8804a21d",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.631585Z",
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"start_time": "2023-04-04T10:51:25.227384Z"
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}
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},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"found_docs = vectara.similarity_search_with_score(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": 9,
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"id": "756a6887",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.642282Z",
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"start_time": "2023-04-04T10:51:25.635947Z"
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}
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},
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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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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender.\n",
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"\n",
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"Score: 1.0046461\n"
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]
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}
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],
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"source": [
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"document, score = found_docs[0]\n",
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"print(document.page_content)\n",
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"print(f\"\\nScore: {score}\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "691a82d6",
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"metadata": {},
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"source": [
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"## Vectara as a Retriever\n",
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"\n",
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"Vectara, as all the other vector stores, is a LangChain Retriever, by using cosine similarity. "
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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": 11,
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"id": "9427195f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:26.031451Z",
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"start_time": "2023-04-04T10:51:26.018763Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"VectorStoreRetriever(vectorstore=<langchain.vectorstores.vectara.Vectara object at 0x156d3e830>, search_type='similarity', search_kwargs={})"
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]
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},
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"execution_count": 11,
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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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"retriever = vectara.as_retriever()\n",
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"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": 15,
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"id": "f3c70c31",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:26.495652Z",
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"start_time": "2023-04-04T10:51:26.046407Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Document(page_content='Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender.', metadata={'source': '../../modules/state_of_the_union.txt'})"
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]
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},
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"execution_count": 15,
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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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"retriever.get_relevant_documents(query)[0]"
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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": "2300e785",
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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": "3.11.3"
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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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