mirror of
https://github.com/hwchase17/langchain
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331 lines
8.9 KiB
Plaintext
331 lines
8.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Redis\n",
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"\n",
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">[Redis (Remote Dictionary Server)](https://en.wikipedia.org/wiki/Redis) is an in-memory data structure store, used as a distributed, in-memory key–value database, cache and message broker, with optional durability.\n",
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"\n",
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"This notebook shows how to use functionality related to the [Redis vector database](https://redis.com/solutions/use-cases/vector-database/).\n",
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"\n",
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"As database either Redis standalone server or Redis Sentinel HA setups are supported for connections with the \"redis_url\"\n",
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"parameter. More information about the different formats of the redis connection url can be found in the LangChain\n",
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"[Redis Readme](/docs/integrations/vectorstores/redis) file"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Installing"
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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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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install redis"
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]
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},
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{
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"cell_type": "markdown",
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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": null,
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"metadata": {},
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"outputs": [],
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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": "markdown",
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"metadata": {},
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"source": [
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"## 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": 1,
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"metadata": {
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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 import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores.redis import Redis"
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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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"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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"\n",
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If you're not interested in the keys of your entries you can also create your redis instance from the documents."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"rds = Redis.from_documents(\n",
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" docs, embeddings, redis_url=\"redis://localhost:6379\", index_name=\"link\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If you're interested in the keys of your entries you have to split your docs in texts and metadatas"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"texts = [d.page_content for d in docs]\n",
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"metadatas = [d.metadata for d in docs]\n",
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"\n",
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"rds, keys = Redis.from_texts_return_keys(\n",
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" texts, embeddings, redis_url=\"redis://localhost:6379\", index_name=\"link\"\n",
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")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"rds.index_name"
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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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"metadata": {},
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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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"results = rds.similarity_search(query)\n",
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"print(results[0].page_content)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"print(rds.add_texts([\"Ankush went to Princeton\"]))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"Princeton\"\n",
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"results = rds.similarity_search(query)\n",
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"print(results[0].page_content)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Load from existing index\n",
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"rds = Redis.from_existing_index(\n",
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" embeddings, redis_url=\"redis://localhost:6379\", index_name=\"link\"\n",
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")\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"results = rds.similarity_search(query)\n",
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"print(results[0].page_content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Redis as Retriever\n",
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"\n",
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"Here we go over different options for using the vector store as a retriever.\n",
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"\n",
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"There are three different search methods we can use to do retrieval. By default, it will use semantic 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"retriever = rds.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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"metadata": {},
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"outputs": [],
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"source": [
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"docs = retriever.get_relevant_documents(query)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can also use similarity_limit as a search method. This is only return documents if they are similar enough"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"retriever = rds.as_retriever(search_type=\"similarity_limit\")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Here we can see it doesn't return any results because there are no relevant documents\n",
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"retriever.get_relevant_documents(\"where did ankush go to college?\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Delete keys"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To delete your entries you have to address them by their keys."
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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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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"Redis.delete(keys, redis_url=\"redis://localhost:6379\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Redis connection Url examples\n",
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"\n",
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"Valid Redis Url scheme are:\n",
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"1. `redis://` - Connection to Redis standalone, unencrypted\n",
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"2. `rediss://` - Connection to Redis standalone, with TLS encryption\n",
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"3. `redis+sentinel://` - Connection to Redis server via Redis Sentinel, unencrypted\n",
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"4. `rediss+sentinel://` - Connection to Redis server via Redis Sentinel, booth connections with TLS encryption\n",
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"\n",
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"More information about additional connection parameter can be found in the redis-py documentation at https://redis-py.readthedocs.io/en/stable/connections.html"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# connection to redis standalone at localhost, db 0, no password\n",
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"redis_url = \"redis://localhost:6379\"\n",
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"# connection to host \"redis\" port 7379 with db 2 and password \"secret\" (old style authentication scheme without username / pre 6.x)\n",
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"redis_url = \"redis://:secret@redis:7379/2\"\n",
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"# connection to host redis on default port with user \"joe\", pass \"secret\" using redis version 6+ ACLs\n",
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"redis_url = \"redis://joe:secret@redis/0\"\n",
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"\n",
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"# connection to sentinel at localhost with default group mymaster and db 0, no password\n",
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"redis_url = \"redis+sentinel://localhost:26379\"\n",
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"# connection to sentinel at host redis with default port 26379 and user \"joe\" with password \"secret\" with default group mymaster and db 0\n",
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"redis_url = \"redis+sentinel://joe:secret@redis\"\n",
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"# connection to sentinel, no auth with sentinel monitoring group \"zone-1\" and database 2\n",
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"redis_url = \"redis+sentinel://redis:26379/zone-1/2\"\n",
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"\n",
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"# connection to redis standalone at localhost, db 0, no password but with TLS support\n",
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"redis_url = \"rediss://localhost:6379\"\n",
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"# connection to redis sentinel at localhost and default port, db 0, no password\n",
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"# but with TLS support for booth Sentinel and Redis server\n",
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"redis_url = \"rediss+sentinel://localhost\""
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]
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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": 4
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}
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