• Client Library Documentation
• Product Documentation

In order to use this library, you first need to go through the following steps:

1. Select or create a Cloud Platform project.
2. Enable billing for your project.
3. Enable the Google Cloud Bigtable API.
4. Setup Authentication.

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Python >= 3.9

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install langchain-google-bigtable

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install langchain-google-bigtable

Use BigtableVectorStore to store documents and their vector embeddings, allowing you to search for the most similar or relevant documents from your database.

• Full VectorStore Implementation: Supports all methods from the LangChain VectorStore abstract class.
• Async/Sync Support: All methods are available in both asynchronous and synchronous versions.
• Metadata Filtering: Supports filtering on metadata fields, including logical AND/OR combinations and filtering on document IDs with a specific prefix.
• Multiple Distance Strategies: Supports both Cosine and Euclidean distance for similarity search.
• Customizable Storage: Full control over how content, embeddings, and metadata are stored in Bigtable columns.

from langchain_google_bigtable import BigtableVectorStore, BigtableEngine

# Your embedding service and other configurations
# embedding_service = ...

engine = await BigtableEngine.async_initialize(project_id="your-project-id")
vector_store = await BigtableVectorStore.create(
    engine=engine,
    instance_id="your-instance-id",
    table_id="your-table-id",
    embedding_service=embedding_service,
    collection="your_collection_name",
)

See the full Vector Store tutorial.

Use BigtableByteStore for a key-value store in LangChain

• ByteStore Interface: Follows LangChain's ByteStore for string keys and byte values.
• Sync/Async: Supports both synchronous and asynchronous operations.
• BigtableEngine: Manages execution context.

from langchain_google_bigtable import BigtableByteStore, BigtableEngine

engine = await BigtableEngine.async_initialize(project_id="your-project-id")
store = await BigtableByteStore.create(
    engine=engine,
    instance_id="your-instance-id",
    table_id="your-table-id",
)
await store.amset([("key", b"value")])
retrieved = await store.amget(["key"])

See the full Key-value Store tutorial.

Use a document loader to load data as LangChain Documents.

from langchain_google_bigtable import BigtableLoader


loader = BigtableLoader(
    instance_id="my-instance",
    table_id="my-table-name"
)
docs = loader.lazy_load()

See the full Document Loader tutorial.

Use ChatMessageHistory to store messages and provide conversation history to LLMs.

from langchain_google_bigtable import BigtableChatMessageHistory


history = BigtableChatMessageHistory(
    instance_id="my-instance",
    table_id="my-message-store",
    session_id="my-session_id"
)

See the full Chat Message History tutorial.

Contributions to this library are always welcome and highly encouraged.

See CONTRIBUTING for more information how to get started.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See Code of Conduct for more information.

Apache 2.0 - See LICENSE for more information.

This is not an officially supported Google product.