Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/singlestore-labs/langchain-singlestore/ below:

singlestore-labs/langchain-singlestore: SingleStore integration for Langchain

This package provides the LangChain integration with SingleStore, enabling efficient storage, retrieval, and management of documents, embeddings, and chat message history using SingleStore's high-performance distributed SQL database.

To install the package, run:

pip install -U langchain-singlestore

This package includes the following components:

The SingleStoreChatMessageHistory class allows you to store and retrieve chat message history in SingleStore.

from langchain_singlestore import SingleStoreChatMessageHistory

chat_history = SingleStoreChatMessageHistory(
    host="127.0.0.1:3306/db",
    table_name="chat_history"
)

# Add a message to the chat history
chat_history.add_message({"role": "user", "content": "Hello, how are you?"})

# Retrieve chat history
messages = chat_history.get_messages()
print(messages)

The SingleStoreSemanticCache class provides a semantic caching mechanism using SingleStore as the backend. It stores embeddings and allows for efficient retrieval based on similarity.

from langchain_singlestore import SingleStoreSemanticCache
from langchain_core.globals import set_llm_cache

set_llm_cache(
    SingleStoreSemanticCache(
        embedding=YourEmbeddings(),
        host="root:pass@localhost:3306/db",
    )
)

The SingleStoreVectorStore class enables storing document embeddings and performing fast vector and full-text searches. It supports advanced search strategies like VECTOR_ONLY, TEXT_ONLY, and hybrid approaches.

from langchain_singlestore.vectorstores import SingleStoreVectorStore
from langchain_core.documents import Document

vector_store = SingleStoreVectorStore(
    embeddings=OpenAIEmbeddings(),
    host="127.0.0.1:3306/db",
    table_name="vector_store"
)

# Add documents to the vector store
documents = [
    Document(page_content="The Eiffel Tower is in Paris.", metadata={"category": "landmark"}),
    Document(page_content="The Louvre is a famous museum in Paris.", metadata={"category": "museum"})
]
vector_store.add_documents(documents)

# Perform a similarity search
results = vector_store.similarity_search(query="famous landmarks in Paris", k=1)
print(results[0].page_content)

The SingleStoreLoader class allows you to load documents directly from a SingleStore database table. This is useful for applications that need to process large datasets stored in SingleStore.

from langchain_singlestore.document_loaders import SingleStoreLoader

loader = SingleStoreLoader(
    host="127.0.0.1:3306/db",
    table_name="documents",
    content_field="content",
    metadata_field="metadata"
)

# Load documents
documents = loader.load()
print(documents[0].page_content)

For detailed documentation, visit the LangChain documentation.

To set up the development environment and run tests, follow these steps:

Install all dependencies, including those for linting, typing, and testing, using poetry:

poetry install --with lint,typing,test,test_integration

Before committing any changes, ensure that the code passes all linting checks. Run the following command:

This will check the code for style and formatting issues.

Run all integration tests to verify that the code works as expected:

The test_add_image2 integration test for SingleStoreVectorStore downloads data to your local machine. The first run may take a significant amount of time due to the data download process. Subsequent runs will be faster as the data will already be available locally.

We welcome contributions to the langchain-singlestore project! Please refer to the CONTRIBUTE.md file for detailed guidelines on how to contribute, including instructions for running tests, linting, and publishing new package versions.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4