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| # Couchbase Capella AI Services Auto-Vectorization with LangChain | ||
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| This guide is a comprehensive tutorial demonstrating how to use Couchbase Capella's AI Services auto-vectorization feature for unstructured data to automatically convert your data into vector embeddings and perform semantic search using LangChain. | ||
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| ## 🚀 Quick Start | ||
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| ### Prerequisites | ||
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| - Python 3.8 or higher | ||
| - A Couchbase Capella account | ||
| - Basic understanding of vector databases and embeddings | ||
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| ### Installation Steps | ||
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| 1. **Clone or download this repository** | ||
| ```bash | ||
| git clone https://github.com/couchbase-examples/vector-search-cookbook.git | ||
| cd vector-search-cookbook/autovec-unstructured | ||
| ``` | ||
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| 2. **Install Python dependencies** | ||
| ```bash | ||
| pip install jupyter | ||
| pip install couchbase | ||
| pip install langchain-couchbase | ||
| pip install langchain-nvidia-ai-endpoints | ||
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|
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| ``` | ||
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| 3. **Start Jupyter Notebook** | ||
| ```bash | ||
| jupyter notebook | ||
| ``` | ||
| or | ||
| ```bash | ||
| jupyter lab | ||
| ``` | ||
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| 4. **Open the tutorial notebook** | ||
| - Navigate to `autovec_unstructured.ipynb` in the Jupyter interface | ||
| - Follow the step-by-step instructions in the notebook | ||
| ``` | ||
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| **Note**: This tutorial is designed for educational purposes. For production deployments, ensure proper security configurations and SSL/TLS verification. | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,18 @@ | ||
| --- | ||
| # frontmatter | ||
| path: "/tutorial-couchbase-autovectorization-langchain" | ||
| title: Auto-Vectorization with Couchbase Capella AI Services and LangChain | ||
| short_title: Auto-Vectorization with Couchbase and LangChain | ||
| description: | ||
| - Learn how to use Couchbase Capella's AI Services auto-vectorization feature to automatically convert your data into vector embeddings. | ||
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|
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| - This tutorial demonstrates how to set up automated embedding generation workflows and perform semantic search using LangChain. | ||
| content_type: tutorial | ||
| filter: sdk | ||
| technology: | ||
| - vector search | ||
| tags: | ||
| - LangChain | ||
| sdk_language: | ||
| - python | ||
| length: 20 Mins | ||
| --- | ||
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