Merge pull request #15 from digidem/luisotee/intent_classification

feat: add supervisor and onboarding crew #11 #1

apps/ai_api/.env.example

@@ -1,8 +1,7 @@
-IS_DEBUG=True
-API_KEY=sample_api_key
-IS_DEBUG=True
-API_KEY=sample_api_key
-OPENAI_API_KEY=sk-proj-xxxx
-CEREBRAS_API_KEY=csk-xxxx
-SERPER_API_KEY=xxxx
-LANGTRACE_API_KEY=xxxx
+IS_DEBUG=True 
+API_KEY=sample_api_key  # NOT USED
+OPENAI_API_KEY=sk-proj-xxxx # NOT USED
+CEREBRAS_API_KEY=csk-xxxx   # NOT USED
+SERPER_API_KEY=xxxx # Needed to use Serper tool
+LANGTRACE_API_KEY=xxxx  
+GROQ_API_KEY=xxxx   # API key for the LLM model

apps/ai_api/eda_ai_api/api/routes/onboarding.py

@@ -0,0 +1,19 @@
+from fastapi import APIRouter
+from onboarding.crew import OnboardingCrew
+from pydantic import BaseModel
+
+
+class OnboardingGuideResponse(BaseModel):
+    guide: str
+
+
+router = APIRouter()
+
+
+@router.get("/guide", response_model=OnboardingGuideResponse, name="guide")
+async def get_bot_guide() -> OnboardingGuideResponse:
+    """Generate a user guide explaining how to use the Earth Defenders Assistant."""
+    crew_output = OnboardingCrew().crew().kickoff()
+    # Extract the raw string content from the CrewOutput object
+    guide_text = str(crew_output.raw)
+    return OnboardingGuideResponse(guide=guide_text)

apps/ai_api/eda_ai_api/api/routes/router.py

@@ -1,7 +1,9 @@
 from fastapi import APIRouter
 
-from eda_ai_api.api.routes import grant, heartbeat
+from eda_ai_api.api.routes import grant, heartbeat, onboarding, supervisor
 
 api_router = APIRouter()
 api_router.include_router(heartbeat.router, tags=["health"], prefix="/health")
 api_router.include_router(grant.router, tags=["discovery"], prefix="/grant")
+api_router.include_router(supervisor.router, tags=["supervisor"], prefix="/supervisor")
+api_router.include_router(onboarding.router, tags=["onboarding"], prefix="/onboarding")

apps/ai_api/eda_ai_api/api/routes/supervisor.py

@@ -0,0 +1,134 @@
+import os
+
+from fastapi import APIRouter
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain_groq import ChatGroq
+from onboarding.crew import OnboardingCrew
+from opportunity_finder.crew import OpportunityFinderCrew
+from proposal_writer.crew import ProposalWriterCrew
+
+from eda_ai_api.models.supervisor import SupervisorRequest, SupervisorResponse
+
+router = APIRouter()
+
+# Setup LLM and prompt
+llm = ChatGroq(
+    model_name="llama3-groq-70b-8192-tool-use-preview",
+    api_key=os.environ.get("GROQ_API_KEY"),
+    temperature=0.5,
+)
+
+ROUTER_TEMPLATE = """
+Given a user message, determine the appropriate service to handle the request.
+Choose between:
+- discovery: For finding grant opportunities
+- proposal: For writing grant proposals
+- onboarding: For getting help using the system
+- heartbeat: For checking system health
+
+User message: {message}
+
+Return only one word (discovery/proposal/onboarding/heartbeat):"""
+
+
+TOPIC_EXTRACTOR_TEMPLATE = """
+Extract up to 5 most relevant topics for grant opportunity research from the user message.
+Return only a comma-separated list of topics (maximum 5), no other text.
+
+User message: {message}
+
+Topics:"""
+
+PROPOSAL_EXTRACTOR_TEMPLATE = """
+Extract the community project name and grant program name from the user message.
+Return in format: project_name|grant_name
+If either cannot be determined, use "unknown" as placeholder.
+
+User message: {message}
+
+Output:"""
+
+# Create prompt templates
+router_prompt = PromptTemplate(input_variables=["message"], template=ROUTER_TEMPLATE)
+topic_prompt = PromptTemplate(
+    input_variables=["message"], template=TOPIC_EXTRACTOR_TEMPLATE
+)
+proposal_prompt = PromptTemplate(
+    input_variables=["message"], template=PROPOSAL_EXTRACTOR_TEMPLATE
+)
+
+# Create LLM chains
+router_chain = LLMChain(llm=llm, prompt=router_prompt)
+topic_chain = LLMChain(llm=llm, prompt=topic_prompt)
+proposal_chain = LLMChain(llm=llm, prompt=proposal_prompt)
+
+
+@router.post("/supervisor", response_model=SupervisorResponse)
+async def supervisor_route(request: SupervisorRequest) -> SupervisorResponse:
+    try:
+        # Get routing decision from LLM
+        decision = router_chain.run(message=request.message).strip().lower()
+
+        # Print input message and decision for debugging
+        print("\n==================================================")
+        print(f"           INPUT MESSAGE: {request.message}")
+        print("==================================================")
+        print(f"               DECISION: {decision}")
+        print("==================================================\n")
+
+        # Handle different decision paths
+        if decision == "discovery":
+            # Extract topics using LLM (limited to 5 in prompt)
+            topics_raw = topic_chain.run(message=request.message)
+            topics = [t.strip() for t in topics_raw.split(",") if t.strip()][
+                :5
+            ]  # Safety check
+            if not topics:
+                topics = ["AI", "Technology"]  # Fallback topics
+
+            print("==================================================")
+            print(f"           EXTRACTED TOPICS: {topics}")
+            print("==================================================\n")
+
+            result = (
+                OpportunityFinderCrew()
+                .crew()
+                .kickoff(inputs={"topics": ", ".join(topics)})
+            )
+
+        elif decision == "proposal":
+            # Extract project and grant details using LLM
+            extracted = proposal_chain.run(message=request.message).split("|")
+            community_project = (
+                extracted[0].strip() if len(extracted) > 0 else "unknown"
+            )
+            grant_call = extracted[1].strip() if len(extracted) > 1 else "unknown"
+
+            print("==================================================")
+            print(f"     PROJECT NAME: {community_project}")
+            print(f"     GRANT PROGRAM: {grant_call}")
+            print("==================================================\n")
+
+            result = (
+                ProposalWriterCrew(
+                    community_project=community_project, grant_call=grant_call
+                )
+                .crew()
+                .kickoff()
+            )
+
+        elif decision == "heartbeat":
+            result = {"is_alive": True}
+
+        elif decision == "onboarding":
+            # Generate guide using OnboardingCrew
+            result = OnboardingCrew().crew().kickoff()
+
+        else:
+            result = {"error": f"Unknown decision type: {decision}"}
+
+    except Exception as e:
+        result = {"error": f"Error processing request: {str(e)}"}
+
+    return SupervisorResponse(result=str(result))

apps/ai_api/eda_ai_api/models/onboarding.py

@@ -0,0 +1,13 @@
+from pydantic import BaseModel
+
+
+class OnboardingResponse(BaseModel):
+    """Response model for onboarding explanations"""
+
+    explanation: str
+
+
+class OnboardingRequest(BaseModel):
+    """Request model for onboarding queries"""
+
+    topic: str | None = None

apps/ai_api/eda_ai_api/models/supervisor.py

@@ -0,0 +1,9 @@
+from pydantic import BaseModel
+
+
+class SupervisorResponse(BaseModel):
+    result: str
+
+
+class SupervisorRequest(BaseModel):
+    message: str

apps/ai_api/pyproject.toml

@@ -12,7 +12,10 @@ dependencies = [
     "joblib>=1.3.2",
     "loguru>=0.7.2",
     "pydantic>=2.5.3",
+    "langchain-groq>=0.2.1",
     "opportunity-finder",
+    "proposal_writer",
+    "onboarding"
 ]
 
 [project.optional-dependencies]
@@ -67,6 +70,8 @@ ignore_missing_imports = true
 
 [tool.uv.sources]
 opportunity-finder = { path = "../../plugins/grant_plugin/opportunity_finder" }
+proposal_writer = { path = "../../plugins/grant_plugin/proposal_writer" }
+onboarding = { path = "../../plugins/onboarding" }
 
 [build-system]
 requires = ["hatchling"]

apps/ai_api/tests/conftest.py

@@ -1,5 +1,3 @@
-
-
 import pytest
 from starlette.config import environ
 from starlette.testclient import TestClient