#!/bin/bash # similar.sh: get a neural net summary (embedding) of a text string (usually an annotation) # Author: Gwern Branwen # Date: 2021-12-05 # When: Time-stamp: "2024-03-15 13:53:06 gwern" # License: CC-0 # # Shell script to pass a document into the OpenAI API Embedding endpoint ( https://beta.openai.com/docs/api-reference/embeddings # https://openai.com/blog/introducing-text-and-code-embeddings/ https://arxiv.org/abs/2201.10005#openai # https://beta.openai.com/docs/guides/embeddings/use-cases ). Authentication via shell environment variable. # # Example: # # $ embed.sh "foo bar" # # text-embedding-ada-002-v2 # # [ # # -0.016606577, # # -0.015486566, # # -0.01253444, # # -0.022013048, # # -0.013647537, # # 0.0068652504, # # ... ] # # Because an embedding is unique to each model, and embeddings generated by different models can't # be easily compared, it is important to track the model+version that generated it. Separated by a # newline, a JSON array of 1024–12288 floats (depending on model size; bigger = more = better) # follows and is the actual embedding. # # The OA API previously provided a semantic Search endpoint # which supports up to 2GB of text file inputs, and # will take short inputs ("One limitation to keep in mind is that the query and longest document # must be below 2000 tokens together.") to return the <=200 most 'similar' results. This could be # used to implement similar-links, but the drawback is that inputs would need to be much shorter, # the set of searched documents would have to be constantly re-uploaded to include new documents, # and it would not support any additional functionality. Using embeddings + vector-search allows for # using the full 2048 BPE context window, allows updating the vector-search database locally without # touching the OA API, and enables downstream tasks like feeding the embeddings into a classifier # for tagging. (Tagging could itself be done using the Classification endpoint # but with caveats of its own, and further # expense.) While the Search endpoint would be a good starting point, particularly for prototyping, # using Embedding is probably better in the long run. # # Requires: curl, jq, valid API key defined in `$OPENAI_API_KEY` # set -e # set -x # Input: X BPEs of text # Output: https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models (all deprecated: https://platform.openai.com/docs/deprecations) # ada-similarity [1024], babbage-similarity [2048], curie-similarity [4096], davinci-similarity [12288, or 12× ada] # Second-generation models: # text-embedding-ada-002 | cl100k_base | 8191 input | 1536 output dimensionality | ENGINE="text-embedding-ada-002" TEXT="$@" if [ "${#TEXT}" == 0 ]; then TEXT=$(> /dev/stderr echo "$RESULT" >> /dev/stderr exit 1 else if [ "$PARSED" = "null null" ] && [ $(( TRUNCATED < 500 )) ]; then echo "Length error? $TEXT_LENGTH $(echo "$RESULT" | jq .)" 1>&2 # drop _n_ characters to try again TEXT_LENGTH="$(( TEXT_LENGTH - 100 ))" TRUNCATED=$(( TRUNCATED + 100 )) TEXT="${TEXT:0:$TEXT_LENGTH}" sleep 1s else echo "$PARSED" break fi fi done # Example output: # $ curl --silent 'https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings' -H "Content-Type: application/json" -H "Authorization: Bearer $OPENAI_API_KEY" -d '{"input": "Sample document text goes here"}' | jq --raw-output '.model, .data[0].embedding' # text-embedding-ada-002-v2 # [ # -0.0030407698, # 0.011684642, # -0.005026957, # -0.02723721, # -0.016361194, # 0.03234503, # -0.016159039, # -0.001036894, # -0.025822116, # -0.0066677933, # 0.020148259, # 0.016657691, # -0.009164426, # 0.023423193, # -0.010121299, # 0.013443403, # 0.025229124, # -0.016873324, # 0.01211591, # -0.016361194, # -0.00426887, # -0.0065026986, # -0.0043699485, # 0.020808637, # -0.010539089, # -0.003652293, # 0.013692729, # -0.0263612, # -0.00031713292, # -0.002218667, # 0.0058221053, # -0.0100876065, # -0.02822104, # -0.016159039, # -0.004218331, # 0.007466311, # -0.0029228453, # -0.031455543, # 0.023881415, # -0.033288427, # -0.0003649345, # 0.013072783, # 0.0070754755, # -0.005680596, # 0.0031064707, # -0.029811336, # 0.026293814, # -0.0046428596, # 0.0066071465, # 0.01749327, # 0.027331552, # 0.01578168, # -0.022196777, # 0.0028537752, # -0.006489222, # 0.0063915127, # -0.016981142, # 0.020067396, # 0.0034838293, # -0.0035074141, # -0.0010613214, # 0.003578169, # -0.0026583571, # -0.012473052, # -0.018072786, # -0.035202175, # -0.031725083, # 0.007574128, # 0.0070619984, # 0.005582887, # 0.020673865, # 0.011010787, # -0.011590303, # -0.017385453, # 0.006654316, # 0.010181946, # -0.012075478, # -0.012419144, # 0.00027564872, # -0.001097541, # 0.004612536, # -0.026603788, # -0.0023787075, # 0.032048535, # 0.009076824, # 0.014083565, # 0.018261464, # 0.029029666, # -0.021078179, # -0.017277637, # -0.0059602456, # 0.01582211, # -0.004326148, # 0.016994618, # -0.013234508, # 0.022008099, # -0.0053807306, # 0.024339635, # 0.0037938026, # -0.04102428, # 0.008456877, # 0.00988545, # -0.012109171, # -0.01025607, # -0.03935312, # 0.0057109194, # 0.016226424, # -0.014824806, # 0.016091652, # 0.014407015, # -0.021401629, # 0.012628039, # -0.0045417813, # -0.033261474, # 0.014218336, # 0.004636121, # -0.0044238567, # -0.021927236, # 0.0011666111, # -0.005215636, # -0.013308632, # 0.026091658, # 0.04000002, # -0.016442057, # 0.014029657, # 0.023679258, # -0.005117927, # -0.031859856, # 0.012769548, # -0.005963615, # 0.01809974, # 0.042479806, # 0.024258774, # 0.020552572, # -0.028247993, # 0.01880055, # -0.045417815, # 0.019137476, # -0.021832896, # -0.017479794, # 0.008921837, # 0.034932632, # -0.0062028333, # -0.006270219, # 0.0068564727, # 0.011192728, # 0.00068901654, # -0.021051224, # 0.0108625395, # -0.013840978, # 0.010849062, # 0.0024241926, # -0.007082214, # 0.01202157, # 0.003679247, # 0.026239906, # 0.012432621, # -0.0038712958, # -0.0113274995, # -0.020525618, # 0.00811995, # 0.01452831, # 0.01939354, # -0.010040437, # 0.010424534, # 0.041374683, # 0.0026566726, # -0.010761461, # 0.0003617758, # -0.017628042, # -0.024474407, # 0.01694071, # -0.045687355, # 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