Gemeni Prompt

backend/src/gemini/__init__.py

@@ -1,6 +1,6 @@
-from src.rag.embeddings import get_embedding
+import os
+from google import genai
 from src.chroma.vector_store import search_documents
-from .client import generate_content
 
 GREENWASHING_ANALYSIS_PROMPT = """
 You are an expert Environmental, Social, and Governance (ESG) Analyst specialized in detecting 'Greenwashing'.
@@ -18,40 +18,31 @@ Based on the context provided, give a final verdict:
 - VERDICT: [Clear/Suspect/High Risk of Greenwashing]
 - REASONING: [Explain your findings clearly]
 - EVIDENCE: [Quote specific parts of the context if possible]
-- BETTER ALTERNATIVES: [Suggest 2-3 similar companies or products that have verified sustainability records or higher transparency in this specific area]
 """
 
-def analyze_greenwashing(query, category=None, num_results=10):
+def ask(prompt):
-    try:
+    client = genai.Client(api_key=os.environ.get("GOOGLE_API_KEY"))
-        query_embedding = get_embedding(query)
+    return client.models.generate_content(model="gemini-2.0-flash-exp", contents=prompt).text
 
-        filter_metadata = None
+def analyze(query, query_embedding, num_results=5, num_alternatives=3):
-        if category:
+    results = search_documents(query_embedding, num_results=num_results + num_alternatives + 5)
-            filter_metadata = {"category": category}
     
-        search_results = search_documents(
+    context = "--- START OF REPORT CONTEXT ---\n"
-            query_embedding, 
+    for res in results[:num_results]:
-            num_results=num_results, 
+        context += f"RELEVANT DATA CHUNK: {res['text']}\n\n"
-            filter_metadata=filter_metadata
+    context += "--- END OF REPORT CONTEXT ---\n"
-        )
     
-        context = ""
+    full_prompt = f"{GREENWASHING_ANALYSIS_PROMPT}\n\n{context}\n\nUSER QUERY/COMPANY FOCUS: {query}"
-        if search_results:
+    analysis_text = ask(full_prompt)
-            context = "--- START OF REPORT CONTEXT ---\n"
-            for res in search_results:
-                context += f"RELEVANT DATA CHUNK: {res['text']}\n\n"
-            context += "--- END OF REPORT CONTEXT ---\n"
     
-        if context:
+    alternatives = []
-            full_prompt = f"{GREENWASHING_ANALYSIS_PROMPT}\n\n{context}\n\nUSER QUERY/COMPANY FOCUS: {query}"
+    seen_texts = set()
-        else:
+    for res in results[num_results:]:
-            return "No data found in the report to analyze. Please upload a report first."
+        text_preview = res['text'][:200]
+        if text_preview not in seen_texts:
+            seen_texts.add(text_preview)
+            alternatives.append({"text": res['text'], "score": res.get('score'), "summary": text_preview})
+        if len(alternatives) >= num_alternatives:
+            break
     
-        response = generate_content(full_prompt)
+    return {"analysis": analysis_text, "alternatives": alternatives}
-        return response
-        
-    except Exception as e:
-        return f"Error in Analysis process: {str(e)}"
-
-def ask_gemini_with_rag(query, category=None, num_results=5):
-    return analyze_greenwashing(query, category, num_results)

backend/src/gemini/client.py

@@ -3,15 +3,9 @@ import os
 
 def generate_content(prompt, model_name="gemini-2.0-flash-exp"):
     api_key = os.environ.get("GOOGLE_API_KEY")
-    if not api_key:
+    client = genai.Client(api_key=api_key)
-        return "Error: GOOGLE_API_KEY not found."
+    response = client.models.generate_content(
-
+        model=model_name,
-    try:
+        contents=prompt,
-        client = genai.Client(api_key=api_key)
+    )
-        response = client.models.generate_content(
+    return response.text
-            model=model_name,
-            contents=prompt,
-        )
-        return response.text
-    except Exception as e:
-        return f"Error interacting with Gemini API: {str(e)}"