Implementing MCP-like Data Retrieval

Using Pinecone, create a mock legal document index. Populate it with simplified tax regulations for two countries (e.g., USA and UK). Then, refine your 'query_legal_database' tool to perform a vector search against this Pinecone index, retrieving top-k relevant document snippets based on the keywords. Demonstrate how your OpenAI Assistant would call this tool to answer a query like 'What are the income tax brackets in the UK?'

```python
# pip install pinecone-client
from pinecone import Pinecone, Index, ServerlessSpec
# Initialize Pinecone (replace with your API key and environment)
# pc = Pinecone(api_key="YOUR_API_KEY")
# index_name = "legal-documents-mcp"
# if index_name not in pc.list_indexes():
#     pc.create_index(name=index_name, dimension=1536, metric='cosine', spec=ServerlessSpec(cloud='aws', region='us-west-2'))
# index = pc.Index(index_name)

# Example of how query_legal_database would use Pinecone
def query_legal_database_with_pinecone(country: str, keywords: str) -> str:
    # ... embed keywords (e.g., with OpenAI embeddings) ...
    # query_vector = get_embedding(keywords)
    # res = index.query(vector=query_vector, top_k=5, filter={"country": country})
    # ... extract and concatenate relevant text from res ...
    return "Relevant legal snippets from Pinecone..."

# OpenAI Assistants SDK tool definition would update to use this function.
```