Update app.py

app.py

@@ -1,163 +1,163 @@
-# main.py
-from fastapi import FastAPI, HTTPException, status, File, UploadFile, Form, Query
-from fastapi.middleware.cors import CORSMiddleware
-from typing import Optional
-import pandas as pd
-import io
-import os
-from text_engine import Text_Search_Engine
-
-app = FastAPI(title="CortexSearch", version="1.0", description="A flexible text search API with multiple FAISS index types and BM25 support.")
-
-# Choose default index_type here: "flat", "ivf", or "hnsw"
-store = Text_Search_Engine(index_type=os.getenv("INDEX_TYPE", "flat"))
-try:
-    store.load()
-except Exception:
-    pass
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"], 
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.get("/")
-async def root():
-    return {"message": "Welcome to the Flexible Text Intelligence API"}
-
-
-# -------------------------
-# Column preview endpoint
-# -------------------------
-@app.post("/list_columns")
-async def list_columns(file: UploadFile = File(...)):
-    """
-    Upload a CSV and get available columns back.
-    Useful to preview before choosing columns to index.
-    """
-    try:
-        contents = await file.read()
-        df = pd.read_csv(io.BytesIO(contents))
-        return {"available_columns": list(df.columns)}
-    except Exception as e:
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e))
-
-
-# -------------------------
-# Health check endpoint
-# -------------------------
-@app.get("/health")
-async def health():
-    return {"status": "ok", "rows_indexed": len(store.rows), "index_type": store.index_type}
-
-
-# -------------------------
-# Upload CSV (build fresh index)
-# -------------------------
-@app.post("/upload_csv")
-async def upload_csv(file: UploadFile = File(...), columns: str = Form(...), index_type: Optional[str] = Form(None)):
-    #Upload CSV and specify columns (comma-separated) to combine into searchable text.
-    #Optional form field 'index_type' can be 'flat', 'ivf', or 'hnsw' to override engine default.
-    try:
-        contents = await file.read()
-        df = pd.read_csv(io.BytesIO(contents))
-
-        column_list = [c.strip() for c in columns.split(",") if c.strip()]
-        # Validate
-        for col in column_list:
-            if col not in df.columns:
-                return {
-                    "status": "error",
-                    "detail": f"Column '{col}' not found.",
-                    "available_columns": list(df.columns),
-                }
-
-        rows = df.dropna(subset=column_list).to_dict(orient="records")
-        for r in rows:
-            r["_search_text"] = " ".join(str(r[col]) for col in column_list if r.get(col) is not None)
-
-        texts = [r["_search_text"] for r in rows]
-
-        if index_type:
-            store.index_type = index_type
-
-        store.encode_store(rows, texts)
-        return {"status": "success", "count": len(rows), "used_columns": column_list, "index_type": store.index_type}
-    except Exception as e:
-        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
-
-
-# -------------------------
-# Add CSV (append new rows)
-# -------------------------
-@app.post("/add_csv")
-async def add_csv(file: UploadFile = File(...), columns: str = Form(...)):
-    try:
-        contents = await file.read()
-        df = pd.read_csv(io.BytesIO(contents))
-
-        column_list = [c.strip() for c in columns.split(",") if c.strip()]
-        for col in column_list:
-            if col not in df.columns:
-                return {
-                    "status": "error",
-                    "detail": f"Column '{col}' not found.",
-                    "available_columns": list(df.columns),
-                }
-
-        new_rows = df.dropna(subset=column_list).to_dict(orient="records")
-        for r in new_rows:
-            r["_search_text"] = " ".join(str(r[col]) for col in column_list if r.get(col) is not None)
-
-        new_texts = [r["_search_text"] for r in new_rows]
-
-        store.add_rows(new_rows, new_texts)
-
-        return {"status": "success", "added_count": len(new_rows), "used_columns": column_list, "total_rows": len(store.rows)}
-    except Exception as e:
-        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
-
-
-# -------------------------
-# Search endpoint
-# -------------------------
-@app.get("/search")
-async def search(
-    query: str,
-    top_k: int = 3,
-    mode: str = Query("semantic", enum=["semantic", "lexical", "hybrid"]),
-    alpha: float = 0.5,):
-    #mode: semantic | lexical | hybrid
-    #alpha: weight for semantic in hybrid (0..1)
-    try:
-        if mode == "semantic":
-            results = store.search(query, top_k=top_k)
-        elif mode == "lexical":
-            if store.bm25 is None:
-                return {"results": []}
-            tokenized_query = query.lower().split()
-            scores = store.bm25.get_scores(tokenized_query)
-            ranked = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)[:top_k]
-            results = [{**store.rows[i], "score": float(score)} for i, score in ranked]
-        else:
-            results = store.hybrid_search(query, top_k=top_k, alpha=alpha)
-
-        return {"results": results}
-    except Exception as e:
-        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
-
-
-# -------------------------
-# Delete all data
-# -------------------------
-@app.delete("/delete_data")
-async def delete_data():
-    try:
-        store.clear_vdb()
-        return {"status": "success", "message": "Vector DB cleared"}
-    except Exception as e:
+# main.py
+from fastapi import FastAPI, HTTPException, status, File, UploadFile, Form, Query
+from fastapi.middleware.cors import CORSMiddleware
+from typing import Optional
+import pandas as pd
+import io
+import os
+from text_engine import Text_Search_Engine
+
+app = FastAPI(title="CortexSearch", version="1.0", description="A flexible text search API with multiple FAISS index types and BM25 support.")
+
+# Choose default index_type here: "flat", "ivf", or "hnsw"
+store = Text_Search_Engine(index_type=os.getenv("INDEX_TYPE", "flat"))
+try:
+    store.load()
+except Exception:
+    pass
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], 
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+async def root():
+    return {"Status": "The CortexSearch API is live!!!"}
+
+
+# -------------------------
+# Column preview endpoint
+# -------------------------
+@app.post("/list_columns")
+async def list_columns(file: UploadFile = File(...)):
+    """
+    Upload a CSV and get available columns back.
+    Useful to preview before choosing columns to index.
+    """
+    try:
+        contents = await file.read()
+        df = pd.read_csv(io.BytesIO(contents))
+        return {"available_columns": list(df.columns)}
+    except Exception as e:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e))
+
+
+# -------------------------
+# Health check endpoint
+# -------------------------
+@app.get("/health")
+async def health():
+    return {"status": "ok", "rows_indexed": len(store.rows), "index_type": store.index_type}
+
+
+# -------------------------
+# Upload CSV (build fresh index)
+# -------------------------
+@app.post("/upload_csv")
+async def upload_csv(file: UploadFile = File(...), columns: str = Form(...), index_type: Optional[str] = Form(None)):
+    #Upload CSV and specify columns (comma-separated) to combine into searchable text.
+    #Optional form field 'index_type' can be 'flat', 'ivf', or 'hnsw' to override engine default.
+    try:
+        contents = await file.read()
+        df = pd.read_csv(io.BytesIO(contents))
+
+        column_list = [c.strip() for c in columns.split(",") if c.strip()]
+        # Validate
+        for col in column_list:
+            if col not in df.columns:
+                return {
+                    "status": "error",
+                    "detail": f"Column '{col}' not found.",
+                    "available_columns": list(df.columns),
+                }
+
+        rows = df.dropna(subset=column_list).to_dict(orient="records")
+        for r in rows:
+            r["_search_text"] = " ".join(str(r[col]) for col in column_list if r.get(col) is not None)
+
+        texts = [r["_search_text"] for r in rows]
+
+        if index_type:
+            store.index_type = index_type
+
+        store.encode_store(rows, texts)
+        return {"status": "success", "count": len(rows), "used_columns": column_list, "index_type": store.index_type}
+    except Exception as e:
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
+
+
+# -------------------------
+# Add CSV (append new rows)
+# -------------------------
+@app.post("/add_csv")
+async def add_csv(file: UploadFile = File(...), columns: str = Form(...)):
+    try:
+        contents = await file.read()
+        df = pd.read_csv(io.BytesIO(contents))
+
+        column_list = [c.strip() for c in columns.split(",") if c.strip()]
+        for col in column_list:
+            if col not in df.columns:
+                return {
+                    "status": "error",
+                    "detail": f"Column '{col}' not found.",
+                    "available_columns": list(df.columns),
+                }
+
+        new_rows = df.dropna(subset=column_list).to_dict(orient="records")
+        for r in new_rows:
+            r["_search_text"] = " ".join(str(r[col]) for col in column_list if r.get(col) is not None)
+
+        new_texts = [r["_search_text"] for r in new_rows]
+
+        store.add_rows(new_rows, new_texts)
+
+        return {"status": "success", "added_count": len(new_rows), "used_columns": column_list, "total_rows": len(store.rows)}
+    except Exception as e:
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
+
+
+# -------------------------
+# Search endpoint
+# -------------------------
+@app.get("/search")
+async def search(
+    query: str,
+    top_k: int = 3,
+    mode: str = Query("semantic", enum=["semantic", "lexical", "hybrid"]),
+    alpha: float = 0.5,):
+    #mode: semantic | lexical | hybrid
+    #alpha: weight for semantic in hybrid (0..1)
+    try:
+        if mode == "semantic":
+            results = store.search(query, top_k=top_k)
+        elif mode == "lexical":
+            if store.bm25 is None:
+                return {"results": []}
+            tokenized_query = query.lower().split()
+            scores = store.bm25.get_scores(tokenized_query)
+            ranked = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)[:top_k]
+            results = [{**store.rows[i], "score": float(score)} for i, score in ranked]
+        else:
+            results = store.hybrid_search(query, top_k=top_k, alpha=alpha)
+
+        return {"results": results}
+    except Exception as e:
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))
+
+
+# -------------------------
+# Delete all data
+# -------------------------
+@app.delete("/delete_data")
+async def delete_data():
+    try:
+        store.clear_vdb()
+        return {"status": "success", "message": "Vector DB cleared"}
+    except Exception as e:
         raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e))