The substratusai/weaviate-gorilla-v4-random-split is a 7 billion parameter causal language model fine-tuned by substratusai. It specializes in generating GraphQL queries from natural language instructions, leveraging provided API references and schemas. This model is specifically optimized for interacting with Weaviate databases, making it suitable for developers building applications that require programmatic GraphQL generation for data retrieval and manipulation.
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Overview
The substratusai/weaviate-gorilla-v4-random-split is a 7 billion parameter language model developed by substratusai, specifically fine-tuned for generating GraphQL queries. This model is trained on a dataset derived from weaviate/WeaviateGraphQLGorilla-RandomSplit-Train, focusing on the task of translating natural language queries into valid GraphQL syntax for Weaviate databases.
Key Capabilities
- Natural Language to GraphQL Conversion: Translates user-provided natural language queries into executable GraphQL code.
- Schema-Aware Generation: Utilizes a provided GraphQL schema to ensure the generated queries are syntactically correct and semantically aligned with the database structure.
- API Reference Integration: Incorporates API reference documentation to guide the generation of accurate and functional GraphQL requests, particularly for Weaviate-specific operations like
bm25searches and result limiting.
Good For
- Weaviate Database Interaction: Ideal for developers and applications that need to programmatically construct complex GraphQL queries for Weaviate without manual coding.
- Automated Query Generation: Suitable for building tools or interfaces that allow users to query Weaviate using natural language, abstracting away the complexities of GraphQL syntax.
- Developer Productivity: Enhances efficiency by automating the creation of GraphQL queries, reducing the need for developers to manually write and debug them.