diff --git a/examples/mcp/groq-mcp.py b/examples/mcp/groq-mcp.py index c93dc9fb9..78283f32d 100644 --- a/examples/mcp/groq-mcp.py +++ b/examples/mcp/groq-mcp.py @@ -2,7 +2,7 @@ search_agent = Agent( instructions="""You help book apartments on Airbnb.""", - llm="groq/llama-3.3-70b-versatile", + llm="groq/llama-3.2-90b-vision-preview", tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt") ) diff --git a/examples/mcp/ollama-python.py b/examples/mcp/ollama-python.py new file mode 100644 index 000000000..0eead5723 --- /dev/null +++ b/examples/mcp/ollama-python.py @@ -0,0 +1,9 @@ +from praisonaiagents import Agent, MCP + +search_agent = Agent( + instructions="""You help book apartments on Airbnb.""", + llm="ollama/llama3.2", + tools=MCP("/Users/praison/miniconda3/envs/mcp/bin/python /Users/praison/stockprice/app.py") +) + +search_agent.start("What is the Stock Price of Apple?") \ No newline at end of file diff --git a/src/praisonai-agents/praisonaiagents/llm/llm.py b/src/praisonai-agents/praisonaiagents/llm/llm.py index 1e12bcf27..20d46b87c 100644 --- a/src/praisonai-agents/praisonaiagents/llm/llm.py +++ b/src/praisonai-agents/praisonaiagents/llm/llm.py @@ -460,9 +460,28 @@ def get_response( for tool_call in tool_calls: # Handle both object and dict access patterns if isinstance(tool_call, dict): - function_name = tool_call["function"]["name"] - arguments = json.loads(tool_call["function"]["arguments"]) - tool_call_id = tool_call["id"] + # Special handling for Ollama provider which may have a different structure + if self.model and self.model.startswith("ollama/"): + try: + # Try standard format first + if "function" in tool_call and isinstance(tool_call["function"], dict): + function_name = tool_call["function"]["name"] + arguments = json.loads(tool_call["function"]["arguments"]) + else: + # Try alternative format that Ollama might return + function_name = tool_call.get("name", "unknown_function") + arguments = json.loads(tool_call.get("arguments", "{}")) + tool_call_id = tool_call.get("id", f"tool_{id(tool_call)}") + except Exception as e: + logging.error(f"Error processing Ollama tool call: {e}") + function_name = "unknown_function" + arguments = {} + tool_call_id = f"tool_{id(tool_call)}" + else: + # Standard format for other providers + function_name = tool_call["function"]["name"] + arguments = json.loads(tool_call["function"]["arguments"]) + tool_call_id = tool_call["id"] else: function_name = tool_call.function.name arguments = json.loads(tool_call.function.arguments) @@ -490,8 +509,55 @@ def get_response( "content": json.dumps(tool_result) if tool_result is not None else "Function returned an empty output" }) + # Special handling for Ollama models that don't automatically process tool results + if self.model and self.model.startswith("ollama/") and tool_result: + # For Ollama models, we need to explicitly ask the model to process the tool results + # First, check if the response is just a JSON tool call + try: + # If the response_text is a valid JSON that looks like a tool call, + # we need to make a follow-up call to process the results + json_response = json.loads(response_text.strip()) + if ('name' in json_response or 'function' in json_response) and not any(word in response_text.lower() for word in ['summary', 'option', 'result', 'found']): + logging.debug("Detected Ollama returning only tool call JSON, making follow-up call to process results") + + # Create a prompt that asks the model to process the tool results + follow_up_prompt = f"I've searched for apartments and found these results. Please analyze them and provide a summary of the best options:\n\n{json.dumps(tool_result, indent=2)}\n\nPlease format your response as a nice summary with the top options." + + # Make a follow-up call to process the results + follow_up_messages = [ + {"role": "user", "content": follow_up_prompt} + ] + + # Get response with streaming + if verbose: + with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live: + response_text = "" + for chunk in litellm.completion( + model=self.model, + messages=follow_up_messages, + temperature=temperature, + stream=True + ): + if chunk and chunk.choices and chunk.choices[0].delta.content: + content = chunk.choices[0].delta.content + response_text += content + live.update(display_generating(response_text, start_time)) + else: + response_text = "" + for chunk in litellm.completion( + model=self.model, + messages=follow_up_messages, + temperature=temperature, + stream=True + ): + if chunk and chunk.choices and chunk.choices[0].delta.content: + response_text += chunk.choices[0].delta.content + except (json.JSONDecodeError, KeyError): + # Not a JSON response or not a tool call format, continue normally + pass + # If reasoning_steps is True, do a single non-streaming call - if reasoning_steps: + elif reasoning_steps: resp = litellm.completion( model=self.model, messages=messages, @@ -969,9 +1035,28 @@ async def get_response_async( for tool_call in tool_calls: # Handle both object and dict access patterns if isinstance(tool_call, dict): - function_name = tool_call["function"]["name"] - arguments = json.loads(tool_call["function"]["arguments"]) - tool_call_id = tool_call["id"] + # Special handling for Ollama provider which may have a different structure + if self.model and self.model.startswith("ollama/"): + try: + # Try standard format first + if "function" in tool_call and isinstance(tool_call["function"], dict): + function_name = tool_call["function"]["name"] + arguments = json.loads(tool_call["function"]["arguments"]) + else: + # Try alternative format that Ollama might return + function_name = tool_call.get("name", "unknown_function") + arguments = json.loads(tool_call.get("arguments", "{}")) + tool_call_id = tool_call.get("id", f"tool_{id(tool_call)}") + except Exception as e: + logging.error(f"Error processing Ollama tool call: {e}") + function_name = "unknown_function" + arguments = {} + tool_call_id = f"tool_{id(tool_call)}" + else: + # Standard format for other providers + function_name = tool_call["function"]["name"] + arguments = json.loads(tool_call["function"]["arguments"]) + tool_call_id = tool_call["id"] else: function_name = tool_call.function.name arguments = json.loads(tool_call.function.arguments) @@ -994,7 +1079,56 @@ async def get_response_async( # Get response after tool calls response_text = "" - if reasoning_steps: + + # Special handling for Ollama models that don't automatically process tool results + if self.model and self.model.startswith("ollama/") and tool_result: + # For Ollama models, we need to explicitly ask the model to process the tool results + # First, check if the response is just a JSON tool call + try: + # If the response_text is a valid JSON that looks like a tool call, + # we need to make a follow-up call to process the results + json_response = json.loads(response_text.strip()) + if ('name' in json_response or 'function' in json_response) and not any(word in response_text.lower() for word in ['summary', 'option', 'result', 'found']): + logging.debug("Detected Ollama returning only tool call JSON in async mode, making follow-up call to process results") + + # Create a prompt that asks the model to process the tool results + follow_up_prompt = f"I've searched for apartments and found these results. Please analyze them and provide a summary of the best options:\n\n{json.dumps(tool_result, indent=2)}\n\nPlease format your response as a nice summary with the top options." + + # Make a follow-up call to process the results + follow_up_messages = [ + {"role": "user", "content": follow_up_prompt} + ] + + # Get response with streaming + if verbose: + response_text = "" + async for chunk in await litellm.acompletion( + model=self.model, + messages=follow_up_messages, + temperature=temperature, + stream=True + ): + if chunk and chunk.choices and chunk.choices[0].delta.content: + content = chunk.choices[0].delta.content + response_text += content + print("\033[K", end="\r") + print(f"Processing results... {time.time() - start_time:.1f}s", end="\r") + else: + response_text = "" + async for chunk in await litellm.acompletion( + model=self.model, + messages=follow_up_messages, + temperature=temperature, + stream=True + ): + if chunk and chunk.choices and chunk.choices[0].delta.content: + response_text += chunk.choices[0].delta.content + except (json.JSONDecodeError, KeyError): + # Not a JSON response or not a tool call format, continue normally + pass + + # If no special handling was needed or if it's not an Ollama model + elif reasoning_steps: # Non-streaming call to capture reasoning resp = await litellm.acompletion( model=self.model, diff --git a/src/praisonai-agents/pyproject.toml b/src/praisonai-agents/pyproject.toml index 377927d56..1bb3fd2ab 100644 --- a/src/praisonai-agents/pyproject.toml +++ b/src/praisonai-agents/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "praisonaiagents" -version = "0.0.69" +version = "0.0.70" description = "Praison AI agents for completing complex tasks with Self Reflection Agents" authors = [ { name="Mervin Praison" } diff --git a/src/praisonai-agents/uv.lock b/src/praisonai-agents/uv.lock index 726772f95..a5b2f9e6d 100644 --- a/src/praisonai-agents/uv.lock +++ b/src/praisonai-agents/uv.lock @@ -1,10 +1,9 @@ version = 1 -requires-python = ">=3.10" +requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.13'", "python_full_version == '3.12.*'", - 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