feat: fix streaming put_inner_thoughts_in_kwargs

letta/llm_api/helpers.py

@@ -1,6 +1,7 @@
 import copy
 import json
 import warnings
+from collections import OrderedDict
 from typing import Any, List, Union
 
 import requests
@@ -10,6 +11,30 @@
 from letta.utils import json_dumps, printd
 
 
+def convert_to_structured_output(openai_function: dict) -> dict:
+    """Convert function call objects to structured output objects
+
+    See: https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
+    """
+    structured_output = {
+        "name": openai_function["name"],
+        "description": openai_function["description"],
+        "strict": True,
+        "parameters": {"type": "object", "properties": {}, "additionalProperties": False, "required": []},
+    }
+
+    for param, details in openai_function["parameters"]["properties"].items():
+        structured_output["parameters"]["properties"][param] = {"type": details["type"], "description": details["description"]}
+
+        if "enum" in details:
+            structured_output["parameters"]["properties"][param]["enum"] = details["enum"]
+
+    # Add all properties to required list
+    structured_output["parameters"]["required"] = list(structured_output["parameters"]["properties"].keys())
+
+    return structured_output
+
+
 def make_post_request(url: str, headers: dict[str, str], data: dict[str, Any]) -> dict[str, Any]:
     printd(f"Sending request to {url}")
     try:
@@ -78,33 +103,34 @@ def add_inner_thoughts_to_functions(
     inner_thoughts_key: str,
     inner_thoughts_description: str,
     inner_thoughts_required: bool = True,
-    # inner_thoughts_to_front: bool = True,  TODO support sorting somewhere, probably in the to_dict?
 ) -> List[dict]:
-    """Add an inner_thoughts kwarg to every function in the provided list"""
-    # return copies
+    """Add an inner_thoughts kwarg to every function in the provided list, ensuring it's the first parameter"""
     new_functions = []
-
-    # functions is a list of dicts in the OpenAI schema (https://platform.openai.com/docs/api-reference/chat/create)
     for function_object in functions:
-        function_params = function_object["parameters"]["properties"]
-        required_params = list(function_object["parameters"]["required"])
+        new_function_object = copy.deepcopy(function_object)
 
-        # if the inner thoughts arg doesn't exist, add it
-        if inner_thoughts_key not in function_params:
-            function_params[inner_thoughts_key] = {
-                "type": "string",
-                "description": inner_thoughts_description,
-            }
+        # Create a new OrderedDict with inner_thoughts as the first item
+        new_properties = OrderedDict()
+        new_properties[inner_thoughts_key] = {
+            "type": "string",
+            "description": inner_thoughts_description,
+        }
 
-        # make sure it's tagged as required
-        new_function_object = copy.deepcopy(function_object)
-        if inner_thoughts_required and inner_thoughts_key not in required_params:
-            required_params.append(inner_thoughts_key)
-            new_function_object["parameters"]["required"] = required_params
+        # Add the rest of the properties
+        new_properties.update(function_object["parameters"]["properties"])
+
+        # Cast OrderedDict back to a regular dict
+        new_function_object["parameters"]["properties"] = dict(new_properties)
+
+        # Update required parameters if necessary
+        if inner_thoughts_required:
+            required_params = new_function_object["parameters"].get("required", [])
+            if inner_thoughts_key not in required_params:
+                required_params.insert(0, inner_thoughts_key)
+                new_function_object["parameters"]["required"] = required_params
 
         new_functions.append(new_function_object)
 
-    # return a list of copies
     return new_functions
 
 

letta/llm_api/llm_api_tools.py

@@ -140,6 +140,7 @@ def create(
             raise ValueError(f"OpenAI key is missing from letta config file")
 
         data = build_openai_chat_completions_request(llm_config, messages, user_id, functions, function_call, use_tool_naming, max_tokens)
+        print(f"Data.tools: {data.tools}")
 
         if stream:  # Client requested token streaming
             data.stream = True

letta/llm_api/openai.py

@@ -9,7 +9,11 @@
 
 from letta.constants import OPENAI_CONTEXT_WINDOW_ERROR_SUBSTRING
 from letta.errors import LLMError
-from letta.llm_api.helpers import add_inner_thoughts_to_functions, make_post_request
+from letta.llm_api.helpers import (
+    add_inner_thoughts_to_functions,
+    convert_to_structured_output,
+    make_post_request,
+)
 from letta.local_llm.constants import (
     INNER_THOUGHTS_KWARG,
     INNER_THOUGHTS_KWARG_DESCRIPTION,
@@ -112,7 +116,7 @@ def build_openai_chat_completions_request(
     use_tool_naming: bool,
     max_tokens: Optional[int],
 ) -> ChatCompletionRequest:
-    if llm_config.put_inner_thoughts_in_kwargs:
+    if functions and llm_config.put_inner_thoughts_in_kwargs:
         functions = add_inner_thoughts_to_functions(
             functions=functions,
             inner_thoughts_key=INNER_THOUGHTS_KWARG,
@@ -154,8 +158,8 @@ def build_openai_chat_completions_request(
         )
         # https://platform.openai.com/docs/guides/text-generation/json-mode
         # only supported by gpt-4o, gpt-4-turbo, or gpt-3.5-turbo
-        if "gpt-4o" in llm_config.model or "gpt-4-turbo" in llm_config.model or "gpt-3.5-turbo" in llm_config.model:
-            data.response_format = {"type": "json_object"}
+        # if "gpt-4o" in llm_config.model or "gpt-4-turbo" in llm_config.model or "gpt-3.5-turbo" in llm_config.model:
+        # data.response_format = {"type": "json_object"}
 
     if "inference.memgpt.ai" in llm_config.model_endpoint:
         # override user id for inference.memgpt.ai
@@ -451,6 +455,13 @@ def openai_chat_completions_request_stream(
         data.pop("tools")
         data.pop("tool_choice", None)  # extra safe,  should exist always (default="auto")
 
+    if "tools" in data:
+        for tool in data["tools"]:
+            # tool["strict"] = True
+            tool["function"] = convert_to_structured_output(tool["function"])
+
+    print(f"\n\n\n\nData[tools]: {json.dumps(data['tools'], indent=2)}")
+
     printd(f"Sending request to {url}")
     try:
         return _sse_post(url=url, data=data, headers=headers)