litellm updates to support only messages llm kwarg

docs/concepts/error_remediation.md

@@ -18,7 +18,6 @@ Note that this list is not exhaustive of the possible errors that could occur.
 ```log
 The callable `fn` passed to `Guard(fn, ...)` failed with the following error:
   {Root error message here!}.
-Make sure that `fn` can be called as a function that takes in a single prompt string and returns a string.
 ```
 
 

docs/concepts/logs.md

@@ -33,17 +33,17 @@ docs/html/single-step-history.html
 
 ## Calls
 ### Initial Input
-Inital inputs like prompt and instructions from a call are available on each call. 
+Initial inputs like messages from a call are available on each call. 
 
 ```py
 first_call = my_guard.history.first
-print("prompt\n-----")
-print(first_call.prompt)
+print("message\n-----")
+print(first_call.messages[0]["content"])
 print("prompt params\n------------- ")
 print(first_call.prompt_params)
 ```
 ```log
-prompt
+message
 -----
 
 You are a human in an enchanted forest. You come across opponents of different types. You should fight smaller opponents, run away from bigger ones, and freeze if the opponent is a bear.
@@ -67,18 +67,6 @@ prompt params
 {'opp_type': 'grizzly'}
 ```
 
-Note: Input messages and msg_history currently can be accessed through iterations
-```py
-print(guard.history.last.iterations.last.inputs.msg_history)
-```
-```log
-[
-  {"role":"system","content":"You are a helpful assistant."},
-  {"role":"user","content":"Tell me a joke"}
-]
-```
-
-
 ### Final Output
 Final output of call is accessible on a call. 
 ```py

docs/concepts/streaming.ipynb

@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,12 +51,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ValidationOutcome</span><span style=\"font-weight: bold\">(</span>\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">call_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'14148119808'</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">raw_llm_output</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'.'</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">validation_summaries</span>=<span style=\"font-weight: bold\">[]</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">validated_output</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'.'</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">reask</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">validation_passed</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">error</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>\n",
+       "<span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;35mValidationOutcome\u001b[0m\u001b[1m(\u001b[0m\n",
+       "    \u001b[33mcall_id\u001b[0m=\u001b[32m'14148119808'\u001b[0m,\n",
+       "    \u001b[33mraw_llm_output\u001b[0m=\u001b[32m'.'\u001b[0m,\n",
+       "    \u001b[33mvalidation_summaries\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m,\n",
+       "    \u001b[33mvalidated_output\u001b[0m=\u001b[32m'.'\u001b[0m,\n",
+       "    \u001b[33mreask\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",
+       "    \u001b[33mvalidation_passed\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
+       "    \u001b[33merror\u001b[0m=\u001b[3;35mNone\u001b[0m\n",
+       "\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fragment_generator = guard(\n",
-    "    litellm.completion,\n",
     "    model=\"gpt-4o\",\n",
     "    messages=[\n",
     "        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
@@ -116,7 +145,6 @@
     "guard = gd.Guard()\n",
     "\n",
     "fragment_generator = await guard(\n",
-    "    litellm.completion,\n",
     "    model=\"gpt-3.5-turbo\",\n",
     "    messages=[\n",
     "        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
@@ -137,7 +165,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "litellm",
    "language": "python",
    "name": "python3"
   },
@@ -151,7 +179,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,