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Vectara Python SDK
The Vectara Python SDK provides a simple and intuitive way to interact with the Vectara platform. This guide walks you through installation, authentication, and core operations to get you building AI-powered applications quickly.
Installation​
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Start by installing the Vectara SDK using pip. The SDK requires Python 3.7 or higher and includes all necessary dependencies for making API calls to the Vectara platform. The installation process is straightforward and should complete within a few seconds.
Authentication​
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The Vectara SDK supports two authentication methods. API keys are perfect for getting started quickly, while OAuth 2.0 provides enterprise-grade security for production applications. API keys provide the simplest authentication method. You can generate API keys in the Vectara Console and use them immediately in your applications. This method is ideal for development, testing, and single-user applications.
Verify Connection​
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Once you've created a client, it's good practice to verify that your authentication credentials are working properly. The health check endpoint provides a quick way to test connectivity and ensure your API key is valid and your account is accessible.
OAuth 2.0 Authentication​
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For production applications requiring enhanced security, OAuth 2.0 provides automatic token refresh and granular permissions. This method is recommended for multi-user applications and enterprise deployments where you need more sophisticated access control and audit trails.
Create a Corpus​
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A corpus is a collection of documents that you can search and query. Think of it as a database for your text content. Each corpus can have its own configuration, metadata schema, and access controls. Creating a corpus requires a unique key and descriptive name. The corpus key acts as an identifier for all future operations, so choose something memorable and descriptive.
List Corpora​
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You can retrieve a list of all corpora in your account. This is useful for applications that need to dynamically discover available document collections or provide users with corpus selection options. The response includes metadata about each corpus including creation dates and document counts.
Upload Files​
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Once you have a corpus, you can upload documents for indexing. Vectara supports various file formats including PDF, Word documents, plain text, and structured data formats. The simplest way to add content is by uploading files directly. The SDK handles the file processing and chunking automatically, making your documents searchable within minutes.
Structured Documents​
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For more control over document organization, you can create structured documents with explicit sections. This approach is ideal when you need precise control over how content is chunked and indexed. Structured documents allow you to define logical sections with titles and metadata, which can improve search relevance and result presentation.
Basic Search​
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Vectara's semantic search capabilities let you find relevant information using natural language queries. The platform understands context and meaning, not just keyword matches. Simple queries return ranked results based on semantic similarity. Each result includes a relevance score and the most relevant text snippet from your documents.
Advanced Search​
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Metadata filters allow you to narrow search results to specific document types, dates, or other criteria. This is particularly useful for large document collections with diverse content types. You can combine multiple filter conditions using AND/OR logic to create sophisticated search queries that return exactly the content you need.
RAG Generation​
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Beyond search, Vectara can generate comprehensive answers using Retrieval Augmented Generation (RAG). This combines search results with large language models to create contextual, accurate responses. RAG generation creates human-like responses based on your document content. The factual consistency score helps you evaluate the reliability of generated answers.
Streaming Responses​
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For real-time applications, you can stream generated responses as they're created. This provides immediate feedback to users and enables responsive user interfaces. Streaming is particularly valuable for longer responses where users can start reading the answer before generation is complete.
Error Handling​
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Production applications need robust error handling to gracefully manage API issues, rate limits, and authentication problems. The SDK provides specific exception types for different error conditions. This allows your application to respond appropriately to various failure scenarios, including network timeouts, rate limiting, and invalid requests.
Next Steps​
You're now ready to build powerful AI applications with the Vectara Python SDK. Consider exploring advanced features like custom rerankers, hybrid search, and batch operations for production deployments.
For complete API reference and advanced examples, visit the Vectara Documentation.