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86cbe3c a0eb181 86cbe3c a0eb181 86cbe3c a0eb181 86cbe3c 6422ca4 86cbe3c 6422ca4 86cbe3c 6422ca4 86cbe3c a0eb181 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import os
import requests
import json
from typing import Dict, Any, List, Optional
from langchain.tools import BaseTool
from pydantic import Field
import logging
logger = logging.getLogger(__name__)
class MCPClient:
"""Client for making authenticated REST API calls to the MCP server."""
def __init__(self, mcp_url: str, api_key: str):
self.mcp_url = mcp_url
self.headers = {
"x-api-key": api_key,
"Content-Type": "application/json"
}
def post(self, endpoint: str, data: Dict[str, Any]) -> Dict[str, Any]:
"""Send a POST request to a given MCP endpoint."""
try:
url = f"{self.mcp_url}/{endpoint}"
response = requests.post(url, headers=self.headers, data=json.dumps(data))
response.raise_for_status()
return response.json()
except requests.exceptions.HTTPError as http_err:
logger.error(f"HTTP error occurred: {http_err} - {response.text}")
return {"status": "error", "message": f"HTTP error: {response.status_code} {response.reason}"}
except requests.exceptions.RequestException as req_err:
logger.error(f"Request error occurred: {req_err}")
return {"status": "error", "message": f"Request failed: {req_err}"}
except json.JSONDecodeError:
logger.error("Failed to decode JSON response.")
return {"status": "error", "message": "Invalid JSON response from server."}
class SchemaSearchTool(BaseTool):
"""LangChain tool for searching database schemas."""
name: str = "schema_search"
description: str = """
Search for relevant database schemas based on a natural language query.
Use this when you need to find which tables/columns are relevant to a user's question.
Input should be a descriptive query like 'patient information' or 'drug trials'.
"""
mcp_client: MCPClient
def _run(self, query: str) -> str:
"""Execute schema search."""
response = self.mcp_client.post("discovery/get_relevant_schemas", {"query": query})
if response.get("status") == "success":
schemas = response.get("schemas", [])
if schemas:
schema_text = "Found relevant schemas:\\n"
for schema in schemas:
schema_text += f"- {schema.get('database', 'Unknown')}.{schema.get('table', 'Unknown')}.{schema.get('name', 'Unknown')} ({schema.get('type', ['Unknown'])[0]})\\n"
return schema_text
else:
return "No relevant schemas found."
else:
return f"Error searching schemas: {response.get('message', 'Unknown error')}"
async def _arun(self, query: str) -> str:
"""Async version - just calls sync version."""
return self._run(query)
class JoinPathFinderTool(BaseTool):
"""LangChain tool for finding join paths between tables."""
name: str = "find_join_path"
description: str = """
Find how to join two tables together using foreign key relationships.
Use this when you need to query across multiple tables.
Input should be two table names separated by a comma, like 'patients,studies'.
"""
mcp_client: MCPClient
def _run(self, table_names: str) -> str:
"""Find join path."""
try:
tables = [t.strip() for t in table_names.split(',')]
if len(tables) != 2:
return "Please provide exactly two table names separated by a comma."
response = self.mcp_client.post(
"graph/find_join_path",
{"table1": tables[0], "table2": tables[1]}
)
if response.get("status") == "success":
path = response.get("path", "No path found")
return f"Join path: {path}"
else:
return f"Error finding join path: {response.get('message', 'Unknown error')}"
except Exception as e:
return f"Failed to find join path: {str(e)}"
async def _arun(self, table_names: str) -> str:
"""Async version - just calls sync version."""
return self._run(table_names)
class QueryExecutorTool(BaseTool):
"""LangChain tool for executing SQL queries."""
name: str = "execute_query"
description: str = """
Execute a SQL query against the databases and return results.
Use this after you have a valid SQL query.
Input should be a valid SQL query string.
"""
mcp_client: MCPClient
def _run(self, sql: str) -> str:
"""Execute query."""
try:
response = self.mcp_client.post(
"intelligence/execute_query",
{"sql": sql}
)
if response.get("status") == "success":
results = response.get("results", [])
if results:
# Format results as a readable table
result_text = f"Query returned {len(results)} rows:\\n"
headers = list(results[0].keys())
result_text += " | ".join(headers) + "\n"
result_text += "-" * (len(" | ".join(headers))) + "\n"
for row in results[:10]: # Limit display to first 10 rows
values = [str(row.get(h, "")) for h in headers]
result_text += " | ".join(values) + "\n"
if len(results) > 10:
result_text += f"... and {len(results) - 10} more rows\n"
return result_text
else:
return "Query executed successfully but returned no results."
else:
return f"Error executing query: {response.get('message', 'Unknown error')}"
except Exception as e:
return f"Failed to execute query: {str(e)}"
async def _arun(self, sql: str) -> str:
"""Async version - just calls sync version."""
return self._run(sql)
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