feat(tool): add web_search_aichat.sh (#120)

This commit is contained in:
sigoden
2024-11-02 20:42:42 +08:00
committed by GitHub
parent 68b1f57a4b
commit 3573a43978
3 changed files with 32 additions and 78 deletions
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#!/usr/bin/env bash
set -e
# @describe Perform a web search to get up-to-date information or additional context.
# Use this when you need current information or feel a search could provide a better answer.
# @option --query! The query to search for.
# @meta require-tools aichat
# @env WEB_SEARCH_MODEL! The model for web-searching.
#
# supported aichat models:
# - cohere:*
# - vertexai:gemini-*
# - perplexity:*-online
# - ernie:*
# - lingyiwanwu:yi-large-rag
main() {
client="${WEB_SEARCH_MODEL%%:*}"
case "$client" in
cohere)
export AICHAT_PATCH_COHERE_CHAT_COMPLETIONS='{".*":{"body":{"connectors":[{"id":"web-search"}]}}}'
;;
vertexai)
export AICHAT_PATCH_VERTEXAI_CHAT_COMPLETIONS='{"gemini-.*":{"body":{"tools":[{"googleSearchRetrieval":{}}]}}}'
;;
esac
aichat -m "$WEB_SEARCH_MODEL" "$argc_query" >> "$LLM_OUTPUT"
}
eval "$(argc --argc-eval "$0" "$@")"
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#!/usr/bin/env bash
set -e
# @describe Perform a web search using Cohere API to get up-to-date information or additional context.
# Use this when you need current information or feel a search could provide a better answer.
# @env COHERE_API_KEY! The api key
# @env COHERE_WEB_SEARCH_MODEL=command-r The LLM model for web search
# @option --query! The query to search for.
main() {
curl -fsS -X POST https://api.cohere.com/v1/chat \
-H "authorization: Bearer $COHERE_API_KEY" \
-H "accept: application/json" \
-H "content-type: application/json" \
--data '
{
"model": "'"$COHERE_WEB_SEARCH_MODEL"'",
"message": "'"$argc_query"'",
"connectors": [{"id": "web-search"}]
}
' | \
jq -r '.text' \
>> "$LLM_OUTPUT"
}
eval "$(argc --argc-eval "$0" "$@")"
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#!/usr/bin/env bash
set -e
# @describe Perform a web search using VertexAI Gemini API to get up-to-date information or additional context.
# Use this when you need current information or feel a search could provide a better answer.
# @env VERTEXAI_PROJECT_ID! The project id
# @env VERTEXAI_LOCATION! The location
# @env VERTEXAI_WEB_SEARCH_MODEL=gemini-1.5-pro-001 The LLM model for web search
# @option --query! The query to search for.
# @meta require-tools gcloud
main() {
curl -fsSL https://$VERTEXAI_LOCATION-aiplatform.googleapis.com/v1beta1/projects/$VERTEXAI_PROJECT_ID/locations/$VERTEXAI_LOCATION/publishers/google/models/$VERTEXAI_WEB_SEARCH_MODEL:generateContent \
-X POST \
-H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json" \
-d '
{
"contents": [{
"role": "user",
"parts": [{
"text": "'"$argc_query"'"
}]
}],
"safetySettings": [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_ONLY_HIGH"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_ONLY_HIGH"
},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_ONLY_HIGH"
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": "BLOCK_ONLY_HIGH"
}
],
"tools": [{
"googleSearchRetrieval": {}
}]
}' | \
jq -r '.candidates[0].content.parts[0].text' >> "$LLM_OUTPUT"
}
eval "$(argc --argc-eval "$0" "$@")"