fix: tool choice for groq, datetime for cohere

src/examples/cohere_example/rerank.py

@@ -1,5 +1,6 @@
 import cohere
 from dotenv import find_dotenv, load_dotenv
+from datetime import datetime
 
 from langtrace_python_sdk import langtrace
 
@@ -16,10 +17,22 @@
 # @with_langtrace_root_span("embed_create")
 def rerank():
     docs = [
-        "Carson City is the capital city of the American state of Nevada.",
-        "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
-        "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.",
-        "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states.",
+        {
+            "text": "Carson City is the capital city of the American state of Nevada.",
+            "date": datetime.now(),
+        },
+        {
+            "text": "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
+            "date": datetime(2020, 5, 17),
+        },
+        {
+            "text": "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.",
+            "date": datetime(1776, 7, 4),
+        },
+        {
+            "text": "Capital punishment (the death penalty) has existed in the United States since before the United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states.",
+            "date": datetime(2023, 9, 14),
+        },
     ]
 
     response = co.rerank(

src/examples/langchain_example/__init__.py

@@ -2,7 +2,7 @@
 from .basic import basic_app, rag, load_and_split
 from langtrace_python_sdk import with_langtrace_root_span
 
-from .groq_example import groq_basic, groq_streaming
+from .groq_example import groq_basic, groq_tool_choice, groq_streaming
 from .langgraph_example_tools import basic_graph_tools
 
 
@@ -20,3 +20,5 @@ class GroqRunner:
     @with_langtrace_root_span("Groq")
     def run(self):
         groq_streaming()
+        groq_basic()
+        groq_tool_choice()

src/examples/langchain_example/groq_example.py

@@ -1,6 +1,6 @@
+import json
+
 from dotenv import find_dotenv, load_dotenv
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_groq import ChatGroq
 from groq import Groq
 
 _ = load_dotenv(find_dotenv())
@@ -30,6 +30,82 @@ def groq_basic():
     return chat_completion
 
 
+def groq_tool_choice():
+
+    user_prompt = "What is 25 * 4 + 10?"
+    MODEL = "llama3-groq-70b-8192-tool-use-preview"
+
+    def calculate(expression):
+        """Evaluate a mathematical expression"""
+        try:
+            result = eval(expression)
+            return json.dumps({"result": result})
+        except:
+            return json.dumps({"error": "Invalid expression"})
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a calculator assistant. Use the calculate function to perform mathematical operations and provide the results.",
+        },
+        {
+            "role": "user",
+            "content": user_prompt,
+        },
+    ]
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "calculate",
+                "description": "Evaluate a mathematical expression",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "expression": {
+                            "type": "string",
+                            "description": "The mathematical expression to evaluate",
+                        }
+                    },
+                    "required": ["expression"],
+                },
+            },
+        }
+    ]
+    response = client.chat.completions.create(
+        model=MODEL,
+        messages=messages,
+        tools=tools,
+        tool_choice={"type": "function", "function": {"name": "calculate"}},
+        max_tokens=4096,
+    )
+
+    response_message = response.choices[0].message
+    tool_calls = response_message.tool_calls
+    if tool_calls:
+        available_functions = {
+            "calculate": calculate,
+        }
+        messages.append(response_message)
+        for tool_call in tool_calls:
+            function_name = tool_call.function.name
+            function_to_call = available_functions[function_name]
+            function_args = json.loads(tool_call.function.arguments)
+            function_response = function_to_call(
+                expression=function_args.get("expression")
+            )
+            messages.append(
+                {
+                    "tool_call_id": tool_call.id,
+                    "role": "tool",
+                    "name": function_name,
+                    "content": function_response,
+                }
+            )
+        second_response = client.chat.completions.create(model=MODEL, messages=messages)
+        return second_response.choices[0].message.content
+
+
 def groq_streaming():
     chat_completion = client.chat.completions.create(
         messages=[

src/langtrace_python_sdk/instrumentation/cohere/patch.py

@@ -27,6 +27,7 @@
 )
 from langtrace.trace_attributes import Event, LLMSpanAttributes
 from langtrace_python_sdk.utils import set_span_attribute
+from langtrace_python_sdk.utils.misc import datetime_encoder
 from opentelemetry.trace import SpanKind
 from opentelemetry.trace.status import Status, StatusCode
 
@@ -50,7 +51,9 @@ def traced_method(wrapped, instance, args, kwargs):
             SpanAttributes.LLM_REQUEST_MODEL: kwargs.get("model") or "command-r-plus",
             SpanAttributes.LLM_URL: APIS["RERANK"]["URL"],
             SpanAttributes.LLM_PATH: APIS["RERANK"]["ENDPOINT"],
-            SpanAttributes.LLM_REQUEST_DOCUMENTS: json.dumps(kwargs.get("documents")),
+            SpanAttributes.LLM_REQUEST_DOCUMENTS: json.dumps(
+                kwargs.get("documents"), cls=datetime_encoder
+            ),
             SpanAttributes.LLM_COHERE_RERANK_QUERY: kwargs.get("query"),
             **get_extra_attributes(),
         }

src/langtrace_python_sdk/utils/llm.py

@@ -124,6 +124,7 @@ def get_llm_request_attributes(kwargs, prompts=None, model=None, operation_name=
 
     top_p = kwargs.get("p", None) or kwargs.get("top_p", None)
     tools = kwargs.get("tools", None)
+    tool_choice = kwargs.get("tool_choice", None)
     return {
         SpanAttributes.LLM_OPERATION_NAME: operation_name,
         SpanAttributes.LLM_REQUEST_MODEL: model
@@ -141,7 +142,7 @@ def get_llm_request_attributes(kwargs, prompts=None, model=None, operation_name=
         SpanAttributes.LLM_FREQUENCY_PENALTY: kwargs.get("frequency_penalty"),
         SpanAttributes.LLM_REQUEST_SEED: kwargs.get("seed"),
         SpanAttributes.LLM_TOOLS: json.dumps(tools) if tools else None,
-        SpanAttributes.LLM_TOOL_CHOICE: kwargs.get("tool_choice"),
+        SpanAttributes.LLM_TOOL_CHOICE: json.dumps(tool_choice) if tool_choice else None,
         SpanAttributes.LLM_REQUEST_LOGPROPS: kwargs.get("logprobs"),
         SpanAttributes.LLM_REQUEST_LOGITBIAS: kwargs.get("logit_bias"),
         SpanAttributes.LLM_REQUEST_TOP_LOGPROPS: kwargs.get("top_logprobs"),

src/langtrace_python_sdk/utils/misc.py

@@ -60,3 +60,11 @@ def is_serializable(value):
 
     # Convert to string representation
     return json.dumps(serializable_args)
+
+
+class datetime_encoder(json.JSONEncoder):
+    def default(self, o):
+        if isinstance(o, datetime):
+            return o.isoformat()
+
+        return json.JSONEncoder.default(self, o)

src/langtrace_python_sdk/version.py

@@ -1 +1 @@
-__version__ = "2.3.15"
+__version__ = "2.3.16"