December 10: Support for OpenAI GPT-4 Turbo, Llama 2 and Mistral models; query by example, bug fixes
New Features
Added query by example to
contents
query. Developers can specify one or more example contents, and query will use vector embeddings to return similar contents.Added query by example to
conversations
query. Developers can specify one or more example conversations, and query will use vector embeddings to return similar conversations.Added vector search support for
conversations
queries. Developers can provide search text which will use vector embeddings to return similar conversations.Added
promptSpecifications
mutation for directly prompting multiple models. This can be used to evaluate prompts against multiple models or compare different specification parameters in parallel.Added
promptStrategy
field to Specification, which supports multiple strategy types for preprocessing the prompt before being sent to the LLM model. For example,REWRITE
prompt strategy will ask LLM to rewrite the incoming user prompt based on the previous conversation messages.Added
suggestConversation
mutation, which returns a list of suggested followup questions based on the specified conversation and related contents. This can be used to auto-suggest questions for chatbot users.Added new summarization types:
CHAPTERS
,QUESTIONS
andPOSTS
. See usage examples in the "LLMs for Podcasters" blog post.Added versioned model enums such as
GPT4_0613
andGPT35_TURBO_16K_1106
. Without version specified, such asGPT35_TURBO_16K
, Graphlit will use the latest production model version, as defined by the LLM vendor.Added
lookupContents
query to get multiple contents by id in one query.
Bugs Fixed
GPLA-1725: Should ignore RSS.xml from web feed sitemap
GPLA-1726: GPT-3.5 Turbo 16k LLM is adding "Citation #" to response
GPLA-1698: Workflow not applied to link-crawled content
GPLA-1692: Mismatched project storage total size, when some content has errored
GPLA-1237: Add relevance threshold for semantic search
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