Azure GenAI + LLM + RAG
Azure Examples
1Azure examples for OpenAI Models with RAG and LLM tests
2
3Azure AI Studio
GenAI
1Prompting:
2------------------------->
3Zero shot prompting
4Few-shot prompting
5Chain-of-Thought prompting
6Advesarial Prompting
7INference parameters
8
9
10Nueral Networks
11------------------------------------------------------------>
12Input Layer --> Hidden Layers --> Output Layer
13
14
15INput Text ---> Transformer Model --> output text
16
17Tokenization ---> Embeddings and HYperParameter Tuning
18
19AI Applications on Azure
20
21LLMOps and Lifecycles of LLM -
22
23Advanced RAG
24
25
26-------------------------------------------------------------------------------------------------------------------->
27
28
29Imperative programing to declarative programing
30
31Paradigm Shift --->
3201. Supervised Learning (like school with children - you need to show multiple examples of cat and dog and allow them to learn)
33
3402. Unsupervised Learning ( its like throwing a baby in an unknown environment asking to figure it out on his own - we didnt give examples )
35
3603. Reinforcement Learning ( train the system - feedback loop )
37
3804. Self-Supervised Learning -- reward or penalise by itself.
39
40
41Natural Language Processing - NLP built on Neural Networks
42
43Idea of GPUs, CUDA and crawling of common datasets ... i.e crwaling of large-datasets leads to new era of tech called --- Artificail Intelligence(AI)
44
45GPUs have enabled parallel training, making large-scale model training feasible.
46
47Transformers in AI... is moslty matrix operations
48
49LLMs - transformers are the key
50
51Gemini
52Llama
53GPT - Generative Pre-trained Transformer
54Claude
55
56
57Neurosymbolic AI
58
59
60
61RLHF -- Re-inforcement Learning from Human Feedback.
62
63Enhanced Model -- Fine-Tuning Process + Human and Machine Feedback.
64
65
66
67Multi-Modality
68------------------>
69Text Modality
70Image Modality
71
72Video Modality
73Audio Modality
74
75
76Tansformer Model:
77
78DALL.E ... CLIP ... Midjourney
Retrieval Augmented Generation
1# RAG
2
3Retrieval Augmented Generation(RAG)
4
5RAG is a powerful hybrid architecture that extends the capabilities of Foundation models by integrating real-time data retrieval from external databases.
6
7Agentic RAG
8
9the process of which external data-source needs to be selected thats where different types of RAG comes into the picture
RAG Vector Search
1Vector search basically is that...
2
3it searches the based on the semantic meaning of the words...
4
5lets say in your Database for all the cars you used the word called vehicle....but IN normal search technique if you search for cars then you want be able to get anything
6
7but if its in Vector Search it is possible to get the result cause vector search stores the semantic meaning of the words that are stored in multi-dimensional space.
8
9https://weaviate.io/blog/distance-metrics-in-vector-search
10
11
12
13Naive RAG:
14------------------------------->
15
16
17
18user sends both query and pdf docs to GenAI lets say to chatgpt
19
20User ---> Query ---> Embedding Model ---> Vectorize ---> Vector Store --------retrieve-------> Augment(prompt, relevantDocs, Query)
Agentic RAG Systems
1Query Expansion (With Multiple Query)
Graph RAG
1Graph Databases
Prompting Techniques
11. Fine-Tuning
22. Conext Learning
3
4
51. Zero-shot prompting - Asking the model to perform task directly without giving any examples
6
72. Few-Shot prompting - Providing a few examples before asking the model to questions
8
93. Chain-of-thought prompting - OpenAIo1
10
114. Adversarial prompting -
12
13Anthropics Approach with Claude
14
15Prompt Management:
16----------------------------------------->
171. Prompt Recording
182. Feedback Mechanism
193. Templates
20
21
225. Inference parameters: - Mastering the output
23
24Hyper-Parameter Tuning
25
26temperature:
27Top-K sampling
28Top-P Sampling
29Presence and Frequency Penalties
30Logit bias
31
32
33https://www.promptingguide.ai/
34
35Anthropic prompting
36
37https://docs.anthropic.com/en/prompt-library/library
LLMs Performance
1LLMs performance
2
3lmsys
4
5chatbot arena
6
7https://lmarena.ai/
8
9
10x.ai
11
12https://huggingface.co/spaces/lmarena-ai/chatbot-arena-leaderboard
13
14
15https://docs.ragas.io/en/stable/
16
17
18Learning Interprertability Tool:
19
20https://pair-code.github.io/lit/
21
22
23
24RLHF: Reinforcement Learning by Human Feedback
25
26
27
28LLMOps:
29------------------------------------------------------------------------>
30
311. Scalabillity: Managing computational resources to handle varying workloads without promising performance.
322. Latency Optimization: Reducing response times for real-time application through model optimization and efficient infrastructure
333. Monitoring and Logging: Continuously Tracking model outputs for quality assurance and compliance, enabling rapid response to issues.
344. Version Control and Updates: Maintaining consistent performance across model updates, with rollback mechanisms in case of unforseen problems
35
36API layer on top of apps - Caching Layer
Transformer Architecture

Standalone LLM vs RAG

RAG Vector Search Diagram

Vector Search Example


Generative LLM Diagram

Advanced RAG

3D Embedding Visualization

Dynamic Embeddings in RAG

Math in Transformers

Inference Parameter Visualizations




AI Studio in Action

Deploying Fine-tuned Models

LLM Leaderboard

Demo: Contoso Chat Setup
1https://github.com/Azure-Samples/contoso-chat.git
2
3The above AI example has the full End2End process of creating RAG applicaiton with Prompty and AI Studio
4
5It includes GPT 3.5 Turbo LLM Application code, evaluations, deployment automation with AZD Cli.
6GithubActions for evaluation and deployment and intent mapping for multiple LLM task Mapping.
7
8
9(.venv) bharathkumardasaraju@azure-samples-contoso-chat$ az login --use-device-code
10To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code NJSKTJT62 to authenticate.
11
12Retrieving tenants and subscriptions for the selection...
13
14[Tenant and subscription selection]
15
16No Subscription name Subscription ID Tenant
17----- --------------------- ------------------------------------ -----------------
18[1] * Azure subscription 1 fda65227-e02e-4d3b-9ae4-f32fb9f280fd Default Directory
19[2] bharath-ai-sg-test-01 6ced41d4-5c31-4325-b2c0-ac5cf2dac444 Default Directory
20[3] BharathTest 2359f93a-8785-4e1e-8b99-8def74b02d69 bharathkumaraju
21
22The default is marked with an *; the default tenant is 'Default Directory' and subscription is 'Azure subscription 1' (fda65227-e02e-4d3b-9ae4-f32fb9f280fd).
23
24Select a subscription and tenant (Type a number or Enter for no changes): 1
25
26Tenant: Default Directory
27Subscription: Azure subscription 1 (fda65227-e02e-4d3b-9ae4-f32fb9f280fd)
28
29[Announcements]
30With the new Azure CLI login experience, you can select the subscription you want to use more easily. Learn more about it and its configuration at https://go.microsoft.com/fwlink/?linkid=2271236
31
32If you encounter any problem, please open an issue at https://aka.ms/azclibug
33
34[Warning] The login output has been updated. Please be aware that it no longer displays the full list of available subscriptions by default.
35
36(.venv) bharathkumardasaraju@azure-samples-contoso-chat$
37
38
39
40(.venv) bharathkumardasaraju@azure-samples-contoso-chat$ azd auth login
41 Logged in to Azure as dbkraju@hotmail.com
42(.venv) bharathkumardasaraju@azure-samples-contoso-chat$
Contoso Chat Workshop PDF
Vector Search and RAG - Shell Demos
1(.venv) bharathkumardasaraju@RAG$ python3.12 ./1.vector_search.py
2
3🔹 List of Sentences to Search From:
4- Best pizza places nearby.
5- Popular breakfast spots in New York.
6- Top-rated seafood restaurants in Miami.
7- Cheap hotels near the beach.
8- Recipes for quick pasta dishes.
9- Is PizzaHut Restaurant Near Me.
10
11🧠 Generating embedding for: "Best pizza places nearby."
12✅ Embedding generated (first 5 values): [-0.08649007230997086, -0.029158486053347588, 0.021387368440628052, -0.007155250757932663, -0.016818758100271225] ...
13
14
15🧠 Generating embedding for: "Popular breakfast spots in New York."
16✅ Embedding generated (first 5 values): [-0.03500208258628845, -0.017171040177345276, -0.011198027059435844, 0.006204014644026756, -0.008464519865810871] ...
17
18
19🧠 Generating embedding for: "Top-rated seafood restaurants in Miami."
20✅ Embedding generated (first 5 values): [-0.008219791576266289, 0.03652282431721687, 0.0037918840534985065, 0.03813983127474785, 0.01901601441204548] ...
21
22
23🧠 Generating embedding for: "Cheap hotels near the beach."
24✅ Embedding generated (first 5 values): [-0.04145326092839241, 0.026635831221938133, 0.029279422014951706, -0.00951359048485756, -0.021059857681393623] ...
25
26
27🧠 Generating embedding for: "Recipes for quick pasta dishes."
28✅ Embedding generated (first 5 values): [-0.053842585533857346, -0.03515124320983887, -0.029104748740792274, -0.05926523730158806, -0.006262439768761396] ...
29
30
31🧠 Generating embedding for: "Is PizzaHut Restaurant Near Me."
32✅ Embedding generated (first 5 values): [-0.09511896222829819, -0.026932721957564354, 0.0025031305849552155, -3.803021172643639e-05, 0.008369842544198036] ...
33
34
35🔍 Searching for sentences similar to: "Find the best pizza restaurant close to me."
36
37🧠 Generating embedding for: "Find the best pizza restaurant close to me."
38✅ Embedding generated (first 5 values): [-0.06838679313659668, -0.009952893480658531, -0.012798307463526726, -0.0091355936601758, -0.006635262630879879] ...
39
40
41📊 **Similarity Scores:**
42- 0.7056 | Best pizza places nearby.
43- 0.6609 | Is PizzaHut Restaurant Near Me.
44- 0.3585 | Top-rated seafood restaurants in Miami.
45- 0.3373 | Cheap hotels near the beach.
46- 0.3328 | Popular breakfast spots in New York.
47- 0.2592 | Recipes for quick pasta dishes.
48
49🏆 **Top Matches:**
50✅ 0.7056 - Best pizza places nearby.
51✅ 0.6609 - Is PizzaHut Restaurant Near Me.
52(.venv) bharathkumardasaraju@RAG$
1(.venv) bharathkumardasaraju@RAG$ python3.12 ./2.vector_search.py
2
3🔹 List of Sentences to Search From:
4- Best pizza places nearby.
5- Popular breakfast spots in New York.
6- Top-rated seafood restaurants in Miami.
7- Cheap hotels near the beach.
8- Recipes for quick pasta dishes.
9- Is PizzaHut Restaurant Near Me.
10
11🧠 Generating embedding for: "Best pizza places nearby."
12✅ Full Embedding Vector (1536 values):
13[-0.08649442344903946, -0.029182350262999535, 0.021388445049524307, -0.007144412491470575, -0.016830801963806152, -0.016987577080726624, 0.01988789439201355, 0.014803939498960972, -0.0005742545472458005, -0.02101890556514263, 0.010688623413443565, 0.013404171913862228, -0.02828649803996086, 0.006315750069916248, 0.0303693525493145, 0.025554152205586433, -0.0010547246783971786, 0.040671639144420624, 0.02842087671160698, 0.015207072719931602, 0.04326960816979408, -0.025419775396585464, -0.01474794838577509, -0.025643737986683846, 0.05343751981854439, 0.0009504420449957252, 0.012295556254684925, 0.020526187494397163, 0.01362813450396061, -0.019831903278827667, 0.006511717569082975, -0.020974114537239075, 0.02322494052350521, -0.04185864329338074, -0.021970748901367188, -0.008129849098622799, 0.014199240133166313, 0.010537448339164257, 0.020391810685396194, -0.025285396724939346, -0.016696425154805183, 0.011326917447149754, 0.03350483253598213, -0.026875533163547516, 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14
15
16🧠 Generating embedding for: "Popular breakfast spots in New York."
17✅ Full Embedding Vector (1536 values):
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19
20
21🧠 Generating embedding for: "Top-rated seafood restaurants in Miami."
22✅ Full Embedding Vector (1536 values):
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26🧠 Generating embedding for: "Cheap hotels near the beach."
27✅ Full Embedding Vector (1536 values):
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29
30
31🧠 Generating embedding for: "Recipes for quick pasta dishes."
32✅ Full Embedding Vector (1536 values):
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36🧠 Generating embedding for: "Is PizzaHut Restaurant Near Me."
37✅ Full Embedding Vector (1536 values):
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39
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41🔍 Searching for sentences similar to: "Find the best pizza restaurant close to me."
42
43🧠 Generating embedding for: "Find the best pizza restaurant close to me."
44✅ Full Embedding Vector (1536 values):
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-0.06402786076068878, 0.009686514735221863, -0.008941863663494587, 0.0006360558909364045, 0.02644418366253376, 0.02439790777862072, 0.012507711537182331, -0.020414330065250397, 0.010394840501248837, -0.004368012771010399, -0.007658400572836399, 0.015752693638205528, 0.0028666029684245586, -0.024204177781939507, -0.003299468895420432, 0.005687800236046314, -0.002211249666288495, -0.007870293222367764, 0.021564602851867676, -0.016019074246287346, 0.006489964667707682, 0.015256260521709919, -0.008572565391659737]
46
47
48📊 **Similarity Scores:**
49- 0.7056 | Best pizza places nearby.
50- 0.6609 | Is PizzaHut Restaurant Near Me.
51- 0.3585 | Top-rated seafood restaurants in Miami.
52- 0.3375 | Cheap hotels near the beach.
53- 0.3328 | Popular breakfast spots in New York.
54- 0.2592 | Recipes for quick pasta dishes.
55
56🏆 **Top Matches:**
57✅ 0.7056 - Best pizza places nearby.
58✅ 0.6609 - Is PizzaHut Restaurant Near Me.
59(.venv) bharathkumardasaraju@RAG$
1(.venv) bharathkumardasaraju@RAG$ python3.12 ./3.visualize_vector_search.py
2
3🔹 List of Sentences to Search From:
4- Best pizza places nearby.
5- Popular breakfast spots in JB.
6- Top-rated seafood restaurants in Singapore.
7- Cheap hotels near the beach.
8- Recipes for quick pasta dishes.
9- Is PizzaHut Restaurant Near Me.
10
11🧠 Generating embedding for: "Best pizza places nearby."
12✅ Embedding generated (first 5 values): [-0.08648238331079483, -0.029155895113945007, 0.02136307582259178, -0.007132221478968859, -0.016806067898869514] ...
13
14
15🧠 Generating embedding for: "Popular breakfast spots in JB."
16✅ Embedding generated (first 5 values): [-0.04536508023738861, -0.009655652567744255, 0.005105708725750446, -0.018480712547898293, -0.008263186551630497] ...
17
18
19🧠 Generating embedding for: "Top-rated seafood restaurants in Singapore."
20✅ Embedding generated (first 5 values): [-0.024301711469888687, 0.015591994859278202, -0.008004389703273773, 0.012343217618763447, 0.06390687823295593] ...
21
22
23🧠 Generating embedding for: "Cheap hotels near the beach."
24✅ Embedding generated (first 5 values): [-0.0414893738925457, 0.026652701199054718, 0.02938460372388363, -0.009517235681414604, -0.02111116051673889] ...
25
26
27🧠 Generating embedding for: "Recipes for quick pasta dishes."
28✅ Embedding generated (first 5 values): [-0.05391743779182434, -0.03516146168112755, -0.02909335121512413, -0.059289995580911636, -0.00627197977155447] ...
29
30
31🧠 Generating embedding for: "Is PizzaHut Restaurant Near Me."
32✅ Embedding generated (first 5 values): [-0.09511896222829819, -0.026932721957564354, 0.0025031305849552155, -3.803021172643639e-05, 0.008369842544198036] ...
33
34
35🔍 Searching for sentences similar to: "Find the best pizza restaurant close to me."
36
37🧠 Generating embedding for: "Find the best pizza restaurant close to me."
38✅ Embedding generated (first 5 values): [-0.06839392334222794, -0.009953930974006653, -0.012751203961670399, -0.009130491875112057, -0.0066601731814444065] ...
39
40
41📊 **Similarity Scores:**
42- 0.7055 | Best pizza places nearby.
43- 0.6608 | Is PizzaHut Restaurant Near Me.
44- 0.3373 | Cheap hotels near the beach.
45- 0.3342 | Top-rated seafood restaurants in Singapore.
46- 0.2989 | Popular breakfast spots in JB.
47- 0.2592 | Recipes for quick pasta dishes.
48
49🏆 **Top Matches:**
50✅ 0.7055 - Best pizza places nearby.
51✅ 0.6608 - Is PizzaHut Restaurant Near Me.
52
53📊 **Visualizing Sentence Embeddings in 3D Space**
542025-03-21 05:45:47.200 Python[59024:4299923] +[IMKClient subclass]: chose IMKClient_Modern
552025-03-21 05:45:47.200 Python[59024:4299923] +[IMKInputSession subclass]: chose IMKInputSession_Modern
1Tokens and Vector Embeddings
2
3(.venv) bharathkumardasaraju@RAG$ python3.12 ./4.tokens_vector_embeddings.py
4
5🔹 List of Sentences to Search From:
6- Best pizza places nearby.
7- Popular breakfast spots in JB.
8- Top-rated seafood restaurants in Singapore.
9- Cheap hotels near the beach.
10- Recipes for quick pasta dishes.
11- Is PizzaHut Restaurant Near Me.
12
13🔤 **Tokenizing:** "Best pizza places nearby."
14🧩 Total Tokens: 5
15📌 Tokens: ['Best', ' pizza', ' places', ' nearby', '.']
16
17🧠 Generating embedding for: "Best pizza places nearby."
18✅ Embedding generated (first 5 values): [-0.08649007230997086, -0.029158486053347588, 0.021387368440628052, -0.007155250757932663, -0.016818758100271225] ...
19
20
21🔤 **Tokenizing:** "Popular breakfast spots in JB."
22🧩 Total Tokens: 6
23📌 Tokens: ['Popular', ' breakfast', ' spots', ' in', ' JB', '.']
24
25🧠 Generating embedding for: "Popular breakfast spots in JB."
26✅ Embedding generated (first 5 values): [-0.04536508023738861, -0.009655652567744255, 0.005105708725750446, -0.018480712547898293, -0.008263186551630497] ...
27
28
29🔤 **Tokenizing:** "Top-rated seafood restaurants in Singapore."
30🧩 Total Tokens: 7
31📌 Tokens: ['Top', '-rated', ' seafood', ' restaurants', ' in', ' Singapore', '.']
32
33🧠 Generating embedding for: "Top-rated seafood restaurants in Singapore."
34✅ Embedding generated (first 5 values): [-0.024301711469888687, 0.015591994859278202, -0.008004389703273773, 0.012343217618763447, 0.06390687823295593] ...
35
36
37🔤 **Tokenizing:** "Cheap hotels near the beach."
38🧩 Total Tokens: 6
39📌 Tokens: ['Cheap', ' hotels', ' near', ' the', ' beach', '.']
40
41🧠 Generating embedding for: "Cheap hotels near the beach."
42✅ Embedding generated (first 5 values): [-0.04145326092839241, 0.026635831221938133, 0.029279422014951706, -0.00951359048485756, -0.021059857681393623] ...
43
44
45🔤 **Tokenizing:** "Recipes for quick pasta dishes."
46🧩 Total Tokens: 6
47📌 Tokens: ['Recipes', ' for', ' quick', ' pasta', ' dishes', '.']
48
49🧠 Generating embedding for: "Recipes for quick pasta dishes."
50✅ Embedding generated (first 5 values): [-0.053842585533857346, -0.03515124320983887, -0.029104748740792274, -0.05926523730158806, -0.006262439768761396] ...
51
52
53🔤 **Tokenizing:** "Is PizzaHut Restaurant Near Me."
54🧩 Total Tokens: 8
55📌 Tokens: ['Is', ' Pizza', 'H', 'ut', ' Restaurant', ' Near', ' Me', '.']
56
57🧠 Generating embedding for: "Is PizzaHut Restaurant Near Me."
58✅ Embedding generated (first 5 values): [-0.09511896222829819, -0.026932721957564354, 0.0025031305849552155, -3.803021172643639e-05, 0.008369842544198036] ...
59
60
61🔍 Searching for sentences similar to: "Find the best pizza restaurant close to me."
62
63🔤 **Tokenizing:** "Find the best pizza restaurant close to me."
64🧩 Total Tokens: 9
65📌 Tokens: ['Find', ' the', ' best', ' pizza', ' restaurant', ' close', ' to', ' me', '.']
66
67🧠 Generating embedding for: "Find the best pizza restaurant close to me."
68✅ Embedding generated (first 5 values): [-0.06838679313659668, -0.009952893480658531, -0.012798307463526726, -0.0091355936601758, -0.006635262630879879] ...
69
70
71📊 **Similarity Scores:**
72- 0.7056 | Best pizza places nearby.
73- 0.6609 | Is PizzaHut Restaurant Near Me.
74- 0.3373 | Cheap hotels near the beach.
75- 0.3342 | Top-rated seafood restaurants in Singapore.
76- 0.2989 | Popular breakfast spots in JB.
77- 0.2592 | Recipes for quick pasta dishes.
78
79🏆 **Top Matches:**
80✅ 0.7056 - Best pizza places nearby.
81✅ 0.6609 - Is PizzaHut Restaurant Near Me.
82
83📊 **Visualizing Sentence Embeddings in 3D Space**
842025-03-21 06:20:55.106 Python[62806:4335110] +[IMKClient subclass]: chose IMKClient_Modern
852025-03-21 06:20:55.106 Python[62806:4335110] +[IMKInputSession subclass]: chose IMKInputSession_Modern
Vector Embeddings in 3D

Python Demos for Vector Search
1import os
2import numpy as np
3from openai import OpenAI
4from dotenv import load_dotenv
5
6# Load environment variables (Make sure you have a .env file with OPENAI_API_KEY)
7load_dotenv()
8
9# Initialize OpenAI client
10client = OpenAI(
11 api_key=os.getenv("OPENAI_API_KEY"),
12)
13
14# Sample sentences
15sentences = [
16 "Best pizza places nearby.",
17 "Popular breakfast spots in JB.",
18 "Top-rated seafood restaurants in Singapore.",
19 "Cheap hotels near the beach.",
20 "Recipes for quick pasta dishes.",
21 "Is PizzaHut Restaurant Near Me.",
22]
23
24print("\n List of Sentences to Search From:")
25for sentence in sentences:
26 print(f"- {sentence}")
27
28
29# Function to get embeddings from OpenAI
30def get_embedding(text, model="text-embedding-3-small"):
31 print(f"\n Generating embedding for: \"{text}\"")
32 embedding = client.embeddings.create(input=[text], model=model).data[0].embedding
33 print(f" Embedding generated (first 5 values): {embedding[:5]} ...\n") # Print only first 5 values for readability
34 return embedding
35
36
37# Generate embeddings for all sentences and store in a dictionary
38sentence_vectors = {}
39for sentence in sentences:
40 sentence_vectors[sentence] = get_embedding(sentence)
41
42
43# Function to calculate cosine similarity
44def calculate_cosine_similarity(query_vector, vector):
45 similarity = np.dot(query_vector, vector) / (np.linalg.norm(query_vector) * np.linalg.norm(vector))
46 return similarity
47
48
49# Function to find top-N most similar sentences
50def get_top_n_similar(query_sentence, n=2):
51 print(f"\n Searching for sentences similar to: \"{query_sentence}\"")
52
53 # Generate embedding for the query
54 query_embedding = get_embedding(query_sentence)
55
56 # Calculate similarity scores for each sentence
57 similarities = {
58 sentence: calculate_cosine_similarity(query_embedding, sentence_vectors[sentence])
59 for sentence in sentences
60 }
61
62 # Sort sentences based on similarity score (higher is better)
63 sorted_similarities = sorted(similarities.items(), key=lambda x: x[1], reverse=True)
64
65 print("\n **Similarity Scores:**")
66 for sentence, score in sorted_similarities:
67 print(f"- {score:.4f} | {sentence}")
68
69 # Get the top N results
70 top_matches = sorted_similarities[:n]
71
72 print("\n **Top Matches:**")
73 for sentence, score in top_matches:
74 print(f" {score:.4f} - {sentence}")
75
76
77# Query Example
78query_sentence = "Find the best pizza restaurant close to me."
79get_top_n_similar(query_sentence, n=2)
1import os
2import numpy as np
3import tiktoken
4from openai import OpenAI
5from dotenv import load_dotenv
6
7# Load environment variables (Make sure you have a .env file with OPENAI_API_KEY)
8load_dotenv()
9
10# Initialize OpenAI client
11client = OpenAI(
12 api_key=os.getenv("OPENAI_API_KEY"),
13)
14
15# Tokenizer for OpenAI models
16tokenizer = tiktoken.encoding_for_model("text-embedding-3-small")
17
18# Sample sentences
19sentences = [
20 "Best pizza places nearby.",
21 "Popular breakfast spots in JB.",
22 "Top-rated seafood restaurants in Singapore.",
23 "Cheap hotels near the beach.",
24 "Recipes for quick pasta dishes.",
25 "Is PizzaHut Restaurant Near Me.",
26]
27
28print("\n🔹 List of Sentences to Search From:")
29for sentence in sentences:
30 print(f"- {sentence}")
31
32
33# Function to tokenize text and print tokens
34def tokenize_text(text):
35 token_ids = tokenizer.encode(text) # Convert text to token IDs
36 tokens = [tokenizer.decode([tid]) for tid in token_ids] # Convert token IDs back to words
37 print(f"\n🔤 **Tokenizing:** \"{text}\"")
38 print(f"🧩 Total Tokens: {len(token_ids)}")
39 print(f"📌 Tokens: {tokens}") # Print tokenized words
40 return token_ids
41
42
43# Function to get embeddings from OpenAI
44def get_embedding(text, model="text-embedding-3-small"):
45 tokenize_text(text) # Print tokenization details before generating embeddings
46 print(f"\n🧠 Generating embedding for: \"{text}\"")
47 embedding = client.embeddings.create(input=[text], model=model).data[0].embedding
48 print(f"✅ Full Embedding Vector ({len(embedding)} values):\n{embedding}\n") # Print all 1536 values
49 return embedding
50
51
52# Generate embeddings for all sentences and store in a dictionary
53sentence_vectors = {sentence: get_embedding(sentence) for sentence in sentences}
54
55
56# Function to calculate cosine similarity
57def calculate_cosine_similarity(query_vector, vector):
58 similarity = np.dot(query_vector, vector) / (np.linalg.norm(query_vector) * np.linalg.norm(vector))
59 return similarity
60
61
62# Function to find top-N most similar sentences
63def get_top_n_similar(query_sentence, n=2):
64 print(f"\n🔍 Searching for sentences similar to: \"{query_sentence}\"")
65
66 # Generate embedding for the query
67 query_embedding = get_embedding(query_sentence)
68
69 # Calculate similarity scores for each sentence
70 similarities = {
71 sentence: calculate_cosine_similarity(query_embedding, sentence_vectors[sentence])
72 for sentence in sentences
73 }
74
75 # Sort sentences based on similarity score (higher is better)
76 sorted_similarities = sorted(similarities.items(), key=lambda x: x[1], reverse=True)
77
78 print("\n📊 **Similarity Scores:**")
79 for sentence, score in sorted_similarities:
80 print(f"- {score:.4f} | {sentence}")
81
82 # Get the top N results
83 top_matches = sorted_similarities[:n]
84
85 print("\n🏆 **Top Matches:**")
86 for sentence, score in top_matches:
87 print(f"✅ {score:.4f} - {sentence}")
88
89
90# Query Example
91query_sentence = "Find the best pizza restaurant close to me."
92get_top_n_similar(query_sentence, n=2)
1import os
2import numpy as np
3import matplotlib.pyplot as plt
4from sklearn.decomposition import PCA
5from openai import OpenAI
6from dotenv import load_dotenv
7
8# Load environment variables (Make sure you have a .env file with OPENAI_API_KEY)
9load_dotenv()
10
11# Initialize OpenAI client
12client = OpenAI(
13 api_key=os.getenv("OPENAI_API_KEY"),
14)
15
16# Sample sentences
17sentences = [
18 "Best pizza places nearby.",
19 "Popular breakfast spots in JB.",
20 "Top-rated seafood restaurants in Singapore.",
21 "Cheap hotels near the beach.",
22 "Recipes for quick pasta dishes.",
23 "Is PizzaHut Restaurant Near Me.",
24]
25
26print("\n🔹 List of Sentences to Search From:")
27for sentence in sentences:
28 print(f"- {sentence}")
29
30
31# Function to get embeddings from OpenAI
32def get_embedding(text, model="text-embedding-3-small"):
33 print(f"\n Generating embedding for: \"{text}\"")
34 embedding = client.embeddings.create(input=[text], model=model).data[0].embedding
35 print(f" Embedding generated (first 5 values): {embedding[:5]} ...\n") # Print only first 5 values for readability
36 return embedding
37
38
39# Generate embeddings for all sentences and store in a dictionary
40sentence_vectors = {sentence: get_embedding(sentence) for sentence in sentences}
41
42
43# Function to calculate cosine similarity
44def calculate_cosine_similarity(query_vector, vector):
45 similarity = np.dot(query_vector, vector) / (np.linalg.norm(query_vector) * np.linalg.norm(vector))
46 return similarity
47
48
49# Function to find top-N most similar sentences
50def get_top_n_similar(query_sentence, n=2):
51 print(f"\n Searching for sentences similar to: \"{query_sentence}\"")
52
53 # Generate embedding for the query
54 query_embedding = get_embedding(query_sentence)
55
56 # Calculate similarity scores for each sentence
57 similarities = {
58 sentence: calculate_cosine_similarity(query_embedding, sentence_vectors[sentence])
59 for sentence in sentences
60 }
61
62 # Sort sentences based on similarity score (higher is better)
63 sorted_similarities = sorted(similarities.items(), key=lambda x: x[1], reverse=True)
64
65 print("\n **Similarity Scores:**")
66 for sentence, score in sorted_similarities:
67 print(f"- {score:.4f} | {sentence}")
68
69 # Get the top N results
70 top_matches = sorted_similarities[:n]
71
72 print("\n **Top Matches:**")
73 for sentence, score in top_matches:
74 print(f" {score:.4f} - {sentence}")
75
76
77# Query Example
78query_sentence = "Find the best pizza restaurant close to me."
79get_top_n_similar(query_sentence, n=2)
80
81
82# 🔹 3D Visualization of Embeddings using PCA
83def visualize_embeddings_3d():
84 print("\n **Visualizing Sentence Embeddings in 3D Space**")
85
86 # Convert dictionary values (embeddings) into a NumPy array
87 sentence_embeddings = np.array(list(sentence_vectors.values()))
88
89 # Reduce dimensions from 1536D → 3D using PCA
90 pca = PCA(n_components=3)
91 reduced_embeddings_3d = pca.fit_transform(sentence_embeddings)
92
93 # Plot in 3D
94 fig = plt.figure(figsize=(10, 7))
95 ax = fig.add_subplot(111, projection='3d')
96
97 for i, sentence in enumerate(sentences):
98 x, y, z = reduced_embeddings_3d[i]
99 ax.scatter(x, y, z, label=sentence, s=100) # s=100 makes the dots larger
100 ax.text(x, y, z, sentence, fontsize=10)
101
102 ax.set_title("Sentence Embeddings Visualized in 3D using PCA")
103 ax.set_xlabel("PC 1")
104 ax.set_ylabel("PC 2")
105 ax.set_zlabel("PC 3")
106 plt.legend()
107 plt.show()
108
109
110# Call the visualization function
111visualize_embeddings_3d()
1import os
2import numpy as np
3import matplotlib.pyplot as plt
4import tiktoken
5from sklearn.decomposition import PCA
6from openai import OpenAI
7from dotenv import load_dotenv
8
9# Load API Key
10load_dotenv()
11client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
12
13# Tokenizer for OpenAI models
14tokenizer = tiktoken.encoding_for_model("text-embedding-3-small")
15
16# Sample sentences
17sentences = [
18 "Best pizza places nearby.",
19 "Popular breakfast spots in JB.",
20 "Top-rated seafood restaurants in Singapore.",
21 "Cheap hotels near the beach.",
22 "Recipes for quick pasta dishes.",
23 "Is PizzaHut Restaurant Near Me.",
24]
25
26print("\n List of Sentences to Search From:")
27for sentence in sentences:
28 print(f"- {sentence}")
29
30
31# Function to tokenize text
32def tokenize_text(text):
33 token_ids = tokenizer.encode(text) # Get token IDs
34 tokens = [tokenizer.decode([tid]) for tid in token_ids] # Convert IDs to readable tokens
35 print(f"\n **Tokenizing:** \"{text}\"")
36 print(f" Total Tokens: {len(token_ids)}")
37 print(f" Tokens: {tokens}")
38 return token_ids
39
40
41# Function to get embeddings
42def get_embedding(text, model="text-embedding-3-small"):
43 tokenize_text(text) # Print tokens before generating embeddings
44 print(f"\n Generating embedding for: \"{text}\"")
45 embedding = client.embeddings.create(input=[text], model=model).data[0].embedding
46 print(f" Embedding generated (first 5 values): {embedding[:5]} ...\n") # Print only first 5 values
47 return embedding
48
49
50# Generate embeddings for all sentences
51sentence_vectors = {sentence: get_embedding(sentence) for sentence in sentences}
52
53
54# Function to calculate cosine similarity
55def cosine_similarity(vec1, vec2):
56 return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
57
58
59# Function to find top-N most similar sentences
60def get_top_n_similar(query_sentence, n=2):
61 print(f"\n🔍 Searching for sentences similar to: \"{query_sentence}\"")
62
63 # Generate embedding for the query
64 query_embedding = get_embedding(query_sentence)
65
66 # Calculate similarity scores for each sentence
67 similarities = {
68 sentence: cosine_similarity(query_embedding, sentence_vectors[sentence])
69 for sentence in sentences
70 }
71
72 # Sort by similarity score (higher is better)
73 sorted_similarities = sorted(similarities.items(), key=lambda x: x[1], reverse=True)
74
75 print("\n **Similarity Scores:**")
76 for sentence, score in sorted_similarities:
77 print(f"- {score:.4f} | {sentence}")
78
79 # Get the top N matches
80 print("\n **Top Matches:**")
81 for sentence, score in sorted_similarities[:n]:
82 print(f" {score:.4f} - {sentence}")
83
84
85# Query Example
86query_sentence = "Find the best pizza restaurant close to me."
87get_top_n_similar(query_sentence, n=2)
88
89
90# 🔹 3D Visualization of Embeddings using PCA
91def visualize_embeddings_3d():
92 print("\n **Visualizing Sentence Embeddings in 3D Space**")
93
94 # Convert embeddings to NumPy array
95 sentence_embeddings = np.array(list(sentence_vectors.values()))
96
97 # Reduce 1536D → 3D using PCA
98 pca = PCA(n_components=3)
99 reduced_embeddings_3d = pca.fit_transform(sentence_embeddings)
100
101 # Plot in 3D
102 fig = plt.figure(figsize=(10, 7))
103 ax = fig.add_subplot(111, projection='3d')
104
105 for i, sentence in enumerate(sentences):
106 x, y, z = reduced_embeddings_3d[i]
107 ax.scatter(x, y, z, label=sentence, s=100)
108 ax.text(x, y, z, sentence, fontsize=10)
109
110 ax.set_title("Sentence Embeddings Visualized in 3D using PCA")
111 ax.set_xlabel("PC 1")
112 ax.set_ylabel("PC 2")
113 ax.set_zlabel("PC 3")
114 plt.legend()
115 plt.show()
116
117
118# Call the visualization function
119visualize_embeddings_3d()
RAG Application Setup
1git clone https://github.com/Azure-Samples/contoso-chat.git
2
3bharathkumardasaraju@external$ git clone https://github.com/Azure-Samples/contoso-chat.git
4Cloning into 'contoso-chat'...
5remote: Enumerating objects: 3779, done.
6remote: Counting objects: 100% (819/819), done.
7remote: Compressing objects: 100% (337/337), done.
8remote: Total 3779 (delta 600), reused 482 (delta 482), pack-reused 2960 (from 2)
9Receiving objects: 100% (3779/3779), 241.04 MiB | 14.62 MiB/s, done.
10Resolving deltas: 100% (1909/1909), done.
11bharathkumardasaraju@external$
12
13
14What are the mathematical methods used to compare the similarity between two vectors?
15
16Euclidean distance
17Cosine Similarity
18dot product
19
20
21Create an AI agentic workflow that builds bunch of promptys that gets customer information and their order history .. and write emails.
22
23How to evaluate answers:
241. Groundedness
252. Fluency
263. Coherence
274. Relevance
28
29https://azure.github.io/Cloud-Native/30-days-of-ia-2024/kick-off


Azure Samples – Contoso Chat (LLM RAG Application)
This project demonstrates a complete Azure-based LLM RAG solution called Contoso Chat, integrating AI, vector search, and enterprise data with Azure infrastructure.
Repository Highlights
🧠 AI chat flow evaluation (evaluate-chat-flow.ipynb)
📊 CosmosDB, Azure Search & Azure Storage provisioning (via Bicep)
💬 Prompt templates and grounding data (src/api/contoso_chat/)
📦 Modular Azure infra (infra/) for AI, database, host, monitoring, etc.
📁 Sample data and customer/product metadata (data/)
🛠 Setup Script
1git clone https://github.com/Azure-Samples/contoso-chat.git
2
3bharathkumardasaraju@external$ git clone https://github.com/Azure-Samples/contoso-chat.git
4Cloning into 'contoso-chat'...
5remote: Enumerating objects: 3779, done.
6remote: Counting objects: 100% (819/819), done.
7remote: Compressing objects: 100% (337/337), done.
8remote: Total 3779 (delta 600), reused 482 (delta 482), pack-reused 2960 (from 2)
9Receiving objects: 100% (3779/3779), 241.04 MiB | 14.62 MiB/s, done.
10Resolving deltas: 100% (1909/1909), done.
11bharathkumardasaraju@external$
📘 Key Resources
README.md – Overview and architecture
evaluate-chat-flow.ipynb – End-to-end evaluation notebook
infra/ – Azure deployment templates (Bicep)
src/api/ – API backend including prompt logic, evaluations, tracing
data/ – CosmosDB, Azure Search and grounding examples
📎 Additional Docs and Policies
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
SECURITY.md
SUPPORT.md
📂 Folder Contents
├── data/
│ ├── customer_info/*.json
│ ├── product_info/products.csv
│ └── manual_info/contoso-manuals-index.ipynb
├── infra/
│ ├── app/
│ ├── core/
│ └── hooks/
├── src/
│ └── api/
│ ├── contoso_chat/
│ └── evaluators/
├── docker-compose.yml
├── azure.yaml
└── img.png
📘 Key Resources
src/api/ – Main backend logic, FastAPI services, tracing, and evaluation flows.
contoso_chat/ – Core chat handler and product logic.
custom_evals/ – Evaluation modules like fluency, coherence, etc.
prompt_templates/ – Prompt templates used in evaluation and experimentation.
**📘 prompty **
.prompty files define reusable prompt templates to evaluate large language models (LLMs) consistently. These prompts encapsulate evaluation metrics such as fluency, coherence, and grounding.
The following .prompty files are included:
Evaluation Prompts:
coherence.prompty – Coherence Prompty
fluency.prompty – Fluency Prompty
groundedness.prompty – Groundedness Prompty
relevance.prompty – Relevance Prompty
Chat & Response Templates:
chat.prompty – Chat prompt template
product.prompty – Product prompt template
Workshop Demo Prompts:
chat-0.prompty chat-0
chat-1.prompty chat-1
chat-2.prompty chat-2
chat-2-jailbreak.prompty chat-2
chat-3.prompty chat-3
chat-4.prompty chat-4
chat-exact.prompty chat-exact
basic.prompty basic
friendliness.prompty friendliness
What is .prompty?
`.prompty` is a custom file format used to define structured LLM prompts. Each file encapsulates:
Prompt instructions
Placeholders or variables
Scoring/evaluation metadata