Merge pull request #164 from NPC-Worldwide/chris/alicanto_knowledge_graph_upgrades

some bug fixes in serve to support npc studio, and up stream in compi…

Author: cagostino

examples/get_llm_response_examples.py

@@ -0,0 +1,156 @@
+from npcpy.llm_funcs import get_llm_response
+
+response = get_llm_response(
+    prompt="What is machine learning?",
+    model="llama3.2",
+    provider="ollama"
+)
+
+print("Response:", response['response'])
+
+
+
+from npcpy.llm_funcs import get_llm_response
+
+response = get_llm_response(
+    prompt="Describe what you see in this image.",
+    model="gemma3:4b",
+    provider="ollama",
+    images=["test_data/markov_chain.png"]
+)
+
+print("Response:", response['response'])
+
+
+
+from npcpy.llm_funcs import get_llm_response
+
+response = get_llm_response(
+    prompt="Summarize the key points in this document.",
+    model="llava:latest",
+    provider="ollama",
+    attachments=["test_data/yuan2004.pdf"]
+)
+
+print("Response:", response['response'])
+
+
+
+from npcpy.llm_funcs import get_llm_response
+
+response = get_llm_response(
+    prompt="Extract data from these files and highlight the most important information.",
+    model="llava:7b",
+    provider="ollama",
+    attachments=["test_data/yuan2004.pdf", "test_data/markov_chain.png", "test_data/sample_data.csv"]
+)
+
+print("Response:", response['response'])
+
+
+
+
+from npcpy.llm_funcs import get_llm_response
+from npcpy.npc_compiler import NPC
+
+# Create a simple NPC with custom system message
+npc = NPC(
+    name="OCR_Assistant",
+    description="An assistant specialized in document processing and OCR.",
+    model="llava:7b",
+    provider="ollama",
+    directive="You are an expert at analyzing documents and extracting valuable information."
+)
+
+response = get_llm_response(
+    prompt="What do you see in this diagram?",
+    images=["test_data/markov_chain.png"],
+    npc=npc
+)
+
+print("Response:", response['response'])
+print("System message used:", response['messages'][0]['content'] if response['messages'] else "No system message")
+
+
+from npcpy.llm_funcs import get_llm_response
+
+# Create a conversation with history
+messages = [
+    {"role": "system", "content": "You are a document analysis assistant."},
+    {"role": "user", "content": "I have some engineering diagrams I need to analyze."},
+    {"role": "assistant", "content": "I'd be happy to help analyze your engineering diagrams. Please share them with me."}
+]
+
+response = get_llm_response(
+    prompt="Here's the diagram I mentioned earlier.",
+    model="llava:7b",
+    provider="ollama",
+    messages=messages,
+    attachments=["test_data/markov_chain.png"]
+)
+
+print("Response:", response['response'])
+print("Updated message history length:", len(response['messages']))
+
+
+
+from npcpy.llm_funcs import get_llm_response
+
+# Create a conversation with history
+messages = [
+    {"role": "system", "content": "You are a document analysis assistant."},
+    {"role": "user", "content": "I have some engineering diagrams I need to analyze."},
+    {"role": "assistant", "content": "I'd be happy to help analyze your engineering diagrams. Please share them with me."}
+]
+
+response = get_llm_response(
+    prompt="Here's the diagram I mentioned earlier.",
+    model="llava:7b",
+    provider="ollama",
+    messages=messages,
+    attachments=["test_data/markov_chain.png"]
+)
+
+print("Response:", response['response'])
+print("Updated message history length:", len(response['messages']))
+
+
+
+from npcpy.llm_funcs import get_llm_response
+
+response = get_llm_response(
+    prompt="Analyze this image and give a detailed explanation.",
+    model="llava:7b",
+    provider="ollama",
+    images=["test_data/markov_chain.png"],
+    stream=True
+)
+
+# For streaming responses, you'd typically iterate through them
+print("Streaming response object type:", type(response['response']))
+
+
+from npcpy.llm_funcs import get_llm_response
+from npcpy.data.load import load_pdf, load_image
+import os
+import pandas as pd
+import json
+from PIL import Image
+import io
+import numpy as np
+
+# Example paths
+pdf_path = 'test_data/yuan2004.pdf'
+image_path = 'test_data/markov_chain.png'
+csv_path = 'test_data/sample_data.csv'
+
+# Method 1: Simple attachment-based approach
+response = get_llm_response(
+    'Extract and analyze all text and images from these files. What are the key concepts presented?', 
+    model='llava:7b', 
+    provider='ollama',
+    attachments=[pdf_path, image_path, csv_path]
+)
+
+print("\nResponse from attachment-based approach:")
+print(response['response'])