How does AI APIs work internally? (Discussions with chatGpt)

Query:

Internal working of ai api: Input text from human (human text) AI server/computer transfer human text to machine input binary (which is highly compressed) Machine input binary is processed to give machine output binary (which is highly compressed) Machine output binary is converted to human text ie output text based on human user location, etc Correct?

You're close, but the actual internal working of an AI API like OpenAI's GPT involves more nuanced steps. Let’s correct and clarify your understanding step by step:

✅ Corrected and Detailed Flow of an AI API:

1. Input: Human Text

Human types: "What is the capital of France?"
This input string is sent to the AI server over HTTPS (usually as JSON).

2. Tokenization (Not Binary Encoding)

AI server tokenizes the text into tokens (subword units, not characters or words).
- Example: "What is the capital of France?" → [What, is, the, cap, ital, of, France, ?]
This is NOT traditional binary compression like gzip, but token IDs, which are integers.
Tokens are mapped to IDs via a vocabulary table.
- Example: "What" → 15496, "cap" → 2243, etc.

3. Machine Input Representation

These token IDs form an input array: [15496, 318, 262, 672, 1042, 286, 4563, 30]
This is passed to the transformer model (the neural network engine).

4. Neural Network Processing

Model processes tokens via layers of attention, MLPs, etc.
This uses floating-point tensors, matrix multiplication, and GPU/TPU hardware.
Output is a probability distribution over next token for each step.

5. Machine Output: Token IDs

Output is a list of generated token IDs, like [496, 13] which may mean "Paris."
This list is passed to the decoder.

6. Detokenization → Human Text

The model maps token IDs back to string tokens: [496, 13] → "Paris."
Sent back to the user as human-readable text.

❌ Clarifications on Misconceptions:

Your Understanding	Correction
Input is binary	Internally, input text is tokenized into integers, not "compressed binary"
Machine input/output are compressed binary	They are floating point tensors, not compressed data
Human output depends on location	Only if system is fine-tuned to consider user profile or locale (e.g. metric vs imperial)

Summary Diagram (Simplified):

scss
Human Text Input
     ↓
[ Tokenizer: text → token IDs ]
     ↓
[ Neural Network: token IDs → next token(s) prediction ]
     ↓
[ Detokenizer: token IDs → text ]
     ↓
Human Text Output

Optional:

AI APIs may consider:
- User locale (e.g., language, timezone)
- Context window (prior messages)
- System instructions (system role in ChatGPT API)

Would you like a code or binary-level demo of how tokenization works internally (e.g. using tiktoken)?

Ok so human text are broken into tokens. Each token has a vocabalary table (id, word/token) From vocabalary table, array of token int id[] send to nural network processing. Form nural network processing, output tokan int id[] is generated Then output token is converted to char** & send to user

Yes, your updated understanding is mostly correct! Here's a refined version with exact technical steps and terminology to match your C/C++ background:

✅ Internal Working of AI API (Step-by-Step, Technical Version):

1. Tokenization (String → `int[]`)

Input:
char* input = "What is the capital of France?";

Tokenizer converts string to token IDs using a vocabulary table (like a dictionary or hash map):

cpp
// Pseudocode:
std::vector<int> token_ids = tokenize("What is the capital of France?");
// token_ids = {15496, 318, 262, 672, 1042, 286, 4563, 30}

Each token_id maps to a subword unit in the vocabulary:

text
15496 => "What"
318   => " is"
262   => " the"
672   => " cap"
...

2. Neural Network Input (Token ID Array → Float Matrix)

This token ID array is converted to an embedding matrix (say float X[n][d], where n = number of tokens, d = embedding dimension).

It enters the transformer model: attention + feedforward layers:

cpp
float logits[n][vocab_size] = transformer_forward(X);

3. Output Prediction (Token ID Output)

Output is probability distribution for the next token.

The model samples (or greedily picks) token IDs:

cpp
std::vector<int> output_token_ids = {496, 13}; // Example: "Paris."

4. Detokenization (Token ID → String)

These output token IDs are converted back using the same vocab table (inverse mapping):
```
cpp
std::string output = detokenize({496, 13}); // → "Paris."
```

5. Response Returned

This output string is wrapped in JSON and returned over HTTP as API response.

🧠 Summary (C-Style View):

c
// User input
char* input_text = "What is the capital of France?";

// Tokenization
int token_ids[] = tokenize(input_text); // -> [15496, 318, 262, 672, ...]

// Model inference
int output_ids[] = run_neural_network(token_ids); // -> [496, 13]

// Detokenize
char* output_text = detokenize(output_ids); // -> "Paris."

// Return to user
send_to_user(output_text);

Notes:

Vocabulary table is static and part of model definition (BPE, SentencePiece, etc.).
Token → ID mapping is not 1:1 with words; many tokens are subwords or partial strings.
Internally, neural networks work on float32/float16 matrices, not compressed binaries.
OpenAI models like GPT-4 use tensors on GPU/TPU for high throughput.

Let me know if you want a real tokenizer demo using tiktoken, HuggingFace, or want to implement a toy one in C/C++?

Now how does this inputIds[] converted to outputIds[] in nural network/ etc?

Great question, Mohit. Now you're diving into the core mechanics of how a transformer neural network (like GPT) works — how inputIds[] is processed into outputIds[]. Below is a step-by-step technical explanation tailored for someone with your C/C++ and systems background:

🔧 Step-by-Step: How `inputIds[]` → `outputIds[]` Happens

1. Input Token IDs → Embedding Vectors

Each token_id is mapped to a dense vector from the embedding table.

c
// Think of this as a lookup:
float embed[token_count][embed_dim]; // e.g., [8][768] or [8][12288]
for(int i=0; i<token_count; ++i)
    embed[i] = embedding_table[inputIds[i]];

Embedding table is a learnable matrix of shape [vocab_size][embedding_dim]
So if vocab size is 50K and dim = 768, then it's a [50000][768] float matrix

2. Add Positional Encoding

Transformer doesn’t have recurrence, so it adds a position encoding vector to each embedded token.

c
for(int i = 0; i < token_count; ++i)
    for(int j = 0; j < embed_dim; ++j)
        embed[i][j] += position_encoding[i][j];

3. Pass Through Transformer Blocks (Layers)

Now, embed[token_count][embed_dim] is fed into multiple Transformer layers. Each layer has:

A. Multi-Head Self-Attention

It allows each token to "attend" to others in the sequence.
Internally:
```
c
Q = X * W_q
K = X * W_k
V = X * W_v

Attention(Q, K, V) = softmax(Q * Kᵗ / sqrt(d_k)) * V
```
- Q, K, V are linear projections of input (X = embeddings)
- Matrix operations over all tokens at once using BLAS/GPU

B. Feedforward MLP Layer

A simple 2-layer network with ReLU/GELU:

c
ff1 = ReLU(X * W1 + b1)
out = ff1 * W2 + b2

C. LayerNorm + Residual Connections

Used before or after attention/MLP:

c
X = LayerNorm(X + Attention(X));
X = LayerNorm(X + MLP(X));

This whole block is repeated N times (12 in GPT-2, 96 in GPT-4).

4. Output: Predict Next Token (Logits)

The final output tensor is of shape [token_count][embed_dim]. It is projected to vocabulary size to get logits:

c
logits[token_count][vocab_size] = X * W_out;

Each row gives probabilities for the next token.

Softmax gives a probability distribution:

c
probs[i][j] = exp(logits[i][j]) / sum(exp(logits[i][:]))

5. Sampling/Decoding → outputIds[]

From the final position (last token's logits), the next token is chosen:

Greedy: pick argmax
Sampling: random sample with temperature
Top-k / Top-p (nucleus): sample from top-k most probable tokens

This becomes the first token of outputIds[].

The process can then be repeated:
- Append new token
- Feed it back in (with full context or using KV caching)
- Repeat until max_tokens, eos_token, or stop condition.

🧠 Summary in Code/Concept

c
// inputIds: int[] --> Embedding Lookup --> Float tensor
float X[N][D] = embed(inputIds);       // Embedding + Position

// Apply N transformer layers
for(int l = 0; l < L; ++l)
    X = transformer_layer(X);         // self-attention + MLP + norm

// Final projection to vocab logits
float logits[N][vocab_size] = X * W_out;

// Decode output tokens
int outputIds[] = sample(logits[N-1]); // Use last token’s logits

Bonus: Performance Tips (as you’re a C++/GPGPU dev)

These operations are fully parallelized via CUDA/ROCm on GPU/TPU
Uses tensor cores (float16/bfloat16 for speed)
Uses KV caching to avoid recomputation for previous tokens in long contexts

Want to see this in action using Python/NumPy/PyTorch or even C++ with ONNX or TinyGrad?

yes in c++ with ONNX or TinyGrad

Perfect. Since you're a C++ dev working on high-performance systems, let's look at a minimal C++ example using ONNX Runtime first (GPU or CPU), followed by TinyGrad (if you want to explore training or running models from scratch).

⚙️ PART 1: Inference using ONNX Runtime (C++)

We’ll use OpenAI-like transformer model exported to .onnx. You load model, provide inputIds[], run inference, get outputIds[].

✅ Requirements

Install ONNX Runtime C++
Pretrained ONNX model (e.g., GPT-2 from HuggingFace exported as ONNX)
Build with CMake or plain g++

🚀 Code: Load GPT2 ONNX and Run Inference

cpp
#include <onnxruntime_cxx_api.h>
#include <vector>
#include <iostream>

int main() {
    Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "test");
    Ort::SessionOptions session_options;
    session_options.SetIntraOpNumThreads(1);

    // Load GPT-2 model (converted from HuggingFace)
    Ort::Session session(env, "gpt2.onnx", session_options);

    // Input ids: e.g., tokenized "Hello"
    std::vector<int64_t> input_ids = {15496}; // token for "Hello"
    std::vector<int64_t> input_shape = {1, input_ids.size()};

    Ort::AllocatorWithDefaultOptions allocator;

    // Input tensor
    Ort::Value input_tensor = Ort::Value::CreateTensor<int64_t>(
        allocator, input_ids.data(), input_ids.size(), input_shape.data(), input_shape.size());

    const char* input_names[] = {"input_ids"};
    const char* output_names[] = {"logits"};

    // Inference
    auto output_tensors = session.Run(Ort::RunOptions{nullptr},
                                      input_names, &input_tensor, 1,
                                      output_names, 1);

    // Access logits
    float* logits = output_tensors[0].GetTensorMutableData<float>();

    // Print top 5 probabilities for next token
    for (int i = 0; i < 5; ++i)
        std::cout << "logit[" << i << "] = " << logits[i] << "\n";

    return 0;
}

🔧 Build it:

bash
g++ -std=c++17 -I/path/to/onnxruntime/include -L/path/to/onnxruntime/lib -lonnxruntime main.cpp -o gpt2_infer

You can get GPT2 in ONNX format from HuggingFace and export using:

python
from transformers import GPT2Model
from transformers.onnx import export
export("gpt2", output="gpt2.onnx", opset=13)

⚙️ PART 2: TinyGrad (Pure Training/Inference Framework in Python)

If you're interested in building a transformer engine from scratch, TinyGrad is a minimal yet fully working deep learning engine (with autograd, GPU, etc.).

📌 Why TinyGrad?

Fully self-contained Python codebase
Shows how transformers process tokens → output
You can port to C++ easily since it's basic matrix ops

Repo: https://github.com/geohot/tinygrad
Transformer example: test/test_gpt2.py

If you want, I can:

Help you port a TinyGrad transformer block to C++
Show tokenizer→embedding→attention→MLP→output step-by-step in C/CUDA

✅ Conclusion

Tool	Use-case	Notes
ONNX Runtime	Production inference in C++	Load models, fast, GPU support
TinyGrad	Learn internals / train own model	Easy to study, Python-based, minimal

Would you like:

A ready GPT2 .onnx file and tokenizer you can test locally?
Or a step-by-step from scratch transformer block in C++/CUDA (TinyGrad-style)?

Let me know your preferred path.

so for conversion of int inputToken[] to int outputToken[], AI computer uses some kind of knowledge base (i.e trained onnx file which have large amout of data & some kind of knowledge that for this inputToken[] these outputToken[] may be the required outputs (internally stored in some kind of matrix) And output is provided in this way?

Yes, you are 100% correct in concept — just needs a few refinements to match what actually happens internally in the AI engine. Here's the clarified, technical explanation with your understanding as the base:

✅ Your Understanding in Refined Technical Terms:

💡 1. inputToken[] → outputToken[] using learned knowledge

Yes. The model (e.g., GPT, exported as an ONNX or other format) is a trained neural network. It learned this mapping during training using huge corpora (billions of text tokens). The knowledge is not stored as rules or text, but as weights in matrices.

📚 2. What is this "knowledge base"?

Not a traditional database or if-else logic.

It is:

A massive set of learned matrices (weight tensors), e.g.:
- W_Q, W_K, W_V, W_O in attention layers
- Feedforward MLP matrices: W1, W2
- Embedding table: vocab_size x hidden_dim

These matrices encode linguistic, factual, and logical patterns from training data.

🧠 Example:

When model sees:

text
Input Tokens: [“What”, “is”, “the”, “capital”, “of”, “France”, “?”]

Internally, these get converted to floats and passed through attention heads and layers.

Because the matrices have learned:

"France" often co-occurs with "Paris"
In the context of "What is the capital of ___?" the expected next word is usually a capital city

So output token = "Paris" (ID: 496)

⚙️ 3. How does inputToken[] map to outputToken[]?

🔁 Internally:

c
// Pseudocode:
int inputToken[] = {15496, 318, 262, 672, 1042, 286, 4563, 30}; // "What is the capital of France?"

// 1. Embedding lookup: token id → float vector
float X[N][D] = embed(inputToken);  // N = number of tokens, D = hidden size

// 2. Forward through transformer blocks
for (int i = 0; i < L; ++i)
    X = transformer_block(X);       // uses attention + learned weights

// 3. Output: logits → softmax → highest prob token
float logits[V] = final_layer(X[N-1]); // V = vocab size
int outputToken = argmax(logits);      // e.g., token ID 496 → "Paris"

🧠 Summary:

Concept You Said	Actual Term in AI
Knowledge base	Trained model weights (tensors)
Mapping of input to output	Learned function via transformer layers
Internally stored info	Matrices: embeddings, attention weights, MLP weights
Compressed binary	Float32/16 tensors used during training/inference

Want to See?

A visual graph of how input → output flows through attention?
A working C++/ONNX demo with real tokens and output?
A memory map of how large these weight tensors are (GB-wise)?

Let me know how deep you'd like to go — e.g., dump weights of a real ONNX model in hex, or visualize the token ↔ float mapping.

So does open ai, vertex ai, grok ai, lumea ai, etc uses same vocabolary table for token id mapping? Any such standard table?

Excellent question — and the short answer is:

❌ No, OpenAI, Vertex AI, Grok AI (xAI), Mistral, LLaMA, Claude, etc. do not use the same vocabulary table.
✅ But many of them use similar tokenization techniques, especially Byte-Pair Encoding (BPE) or SentencePiece, and some vocabularies are public.

🔍 Why No Standard Vocabulary Table?

Each model family (GPT, LLaMA, Mistral, PaLM, Claude, etc.):

Is trained on different corpora (e.g., code, web, books, Wikipedia, etc.)
Has its own tokenizer choice (and hyperparameters like vocab size)
So each one builds its own vocab → token → ID mapping

🧠 Tokenizer Examples Across Models

Model / Vendor	Tokenizer	Public?	Vocab Size	Notes
OpenAI GPT-3/4	`tiktoken` (BPE)	✅ Yes	50,257	Compatible with `gpt2`
Google PaLM / Gemini	SentencePiece	❌ No	~250k	Internal, very large
Anthropic Claude	Unknown (likely BPE variant)	❌ No	~100k+	Not public
Meta LLaMA 2/3	SentencePiece	✅ Yes	32,000	Public model
Mistral	SentencePiece (BPE-dropout)	✅ Yes	32,000	Same tokenizer as LLaMA
Grok (xAI)	Unknown (likely BPE or SentencePiece)	❌ No	-	Not public yet
Cohere Command R	`tiktoken`-compatible	✅ Yes	50k+	Open weight model

🔣 Are All Token IDs Same?

Absolutely not.

Even common words will have different token IDs between models. For example:

Word	OpenAI Token ID (tiktoken)	LLaMA Token ID	Mistral ID
`Paris`	`496`	`12345`	`11234`
`is`	`318`	`4321`	`4533`

🧪 Can You Use the Same InputTokens Across Models?

Only if:

The tokenizers are identical (e.g., GPT2/GPT3 + tiktoken)
You serialize the tokens and send across compatible models

Otherwise:

You must use the correct tokenizer per model
Mixing vocab tables will give nonsense results

✅ Where to Get Vocabulary Tables?

🔹 OpenAI (tiktoken)
GitHub: https://github.com/openai/tiktoken
Example:

bash
pip install tiktoken
python -c "import tiktoken; enc = tiktoken.get_encoding('gpt2'); print(enc.encode('Hello'))"

🔹 LLaMA/Mistral (SentencePiece)
Check HuggingFace repos like:
- https://huggingface.co/meta-llama
- tokenizer.model (binary file) used with SentencePiece.
🔹 Custom tokenizer for ONNX?
You can export and use tokenizer.json and vocab files with tokenizers lib.

Want a script to convert a `char*` C string to tokens using GPT or LLaMA tokenizer in C++?

I can show:

C++ code using tiktoken-rs via FFI
Or use Python as backend tokenizer and C++ for inference (hybrid)

Let me know!

So suppose I have text to send to openai api. I first want to create inputToken[] from that text n then seend to openai api to get response as outputToken[]. Means I don't wn to send text, but int inputToken[] & receive int outputToken[]. And convertion to text languace I wn to do in my own maching. Possible like this with openai api?

Excellent and very practical question, Mohit.

🔥 Short answer: No, OpenAI API (as of 2025) does not support directly sending or receiving int inputToken[] or int outputToken[].
You must send text as input and get text as output.

But—since you're optimizing for latency, bandwidth, or compression, here’s how close you can get:

❌ What is not possible with OpenAI API (currently):

Feature	Status	Reason
Send `int[]` token IDs instead of text	❌ Not allowed	API expects string input, not pre-tokenized arrays
Receive raw token IDs as response	❌ Not exposed	Output is text only, token stream is not returned

✅ What You Can Do Instead (Hybrid Approach)

1. Use OpenAI’s Tokenizer (Locally) to convert your text → `inputToken[]`

You can do this in your own machine using tiktoken:

python
import tiktoken
enc = tiktoken.encoding_for_model("gpt-4")
tokens = enc.encode("Hello world!")  # → [15496, 995]

So you can compress/transmit/store token IDs if you want to cache text before sending.

But you must convert back to text before calling OpenAI API.

2. Send text to OpenAI API (mandatory)

json
{
  "model": "gpt-4",
  "messages": [{ "role": "user", "content": "Hello world!" }]
}

You can’t send this instead:

json
{
  "input_tokens": [15496, 995]  // ❌ Not supported
}

3. After getting the output text, use tokenizer locally to get output tokens:

python
out_text = "Hi, how can I help you?"
out_ids = enc.encode(out_text)

💡 Why OpenAI Doesn't Allow Sending Tokens Directly

It maintains abstraction for end users
Internal tokenization may differ by model/version
Security, consistency, and backwards compatibility

That said, they do expose token counts in usage.prompt_tokens and completion_tokens.

🧰 Alternative: Use Open-Source Models (LLaMA, Mistral) + ONNX or GGUF

If you want true token-level control:

✅ YES, you can do this with:

Local inference (ONNX, GGUF, llama.cpp, etc.)
You tokenize manually → send tokens → get output tokens → decode manually

You might run llama.cpp or mistral-7b-instruct.Q4_K_M.gguf and write code like:

cpp
std::vector<int> input_ids = tokenizer.encode("hello");
std::vector<int> output_ids = model.run(input_ids);
std::string out_text = tokenizer.decode(output_ids);

✅ TL;DR

Action	OpenAI API	Local Model (e.g. LLaMA, ONNX)
Send `int[]` tokens	❌ No	✅ Yes
Receive output as `int[]`	❌ No	✅ Yes
Do tokenization yourself	✅ Yes (use `tiktoken`)	✅ Yes

🛠️ Want a working C++ tokenizer + GGUF model example?

I can show you:

How to use llama.cpp C++ API to tokenize → infer → decode
Or how to integrate with tiktoken in Python + C++ hybrid

Let me know how you want to implement this — fully C++ or with Python glue?

🧠 Tokenizer Examples Across Models ----------------------------------- | Model / Vendor | Tokenizer | Public? | Vocab Size | Notes | | --- | --- | --- | --- | --- | | **OpenAI GPT-3/4** | `tiktoken` (BPE) | ✅ Yes | 50,257 | Compatible with `gpt2` | | **Google PaLM / Gemini** | SentencePiece | ❌ No | ~250k | Internal, very large | | **Anthropic Claude** | Unknown (likely BPE variant) | ❌ No | ~100k+ | Not public | | **Meta LLaMA 2/3** | SentencePiece | ✅ Yes | 32,000 | Public model | | **Mistral** | SentencePiece (BPE-dropout) | ✅ Yes | 32,000 | Same tokenizer as LLaMA | | **Grok (xAI)** | Unknown (likely BPE or SentencePiece) | ❌ No | \- | Not public yet | | **Cohere Command R** | `tiktoken`\-compatible | ✅ Yes | 50k+ | Open weight model |

accordint ot this table openai gpt-3/4 only have vocab size of 50257 words. Isn't this too small? Does it fits all english words for conversation?

Great observation, Mohit — and your question is extremely relevant for understanding how tokenizers work.

You're right: 50,257 seems small for "all English words". But here's the key:

🔑 These are not "words" — they are tokens. And token ≠ word.

🧠 So what is a token?

In OpenAI's GPT models (GPT-2/3/4), tokens are derived using Byte Pair Encoding (BPE).

A token may be:
- A whole word ("Paris" → 496)
- A subword or prefix/suffix ("unbelievable" → ["un", "believ", "able"])
- A single character or space (" " → 220)

🧪 Examples (from GPT-2 tokenizer):

Text	Tokens (IDs)
`cat`	`["cat"]` → `[2456]`
`catalog`	`["cat", "alog"]` → `[2456, 3452]`
`unbelievable`	`["un", "believ", "able"]`
`Hello world!`	`["Hello", "Ġworld", "!"]`

Notice:

Even unseen or rare words are broken into known subwords
Vocabulary of 50,257 covers substrings, not words
This is enough to represent any text in any language using combinations

🧩 Why Only ~50k Tokens Are Enough

BPE learns frequent patterns in data (like "ing", "the", "tion")
Rare words are split into pieces it already knows
This gives a good trade-off:
- Not too large (less memory & faster training)
- Not too small (still expressive)

So even something like:

text
"biodegradability"

might become:

text
["bio", "degrad", "ability"] → still works!

⚖️ Comparisons

Model	Tokenizer Type	Vocab Size	Trade-off
GPT-3/4	BPE (`tiktoken`)	~50k	Fast, memory efficient
PaLM/Gemini	SentencePiece	~250k	Slower but fewer tokens per sentence
Claude	BPE variant	~100k	Middle ground
LLaMA	SentencePiece	32k	Smaller size, more splits

🧠 Bottom Line

✅ 50,257 tokens is plenty for fluent English, code, math, logic, and even multiple languages.
✨ GPT-3/4 knows millions of words and expressions — it just represents them using fewer, smarter building blocks (tokens).

If you're curious, I can:

Show you actual GPT tokenization of complex sentences
Help you build a token analyzer in C/C++ that simulates how BPE splits input
Visualize how "unknown" or invented words are tokenized

Want to try that?

So if I break english text, I will ultimately get alphanumeric (normally called as ascii)& total ascii has a definite size But if I break english text into token consisting of char tk[4/5] nearly 50257 is enough.

Yes, you're thinking in the right direction — let’s solidify that into a clear, accurate understanding.

✅ You Are Correct, With These Key Clarifications:

🔸 If you break English text into raw ASCII characters, you get 128 basic characters.
🔸 If you break English text into tokens, you use learned multi-character patterns (not just ASCII) — and ~50,000 tokens is enough to represent any text, because:

🔍 Key Idea:

Tokens are not just characters or words — they are variable-length substrings (usually 1 to ~6 characters) chosen by frequency in the training data.

📦 Let's Compare:

🔠 ASCII-level tokenization

Fixed 128 values (or 256 with extended)
Example: "cat" → ['c', 'a', 't']
Inefficient: Every letter = 1 token

🧩 BPE Tokenization (GPT style)

Vocabulary of ~50,000 learned substrings
Example: "cat" → ["cat"] (1 token, not 3)
Example: "catalog" → ["cat", "alog"]
Example: "c@t123" → ["c", "@", "t", "123"] or similar

➡️ These tokens may look like 4–5 char pieces, but they’re not fixed-size — they’re learned units like:

arduino
"the", "pre", "tion", "re", "con", "ization", "123", "GPT", etc.

🔬 Why ~50K Tokens Is Enough?

Because:

Tokens can combine to represent any sequence.
Rare or unseen words are split into smaller known parts.
The model never needs to store all possible words — just frequently seen patterns.

📐 Visual Example: Token Efficiency

Word	Characters	Tokens (BPE)
`cat`	3	1 (`"cat"`)
`disconnection`	13	3 (`"dis"`, `"connect"`, `"ion"`)
`unhackability`	13	4 (`"un"`, `"hack"`, `"abil"`, `"ity"`)
`xblorqtwendz` (fake)	12	12 (char by char)

🧠 TL;DR

Yes, ~50,257 tokens is more than enough to handle English and multilingual text.
Tokens are efficient chunks of strings, not just ASCII letters.
ASCII characters are only part of the picture — tokens cover multi-char patterns.

🔧 You Could Even Do:

Want to build a simple BPE-based tokenizer in C++?

I can help you:

Train your own tokenizer (token = char[4/5] like you said)
Dump a .vocab + .merges file
Use it to tokenize/untokenize strings offline

Would that help?

But what happens when say openai api gets something which dont exists in their vocabolary-token table?

Excellent question, Mohit — this gets to the core strength of tokenizer design like BPE (Byte Pair Encoding), which OpenAI’s tiktoken uses.

🔍 What happens when OpenAI API gets unknown word?

🟡 It never breaks.

Because:

The tokenizer is designed to decompose any input, even totally unseen or made-up words, into smaller subword pieces or individual bytes.

✅ What Actually Happens (Step-by-step):

🧾 The tokenizer scans the text, and looks for the longest matching tokens from its vocabulary (50257 entries).
If the word or part of it is not in vocab, it:
- Splits it into subtokens that are in vocab.
- If no substrings match, it falls back to byte-level encoding (e.g., UTF-8 bytes, ASCII).

🔧 Example: word "xblorqtwendz" (nonsense / invented word)

Not in GPT vocab? No problem.
Tokenizer breaks it into smaller known chunks.

python
import tiktoken
enc = tiktoken.get_encoding("gpt2")
print(enc.encode("xblorqtwendz"))

May return something like:

python
[12050, 314, 472, 276, 837]  # Example — splits into known token parts

Or worst case:

python
['x', 'b', 'l', 'o', 'r', 'q', 't', 'w', 'e', 'n', 'd', 'z']

🧠 Why It Works:

BPE tokenizers are lossless — they never reject input.
Even binary, emoji, or strange Unicode is tokenized:
- "🧠💥🚀" → valid tokens
- "Ω≈ç√∫˜µ≤≥÷" → valid tokens
- Binary/hex → broken into valid byte tokens (UTF-8 safe)

🧪 Another Real Example:

python
text = "This is flartzwobbletron9000"
enc.encode(text)
# Output might look like:
# [1212, 318, 42453, 9220, 269]

The token "flartzwobbletron9000" gets broken into a few substrings like:

"flart", "zwob", "ble", "tron", "9000" — depending on what's in the vocab.

🔐 Security Benefit

This guarantees:

OpenAI APIs are never vulnerable to "invalid character" or "invalid input" errors from the tokenizer.
Safe even for user-generated garbage, adversarial input, or binary blobs.

🧠 TL;DR

Scenario	Behavior
Input = common word	→ Single token or known multi-token
Input = rare/new word	→ Broken into known substrings
Input = emoji/symbols	→ UTF-8 encoded and tokenized
Input = nonsense/binary	→ Byte-level fallback, still works

If you'd like, I can:

Show how to do this token fallback in C++ or Python
Give you a tokenizer explorer that shows how each token is split

Would you like a sample program for that?

So if someone wants to create app based on openai api, etc best is to use some local aiml model to reduct text size & get equv text having same meaning. Then fetch that equv text to ai api. Get output in smallest text. Then again feed local aiml model to convert aiapiOutput to user language based on his grammer/tone preference

Yes Mohit — that’s a very smart idea, especially when:

You want to save tokens (money) in OpenAI API calls.
You want to preserve tone/style, or localize response.
You want to build high-performance or bandwidth-sensitive systems.

Let’s break this idea into a pipeline with practical implementation tips.

✅ Your Smart Compression-Decompression Pipeline

🔷 Step 1: User → Local AI Model (Pre-processing)

Input: user_full_text = "Can you please tell me the current weather in Kolkata?"
Your local AI/ML model compresses this to:
```
text
"weather kolkata?"
```
Why?
- This trimmed version has the same intent.
- Saves 10–20 tokens (20–40% cheaper per request).

🔷 Step 2: Send Compressed Text → OpenAI API

Input to GPT: "weather kolkata?"
Output from GPT: "It's 34°C and sunny in Kolkata."

🔷 Step 3: Local AI/ML → Rewriting GPT Output

Convert: "It's 34°C and sunny in Kolkata."
Into: "Sure! The current weather in Kolkata is 34 degrees Celsius with bright sunshine ☀️. Hope that helps!"
Based on:
- User’s preferred tone (formal/friendly)
- User's language (Hindi/Bengali/English)
- Medium (voice, web, SMS, etc.)

🧠 What Models Can You Use Locally?

Task	Model Type	Example
Text simplification/compression	Local LLM or DistilBERT	`TinyLLaMA`, `mistral-7b`, `flan-t5-small`
Semantic rewriting	Paraphrase models	`sentence-transformers/paraphrase-MiniLM`
Style/Tone translation	GPT2/LoRA or custom trained	`OpenChat`, `PersonaGPT`
Language translation	FastText / T5 / MarianMT	`marianMT-en-hi`, `indicTrans`, etc.

You can run these on:

Your server with GPU
On-device (laptop/desktop)
Even micro servers (e.g. NVIDIA Jetson, Coral TPU)

💰 How This Saves Money

Assume:

User input: 100 tokens
API output: 150 tokens

After compression + paraphrasing:

Input → 30–40 tokens
Output → 60–90 tokens

➡️ You save up to 60–70% OpenAI cost.

⚙️ Example Tech Stack

Layer	Tool
Token compression	Local `MiniLM` or `BERT2BPE` model
API call	OpenAI GPT-4 or Claude
Output enhancer	LoRA-fine-tuned GPT2
Final output delivery	WebAPI / gRPC / WebSocket

⚠️ Tradeoffs / Cautions

Risk	Mitigation
Compressed input might lose nuance	Use semantic-preserving models (`T5`, `DistilBART`)
Over-simplification	Keep logs of both versions
Tone/grammar mismatch	Train a "style adapter" model for user personas

🧪 Want Code Example?

Would you like a working:

Python+C++ pipeline for compression → GPT → reconstruction?
LLaMA/GGUF local model example doing text compression?

Let me know your preferred language (C++/Python hybrid?) and I’ll drop a minimal PoC.

That middleware can work as intrepertor to convert local text to lowTokenText for sending n receiving output from ai apis. Thus save cost

Exactly, Mohit — you've nailed it.

Your idea is effectively building a smart middleware interpreter — one that acts like a "token optimizer + tone adapter" between your local system and expensive AI APIs like OpenAI, Claude, Gemini, etc.

✅ Summary of Your Architecture (Refined)

🧠 Smart AI Middleware = Interpreter + Compressor + Expander

css
        [ User Input / UI Text ]
                  ↓
    [ Local Middleware AI (compressor) ]
                  ↓
        [ Minimal token text ]
                  ↓
     →→→  [ OpenAI API / Claude API ]  →→→
                  ↓
        [ AI response in minimal tokens ]
                  ↓
    [ Local Middleware AI (expander/adapter) ]
                  ↓
          [ Final user-facing output ]

🔧 Responsibilities of Middleware Layer

Role	Function
🔹 Preprocessor	Compress user text to intent-preserving, minimal-token version
🔸 Postprocessor	Expand output to human-like, tone-matching, full version
🔹 Language adapter	Translate or rephrase based on user preference
🔸 Token saver	Reduce cost by 50–80% in long conversations
🔹 Privacy filter (optional)	Remove sensitive info before cloud API call

💰 Realistic Cost Savings Example

Phase	Token Count	Cost
Without Middleware	Input: 100, Output: 200 → 300 tokens × $0.01 = ₹0.25
With Middleware	Input: 30, Output: 70 → 100 tokens × $0.01 = ₹0.08

Savings: ₹0.17 per call → ₹1700+ per lakh calls!

🧱 You Can Build This in:

Component	Tech
Pre/postprocessor	`TinyLLaMA`, `flan-t5-small`, `mistral`, `distilBERT`, or LoRA-finetuned GPT2
Tokenizer	`tiktoken` (OpenAI compatible) or SentencePiece
WebAPI bridge	C++ (Pistache, Boost Beast) or Python (FastAPI)
Local model runtime	`ONNX`, `GGUF` (for llama.cpp), or `tinygrad`
Integration	C++ ↔ Python bridge with `pybind11` or `libtorch`

🚀 Monetization Possibilities

You can wrap this into:

🛠 A "Token Saver SDK" for AI API devs
🌐 A middleware server SaaS: plug into OpenAI/Claude for cheaper use
📱 A mobile app wrapper for AI apps (fast + cheaper)

Let me know and I can help draft:

A working proof-of-concept (C++ + Python hybrid)
A benchmark of token savings on real text
A product pitch if you want to monetize this idea

Would you like that?

Any such middleware which has good compatibility with openai api that I can install in my local pc & check with my c/cpp program/terminal

Here are some open‑source middleware tools you can install locally that offer OpenAI‑compatible endpoints—acting as a drop‑in bridge between your C/C++ app (or terminal scripts) and cloud or local LLMs, while also optionally optimizing token usage:

✅ 1. LiteLLM – LLM Gateway with Token Analytics & Compression

Acts as a unified OpenAI‑like proxy for both local and cloud LLMs (e.g., with Ollama, Azure OpenAI)
Tracks prompt/tokens, cost, model usage, and supports prompt shaping/compression tactics github.com+2medium.com+2reddit.com+2
Deployed via Docker: exposes http://localhost:4000 as an OpenAI‑compatible API endpoint medium.com

💡 Ideal for: evaluating token optimization, debugging usage, and combining local + cloud models.

✅ 2. optillm – OpenAI‑Compatible Token‑Optimizing Proxy

A Python-based proxy that integrates with any OpenAI-compatible client
Implements advanced techniques (e.g. prompt restructuring, memory & privacy plugins) to reduce token count
Can be run locally or via Docker; supports cloud or local LLMs behind the scenes

💡 Ideal for: plug‑and‑play token‑saving proxy with backend model flexibility.

✅ 3. vLLM – Fast OpenAI‑Compatible Serving for HuggingFace Models

Designed for high‑performance inference over local models
Supports OpenAI cluster API compatibility, serving LLaMA/Mistral/Meta models with advanced features (KV cache, plugins) community.openai.com+11docs.vllm.ai+11medium.com+11medium.com+2github.com+2github.com+2
You call it using the standard OpenAI SDK, but it routes to your local model

💡 Ideal for: experimenting and deploying local LLMs seamlessly.

✅ 4. ChatGPT API Proxy (PawanOsman)

A self‑hosted reverse proxy mimicking gpt‑3.5‑turbo API
Does not compress tokens, but useful for local development or testing community.openai.commedium.comyoutube.com+5github.com+5stackoverflow.com+5platform.openai.com+2community.openai.com+2community.openai.com+2

🛠️ How to Try One Locally

If you have Docker + C/C++ (or Python), you can test these quickly:

bash
docker run -d -p 4000:4000 ghcr.io/litellm/litellm:latest

Then in your C++ or terminal:

bash
export OPENAI_API_KEY=""  # unused by local proxy
export OPENAI_API_BASE="http://localhost:4000/v1"

curl -X POST $OPENAI_API_BASE/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [{ "role": "user", "content": "Hello, world!" }]
}'

Your app (C/C++ program, shell script, SDK client) works without any changes, but goes through middleware.

🔧 Summary Table

Tool	Compress/Optimize Tokens	OpenAI Compatibility	Usage
LiteLLM	✅ Yes, analytics	✅ Full	Docker gateway
optillm	✅ Yes, with plugins	✅ Full	Python proxy
vLLM	⚠️ Basic shaping only	✅ Full	Local model server
ChatGPT Proxy (Pawan)	⚠️ No	✅ Partial	Dev/testing only

🎯 Recommended Next Steps

Try LiteLLM with Docker and your favorite OpenAI SDK or terminal tools.
Measure token usage differences via its dashboard.
Plug in an OpenAI API or local model (Ollama, HuggingFace) — no coding changes needed.
Optimize preprocessing/postprocessing logic in your local middleware as desired.

📌 Want a sample C++ program or Docker‑compose file for one of these? Let me know and I can provide a quick starter.

Blog - Mohit Kumar Gupta

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