mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-27 08:58:48 +00:00
docs: google provider page fixes (#18290)
Several URL-s were broken (in the yesterday PR). Like [Integrations/platforms/google/Document Loaders](https://python.langchain.com/docs/integrations/platforms/google#document-loaders) page, Example link to "Document Loaders / Cloud SQL for PostgreSQL" and most of the new example links in the Document Loaders, Vectorstores, Memory sections. - fixed URL-s (manually verified all example links) - sorted sections in page to follow the "integrations/components" menu item order. - fixed several page titles to fix Navbar item order --------- Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Leonid Ganeline
GitHub
parent
0486404a74
commit
1d865a7e86
@ -2,6 +2,56 @@
|
||||
|
||||
All functionality related to [Google Cloud Platform](https://cloud.google.com/) and other `Google` products.
|
||||
|
||||
## LLMs
|
||||
|
||||
### Google Generative AI
|
||||
|
||||
Access GoogleAI `Gemini` models such as `gemini-pro` and `gemini-pro-vision` through the `GoogleGenerativeAI` class.
|
||||
|
||||
Install python package.
|
||||
|
||||
```bash
|
||||
pip install langchain-google-genai
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/llms/google_ai).
|
||||
|
||||
```python
|
||||
from langchain_google_genai import GoogleGenerativeAI
|
||||
```
|
||||
|
||||
### Vertex AI
|
||||
|
||||
Access to `Gemini` and `PaLM` LLMs (like `text-bison` and `code-bison`) via `Vertex AI` on Google Cloud.
|
||||
|
||||
We need to install `langchain-google-vertexai` python package.
|
||||
|
||||
```bash
|
||||
pip install langchain-google-vertexai
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
|
||||
|
||||
```python
|
||||
from langchain_google_vertexai import VertexAI
|
||||
```
|
||||
|
||||
### Model Garden
|
||||
|
||||
Access PaLM and hundreds of OSS models via `Vertex AI Model Garden` on Google Cloud.
|
||||
|
||||
We need to install `langchain-google-vertexai` python package.
|
||||
|
||||
```bash
|
||||
pip install langchain-google-vertexai
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/llms/google_vertex_ai_palm#vertex-model-garden).
|
||||
|
||||
```python
|
||||
from langchain_google_vertexai import VertexAIModelGarden
|
||||
```
|
||||
|
||||
## Chat models
|
||||
|
||||
### Google Generative AI
|
||||
@ -69,61 +119,273 @@ See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
|
||||
from langchain_google_vertexai import ChatVertexAI
|
||||
```
|
||||
|
||||
## LLMs
|
||||
## Document Loaders
|
||||
|
||||
### Google Generative AI
|
||||
### AlloyDB for PostgreSQL
|
||||
|
||||
Access GoogleAI `Gemini` models such as `gemini-pro` and `gemini-pro-vision` through the `GoogleGenerativeAI` class.
|
||||
> [Google Cloud AlloyDB](https://cloud.google.com/alloydb) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability on Google Cloud. AlloyDB is 100% compatible with PostgreSQL.
|
||||
|
||||
Install python package.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-genai
|
||||
pip install langchain-google-alloydb-pg
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/llms/google_ai).
|
||||
See [usage example](/docs/integrations/document_loaders/google_alloydb).
|
||||
|
||||
```python
|
||||
from langchain_google_genai import GoogleGenerativeAI
|
||||
from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBLoader
|
||||
```
|
||||
|
||||
### Vertex AI
|
||||
### BigQuery
|
||||
|
||||
Access to `Gemini` and `PaLM` LLMs (like `text-bison` and `code-bison`) via `Vertex AI` on Google Cloud.
|
||||
> [Google Cloud BigQuery](https://cloud.google.com/bigquery) is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data in Google Cloud.
|
||||
|
||||
We need to install `langchain-google-vertexai` python package.
|
||||
We need to install `google-cloud-bigquery` python package.
|
||||
|
||||
```bash
|
||||
pip install langchain-google-vertexai
|
||||
pip install google-cloud-bigquery
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
|
||||
See a [usage example](/docs/integrations/document_loaders/google_bigquery).
|
||||
|
||||
```python
|
||||
from langchain_google_vertexai import VertexAI
|
||||
from langchain_community.document_loaders import BigQueryLoader
|
||||
```
|
||||
|
||||
### Model Garden
|
||||
### Bigtable
|
||||
|
||||
Access PaLM and hundreds of OSS models via `Vertex AI Model Garden` on Google Cloud.
|
||||
|
||||
We need to install `langchain-google-vertexai` python package.
|
||||
> [Google Cloud Bigtable](https://cloud.google.com/bigtable/docs) is Google's fully managed NoSQL Big Data database service in Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-vertexai
|
||||
pip install langchain-google-bigtable
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/llms/google_vertex_ai_palm#vertex-model-garden).
|
||||
See [Googel Cloud usage example](/docs/integrations/document_loaders/google_bigtable).
|
||||
|
||||
```python
|
||||
from langchain_google_vertexai import VertexAIModelGarden
|
||||
from langchain_google_bigtable import BigtableLoader
|
||||
```
|
||||
|
||||
### SQL for MySQL
|
||||
|
||||
> [Google Cloud SQL for MySQL](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your MySQL relational databases on Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-cloud-sql-mysql
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/document_loaders/google_cloud_sql_mysql).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_mysql import MySQLEngine, MySQLDocumentLoader
|
||||
```
|
||||
|
||||
### SQL for SQL Server
|
||||
|
||||
> [Google Cloud SQL for SQL Server](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your SQL Server databases on Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-cloud-sql-mssql
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/document_loaders/google_cloud_sql_mssql).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_mssql import MSSQLEngine, MSSQLLoader
|
||||
```
|
||||
|
||||
### SQL for PostgreSQL
|
||||
|
||||
> [Google Cloud SQL for PostgreSQL](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your PostgreSQL relational databases on Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-cloud-sql-pg
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/document_loaders/google_cloud_sql_pg).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_pg import PostgreSQLEngine, PostgreSQLLoader
|
||||
```
|
||||
|
||||
### Cloud Storage
|
||||
|
||||
>[Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data in Google Cloud.
|
||||
|
||||
We need to install `google-cloud-storage` python package.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-storage
|
||||
```
|
||||
|
||||
There are two loaders for the `Google Cloud Storage`: the `Directory` and the `File` loaders.
|
||||
|
||||
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_directory).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import GCSDirectoryLoader
|
||||
```
|
||||
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_file).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import GCSFileLoader
|
||||
```
|
||||
|
||||
### Google Drive
|
||||
|
||||
>[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google.
|
||||
|
||||
Currently, only `Google Docs` are supported.
|
||||
|
||||
We need to install several python packages.
|
||||
|
||||
```bash
|
||||
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
|
||||
```
|
||||
|
||||
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_drive).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import GoogleDriveLoader
|
||||
```
|
||||
|
||||
### Firestore (Native Mode)
|
||||
|
||||
> [Google Cloud Firestore](https://cloud.google.com/firestore/docs/) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-firestore
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/document_loaders/google_firestore).
|
||||
|
||||
```python
|
||||
from langchain_google_firestore import FirestoreLoader
|
||||
```
|
||||
|
||||
### Firestore (Datastore Mode)
|
||||
|
||||
> [Google Cloud Firestore in Datastore mode](https://cloud.google.com/datastore/docs) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
|
||||
> Firestore is the newest version of Datastore and introduces several improvements over Datastore.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-datastore
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/document_loaders/google_datastore).
|
||||
|
||||
```python
|
||||
from langchain_google_datastore import DatastoreLoader
|
||||
```
|
||||
|
||||
### Memorystore for Redis
|
||||
|
||||
> [Google Cloud Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis) is a fully managed Redis service for Google Cloud. Applications running on Google Cloud can achieve extreme performance by leveraging the highly scalable, available, secure Redis service without the burden of managing complex Redis deployments.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-memorystore-redis
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/document_loaders/google_memorystore_redis).
|
||||
|
||||
```python
|
||||
from langchain_google_memorystore_redis import MemorystoreLoader
|
||||
```
|
||||
|
||||
### Spanner
|
||||
|
||||
> [Google Cloud Spanner](https://cloud.google.com/spanner/docs) is a fully managed, mission-critical, relational database service on Google Cloud that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL dialects: GoogleSQL (ANSI 2011 with extensions) and PostgreSQL.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-spanner
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/document_loaders/google_spanner).
|
||||
|
||||
```python
|
||||
from langchain_google_spanner import SpannerLoader
|
||||
```
|
||||
|
||||
### Speech-to-Text
|
||||
|
||||
> [Google Cloud Speech-to-Text](https://cloud.google.com/speech-to-text) is an audio transcription API powered by Google's speech recognition models in Google Cloud.
|
||||
|
||||
This document loader transcribes audio files and outputs the text results as Documents.
|
||||
|
||||
First, we need to install the python package.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-speech
|
||||
```
|
||||
|
||||
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_speech_to_text).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import GoogleSpeechToTextLoader
|
||||
```
|
||||
|
||||
## Document Transformers
|
||||
|
||||
### Document AI
|
||||
|
||||
>[Google Cloud Document AI](https://cloud.google.com/document-ai/docs/overview) is a Google Cloud
|
||||
> service that transforms unstructured data from documents into structured data, making it easier
|
||||
> to understand, analyze, and consume.
|
||||
|
||||
We need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)
|
||||
The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`)
|
||||
and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`.
|
||||
We can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details`
|
||||
tab in the Google Cloud Console.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-documentai
|
||||
pip install google-cloud-documentai-toolbox
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/document_transformers/google_docai).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders.blob_loaders import Blob
|
||||
from langchain_community.document_loaders.parsers import DocAIParser
|
||||
```
|
||||
|
||||
### Google Translate
|
||||
|
||||
> [Google Translate](https://translate.google.com/) is a multilingual neural machine
|
||||
> translation service developed by Google to translate text, documents and websites
|
||||
> from one language into another.
|
||||
|
||||
The `GoogleTranslateTransformer` allows you to translate text and HTML with the [Google Cloud Translation API](https://cloud.google.com/translate).
|
||||
|
||||
To use it, you should have the `google-cloud-translate` python package installed, and a Google Cloud project with the [Translation API enabled](https://cloud.google.com/translate/docs/setup). This transformer uses the [Advanced edition (v3)](https://cloud.google.com/translate/docs/intro-to-v3).
|
||||
|
||||
First, we need to install the python package.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-translate
|
||||
```
|
||||
|
||||
See a [usage example and authorization instructions](/docs/integrations/document_transformers/google_translate).
|
||||
|
||||
```python
|
||||
from langchain_community.document_transformers import GoogleTranslateTransformer
|
||||
```
|
||||
|
||||
## Vector Stores
|
||||
|
||||
### AlloyDB for PostgreSQL
|
||||
|
||||
> [AlloyDB](https://cloud.google.com/alloydb) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability on Google Cloud. AlloyDB is 100% compatible with PostgreSQL.
|
||||
> [Google Cloud AlloyDB](https://cloud.google.com/alloydb) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability on Google Cloud. AlloyDB is 100% compatible with PostgreSQL.
|
||||
|
||||
Install the python package:
|
||||
|
||||
@ -137,54 +399,76 @@ See [usage example](/docs/integrations/vectorstores/google_alloydb).
|
||||
from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBVectorStore
|
||||
```
|
||||
|
||||
### Cloud SQL for PostgreSQL
|
||||
### BigQuery Vector Search
|
||||
|
||||
> [Cloud SQL for PostgreSQL](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your PostgreSQL relational databases on Google Cloud.
|
||||
Install the python package:
|
||||
> [Google Cloud BigQuery](https://cloud.google.com/bigquery),
|
||||
> BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud.
|
||||
>
|
||||
> [Google Cloud BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro)
|
||||
> BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results.
|
||||
|
||||
> It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance.
|
||||
|
||||
We need to install several python packages.
|
||||
|
||||
```bash
|
||||
pip install langchain-google-cloud-sql-pg
|
||||
pip install google-cloud-bigquery
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/vectorstores/google_cloud_sql_pg).
|
||||
See a [usage example](/docs/integrations/vectorstores/google_bigquery_vector_search).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_pg import PostgreSQLEngine, PostgresVectorStore
|
||||
```
|
||||
|
||||
### Spanner
|
||||
|
||||
> [Spanner](https://cloud.google.com/spanner/docs) is a fully managed, mission-critical, relational database service on Google Cloud that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL dialects: GoogleSQL (ANSI 2011 with extensions) and PostgreSQL.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-spanner
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/vectorstores/google_spanner).
|
||||
|
||||
```python
|
||||
from langchain_google_spanner import SpannerVectorStore
|
||||
from langchain.vectorstores import BigQueryVectorSearch
|
||||
```
|
||||
|
||||
### Memorystore for Redis
|
||||
|
||||
> [Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis) is a fully managed Redis service for Google Cloud. Applications running on Google Cloud can achieve extreme performance by leveraging the highly scalable, available, secure Redis service without the burden of managing complex Redis deployments.
|
||||
> [Google Cloud Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis) is a fully managed Redis service for Google Cloud. Applications running on Google Cloud can achieve extreme performance by leveraging the highly scalable, available, secure Redis service without the burden of managing complex Redis deployments.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-memorystore-redis
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/vectorstores/google_memorystore_redis).
|
||||
See [usage example](/docs/integrations/vectorstores/google_memorystore_redis).
|
||||
|
||||
```python
|
||||
from langchain_google_memorystore_redis import RedisVectorStore
|
||||
```
|
||||
|
||||
### Spanner
|
||||
|
||||
> [Google Cloud Spanner](https://cloud.google.com/spanner/docs) is a fully managed, mission-critical, relational database service on Google Cloud that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL dialects: GoogleSQL (ANSI 2011 with extensions) and PostgreSQL.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-spanner
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/vectorstores/google_spanner).
|
||||
|
||||
```python
|
||||
from langchain_google_spanner import SpannerVectorStore
|
||||
```
|
||||
|
||||
### SQL for PostgreSQL
|
||||
|
||||
> [Google Cloud SQL for PostgreSQL](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your PostgreSQL relational databases on Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-cloud-sql-pg
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/vectorstores/google_sql_pg).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_pg import PostgreSQLEngine, PostgresVectorStore
|
||||
```
|
||||
|
||||
### Vertex AI Vector Search
|
||||
|
||||
> [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) from Google Cloud,
|
||||
> [Google Cloud Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) from Google Cloud,
|
||||
> formerly known as `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 approximate nearest neighbor (ANN) service.
|
||||
@ -201,29 +485,7 @@ See a [usage example](/docs/integrations/vectorstores/google_vertex_ai_vector_se
|
||||
from langchain_community.vectorstores import MatchingEngine
|
||||
```
|
||||
|
||||
### BigQuery
|
||||
|
||||
> [BigQuery](https://cloud.google.com/bigquery),
|
||||
> BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud.
|
||||
>
|
||||
> [BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro)
|
||||
> BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results.
|
||||
|
||||
> It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance.
|
||||
|
||||
We need to install several python packages.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-bigquery
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/vectorstores/bigquery_vector_search).
|
||||
|
||||
```python
|
||||
from langchain.vectorstores import BigQueryVectorSearch
|
||||
```
|
||||
|
||||
### Google ScaNN
|
||||
### ScaNN
|
||||
|
||||
>[Google ScaNN](https://github.com/google-research/google-research/tree/master/scann)
|
||||
> (Scalable Nearest Neighbors) is a python package.
|
||||
@ -298,273 +560,11 @@ documents = docai_wh_retriever.get_relevant_documents(
|
||||
)
|
||||
```
|
||||
|
||||
## Document Loaders
|
||||
|
||||
### AlloyDB for PostgreSQL
|
||||
|
||||
> [AlloyDB](https://cloud.google.com/alloydb) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability on Google Cloud. AlloyDB is 100% compatible with PostgreSQL.
|
||||
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-alloydb-pg
|
||||
```
|
||||
|
||||
See [usage example](/docs/integrations/document_loaders/google_alloydb).
|
||||
|
||||
```python
|
||||
from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBLoader
|
||||
```
|
||||
|
||||
### Cloud SQL for PostgreSQL
|
||||
|
||||
> [Cloud SQL for PostgreSQL](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your PostgreSQL relational databases on Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-cloud-sql-pg
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loaders/google_cloud_sql_pg).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_pg import PostgreSQLEngine, PostgreSQLLoader
|
||||
```
|
||||
|
||||
### Cloud SQL for MySQL
|
||||
|
||||
> [Cloud SQL for MySQL](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your MySQL relational databases on Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-cloud-sql-mysql
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loader/cloud_sql_mysql).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_mysql import MySQLEngine, MySQLDocumentLoader
|
||||
```
|
||||
|
||||
### Cloud SQL for SQL Server
|
||||
|
||||
> [Cloud SQL for SQL Server](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your SQL Server databases on Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-cloud-sql-mssql
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loaders/google_cloud_sql_mssql).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_mssql import MSSQLEngine, MSSQLLoader
|
||||
```
|
||||
|
||||
### Bigtable
|
||||
|
||||
> [Bigtable](https://cloud.google.com/bigtable/docs) is Google's fully managed NoSQL Big Data database service in Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-bigtable
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loaders/google_bigtable).
|
||||
|
||||
```python
|
||||
from langchain_google_bigtable import BigtableLoader
|
||||
```
|
||||
|
||||
### Spanner
|
||||
|
||||
> [Spanner](https://cloud.google.com/spanner/docs) is a fully managed, mission-critical, relational database service on Google Cloud that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL dialects: GoogleSQL (ANSI 2011 with extensions) and PostgreSQL.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-spanner
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loaders/google_spanner).
|
||||
|
||||
```python
|
||||
from langchain_google_spanner import SpannerLoader
|
||||
```
|
||||
|
||||
### Memorystore for Redis
|
||||
|
||||
> [Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis) is a fully managed Redis service for Google Cloud. Applications running on Google Cloud can achieve extreme performance by leveraging the highly scalable, available, secure Redis service without the burden of managing complex Redis deployments.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-memorystore-redis
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loaders/google_memorystore_redis).
|
||||
|
||||
```python
|
||||
from langchain_google_memorystore_redis import MemorystoreLoader
|
||||
```
|
||||
|
||||
### Firestore (Native Mode)
|
||||
|
||||
> [Firestore](https://cloud.google.com/firestore/docs/) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-firestore
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loader/google_firestore).
|
||||
|
||||
```python
|
||||
from langchain_google_firestore import FirestoreLoader
|
||||
```
|
||||
|
||||
### Firestore in Datastore Mode
|
||||
|
||||
> [Firestore in Datastore mode](https://cloud.google.com/datastore/docs) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
|
||||
> Firestore is the newest version of Datastore and introduces several improvements over Datastore.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-datastore
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loaders/google_datastore).
|
||||
|
||||
```python
|
||||
from langchain_google_datastore import DatastoreLoader
|
||||
```
|
||||
|
||||
### BigQuery
|
||||
|
||||
> [BigQuery](https://cloud.google.com/bigquery) is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data in Google Cloud.
|
||||
|
||||
We need to install `google-cloud-bigquery` python package.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-bigquery
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/document_loaders/google_bigquery).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import BigQueryLoader
|
||||
```
|
||||
|
||||
### Cloud Storage
|
||||
|
||||
>[Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data in Google Cloud.
|
||||
|
||||
We need to install `google-cloud-storage` python package.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-storage
|
||||
```
|
||||
|
||||
There are two loaders for the `Google Cloud Storage`: the `Directory` and the `File` loaders.
|
||||
|
||||
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_directory).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import GCSDirectoryLoader
|
||||
```
|
||||
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_file).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import GCSFileLoader
|
||||
```
|
||||
|
||||
### Google Drive
|
||||
|
||||
>[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google.
|
||||
|
||||
Currently, only `Google Docs` are supported.
|
||||
|
||||
We need to install several python packages.
|
||||
|
||||
```bash
|
||||
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
|
||||
```
|
||||
|
||||
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_drive).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import GoogleDriveLoader
|
||||
```
|
||||
|
||||
### Speech-to-Text
|
||||
|
||||
> [Speech-to-Text](https://cloud.google.com/speech-to-text) is an audio transcription API powered by Google's speech recognition models in Google Cloud.
|
||||
|
||||
This document loader transcribes audio files and outputs the text results as Documents.
|
||||
|
||||
First, we need to install the python package.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-speech
|
||||
```
|
||||
|
||||
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_speech_to_text).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders import GoogleSpeechToTextLoader
|
||||
```
|
||||
|
||||
## Document Transformers
|
||||
|
||||
### Document AI
|
||||
|
||||
>[Document AI](https://cloud.google.com/document-ai/docs/overview) is a Google Cloud
|
||||
> service that transforms unstructured data from documents into structured data, making it easier
|
||||
> to understand, analyze, and consume.
|
||||
|
||||
We need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)
|
||||
The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`)
|
||||
and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`.
|
||||
We can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details`
|
||||
tab in the Google Cloud Console.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-documentai
|
||||
pip install google-cloud-documentai-toolbox
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/document_transformers/docai).
|
||||
|
||||
```python
|
||||
from langchain_community.document_loaders.blob_loaders import Blob
|
||||
from langchain_community.document_loaders.parsers import DocAIParser
|
||||
```
|
||||
|
||||
### Google Translate
|
||||
|
||||
> [Google Translate](https://translate.google.com/) is a multilingual neural machine
|
||||
> translation service developed by Google to translate text, documents and websites
|
||||
> from one language into another.
|
||||
|
||||
The `GoogleTranslateTransformer` allows you to translate text and HTML with the [Google Cloud Translation API](https://cloud.google.com/translate).
|
||||
|
||||
To use it, you should have the `google-cloud-translate` python package installed, and a Google Cloud project with the [Translation API enabled](https://cloud.google.com/translate/docs/setup). This transformer uses the [Advanced edition (v3)](https://cloud.google.com/translate/docs/intro-to-v3).
|
||||
|
||||
First, we need to install the python package.
|
||||
|
||||
```bash
|
||||
pip install google-cloud-translate
|
||||
```
|
||||
|
||||
See a [usage example and authorization instructions](/docs/integrations/document_transformers/google_translate).
|
||||
|
||||
```python
|
||||
from langchain_community.document_transformers import GoogleTranslateTransformer
|
||||
```
|
||||
|
||||
## Tools
|
||||
|
||||
### Text-to-Speech
|
||||
|
||||
>[Text-to-Speech](https://cloud.google.com/text-to-speech) is a Google Cloud service that enables developers to
|
||||
>[Google Cloud Text-to-Speech](https://cloud.google.com/text-to-speech) is a Google Cloud service that enables developers to
|
||||
> synthesize natural-sounding speech with 100+ voices, available in multiple languages and variants.
|
||||
> It applies DeepMind’s groundbreaking research in WaveNet and Google’s powerful neural networks
|
||||
> to deliver the highest fidelity possible.
|
||||
@ -596,39 +596,6 @@ from langchain_community.utilities.google_drive import GoogleDriveAPIWrapper
|
||||
from langchain_community.tools.google_drive.tool import GoogleDriveSearchTool
|
||||
```
|
||||
|
||||
### Google Places
|
||||
|
||||
We need to install a python package.
|
||||
|
||||
```bash
|
||||
pip install googlemaps
|
||||
```
|
||||
|
||||
See a [usage example and authorization instructions](/docs/integrations/tools/google_places).
|
||||
|
||||
```python
|
||||
from langchain.tools import GooglePlacesTool
|
||||
```
|
||||
|
||||
### Google Search
|
||||
|
||||
- Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
|
||||
- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables
|
||||
`GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively.
|
||||
|
||||
```python
|
||||
from langchain_community.utilities import GoogleSearchAPIWrapper
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search).
|
||||
|
||||
We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:
|
||||
|
||||
```python
|
||||
from langchain.agents import load_tools
|
||||
tools = load_tools(["google-search"])
|
||||
```
|
||||
|
||||
### Google Finance
|
||||
|
||||
We need to install a python package.
|
||||
@ -668,6 +635,20 @@ from langchain_community.tools.google_lens import GoogleLensQueryRun
|
||||
from langchain_community.utilities.google_lens import GoogleLensAPIWrapper
|
||||
```
|
||||
|
||||
### Google Places
|
||||
|
||||
We need to install a python package.
|
||||
|
||||
```bash
|
||||
pip install googlemaps
|
||||
```
|
||||
|
||||
See a [usage example and authorization instructions](/docs/integrations/tools/google_places).
|
||||
|
||||
```python
|
||||
from langchain.tools import GooglePlacesTool
|
||||
```
|
||||
|
||||
### Google Scholar
|
||||
|
||||
We need to install a python package.
|
||||
@ -683,6 +664,25 @@ from langchain_community.tools.google_scholar import GoogleScholarQueryRun
|
||||
from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper
|
||||
```
|
||||
|
||||
### Google Search
|
||||
|
||||
- Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
|
||||
- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables
|
||||
`GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively.
|
||||
|
||||
```python
|
||||
from langchain_community.utilities import GoogleSearchAPIWrapper
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search).
|
||||
|
||||
We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:
|
||||
|
||||
```python
|
||||
from langchain.agents import load_tools
|
||||
tools = load_tools(["google-search"])
|
||||
```
|
||||
|
||||
### Google Trends
|
||||
|
||||
We need to install a python package.
|
||||
@ -702,7 +702,7 @@ from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
|
||||
|
||||
### GMail
|
||||
|
||||
> [Gmail](https://en.wikipedia.org/wiki/Gmail) is a free email service provided by Google.
|
||||
> [Google Gmail](https://en.wikipedia.org/wiki/Gmail) is a free email service provided by Google.
|
||||
This toolkit works with emails through the `Gmail API`.
|
||||
|
||||
We need to install several python packages.
|
||||
@ -744,7 +744,7 @@ Install the python package:
|
||||
pip install langchain-google-cloud-sql-pg
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/memory/google_cloud_sql_pg).
|
||||
See [usage example](/docs/integrations/memory/google_cloud_sql_pg).
|
||||
|
||||
|
||||
```python
|
||||
@ -760,7 +760,7 @@ Install the python package:
|
||||
pip install langchain-google-cloud-sql-mysql
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/memory/google_cloud_sql_mysql).
|
||||
See [usage example](/docs/integrations/memory/google_cloud_sql_mysql).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_mysql import MySQLEngine, MySQLChatMessageHistory
|
||||
@ -775,7 +775,7 @@ Install the python package:
|
||||
pip install langchain-google-cloud-sql-mssql
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/memory/google_cloud_sql_mssql).
|
||||
See [usage example](/docs/integrations/memory/google_cloud_sql_mssql).
|
||||
|
||||
```python
|
||||
from langchain_google_cloud_sql_mssql import MSSQLEngine, MSSQLChatMessageHistory
|
||||
@ -783,14 +783,14 @@ from langchain_google_cloud_sql_mssql import MSSQLEngine, MSSQLChatMessageHistor
|
||||
|
||||
### Spanner
|
||||
|
||||
> [Spanner](https://cloud.google.com/spanner/docs) is a fully managed, mission-critical, relational database service on Google Cloud that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL dialects: GoogleSQL (ANSI 2011 with extensions) and PostgreSQL.
|
||||
> [Google Cloud Spanner](https://cloud.google.com/spanner/docs) is a fully managed, mission-critical, relational database service on Google Cloud that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL dialects: GoogleSQL (ANSI 2011 with extensions) and PostgreSQL.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-spanner
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/memory/google_spanner).
|
||||
See [usage example](/docs/integrations/memory/google_spanner).
|
||||
|
||||
```python
|
||||
from langchain_google_spanner import SpannerChatMessageHistory
|
||||
@ -798,14 +798,14 @@ from langchain_google_spanner import SpannerChatMessageHistory
|
||||
|
||||
### Memorystore for Redis
|
||||
|
||||
> [Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis) is a fully managed Redis service for Google Cloud. Applications running on Google Cloud can achieve extreme performance by leveraging the highly scalable, available, secure Redis service without the burden of managing complex Redis deployments.
|
||||
> [Google Cloud Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis) is a fully managed Redis service for Google Cloud. Applications running on Google Cloud can achieve extreme performance by leveraging the highly scalable, available, secure Redis service without the burden of managing complex Redis deployments.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-memorystore-redis
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/document_loaders/google_memorystore_redis).
|
||||
See [usage example](/docs/integrations/document_loaders/google_memorystore_redis).
|
||||
|
||||
```python
|
||||
from langchain_google_memorystore_redis import MemorystoreChatMessageHistory
|
||||
@ -813,14 +813,14 @@ from langchain_google_memorystore_redis import MemorystoreChatMessageHistory
|
||||
|
||||
### Bigtable
|
||||
|
||||
> [Bigtable](https://cloud.google.com/bigtable/docs) is Google's fully managed NoSQL Big Data database service in Google Cloud.
|
||||
> [Google Cloud Bigtable](https://cloud.google.com/bigtable/docs) is Google's fully managed NoSQL Big Data database service in Google Cloud.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-bigtable
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/memory/google_bigtable).
|
||||
See [usage example](/docs/integrations/memory/google_bigtable).
|
||||
|
||||
```python
|
||||
from langchain_google_bigtable import BigtableChatMessageHistory
|
||||
@ -828,22 +828,22 @@ from langchain_google_bigtable import BigtableChatMessageHistory
|
||||
|
||||
### Firestore (Native Mode)
|
||||
|
||||
> [Firestore](https://cloud.google.com/firestore/docs/) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
|
||||
> [Google Cloud Firestore](https://cloud.google.com/firestore/docs/) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
|
||||
Install the python package:
|
||||
|
||||
```bash
|
||||
pip install langchain-google-firestore
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/memory/google_firestore).
|
||||
See [usage example](/docs/integrations/memory/google_firestore).
|
||||
|
||||
```python
|
||||
from langchain_google_firestore import FirestoreChatMessageHistory
|
||||
```
|
||||
|
||||
### Firestore in Datastore Mode
|
||||
### Firestore (Datastore Mode)
|
||||
|
||||
> [Firestore in Datastore mode](https://cloud.google.com/datastore/docs) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
|
||||
> [Google Cloud Firestore in Datastore mode](https://cloud.google.com/datastore/docs) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
|
||||
> Firestore is the newest version of Datastore and introduces several improvements over Datastore.
|
||||
Install the python package:
|
||||
|
||||
@ -851,7 +851,7 @@ Install the python package:
|
||||
pip install langchain-google-datastore
|
||||
```
|
||||
|
||||
See [usage example](docs/docs/integrations/memory/google_datastore).
|
||||
See [usage example](/docs/integrations/memory/google_datastore).
|
||||
|
||||
```python
|
||||
from langchain_google_datastore import DatastoreChatMessageHistory
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
"source": [
|
||||
"# Google AlloyDB for PostgreSQL\n",
|
||||
"\n",
|
||||
"> [AlloyDB](https://cloud.google.com/alloydb) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. AlloyDB is 100% compatible with PostgreSQL. Extend your database application to build AI-powered experiences leveraging AlloyDB's Langchain integrations.\n",
|
||||
"> [Google Cloud AlloyDB](https://cloud.google.com/alloydb) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. AlloyDB is 100% compatible with PostgreSQL. Extend your database application to build AI-powered experiences leveraging AlloyDB's Langchain integrations.\n",
|
||||
"\n",
|
||||
"This notebook goes over how to use `AlloyDB for PostgreSQL` to store vector embeddings with the `AlloyDBVectorStore` class."
|
||||
]
|
||||
@ -64,7 +64,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "v6jBDnYnNM08",
|
||||
"metadata": {
|
||||
"id": "v6jBDnYnNM08"
|
||||
},
|
||||
@ -79,7 +78,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "yygMe6rPWxHS",
|
||||
"metadata": {
|
||||
"id": "yygMe6rPWxHS"
|
||||
},
|
||||
@ -94,7 +92,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "PTXN1_DSXj2b",
|
||||
"metadata": {
|
||||
"id": "PTXN1_DSXj2b"
|
||||
},
|
||||
@ -107,7 +104,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "NEvB9BoLEulY",
|
||||
"metadata": {
|
||||
"id": "NEvB9BoLEulY"
|
||||
},
|
||||
@ -125,7 +121,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "gfkS3yVRE4_W",
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "gfkS3yVRE4_W"
|
||||
@ -142,7 +137,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "rEWWNoNnKOgq",
|
||||
"metadata": {
|
||||
"id": "rEWWNoNnKOgq"
|
||||
},
|
||||
@ -154,7 +148,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "5utKIdq7KYi5",
|
||||
"metadata": {
|
||||
"id": "5utKIdq7KYi5"
|
||||
},
|
||||
@ -166,7 +159,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f8f2830ee9ca1e01",
|
||||
"metadata": {
|
||||
"id": "f8f2830ee9ca1e01"
|
||||
},
|
||||
@ -176,7 +168,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "OMvzMWRrR6n7",
|
||||
"metadata": {
|
||||
"id": "OMvzMWRrR6n7"
|
||||
},
|
||||
@ -188,7 +179,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "irl7eMFnSPZr",
|
||||
"metadata": {
|
||||
"id": "irl7eMFnSPZr"
|
||||
},
|
||||
@ -204,7 +194,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "QuQigs4UoFQ2",
|
||||
"metadata": {
|
||||
"id": "QuQigs4UoFQ2"
|
||||
},
|
||||
@ -290,7 +279,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "5utKIdq7KYi5",
|
||||
"metadata": {
|
||||
"id": "5utKIdq7KYi5"
|
||||
},
|
||||
@ -546,7 +534,8 @@
|
||||
"toc_visible": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
@ -559,9 +548,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -6,8 +6,9 @@
|
||||
"id": "E_RJy7C1bpCT"
|
||||
},
|
||||
"source": [
|
||||
"# BigQuery Vector Search\n",
|
||||
"> [**BigQuery Vector Search**](https://cloud.google.com/bigquery/docs/vector-search-intro) lets you use GoogleSQL to do semantic search, using vector indexes for fast approximate results, or using brute force for exact results.\n",
|
||||
"# Google BigQuery Vector Search\n",
|
||||
"\n",
|
||||
"> [Google Cloud BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro) lets you use GoogleSQL to do semantic search, using vector indexes for fast approximate results, or using brute force for exact results.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"This tutorial illustrates how to work with an end-to-end data and embedding management system in LangChain, and provide scalable semantic search in BigQuery."
|
||||
@ -349,7 +350,8 @@
|
||||
"toc_visible": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
@ -362,9 +364,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.0"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@ -4,9 +4,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Google Cloud SQL for PostgreSQL\n",
|
||||
"# Google SQL for PostgreSQL\n",
|
||||
"\n",
|
||||
"> [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers PostgreSQL, PostgreSQL, and SQL Server database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
|
||||
"> [Google Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers PostgreSQL, PostgreSQL, and SQL Server database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
|
||||
"\n",
|
||||
"This notebook goes over how to use `Cloud SQL for PostgreSQL` to store vector embeddings with the `PostgresVectorStore` class."
|
||||
]
|
||||
@ -64,7 +64,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "v6jBDnYnNM08",
|
||||
"metadata": {
|
||||
"id": "v6jBDnYnNM08"
|
||||
},
|
||||
@ -79,7 +78,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "yygMe6rPWxHS",
|
||||
"metadata": {
|
||||
"id": "yygMe6rPWxHS"
|
||||
},
|
||||
@ -94,7 +92,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "PTXN1_DSXj2b",
|
||||
"metadata": {
|
||||
"id": "PTXN1_DSXj2b"
|
||||
},
|
||||
@ -107,7 +104,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "NEvB9BoLEulY",
|
||||
"metadata": {
|
||||
"id": "NEvB9BoLEulY"
|
||||
},
|
||||
@ -125,7 +121,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "gfkS3yVRE4_W",
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "gfkS3yVRE4_W"
|
||||
@ -142,7 +137,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "rEWWNoNnKOgq",
|
||||
"metadata": {
|
||||
"id": "rEWWNoNnKOgq"
|
||||
},
|
||||
@ -154,7 +148,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "5utKIdq7KYi5",
|
||||
"metadata": {
|
||||
"id": "5utKIdq7KYi5"
|
||||
},
|
||||
@ -166,7 +159,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f8f2830ee9ca1e01",
|
||||
"metadata": {
|
||||
"id": "f8f2830ee9ca1e01"
|
||||
},
|
||||
@ -176,7 +168,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "OMvzMWRrR6n7",
|
||||
"metadata": {
|
||||
"id": "OMvzMWRrR6n7"
|
||||
},
|
||||
@ -188,7 +179,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "irl7eMFnSPZr",
|
||||
"metadata": {
|
||||
"id": "irl7eMFnSPZr"
|
||||
},
|
||||
@ -203,7 +193,6 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "QuQigs4UoFQ2",
|
||||
"metadata": {
|
||||
"id": "QuQigs4UoFQ2"
|
||||
},
|
||||
@ -291,7 +280,6 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "5utKIdq7KYi5",
|
||||
"metadata": {
|
||||
"id": "5utKIdq7KYi5"
|
||||
},
|
||||
@ -547,7 +535,8 @@
|
||||
"toc_visible": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
@ -560,9 +549,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user