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docs/design_pattern/rag.md

@@ -8,7 +8,11 @@ nav_order: 4
 # RAG (Retrieval Augmented Generation)
 
 For certain LLM tasks like answering questions, providing context is essential.
-Use [vector search](../utility_function/tool.md) to find relevant context for LLM responses.
+Most common way to retrive text-based context is through embedding:
+1. Given texts, you first [chunk](../utility_function/chunking.md) them.
+2. Next, you [embed](../utility_function/embedding.md) each chunk.
+3. Then you store the chunks in [vector databases](../utility_function/vector.md).
+4. Finally, given a query, you embed the query and find the closest chunk in the vector databases.
 
 ### Example: Question Answering
 

docs/index.md

@@ -41,7 +41,8 @@ We model the LLM workflow as a **Graph + Shared Store**:
 - [(Optional) Web Search](./utility_function/websearch.md)
 - [(Optional) Chunking](./utility_function/chunking.md)
 - [(Optional) Embedding](./utility_function/embedding.md)
-- [(Optional) Vector](./utility_function/vector.md)
+- [(Optional) Vector Databases](./utility_function/vector.md)
+- [(Optional) Text-to-Speech](./utility_function/text_to_speech.md)
 
 > We do not provide built-in utility functions. Example implementations are provided as reference.
 {: .warning }

docs/utility_function/chunking.md

@@ -32,7 +32,7 @@ def fixed_size_chunk(text, chunk_size=100):
 
 However, sentences are often cut awkwardly, losing coherence.
 
-### Sentence-Based Chunking
+### 2. Sentence-Based Chunking
 
 ```python
 import nltk
@@ -47,7 +47,7 @@ def sentence_based_chunk(text, max_sentences=2):
 
 However, might not handle very long sentences or paragraphs well.
 
-### Other Chunking
+### 3. Other Chunking
 
 - **Paragraph-Based**: Split text by paragraphs (e.g., newlines). Large paragraphs can create big chunks.
 - **Semantic**: Use embeddings or topic modeling to chunk by semantic boundaries.

docs/utility_function/embedding.md

@@ -1,7 +1,7 @@
 ---
 layout: default
-title: "Web Search"
+title: "Embedding"
-parent: "Embedding"
+parent: "Utility Function"
 nav_order: 6
 ---
 
@@ -15,7 +15,7 @@ Below you will find an overview table of various text embedding APIs, along with
 {: .best-practice }
 
 
-| **API** | **Free Tier** | **Pricing** | **Docs** |
+| **API** | **Free Tier** | **Pricing Model** | **Docs** |
 | --- | --- | --- | --- |
 | **OpenAI** | ~$5 credit | ~$0.0001/1K tokens | [OpenAI Embeddings](https://platform.openai.com/docs/api-reference/embeddings) |
 | **Azure OpenAI** | $200 credit | Same as OpenAI (~$0.0001/1K tokens) | [Azure OpenAI Embeddings](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?tabs=portal) |

docs/utility_function/text_to_speech.md

@@ -0,0 +1,107 @@
+---
+layout: default
+title: "Text-to-Speech"
+parent: "Utility Function"
+nav_order: 8
+---
+
+## Text-to-Speech
+
+| **Service**          | **Free Tier**         | **Pricing Model**                                            | **Docs**                                                            |
+|----------------------|-----------------------|--------------------------------------------------------------|---------------------------------------------------------------------|
+| **Amazon Polly**     | 5M std + 1M neural   | ~$4 /M (std), ~$16 /M (neural) after free tier               | [Polly Docs](https://aws.amazon.com/polly/)                         |
+| **Google Cloud TTS** | 4M std + 1M WaveNet  | ~$4 /M (std), ~$16 /M (WaveNet) pay-as-you-go                | [Cloud TTS Docs](https://cloud.google.com/text-to-speech)           |
+| **Azure TTS**        | 500K neural ongoing  | ~$15 /M (neural), discount at higher volumes                 | [Azure TTS Docs](https://azure.microsoft.com/products/cognitive-services/text-to-speech/) |
+| **IBM Watson TTS**   | 10K chars Lite plan  | ~$0.02 /1K (i.e. ~$20 /M). Enterprise options available       | [IBM Watson Docs](https://www.ibm.com/cloud/watson-text-to-speech)   |
+| **ElevenLabs**       | 10K chars monthly    | From ~$5/mo (30K chars) up to $330/mo (2M chars). Enterprise  | [ElevenLabs Docs](https://elevenlabs.io)                            |
+
+## Example Python Code
+
+### Amazon Polly
+```python
+import boto3
+
+polly = boto3.client("polly", region_name="us-east-1",
+                     aws_access_key_id="YOUR_AWS_ACCESS_KEY_ID",
+                     aws_secret_access_key="YOUR_AWS_SECRET_ACCESS_KEY")
+
+resp = polly.synthesize_speech(
+    Text="Hello from Polly!",
+    OutputFormat="mp3",
+    VoiceId="Joanna"
+)
+
+with open("polly.mp3", "wb") as f:
+    f.write(resp["AudioStream"].read())
+```
+
+### Google Cloud TTS
+```python
+from google.cloud import texttospeech
+
+client = texttospeech.TextToSpeechClient()
+input_text = texttospeech.SynthesisInput(text="Hello from Google Cloud TTS!")
+voice = texttospeech.VoiceSelectionParams(language_code="en-US")
+audio_cfg = texttospeech.AudioConfig(audio_encoding=texttospeech.AudioEncoding.MP3)
+
+resp = client.synthesize_speech(input=input_text, voice=voice, audio_config=audio_cfg)
+
+with open("gcloud_tts.mp3", "wb") as f:
+    f.write(resp.audio_content)
+```
+
+### Azure TTS
+```python
+import azure.cognitiveservices.speech as speechsdk
+
+speech_config = speechsdk.SpeechConfig(
+    subscription="AZURE_KEY", region="AZURE_REGION")
+audio_cfg = speechsdk.audio.AudioConfig(filename="azure_tts.wav")
+
+synthesizer = speechsdk.SpeechSynthesizer(
+    speech_config=speech_config,
+    audio_config=audio_cfg
+)
+
+synthesizer.speak_text_async("Hello from Azure TTS!").get()
+```
+
+### IBM Watson TTS
+```python
+from ibm_watson import TextToSpeechV1
+from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
+
+auth = IAMAuthenticator("IBM_API_KEY")
+service = TextToSpeechV1(authenticator=auth)
+service.set_service_url("IBM_SERVICE_URL")
+
+resp = service.synthesize(
+    "Hello from IBM Watson!",
+    voice="en-US_AllisonV3Voice",
+    accept="audio/mp3"
+).get_result()
+
+with open("ibm_tts.mp3", "wb") as f:
+    f.write(resp.content)
+```
+
+### ElevenLabs
+```python
+import requests
+
+api_key = "ELEVENLABS_KEY"
+voice_id = "ELEVENLABS_VOICE"
+url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
+headers = {"xi-api-key": api_key, "Content-Type": "application/json"}
+
+json_data = {
+    "text": "Hello from ElevenLabs!",
+    "voice_settings": {"stability": 0.75, "similarity_boost": 0.75}
+}
+
+resp = requests.post(url, headers=headers, json=json_data)
+
+with open("elevenlabs.mp3", "wb") as f:
+    f.write(resp.content)
+```
+

docs/utility_function/vector.md

@@ -0,0 +1,218 @@
+---
+layout: default
+title: "Vector Databases"
+parent: "Utility Function"
+nav_order: 7
+---
+
+# Vector Databases
+
+
+Below is a  table of the popular vector search solutions:
+
+| **Tool** | **Free Tier** | **Pricing Model** | **Docs** |
+| --- | --- | --- | --- |
+| **FAISS** | N/A, self-host | Open-source | [Faiss.ai](https://faiss.ai) |
+| **Pinecone** | 2GB free | From $25/mo | [pinecone.io](https://pinecone.io) |
+| **Qdrant** | 1GB free cloud | Pay-as-you-go | [qdrant.tech](https://qdrant.tech) |
+| **Weaviate** | 14-day sandbox | From $25/mo | [weaviate.io](https://weaviate.io) |
+| **Milvus** | 5GB free cloud | PAYG or $99/mo dedicated | [milvus.io](https://milvus.io) |
+| **Chroma** | N/A, self-host | Free (Apache 2.0) | [trychroma.com](https://trychroma.com) |
+| **Redis** | 30MB free | From $5/mo | [redis.io](https://redis.io) |
+
+---
+## Example Python Code
+
+Below are basic usage snippets for each tool.
+
+### FAISS
+```python
+import faiss
+import numpy as np
+
+# Dimensionality of embeddings
+d = 128
+
+# Create a flat L2 index
+index = faiss.IndexFlatL2(d)
+
+# Random vectors
+data = np.random.random((1000, d)).astype('float32')
+index.add(data)
+
+# Query
+query = np.random.random((1, d)).astype('float32')
+D, I = index.search(query, k=5)
+
+print("Distances:", D)
+print("Neighbors:", I)
+```
+
+### Pinecone
+```python
+import pinecone
+
+pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENV")
+
+index_name = "my-index"
+
+# Create the index if it doesn't exist
+if index_name not in pinecone.list_indexes():
+    pinecone.create_index(name=index_name, dimension=128)
+
+# Connect
+index = pinecone.Index(index_name)
+
+# Upsert
+vectors = [
+    ("id1", [0.1]*128),
+    ("id2", [0.2]*128)
+]
+index.upsert(vectors)
+
+# Query
+response = index.query([[0.15]*128], top_k=3)
+print(response)
+```
+
+### Qdrant
+```python
+import qdrant_client
+from qdrant_client.models import Distance, VectorParams, PointStruct
+
+client = qdrant_client.QdrantClient(
+    url="https://YOUR-QDRANT-CLOUD-ENDPOINT",
+    api_key="YOUR_API_KEY"
+)
+
+collection = "my_collection"
+client.recreate_collection(
+    collection_name=collection,
+    vectors_config=VectorParams(size=128, distance=Distance.COSINE)
+)
+
+points = [
+    PointStruct(id=1, vector=[0.1]*128, payload={"type": "doc1"}),
+    PointStruct(id=2, vector=[0.2]*128, payload={"type": "doc2"}),
+]
+
+client.upsert(collection_name=collection, points=points)
+
+results = client.search(
+    collection_name=collection,
+    query_vector=[0.15]*128,
+    limit=2
+)
+print(results)
+```
+
+### Weaviate
+```python
+import weaviate
+
+client = weaviate.Client("https://YOUR-WEAVIATE-CLOUD-ENDPOINT")
+
+schema = {
+    "classes": [
+        {
+            "class": "Article",
+            "vectorizer": "none"
+        }
+    ]
+}
+client.schema.create(schema)
+
+obj = {
+    "title": "Hello World",
+    "content": "Weaviate vector search"
+}
+client.data_object.create(obj, "Article", vector=[0.1]*128)
+
+resp = (
+    client.query
+    .get("Article", ["title", "content"])
+    .with_near_vector({"vector": [0.15]*128})
+    .with_limit(3)
+    .do()
+)
+print(resp)
+```
+
+### Milvus
+```python
+from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection
+import numpy as np
+
+connections.connect(alias="default", host="localhost", port="19530")
+
+fields = [
+    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
+    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128)
+]
+schema = CollectionSchema(fields)
+collection = Collection("MyCollection", schema)
+
+emb = np.random.rand(10, 128).astype('float32')
+ids = list(range(10))
+collection.insert([ids, emb])
+
+index_params = {
+    "index_type": "IVF_FLAT",
+    "params": {"nlist": 128},
+    "metric_type": "L2"
+}
+collection.create_index("embedding", index_params)
+collection.load()
+
+query_emb = np.random.rand(1, 128).astype('float32')
+results = collection.search(query_emb, "embedding", param={"nprobe": 10}, limit=3)
+print(results)
+```
+
+### Chroma
+```python
+import chromadb
+from chromadb.config import Settings
+
+client = chromadb.Client(Settings(
+    chroma_db_impl="duckdb+parquet",
+    persist_directory="./chroma_data"
+))
+
+coll = client.create_collection("my_collection")
+
+vectors = [[0.1, 0.2, 0.3], [0.2, 0.2, 0.2]]
+metas = [{"doc": "text1"}, {"doc": "text2"}]
+ids = ["id1", "id2"]
+coll.add(embeddings=vectors, metadatas=metas, ids=ids)
+
+res = coll.query(query_embeddings=[[0.15, 0.25, 0.3]], n_results=2)
+print(res)
+```
+
+### Redis
+```python
+import redis
+import struct
+
+r = redis.Redis(host="localhost", port=6379)
+
+# Create index
+r.execute_command(
+    "FT.CREATE", "my_idx", "ON", "HASH",
+    "SCHEMA", "embedding", "VECTOR", "FLAT", "6",
+    "TYPE", "FLOAT32", "DIM", "128",
+    "DISTANCE_METRIC", "L2"
+)
+
+# Insert
+vec = struct.pack('128f', *[0.1]*128)
+r.hset("doc1", mapping={"embedding": vec})
+
+# Search
+qvec = struct.pack('128f', *[0.15]*128)
+q = "*=>[KNN 3 @embedding $BLOB AS dist]"
+res = r.ft("my_idx").search(q, query_params={"BLOB": qvec})
+print(res.docs)
+```
+

docs/utility_function/websearch.md

@@ -8,7 +8,7 @@ nav_order: 4
 
 We recommend some implementations of commonly used web search tools.
 
-| **API**                         | **Free Tier**                                | **Pricing Model**                                              | **Official API Page**                                                  |
+| **API**                         | **Free Tier**                                | **Pricing Model**                                              | **Docs**                                                  |
 |---------------------------------|-----------------------------------------------|-----------------------------------------------------------------|------------------------------------------------------------------------|
 | **Google Custom Search JSON API** | 100 queries/day free       | $5 per 1000 queries.           | [Link](https://developers.google.com/custom-search/v1/overview)        |
 | **Bing Web Search API**         | 1,000 queries/month               | $15–$25 per 1,000 queries. | [Link](https://azure.microsoft.com/en-us/services/cognitive-services/bing-web-search-api/) |