Package: datasette-openai
datasette-openaiSQL functions for calling OpenAI APIsSeeSemantic search answers: Q&A against documentation with GPT3 + OpenAI embeddingsfor background on this project.InstallationInstall this plugin in the same environment as Datasette.datasette install datasette-openai⚠️ Warning ⚠️This plugin allows you to call a commercial, priced API using SQL queries.Use this with care! You could accidentally spend a lot of money.For example, the following query:selectopenai_davinci('Summarize this text: '||content,200,1,:api_key)assummaryfromdocumentsWould execute one paid API call for every item in thedocumentsdatabase. This could become very expensive.Be sure to familiarize yourself withOpenAI pricing. You will need to obtain anAPI key.UsageThis extension provides three new SQL functions:openai_davinci(prompt, max_tokens, temperature, api_key)This function runs atext-davinci-003completion against the provided prompt, with the specified values for max tokens and temperature.Da Vinci is currently 2 cents per thousand tokens.openai_embedding(text, api_key)This calls the OpenAI embedding endpoint and returns a binary object representing the floating point embedding for the provided text.selectopenai_embedding(:query,:api_key)An embedding is an array of 1536 floating point values. The returned value from this is ablobencoding of those values.It's mainly useful for using with theopenai_embedding_similarity()function.The embedding API is very inexpensive: at time of writing, $0.0004 cents per thousand tokens, where a token is more-or-less a single word.openai_embedding_similarity(a, b)This function does not make any API calls. It takes two embedding blobs and returns the cosine similarity between the two.This function is particularly useful if you have stored embeddings of documents in a database table, and you want to find the most similar documents to a query or to another document.A simple search query could look like this:withqueryas(selectopenai_embedding(:query,:token)asq)selectid,title,openai_embedding_similarity(query.q,embedding)asscorefromcontent,queryorderbyscoredesclimit10openai_build_prompt(text, prefix, suffix, completion_tokens, token_limit=4000)This aggregate function helps build a prompt from a number of inputs in a way that fits the GPT-3 prompt size limit.It takes the following argument:text- this is the column that is being aggregated, so the function expects to have multiple values for this. All other arguments will only be read the first time they are passed, so should be consistent across all calls to the function.prefix- text to use for the prefix of the promptsuffix- text to use for the suffix of the promptcompletion_tokens- the number of tokens to reserve for the prompt response - this will be subtracted from the token limittoken_limit- this value is optional (there are 4-argument and 5-argument versions of the function registered). It defaults to the GPT-3 Da Vinci size limit of 4,000 tokens but can be changed if the model the prompt is being used with has a different size limit.Here's an example usage of this function, adapted fromthis article:withtop_nas(selectbodyfromblog_entryorderbyiddesclimit3)selectopenai_build_prompt(body,'Context:------------','------------Given the above context, answer the following question: '||:question,500,2000)fromtop_nTry that here.This query first retrieves the three most recent blog entries, then constructs a prompt that with the provided prefix and suffix designed to fit 1500 tokens (2000 total, minus 500 reserved for the response).The output looks something like this (truncated for space):Context:
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< p > If you 've spent any time with GPT - 3 or ChatGPT , you 've likely thought about how ...
I release Datasette 0 . 64 this morning . This release is mainly a response to the realization that it 's not safe to run Datasette with the SpatiaLite extension loaded if that Datasette instance is configured to enable arbitrary SQL queries from untrusted users ...
In lieu of my regular weeknotes ( I took two weeks off for the holidays ) here 's a look back at 2022 , mainly in terms of projects and things I 've written about ...
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Given the above context, answer the following question: Examples of a language model?The body of each entry has been truncated to the number of tokens that will allow examples from all three entries to be included in the generated prompt.openai_strip_tags(text)Sometimes it can be useful to strip HTML tags from text in order to reduce the number of tokens used. This function does a very simple version of tag stripping - just removing anything that matches<...>.openai_tokenize(text)Returns a JSON array of tokens for the provided text.This uses a regular expressionextracted from OpenAI's GPT-2.openai_count_tokens(text)Returns a count of the number of tokens in the provided text.DevelopmentTo set up this plugin locally, first checkout the code. Then create a new virtual environment:cd datasette-openai
python3 -m venv venv
source venv/bin/activateNow install the dependencies and test dependencies:pip install -e '.[test]'To run the tests:pytest