feat: Generate grouped arguments for electric vehicle speeches

This commit adds code to generate grouped arguments for electric vehicle speeches. It retrieves speeches from the 'ev_speeches' collection and categorizes the arguments into negative, positive, and neutral sentiments. The grouped arguments are generated using the Ollama library and stored in separate lists. This enhancement improves the analysis of electric vehicle arguments and provides a clearer understanding of the sentiments expressed in the speeches.

analyze_arguments.py

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+from arango_class import ArangoDB
+from ollama_class import Ollama
+arango = ArangoDB()
+db = arango.db
+
+ollama = Ollama()
+
+# Get all speeches from the 'ev_speeches' collection, containing the 400 speeches filtered out from the European Parliament
+speeches = arango.all_ev_speeches()
+
+negative_arguments = []
+positive_arguments = []
+neutral_arguments = []
+
+for speech in speeches:
+    if speech['llm_sentiment'] == 'negative':
+        negative_arguments.extend(speech['llm_arguments'])
+    elif speech['llm_sentiment'] == 'positive':
+        positive_arguments.extend(speech['llm_arguments'])
+    else:
+        neutral_arguments.extend(speech['llm_arguments'])
+
+negative_arguments = list(set(negative_arguments))
+positive_arguments = list(set(positive_arguments))
+neutral_arguments = list(set(neutral_arguments))
+
+
+for sentiment, arguments in zip(['negative', 'positive', 'neutral'], [negative_arguments, positive_arguments, neutral_arguments]):
+    prompt = f'''Below is a list of arguments related to electric vehicles. They are mostly {sentiment} towards electric vehicles.
+    \n{negative_arguments}\n
+    What arguments are there? Give me a list where you group the arguments into categories.
+    Answer ONLY with the grouped arguments, no greeting or explanation. Keep to the information in the list above.
+    '''
+    grouped_arguments = ollama.generate(prompt=prompt)
+    print(sentiment.upper())
+    print(grouped_arguments)
+    print('-'*30)