id stringlengths 14 15 | text stringlengths 17 2.72k | source stringlengths 47 115 |
|---|---|---|
fb1b9029f3b3-1 | client = vectordb.Client()
vector_store = Epsilla.from_documents(
documents,
embeddings,
client,
db_path="/tmp/mypath",
db_name="MyDB",
collection_name="MyCollection"
)
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_store.similarity_search(query)
print(docs[0].page_content)
In state afte... | https://python.langchain.com/docs/integrations/vectorstores/epsilla |
806d1e9ef3a4-0 | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. Hologres supports standard SQL syntax, is compatible with PostgreSQL, and supports most PostgreSQL functions. Hologres supports online anal... | https://python.langchain.com/docs/integrations/vectorstores/hologres |
9d6fefeef2e6-0 | Faiss
Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.
Faiss documentati... | https://python.langchain.com/docs/integrations/vectorstores/faiss |
9d6fefeef2e6-1 | 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.
Similarity Search with score
There are some FAISS specific methods. One of them is similarity_search_with_score, which allows y... | https://python.langchain.com/docs/integrations/vectorstores/faiss |
9d6fefeef2e6-2 | docs = new_db.similarity_search(query)
Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated ... | https://python.langchain.com/docs/integrations/vectorstores/faiss |
9d6fefeef2e6-3 | {'068c473b-d420-487a-806b-fb0ccea7f711': Document(page_content='foo', metadata={}),
'807e0c63-13f6-4070-9774-5c6f0fbb9866': Document(page_content='bar', metadata={})}
Similarity Search with filtering
FAISS vectorstore can also support filtering, since the FAISS does not natively support filtering we have to do it manu... | https://python.langchain.com/docs/integrations/vectorstores/faiss |
9d6fefeef2e6-4 | list_of_documents = [
Document(page_content="foo", metadata=dict(page=1)),
Document(page_content="bar", metadata=dict(page=1)),
Document(page_content="foo", metadata=dict(page=2)),
Document(page_content="barbar", metadata=dict(page=2)),
Document(page_content="foo", metadata=dict(page=3)),
Document(page_content="bar bur... | https://python.langchain.com/docs/integrations/vectorstores/faiss |
9d6fefeef2e6-5 | Content: bar, Metadata: {'page': 1}
Here is an example of how to set fetch_k parameter when calling similarity_search. Usually you would want the fetch_k parameter >> k parameter. This is because the fetch_k parameter is the number of documents that will be fetched before filtering. If you set fetch_k to a low number, ... | https://python.langchain.com/docs/integrations/vectorstores/faiss |
8fe6552faa14-0 | This notebook shows how to use functionality related to the LanceDB vector database based on the Lance data format.
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import lancedb
db = lancedb.connect("/tmp/lancedb")
table = db.create_table(
"my_table",
data=[
{
"vector": embeddings.embed_query("... | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
8fe6552faa14-1 | I ask Democrats and Republicans alike: Pass my budget and keep our neighborhoods safe.
And I will keep doing everything in my power to crack down on gun trafficking and ghost guns you can buy online and make at home—they have no serial numbers and can’t be traced.
And I ask Congress to pass proven measures to reduc... | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
8fe6552faa14-2 | We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
711af5008774-0 | Marqo
This notebook shows how to use functionality related to the Marqo vectorstore.
Marqo is an open-source vector search engine. Marqo allows you to store and query multimodal data such as text and images. Marqo creates the vectors for you using a huge selection of opensource models, you can also provide your own fin... | https://python.langchain.com/docs/integrations/vectorstores/marqo |
711af5008774-1 | 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 ... | https://python.langchain.com/docs/integrations/vectorstores/marqo |
711af5008774-2 | # incase the demo is re-run
try:
client.delete_index(index_name)
except Exception:
print(f"Creating {index_name}")
# This index could have been created by another system
settings = {"treat_urls_and_pointers_as_images": True, "model": "ViT-L/14"}
client.create_index(index_name, **settings)
client.index(index_name).add_... | https://python.langchain.com/docs/integrations/vectorstores/marqo |
711af5008774-3 | # This index could have been created by another system
client.create_index(index_name)
client.index(index_name).add_documents(
[
{
"Title": "Smartphone",
"Description": "A smartphone is a portable computer device that combines mobile telephone "
"functions and computing functions into one unit.",
},
{
"Title": "Telepho... | https://python.langchain.com/docs/integrations/vectorstores/marqo |
711af5008774-4 | print(doc_results[0].page_content)
This is a document that is about elephants
Weighted Queries
We also expose marqos weighted queries which are a powerful way to compose complex semantic searches.
query = {"communications devices": 1.0}
doc_results = docsearch.similarity_search(query)
print(doc_results[0].page_content... | https://python.langchain.com/docs/integrations/vectorstores/marqo |
ae6c4b0fdd74-0 | Google Vertex AI MatchingEngine
This notebook shows how to use functionality related to the GCP Vertex AI MatchingEngine vector database.
Vertex AI Matching Engine provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an ... | https://python.langchain.com/docs/integrations/vectorstores/matchingengine |
ae6c4b0fdd74-1 | # Change this if you need the VPC to be created.
CREATE_VPC = False
# Set the project id
gcloud config set project {PROJECT_ID}
# Remove the if condition to run the encapsulated code
if CREATE_VPC:
# Create a VPC network
gcloud compute networks create {VPC_NETWORK} --bgp-routing-mode=regional --subnet-mode=auto --proje... | https://python.langchain.com/docs/integrations/vectorstores/matchingengine |
ae6c4b0fdd74-2 | with open("data.json", "w") as f:
json.dump(initial_config, f)
gsutil cp data.json {EMBEDDING_DIR}/file.json
aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)
Creating Index
my_index = aiplatform.MatchingEngineIndex.create_tree_ah_index(
display_name=DISPLAY_NAME,
contents_delta_uri=EMBED... | https://python.langchain.com/docs/integrations/vectorstores/matchingengine |
848a0967d864-0 | Milvus
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
This notebook shows how to use functionality related to the Milvus vector database.
To run, you should have a Milvus instance up and running.
We want to use O... | https://python.langchain.com/docs/integrations/vectorstores/milvus |
6a4ee69f16de-0 | Meilisearch
Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences.
You can self-host Meilisearch or run on Meilisearch Cloud.
Meilisearch v1.3 supports vector search. This page guides you through integrating Meil... | https://python.langchain.com/docs/integrations/vectorstores/meilisearch |
6a4ee69f16de-1 | os.environ["MEILI_HTTP_ADDR"] = getpass.getpass("Meilisearch HTTP address and port:")
os.environ["MEILI_MASTER_KEY"] = getpass.getpass("Meilisearch API Key:")
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
Adding text and embedding... | https://python.langchain.com/docs/integrations/vectorstores/meilisearch |
6a4ee69f16de-2 | client = meilisearch.Client(url="http://127.0.0.1:7700", api_key="***")
vector_store = Meilisearch(
embedding=embeddings, client=client, index_name="langchain_demo", text_key="text"
)
vector_store.add_documents(documents)
Similarity Search with score
This specific method allows you to return the documents and the dist... | https://python.langchain.com/docs/integrations/vectorstores/meilisearch |
96e0d4c3960a-0 | MongoDB Atlas
MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. It now has support for native Vector Search on your MongoDB document data.
This notebook shows how to use MongoDB Atlas Vector Search to store your embeddings in MongoDB documents, create a vector search index, and perform K... | https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas |
96e0d4c3960a-1 | # initialize MongoDB python client
client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
db_name = "langchain_db"
collection_name = "langchain_col"
collection = client[db_name][collection_name]
index_name = "langchain_demo"
# insert the documents in MongoDB Atlas with their embedding
docsearch = MongoDBAtlasVectorSearch.fr... | https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas |
17f2855cf2a8-0 | MyScale
MyScale is a cloud-based database optimized for AI applications and solutions, built on the open-source ClickHouse.
This notebook shows how to use functionality related to the MyScale vector database.
Setting up envrionments
pip install clickhouse-connect
We want to use OpenAIEmbeddings so we have to get the ... | https://python.langchain.com/docs/integrations/vectorstores/myscale |
17f2855cf2a8-1 | query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
Get connection info and data schema
Filtering
You can have direct access to myscale SQL where statement. You can write WHERE clause following standard SQL.
NOTE: Please be aware of SQ... | https://python.langchain.com/docs/integrations/vectorstores/myscale |
8074ad1d06d8-0 | Neo4j Vector Index
Neo4j is an open-source graph database with integrated support for vector similarity search
It supports:
approximate nearest neighbor search
L2 distance and cosine distance
This notebook shows how to use the Neo4j vector index (Neo4jVector).
See the installation instruction.
# Pip install necessary p... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector |
8074ad1d06d8-1 | Requirement already satisfied: certifi>=2017.4.17 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.20->openai) (2023.7.22)
Requirement already satisfied: attrs>=17.3.0 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from aiohttp->openai) (23.1.0)
Requirement already s... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector |
8074ad1d06d8-2 | Requirement already satisfied: charset-normalizer<4,>=2 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken) (3.2.0)
Requirement already satisfied: idna<4,>=2.5 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken) (3.4)
Requirem... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector |
8074ad1d06d8-3 | os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Neo4jVector
from langchain.document_loaders import TextLoader
from langchain.docstore.document import Do... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector |
8074ad1d06d8-4 | 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.
--------------------------------------------------------------------------------
-----------------------------------------------... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector |
8074ad1d06d8-5 | We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.891287088394165
A... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector |
8074ad1d06d8-6 | store = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name=index_name,
)
Add documents
We can add documents to the existing vectorstore.
store.add_documents([Document(page_content="foo")])
['2f70679a-4416-11ee-b7c3-d46a6aa24f5b']
docs_with_score = store.simil... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector |
8074ad1d06d8-7 | return_only_outputs=True,
)
{'answer': "The president honored Justice Stephen Breyer, who is retiring from the United States Supreme Court, and thanked him for his service. The president also mentioned that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy of excellen... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector |
43d1023f68b3-0 | OpenSearch
OpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on Apache Lucene.
This notebook shows how to use functionality related to the OpenSearch... | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
43d1023f68b3-1 | # If using the default Docker installation, use this instantiation instead:
# docsearch = OpenSearchVectorSearch.from_documents(
# docs,
# embeddings,
# opensearch_url="https://localhost:9200",
# http_auth=("admin", "admin"),
# use_ssl = False,
# verify_certs = False,
# ssl_assert_hostname = False,
# ssl_show_warn = Fa... | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
43d1023f68b3-2 | query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(
"What did the president say about Ketanji Brown Jackson",
k=1,
search_type="script_scoring",
)
print(docs[0].page_content)
similarity_search using Painless Scripting
similarity_search using Painless Scripting with Cust... | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
43d1023f68b3-3 | service = 'aoss' # must set the service as 'aoss'
region = 'us-east-2'
credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()
awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)
docsearch = OpenSearchVectorSearch.from_documents(
docs,
... | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
6ea442208a3a-0 | Postgres Embedding
Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search.
It supports:
exact and approximate nearest neighbor search using HNSW
L2 distance
This notebook shows how to use the Postgres vector... | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
6ea442208a3a-1 | query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
Working with vectorstore in Postgres
Uploading a vec... | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
6ea442208a3a-2 | m: Defines the maximum number of bi-directional links (also referred to as "edges") created for each node during graph construction. The following additional index options are supported:
efConstruction: Defines the number of nearest neighbors considered during index construction. The default value is 32.
efsearch: Defi... | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
6ea442208a3a-3 | retriever = store.as_retriever()
VectorStoreRetriever(vectorstore=<langchain.vectorstores.pghnsw.HNSWVectoreStore object at 0x121d3c8b0>, search_type='similarity', search_kwargs={})
db1 = PGEmbedding.from_existing_index(
embedding=embeddings,
collection_name=collection_name,
pre_delete_collection=False,
connection_stri... | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
a822e2d9cc5f-0 | This notebook shows how to use the Postgres vector database (PGVector).
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
# PGVector needs the connection string to the database.
CONNECTION_STRING = "postgresql+psycopg2://harrisonchase@localhost:5432/test3"
# # Alternatively, you can create it from ... | https://python.langchain.com/docs/integrations/vectorstores/pgvector |
a822e2d9cc5f-1 | 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.
--------------------------------------------------------------------------------
-----------------------------------------------... | https://python.langchain.com/docs/integrations/vectorstores/pgvector |
a822e2d9cc5f-2 | And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
... | https://python.langchain.com/docs/integrations/vectorstores/pgvector |
83997d0ea1ff-0 | Pinecone
Pinecone is a vector database with broad functionality.
This notebook shows how to use functionality related to the Pinecone vector database.
To use Pinecone, you must have an API key. Here are the installation instructions.
pip install pinecone-client openai tiktoken langchain
import os
import getpass
os.env... | https://python.langchain.com/docs/integrations/vectorstores/pinecone |
83997d0ea1ff-1 | query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
Adding More Text to an Existing Index
More text can embedded and upserted to an existing Pinecone index using the add_texts function
index = pinecone.Index("langchain-demo")
vectorstor... | https://python.langchain.com/docs/integrations/vectorstores/pinecone |
cde62f900797-0 | Qdrant
Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-ba... | https://python.langchain.com/docs/integrations/vectorstores/qdrant |
cde62f900797-1 | embeddings = OpenAIEmbeddings()
Connecting to Qdrant from LangChain
Local mode
Python client allows you to run the same code in local mode without running the Qdrant server. That's great for testing things out and debugging or if you plan to store just a small amount of vectors. The embeddings might be fully kepy in ... | https://python.langchain.com/docs/integrations/vectorstores/qdrant |
cde62f900797-2 | prefer_grpc=True,
api_key=api_key,
collection_name="my_documents",
)
Recreating the collection
Both Qdrant.from_texts and Qdrant.from_documents methods are great to start using Qdrant with Langchain. In the previous versions the collection was recreated every time you called any of them. That behaviour has changed. Cu... | https://python.langchain.com/docs/integrations/vectorstores/qdrant |
cde62f900797-3 | 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 ... | https://python.langchain.com/docs/integrations/vectorstores/qdrant |
cde62f900797-4 | query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search_with_score(query, filter=rest.Filter(...))
Maximum marginal relevance search (MMR)
If you'd like to look up for some similar documents, but you'd also like to receive diverse results, MMR is method you should conside... | https://python.langchain.com/docs/integrations/vectorstores/qdrant |
cde62f900797-5 | I’ve worked on these issues a long time.
I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. | https://python.langchain.com/docs/integrations/vectorstores/qdrant |
cde62f900797-6 | Qdrant as a Retriever
Qdrant, as all the other vector stores, is a LangChain Retriever, by using cosine similarity.
retriever = qdrant.as_retriever()
retriever
VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='similarity', search_kwargs={})
It might be also... | https://python.langchain.com/docs/integrations/vectorstores/qdrant |
cde62f900797-7 | Named vectors
Qdrant supports multiple vectors per point by named vectors. Langchain requires just a single embedding per document and, by default, uses a single vector. However, if you work with a collection created externally or want to have the named vector used, you can configure it by providing its name.
Qdrant.f... | https://python.langchain.com/docs/integrations/vectorstores/qdrant |
8edda8e85cae-0 | Redis
Redis vector database introduction and langchain integration guide.
What is Redis?
Most developers from a web services background are probably familiar with Redis. At it's core, Redis is an open-source key-value store that can be used as a cache, message broker, and database. Developers choice Redis because it i... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-1 | redis-py Python MIT Redis
node-redis Node.js MIT Redis
nredisstack .NET MIT Redis
Deployment Options
There are many ways to deploy Redis with RediSearch. The easiest way to get started is to use Docker, but there are are many potential options for deployment such as
Redis Cloud
Docker (Redis Stack)
Cloud marketp... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-2 | os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
Sample Data
First we will describe some sample data so that the various attributes of the Redis vector store can be demonstrated.
metadata = [
{
"user": "john",
"age": 18... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-3 | documents = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadata)]
rds = Redis.from_documents(
documents,
embeddings,
redis_url="redis://localhost:6379",
index_name="users"
)
Inspecting the Created Index
Once the Redis VectorStore object has been constructed, an index will have been created in Redis i... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-4 | Index Information:
╭──────────────┬────────────────┬───────────────┬─────────────────┬────────────╮
│ Index Name │ Storage Type │ Prefixes │ Index Options │ Indexing │
├──────────────┼────────────────┼───────────────┼─────────────────┼────────────┤
│ users │ HASH │ ['doc:users'] │ [] │ 0 │
╰──────────────┴─────────────... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-5 | Statistics:
╭─────────────────────────────┬─────────────╮
│ Stat Key │ Value │
├─────────────────────────────┼─────────────┤
│ num_docs │ 5 │
│ num_terms │ 15 │
│ max_doc_id │ 5 │
│ num_records │ 33 │
│ percent_indexed │ 1 │
│ hash_indexing_failures │ 0 │
│ number_of_uses │ 4 │
│ bytes_per_record_avg │ 4.60606 │
│ doc_... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-6 | meta = results[1].metadata
print("Key of the document in Redis: ", meta.pop("id"))
print("Metadata of the document: ", meta)
Key of the document in Redis: doc:users:a70ca43b3a4e4168bae57c78753a200f
Metadata of the document: {'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}
# with scores (distance... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-7 | Content: foo --- Similarity: 1.0
Content: foo --- Similarity: 1.0
Content: foo --- Similarity: 1.0
# you can also add new documents as follows
new_document = ["baz"]
new_metadata = [{
"user": "sam",
"age": 50,
"job": "janitor",
"credit_score": "high"
}]
# both the document and metadata must be lists
rds.add_texts(new_d... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-8 | dims: 1536
distance_metric: COSINE
initial_cap: 20000
name: content_vector
Notice, this include all possible fields for the schema. You can remove any fields that you don't need.
# now we can connect to our existing index as follows | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-9 | new_rds = Redis.from_existing_index(
embeddings,
index_name="users",
redis_url="redis://localhost:6379",
schema="redis_schema.yaml"
)
results = new_rds.similarity_search("foo", k=3)
print(results[0].metadata)
{'id': 'doc:users:8484c48a032d4c4cbe3cc2ed6845fabb', 'user': 'john', 'job': 'engineer', 'credit_score': 'high',... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-10 | rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users_modified",
index_schema=index_schema, # pass in the new index schema
)
`index_schema` does not match generated metadata schema.
If you meant to manually override the schema, please igno... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-11 | # numeric filtering
age_is_18 = RedisNum("age") == 18
age_is_not_18 = RedisNum("age") != 18
age_is_greater_than_18 = RedisNum("age") > 18
age_is_less_than_18 = RedisNum("age") < 18
age_is_greater_than_or_equal_to_18 = RedisNum("age") >= 18
age_is_less_than_or_equal_to_18 = RedisNum("age") <= 18
The RedisFilter class c... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-12 | for result in results:
print("User:", result.metadata["user"], "is", result.metadata["age"])
User: derrick is 45
User: nancy is 94
User: joe is 35
# make sure to use parenthesis around FilterExpressions
# if initializing them while constructing them
age_range = (RedisNum("age") > 18) & (RedisNum("age") < 99)
results = ... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-13 | for result in results:
print("Content:", result[0].page_content, " --- Score: ", result[1])
Content: foo --- Score: 0.0
Content: foo --- Score: 0.0
Content: foo --- Score: 0.0
retriever = rds.as_retriever(search_type="similarity", search_kwargs={"k": 4})
docs = retriever.get_relevant_documents(query)
docs
[Document(pag... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-14 | Document(page_content='foo', metadata={'id': 'doc:users_modified:009b1afeb4084cc6bdef858c7a99b48e', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}),
Document(page_content='foo', metadata={'id': 'doc:users_modified:7087cee9be5b4eca93c30fbdd09a2731', 'user': 'nancy', 'job': 'doctor', 'credit_scor... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-15 | Valid Redis Url scheme are:
redis:// - Connection to Redis standalone, unencrypted
rediss:// - Connection to Redis standalone, with TLS encryption
redis+sentinel:// - Connection to Redis server via Redis Sentinel, unencrypted
rediss+sentinel:// - Connection to Redis server via Redis Sentinel, booth connections with TLS... | https://python.langchain.com/docs/integrations/vectorstores/redis |
8edda8e85cae-16 | # connection to sentinel at localhost with default group mymaster and db 0, no password
redis_url = "redis+sentinel://localhost:26379"
# connection to sentinel at host redis with default port 26379 and user "joe" with password "secret" with default group mymaster and db 0
redis_url = "redis+sentinel://joe:secret@redis"... | https://python.langchain.com/docs/integrations/vectorstores/redis |
37a715ec1525-0 | ScaNN
ScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale.
ScaNN includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is optimized for x86 processors with AVX2 ... | https://python.langchain.com/docs/integrations/vectorstores/scann |
37a715ec1525-1 | docs[0]
Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country:... | https://python.langchain.com/docs/integrations/vectorstores/scann |
9577cdbcccad-0 | Rockset
Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly u... | https://python.langchain.com/docs/integrations/vectorstores/rockset |
9577cdbcccad-1 | loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
3. Insert Documents
embeddings = OpenAIEmbeddings() # Verify OPENAI_API_KEY environment variable
docsearch = Rockset(... | https://python.langchain.com/docs/integrations/vectorstores/rockset |
9577cdbcccad-2 | ##
# output length: 4
# 0.7651359650263554 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...
# 0.7486265516824893 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...
# 0.7469625542348115 {'source': '../../../state_of_the_union.txt'} And so many families...
# 0.7344177777547739 {'so... | https://python.langchain.com/docs/integrations/vectorstores/rockset |
82354a158103-0 | SingleStoreDB
SingleStoreDB is a high-performance distributed SQL database that supports deployment both in the cloud and on-premises. It provides vector storage, and vector functions including dot_product and euclidean_distance, thereby supporting AI applications that require text similarity matching.
This tutorial i... | https://python.langchain.com/docs/integrations/vectorstores/singlestoredb |
cf7cec8a0245-0 | scikit-learn
scikit-learn is an open source collection of machine learning algorithms, including some implementations of the k nearest neighbors. SKLearnVectorStore wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.
This notebook shows ho... | https://python.langchain.com/docs/integrations/vectorstores/sklearn |
cf7cec8a0245-1 | 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... | https://python.langchain.com/docs/integrations/vectorstores/sklearn |
4492ae6f4509-0 | StarRocks
StarRocks is a High-Performance Analytical Database. StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.
Usually StarRocks is categorized into OLAP, and it has showed excellent performance in ClickBench — a ... | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
4492ae6f4509-1 | update_vectordb = False
API Reference:
OpenAIEmbeddings
StarRocks
StarRocksSettings
Chroma
CharacterTextSplitter
TokenTextSplitter
DirectoryLoader
RetrievalQA
TextLoader
UnstructuredMarkdownLoader
/Users/dirlt/utils/py3env/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.7)... | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
4492ae6f4509-2 | # tell vectordb to update text embeddings
update_vectordb = True
Document(page_content='Compile StarRocks with Docker\n\nThis topic describes how to compile StarRocks using Docker.\n\nOverview\n\nStarRocks provides development environment images for both Ubuntu 22.04 and CentOS 7.9. With the image, you can launch a Doc... | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
4492ae6f4509-3 | Configuring StarRocks instance is pretty much like configuring mysql instance. You need to specify:
host/port
username(default: 'root')
password(default: '')
database(default: 'default')
table(default: 'langchain')
embeddings = OpenAIEmbeddings() | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
4492ae6f4509-4 | # configure starrocks settings(host/port/user/pw/db)
settings = StarRocksSettings()
settings.port = 41003
settings.host = "127.0.0.1"
settings.username = "root"
settings.password = ""
settings.database = "zya"
docsearch = gen_starrocks(update_vectordb, embeddings, settings)
print(docsearch)
update_vectordb = False
In... | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
37f5d5e493a1-0 | Supabase (Postgres)
Supabase is an open source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.
PostgreSQL also known as Postgres, is a free and open-source relational database managemen... | https://python.langchain.com/docs/integrations/vectorstores/supabase |
37f5d5e493a1-1 | load_dotenv()
import os
from supabase.client import Client, create_client
supabase_url = os.environ.get("SUPABASE_URL")
supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
supabase: Client = create_client(supabase_url, supabase_key)
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitte... | https://python.langchain.com/docs/integrations/vectorstores/supabase |
37f5d5e493a1-2 | 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.
Similarity search with score
The returned distance score is cosine distance. Therefore, a lower score is better.
matched_docs =... | https://python.langchain.com/docs/integrations/vectorstores/supabase |
37f5d5e493a1-3 | 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... | https://python.langchain.com/docs/integrations/vectorstores/supabase |
37f5d5e493a1-4 | While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly.
## Document 3
We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together.
I recently visited the New York City Po... | https://python.langchain.com/docs/integrations/vectorstores/supabase |
946bb18472e5-0 | Tair
Tair is a cloud native in-memory database service developed by Alibaba Cloud. It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open source Redis. Tair also introduces persistent memory-optimized instances that are ba... | https://python.langchain.com/docs/integrations/vectorstores/tair |
946bb18472e5-1 | RuntimeError: Error loading ../../../state_of_the_union.txt
Connect to Tair using the TAIR_URL environment variable
export TAIR_URL="redis://{username}:{password}@{tair_address}:{tair_port}"
or the keyword argument tair_url.
Then store documents and embeddings into Tair.
tair_url = "redis://localhost:6379"
# drop fir... | https://python.langchain.com/docs/integrations/vectorstores/tair |
946bb18472e5-2 | vector_store = Tair.from_documents(docs, embeddings, tair_url=tair_url, index_params={"lexical_algorithm":"bm25"})
Tair Hybrid Search
query = "What did the president say about Ketanji Brown Jackson"
# hybrid_ratio: 0.5 hybrid search, 0.9999 vector search, 0.0001 text search
kwargs = {"TEXT" : query, "hybrid_ratio" : 0.... | https://python.langchain.com/docs/integrations/vectorstores/tair |
41312aadf7ab-0 | Tencent Cloud VectorDB
Tencent Cloud VectorDB is a fully managed, self-developed, enterprise-level distributed database service designed for storing, retrieving, and analyzing multi-dimensional vector data. The database supports multiple index types and similarity calculation methods. A single index can support a vecto... | https://python.langchain.com/docs/integrations/vectorstores/tencentvectordb |
0064c17e5be0-0 | Tigris
Tigris is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications. Tigris eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead.... | https://python.langchain.com/docs/integrations/vectorstores/tigris |
0064c17e5be0-1 | embeddings = OpenAIEmbeddings()
vector_store = Tigris.from_documents(docs, embeddings, index_name="my_embeddings")
Similarity Search
query = "What did the president say about Ketanji Brown Jackson"
found_docs = vector_store.similarity_search(query)
print(found_docs)
Similarity Search with score (vector distance)
quer... | https://python.langchain.com/docs/integrations/vectorstores/tigris |
5f4e50dc9d42-0 | Typesense
Typesense is an open source, in-memory search engine, that you can either self-host or run on Typesense Cloud.
Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and s... | https://python.langchain.com/docs/integrations/vectorstores/typesense |
107a694eb3d6-0 | USearch
USearch is a Smaller & Faster Single-File Vector Search Engine
USearch's base functionality is identical to FAISS, and the interface should look familiar if you have ever investigated Approximate Nearest Neigbors search. FAISS is a widely recognized standard for high-performance vector search engines. USearch a... | https://python.langchain.com/docs/integrations/vectorstores/usearch |
107a694eb3d6-1 | 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.
Similarity Search with score
The similarity_search_with_score method allows you to return not only the documents but also the d... | https://python.langchain.com/docs/integrations/vectorstores/usearch |
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