InvokeURL butchering JSON for OpenAI API calls

InvokeURL butchering JSON for OpenAI API calls

My organization works with mostly educational institutions. We have a custom module called "Schools", which is the user-entered school name they put when using our service (which they enter along with their state and zip code). We want to map this to a list of known schools from a public database that has both a standardized school name, address, phone number, and other data about the school (along with a unique identifier) stored in our org's Zoho Analytics database.

To accomplish this, we are trying to leverage OpenAI's API to find a "best match" of the unique ID. When a school is created in our system, I am getting the "State", "Zip", and entered school name. This runs a query in Zoho Analytics to return batches of 250 schools in that state at a time (to avoid API size problems), and runs this through OpenAI to find which in each batch is the closest match to what was entered, and saves that to a new array. This repeats for all schools in the state, then afterwards runs the set of "winners" through OpenAI one more time to get the best overall winner.

The problem is our API call to OpenAI - it continually returns 

{"error":{"message":"We could not parse the JSON body of your request. (HINT: This likely means you aren't using your HTTP library correctly. The OpenAI API expects a JSON payload, but what was sent was not valid JSON. If you have trouble figuring out how to fix this, please contact us through our help center at help.openai.com.)","type":"invalid_request_error","param":null,"code":null}}

I verified several times that my JSON body is indeed correct. I also have alternated between using "parameters" and "body", and various ways to stringify the payload - none of which have any improvement. Recommendations from ChatGPT believe that regardless of me specifying "content-type: application/json", it is still mangling the payload.

The worst part is - this actually worked just fine until last night (October 17, 2025), sometime around 5pm. I had been running the version of the script below to back-fill our existing school database, which had been calling this API every 45 seconds for a couple days without any errors. OpenAI insists they have not changed anything on their end, and zoho insists the same on theirs. Any help would be appreciated.

string standalone.SetSchoolAddressAPI()
{
PAGE_SIZE = 250;
random = randomNumber(1,50);
school = zoho.crm.searchRecords("Schools","(NCES_School_ID:equals:99)",1,random);
st_school_id = school.get(0).get("id");
info st_school_id;
school = zoho.crm.getRecordById("Schools",st_school_id);
school_name = ifnull(school.get("Name"),"");
acc_id = null;
if(school.containsKey("Account") && school.get("Account") != null && school.get("Account").containsKey("id"))
{
acc_id = school.get("Account").get("id");
}
account = Map();
if(acc_id != null)
{
account = zoho.crm.getRecordById("Accounts",acc_id);
}
account = ifnull(account,Map());
school_zip = ifnull(school.get("Zip"),account.get("Billing_Code"));
school_state = ifnull(school.get("State"),account.get("Billing_State"));
target_name = ifnull(school_name,"");
state_input = ifnull(school_state,"");
zip_input = ifnull(school_zip,"");
/***** --- Enforce state presence & normalize --- *****/
if(state_input == null || state_input == "")
{
update_map = Map();
update_map.put("NCES_School_ID",null);
update_map.put("Country","");
update_map.put("State",school_state);
zoho.crm.updateRecord("Schools",st_school_id,update_map);
return "No state on record; aborting NCES match.";
}
state_input = state_input.trim();
/***** === Zoho Analytics fixed config (unchanged) === *****/
workspace_name = "Zoho CRM Analytics";
view_name = "NCES Schools";
owner_email = "xxx";
ws_enc = zoho.encryption.urlEncode(workspace_name);
view_enc = zoho.encryption.urlEncode(view_name);
endpoint = "https://analyticsapi.zoho.com/api/" + owner_email + "/" + ws_enc + "/" + view_enc;
/***** === Step 1: Count rows by state === *****/
state_esc = state_input.replaceAll("'","''");
count_sql = "select count(*) as total_count from \"" + view_name + "\" where \"State\"='" + state_esc + "'";
count_params = Map();
count_params.put("ZOHO_ACTION","EXPORT");
count_params.put("ZOHO_OUTPUT_FORMAT","JSON");
count_params.put("ZOHO_SQLQUERY",count_sql);
count_params.put("ZOHO_API_VERSION","1.0");
count_resp = invokeurl
[
url :endpoint
type :POST
parameters:count_params
connection:"zoho_analytics_conn"
];
total_count = 0;
// First try KV shape: {"data":[{"total_count":"10398"}]}
if(count_resp != null && count_resp.containsKey("data") && count_resp.get("data") != null && count_resp.get("data").size() > 0)
{
tc_raw = count_resp.get("data").get(0).get("total_count");
if(tc_raw != null)
{
total_count = tc_raw.toLong();
}
}
else
{
// Fallback to rows shape:
// {"response":{"result":{"column_order":["total_count"],"rows":[["10398"]]}}}
if(count_resp != null && count_resp.containsKey("response"))
{
resp_obj = count_resp.get("response");
if(resp_obj != null && resp_obj.containsKey("result"))
{
result_obj = resp_obj.get("result");
if(result_obj != null && result_obj.containsKey("rows"))
{
rows_list = result_obj.get("rows");
if(rows_list != null && rows_list.size() > 0)
{
first_row = rows_list.get(0);
// rows are arrays, so first cell holds the count as string
if(first_row != null && first_row.size() > 0)
{
tc_raw2 = first_row.get(0);
if(tc_raw2 != null)
{
total_count = tc_raw2.toLong();
}
}
}
}
}
}
}
/***** === Step 2: Build list of page indexes (Deluge compliant, no while) === *****/
page_map = list();
num_pages = (total_count / PAGE_SIZE).toLong();
if(total_count % PAGE_SIZE > 0)
{
num_pages = num_pages + 1;
}
// use nested for-each loops over a fixed dummy list to reach num_pages
dummy = list();
for each  d in {"a","b","c","d","e","f","g","h","i","j"}
{
// outer loop (10×)
for each  e in {"1","2","3","4","5","6","7","8","9","10"}
{
// inner loop (10×)
if(page_map.size() < num_pages)
{
page_map.add(page_map.size());
}
}
}
/***** === Step 3: For each page, fetch candidates and pick a batch winner via OpenAI === *****/
batch_winners = list();
for each  p in page_map
{
offset_val = p * PAGE_SIZE;
sql_batch = "select \"State\",\"School Name\",\"NCES School ID\",\"Zip\"" + "from \"" + view_name + "\" where \"State\"='" + state_esc + "' " + "limit " + PAGE_SIZE.toString() + " offset " + offset_val.toString();
params = Map();
params.put("ZOHO_ACTION","EXPORT");
params.put("ZOHO_OUTPUT_FORMAT","JSON");
params.put("ZOHO_API_VERSION","1.0");
params.put("KEY_VALUE_FORMAT","true");
params.put("ZOHO_SQLQUERY",sql_batch);
count_params.put("ZOHO_API_VERSION","1.0");
resp_batch = invokeurl
[
url :endpoint
type :POST
parameters:params
connection:"zoho_analytics_conn"
];
/***** Parse candidates safely no matter the response shape *****/
candidates = list();
if(resp_batch != null)
{
// Case 1: Key-value format (normal Zoho Analytics JSON)
if(resp_batch.containsKey("data") && resp_batch.get("data") != null)
{
candidates = resp_batch.get("data");
}
// Case 2: Nested "response" > "result" > "rows" format
else if(resp_batch.containsKey("response"))
{
resp_obj = resp_batch.get("response");
if(resp_obj != null && resp_obj.containsKey("result"))
{
result_obj = resp_obj.get("result");
if(result_obj != null && result_obj.containsKey("rows"))
{
rows_list = result_obj.get("rows");
// convert matrix rows into list of maps for consistency
for each  row_item in rows_list
{
row_map = Map();
// optional: if you have column_order, map by index
if(result_obj.containsKey("column_order"))
{
cols = result_obj.get("column_order");
idx = 0;
for each  col in cols
{
if(row_item.size() > idx)
{
row_map.put(col,row_item.get(idx));
}
idx = idx + 1;
}
}
candidates.add(row_map);
}
}
}
}
}
if(candidates.isEmpty())
{
info "No candidates found for this page (state=" + state_input + ")";
continue;
}
/***** Build compact OpenAI prompt for this batch *****/
cand_lines = "";
for each  c in candidates
{
id_short = ifnull(c.get("NCES School ID"),"");
name_short = ifnull(c.get("School Name"),"");
if(name_short != null && name_short.length() > 60)
{
name_short = name_short.substring(0,60);
}
zip_short = ifnull(c.get("Zip"),"");
if(zip_short != null && zip_short.length() > 10)
{
zip_short = zip_short.substring(0,10);
}
delimiter = "###";
// define this once before the loop if not already defined
record_sep = " || ";
// separates candidate rows, readable and single-line
cand_lines = cand_lines + id_short + "|" + name_short + "|" + zip_short + record_sep;
}
// === Build simplified OpenAI request (no line breaks) ===
prompt = "TARGET=" + target_name + "|" + zip_input + delimiter + "CANDIDATES=" + cand_lines + delimiter + "Return ONLY JSON {\"nces_school_id\": string|null, \"confidence\": number}";
messages = List();
messages.add({"role":"system","content":"Match the user-entered school to the best NCES candidate using NAME similarity and ZIP proximity. The delimiter is " + delimiter + "."});
messages.add({"role":"user","content":prompt});
req_body = Map();
req_body.put("model","gpt-4o-mini");
req_body.put("temperature",0);
req_body.put("messages",messages);
ai_resp = invokeurl
[
url :"https://api.openai.com/v1/chat/completions"
type :POST
parameters:req_body.toString()
connection:"openai_custom"
];
// ========== Step 4: Inspect result ==========
best_id = "";
best_conf = 0.0;
raw_content = ai_resp.get("choices").get(0).get("message").get("content");
// Remove markdown fences if present
cleaned = raw_content.replaceAll("(?s)```json|```","").trim();
parsed = cleaned.toMap();
school_id = parsed.get("nces_school_id");
confidence = parsed.get("confidence");
for each  c in candidates
{
if(c.get("NCES School ID") == school_id)
{
if(confidence > 0.8)
{
//do not put low-confidence candidates in the winner batch
c.put("confidence",confidence);
batch_winners.add(c);
break;
}
}
}
}
/***** === Step 4: Final round — pick overall best among batch winners === *****/
if(batch_winners.size() == 0)
{
update_map = Map();
update_map.put("NCES_School_ID",null);
update_map.put("Country","");
update_map.put("State",school_state);
zoho.crm.updateRecord("Schools",st_school_id,update_map);
info "No candidates found for state=" + state_input;
}
winner_lines = "";
for each  w in batch_winners
{
id_short = ifnull(w.get("NCES School ID"),"");
name_short = ifnull(w.get("School Name"),"");
if(name_short != null && name_short.length() > 60)
{
name_short = name_short.substring(0,60);
}
zip_short = ifnull(w.get("Zip"),"");
if(zip_short != null && zip_short.length() > 10)
{
zip_short = zip_short.substring(0,10);
}
delimiter = "###";
// define this once before the loop if not already defined
record_sep = " || ";
// separates candidate rows, readable and single-line
winner_lines = winner_lines + id_short + "|" + name_short + "|" + zip_short + record_sep;
}
// === Build simplified OpenAI request (no line breaks) ===
prompt = "TARGET=" + target_name + "|" + zip_input + delimiter + "CANDIDATES=" + winner_lines + delimiter + "Return ONLY JSON {\"nces_school_id\": string|null, \"confidence\": number}";
messages = List();
messages.add({"role":"system","content":"Match the user-entered school to the best NCES candidate using NAME similarity and ZIP proximity. The delimiter is " + delimiter + "."});
messages.add({"role":"user","content":prompt});
req_body = Map();
req_body.put("model","gpt-4o-mini");
req_body.put("temperature",0);
req_body.put("messages",messages);
final_ai = invokeurl
[
url :"https://api.openai.com/v1/chat/completions"
type :POST
parameters:req_body.toString()
connection:"openai_custom"
];
selected_nces = "";
confidence = 0.0;
contentText = final_ai.get("choices").get(0).get("message").get("content");
// Step 2: Sanitize it — remove code block markers and trim spaces/newlines
cleanText = contentText.replaceAll("```json","");
cleanText = cleanText.replaceAll("```","");
cleanText = cleanText.trim();
// Step 3: Parse the cleaned string as JSON
contentJSON = cleanText.toMap();
// Step 4: Extract the NCES/NCAS ID
selected_nces = contentJSON.get("nces_school_id");
/***** === Step 5: Find selected record and update CRM (unchanged) === *****/
/***** === Step 2: Fetch single row by NCES School ID === *****/
row_sql = "select \"NCES School ID\", \"Website\", \"Address\", \"Address 2\", \"City\", \"State\", \"Zip\", \"Phone\", \"Grade Level\" " + "from \"" + view_name + "\" " + "where \"NCES School ID\" = '" + selected_nces + "' " + "limit 1";
row_params = Map();
row_params.put("ZOHO_ACTION","EXPORT");
row_params.put("ZOHO_OUTPUT_FORMAT","JSON");
row_params.put("ZOHO_SQLQUERY",row_sql);
row_params.put("ZOHO_API_VERSION","1.0");
row_resp = invokeurl
[
url :endpoint
type :POST
parameters:row_params
connection:"zoho_analytics_conn"
];
/***** === Step 3: Parse response and assign variables === *****/
nces_id_final = "";
Website_final = "";
Address_final = "";
Address2_final = "";
City_final = "";
State_final = "";
Zip_final = "";
Phone_final = "";
Grade_final = "";
// First check for JSON KV structure
if(row_resp != null && row_resp.containsKey("data") && row_resp.get("data") != null && row_resp.get("data").size() > 0)
{
row_data = row_resp.get("data").get(0);
nces_id_final = selected_nces;
Website_final = ifnull(row_data.get("Website"),"");
Address_final = ifnull(row_data.get("Address"),"");
Address2_final = ifnull(row_data.get("Address 2"),"");
City_final = ifnull(row_data.get("City"),"");
State_final = ifnull(row_data.get("State"),"");
Zip_final = ifnull(row_data.get("Zip"),"");
Phone_final = ifnull(row_data.get("Phone"),"");
Grade_final = ifnull(row_data.get("Grade Level"),"");
}
else
{
// Fallback for "rows" format
if(row_resp != null && row_resp.containsKey("response"))
{
resp_obj = row_resp.get("response");
if(resp_obj != null && resp_obj.containsKey("result"))
{
result_obj = resp_obj.get("result");
if(result_obj != null && result_obj.containsKey("rows"))
{
rows_list = result_obj.get("rows");
if(rows_list != null && rows_list.size() > 0)
{
first_row = rows_list.get(0);
if(first_row != null && first_row.size() >= 9)
{
nces_id_final = ifnull(first_row.get(0),"");
Website_final = ifnull(first_row.get(1),"");
Address_final = ifnull(first_row.get(2),"");
Address2_final = ifnull(first_row.get(3),"");
City_final = ifnull(first_row.get(4),"");
State_final = ifnull(first_row.get(5),"");
Zip_final = ifnull(first_row.get(6),"");
Phone_final = ifnull(first_row.get(7),"");
Grade_final = ifnull(first_row.get(8),"");
}
}
}
}
}
}
country = "USA";
if(isNull(State_final))
{
country = "";
}
update_map = Map();
update_map.put("NCES_School_ID",nces_id_final);
update_map.put("School_Website",Website_final);
update_map.put("Address",Address_final);
update_map.put("Address_2",Address2_final);
update_map.put("City",City_final);
update_map.put("State",State_final);
update_map.put("Zip",Zip_final);
update_map.put("School_Phone",Phone_final);
update_map.put("Grade_Level",Grade_final);
update_map.put("Country",country);
update_rec = zoho.crm.updateRecord("Schools",st_school_id,update_map);
info st_school_id + " Updated";
/***** === Diagnostics === *****/
diag = Map();
diag.put("school_id",st_school_id);
diag.put("selected_nces_school_id",nces_id_final);
diag.put("model_confidence",confidence);
used_fallback = false;
if(selected_nces == "" || selected_nces == null)
{
used_fallback = true;
}
diag.put("used_fallback",used_fallback);
return diag.toString();
}

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