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updated data to 400+ use cases

README.md

@@ -4,7 +4,7 @@ emoji: 🧠
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 5.44.0
+sdk_version: 4.0.0
 app_file: app.py
 pinned: false
 license: mit
@@ -12,7 +12,7 @@ license: mit
 
 # ML/AI Use Cases RAG Assistant (Bring Your Own Key)
 
-An AI-powered assistant that provides business advice based on real ML/AI implementations from 310+ companies. This app uses Retrieval-Augmented Generation (RAG) to find relevant company examples and provides actionable recommendations.
+An AI-powered assistant that provides business advice based on real ML/AI implementations from 60+ companies with 400+ use cases. This app uses Retrieval-Augmented Generation (RAG) to find relevant company examples and provides actionable recommendations.
 
 **🔑 Bring Your Own Key:** This version requires users to provide their own HuggingFace API key, ensuring zero cost to the space owner while maintaining full functionality.
 
@@ -30,7 +30,7 @@ An AI-powered assistant that provides business advice based on real ML/AI implem
 
 1. **🔑 API Key Setup**: Provide your HuggingFace API key for secure access
 2. **📝 Query Processing**: Your business problem is analyzed and converted to embeddings
-3. **🔍 Semantic Search**: The system searches through 310+ pre-processed ML use cases
+3. **🔍 Semantic Search**: The system searches through 400+ pre-processed ML use cases
 4. **📚 Context Building**: Relevant company examples are selected as context
 5. **🤖 AI Generation**: Your API key powers the language model to generate tailored advice
 6. **📊 Model Matching**: HuggingFace API provides relevant model recommendations using your key

app.py

@@ -480,7 +480,7 @@ def generate_template_response(prompt: str) -> str:
 - Business metrics alignment
 - Technical infrastructure requirements
 
-This analysis is based on patterns from 310+ real-world ML implementations across various industries."""
+This analysis is based on patterns from 400+ real-world ML implementations across various industries."""
 
 @app.post("/chat", response_model=ChatResponse)
 async def chat_with_rag(request: ChatRequest, x_hf_api_key: str = Header(None, alias="X-HF-API-Key")):

chroma_db_complete.tar.gz

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