Fix async LLM calls and agents orchestration to resolve chat connectivity error

backend/app/services/agents.py

@@ -18,7 +18,7 @@ async def legal_agent_task(tender: Tender, company: CompanyProfile, document_tex
         f"EXTRACTED TEXT: {document_text[:5000]}\n"
         f"TASK: Identify 3 legal gaps. Analyze if the timeline is 'Express' (suspiciously short) for the tender type {tender.type}. Verify if company documents meet common requirements for this tender level."
     )
-    return await asyncio.to_thread(call_gemini_with_model, prompt, model)
+    return await call_gemini_with_model(prompt, model)
 
 async def technical_agent_task(tender: Tender, company: CompanyProfile, document_text: str = "", model: str | None = None) -> str:
     items_summary = ", ".join([f"{i.name} ({i.quantity} {i.unit})" for i in (tender.items or [])[:10]])
@@ -31,7 +31,7 @@ async def technical_agent_task(tender: Tender, company: CompanyProfile, document
         f"EXTRACTED TEXT: {document_text[:5000]}\n"
         f"TASK: Analyze specific LINE ITEMS against company capabilities. Identify 3 technical challenges. Is this a generic 'buy' or a complex project?"
     )
-    return await asyncio.to_thread(call_gemini_with_model, prompt, model)
+    return await call_gemini_with_model(prompt, model)
 
 async def strategy_agent_task(tender: Tender, company: CompanyProfile, document_text: str = "", model: str | None = None) -> str:
     prompt = (
@@ -43,7 +43,7 @@ async def strategy_agent_task(tender: Tender, company: CompanyProfile, document_
         f"COMPANY: {company.name}\n"
         f"TASK: Identify 3 strategic risks. Pay special attention to CURRENCY RISK if not CLP ({tender.currency}). Suggest a 'Win Strategy' based on the tender type {tender.type}."
     )
-    return await asyncio.to_thread(call_gemini_with_model, prompt, model)
+    return await call_gemini_with_model(prompt, model)
 
 async def run_full_analysis(tender: Tender, company_profile: CompanyProfile, document_text: str | None = None, models: dict | None = None) -> AnalysisResult:
     audit_log = ["🚀 Iniciando mesa de expertos agéntica..."]
@@ -87,7 +87,7 @@ async def run_full_analysis(tender: Tender, company_profile: CompanyProfile, doc
         f"Responde ÚNICAMENTE con el JSON."
     )
     
-    final_output = await asyncio.to_thread(call_gemini, synthesis_prompt)
+    final_output = await call_gemini(synthesis_prompt)
     parse_result = _parse_gemini_response(final_output)
     
     if parse_result:
@@ -105,4 +105,4 @@ async def run_full_analysis(tender: Tender, company_profile: CompanyProfile, doc
             
     # Fallback
     from app.services.llm import generate_analysis
-    return generate_analysis(tender, company_profile, doc_text, models)
+    return await generate_analysis(tender, company_profile, doc_text, models)

backend/app/services/llm.py

@@ -27,26 +27,27 @@ def get_gemini_model():
         }
     )
 
-def call_gemini(prompt: str) -> str:
+async def call_gemini(prompt: str) -> str:
     if not settings.gemini_api_key:
         # Fallback to Featherless if Gemini not available
-        return call_featherless(prompt, "meta-llama/Llama-3.3-70B-Instruct")
+        return await call_featherless(prompt, "meta-llama/Llama-3.3-70B-Instruct")
 
     try:
         model = get_gemini_model()
-        response = model.generate_content(prompt)
+        # Use run_in_executor if the library is blocking, or use async generate_content if supported
+        response = await model.generate_content_async(prompt)
         return response.text
     except Exception as e:
         print(f"Error calling Gemini: {e}, trying Featherless fallback...")
-        return call_featherless(prompt, "meta-llama/Llama-3.3-70B-Instruct")
+        return await call_featherless(prompt, "meta-llama/Llama-3.3-70B-Instruct")
 
-def call_featherless(prompt: str, model: str = "deepseek-ai/DeepSeek-V3.2") -> str:
+async def call_featherless(prompt: str, model: str = "deepseek-ai/DeepSeek-V3.2") -> str:
     if not settings.featherless_api_key:
         return ""
 
     try:
-        with httpx.Client(timeout=60.0) as client:
-            response = client.post(
+        async with httpx.AsyncClient(timeout=60.0) as client:
+            response = await client.post(
                 "https://api.featherless.ai/v1/chat/completions",
                 headers={
                     "Authorization": f"Bearer {settings.featherless_api_key}",
@@ -65,7 +66,7 @@ def call_featherless(prompt: str, model: str = "deepseek-ai/DeepSeek-V3.2") -> s
         print(f"Error calling Featherless ({model}): {e}")
         return ""
 
-def call_gemini_with_model(prompt: str, model_name: str | None = None) -> str:
+async def call_gemini_with_model(prompt: str, model_name: str | None = None) -> str:
     # Default model mapping
     model_map = {
         "Gemini 2.5 Flash": "gemini",
@@ -79,9 +80,9 @@ def call_gemini_with_model(prompt: str, model_name: str | None = None) -> str:
     model_id = model_map.get(model_name, "gemini")
     
     if model_id == "gemini":
-        return call_gemini(prompt)
+        return await call_gemini(prompt)
     else:
-        return call_featherless(prompt, model=model_id)
+        return await call_featherless(prompt, model=model_id)
 
 def _normalize_gemini_output(output: str) -> str:
     if not output:
@@ -158,7 +159,7 @@ def generate_mock_analysis(tender: Tender, company: CompanyProfile) -> AnalysisR
         audit_log=["Iniciando análisis de respaldo...", "Generando datos mock."]
     )
 
-def generate_analysis(tender: Tender, company: CompanyProfile, document_text: str | None = None, models: dict | None = None) -> AnalysisResult:
+async def generate_analysis(tender: Tender, company: CompanyProfile, document_text: str | None = None, models: dict | None = None) -> AnalysisResult:
     # Default model mapping
     model_map = {
         "Gemini 2.5 Flash": "gemini",
@@ -202,13 +203,13 @@ def generate_analysis(tender: Tender, company: CompanyProfile, document_text: st
         
         res = ""
         if model_id == "gemini":
-            res = call_gemini(agent_prompt)
+            res = await call_gemini(agent_prompt)
             # Failover to Featherless if Gemini fails (e.g. 429)
             if not res and settings.featherless_api_key:
                 audit_messages.append(f"🔄 Gemini failed/rate-limited. Switching to DeepSeek for {agent_id.upper()}...")
-                res = call_featherless(agent_prompt, model="deepseek-ai/DeepSeek-V3.2")
+                res = await call_featherless(agent_prompt, model="deepseek-ai/DeepSeek-V3.2")
         else:
-            res = call_featherless(agent_prompt, model=model_id)
+            res = await call_featherless(agent_prompt, model=model_id)
             
         agent_outputs[agent_id] = res or "Análisis no disponible por error de API."
 
@@ -233,9 +234,9 @@ def generate_analysis(tender: Tender, company: CompanyProfile, document_text: st
     RESPONDE SOLO EL JSON.
     """
     
-    final_json = call_gemini(synthesis_prompt)
+    final_json = await call_gemini(synthesis_prompt)
     if not final_json and settings.featherless_api_key:
-        final_json = call_featherless(synthesis_prompt, model="deepseek-ai/DeepSeek-V3.2")
+        final_json = await call_featherless(synthesis_prompt, model="deepseek-ai/DeepSeek-V3.2")
 
     parse_result = _parse_gemini_response(final_json)
     
@@ -244,7 +245,7 @@ def generate_analysis(tender: Tender, company: CompanyProfile, document_text: st
             # FORCE PROPOSAL GENERATION
             if not parse_result.get("proposal_draft") or len(parse_result["proposal_draft"]) < 100:
                 audit_messages.append("📝 Generating specialized proposal draft...")
-                parse_result["proposal_draft"] = generate_proposal_draft(parse_result, company)
+                parse_result["proposal_draft"] = await generate_proposal_draft(parse_result, company)
             
             result = AnalysisResult(**parse_result)
             result.audit_log = audit_messages + (result.audit_log or [])
@@ -257,7 +258,7 @@ def generate_analysis(tender: Tender, company: CompanyProfile, document_text: st
     analysis.audit_log = audit_messages + ["⚠️ Synthesis failed, using emergency fallback."]
     return analysis
 
-def generate_proposal_draft(analysis: dict, company: CompanyProfile) -> str:
+async def generate_proposal_draft(analysis: dict, company: CompanyProfile) -> str:
     prompt = f"""
     Como experto redactor de propuestas de licitación, genera un borrador profesional (en Markdown) basado en este análisis técnico:
     {analysis.get('executive_summary', 'Analizar bases adjuntas.')}
@@ -274,13 +275,13 @@ def generate_proposal_draft(analysis: dict, company: CompanyProfile) -> str:
     
     # Use DeepSeek for high-quality drafting if available
     if settings.featherless_api_key:
-        draft = call_featherless(prompt, model="deepseek-ai/DeepSeek-V3.2")
+        draft = await call_featherless(prompt, model="deepseek-ai/DeepSeek-V3.2")
         if draft: return draft
 
     # Fallback to Gemini
-    return call_gemini(prompt) or "Error al generar el borrador de la propuesta."
+    return await call_gemini(prompt) or "Error al generar el borrador de la propuesta."
 
-def generate_synthetic_tenders(keyword: str) -> list[Tender]:
+async def generate_synthetic_tenders(keyword: str) -> list[Tender]:
     """
     Generates realistic-looking synthetic tenders using an LLM.
     Used as a fallback when the real scraper is blocked.
@@ -302,9 +303,9 @@ def generate_synthetic_tenders(keyword: str) -> list[Tender]:
     Responde ÚNICAMENTE un JSON con una lista de objetos bajo la llave 'tenders'.
     """
     
-    res = call_gemini(prompt)
+    res = await call_gemini(prompt)
     if not res and settings.featherless_api_key:
-        res = call_featherless(prompt)
+        res = await call_featherless(prompt)
         
     data = _parse_gemini_response(res)
     results = []

backend/app/services/scraper.py

@@ -42,14 +42,14 @@ async def scrape_compra_agil(keywords: str) -> List[Tender]:
             
             if response.status_code != 200:
                 print(f"⚠️ API blocked (Status {response.status_code}). Activating Synthetic Fallback...")
-                return generate_synthetic_tenders(keywords)
+                return await generate_synthetic_tenders(keywords)
 
             raw_data = response.json()
             items = raw_data.get("data", [])
             
             if not items:
                 print(f"ℹ️ No real results found for '{keywords}'. Using Synthetic Intelligence to find potential leads.")
-                return generate_synthetic_tenders(keywords)
+                return await generate_synthetic_tenders(keywords)
 
             tenders = []
             for item in items:
@@ -85,6 +85,6 @@ async def scrape_compra_agil(keywords: str) -> List[Tender]:
     except Exception as e:
         print(f"❌ Scraper failure: {e}. Activating emergency fallback.")
         try:
-            return generate_synthetic_tenders(keywords)
+            return await generate_synthetic_tenders(keywords)
         except:
             return []