ClickHouse
ClickHouse is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like
L2Distance) as well asapproximate nearest neighbor search indexes enable ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL.
This notebook shows how to use functionality related to theClickHouse vector store.
Setup
First set up a local clickhouse server with docker:
! docker run-d-p8123:8123-p9000:9000--name langchain-clickhouse-server--ulimit nofile=262144:262144 clickhouse/clickhouse-server:24.7.6.8
You'll need to installlangchain-community andclickhouse-connect to use this integration
pip install-qU langchain-community clickhouse-connect
Credentials
There are no credentials for this notebook, just make sure you have installed the packages as shown above.
If you want to get best in-class automated tracing of your model calls you can also set yourLangSmith API key by uncommenting below:
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
Instantiation
pip install -qU langchain-openai
import getpass
import os
ifnot os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"]= getpass.getpass("Enter API key for OpenAI: ")
from langchain_openaiimport OpenAIEmbeddings
embeddings= OpenAIEmbeddings(model="text-embedding-3-large")
from langchain_community.vectorstoresimport Clickhouse, ClickhouseSettings
settings= ClickhouseSettings(table="clickhouse_example")
vector_store= Clickhouse(embeddings, config=settings)
Manage vector store
Once you have created your vector store, we can interact with it by adding and deleting different items.
Add items to vector store
We can add items to our vector store by using theadd_documents function.
from uuidimport uuid4
from langchain_core.documentsimport Document
document_1= Document(
page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
metadata={"source":"tweet"},
)
document_2= Document(
page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
metadata={"source":"news"},
)
document_3= Document(
page_content="Building an exciting new project with LangChain - come check it out!",
metadata={"source":"tweet"},
)
document_4= Document(
page_content="Robbers broke into the city bank and stole $1 million in cash.",
metadata={"source":"news"},
)
document_5= Document(
page_content="Wow! That was an amazing movie. I can't wait to see it again.",
metadata={"source":"tweet"},
)
document_6= Document(
page_content="Is the new iPhone worth the price? Read this review to find out.",
metadata={"source":"website"},
)
document_7= Document(
page_content="The top 10 soccer players in the world right now.",
metadata={"source":"website"},
)
document_8= Document(
page_content="LangGraph is the best framework for building stateful, agentic applications!",
metadata={"source":"tweet"},
)
document_9= Document(
page_content="The stock market is down 500 points today due to fears of a recession.",
metadata={"source":"news"},
)
document_10= Document(
page_content="I have a bad feeling I am going to get deleted :(",
metadata={"source":"tweet"},
)
documents=[
document_1,
document_2,
document_3,
document_4,
document_5,
document_6,
document_7,
document_8,
document_9,
document_10,
]
uuids=[str(uuid4())for _inrange(len(documents))]
vector_store.add_documents(documents=documents, ids=uuids)
Delete items from vector store
We can delete items from our vector store by ID by using thedelete function.
vector_store.delete(ids=uuids[-1])
Query vector store
Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.
Query directly
Similarity search
Performing a simple similarity search can be done as follows:
results= vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy", k=2
)
for resin results:
print(f"*{res.page_content} [{res.metadata}]")
Similarity search with score
You can also search with score:
results= vector_store.similarity_search_with_score("Will it be hot tomorrow?", k=1)
for res, scorein results:
print(f"* [SIM={score:3f}]{res.page_content} [{res.metadata}]")
Filtering
You can have direct access to ClickHouse SQL where statement. You can writeWHERE clause following standard SQL.
NOTE: Please be aware of SQL injection, this interface must not be directly called by end-user.
If you custimized yourcolumn_map under your setting, you search with filter like this:
meta= vector_store.metadata_column
results= vector_store.similarity_search_with_relevance_scores(
"What did I eat for breakfast?",
k=4,
where_str=f"{meta}.source = 'tweet'",
)
for resin results:
print(f"*{res.page_content} [{res.metadata}]")
Other search methods
There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available forClickhouse vector store check out theAPI reference.
Query by turning into retriever
You can also transform the vector store into a retriever for easier usage in your chains.
Here is how to transform your vector store into a retriever and then invoke the retreiever with a simple query and filter.
retriever= vector_store.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k":1,"score_threshold":0.5},
)
retriever.invoke("Stealing from the bank is a crime",filter={"source":"news"})
Usage for retrieval-augmented generation
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
For more, check out a complete RAG template using Astra DBhere.
API reference
For detailed documentation of allClickhouse features and configurations head to the API reference:https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html
Related
- Vector storeconceptual guide
- Vector storehow-to guides