Merge branch 'kyegomez:master' into master

cerebas_example.py

@@ -0,0 +1,13 @@
+from swarms.structs.agent import Agent
+
+agent = Agent(
+    agent_name="Financial-Analysis-Agent",
+    agent_description="Personal finance advisor agent",
+    max_loops=4,
+    model_name="cerebras/llama3-70b-instruct",
+    dynamic_temperature_enabled=True,
+    interactive=False,
+    output_type="all",
+)
+
+agent.run("Conduct an analysis of the best real undervalued ETFs")

docs/mkdocs.yml

@@ -347,6 +347,7 @@ nav:
       - OpenAIChat: "swarms/models/openai.md"
       - OpenAIFunctionCaller: "swarms/models/openai_function_caller.md"
       - Groq: "swarms/models/groq.md"
+      - Cerebras: "swarms/models/cerebras.md"
     - MultiModal Models:
       - BaseMultiModalModel: "swarms/models/base_multimodal_model.md"
       - Multi Modal Models Available: "swarms/models/multimodal_models.md"

docs/swarms/models/cerebras.md

@@ -0,0 +1,89 @@
+# Using Cerebras LLaMA with Swarms
+
+This guide demonstrates how to create and use an AI agent powered by the Cerebras LLaMA 3 70B model using the Swarms framework.
+
+## Prerequisites
+
+- Python 3.7+
+
+- Swarms library installed (`pip install swarms`)
+
+- Set your ENV key `CEREBRAS_API_KEY`
+
+## Step-by-Step Guide
+
+### 1. Import Required Module
+
+```python
+from swarms.structs.agent import Agent
+```
+
+This imports the `Agent` class from Swarms, which is the core component for creating AI agents.
+
+### 2. Create an Agent Instance
+
+```python
+agent = Agent(
+    agent_name="Financial-Analysis-Agent",
+    agent_description="Personal finance advisor agent",
+    max_loops=4,
+    model_name="cerebras/llama3-70b-instruct",
+    dynamic_temperature_enabled=True,
+    interactive=False,
+    output_type="all",
+)
+```
+
+Let's break down each parameter:
+
+- `agent_name`: A descriptive name for your agent (here, "Financial-Analysis-Agent")
+
+- `agent_description`: A brief description of the agent's purpose
+
+- `max_loops`: Maximum number of interaction loops the agent can perform (set to 4)
+
+- `model_name`: Specifies the Cerebras LLaMA 3 70B model to use
+
+- `dynamic_temperature_enabled`: Enables dynamic adjustment of temperature for varied responses
+
+- `interactive`: When False, runs without requiring user interaction
+
+- `output_type`: Set to "all" to return complete response information
+
+### 3. Run the Agent
+
+```python
+agent.run("Conduct an analysis of the best real undervalued ETFs")
+```
+
+This command:
+
+1. Activates the agent
+
+2. Processes the given prompt about ETF analysis
+
+3. Returns the analysis based on the model's knowledge
+
+## Notes
+
+- The Cerebras LLaMA 3 70B model is a powerful language model suitable for complex analysis tasks
+
+- The agent can be customized further with additional parameters
+
+- The `max_loops=4` setting prevents infinite loops while allowing sufficient processing depth
+
+- Setting `interactive=False` makes the agent run autonomously without user intervention
+
+## Example Output
+
+The agent will provide a detailed analysis of undervalued ETFs, including:
+
+- Market analysis
+
+- Performance metrics
+
+- Risk assessment
+
+- Investment recommendations
+
+Note: Actual output will vary based on current market conditions and the model's training data.

examples/4o_mini_demo.py

@@ -0,0 +1,249 @@
+"""
+- For each diagnosis, pull lab results, 
+- egfr 
+- for each diagnosis, pull lab ranges, 
+- pull ranges for diagnosis
+
+- if the diagnosis is x, then the lab ranges should be a to b
+- train the agents, increase the load of input 
+- medical history sent to the agent
+- setup rag for the agents
+- run the first agent -> kidney disease -> don't know the stage -> stage 2 -> lab results -> indicative of stage 3 -> the case got elavated -> 
+- how to manage diseases and by looking at correlating lab, docs, diagnoses
+- put docs in rag -> 
+- monitoring, evaluation, and treatment
+- can we confirm for every diagnosis -> monitoring, evaluation, and treatment, specialized for these things
+- find diagnosis -> or have diagnosis, -> for each diagnosis are there evidence of those 3 things
+- swarm of those 4 agents, -> 
+- fda api for healthcare for commerically available papers
+- 
+
+"""
+
+from datetime import datetime
+
+from swarms import Agent, AgentRearrange, create_file_in_folder
+
+
+chief_medical_officer = Agent(
+    agent_name="Chief Medical Officer",
+    system_prompt="""You are the Chief Medical Officer coordinating a team of medical specialists for viral disease diagnosis.
+    Your responsibilities include:
+    - Gathering initial patient symptoms and medical history
+    - Coordinating with specialists to form differential diagnoses
+    - Synthesizing different specialist opinions into a cohesive diagnosis
+    - Ensuring all relevant symptoms and test results are considered
+    - Making final diagnostic recommendations
+    - Suggesting treatment plans based on team input
+    - Identifying when additional specialists need to be consulted
+    - For each diferrential diagnosis provide minimum lab ranges to meet that diagnosis or be indicative of that diagnosis minimum and maximum
+    
+    Format all responses with clear sections for:
+    - Initial Assessment (include preliminary ICD-10 codes for symptoms)
+    - Differential Diagnoses (with corresponding ICD-10 codes)
+    - Specialist Consultations Needed
+    - Recommended Next Steps
+    
+    
+    """,
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+virologist = Agent(
+    agent_name="Virologist",
+    system_prompt="""You are a specialist in viral diseases. For each case, provide:
+    
+    Clinical Analysis:
+    - Detailed viral symptom analysis
+    - Disease progression timeline
+    - Risk factors and complications
+    
+    Coding Requirements:
+    - List relevant ICD-10 codes for:
+        * Confirmed viral conditions
+        * Suspected viral conditions
+        * Associated symptoms
+        * Complications
+    - Include both:
+        * Primary diagnostic codes
+        * Secondary condition codes
+    
+    Document all findings using proper medical coding standards and include rationale for code selection.""",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+internist = Agent(
+    agent_name="Internist",
+    system_prompt="""You are an Internal Medicine specialist responsible for comprehensive evaluation.
+    
+    For each case, provide:
+    
+    Clinical Assessment:
+    - System-by-system review
+    - Vital signs analysis
+    - Comorbidity evaluation
+    
+    Medical Coding:
+    - ICD-10 codes for:
+        * Primary conditions
+        * Secondary diagnoses
+        * Complications
+        * Chronic conditions
+        * Signs and symptoms
+    - Include hierarchical condition category (HCC) codes where applicable
+    
+    Document supporting evidence for each code selected.""",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+medical_coder = Agent(
+    agent_name="Medical Coder",
+    system_prompt="""You are a certified medical coder responsible for:
+    
+    Primary Tasks:
+    1. Reviewing all clinical documentation
+    2. Assigning accurate ICD-10 codes
+    3. Ensuring coding compliance
+    4. Documenting code justification
+    
+    Coding Process:
+    - Review all specialist inputs
+    - Identify primary and secondary diagnoses
+    - Assign appropriate ICD-10 codes
+    - Document supporting evidence
+    - Note any coding queries
+    
+    Output Format:
+    1. Primary Diagnosis Codes
+        - ICD-10 code
+        - Description
+        - Supporting documentation
+    2. Secondary Diagnosis Codes
+        - Listed in order of clinical significance
+    3. Symptom Codes
+    4. Complication Codes
+    5. Coding Notes""",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+synthesizer = Agent(
+    agent_name="Diagnostic Synthesizer",
+    system_prompt="""You are responsible for creating the final diagnostic and coding assessment.
+    
+    Synthesis Requirements:
+    1. Integrate all specialist findings
+    2. Reconcile any conflicting diagnoses
+    3. Verify coding accuracy and completeness
+    
+    Final Report Sections:
+    1. Clinical Summary
+        - Primary diagnosis with ICD-10
+        - Secondary diagnoses with ICD-10
+        - Supporting evidence
+    2. Coding Summary
+        - Complete code list with descriptions
+        - Code hierarchy and relationships
+        - Supporting documentation
+    3. Recommendations
+        - Additional testing needed
+        - Follow-up care
+        - Documentation improvements needed
+    
+    Include confidence levels and evidence quality for all diagnoses and codes.""",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+# Create agent list
+agents = [
+    chief_medical_officer,
+    virologist,
+    internist,
+    medical_coder,
+    synthesizer,
+]
+
+# Define diagnostic flow
+flow = f"""{chief_medical_officer.agent_name} -> {virologist.agent_name} -> {internist.agent_name} -> {medical_coder.agent_name} -> {synthesizer.agent_name}"""
+
+# Create the swarm system
+diagnosis_system = AgentRearrange(
+    name="Medical-coding-diagnosis-swarm",
+    description="Comprehensive medical diagnosis and coding system",
+    agents=agents,
+    flow=flow,
+    max_loops=1,
+    output_type="all",
+)
+
+
+def generate_coding_report(diagnosis_output: str) -> str:
+    """
+    Generate a structured medical coding report from the diagnosis output.
+    """
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    report = f"""# Medical Diagnosis and Coding Report
+    Generated: {timestamp}
+
+    ## Clinical Summary
+    {diagnosis_output}
+
+    ## Coding Summary
+    ### Primary Diagnosis Codes
+    [Extracted from synthesis]
+
+    ### Secondary Diagnosis Codes
+    [Extracted from synthesis]
+
+    ### Symptom Codes
+    [Extracted from synthesis]
+
+    ### Procedure Codes (if applicable)
+    [Extracted from synthesis]
+
+    ## Documentation and Compliance Notes
+    - Code justification
+    - Supporting documentation references
+    - Any coding queries or clarifications needed
+
+    ## Recommendations
+    - Additional documentation needed
+    - Suggested follow-up
+    - Coding optimization opportunities
+    """
+    return report
+
+
+if __name__ == "__main__":
+    # Example patient case
+    patient_case = """
+    Patient: 45-year-old White Male
+
+    Lab Results:
+    - egfr 
+    - 59 ml / min / 1.73
+    - non african-american
+    
+    """
+
+    # Add timestamp to the patient case
+    case_info = f"Timestamp: {datetime.now()}\nPatient Information: {patient_case}"
+
+    # Run the diagnostic process
+    diagnosis = diagnosis_system.run(case_info)
+
+    # Generate coding report
+    coding_report = generate_coding_report(diagnosis)
+
+    # Create reports
+    create_file_in_folder(
+        "reports", "medical_diagnosis_report.md", diagnosis
+    )
+    create_file_in_folder(
+        "reports", "medical_coding_report.md", coding_report
+    )

examples/ollama_demo.py

@@ -24,9 +24,6 @@ from datetime import datetime
 
 from swarms import Agent, AgentRearrange, create_file_in_folder
 
-from swarm_models import OllamaModel
-
-model = OllamaModel(model_name="llama3.2")
 
 chief_medical_officer = Agent(
     agent_name="Chief Medical Officer",
@@ -49,7 +46,7 @@ chief_medical_officer = Agent(
     
     
     """,
-    llm=model,
+    model_name="ollama/llama3.2",
     max_loops=1,
 )
 
@@ -73,7 +70,7 @@ virologist = Agent(
         * Secondary condition codes
     
     Document all findings using proper medical coding standards and include rationale for code selection.""",
-    llm=model,
+    model_name="ollama/llama3.2",
     max_loops=1,
 )
 
@@ -98,7 +95,7 @@ internist = Agent(
     - Include hierarchical condition category (HCC) codes where applicable
     
     Document supporting evidence for each code selected.""",
-    llm=model,
+    model_name="ollama/llama3.2",
     max_loops=1,
 )
 
@@ -129,7 +126,7 @@ medical_coder = Agent(
     3. Symptom Codes
     4. Complication Codes
     5. Coding Notes""",
-    llm=model,
+    model_name="ollama/llama3.2",
     max_loops=1,
 )
 
@@ -157,7 +154,7 @@ synthesizer = Agent(
         - Documentation improvements needed
     
     Include confidence levels and evidence quality for all diagnoses and codes.""",
-    llm=model,
+    model_name="ollama/llama3.2",
     max_loops=1,
 )
 

swarms/structs/agent.py

@@ -279,7 +279,6 @@ class Agent:
 
     def __init__(
         self,
-        agent_id: Optional[str] = agent_id(),
         id: Optional[str] = agent_id(),
         llm: Optional[Any] = None,
         template: Optional[str] = None,
@@ -403,7 +402,6 @@ class Agent:
         **kwargs,
     ):
         # super().__init__(*args, **kwargs)
-        self.agent_id = agent_id
         self.id = id
         self.llm = llm
         self.template = template
@@ -2270,7 +2268,7 @@ class Agent:
             time=time.time(),
             tokens=total_tokens,
             response=AgentChatCompletionResponse(
-                id=self.agent_id,
+                id=self.id,
                 agent_name=self.agent_name,
                 object="chat.completion",
                 choices=ChatCompletionResponseChoice(