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ChatGPT Retrieval Plugin: Difference between revisions
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This functionality allows for regular updates to content stored in connected vector databases, giving the model awareness of new information without the need for costly and time-consuming retraining of the [[large language model]] ([[LLM]]). | This functionality allows for regular updates to content stored in connected vector databases, giving the model awareness of new information without the need for costly and time-consuming retraining of the [[large language model]] ([[LLM]]). | ||
==Overview of Vector Database Providers== | |||
This article presents an overview of various vector database providers supported by the plugin, highlighting their unique features, performance, and pricing. The choice of a vector database provider depends on specific use cases and requirements, and each provider necessitates the use of distinct Dockerfiles and environment variables. Detailed instructions for setting up and using each provider can be found in their respective documentation at /docs/providers/<datastore_name>/setup.md. | |||
===Pinecone=== | |||
[[Pinecone]] is a managed vector database engineered for rapid deployment, speed, and scalability. It uniquely supports hybrid search and is the sole datastore that natively accommodates SPLADE sparse vectors. For comprehensive setup guidance, refer to /docs/providers/pinecone/setup.md. | |||
===Weaviate=== | |||
[[Weaviate]] is an open-source vector search engine designed to scale effortlessly to billions of data objects. Its out-of-the-box support for hybrid search makes it ideal for users who need efficient keyword searches. Weaviate can be self-hosted or managed, offering flexible deployment options. For extensive setup guidance, refer to /docs/providers/weaviate/setup.md. | |||
===Zilliz=== | |||
[[Zilliz]] is a managed, cloud-native vector database tailored for billion-scale data. It boasts a plethora of features, including numerous indexing algorithms, distance metrics, scalar filtering, time-travel searches, rollback with snapshots, full RBAC, 99.9% uptime, separated storage and compute, and multi-language SDKs. For comprehensive setup guidance, refer to /docs/providers/zilliz/setup.md. | |||
===Milvus=== | |||
[[Milvus]] is an open-source, cloud-native vector database that scales to billions of vectors. As the open-source variant of Zilliz, Milvus shares many features with it, such as various indexing algorithms, distance metrics, scalar filtering, time-travel searches, rollback with snapshots, multi-language SDKs, storage and compute separation, and cloud scalability. For extensive setup guidance, refer to /docs/providers/milvus/setup.md. | |||
===Qdrant=== | |||
[[Qdrant]] is a vector database that can store documents and vector embeddings. It provides self-hosted and managed Qdrant Cloud deployment options, catering to users with diverse requirements. For comprehensive setup guidance, refer to /docs/providers/qdrant/setup.md. | |||
===Redis=== | |||
[[Redis]] is a real-time data platform suitable for an array of applications, including AI/ML workloads and everyday use. By creating a Redis database with the Redis Stack docker container, Redis can be employed as a low-latency vector engine. For a hosted or managed solution, Redis Cloud is available. For extensive setup guidance, refer to /docs/providers/redis/setup.md. | |||
===LlamaIndex=== | |||
[[LlamaIndex]] serves as a central interface to connect your LLMs with external data. It offers a collection of in-memory indices over structured and unstructured data for use with ChatGPT. Unlike conventional vector databases, LlamaIndex supports a wide array of indexing strategies (e.g., tree, keyword table, knowledge graph) optimized for various use-cases. It is lightweight, user-friendly, and requires no additional deployment. Users need only specify a few environment variables and, optionally, point to an existing saved Index JSON file. However, metadata filters in queries are not yet supported. For comprehensive setup guidance, refer to /docs/providers/llama/setup.md. | |||
==Weaviate Retrieval Plugin in Action== | ==Weaviate Retrieval Plugin in Action== | ||
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One of the most powerful applications of the Weaviate Retrieval Plugin is its ability to store and reference previous conversations with ChatGPT. By persisting these conversations in Weaviate, ChatGPT can recall past interactions and provide more contextually relevant responses. | One of the most powerful applications of the Weaviate Retrieval Plugin is its ability to store and reference previous conversations with ChatGPT. By persisting these conversations in Weaviate, ChatGPT can recall past interactions and provide more contextually relevant responses. | ||
[[Category:Plugins]] [[Category:ChatGPT Plugins]] | [[Category:Plugins]] [[Category:ChatGPT Plugins]] | ||