Pavan Kumar
3 weeks ago
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GitHub
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cd074d0f6e
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from swarms.structs.agent import Agent
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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agent_description="Personal finance advisor agent",
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max_loops=4,
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model_name="cerebras/llama3-70b-instruct",
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dynamic_temperature_enabled=True,
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interactive=False,
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output_type="all",
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)
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agent.run("Conduct an analysis of the best real undervalued ETFs")
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# Using Cerebras LLaMA with Swarms
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This guide demonstrates how to create and use an AI agent powered by the Cerebras LLaMA 3 70B model using the Swarms framework.
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## Prerequisites
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- Python 3.7+
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- Swarms library installed (`pip install swarms`)
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- Set your ENV key `CEREBRAS_API_KEY`
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## Step-by-Step Guide
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### 1. Import Required Module
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```python
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from swarms.structs.agent import Agent
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```
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This imports the `Agent` class from Swarms, which is the core component for creating AI agents.
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### 2. Create an Agent Instance
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```python
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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agent_description="Personal finance advisor agent",
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max_loops=4,
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model_name="cerebras/llama3-70b-instruct",
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dynamic_temperature_enabled=True,
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interactive=False,
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output_type="all",
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)
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```
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Let's break down each parameter:
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- `agent_name`: A descriptive name for your agent (here, "Financial-Analysis-Agent")
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- `agent_description`: A brief description of the agent's purpose
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- `max_loops`: Maximum number of interaction loops the agent can perform (set to 4)
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- `model_name`: Specifies the Cerebras LLaMA 3 70B model to use
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- `dynamic_temperature_enabled`: Enables dynamic adjustment of temperature for varied responses
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- `interactive`: When False, runs without requiring user interaction
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- `output_type`: Set to "all" to return complete response information
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### 3. Run the Agent
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```python
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agent.run("Conduct an analysis of the best real undervalued ETFs")
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```
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This command:
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1. Activates the agent
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2. Processes the given prompt about ETF analysis
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3. Returns the analysis based on the model's knowledge
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## Notes
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- The Cerebras LLaMA 3 70B model is a powerful language model suitable for complex analysis tasks
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- The agent can be customized further with additional parameters
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- The `max_loops=4` setting prevents infinite loops while allowing sufficient processing depth
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- Setting `interactive=False` makes the agent run autonomously without user intervention
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## Example Output
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The agent will provide a detailed analysis of undervalued ETFs, including:
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- Market analysis
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- Performance metrics
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- Risk assessment
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- Investment recommendations
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Note: Actual output will vary based on current market conditions and the model's training data.
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@ -0,0 +1,249 @@
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"""
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- For each diagnosis, pull lab results,
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- egfr
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- for each diagnosis, pull lab ranges,
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- pull ranges for diagnosis
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- if the diagnosis is x, then the lab ranges should be a to b
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- train the agents, increase the load of input
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- medical history sent to the agent
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- setup rag for the agents
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- run the first agent -> kidney disease -> don't know the stage -> stage 2 -> lab results -> indicative of stage 3 -> the case got elavated ->
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- how to manage diseases and by looking at correlating lab, docs, diagnoses
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- put docs in rag ->
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- monitoring, evaluation, and treatment
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- can we confirm for every diagnosis -> monitoring, evaluation, and treatment, specialized for these things
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- find diagnosis -> or have diagnosis, -> for each diagnosis are there evidence of those 3 things
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- swarm of those 4 agents, ->
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- fda api for healthcare for commerically available papers
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-
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"""
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from datetime import datetime
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from swarms import Agent, AgentRearrange, create_file_in_folder
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chief_medical_officer = Agent(
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agent_name="Chief Medical Officer",
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system_prompt="""You are the Chief Medical Officer coordinating a team of medical specialists for viral disease diagnosis.
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Your responsibilities include:
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- Gathering initial patient symptoms and medical history
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- Coordinating with specialists to form differential diagnoses
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- Synthesizing different specialist opinions into a cohesive diagnosis
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- Ensuring all relevant symptoms and test results are considered
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- Making final diagnostic recommendations
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- Suggesting treatment plans based on team input
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- Identifying when additional specialists need to be consulted
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- For each diferrential diagnosis provide minimum lab ranges to meet that diagnosis or be indicative of that diagnosis minimum and maximum
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Format all responses with clear sections for:
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- Initial Assessment (include preliminary ICD-10 codes for symptoms)
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- Differential Diagnoses (with corresponding ICD-10 codes)
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- Specialist Consultations Needed
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- Recommended Next Steps
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""",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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virologist = Agent(
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agent_name="Virologist",
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system_prompt="""You are a specialist in viral diseases. For each case, provide:
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Clinical Analysis:
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- Detailed viral symptom analysis
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- Disease progression timeline
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- Risk factors and complications
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Coding Requirements:
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- List relevant ICD-10 codes for:
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* Confirmed viral conditions
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* Suspected viral conditions
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* Associated symptoms
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* Complications
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- Include both:
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* Primary diagnostic codes
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* Secondary condition codes
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Document all findings using proper medical coding standards and include rationale for code selection.""",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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internist = Agent(
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agent_name="Internist",
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system_prompt="""You are an Internal Medicine specialist responsible for comprehensive evaluation.
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For each case, provide:
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Clinical Assessment:
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- System-by-system review
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- Vital signs analysis
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- Comorbidity evaluation
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Medical Coding:
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- ICD-10 codes for:
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* Primary conditions
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* Secondary diagnoses
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* Complications
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* Chronic conditions
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* Signs and symptoms
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- Include hierarchical condition category (HCC) codes where applicable
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Document supporting evidence for each code selected.""",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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medical_coder = Agent(
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agent_name="Medical Coder",
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system_prompt="""You are a certified medical coder responsible for:
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Primary Tasks:
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1. Reviewing all clinical documentation
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2. Assigning accurate ICD-10 codes
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3. Ensuring coding compliance
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4. Documenting code justification
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Coding Process:
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- Review all specialist inputs
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- Identify primary and secondary diagnoses
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- Assign appropriate ICD-10 codes
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- Document supporting evidence
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- Note any coding queries
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Output Format:
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1. Primary Diagnosis Codes
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- ICD-10 code
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- Description
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- Supporting documentation
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2. Secondary Diagnosis Codes
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- Listed in order of clinical significance
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3. Symptom Codes
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4. Complication Codes
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5. Coding Notes""",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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synthesizer = Agent(
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agent_name="Diagnostic Synthesizer",
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system_prompt="""You are responsible for creating the final diagnostic and coding assessment.
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Synthesis Requirements:
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1. Integrate all specialist findings
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2. Reconcile any conflicting diagnoses
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3. Verify coding accuracy and completeness
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Final Report Sections:
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1. Clinical Summary
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- Primary diagnosis with ICD-10
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- Secondary diagnoses with ICD-10
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- Supporting evidence
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2. Coding Summary
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- Complete code list with descriptions
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- Code hierarchy and relationships
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- Supporting documentation
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3. Recommendations
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- Additional testing needed
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- Follow-up care
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- Documentation improvements needed
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Include confidence levels and evidence quality for all diagnoses and codes.""",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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# Create agent list
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agents = [
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chief_medical_officer,
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virologist,
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internist,
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medical_coder,
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synthesizer,
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]
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# Define diagnostic flow
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flow = f"""{chief_medical_officer.agent_name} -> {virologist.agent_name} -> {internist.agent_name} -> {medical_coder.agent_name} -> {synthesizer.agent_name}"""
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# Create the swarm system
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diagnosis_system = AgentRearrange(
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name="Medical-coding-diagnosis-swarm",
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description="Comprehensive medical diagnosis and coding system",
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agents=agents,
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flow=flow,
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max_loops=1,
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output_type="all",
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)
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def generate_coding_report(diagnosis_output: str) -> str:
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"""
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Generate a structured medical coding report from the diagnosis output.
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"""
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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report = f"""# Medical Diagnosis and Coding Report
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Generated: {timestamp}
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## Clinical Summary
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{diagnosis_output}
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## Coding Summary
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### Primary Diagnosis Codes
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[Extracted from synthesis]
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### Secondary Diagnosis Codes
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[Extracted from synthesis]
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### Symptom Codes
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[Extracted from synthesis]
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### Procedure Codes (if applicable)
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[Extracted from synthesis]
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## Documentation and Compliance Notes
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- Code justification
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- Supporting documentation references
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- Any coding queries or clarifications needed
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## Recommendations
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- Additional documentation needed
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- Suggested follow-up
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- Coding optimization opportunities
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"""
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return report
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if __name__ == "__main__":
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# Example patient case
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patient_case = """
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Patient: 45-year-old White Male
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Lab Results:
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- egfr
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- 59 ml / min / 1.73
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- non african-american
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"""
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# Add timestamp to the patient case
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case_info = f"Timestamp: {datetime.now()}\nPatient Information: {patient_case}"
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# Run the diagnostic process
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diagnosis = diagnosis_system.run(case_info)
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# Generate coding report
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coding_report = generate_coding_report(diagnosis)
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# Create reports
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create_file_in_folder(
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"reports", "medical_diagnosis_report.md", diagnosis
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)
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create_file_in_folder(
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"reports", "medical_coding_report.md", coding_report
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)
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