langchain/docs/extras/integrations/vectorstores/redis.ipynb
2023-07-24 21:20:32 -07:00

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{
"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 keyvalue 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/integrations/vectorstores/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\""
]
}
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