feat: sql server

servers/sql/main.py

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+import os
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from typing import Optional
+
+# --- LLM/SQL libraries ---
+from langchain_experimental.sql import SQLDatabaseChain
+from langchain_community.llms.openai import OpenAI
+from langchain_community.utilities import SQLDatabase
+
+from sqlalchemy.exc import SQLAlchemyError
+
+# -- Load DB URL from environment variable --
+DATABASE_URL = os.getenv("DATABASE_URL")
+if not DATABASE_URL:
+    raise RuntimeError("DATABASE_URL environment variable must be set.")
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")  # Set this in your environment
+
+
+# -------------------------------
+# Pydantic models
+# -------------------------------
+class SQLChatInput(BaseModel):
+    query: str = Field(
+        ...,
+        description="Your question or task in natural language (e.g. 'Show me the top 10 customers by sales.')",
+    )
+
+
+class SQLChatOutput(BaseModel):
+    sql: str = Field(..., description="SQL that was executed")
+    answer: str = Field(..., description="Answer to your query, from the database")
+    raw_result: Optional[list] = Field(
+        None, description="Raw result rows (list of dict/tuples)"
+    )
+
+
+# -------------------------------
+# API Setup
+# -------------------------------
+app = FastAPI(
+    title="Chat with SQL API",
+    version="1.0.0",
+    description=(
+        "Chat in natural language with any SQL database using LLMs. "
+        "Query and analyze your data conversationally!"
+    ),
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# -------------------------------
+# LLM + SQL Chain Setup (singleton)
+# -------------------------------
+def get_chain():
+    # Initiate reflected SQLAlchemy DB
+    db = SQLDatabase.from_uri(DATABASE_URL)
+    # LLM instance: using OpenAI GPT (or swap for your preferred)
+    llm = OpenAI(
+        temperature=0, openai_api_key=OPENAI_API_KEY, model_name="gpt-3.5-turbo"
+    )
+    return SQLDatabaseChain.from_llm(
+        llm, db, verbose=True, return_sql=True, return_intermediate_steps=True
+    )
+
+
+sql_chain = get_chain()
+
+
+# -------------------------------
+# Schema endpoint
+# -------------------------------
+@app.get("/schema", summary="Get database schema overview")
+def get_db_schema():
+    """
+    Returns the tables and columns for the currently connected database.
+    """
+    try:
+        db = sql_chain.database
+        return db.get_table_info()
+    except Exception as e:
+        raise HTTPException(
+            status_code=500, detail=f"Failed to retrieve schema info: {e}"
+        )
+
+
+# -------------------------------
+# Chatting endpoint
+# -------------------------------
+@app.post(
+    "/chat_sql", response_model=SQLChatOutput, summary="Chat with your SQL database"
+)
+def chat_sql(data: SQLChatInput):
+    """
+    Enter a natural language instruction/question, get answer from your database.
+    """
+    try:
+        # Run chain
+        result = sql_chain({"query": data.query})
+        # result example: {'result': 'Answer in plain text', 'intermediate_steps': {'sql_cmd': sql, ...}}
+        answer = result["result"]
+        sql = None
+        raw_result = None
+        if "intermediate_steps" in result and "sql_cmd" in result["intermediate_steps"]:
+            sql = result["intermediate_steps"]["sql_cmd"]
+        if "intermediate_steps" in result and "result" in result["intermediate_steps"]:
+            raw_result = result["intermediate_steps"]["result"]
+        return SQLChatOutput(sql=sql or "", answer=answer, raw_result=raw_result)
+    except SQLAlchemyError as e:
+        raise HTTPException(status_code=400, detail=f"Database error: {str(e)}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error: {e}")

servers/sql/requirements.txt

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+fastapi
+uvicorn[standard]
+pydantic
+python-multipart
+
+langchain
+langchain_community
+langchain-openai
+langchain-experimental
+sentence_transformers
+sqlalchemy