feat: add Bigtable documentation

Author: ad548

reference/packages.yml

@@ -784,3 +784,7 @@ packages:
   provider_page: oracleai
   downloads: 700
   downloads_updated_at: "2025-11-25T00:06:22.543193+00:00"
+- name: langchain-google-bigtable
+  name_title: Bigtable
+  repo: googleapis/langchain-google-bigtable-python
+  provider_page: google

src/oss/python/integrations/document_loaders/google_bigtable

@@ -0,0 +1,128 @@
+# Bigtable
+
+Bigtable is a scalable, fully managed key-value and wide-column store ideal for fast access to structured, semi-structured, or unstructured data. This page provides an overview of Bigtable's LangChain integrations.
+
+**Client Library Documentation:** [cloud.google.com/python/docs/reference/langchain-google-bigtable/latest](https://cloud.google.com/python/docs/reference/langchain-google-bigtable/latest)
+
+**Product Documentation:** [cloud.google.com/bigtable](https://cloud.google.com/bigtable)
+
+## Quick Start
+
+To use this library, you first need to:
+
+1.  Select or create a Cloud Platform project.
+2.  Enable billing for your project.
+3.  Enable the Google Cloud Bigtable API.
+4.  Set up Authentication.
+
+## Installation
+
+The main package for this integration is `langchain-google-bigtable`.
+
+```bash
+pip install -U langchain-google-bigtable
+```
+
+## Integrations
+
+The `langchain-google-bigtable` package provides the following integrations:
+
+### Document Loader
+
+Use the `BigtableLoader` to load data from a Bigtable table and represent it as LangChain `Document` objects.
+
+```python
+from langchain_google_bigtable import BigtableLoader
+
+loader = BigtableLoader(
+    project_id="your-project-id",
+    instance_id="your-instance-id",
+    table_id="your-table-name"
+)
+docs = loader.load()
+```
+
+Learn more in the [Document Loader how-to guide](https://cloud.google.com/python/docs/reference/langchain-google-bigtable/latest/document-loader).
+
+### Chat Message History
+
+Use `BigtableChatMessageHistory` to store conversation histories, enabling stateful chains and agents.
+
+```python
+from langchain_google_bigtable import BigtableChatMessageHistory
+
+history = BigtableChatMessageHistory(
+    project_id="your-project-id",
+    instance_id="your-instance-id",
+    table_id="your-message-store",
+    session_id="user-session-123"
+)
+
+history.add_user_message("Hello!")
+history.add_ai_message("Hi there!")
+```
+
+Learn more in the [Chat Message History how-to guide](https://cloud.google.com/python/docs/reference/langchain-google-bigtable/latest/chat-message-history).
+
+### Vector Store
+
+With `BigtableVectorStore`, you can store documents and their vector embeddings to find the most similar or relevant information in 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:** Powerful filtering on metadata fields, including logical AND/OR combinations.
+* **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.
+
+```python
+from langchain_google_bigtable import BigtableVectorStore
+
+# 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",
+)
+await vector_store.aadd_documents([your_documents])
+results = await vector_store.asimilarity_search("your query")
+```
+
+Learn more in the [Vector Store how-to guide](https://colab.research.google.com/github/googleapis/langchain-google-bigtable-python/blob/main/docs/vector_store.ipynb).
+
+### Key-value Store
+
+Use `BigtableByteStore` as a persistent, scalable key-value store for caching, session management, or other storage needs. It supports both synchronous and asynchronous operations.
+
+```python
+from langchain_google_bigtable import BigtableByteStore
+
+# Initialize the store
+store = await BigtableByteStore.create(
+    project_id="your-project-id",
+    instance_id="your-instance-id",
+    table_id="your-table-id",
+)
+
+# Set and get values
+await store.amset([("key1", b"value1")])
+retrieved = await store.amget(["key1"])
+```
+
+Learn more in the [Key-value Store how-to guide](https://cloud.google.com/python/docs/reference/langchain-google-bigtable/latest/key-value-store).
+
+## Contributions
+
+Contributions to this library are welcome. Please see the CONTRIBUTING guide in the [package repo](https://github.com/googleapis/langchain-google-bigtable-python/) for more details
+
+## License
+
+This project is licensed under the Apache 2.0 License - see the LICENSE file in the [package repo](https://github.com/googleapis/langchain-google-bigtable-python/blob/main/LICENSE) for details.
+
+## Disclaimer
+
+This is not an officially supported Google product.

src/oss/python/integrations/providers/google.mdx

@@ -225,6 +225,9 @@ Load documents from various Google Cloud sources.
     <Card title="Spanner" href="/oss/integrations/document_loaders/google_spanner" cta="Get started" arrow>
         Google Cloud Spanner is a fully managed, globally distributed relational database service.
     </Card>
+    <Card title="Bigtable" href="/oss/integrations/document_loaders/google_bigtable" cta="Get started" arrow>
+        Google Cloud Bigtable is a scalable, fully managed key-value and wide-column store ideal for fast access to structured, semi-structured, or unstructured data. 
+    </Card>
     <Card title="Speech-to-Text" href="/oss/integrations/document_loaders/google_speech_to_text" cta="Get started" arrow>
         Google Cloud Speech-to-Text transcribes audio files.
     </Card>
@@ -268,6 +271,9 @@ Store and search vectors using Google Cloud databases and Vertex AI Vector Searc
     <Card title="Spanner" href="/oss/integrations/vectorstores/google_spanner" cta="Get started" arrow>
         Vector store using Cloud Spanner
     </Card>
+    <Card title="Bigtable" href="/oss/integrations/vectorstores/google_bigtable" cta="Get started" arrow>
+        Vector store using Cloud Bigtable
+    </Card>
     <Card title="Firestore (Native Mode)" href="/oss/integrations/vectorstores/google_firestore" cta="Get started" arrow>
         Vector store using Firestore
     </Card>