18082025

ollama_remote_query/ollama_prompt.py

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+#! /usr/bin/env python
+from ollama import Client
+import datetime
+import os
+
+output_folder = "output"
+
+if not os.path.exists(output_folder):
+    os.makedirs(output_folder)
+
+# Create_Timestamp
+now = int(datetime.datetime.now().timestamp())
+# res = int(now.timestamp())
+
+# PROMT
+prompt = input("Was möchtest du Fragen: ")
+
+client = Client(
+    host='http://localhost:11434',
+    headers={'x-some-header': 'some-value'}
+)
+
+response = client.chat(
+    model='gemma3n:e2b',
+    messages=[
+        {
+            'role': 'user',
+            'content': prompt,
+        }],
+    stream=True)
+
+for chunk in response:
+    print(chunk['message']["content"], end='', flush=True)
+    with open(str(output_folder + "/ollama_output_" + str(now) + ".md"), "a") as dm:
+        print(chunk['message']["content"], end='', flush=True, file=dm)
+
+# with open("test.md", "w") as dm:

ollama_remote_query/output/ollama_output_1755524506.md

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+Okay, I'm exiting. Have a good one! 
+
+If you need anything in the future, feel free to ask. 😊
+
+
+

ollama_remote_query/translate_func.py

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+# %% packages
+# from langchain_openai import ChatOpenAI
+import langchain_ollama
+from langchain_core.prompts import ChatPromptTemplate
+# from dotenv import load_dotenv
+from langchain_core.output_parsers import StrOutputParser
+# load_dotenv('.env')
+
+
+def translate_func(select_lang="Germany", target_lang="English", query_trans="prompt"):
+    if query_trans == "prompt":
+        query_trans = input("Was soll Übersetzt werden ? ")
+    # %% set up prompt template
+    prompt_template = ChatPromptTemplate.from_messages([
+        ("system", f"You are an AI assistant that translates {
+            select_lang} into another language."),
+        ("user", "Translate this sentence: '{input}' into {target_language}"),
+    ])
+
+    # %% model
+    model = langchain_ollama.llms.OllamaLLM(base_url='http://localhost:11434', model="gemma3n:e2b",
+                                            temperature=0)
+
+    # %% chain
+    chain = prompt_template | model | StrOutputParser()
+
+    # %% invoke chain
+    res = chain.invoke({"input": query_trans,
+                        "target_language": target_lang})
+    print(res)
+
+    # %%
+
+
+if __name__ == "__main__":
+    select_lang = "Germany"
+    target_lang = "English"
+    translate_func(select_lang=target_lang, target_lang=select_lang)