add structured output llm call

.env.example

@@ -1,3 +1,4 @@
 API_URI=
 API_KEY=
-API_TEAM=
+API_TEAM=
+GROQ_API_KEY=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

requirements.txt

@@ -14,4 +14,6 @@ pypdfium2==4.30.1
 pytesseract==0.3.13
 requests==2.32.3
 urllib3==2.4.0
-pydantic==2.11.3
+pydantic==2.11.3
+langchain==0.3.23
+langchain-groq==0.3.2

utils/decisions_makers/llm_call_structured_output.py

@@ -0,0 +1,35 @@
+from langchain_core.runnables import Runnable
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_groq import ChatGroq
+from pydantic import BaseModel, Field
+
+
+# Step 1: Define the structured output
+class CountryAnswer(BaseModel):
+    answer: str = Field(..., description="La réponse à la question")
+    country: str = Field(..., description="Le pays concerné")
+
+# Step 2: Create the output parser
+parser = PydanticOutputParser(pydantic_object=CountryAnswer)
+
+# Step 3: Create the prompt
+prompt = ChatPromptTemplate.from_template(
+    "Tu es un assistant utile. Réponds à la question : {question}\n"
+    "Réponds uniquement en JSON avec ce format :\n{format_instructions}"
+)
+
+# Step 4: LLM configuration
+llm = ChatGroq(model_name="llama3-70b-8192", temperature=0.7)
+
+# Step 5: Combine everything
+chain: Runnable = prompt | llm | parser
+
+# Step 6: Run the chain
+response = chain.invoke({
+    "question": "Quelle est la capitale de la Suisse ?",
+    "format_instructions": parser.get_format_instructions()
+})
+
+# Result
+print(response)