mirror of
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8be3c88711
This PR adds a collection of pre-built store agents that can be loaded into test databases for development and testing purposes. ### Changes 🏗️ - Add 17 exported agent JSON files in `backend/agents/` directory - Add `StoreAgent_rows.csv` containing store listing metadata (titles, descriptions, categories, images) - Add `load_store_agents.py` script to load agents into the test database - Add `load-store-agents` Makefile target for easy execution **Included Agents:** - Flux AI Image Generator - YouTube Transcription Scraper - Decision Maker Lead Finder - Smart Meeting Prep - Automated Support Agent - Unspirational Poster Maker - AI Video Generator - Automated SEO Blog Writer - Lead Finder (Local Businesses) - LinkedIn Post Generator - YouTube to LinkedIn Post Converter - Personal Newsletter - Email Scout - Contact Finder Assistant - YouTube Video to SEO Blog Writer - AI Webpage Copy Improver - Domain Name Finder - AI Function ### Checklist 📋 #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Run `make load-store-agents` and verify agents are loaded into the database - [x] Verify store listings appear correctly with metadata from CSV - [x] Confirm no sensitive information (API keys, secrets) is included in the exported agents #### For configuration changes: - [x] `.env.default` is updated or already compatible with my changes - [x] `docker-compose.yml` is updated or already compatible with my changes - [x] I have included a list of my configuration changes in the PR description (under **Changes**) No configuration changes required - this only adds test data and a loading script.
5032 lines
171 KiB
JSON
5032 lines
171 KiB
JSON
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"prompt": "CUSTOMER EMAIL:\n{{customer_email_body | safe}}\n\nDOCUMENTATION RESEARCH FINDINGS (XML FORMAT):\n{{relevant_information | safe}}\n\nAnalyze the XML research findings above to determine if you can provide a complete, accurate answer to this customer's question. The XML contains:\n- Research confidence score\n- Verbatim documentation quotes with sources \n- Pre-drafted answer based on documentation\n- Source URLs\n\nUse a {{company_tone}} tone and only auto-reply if confidence is {{confidence_threshold}}% or higher.\n\nStart your email response with \"Hello,\" followed by two line breaks, then provide the email body content. Do not include a signature or closing.\n\nRespond in this JSON format:\n{\n \"confidence_score\": [number 0-100],\n \"should_auto_reply\": [string \"true\" or \"false\" with quotes],\n \"email_response\": \"[start with 'Hello,' two enter characters, then email body content - no signature]\",\n \"reasoning\": \"[brief explanation of your confidence assessment and decision]\"\n}\n\n\u2705 CORRECT Adaptation to Threshold 70%:\n{\n \"confidence_score\": 75,\n \"should_auto_reply\": \"true\",\n \"email_response\": \"Hello,\\n\\nBased on our documentation, PandaDoc API has rate limiting in place, though specific limits may vary by endpoint. For detailed rate limit information specific to your use case, I recommend checking the headers in API responses or contacting our technical team.\",\n \"reasoning\": \"Documentation mentions rate limiting exists but lacks specific numbers. At 70% threshold, I can provide helpful guidance even with partial information.\"\n}\n\n\u2705 CORRECT Adaptation to Threshold 90%:\n{\n \"confidence_score\": 75,\n \"should_auto_reply\": \"false\",\n \"email_response\": \"Hello,\\n\\nYour question about API rate limits requires specific technical details that would benefit from personalized assistance. I'm escalating this to our technical support team who can provide you with exact rate limit specifications.\",\n \"reasoning\": \"Documentation provides partial information about rate limiting, but at 90% threshold, I need more complete details to auto-reply confidently.\"\n}\n\n\u274c WRONG Examples (Ignoring Threshold):\nWrong - Same Response Regardless of Threshold:\n// At 70% threshold - WRONG\n{\n \"confidence_score\": 75,\n \"should_auto_reply\": \"false\", // \u2190 Should be true at 70%\n \"email_response\": \"drafted email response (best try) goes here\"\n \"reasoning\": \"Not confident enough\" // \u2190 Ignoring 70% threshold\n}\n// At 90% threshold - WRONG \n{\n \"confidence_score\": 75,\n \"should_auto_reply\": \"true\", // \u2190 Should be false at 90%\n \"email_response\": \"Hello,\\n\\nHere's partial information...\",\n \"reasoning\": \"Providing available info\" // \u2190 Ignoring 90% threshold\n}\n\nDo not ask people to contact human support on their own, as they have already contacted support, and setting should_auto_reply to false results in this ticket being escalated to a human.",
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"prompt": "**Current Task:**\nDocumentation Base URL: {{documentation_base_url}}\nCustomer Question: {{question}}\nRequired Confidence Threshold: {{confidence_threshold}}%\nSitemap: {{sitemap}}\n\n**Your Available Tools:**\n1. **FireCrawl Crawl** - Pass in a base URL to crawl through multiple related pages agentically\n2. **FireCrawl Extract** - Pass in a list of specific URLs and a custom prompt for targeted extraction\n\n**Search Process:**\n1. Analyze the sitemap and customer's question to identify key concepts and requirements\n2. Choose the optimal tool strategy:\n - Use **FireCrawl Crawl** when you need to explore multiple related pages in a section\n - Use **FireCrawl Extract** when you know specific URLs and want targeted information with custom prompts\n3. For each search result, perform documentation analysis:\n - Extract relevant quotes from documentation, cited verbatim\n - Identify specific instructions, troubleshooting steps, or policies\n - Note any limitations that might prevent a complete answer\n - Consider edge cases or limitations in potential answers\n4. Assess confidence level (0-100%) based on completeness and accuracy of found information\n5. Continue searching if below {{confidence_threshold}}% threshold using different tools/strategies\n6. Only include information explicitly stated in the documentation\n\n**Tool Usage Guidelines:**\n- **FireCrawl Crawl:** When sitemap shows related pages that need exploration (e.g., API section with multiple endpoints)\n- **FireCrawl Extract:** When sitemap reveals specific relevant pages and you need precise information with targeted prompts\n\n**Stop Conditions:**\n- Confidence reaches {{confidence_threshold}}% or higher\n- Documentation has been thoroughly searched with appropriate tools\n- No additional relevant information can be found\n\n**Final Output Format:**\n{\n \"confidence_score\": [X]%,\n \"relevant_information\": \"Exact text copied verbatim from documentation\",\n \"answer\": \"Complete response based solely on documentation findings\",\n \"source_urls\": [\"exact page URLs relevant to this answer\"],\n \"reasoning\": \"Why this confidence level was reached and which tools were used\"\n}\n\n**Important:** \n- Never make assumptions or include information not present in documentation\n- If insufficient information found, clearly state limitations\n- Ensure responses are accurate, professional, and solution-oriented\n\nBegin searching for: \"{{question}}\" in documentation at {{documentation_base_url}}",
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"sys_prompt": "You are a documentation search agent that uses FireCrawl tools to find accurate answers to customer questions. Your goal is to thoroughly search documentation until you reach the specified confidence threshold or exhaust available sources.\n\n**Available Functions:**\n1. firecrawl_crawl(url, limit, only_main_content) - Explore multiple pages from a base URL\n2. firecrawl_extract(urls, prompt) - Extract specific information from known URLs with targeted prompts\n\n**Search Strategy:**\n- Analyze the sitemap to identify relevant documentation areas\n- Use firecrawl_extract when you know specific URLs contain the answer\n- Use firecrawl_crawl when you need to explore and discover relevant pages\n- Continue searching until your confidence reaches the specified confidence threshold OR you've exhausted relevant sources\n- Always provide complete function arguments matching the required schema\n\n**MANDATORY STOP CONDITIONS:**\nYou MUST stop searching and provide your final answer when ANY of these conditions are met:\n- You have made 5 tool calls total (hard limit)\n- Your confidence reaches the confidence threshold ({{CONFIDENCE_THRESHOLD}}%) or higher\n- You find no new relevant information in your last 2 consecutive searches\n- You are about to crawl/extract from URLs you've already processed\n- No more relevant URLs are available to search\n\n**Goal:** Raise your confidence level as high as possible through thorough documentation search.\n\n**Confidence Assessment:**\n- Evaluate how completely and accurately you can answer the customer's question\n- Consider: completeness of information, clarity of documentation, presence of contradictions\n- You must explicitly state your confidence percentage (0-100%)\n- Continue searching if below the confidence threshold ({{CONFIDENCE_THRESHOLD}}%) AND more sources are available AND stop conditions not met\n\n**Output Requirements:**\n- Extract verbatim quotes from documentation sources\n- Provide comprehensive answers based solely on documentation\n- Include relevant source URLs (limit to 2-3 most important)\n- Assess your final confidence level objectively\n\n**Tool Call Tracking:**\n- Keep count of your tool calls (1/5, 2/5, etc.)\n- After each tool call, evaluate if stop conditions are met\n- If stop conditions are met, immediately provide final output in XML format\n\nAlways choose a function call from the available tools and provide complete arguments. When finished, output your findings in the specified XML format.",
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"ollama_host": "localhost:11434",
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|
|
"title": "CredentialsMetaInput[Literal[<ProviderName.AIRTABLE: 'airtable'>], Literal['api_key', 'oauth2']]",
|
|
"type": "object",
|
|
"discriminator_values": []
|
|
},
|
|
"anthropic_api_key_credentials": {
|
|
"credentials_provider": [
|
|
"anthropic"
|
|
],
|
|
"credentials_types": [
|
|
"api_key"
|
|
],
|
|
"properties": {
|
|
"id": {
|
|
"title": "Id",
|
|
"type": "string"
|
|
},
|
|
"title": {
|
|
"anyOf": [
|
|
{
|
|
"type": "string"
|
|
},
|
|
{
|
|
"type": "null"
|
|
}
|
|
],
|
|
"default": null,
|
|
"title": "Title"
|
|
},
|
|
"provider": {
|
|
"const": "anthropic",
|
|
"title": "Provider",
|
|
"type": "string"
|
|
},
|
|
"type": {
|
|
"const": "api_key",
|
|
"title": "Type",
|
|
"type": "string"
|
|
}
|
|
},
|
|
"required": [
|
|
"id",
|
|
"provider",
|
|
"type"
|
|
],
|
|
"title": "CredentialsMetaInput[Literal[<ProviderName.ANTHROPIC: 'anthropic'>], Literal['api_key']]",
|
|
"type": "object",
|
|
"discriminator": "model",
|
|
"discriminator_mapping": {
|
|
"Llama-3.3-70B-Instruct": "llama_api",
|
|
"Llama-3.3-8B-Instruct": "llama_api",
|
|
"Llama-4-Maverick-17B-128E-Instruct-FP8": "llama_api",
|
|
"Llama-4-Scout-17B-16E-Instruct-FP8": "llama_api",
|
|
"Qwen/Qwen2.5-72B-Instruct-Turbo": "aiml_api",
|
|
"amazon/nova-lite-v1": "open_router",
|
|
"amazon/nova-micro-v1": "open_router",
|
|
"amazon/nova-pro-v1": "open_router",
|
|
"claude-3-7-sonnet-20250219": "anthropic",
|
|
"claude-3-haiku-20240307": "anthropic",
|
|
"claude-haiku-4-5-20251001": "anthropic",
|
|
"claude-opus-4-1-20250805": "anthropic",
|
|
"claude-opus-4-20250514": "anthropic",
|
|
"claude-opus-4-5-20251101": "anthropic",
|
|
"claude-sonnet-4-20250514": "anthropic",
|
|
"claude-sonnet-4-5-20250929": "anthropic",
|
|
"cohere/command-r-08-2024": "open_router",
|
|
"cohere/command-r-plus-08-2024": "open_router",
|
|
"deepseek/deepseek-chat": "open_router",
|
|
"deepseek/deepseek-r1-0528": "open_router",
|
|
"dolphin-mistral:latest": "ollama",
|
|
"google/gemini-2.0-flash-001": "open_router",
|
|
"google/gemini-2.0-flash-lite-001": "open_router",
|
|
"google/gemini-2.5-flash": "open_router",
|
|
"google/gemini-2.5-flash-lite-preview-06-17": "open_router",
|
|
"google/gemini-2.5-pro-preview-03-25": "open_router",
|
|
"google/gemini-3-pro-preview": "open_router",
|
|
"gpt-3.5-turbo": "openai",
|
|
"gpt-4-turbo": "openai",
|
|
"gpt-4.1-2025-04-14": "openai",
|
|
"gpt-4.1-mini-2025-04-14": "openai",
|
|
"gpt-4o": "openai",
|
|
"gpt-4o-mini": "openai",
|
|
"gpt-5-2025-08-07": "openai",
|
|
"gpt-5-chat-latest": "openai",
|
|
"gpt-5-mini-2025-08-07": "openai",
|
|
"gpt-5-nano-2025-08-07": "openai",
|
|
"gpt-5.1-2025-11-13": "openai",
|
|
"gryphe/mythomax-l2-13b": "open_router",
|
|
"llama-3.1-8b-instant": "groq",
|
|
"llama-3.3-70b-versatile": "groq",
|
|
"llama3": "ollama",
|
|
"llama3.1:405b": "ollama",
|
|
"llama3.2": "ollama",
|
|
"llama3.3": "ollama",
|
|
"meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
|
|
"meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
|
|
"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
|
|
"meta-llama/llama-4-maverick": "open_router",
|
|
"meta-llama/llama-4-scout": "open_router",
|
|
"microsoft/wizardlm-2-8x22b": "open_router",
|
|
"mistralai/mistral-nemo": "open_router",
|
|
"moonshotai/kimi-k2": "open_router",
|
|
"nousresearch/hermes-3-llama-3.1-405b": "open_router",
|
|
"nousresearch/hermes-3-llama-3.1-70b": "open_router",
|
|
"nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
|
|
"o1": "openai",
|
|
"o1-mini": "openai",
|
|
"o3-2025-04-16": "openai",
|
|
"o3-mini": "openai",
|
|
"openai/gpt-oss-120b": "open_router",
|
|
"openai/gpt-oss-20b": "open_router",
|
|
"perplexity/sonar": "open_router",
|
|
"perplexity/sonar-deep-research": "open_router",
|
|
"perplexity/sonar-pro": "open_router",
|
|
"qwen/qwen3-235b-a22b-thinking-2507": "open_router",
|
|
"qwen/qwen3-coder": "open_router",
|
|
"v0-1.0-md": "v0",
|
|
"v0-1.5-lg": "v0",
|
|
"v0-1.5-md": "v0",
|
|
"x-ai/grok-4": "open_router",
|
|
"x-ai/grok-4-fast": "open_router",
|
|
"x-ai/grok-4.1-fast": "open_router",
|
|
"x-ai/grok-code-fast-1": "open_router"
|
|
},
|
|
"discriminator_values": [
|
|
"claude-sonnet-4-20250514"
|
|
]
|
|
},
|
|
"firecrawl_api_key_credentials": {
|
|
"credentials_provider": [
|
|
"firecrawl"
|
|
],
|
|
"credentials_types": [
|
|
"api_key"
|
|
],
|
|
"properties": {
|
|
"id": {
|
|
"title": "Id",
|
|
"type": "string"
|
|
},
|
|
"title": {
|
|
"anyOf": [
|
|
{
|
|
"type": "string"
|
|
},
|
|
{
|
|
"type": "null"
|
|
}
|
|
],
|
|
"default": null,
|
|
"title": "Title"
|
|
},
|
|
"provider": {
|
|
"const": "firecrawl",
|
|
"title": "Provider",
|
|
"type": "string"
|
|
},
|
|
"type": {
|
|
"const": "api_key",
|
|
"title": "Type",
|
|
"type": "string"
|
|
}
|
|
},
|
|
"required": [
|
|
"id",
|
|
"provider",
|
|
"type"
|
|
],
|
|
"title": "CredentialsMetaInput[Literal[<ProviderName.FIRECRAWL: 'firecrawl'>], Literal['api_key']]",
|
|
"type": "object",
|
|
"discriminator_values": []
|
|
}
|
|
},
|
|
"required": [
|
|
"google_oauth2_credentials",
|
|
"openai_api_key_credentials",
|
|
"airtable_api_key-oauth2_credentials",
|
|
"anthropic_api_key_credentials",
|
|
"firecrawl_api_key_credentials"
|
|
],
|
|
"title": "AutomatedSupportAgentCredentialsInputSchema",
|
|
"type": "object"
|
|
}
|
|
} |