{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Redis\n", "\n", ">[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", "\n", "This notebook shows how to use functionality related to the [Redis vector database](https://redis.com/solutions/use-cases/vector-database/).\n", "\n", "As database either Redis standalone server or Redis Sentinel HA setups are supported for connections with the \"redis_url\"\n", "parameter. More information about the different formats of the redis connection url can be found in the LangChain\n", "[Redis Readme](/docs/modules/data_connection/vectorstores/integrations/redis) file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "!pip install redis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import getpass\n", "\n", "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "from langchain.embeddings import OpenAIEmbeddings\n", "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain.vectorstores.redis import Redis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain.document_loaders import TextLoader\n", "\n", "loader = TextLoader(\"../../../state_of_the_union.txt\")\n", "documents = loader.load()\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", "docs = text_splitter.split_documents(documents)\n", "\n", "embeddings = OpenAIEmbeddings()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're not interested in the keys of your entries you can also create your redis instance from the documents." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rds = Redis.from_documents(\n", " docs, embeddings, redis_url=\"redis://localhost:6379\", index_name=\"link\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're interested in the keys of your entries you have to split your docs in texts and metadatas" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "texts = [d.page_content for d in docs]\n", "metadatas = [d.metadata for d in docs]\n", "\n", "rds, keys = Redis.from_texts_return_keys(\n", " texts, embeddings, redis_url=\"redis://localhost:6379\", index_name=\"link\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rds.index_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "query = \"What did the president say about Ketanji Brown Jackson\"\n", "results = rds.similarity_search(query)\n", "print(results[0].page_content)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(rds.add_texts([\"Ankush went to Princeton\"]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "query = \"Princeton\"\n", "results = rds.similarity_search(query)\n", "print(results[0].page_content)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Load from existing index\n", "rds = Redis.from_existing_index(\n", " embeddings, redis_url=\"redis://localhost:6379\", index_name=\"link\"\n", ")\n", "\n", "query = \"What did the president say about Ketanji Brown Jackson\"\n", "results = rds.similarity_search(query)\n", "print(results[0].page_content)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Redis as Retriever\n", "\n", "Here we go over different options for using the vector store as a retriever.\n", "\n", "There are three different search methods we can use to do retrieval. By default, it will use semantic similarity." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "retriever = rds.as_retriever()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "docs = retriever.get_relevant_documents(query)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use similarity_limit as a search method. This is only return documents if they are similar enough" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "retriever = rds.as_retriever(search_type=\"similarity_limit\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Here we can see it doesn't return any results because there are no relevant documents\n", "retriever.get_relevant_documents(\"where did ankush go to college?\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Delete keys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To delete your entries you have to address them by their keys." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "Redis.delete(keys, redis_url=\"redis://localhost:6379\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Redis connection Url examples\n", "\n", "Valid Redis Url scheme are:\n", "1. `redis://` - Connection to Redis standalone, unencrypted\n", "2. `rediss://` - Connection to Redis standalone, with TLS encryption\n", "3. `redis+sentinel://` - Connection to Redis server via Redis Sentinel, unencrypted\n", "4. `rediss+sentinel://` - Connection to Redis server via Redis Sentinel, booth connections with TLS encryption\n", "\n", "More information about additional connection parameter can be found in the redis-py documentation at https://redis-py.readthedocs.io/en/stable/connections.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# connection to redis standalone at localhost, db 0, no password\n", "redis_url = \"redis://localhost:6379\"\n", "# connection to host \"redis\" port 7379 with db 2 and password \"secret\" (old style authentication scheme without username / pre 6.x)\n", "redis_url = \"redis://:secret@redis:7379/2\"\n", "# connection to host redis on default port with user \"joe\", pass \"secret\" using redis version 6+ ACLs\n", "redis_url = \"redis://joe:secret@redis/0\"\n", "\n", "# connection to sentinel at localhost with default group mymaster and db 0, no password\n", "redis_url = \"redis+sentinel://localhost:26379\"\n", "# connection to sentinel at host redis with default port 26379 and user \"joe\" with password \"secret\" with default group mymaster and db 0\n", "redis_url = \"redis+sentinel://joe:secret@redis\"\n", "# connection to sentinel, no auth with sentinel monitoring group \"zone-1\" and database 2\n", "redis_url = \"redis+sentinel://redis:26379/zone-1/2\"\n", "\n", "# connection to redis standalone at localhost, db 0, no password but with TLS support\n", "redis_url = \"rediss://localhost:6379\"\n", "# connection to redis sentinel at localhost and default port, db 0, no password\n", "# but with TLS support for booth Sentinel and Redis server\n", "redis_url = \"rediss+sentinel://localhost\"" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 4 }