machine-learning Archives

Creating a local LLM Cluster Server using Apple Silicon GPU

Posted on February 27, 2025March 10, 2025 by SatyakiDe in ai, api, Apple, call, cloud, code, combining, computing, CPU, Crossplatform, Data Science, design, exo, features, GPU, gui, HuggingFace, integration, IoT, json, llm, machine-learning, Model, numpy, objects, Open-CV, openai, Pandas, Python, React, Real-time, Technology, Tensorflow, Torch

Today, we’re going to discuss creating a local LLM server and then utilizing it to execute various popular LLM models. We will club the local Apple GPUs together via a new framework that binds all the available Apple Silicon devices into one big LLM server. This enables people to run many large models, which was otherwise not possible due to the lack of GPUs.

This is certainly a new way; One can create virtual computation layers by adding nodes to the resource pool, increasing the computation capacity.

Why not witness a small demo to energize ourselves –

Let us understand the scenario. I’ve one Mac Book Pro M4 & 2 Mac Mini Pro M4 (Base models). So, I want to add them & expose them as a cluster as follows –

As you can see, I’ve connected my MacBook Pro with both the Mac Mini using high-speed thunderbolt cables for better data transmissions. And, I’ll be using an open-source framework called “Exo” to create it.

Also, you can see that my total computing capacity is 53.11 TFlops, which is slightly more than the last category.

What is Exo?

“Exo” is an open-source framework that helps you merge all your available devices into a large cluster of available resources. This extracts all the computing juice needed to handle complex tasks, including the big LLMs, which require very expensive GPU-based servers.

For more information on “Exo”, please refer to the following link.

In our previous diagram, we can see that the framework also offers endpoints.

One option is a local ChatGPT interface, where any question you ask will receive a response from models by combining all available computing power.
The other endpoint offers users a choice of any standard LLM API endpoint, which helps them integrate it into their solutions.

Let us see, how the devices are connected together –

How to establish the Cluster?

To proceed with this, you need to have at least Python 3.12, Anaconda or Miniconda & Xcode installed in all of your machines. Also, you need to install some Apple-specific MLX packages or libraries to get the best performance.

Depending on your choice, you need to use the following link to download Anaconda or Miniconda.

You can download the following link to download the Python 3.12. However, I’ve used Python 3.13 on some machines & some machines, I’ve used Python 3.12. And it worked without any problem.

Sometimes, after installing Anaconda or Miniconda, the environment may not implicitly be activated after successful installation. In that case, you may need to use the following commands in the terminal -> source ~/.bash_profile

To verify, whether the conda has been successfully installed & activated, you need to type the following command –

(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % conda --version
conda 24.11.3
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas %

Once you verify it. Now, we need to install the following supplemental packages in all the machines as –

satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
satyaki_de@Satyakis-MacBook-Pro-Max Pandas % conda install anaconda::m4
Channels:
 - defaults
 - anaconda
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /opt/anaconda3

  added / updated specs:
    - anaconda::m4


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    m4-1.4.18                  |       h1230e6a_1         202 KB  anaconda
    ------------------------------------------------------------
                                           Total:         202 KB

The following NEW packages will be INSTALLED:

  m4                 anaconda/osx-arm64::m4-1.4.18-h1230e6a_1 


Proceed ([y]/n)? y


Downloading and Extracting Packages:
                                                                                                                                                                                                                      
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

Also, you can use this package to install in your machines –

(base) satyakidemini2@Satyakis-Mac-mini-2 exo % 
(base) satyakidemini2@Satyakis-Mac-mini-2 exo % pip install mlx
Collecting mlx
  Downloading mlx-0.23.2-cp312-cp312-macosx_14_0_arm64.whl.metadata (5.3 kB)
Downloading mlx-0.23.2-cp312-cp312-macosx_14_0_arm64.whl (27.6 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 27.6/27.6 MB 8.8 MB/s eta 0:00:00
Installing collected packages: mlx
Successfully installed mlx-0.23.2
(base) satyakidemini2@Satyakis-Mac-mini-2 exo % 
(base) satyakidemini2@Satyakis-Mac-mini-2 exo %

Main Setup:

Till now, we’ve installed all the important packages. Now, we need to setup the final “eco” framework in all the machines like our previous steps.

Now, we’ll first clone the “eco” framework by the following commands –

(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % git clone https://github.com/exo-explore/exo.git
Cloning into 'exo'...
remote: Enumerating objects: 9736, done.
remote: Counting objects: 100% (411/411), done.
remote: Compressing objects: 100% (148/148), done.
remote: Total 9736 (delta 333), reused 263 (delta 263), pack-reused 9325 (from 3)
Receiving objects: 100% (9736/9736), 12.18 MiB | 8.41 MiB/s, done.
Resolving deltas: 100% (5917/5917), done.
Updating files: 100% (178/178), done.
Filtering content: 100% (9/9), 3.16 MiB | 2.45 MiB/s, done.
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas %

And, the content of the “Exo” folder should look like this –

total 28672
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 docs
-rwx------  1 satyaki_de  staff     1337 Mar  9 17:06 configure_mlx.sh
-rwx------  1 satyaki_de  staff    11107 Mar  9 17:06 README.md
-rwx------  1 satyaki_de  staff    35150 Mar  9 17:06 LICENSE
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 examples
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 exo
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 extra
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 scripts
-rwx------  1 satyaki_de  staff      390 Mar  9 17:06 install.sh
-rwx------  1 satyaki_de  staff      792 Mar  9 17:06 format.py
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 test
-rwx------  1 satyaki_de  staff     2476 Mar  9 17:06 setup.py
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:10 build
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:17 exo.egg-info

Similar commands need to fire to other devices. Here, I’m showing one Mac-Mini examples –

(base) satyakidemini2@Satyakis-Mac-mini-2 Pandas % 
(base) satyakidemini2@Satyakis-Mac-mini-2 Pandas % git clone https://github.com/exo-explore/exo.git
Cloning into 'exo'...
remote: Enumerating objects: 9736, done.
remote: Counting objects: 100% (424/424), done.
remote: Compressing objects: 100% (146/146), done.
remote: Total 9736 (delta 345), reused 278 (delta 278), pack-reused 9312 (from 4)
Receiving objects: 100% (9736/9736), 12.18 MiB | 6.37 MiB/s, done.
Resolving deltas: 100% (5920/5920), done.
(base) satyakidemini2@Satyakis-Mac-mini-2 Pandas %

After that, I’ll execute the following sets of commands to install the framework –

(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % cd exo
(base) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max exo % conda create --name exo1 python=3.13
WARNING: A conda environment already exists at '/opt/anaconda3/envs/exo1'

Remove existing environment?
This will remove ALL directories contained within this specified prefix directory, including any other conda environments.

 (y/[n])? y

Channels:
 - defaults
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /opt/anaconda3/envs/exo1

  added / updated specs:
    - python=3.13


The following NEW packages will be INSTALLED:

  bzip2              pkgs/main/osx-arm64::bzip2-1.0.8-h80987f9_6 
  ca-certificates    pkgs/main/osx-arm64::ca-certificates-2025.2.25-hca03da5_0 
  expat              pkgs/main/osx-arm64::expat-2.6.4-h313beb8_0 
  libcxx             pkgs/main/osx-arm64::libcxx-14.0.6-h848a8c0_0 
  libffi             pkgs/main/osx-arm64::libffi-3.4.4-hca03da5_1 
  libmpdec           pkgs/main/osx-arm64::libmpdec-4.0.0-h80987f9_0 
  ncurses            pkgs/main/osx-arm64::ncurses-6.4-h313beb8_0 
  openssl            pkgs/main/osx-arm64::openssl-3.0.16-h02f6b3c_0 
  pip                pkgs/main/osx-arm64::pip-25.0-py313hca03da5_0 
  python             pkgs/main/osx-arm64::python-3.13.2-h4862095_100_cp313 
  python_abi         pkgs/main/osx-arm64::python_abi-3.13-0_cp313 
  readline           pkgs/main/osx-arm64::readline-8.2-h1a28f6b_0 
  setuptools         pkgs/main/osx-arm64::setuptools-75.8.0-py313hca03da5_0 
  sqlite             pkgs/main/osx-arm64::sqlite-3.45.3-h80987f9_0 
  tk                 pkgs/main/osx-arm64::tk-8.6.14-h6ba3021_0 
  tzdata             pkgs/main/noarch::tzdata-2025a-h04d1e81_0 
  wheel              pkgs/main/osx-arm64::wheel-0.45.1-py313hca03da5_0 
  xz                 pkgs/main/osx-arm64::xz-5.6.4-h80987f9_1 
  zlib               pkgs/main/osx-arm64::zlib-1.2.13-h18a0788_1 


Proceed ([y]/n)? y


Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate exo1
#
# To deactivate an active environment, use
#
#     $ conda deactivate

(base) satyaki_de@Satyakis-MacBook-Pro-Max exo % conda activate exo1
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % ls -lrt
total 24576
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 docs
-rwx------  1 satyaki_de  staff     1337 Mar  9 17:06 configure_mlx.sh
-rwx------  1 satyaki_de  staff    11107 Mar  9 17:06 README.md
-rwx------  1 satyaki_de  staff    35150 Mar  9 17:06 LICENSE
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 examples
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 exo
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 extra
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 scripts
-rwx------  1 satyaki_de  staff      390 Mar  9 17:06 install.sh
-rwx------  1 satyaki_de  staff      792 Mar  9 17:06 format.py
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 test
-rwx------  1 satyaki_de  staff     2476 Mar  9 17:06 setup.py
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % pip install .
Processing /Volumes/WD_BLACK/PythonCourse/Pandas/exo
  Preparing metadata (setup.py) ... done
Collecting tinygrad@ git+https://github.com/tinygrad/tinygrad.git@ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8 (from exo==0.0.1)
  Cloning https://github.com/tinygrad/tinygrad.git (to revision ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8) to /private/var/folders/26/dj11b57559b8r8rl6ztdpc840000gn/T/pip-install-q18fzk3r/tinygrad_7917114c483a4d9c83c795b69dbeb5c7
  Running command git clone --filter=blob:none --quiet https://github.com/tinygrad/tinygrad.git /private/var/folders/26/dj11b57559b8r8rl6ztdpc840000gn/T/pip-install-q18fzk3r/tinygrad_7917114c483a4d9c83c795b69dbeb5c7
  Running command git rev-parse -q --verify 'sha^ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8'
  Running command git fetch -q https://github.com/tinygrad/tinygrad.git ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8
  Running command git checkout -q ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8
  Resolved https://github.com/tinygrad/tinygrad.git to commit ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8
  Preparing metadata (setup.py) ... done
Collecting aiohttp==3.10.11 (from exo==0.0.1)
.
.
(Installed many more dependant packages)
.
.
Downloading propcache-0.3.0-cp313-cp313-macosx_11_0_arm64.whl (44 kB)
Building wheels for collected packages: exo, nuitka, numpy, uuid, tinygrad
  Building wheel for exo (setup.py) ... done
  Created wheel for exo: filename=exo-0.0.1-py3-none-any.whl size=901357 sha256=5665297f8ea09d06670c9dea91e40270acc4a3cf99a560bf8d268abb236050f7
  Stored in directory: /private/var/folders/26/dj118r8rl6ztdpc840000gn/T/pip-ephem-wheel-cache-0k8zloo3/wheels/b6/91/fb/c1c7d8ca90cf16b9cd8203c11bb512614bee7f6d34
  Building wheel for nuitka (pyproject.toml) ... done
  Created wheel for nuitka: filename=nuitka-2.5.1-cp313-cp313-macosx_11_0_arm64.whl size=3432720 sha256=ae5a280a1684fde98c334516ee8a99f9f0acb6fc2f625643b7f9c5c0887c2998
  Stored in directory: /Users/satyaki_de/Library/Caches/pip/wheels/f6/c9/53/9e37c6fb34c27e892e8357aaead46da610f82117ab2825
  Building wheel for numpy (pyproject.toml) ... done
  Created wheel for numpy: filename=numpy-2.0.0-cp313-cp313-macosx_15_0_arm64.whl size=4920701 sha256=f030b0aa51ec6628f708fab0af14ff765a46d210df89aa66dd8d9482e59b5
  Stored in directory: /Users/satyaki_de/Library/Caches/pip/wheels/e0/d3/66/30d07c18e56ac85e8d3ceaf22f093a09bae124a472b85d1
  Building wheel for uuid (setup.py) ... done
  Created wheel for uuid: filename=uuid-1.30-py3-none-any.whl size=6504 sha256=885103a90d1dc92d9a75707fc353f4154597d232f2599a636de1bc6d1c83d
  Stored in directory: /Users/satyaki_de/Library/Caches/pip/wheels/cc/9d/72/13ff6a181eacfdbd6d761a4ee7c5c9f92034a9dc8a1b3c
  Building wheel for tinygrad (setup.py) ... done
  Created wheel for tinygrad: filename=tinygrad-0.10.0-py3-none-any.whl size=1333964 sha256=1f08c5ce55aa3c87668675beb80810d609955a81b99d416459d2489b36a
  Stored in directory: /Users/satyaki_de/Library/Caches/pip/wheels/c7/bd/02/bd91c1303002619dad23f70f4c1f1c15d0c24c60b043e
Successfully built exo nuitka numpy uuid tinygrad
Installing collected packages: uuid, sentencepiece, nvidia-ml-py, zstandard, uvloop, urllib3, typing-extensions, tqdm, tinygrad, scapy, safetensors, regex, pyyaml, pygments, psutil, protobuf, propcache, prometheus-client, pillow, packaging, ordered-set, numpy, multidict, mlx, mdurl, MarkupSafe, idna, grpcio, fsspec, frozenlist, filelock, charset-normalizer, certifi, attrs, annotated-types, aiohappyeyeballs, aiofiles, yarl, requests, pydantic-core, opencv-python, nuitka, markdown-it-py, Jinja2, grpcio-tools, aiosignal, rich, pydantic, huggingface-hub, aiohttp, tokenizers, aiohttp_cors, transformers, mlx-lm, exo
Successfully installed Jinja2-3.1.4 MarkupSafe-3.0.2 aiofiles-24.1.0 aiohappyeyeballs-2.5.0 aiohttp-3.10.11 aiohttp_cors-0.7.0 aiosignal-1.3.2 annotated-types-0.7.0 attrs-25.1.0 certifi-2025.1.31 charset-normalizer-3.4.1 exo-0.0.1 filelock-3.17.0 frozenlist-1.5.0 fsspec-2025.3.0 grpcio-1.67.0 grpcio-tools-1.67.0 huggingface-hub-0.29.2 idna-3.10 markdown-it-py-3.0.0 mdurl-0.1.2 mlx-0.22.0 mlx-lm-0.21.1 multidict-6.1.0 nuitka-2.5.1 numpy-2.0.0 nvidia-ml-py-12.560.30 opencv-python-4.10.0.84 ordered-set-4.1.0 packaging-24.2 pillow-10.4.0 prometheus-client-0.20.0 propcache-0.3.0 protobuf-5.28.1 psutil-6.0.0 pydantic-2.9.2 pydantic-core-2.23.4 pygments-2.19.1 pyyaml-6.0.2 regex-2024.11.6 requests-2.32.3 rich-13.7.1 safetensors-0.5.3 scapy-2.6.1 sentencepiece-0.2.0 tinygrad-0.10.0 tokenizers-0.20.3 tqdm-4.66.4 transformers-4.46.3 typing-extensions-4.12.2 urllib3-2.3.0 uuid-1.30 uvloop-0.21.0 yarl-1.18.3 zstandard-0.23.0
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo %

And, you need to perform the same process in other available devices as well.

Now, we’re ready to proceed with the final command –

(.venv) (exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % exo
/opt/anaconda3/envs/exo1/lib/python3.13/site-packages/google/protobuf/runtime_version.py:112: UserWarning: Protobuf gencode version 5.27.2 is older than the runtime version 5.28.1 at node_service.proto. Please avoid checked-in Protobuf gencode that can be obsolete.
  warnings.warn(
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
Selected inference engine: None

  _____  _____  
 / _ \ \/ / _ \ 
|  __/>  < (_) |
 \___/_/\_\___/ 
    
Detected system: Apple Silicon Mac
Inference engine name after selection: mlx
Using inference engine: MLXDynamicShardInferenceEngine with shard downloader: SingletonShardDownloader
[60771, 54631, 54661]
Chat interface started:
 - http://127.0.0.1:52415
 - http://XXX.XXX.XX.XX:52415
 - http://XXX.XXX.XXX.XX:52415
 - http://XXX.XXX.XXX.XXX:52415
ChatGPT API endpoint served at:
 - http://127.0.0.1:52415/v1/chat/completions
 - http://XXX.XXX.X.XX:52415/v1/chat/completions
 - http://XXX.XXX.XXX.XX:52415/v1/chat/completions
 - http://XXX.XXX.XXX.XXX:52415/v1/chat/completions
has_read=True, has_write=True
╭────────────────────────────────────────────────────────────────────────────────────────────── Exo Cluster (2 nodes) ───────────────────────────────────────────────────────────────────────────────────────────────╮
Received exit signal SIGTERM...
Thank you for using exo.

  _____  _____  
 / _ \ \/ / _ \ 
|  __/>  < (_) |
 \___/_/\_\___/

Note that I’ve masked the IP addresses for security reasons.

Run Time:

At the beginning, if we trigger the main MacBook Pro Max, the “Exo” screen should looks like this –

And if you open the URL, you will see the following ChatGPT-like interface –

Connecting without the Thunderbolt bridge with the relevant port or a hub may cause performance degradation. Hence, how you connect will play a major role in the success of this intention. However, this is certainly a great idea to proceed with.

So, we’ve done it.

We’ll cover the detailed performance testing, Optimized configurations & many other useful details in our next post.

Till then, Happy Avenging! 🙂

Note: All the data & scenarios posted here are representational data & scenarios & available over the internet & for educational purposes only. There is always room for improvement in this kind of model & the solution associated with it. I’ve shown the basic ways to achieve the same for educational purposes only.

Enabling & Exploring Stable Defussion – Part 3

Posted on December 4, 2024December 4, 2024 by SatyakiDe in ai, Apple, cloud, code, Computer-Vision, CPU, Crossplatform, Data Science, design, function, GPU, json, machine-learning, Model, numpy, Open-CV, Pandas, Performance, pytesseract, Python, Real-time, Silicon, StableDefussion, Technology, Torch

Before we dive into the details of this post, let us provide the previous two links that precede it.

Enabling & Exploring Stable Defussion – Part 1

Enabling & Exploring Stable Defussion – Part 2

For, reference, we’ll share the demo before deep dive into the actual follow-up analysis in the below section –

Now, let us continue our discussions from where we left.

CODE:

clsText2Image.py (This class converts input prompts to an intermediate image)

class clsText2Image:
    def __init__(self, pipe, output_path, filename):

        self.pipe = pipe
        
        # More aggressive attention slicing
        self.pipe.enable_attention_slicing(slice_size=1)

        self.output_path = f"{output_path}{filename}"
        
        # Warm up the pipeline
        self._warmup()
    
    def _warmup(self):
        """Warm up the pipeline to optimize memory allocation"""
        with torch.no_grad():
            _ = self.pipe("warmup", num_inference_steps=1, height=512, width=512)
        torch.mps.empty_cache()
        gc.collect()
    
    def generate(self, prompt, num_inference_steps=12, guidance_scale=3.0):
        try:
            torch.mps.empty_cache()
            gc.collect()
            
            with torch.autocast(device_type="mps"):
                with torch.no_grad():
                    image = self.pipe(
                        prompt,
                        num_inference_steps=num_inference_steps,
                        guidance_scale=guidance_scale,
                        height=1024,
                        width=1024,
                    ).images[0]
            
            image.save(self.output_path)
            return 0
        except Exception as e:
            print(f'Error: {str(e)}')
            return 1
        finally:
            torch.mps.empty_cache()
            gc.collect()

    def genImage(self, prompt):
        try:

            # Initialize generator
            x = self.generate(prompt)

            if x == 0:
                print('Successfully processed first pass!')
            else:
                print('Failed complete first pass!')
                raise 

            return 0

        except Exception as e:
            print(f"\nAn unexpected error occurred: {str(e)}")

            return 1

1. `CLASS INSTANTIATE(pipe, output_path, filename)`

This is the initialization method for the clsText2Image class:

Takes a pre-configured pipe (text-to-image pipeline), an output_path, and a filename.
Enables more aggressive memory optimization by setting “attention slicing.”
Prepares the full file path for saving generated images.
Calls a _warmup method to pre-load the pipeline and optimize memory allocation.

2. `_warmup`

This private method warms up the pipeline:

Sends a dummy “warmup” request with basic parameters to allocate memory efficiently.
Clears any cached memory (torch.mps.empty_cache()) and performs garbage collection (gc.collect()).
Ensures smoother operation for future image generation tasks.

3. `generate(prompt, num_inference_steps=12, guidance_scale=3.0)`

This method generates an image from a text prompt:

Clears memory cache and performs garbage collection before starting.
Uses the text-to-image pipeline (pipe) to generate an image:
- Takes the prompt, number of inference steps, and guidance scale as input.
- Outputs an image at 1024×1024 resolution.
Saves the generated image to the specified output path.
Returns 0 on success or 1 on failure.
Ensures cleanup by clearing memory and collecting garbage, even in case of errors.

4. `genImage(prompt)`

This method simplifies image generation:

Calls the generate method with the given prompt.
Prints a success message if the image is generated (0 return value).
On failure, logs the error and raises an exception.
Returns 0 on success or 1 on failure.

clsImage2Video.py (This class converts an image to video as part of the second pass)

class clsImage2Video:
    def __init__(self, pipeline):
        
        # Optimize model loading
        torch.mps.empty_cache()
        self.pipeline = pipeline

    def generate_frames(self, pipeline, init_image, prompt, duration_seconds=10):
        try:
            torch.mps.empty_cache()
            gc.collect()

            base_frames = []
            img = Image.open(init_image).convert("RGB").resize((1024, 1024))
            
            for _ in range(10):
                result = pipeline(
                    prompt=prompt,
                    image=img,
                    strength=0.45,
                    guidance_scale=7.5,
                    num_inference_steps=25
                ).images[0]

                base_frames.append(np.array(result))
                img = result
                torch.mps.empty_cache()

            frames = []
            for i in range(len(base_frames)-1):
                frame1, frame2 = base_frames[i], base_frames[i+1]
                for t in np.linspace(0, 1, int(duration_seconds*24/10)):
                    frame = (1-t)*frame1 + t*frame2
                    frames.append(frame.astype(np.uint8))
            
            return frames
        except Exception as e:
            frames = []
            print(f'Error: {str(e)}')

            return frames
        finally:
            torch.mps.empty_cache()
            gc.collect()

    # Main method
    def genVideo(self, prompt, inputImage, targetVideo, fps):
        try:
            print("Starting animation generation...")
            
            init_image_path = inputImage
            output_path = targetVideo
            fps = fps
            
            frames = self.generate_frames(
                pipeline=self.pipeline,
                init_image=init_image_path,
                prompt=prompt,
                duration_seconds=20
            )
            
            imageio.mimsave(output_path, frames, fps=30)

            print("Animation completed successfully!")

            return 0
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return 1

1. `CLASS INSTANTIATE (pipeline)`

This initializes the clsImage2Video class:

Clears the GPU cache to optimize memory before loading.
Sets up the pipeline for generating frames, which uses an image-to-video transformation model.

2. `generate_frames(pipeline, init_image, prompt, duration_seconds=10)`

This function generates frames for a video:

Starts by clearing GPU memory and running garbage collection.
Loads the init_image, resizes it to 1024×1024 pixels, and converts it to RGB format.
Iteratively applies the pipeline to transform the image:
- Uses the prompt and specified parameters like strength, guidance_scale, and num_inference_steps.
- Stores the resulting frames in a list.
Interpolates between consecutive frames to create smooth transitions:
- Uses linear blending for smooth animation across a specified duration and frame rate (24 fps for 10 segments).
Returns the final list of generated frames or an empty list if an error occurs.
Always clears memory after execution.

3. `genVideo(prompt, inputImage, targetVideo, fps)`

This is the main function for creating a video from an image and text prompt:

Logs the start of the animation generation process.
Calls generate_frames() with the given pipeline, inputImage, and prompt to create frames.
Saves the generated frames as a video using the imageio library, setting the specified frame rate (fps).
Logs a success message and returns 0 if the process is successful.
On error, logs the issue and returns 1.

Now, let us understand the performance. But, before that let us explore the device on which we’ve performed these stress test that involves GPU & CPUs as well.

And, here is the performance stats –

From the above snapshot, we can clearly communicate that the GPU is 100% utilized. However, the CPU has shown a significant % of availability.

As you can see, the first pass converts the input prompt to intermediate images within 1 min 30 sec. However, the second pass constitutes multiple hops (11 hops) on an avg 22 seconds. Overall, the application will finish in 5 minutes 36 seconds for a 10-second video clip.

So, we’ve done it.

You can find the detailed code at the GitHub lin k.

I’ll bring some more exciting topics in the coming days from the Python verse.

Till then, Happy Avenging! 🙂

Enabling & Exploring Stable Defussion – Part 2

Posted on November 30, 2024December 4, 2024 by SatyakiDe in ai, cloud, code, Data Science, design, function, HuggingFace, json, machine-learning, numpy, objects, Open-CV, Pandas, pytesseract, Python, Real-time, snippet, StableDefussion

As we’ve started explaining, the importance & usage of Stable Defussion in our previous post:

Enabling & Exploring Stable Defussion – Part 1

In today’s post, we’ll discuss another approach, where we built the custom Python-based SDK solution that consumes HuggingFace Library, which generates video out of the supplied prompt.

But, before that, let us view the demo generated from a custom solution.

Isn’t it exciting? Let us dive deep into the details.

FLOW:

Let us understand basic flow of events for the custom solution –

So, the application will interact with the python-sdk like “stable-diffusion-3.5-large” & “dreamshaper-xl-1-0”, which is available in HuggingFace. As part of the process, these libraries will load all the large models inside the local laptop that require some time depend upon the bandwidth of your internet.

Before we even deep dive into the code, let us understand the flow of Python scripts as shown below:

From the above diagram, we can understand that the main application will be triggered by “generateText2Video.py”. As you can see that “clsConfigClient.py” has all the necessary parameter information that will be supplied to all the scripts.

“generateText2Video.py” will trigger the main class named “clsText2Video.py”, which then calls all the subsequent classes.

Great! Since we now have better visibility of the script flow, let’s examine the key snippets individually.

CODE:

clsText2Video.py (The main class that initiates the fellow classes to convert from prompt to video):

class clsText2Video:
    def __init__(self, model_id_1, model_id_2, output_path, filename, vidfilename, fps, force_cpu=False):
        self.model_id_1 = model_id_1
        self.model_id_2 = model_id_2
        self.output_path = output_path
        self.filename = filename
        self.vidfilename = vidfilename
        self.force_cpu = force_cpu
        self.fps = fps

        # Initialize in main process
        os.environ["TOKENIZERS_PARALLELISM"] = "true"
        self.r1 = cm.clsMaster(force_cpu)
        self.torch_type = self.r1.getTorchType()
        
        torch.mps.empty_cache()
        self.pipe = self.r1.getText2ImagePipe(self.model_id_1, self.torch_type)
        self.pipeline = self.r1.getImage2VideoPipe(self.model_id_2, self.torch_type)

        self.text2img = cti.clsText2Image(self.pipe, self.output_path, self.filename)
        self.img2vid = civ.clsImage2Video(self.pipeline)

    def getPrompt2Video(self, prompt):
        try:
            input_image = self.output_path + self.filename
            target_video = self.output_path + self.vidfilename

            if self.text2img.genImage(prompt) == 0:
                print('Pass 1: Text to intermediate images generated!')
                
                if self.img2vid.genVideo(prompt, input_image, target_video, self.fps) == 0:
                    print('Pass 2: Successfully generated!')
                    return 0
            return 1
        except Exception as e:
            print(f"\nAn unexpected error occurred: {str(e)}")
            return 1

Now, let us interpret:

1. `CLASS INSTANTIATION:`

This is the initialization method for the class. It does the following:

Sets up configurations like model IDs, output paths, filenames, video filename, frames per second (fps), and whether to use the CPU (force_cpu).
Configures an environment variable for tokenizer parallelism.
Initializes helper classes (clsMaster) to manage system resources and retrieve appropriate PyTorch settings.
Creates two pipelines:
- pipe: For converting text to images using the first model.
- pipeline: For converting images to video using the second model.
Initializes text2img and img2vid objects:
- text2img handles text-to-image conversions.
- img2vid handles image-to-video conversions.

2. `getPrompt2Video(prompt)`

This method generates a video from a text prompt in two steps:

Text-to-Image Conversion:
- Calls genImage(prompt) using the text2img object to create an intermediate image file.
- If successful, it prints confirmation.
Image-to-Video Conversion:
- Uses the img2vid object to convert the intermediate image into a video file.
- Includes the input image path, target video path, and frames per second (fps).
- If successful, it prints confirmation.

If either step fails, the method returns 1.
Logs any unexpected errors and returns 1 in such cases.

clsMaster.py (This class packaged all the necessary common capabilities that can be used from a single class)

# Set device for Apple Silicon GPU
def setup_gpu(force_cpu=False):
    if not force_cpu and torch.backends.mps.is_available() and torch.backends.mps.is_built():
        print('Running on Apple Silicon MPS GPU!')
        return torch.device("mps")
    return torch.device("cpu")

######################################
####         Global Flag      ########
######################################

class clsMaster:
    def __init__(self, force_cpu=False):
        self.device = setup_gpu(force_cpu)

    def getTorchType(self):
        try:
            # Check if MPS (Apple Silicon GPU) is available
            if not torch.backends.mps.is_available():
                torch_dtype = torch.float32
                raise RuntimeError("MPS (Metal Performance Shaders) is not available on this system.")
            else:
                torch_dtype = torch.float16
            
            return torch_dtype
        except Exception as e:
            torch_dtype = torch.float16
            print(f'Error: {str(e)}')

            return torch_dtype

    def getText2ImagePipe(self, model_id, torchType):
        try:
            device = self.device

            torch.mps.empty_cache()
            self.pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torchType, use_safetensors=True, variant="fp16",).to(device)

            return self.pipe
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            torch.mps.empty_cache()
            self.pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torchType,).to(device)

            return self.pipe
        
    def getImage2VideoPipe(self, model_id, torchType):
        try:
            device = self.device

            torch.mps.empty_cache()
            self.pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torchType, use_safetensors=True, use_fast=True).to(device)

            return self.pipeline
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            torch.mps.empty_cache()
            self.pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torchType).to(device)

            return self.pipeline

Let us interpret:

1. `setup_gpu(force_cpu=False)`

This function determines whether to use the Apple Silicon GPU (MPS) or the CPU:

If force_cpu is False and the MPS GPU is available, it sets the device to “mps” (Apple GPU) and prints a message.
Otherwise, it defaults to the CPU.

2. `CLASS INSTANTIATION (force_cpu=False)`

This is the initializer for the clsMaster class:

It sets the device to either GPU or CPU using the setup_gpu function (mentioned above) based on the force_cpu flag.

3. `getTorchType`

This method determines the PyTorch data type to use:

Checks if MPS GPU is available:
- If available, uses torch.float16 for optimized performance.
- If unavailable, defaults to torch.float32 and raises a warning.
Handles errors gracefully by defaulting to torch.float16 and printing the error.

4. `getText2ImagePipe(model_id, torchType)`

This method initializes a text-to-image pipeline:

Loads the Stable Diffusion model with the given model_id and torchType.
Configures it for MPS GPU or CPU, based on the device.
Clears the GPU cache before loading the model to optimize memory usage.
If an error occurs, attempts to reload the pipeline without safetensors.

5. `getImage2VideoPipe(model_id, torchType)`

This method initializes an image-to-video pipeline:

Similar to getText2ImagePipe, it loads the Stable Diffusion XL Img2Img pipeline with the specified model_id and torchType.
Configures it for MPS GPU or CPU and clears the cache before loading.
On error, reloads the pipeline without additional optimization settings and prints the error.

Let us continue this in the next post:

E nabling & Exploring Stable Defussion – Part 3

Till then, Happy Avenging! 🙂

Text2SQL Data Extractor (T2SDE) using Python & Open AI LLM

Posted on December 31, 2023December 31, 2023 by SatyakiDe in ai, analytic function, api, Azure, call, cloud, code, Data Science, function, gpt3, integration, json, machine-learning, Model, numpy, objects, openai, Pandas, Performance, Python, snippet, sql, Technology

Today, I will share a new post that will contextualize the source files & then read the data into the pandas data frame, and then dynamically create the SQL & execute it. Then, fetch the data from the sources based on the query generated dynamically. This project is for the advanced Python developer and data Science Newbie.

In this post, I’ve directly subscribed to OpenAI & I’m not using OpenAI from Azure. However, I’ll explore that in the future as well.

Before I explain the process to invoke this new library, why not view the demo first & then discuss it?

Demo

FLOW OF EVENTS:

Let us look at the flow diagram as it captures the sequence of events that unfold as part of the process.

The application will take the metadata captured from source data dynamically. It blends the metadata and enhances the prompt to pass to the Flask server. The Flask server has all the limits of contexts.

Once the application receives the correct generated SQL, it will then apply the SQL using the SQLAlchemy package to get the desired results.

IMPORTANT PACKAGES:

The following are the important packages that are essential to this project –

pip install openai==1.6.1
pip install pandas==2.1.4
pip install Flask==3.0.0
pip install SQLAlchemy==2.0.23

CODE:

We’ll have both the server and the main application. Today, we’ll be going in reverse mode. We first discuss the main script & then explain all the other class scripts.

1_invokeSQLServer.py (This is the main calling Python script to invoke the OpenAI-Server.)

Please find some of the key snippet from this discussion –

@app.route('/message', methods=['POST'])
def message():
    input_text = request.json.get('input_text', None)
    session_id = request.json.get('session_id', None)

    print('*' * 240)
    print('User Input:')
    print(str(input_text))
    print('*' * 240)

    # Retrieve conversation history from the session or database
    conversation_history = session.get(session_id, [])

    # Add the new message to the conversation history
    conversation_history.append(input_text)

    # Call OpenAI API with the updated conversation
    response = client.with_options(max_retries=0).chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": input_text,
            }
        ],
        model=cf.conf['MODEL_NAME'],
    )

    # Extract the content from the first choice's message
    chat_response = response.choices[0].message.content
    print('*' * 240)
    print('Resposne::')
    print(chat_response)
    print('*' * 240)

    conversation_history.append(chat_response)

    # Store the updated conversation history in the session or database
    session[session_id] = conversation_history

    return chat_response

This code defines a web application route that handles POST requests sent to the /message endpoint:

Route Declaration: The @app.route('/message', methods=['POST']) part specifies that the function message() is executed when the server receives a POST request at the /message URL.
Function Definition: Inside the message() function:
- It retrieves two pieces of data from the request’s JSON body: input_text (the user’s input message) and session_id (a unique identifier for the user’s session).
- It prints the user’s input message, surrounded by lines of asterisks for emphasis.
Conversation History Management:
- The code retrieves the conversation history associated with the given session_id. This history is a list of messages.
- It then adds the new user message (input_text) to this conversation history.
OpenAI API Call:
- The function makes a call to the OpenAI API, passing the user’s message. It specifies not to retry the request if it fails (max_retries=0).
- The model used for the OpenAI API call is taken from some configurations (cf.conf['MODEL_NAME']).
Processing API Response:
- The response from the OpenAI API is processed to extract the content of the chat response.
- This chat response is printed.
Updating Conversation History:
- The chat response is added to the conversation history.
- The updated conversation history is then stored back in the session or database, associated with the session_id.
Returning the Response: Finally, the function returns the chat response.

clsDynamicSQLProcess.py (This Python class generates the SQL & then executes the flask server to invoke the OpenAI-Server.)

Now, let us understand the few important piece of snippet –

def text2SQLBegin(self, DBFileNameList, fileDBPath, srcQueryPrompt, joinCond, debugInd='N'):

        question = srcQueryPrompt
        create_table_statement = ''
        jStr = ''

        print('DBFileNameList::', DBFileNameList)
        print('prevSessionDBFileNameList::', self.prevSessionDBFileNameList)

        if set(self.prevSessionDBFileNameList) == set(DBFileNameList):
            self.flag = 'Y'
        else:
            self.flag = 'N'

        if self.flag == 'N':

            for i in DBFileNameList:
                DBFileName = i

                FullDBname = fileDBPath + DBFileName
                print('File: ', str(FullDBname))

                tabName, _ = DBFileName.split('.')

                # Reading the source data
                df = pd.read_csv(FullDBname)

                # Convert all string columns to lowercase
                df = df.apply(lambda x: x.str.lower() if x.dtype == "object" else x)

                # Convert DataFrame to SQL table
                df.to_sql(tabName, con=engine, index=False)

                # Create a MetaData object and reflect the existing database
                metadata = MetaData()
                metadata.reflect(bind=engine)

                # Access the 'users' table from the reflected metadata
                table = metadata.tables[tabName]

                # Generate the CREATE TABLE statement
                create_table_statement = create_table_statement + str(CreateTable(table)) + '; \n'

                tabName = ''

            for joinS in joinCond:
                jStr = jStr + joinS + '\n'

            self.prevSessionDBFileNameList = DBFileNameList
            self.prev_create_table_statement = create_table_statement

            masterSessionDBFileNameList = self.prevSessionDBFileNameList
            mast_create_table_statement = self.prev_create_table_statement

        else:
            masterSessionDBFileNameList = self.prevSessionDBFileNameList
            mast_create_table_statement = self.prev_create_table_statement

        inputPrompt = (templateVal_1 + mast_create_table_statement + jStr + templateVal_2).format(question=question)

        if debugInd == 'Y':
            print('INPUT PROMPT::')
            print(inputPrompt)

        print('*' * 240)
        print('Find the Generated SQL:')
        print()

        DBFileNameList = []
        create_table_statement = ''

        return inputPrompt

Function Overview: The text2SQLBegin function processes a list of database file names (DBFileNameList), a file path (fileDBPath), a query prompt (srcQueryPrompt), join conditions (joinCond), and a debug indicator (debugInd) to generate SQL commands.
Initial Setup: It starts by initializing variables for the question, the SQL table creation statement, and a string for join conditions.
Debug Prints: The function prints the current and previous session database file names for debugging purposes.
Flag Setting: A flag is set to ‘Y’ if the current session’s database file names match the previous session’s; otherwise, it’s set to ‘N’.
Processing New Session Data: If the flag is ‘N’, indicating new session data:
- For each database file, it reads the data, converts string columns to lowercase, and creates a corresponding SQL table in a database using the pandas library.
- Metadata is generated for each table and a CREATE TABLE SQL statement is created.
Join Conditions and Statement Aggregation: Join conditions are concatenated, and previous session information is updated with the current session’s data.
Handling Repeated Sessions: If the session data is repeated (flag is ‘Y’), it uses the previous session’s SQL table creation statements and database file names.
Final Input Prompt Creation: It constructs the final input prompt by combining template values with the create table statement, join conditions, and the original question.
Debug Printing: If debug mode is enabled, it prints the final input prompt.
Conclusion: The function clears the DBFileNameList and create_table_statement variables, and returns the constructed input prompt.

  def text2SQLEnd(self, srcContext, debugInd='N'):
      url = self.url

      payload = json.dumps({"input_text": srcContext,"session_id": ""})
      headers = {'Content-Type': 'application/json', 'Cookie': cf.conf['HEADER_TOKEN']}

      response = requests.request("POST", url, headers=headers, data=payload)

      return response.text

The text2SQLEnd function sends an HTTP POST request to a specified URL and returns the response. It takes two parameters: srcContext which contains the input text, and an optional debugInd for debugging purposes. The function constructs the request payload by converting the input text and an empty session ID to JSON format. It sets the request headers, including a content type of ‘application/json’ and a token from the configuration file. The function then sends the POST request using the requests library and returns the text content of the response.

  def sql2Data(self, srcSQL):
      # Executing the query on top of your data
      resultSQL = pd.read_sql_query(srcSQL, con=engine)

      return resultSQL

The sql2Data function is designed to execute a SQL query on a database and return the result. It takes a single parameter, srcSQL, which contains the SQL query to be executed. The function uses the pandas library to run the provided SQL query (srcSQL) against a database connection (engine). It then returns the result of this query, which is typically a DataFrame object containing the data retrieved from the database.

def genData(self, srcQueryPrompt, fileDBPath, DBFileNameList, joinCond, debugInd='N'):
    try:
        authorName = self.authorName
        website = self.website
        var = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")

        print('*' * 240)
        print('SQL Start Time: ' + str(var))
        print('*' * 240)

        print('*' * 240)
        print()

        if debugInd == 'Y':
            print('Author Name: ', authorName)
            print('For more information, please visit the following Website: ', website)
            print()

            print('*' * 240)
        print('Your Data for Retrieval:')
        print('*' * 240)

        if debugInd == 'Y':

            print()
            print('Converted File to Dataframe Sample:')
            print()

        else:
            print()

        context = self.text2SQLBegin(DBFileNameList, fileDBPath, srcQueryPrompt, joinCond, debugInd)
        srcSQL = self.text2SQLEnd(context, debugInd)

        print(srcSQL)
        print('*' * 240)
        print()
        resDF = self.sql2Data(srcSQL)

        print('*' * 240)
        print('SQL End Time: ' + str(var))
        print('*' * 240)

        return resDF

    except Exception as e:
        x = str(e)
        print('Error: ', x)

        df = pd.DataFrame()

        return df

Initialization and Debug Information: The function begins by initializing variables like authorName, website, and a timestamp (var). It then prints the start time of the SQL process. If the debug indicator (debugInd) is ‘Y’, it prints additional information like the author’s name and website.
Generating SQL Context and Query: The function calls text2SQLBegin with various parameters (file paths, database file names, query prompt, join conditions, and the debug indicator) to generate an SQL context. Then it calls text2SQLEnd with this context and the debug indicator to generate the actual SQL query.
Executing the SQL Query: It prints the generated SQL query for visibility, especially in debug mode. The query is then executed by calling sql2Data, which returns the result as a data frame (resDF).
Finalization and Error Handling: After executing the query, it prints the SQL end time. In case of any exceptions during the process, it catches the error, prints it, and returns an empty DataFrame.
Return Value: The function returns the DataFrame (resDF) containing the results of the executed SQL query. If an error occurs, it returns an empty DataFrame instead.

DIRECTORY STRUCTURES:

Let us explore the directory structure starting from the parent to some of the important child folder should look like this –

Let us understand the important screenshots of this entire process –

So, finally, we’ve done it.

You will get the complete codebase in the following GitHub link.

I’ll bring some more exciting topics in the coming days from the Python verse. Please share & subscribe to my post & let me know your feedback.

Till then, Happy Avenging! 🙂

Note: All the data & scenarios posted here are representational data & scenarios & available over the internet & for educational purposes only. Some of the images (except my photo) we’ve used are available over the net. We don’t claim ownership of these images. There is always room for improvement & especially in the prediction quality.

Validating source data against RAG-response using Open AI, GloVe, FAISS using Python

Posted on November 27, 2023November 27, 2023 by SatyakiDe in analytic function, api, Azure, BOT, call, cloud, code, computing, design, faiss, gensim, gpt3, Linear-Regression, machine-learning, Model, natural-language, numpy, objects, openai, Pandas, Performance, Pickle, Python, Real-time, regex, snippet, Technology, vector

Today, I’ll be presenting another exciting capability of architecture in the world of LLMs, where you need to answer one crucial point & that is how valid the response generated by these LLMs is against your data. This response is critical when discussing business growth & need to take the right action at the right time.

Why not view the demo before going through it?

Demo

Isn’t it exciting? Great! Let us understand this in detail.

Flow of Architecture:

The first dotted box (extreme-left) represents the area that talks about the data ingestion from different sources, including third-party PDFs. It is expected that organizations should have ready-to-digest data sources. Examples: Data Lake, Data Mart, One Lake, or any other equivalent platforms. Those PDFs will provide additional insights beyond the conventional advanced analytics.

You need to have some kind of OCR solution that will extract all the relevant information in the form of text from the documents.

The next important part is how you define the chunking & embedding of data chunks into Vector DB. Chunking & indexing strategies, along with the overlapping chain, play a crucial importance in tying that segregated piece of context into a single context that will be fed into the source for your preferred LLMs.

This system employs a vector similarity search to browse through unstructured information and concurrently accesses the database to retrieve the context, ensuring that the responses are not only comprehensive but also anchored in validated knowledge.

This approach is particularly vital for addressing multi-hop questions, where a single query can be broken down into multiple sub-questions and may require information from numerous documents to generate an accurate answer.

Python Packages:

pip install openai==0.27.8
pip install pandas==2.0.3
pip install tensorflow==2.11.1
pip install faiss-cpu==1.7.4
pip install gensim==4.3.2

Let us understand the key class & snippets.

clsFeedVectorDB.py (This is the main class that will invoke the Faiss framework to contextualize the docs inside the vector DB with the source file name to validate the answer from Gen AI using Globe.6B embedding models.)

Let us understand some of the key snippets from the above script (Full scripts will be available in the GitHub Repo) –

# Sample function to convert text to a vector
def text2Vector(self, text):
    # Encode the text using the tokenizer
    words = [word for word in text.lower().split() if word in self.model]

    # If no words in the model, return a zero vector
    if not words:
        return np.zeros(self.model.vector_size)

    # Compute the average of the word vectors
    vector = np.mean([self.model[word] for word in words], axis=0)
    return vector.reshape(1, -1)

This code is for a function called “text2Vector” that takes some text as input and converts it into a numerical vector. Let me break it down step by step:

It starts by taking some text as input, and this text is expected to be a sentence or a piece of text.
The text is then split into individual words, and each word is converted to lowercase.
It checks if each word is present in a pre-trained language model (probably a word embedding model like Word2Vec or GloVe). If a word is not in the model, it’s ignored.
If none of the words from the input text are found in the model, the function returns a vector filled with zeros. This vector has the same size as the word vectors in the model.
If there are words from the input text in the model, the function calculates the average vector of these words. It does this by taking the word vectors for each word found in the model and computing their mean (average). This results in a single vector that represents the input text.
Finally, the function reshapes this vector into a 2D array with one row and as many columns as there are elements in the vector. The reason for this reshaping is often related to compatibility with other parts of the code or libraries used in the project.

So, in simple terms, this function takes a piece of text, looks up the word vectors for the words in that text, and calculates the average of those vectors to create a single numerical representation of the text. If none of the words are found in the model, it returns a vector of zeros.

    def genData(self):
        try:
            basePath = self.basePath
            modelFileName = self.modelFileName
            vectorDBPath = self.vectorDBPath
            vectorDBFileName = self.vectorDBFileName

            # Create a FAISS index
            dimension = int(cf.conf['NO_OF_MODEL_DIM'])  # Assuming 100-dimensional vectors 
            index = faiss.IndexFlatL2(dimension)

            print('*' * 240)
            print('Vector Index Your Data for Retrieval:')
            print('*' * 240)

            FullVectorDBname = vectorDBPath + vectorDBFileName
            indexFile = str(vectorDBPath) + str(vectorDBFileName) + '.index'

            print('File: ', str(indexFile))

            data = {}
            # List all files in the specified directory
            files = os.listdir(basePath)

            # Filter out files that are not text files
            text_files = [file for file in files if file.endswith('.txt')]

            # Read each text file
            for file in text_files:
                file_path = os.path.join(basePath, file)
                print('*' * 240)
                print('Processing File:')
                print(str(file_path))
                try:
                    # Attempt to open with utf-8 encoding
                    with open(file_path, 'r', encoding='utf-8') as file:
                        for line_number, line in enumerate(file, start=1):
                            # Assume each line is a separate document
                            vector = self.text2Vector(line)
                            vector = vector.reshape(-1)
                            index_id = index.ntotal

                            index.add(np.array([vector]))  # Adding the vector to the index
                            data[index_id] = {'text': line, 'line_number': line_number, 'file_name': file_path}  # Storing the line and file name
                except UnicodeDecodeError:
                    # If utf-8 fails, try a different encoding
                    try:
                        with open(file_path, 'r', encoding='ISO-8859-1') as file:
                            for line_number, line in enumerate(file, start=1):
                                # Assume each line is a separate document
                                vector = self.text2Vector(line)
                                vector = vector.reshape(-1)
                                index_id = index.ntotal
                                index.add(np.array([vector]))  # Adding the vector to the index
                                data[index_id] = {'text': line, 'line_number': line_number, 'file_name': file_path}  # Storing the line and file name
                    except Exception as e:
                        print(f"Could not read file {file}: {e}")
                        continue

                print('*' * 240)

            # Save the data dictionary using pickle
            dataCache = vectorDBPath + modelFileName
            with open(dataCache, 'wb') as f:
                pickle.dump(data, f)

            # Save the index and data for later use
            faiss.write_index(index, indexFile)

            print('*' * 240)

            return 0

        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return 1

This code defines a function called genData, and its purpose is to prepare and store data for later retrieval using a FAISS index. Let’s break down what it does step by step:
It starts by assigning several variables, such as basePath, modelFileName, vectorDBPath, and vectorDBFileName. These variables likely contain file paths and configuration settings.
It creates a FAISS index with a specified dimension (assuming 100-dimensional vectors in this case) using faiss.IndexFlatL2. FAISS is a library for efficient similarity search and clustering of high-dimensional data.
It prints the file name and lines where the index will be stored. It initializes an empty dictionary called data to store information about the processed text data.
It lists all the files in a directory specified by basePath. It filters out only the files that have a “.txt” extension as text files.
It then reads each of these text files one by one. For each file:

It attempts to open the file with UTF-8 encoding.
- It reads the file line by line.
- For each line, it calls a function text2Vector to convert the text into a numerical vector representation. This vector is added to the FAISS index.
- It also stores some information about the line, such as the line number and the file name, in the data dictionary.
- If there is an issue with UTF-8 encoding, it tries to open the file with a different encoding, “ISO-8859-1”. The same process of reading and storing data continues.

If there are any exceptions (errors) during this process, it prints an error message but continues processing other files.
Once all the files are processed, it saves the data dictionary using the pickle library to a file specified by dataCache.
It also saves the FAISS index to a file specified by indexFile.
Finally, it returns 0 if the process completes successfully or 1 if there was an error during execution.

In summary, this function reads text files, converts their contents into numerical vectors, and builds a FAISS index for efficient similarity search. It also saves the processed data and the index for later use. If there are any issues during the process, it prints error messages but continues processing other files.

clsRAGOpenAI.py (This is the main class that will invoke the RAG class, which will get the contexts with references including source files, line numbers, and source texts. This will help the customer to validate the source against the OpenAI response to understand & control the data bias & other potential critical issues.)

Let us understand some of the key snippets from the above script (Full scripts will be available in the GitHub Repo) –

def ragAnswerWithHaystackAndGPT3(self, queryVector, k, question):
    modelName = self.modelName
    maxToken = self.maxToken
    temp = self.temp

    # Assuming getTopKContexts is a method that returns the top K contexts
    contexts = self.getTopKContexts(queryVector, k)
    messages = []

    # Add contexts as system messages
    for file_name, line_number, text in contexts:
        messages.append({"role": "system", "content": f"Document: {file_name} \nLine Number: {line_number} \nContent: {text}"})

    prompt = self.generateOpenaiPrompt(queryVector, k)
    prompt = prompt + "Question: " + str(question) + ". \n Answer based on the above documents."

    # Add user question
    messages.append({"role": "user", "content": prompt})

    # Create chat completion
    completion = client.chat.completions.create(
    model=modelName,
    messages=messages,
    temperature = temp,
    max_tokens = maxToken
    )

    # Assuming the last message in the response is the answer
    last_response = completion.choices[0].message.content
    source_refernces = ['FileName: ' + str(context[0]) + ' - Line Numbers: ' + str(context[1]) + ' - Source Text (Reference): ' + str(context[2]) for context in contexts]

    return last_response, source_refernces

This code defines a function called ragAnswerWithHaystackAndGPT3. Its purpose is to use a combination of the Haystack search method and OpenAI’s GPT-3 model to generate an answer to a user’s question. Let’s break down what it does step by step:
It starts by assigning several variables, such as modelName, maxToken, and temp. These variables likely contain model-specific information and settings for GPT-3.
It calls a method getTopKContexts to retrieve the top K contexts (which are likely documents or pieces of text) related to the user’s query. These contexts are stored in the contexts variable.
It initializes an empty list called messages to store messages that will be used in the conversation with the GPT-3 model.
It iterates through each context and adds them as system messages to the messages list. These system messages provide information about the documents or sources being used in the conversation.
It creates a prompt that combines the query, retrieved contexts, and the user’s question. This prompt is then added as a user message to the messages list. It effectively sets up the conversation for GPT-3, where the user’s question is followed by context.
It makes a request to the GPT-3 model using the client.chat.completions.create method, passing in the model name, the constructed messages, and other settings such as temperature and maximum tokens.
After receiving a response from GPT-3, it assumes that the last message in the response contains the answer generated by the model.
It also constructs source_references, which is a list of references to the documents or sources used in generating the answer. This information includes the file name, line numbers, and source text for each context.
Finally, it returns the generated answer (last_response) and the source references to the caller.

In summary, this function takes a user’s query, retrieves relevant contexts or documents, sets up a conversation with GPT-3 that includes the query and contexts, and then uses GPT-3 to generate an answer. It also provides references to the sources used in generating the answer.

    def getTopKContexts(self, queryVector, k):
        try:
            distances, indices = index.search(queryVector, k)
            resDict = [(data[i]['file_name'], data[i]['line_number'], data[i]['text']) for i in indices[0]]
            return resDict
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return x

This code defines a function called getTopKContexts. Its purpose is to retrieve the top K relevant contexts or pieces of information from a pre-built index based on a query vector. Here’s a breakdown of what it does:

It takes two parameters as input: queryVector, which is a numerical vector representing a query, and k, which specifies how many relevant contexts to retrieve.
Inside a try-except block, it attempts the following steps:
- It uses the index.search method to find the top K closest contexts to the given queryVector. This method returns two arrays: distances (measuring how similar the contexts are to the query) and indices (indicating the positions of the closest contexts in the data).
- It creates a list called “resDict", which contains tuples for each of the top K contexts. Each tuple contains three pieces of information: the file name (file_name), the line number (line_number), and the text content (text) of the context. These details are extracted from a data dictionary.
If the process completes successfully, it returns the list of top K contexts (resDict) to the caller.
If there’s an exception (an error) during this process, it captures the error message as a string (x), prints the error message, and then returns the error message itself.

In summary, this function takes a query vector and finds the K most relevant contexts or pieces of information based on their similarity to the query. It returns these contexts as a list of tuples containing file names, line numbers, and text content. If there’s an error, it prints an error message and returns the error message string.

def generateOpenaiPrompt(self, queryVector, k):
    contexts = self.getTopKContexts(queryVector, k)
    template = ct.templateVal_1
    prompt = template
    for file_name, line_number, text in contexts:
        prompt += f"Document: {file_name}\n Line Number: {line_number} \n Content: {text}\n\n"
    return prompt

This code defines a function called generateOpenaiPrompt. Its purpose is to create a prompt or a piece of text that combines a template with information from the top K relevant contexts retrieved earlier. Let’s break down what it does:

It starts by calling the getTopKContexts function to obtain the top K relevant contexts based on a given queryVector.
It initializes a variable called template with a predefined template value (likely defined elsewhere in the code).
It sets the prompt variable to the initial template.
Then, it enters a loop where it iterates through each of the relevant contexts retrieved earlier (contexts are typically documents or text snippets).
For each context, it appends information to the prompt. Specifically, it adds lines to the prompt that include:
- The document’s file name (Document: [file_name]).
- The line number within the document (Line Number: [line_number]).
- The content of the context itself (Content: [text]).
It adds some extra spacing (newlines) between each context to ensure readability.
Finally, it returns the complete – prompt, which is a combination of the template and information from the relevant contexts.

In summary, this function takes a query vector, retrieves relevant contexts, and creates a prompt by combining a template with information from these contexts. This prompt can then be used as input for an AI model or system, likely for generating responses or answers based on the provided context.

Let us understand the directory structure of this entire application –

To learn more about this package, please visit the following GitHub link.

So, finally, we’ve done it. I know that this post is relatively smaller than my earlier post. But, I think, you can get a good hack to improve some of your long-running jobs by applying this trick.

I’ll bring some more exciting topics in the coming days from the Python verse. Please share & subscribe to my post & let me know your feedback.

Till then, Happy Avenging! 🙂

RAG implementation of LLMs by using Python, Haystack & React (Part – 2)

Posted on September 29, 2023September 29, 2023 by SatyakiDe in api, Azure, cloud, code, computing, Data Science, design, faiss, gpt3, Haystack, json, Keras, machine-learning, natural-language, numpy, objects, openai, Pandas, Performance, Python, Technology

Today, we’ll share the second installment of the RAG implementation. If you are new here, please visit the previous post for full context.

In this post, we’ll be discussing the Haystack framework more. Again, before discussing the main context, I want to present the demo here.

Demo

FLOW OF EVENTS:

Let us look at the flow diagram as it captures the sequence of events that unfold as part of the process, where today, we’ll pay our primary attention.

As you can see today, we’ll discuss the red dotted line, which contextualizes the source data into the Vector DBs.

Let us understand the flow of events here –

The main Python application will consume the nested JSON by invoking the museum API in multiple threads.
The application will clean the nested data & extract the relevant attributes after flattening the JSON.
It will create the unstructured text-based context, which is later fed to the Vector DB framework.

IMPORTANT PACKAGES:

pip install farm-haystack==1.19.0
pip install Flask==2.2.5
pip install Flask-Cors==4.0.0
pip install Flask-JWT-Extended==4.5.2
pip install Flask-Session==0.5.0
pip install openai==0.27.8
pip install pandas==2.0.3
pip install tensorflow==2.11.1

We’re using the Metropolitan Museum API to feed the data to our Vector DB. For more information, please visit the following lin k. And this is free to use & moreover, we’re using it for education scenarios.

CODE:

We’ll discuss the tokenization part highlighted in a red dotted line from the above picture.

Python:

We’ll discuss the scripts in the diagram as part of the flow mentioned above.

clsExtractJSON.py (This is the main class that will extract the content from the museum API using parallel calls.)

def genData(self):
    try:
        base_url = self.base_url
        header_token = self.header_token
        basePath = self.basePath
        outputPath = self.outputPath
        mergedFile = self.mergedFile
        subdir = self.subdir
        Ind = self.Ind
        var_1 = datetime.now().strftime("%H.%M.%S")


        devVal = list()
        objVal = list()

        # Main Details
        headers = {'Cookie':header_token}
        payload={}

        url = base_url + '/departments'

        date_ranges = self.generateFirstDayOfLastTenYears()

        # Getting all the departments
        try:
            print('Department URL:')
            print(str(url))

            response = requests.request("GET", url, headers=headers, data=payload)
            parsed_data = json.loads(response.text)

            print('Department JSON:')
            print(str(parsed_data))

            # Extract the "departmentId" values into a Python list
            for dept_det in parsed_data['departments']:
                for info in dept_det:
                    if info == 'departmentId':
                        devVal.append(dept_det[info])

        except Exception as e:
            x = str(e)
            print('Error: ', x)
            devVal = list()

        # List to hold thread objects
        threads = []

        # Calling the Data using threads
        for dep in devVal:
            t = threading.Thread(target=self.getDataThread, args=(dep, base_url, headers, payload, date_ranges, objVal, subdir, Ind,))
            threads.append(t)
            t.start()

        # Wait for all threads to complete
        for t in threads:
            t.join()

        res = self.mergeCsvFilesInDirectory(basePath, outputPath, mergedFile)

        if res == 0:
            print('Successful!')
        else:
            print('Failure!')

        return 0

    except Exception as e:
        x = str(e)
        print('Error: ', x)

        return 1

The above code translates into the following steps –

The above method first calls the generateFirstDayOfLastTenYears() plan to populate records for every department after getting all the unique departments by calling another API.
Then, it will call the getDataThread() methods to fetch all the relevant APIs simultaneously to reduce the overall wait time & create individual smaller files.
Finally, the application will invoke the mergeCsvFilesInDirectory() method to merge all the chunk files into one extensive historical data.

def generateFirstDayOfLastTenYears(self):
    yearRange = self.yearRange
    date_format = "%Y-%m-%d"
    current_year = datetime.now().year

    date_ranges = []
    for year in range(current_year - yearRange, current_year + 1):
        first_day_of_year_full = datetime(year, 1, 1)
        first_day_of_year = first_day_of_year_full.strftime(date_format)
        date_ranges.append(first_day_of_year)

    return date_ranges

The first method will generate the first day of each year for the last ten years, including the current year.

def getDataThread(self, dep, base_url, headers, payload, date_ranges, objVal, subdir, Ind):
    try:
        cnt = 0
        cnt_x = 1
        var_1 = datetime.now().strftime("%H.%M.%S")

        for x_start_date in date_ranges:
            try:
                urlM = base_url + '/objects?metadataDate=' + str(x_start_date) + '&departmentIds=' + str(dep)

                print('Nested URL:')
                print(str(urlM))

                response_obj = requests.request("GET", urlM, headers=headers, data=payload)
                objectDets = json.loads(response_obj.text)

                for obj_det in objectDets['objectIDs']:
                    objVal.append(obj_det)

                for objId in objVal:
                    urlS = base_url + '/objects/' + str(objId)

                    print('Final URL:')
                    print(str(urlS))

                    response_det = requests.request("GET", urlS, headers=headers, data=payload)
                    objDetJSON = response_det.text

                    retDB = self.createData(objDetJSON)
                    retDB['departmentId'] = str(dep)

                    if cnt == 0:
                        df_M = retDB
                    else:
                        d_frames = [df_M, retDB]
                        df_M = pd.concat(d_frames)

                    if cnt == 1000:
                        cnt = 0
                        clog.logr('df_M_' + var_1 + '_' + str(cnt_x) + '_' + str(dep) +'.csv', Ind, df_M, subdir)
                        cnt_x += 1
                        df_M = pd.DataFrame()

                    cnt += 1

            except Exception as e:
                x = str(e)
                print('Error X:', x)
        return 0

    except Exception as e:
        x = str(e)
        print('Error: ', x)

        return 1

The above method will invoke the individual API call to fetch the relevant artifact information.

def mergeCsvFilesInDirectory(self, directory_path, output_path, output_file):
    try:
        csv_files = [file for file in os.listdir(directory_path) if file.endswith('.csv')]
        data_frames = []

        for file in csv_files:
            encodings_to_try = ['utf-8', 'utf-8-sig', 'latin-1', 'cp1252']
            for encoding in encodings_to_try:
                try:
                    FullFileName = directory_path + file
                    print('File Name: ', FullFileName)
                    df = pd.read_csv(FullFileName, encoding=encoding)
                    data_frames.append(df)
                    break  # Stop trying other encodings if the reading is successful
                except UnicodeDecodeError:
                    continue

        if not data_frames:
            raise Exception("Unable to read CSV files. Check encoding or file format.")

        merged_df = pd.concat(data_frames, ignore_index=True)

        merged_full_name = os.path.join(output_path, output_file)
        merged_df.to_csv(merged_full_name, index=False)

        for file in csv_files:
            os.remove(os.path.join(directory_path, file))

        return 0

    except Exception as e:
        x = str(e)
        print('Error: ', x)
        return 1

The above method will merge all the small files into a single, more extensive historical data that contains over ten years of data (the first day of ten years of data, to be precise).

For the complete code, please visit the GitHub.

1_ReadMuseumJSON.py (This is the main class that will invoke the class, which will extract the content from the museum API using parallel calls.)

#########################################################
#### Written By: SATYAKI DE                          ####
#### Written On: 27-Jun-2023                         ####
#### Modified On 28-Jun-2023                         ####
####                                                 ####
#### Objective: This is the main calling             ####
#### python script that will invoke the              ####
#### shortcut application created inside MAC         ####
#### enviornment including MacBook, IPad or IPhone.  ####
####                                                 ####
#########################################################
import datetime
from clsConfigClient import clsConfigClient as cf

import clsExtractJSON as cej

########################################################
################    Global Area   ######################
########################################################

cJSON = cej.clsExtractJSON()

basePath = cf.conf['DATA_PATH']
outputPath = cf.conf['OUTPUT_PATH']
mergedFile = cf.conf['MERGED_FILE']

########################################################
################  End Of Global Area   #################
########################################################

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

def main():
    try:
        var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        print('*'*120)
        print('Start Time: ' + str(var))
        print('*'*120)

        r1 = cJSON.genData()

        if r1 == 0:
            print()
            print('Successfully Scrapped!')
        else:
            print()
            print('Failed to Scrappe!')

        print('*'*120)
        var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        print('End Time: ' + str(var1))

    except Exception as e:
        x = str(e)
        print('Error: ', x)

if __name__ == '__main__':
    main()

The above script calls the main class after instantiating the class.

clsCreateList.py (This is the main class that will extract the relevant attributes from the historical files & then create the right input text to create the documents for contextualize into the Vector DB framework.)

def createRec(self):
    try:
        basePath = self.basePath
        fileName = self.fileName
        Ind = self.Ind
        subdir = self.subdir
        base_url = self.base_url
        outputPath = self.outputPath
        mergedFile = self.mergedFile
        cleanedFile = self.cleanedFile

        FullFileName = outputPath + mergedFile

        df = pd.read_csv(FullFileName)
        df2 = df[listCol]
        dfFin = df2.drop_duplicates().reset_index(drop=True)

        dfFin['artist_URL'] = dfFin['artistWikidata_URL'].combine_first(dfFin['artistULAN_URL'])
        dfFin['object_URL'] = dfFin['objectURL'].combine_first(dfFin['objectWikidata_URL'])
        dfFin['Wiki_URL'] = dfFin['Wikidata_URL'].combine_first(dfFin['AAT_URL']).combine_first(dfFin['URL']).combine_first(dfFin['object_URL'])

        # Dropping the old Dtype Columns
        dfFin.drop(['artistWikidata_URL'], axis=1, inplace=True)
        dfFin.drop(['artistULAN_URL'], axis=1, inplace=True)
        dfFin.drop(['objectURL'], axis=1, inplace=True)
        dfFin.drop(['objectWikidata_URL'], axis=1, inplace=True)
        dfFin.drop(['AAT_URL'], axis=1, inplace=True)
        dfFin.drop(['Wikidata_URL'], axis=1, inplace=True)
        dfFin.drop(['URL'], axis=1, inplace=True)

        # Save the filtered DataFrame to a new CSV file
        #clog.logr(cleanedFile, Ind, dfFin, subdir)
        res = self.addHash(dfFin)

        if res == 0:
            print('Added Hash!')
        else:
            print('Failed to add hash!')

        # Generate the text for each row in the dataframe
        for _, row in dfFin.iterrows():
            x = self.genPrompt(row)
            self.addDocument(x, cleanedFile)

        return documents

    except Exception as e:
        x = str(e)
        print('Record Error: ', x)

        return documents

The above code will read the data from the extensive historical file created from the earlier steps & then it will clean the file by removing all the duplicate records (if any) & finally, it will create three unique URLs that constitute artist, object & wiki.

Also, this application will remove the hyperlink with a specific hash value, which will feed into the vector DB. Vector DB could be better with the URLs. Hence, we will store the URLs in a separate file by storing the associate hash value & later, we’ll fetch it in a lookup from the open AI response.

Then, this application will generate prompts dynamically & finally create the documents for later steps of vector DB consumption by invoking the addDocument() methods.

For more details, please visit the GitHub link.

1_1_testCreateRec.py (This is the main class that will call the above class.)

#########################################################
#### Written By: SATYAKI DE                          ####
#### Written On: 27-Jun-2023                         ####
#### Modified On 28-Jun-2023                         ####
####                                                 ####
#### Objective: This is the main calling             ####
#### python script that will invoke the              ####
#### shortcut application created inside MAC         ####
#### enviornment including MacBook, IPad or IPhone.  ####
####                                                 ####
#########################################################

from clsConfigClient import clsConfigClient as cf
import clsL as log
import clsCreateList as ccl

from datetime import datetime, timedelta

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

###############################################
###           Global Section                ###
###############################################

#Initiating Logging Instances
clog = log.clsL()
cl = ccl.clsCreateList()

var = datetime.now().strftime(".%H.%M.%S")

documents = []

###############################################
###    End of Global Section                ###
###############################################
def main():
    try:
        var = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        print('*'*120)
        print('Start Time: ' + str(var))
        print('*'*120)

        print('*'*240)
        print('Creating Index store:: ')
        print('*'*240)

        documents = cl.createRec()

        print('Inserted Sample Records: ')
        print(str(documents))
        print('\n')

        r1 = len(documents)

        if r1 > 0:
            print()
            print('Successfully Indexed sample records!')
        else:
            print()
            print('Failed to sample Indexed recrods!')

        print('*'*120)
        var1 = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        print('End Time: ' + str(var1))

    except Exception as e:
        x = str(e)
        print('Error: ', x)

if __name__ == '__main__':
    main()

The above script invokes the main class after instantiating it & invokes the createRec() methods to tokenize the data into the vector DB.

This above test script will be used to test the above clsCreateList class. However, the class will be used inside another class.
– Satyaki

clsFeedVectorDB.py (This is the main class that will feed the documents into the vector DB.)

#########################################################
#### Written By: SATYAKI DE                          ####
#### Written On: 27-Jun-2023                         ####
#### Modified On 28-Sep-2023                         ####
####                                                 ####
#### Objective: This is the main calling             ####
#### python script that will invoke the              ####
#### haystack frameowrk to contextulioze the docs    ####
#### inside the vector DB.                           ####
####                                                 ####
#########################################################

from haystack.document_stores.faiss import FAISSDocumentStore
from haystack.nodes import DensePassageRetriever
import openai
import pandas as pd
import os
import clsCreateList as ccl

from clsConfigClient import clsConfigClient as cf
import clsL as log

from datetime import datetime, timedelta

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

###############################################
###           Global Section                ###
###############################################

Ind = cf.conf['DEBUG_IND']
openAIKey = cf.conf['OPEN_AI_KEY']

os.environ["TOKENIZERS_PARALLELISM"] = "false"

#Initiating Logging Instances
clog = log.clsL()
cl = ccl.clsCreateList()

var = datetime.now().strftime(".%H.%M.%S")

# Encode your data to create embeddings
documents = []

var_1 = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
print('*'*120)
print('Start Time: ' + str(var_1))
print('*'*120)

print('*'*240)
print('Creating Index store:: ')
print('*'*240)

documents = cl.createRec()

print('Inserted Sample Records: ')
print(documents[:5])
print('\n')
print('Type:')
print(type(documents))

r1 = len(documents)

if r1 > 0:
    print()
    print('Successfully Indexed records!')
else:
    print()
    print('Failed to Indexed recrods!')

print('*'*120)
var_2 = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
print('End Time: ' + str(var_2))

# Passing OpenAI API Key
openai.api_key = openAIKey

###############################################
###    End of Global Section                ###
###############################################

class clsFeedVectorDB:
    def __init__(self):
        self.basePath = cf.conf['DATA_PATH']
        self.modelFileName = cf.conf['CACHE_FILE']
        self.vectorDBPath = cf.conf['VECTORDB_PATH']
        self.vectorDBFileName = cf.conf['VECTORDB_FILE_NM']
        self.queryModel = cf.conf['QUERY_MODEL']
        self.passageModel = cf.conf['PASSAGE_MODEL']

    def retrieveDocuments(self, question, retriever, top_k=3):
        return retriever.retrieve(question, top_k=top_k)

    def generateAnswerWithGPT3(self, retrievedDocs, question):
        documents_text = " ".join([doc.content for doc in retrievedDocs])
        prompt = f"Given the following documents: {documents_text}, answer the question: {question}"

        response = openai.Completion.create(
            model="text-davinci-003",
            prompt=prompt,
            max_tokens=150
        )
        return response.choices[0].text.strip()

    def ragAnswerWithHaystackAndGPT3(self, question, retriever):
        retrievedDocs = self.retrieveDocuments(question, retriever)
        return self.generateAnswerWithGPT3(retrievedDocs, question)

    def genData(self, strVal):
        try:
            basePath = self.basePath
            modelFileName = self.modelFileName
            vectorDBPath = self.vectorDBPath
            vectorDBFileName = self.vectorDBFileName
            queryModel = self.queryModel
            passageModel = self.passageModel

            print('*'*120)
            print('Index Your Data for Retrieval:')
            print('*'*120)

            FullFileName = basePath + modelFileName
            FullVectorDBname = vectorDBPath + vectorDBFileName

            sqlite_path = "sqlite:///" + FullVectorDBname + '.db'
            print('Vector DB Path: ', str(sqlite_path))

            indexFile = "vectorDB/" + str(vectorDBFileName) + '.faiss'
            indexConfig = "vectorDB/" + str(vectorDBFileName) + ".json"

            print('File: ', str(indexFile))
            print('Config: ', str(indexConfig))

            # Initialize DocumentStore
            document_store = FAISSDocumentStore(sql_url=sqlite_path)

            libName = "vectorDB/" + str(vectorDBFileName) + '.faiss'

            document_store.write_documents(documents)

            # Initialize Retriever
            retriever = DensePassageRetriever(document_store=document_store,
                                              query_embedding_model=queryModel,
                                              passage_embedding_model=passageModel,
                                              use_gpu=False)

            document_store.update_embeddings(retriever=retriever)

            document_store.save(index_path=libName, config_path="vectorDB/" + str(vectorDBFileName) + ".json")

            print('*'*120)
            print('Testing with RAG & OpenAI...')
            print('*'*120)

            answer = self.ragAnswerWithHaystackAndGPT3(strVal, retriever)

            print('*'*120)
            print('Testing Answer:: ')
            print(answer)
            print('*'*120)

            return 0

        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return 1

In the above script, the following essential steps took place –

First, the application calls the clsCreateList class to store all the documents inside a dictionary.
Then it stores the data inside the vector DB & creates & stores the model, which will be later reused (If you remember, we’ve used this as a model in our previous post).
Finally, test with some sample use cases by providing the proper context to OpenAI & confirm the response.

Here is a short clip of how the RAG models contextualize with the source data.

RAG-Model Contextualization

So, finally, we’ve done it.

I know that this post is relatively bigger than my earlier post. But, I think, you can get all the details once you go through it.

You will get the complete codebase in the following GitHub link.

I’ll bring some more exciting topics in the coming days from the Python verse. Please share & subscribe to my post & let me know your feedback.

Till then, Happy Avenging! 🙂

RAG implementation of LLMs by using Python, Haystack & React (Part – 1)

Posted on August 31, 2023August 31, 2023 by SatyakiDe in analytic function, api, Azure, chainlang, cloud, code, faiss, gpt3, Haystack, integration, IoT, java, json, Keras, machine-learning, mobile, natural-language, numpy, openai, Pandas, Performance, Python, React, Real-time, regex, replace, snippet, sql, String Manipulation, Technology, Tensorflow

Today, I will share a new post in a part series about creating end-end LLMs that feed source data with RAG implementation. I’ll also use OpenAI python-based SDK and Haystack embeddings in this case.

In this post, I’ve directly subscribed to OpenAI & I’m not using OpenAI from Azure. However, I’ll explore that in the future as well.

Before I explain the process to invoke this new library, why not view the demo first & then discuss it?

Demo

FLOW OF EVENTS:

Let us look at the flow diagram as it captures the sequence of events that unfold as part of the process.

As you can see, to enable this large & complex solution, we must first establish the capabilities to build applications powered by LLMs, Transformer models, vector search, and more. You can use state-of-the-art NLP models to perform question-answering, answer generation, semantic document search, or build tools capable of complex decision-making and query resolution. Hence, steps no. 1 & 2 showcased the data embedding & creating that informed repository. We’ll be discussing that in our second part.

Once you have the informed repository, the system can interact with the end-users. As part of the query (shown in step 3), the prompt & the question are shared with the process engine, which then turned to reduce the volume & get relevant context from our informed repository & get the tuned context as part of the response (Shown in steps 4, 5 & 6).

Then, this tuned context is shared with the OpenAI for better response & summary & concluding remarks that are very user-friendly & easier to understand for end-users (Shown in steps 8 & 9).

IMPORTANT PACKAGES:

The following are the important packages that are essential to this project –

pip install farm-haystack==1.19.0
pip install Flask==2.2.5
pip install Flask-Cors==4.0.0
pip install Flask-JWT-Extended==4.5.2
pip install Flask-Session==0.5.0
pip install openai==0.27.8
pip install pandas==2.0.3
pip install tensorflow==2.11.1

CODE:

We’ve both the front-end using react & back-end APIs with Python-flask and the Open AI to create this experience.

Python:

Today, we’ll be going in reverse mode. We first discuss the main script & then explain all the other class scripts.

flaskServer.py (This is the main calling Python script to invoke the RAG-Server.)

#########################################################
#### Written By: SATYAKI DE                          ####
#### Written On: 27-Jun-2023                         ####
#### Modified On 28-Jun-2023                         ####
####                                                 ####
#### Objective: This is the main calling             ####
#### python script that will invoke the              ####
#### shortcut application created inside MAC         ####
#### enviornment including MacBook, IPad or IPhone.  ####
####                                                 ####
#########################################################

from flask import Flask, jsonify, request, session
from flask_cors import CORS
from werkzeug.security import check_password_hash, generate_password_hash
from flask_jwt_extended import JWTManager, jwt_required, create_access_token
import pandas as pd
from clsConfigClient import clsConfigClient as cf
import clsL as log
import clsContentScrapper as csc
import clsRAGOpenAI as crao
import csv
from datetime import timedelta
import os
import re
import json

########################################################
################    Global Area   ######################
########################################################
#Initiating Logging Instances
clog = log.clsL()

admin_key = cf.conf['ADMIN_KEY']
secret_key = cf.conf['SECRET_KEY']
session_path = cf.conf['SESSION_PATH']
sessionFile = cf.conf['SESSION_CACHE_FILE']

app = Flask(__name__)
CORS(app)  # This will enable CORS for all routes
app.config['JWT_SECRET_KEY'] = admin_key  # Change this!
app.secret_key = secret_key

jwt = JWTManager(app)

users = cf.conf['USER_NM']
passwd = cf.conf['USER_PWD']

cCScrapper = csc.clsContentScrapper()
cr = crao.clsRAGOpenAI()

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

# Define the aggregation functions
def join_unique(series):
    unique_vals = series.drop_duplicates().astype(str)
    return ', '.join(filter(lambda x: x != 'nan', unique_vals))

# Building the preaggregate cache
def groupImageWiki():
    try:
        base_path = cf.conf['OUTPUT_PATH']
        inputFile = cf.conf['CLEANED_FILE']
        outputFile = cf.conf['CLEANED_FILE_SHORT']
        subdir = cf.conf['SUBDIR_OUT']
        Ind = cf.conf['DEBUG_IND']

        inputCleanedFileLookUp = base_path + inputFile

        #Opening the file in dataframe
        df = pd.read_csv(inputCleanedFileLookUp)
        hash_values = df['Total_Hash'].unique()

        dFin = df[['primaryImage','Wiki_URL','Total_Hash']]

        # Ensure columns are strings and not NaN
        # Convert columns to string and replace 'nan' with an empty string
        dFin['primaryImage'] = dFin['primaryImage'].astype(str).replace('nan', '')
        dFin['Wiki_URL'] = dFin['Wiki_URL'].astype(str).replace('nan', '')

        dFin.drop_duplicates()

        # Group by 'Total_Hash' and aggregate
        dfAgg = dFin.groupby('Total_Hash').agg({'primaryImage': join_unique,'Wiki_URL': join_unique}).reset_index()

        return dfAgg

    except Exception as e:
        x = str(e)
        print('Error: ', x)

        df = pd.DataFrame()

        return df

resDf = groupImageWiki()

########################################################
################  End  Global Area  ####################
########################################################

def extractRemoveUrls(hash_value):
    image_urls = ''
    wiki_urls = ''
    # Parse the inner message JSON string
    try:

        resDf['Total_Hash'] = resDf['Total_Hash'].astype(int)
        filtered_df = resDf[resDf['Total_Hash'] == int(hash_value)]

        if not filtered_df.empty:
            image_urls = filtered_df['primaryImage'].values[0]
            wiki_urls = filtered_df['Wiki_URL'].values[0]

        return image_urls, wiki_urls

    except Exception as e:
        x = str(e)
        print('extractRemoveUrls Error: ', x)
        return image_urls, wiki_urls

def isIncomplete(line):
    """Check if a line appears to be incomplete."""

    # Check if the line ends with certain patterns indicating it might be incomplete.
    incomplete_patterns = [': [Link](', ': Approximately ', ': ']
    return any(line.endswith(pattern) for pattern in incomplete_patterns)

def filterData(data):
    """Return only the complete lines from the data."""

    lines = data.split('\n')
    complete_lines = [line for line in lines if not isIncomplete(line)]

    return '\n'.join(complete_lines)

def updateCounter(sessionFile):
    try:
        counter = 0

        # Check if the CSV file exists
        if os.path.exists(sessionFile):
            with open(sessionFile, 'r') as f:
                reader = csv.reader(f)
                for row in reader:
                    # Assuming the counter is the first value in the CSV
                    counter = int(row[0])

        # Increment counter
        counter += 1

        # Write counter back to CSV
        with open(sessionFile, 'w', newline='') as f:
            writer = csv.writer(f)
            writer.writerow([counter])

        return counter
    except Exception as e:
        x = str(e)
        print('Error: ', x)

        return 1

def getPreviousResult():
    try:
        fullFileName = session_path + sessionFile
        newCounterValue = updateCounter(fullFileName)

        return newCounterValue
    except Exception as e:
        x = str(e)
        print('Error: ', x)

        return 1

@app.route('/login', methods=['POST'])
def login():
    username = request.json.get('username', None)
    password = request.json.get('password', None)

    print('User Name: ', str(username))
    print('Password: ', str(password))

    #if username not in users or not check_password_hash(users.get(username), password):
    if ((username not in users) or (password not in passwd)):
        return jsonify({'login': False}), 401

    access_token = create_access_token(identity=username)
    return jsonify(access_token=access_token)

@app.route('/chat', methods=['POST'])
def get_chat():
    try:
        #session["key"] = "1D98KI"
        #session_id = session.sid
        #print('Session Id: ', str(session_id))

        cnt = getPreviousResult()
        print('Running Session Count: ', str(cnt))

        username = request.json.get('username', None)
        message = request.json.get('message', None)

        print('User: ', str(username))
        print('Content: ', str(message))

        if cnt == 1:
            retList = cCScrapper.extractCatalog()
        else:
            hashValue, cleanedData = cr.getData(str(message))
            print('Main Hash Value:', str(hashValue))

            imageUrls, wikiUrls = extractRemoveUrls(hashValue)
            print('Image URLs: ', str(imageUrls))
            print('Wiki URLs: ', str(wikiUrls))
            print('Clean Text:')
            print(str(cleanedData))
            retList = '{"records":[{"Id":"' + str(cleanedData) + '", "Image":"' + str(imageUrls) + '", "Wiki": "' + str(wikiUrls) + '"}]}'

        response = {
            'message': retList
        }

        print('JSON: ', str(response))
        return jsonify(response)

    except Exception as e:
        x = str(e)

        response = {
            'message': 'Error: ' + x
        }
        return jsonify(response)

@app.route('/api/data', methods=['GET'])
@jwt_required()
def get_data():
    response = {
        'message': 'Hello from Flask!'
    }
    return jsonify(response)

if __name__ == '__main__':
    app.run(debug=True)

Let us understand some of the important sections of the above script –

Function – login():

The login function retrieves a ‘username’ and ‘password’ from a JSON request and prints them. It checks if the provided credentials are missing from users or password lists, returning a failure JSON response if so. It creates and returns an access token in a JSON response if valid.

Function – get_chat():

The get_chat function retrieves the running session count and user input from a JSON request. Based on the session count, it extracts catalog data or processes the user’s message from the RAG framework that finally receives the refined response from the OpenAI, extracting hash values, image URLs, and wiki URLs. If an error arises, the function captures and returns the error as a JSON message.

Function – updateCounter():

The updateCounter function checks if a given CSV file exists and retrieves its counter value. It then increments the counter and writes it back to the CSV. If any errors occur, an error message is printed, and the function returns a value of 1.

Function – extractRemoveUrls():

The extractRemoveUrls function attempts to filter a data frame, resDf, based on a provided hash value to extract image and wiki URLs. If the data frame contains matching entries, it retrieves the corresponding URLs. Any errors encountered are printed, but the function always returns the image and wiki URLs, even if they are empty.

clsContentScrapper.py (This is the main class that brings the default options for the users if they agree with the initial prompt by the bot.)

#####################################################
#### Written By: SATYAKI DE                      ####
#### Written On: 27-May-2023                     ####
#### Modified On 28-May-2023                     ####
####                                             ####
#### Objective: This is the main calling         ####
#### python class that will invoke the           ####
#### LangChain of package to extract             ####
#### the transcript from the YouTube videos &    ####
#### then answer the questions based on the      ####
#### topics selected by the users.               ####
####                                             ####
#####################################################

from langchain.document_loaders import YoutubeLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain

from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)

from googleapiclient.discovery import build

import clsTemplate as ct
from clsConfigClient import clsConfigClient as cf

import os

from flask import jsonify
import requests

###############################################
###           Global Section                ###
###############################################
open_ai_Key = cf.conf['OPEN_AI_KEY']
os.environ["OPENAI_API_KEY"] = open_ai_Key
embeddings = OpenAIEmbeddings(openai_api_key=open_ai_Key)

YouTube_Key = cf.conf['YOUTUBE_KEY']
youtube = build('youtube', 'v3', developerKey=YouTube_Key)

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

###############################################
###    End of Global Section                ###
###############################################

class clsContentScrapper:
    def __init__(self):
        self.model_name = cf.conf['MODEL_NAME']
        self.temp_val = cf.conf['TEMP_VAL']
        self.max_cnt = int(cf.conf['MAX_CNT'])
        self.url = cf.conf['BASE_URL']
        self.header_token = cf.conf['HEADER_TOKEN']

    def extractCatalog(self):
        try:
            base_url = self.url
            header_token = self.header_token

            url = base_url + '/departments'

            print('Full URL: ', str(url))

            payload={}
            headers = {'Cookie': header_token}

            response = requests.request("GET", url, headers=headers, data=payload)

            x = response.text

            return x
        except Exception as e:
            discussedTopic = []
            x = str(e)
            print('Error: ', x)

            return x

Let us understand the the core part that require from this class.

Function – extractCatalog():

The extractCatalog function uses specific headers to make a GET request to a constructed URL. The URL is derived by appending ‘/departments’ to a base_url, and a header token is used in the request headers. If successful, it returns the text of the response; if there’s an exception, it prints the error and returns the error message.

clsRAGOpenAI.py (This is the main class that brings the RAG-enabled context that is fed to OpenAI for fine-tuned response with less cost.)

#########################################################
#### Written By: SATYAKI DE                          ####
#### Written On: 27-Jun-2023                         ####
#### Modified On 28-Jun-2023                         ####
####                                                 ####
#### Objective: This is the main calling             ####
#### python script that will invoke the              ####
#### shortcut application created inside MAC         ####
#### enviornment including MacBook, IPad or IPhone.  ####
####                                                 ####
#########################################################

from haystack.document_stores.faiss import FAISSDocumentStore
from haystack.nodes import DensePassageRetriever
import openai

from clsConfigClient import clsConfigClient as cf
import clsL as log

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

import os
import re
###############################################
###           Global Section                ###
###############################################
Ind = cf.conf['DEBUG_IND']
queryModel = cf.conf['QUERY_MODEL']
passageModel = cf.conf['PASSAGE_MODEL']

#Initiating Logging Instances
clog = log.clsL()

os.environ["TOKENIZERS_PARALLELISM"] = "false"

vectorDBFileName = cf.conf['VECTORDB_FILE_NM']

indexFile = "vectorDB/" + str(vectorDBFileName) + '.faiss'
indexConfig = "vectorDB/" + str(vectorDBFileName) + ".json"

print('File: ', str(indexFile))
print('Config: ', str(indexConfig))

# Also, provide `config_path` parameter if you set it when calling the `save()` method:
new_document_store = FAISSDocumentStore.load(index_path=indexFile, config_path=indexConfig)

# Initialize Retriever
retriever = DensePassageRetriever(document_store=new_document_store,
                                  query_embedding_model=queryModel,
                                  passage_embedding_model=passageModel,
                                  use_gpu=False)


###############################################
###    End of Global Section                ###
###############################################

class clsRAGOpenAI:
    def __init__(self):
        self.basePath = cf.conf['DATA_PATH']
        self.fileName = cf.conf['FILE_NAME']
        self.Ind = cf.conf['DEBUG_IND']
        self.subdir = str(cf.conf['OUT_DIR'])
        self.base_url = cf.conf['BASE_URL']
        self.outputPath = cf.conf['OUTPUT_PATH']
        self.vectorDBPath = cf.conf['VECTORDB_PATH']
        self.openAIKey = cf.conf['OPEN_AI_KEY']
        self.temp = cf.conf['TEMP_VAL']
        self.modelName = cf.conf['MODEL_NAME']
        self.maxToken = cf.conf['MAX_TOKEN']

    def extractHash(self, text):
        try:
            # Regular expression pattern to match 'Ref: {' followed by a number and then '}'
            pattern = r"Ref: \{'(\d+)'\}"
            match = re.search(pattern, text)

            if match:
                return match.group(1)
            else:
                return None
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return None

    def removeSentencesWithNaN(self, text):
        try:
            # Split text into sentences using regular expression
            sentences = re.split('(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
            # Filter out sentences containing 'nan'
            filteredSentences = [sentence for sentence in sentences if 'nan' not in sentence]
            # Rejoin the sentences
            return ' '.join(filteredSentences)
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return ''

    def retrieveDocumentsReader(self, question, top_k=9):
        return retriever.retrieve(question, top_k=top_k)

    def generateAnswerWithGPT3(self, retrieved_docs, question):
        try:
            openai.api_key = self.openAIKey
            temp = self.temp
            modelName = self.modelName
            maxToken = self.maxToken

            documentsText = " ".join([doc.content for doc in retrieved_docs])

            filteredDocs = self.removeSentencesWithNaN(documentsText)
            hashValue = self.extractHash(filteredDocs)

            print('RAG Docs:: ')
            print(filteredDocs)
            #prompt = f"Given the following documents: {documentsText}, answer the question accurately based on the above data with the supplied http urls: {question}"

            # Set up a chat-style prompt with your data
            messages = [
                {"role": "system", "content": "You are a helpful assistant, answer the question accurately based on the above data with the supplied http urls. Only relevant content needs to publish. Please do not provide the facts or the texts that results crossing the max_token limits."},
                {"role": "user", "content": filteredDocs}
            ]

            # Chat style invoking the latest model
            response = openai.ChatCompletion.create(
                model=modelName,
                messages=messages,
                temperature = temp,
                max_tokens=maxToken
            )
            return hashValue, response.choices[0].message['content'].strip().replace('\n','\\n')
        except Exception as e:
            x = str(e)
            print('failed to get from OpenAI: ', x)
            return 'Not Available!'

    def ragAnswerWithHaystackAndGPT3(self, question):
        retrievedDocs = self.retrieveDocumentsReader(question)
        return self.generateAnswerWithGPT3(retrievedDocs, question)

    def getData(self, strVal):
        try:
            print('*'*120)
            print('Index Your Data for Retrieval:')
            print('*'*120)

            print('Response from New Docs: ')
            print()

            hashValue, answer = self.ragAnswerWithHaystackAndGPT3(strVal)

            print('GPT3 Answer::')
            print(answer)
            print('Hash Value:')
            print(str(hashValue))

            print('*'*240)
            print('End Of Use RAG to Generate Answers:')
            print('*'*240)

            return hashValue, answer
        except Exception as e:
            x = str(e)
            print('Error: ', x)
            answer = x
            hashValue = 1

            return hashValue, answer

Let us understand some of the important block –

Function – ragAnswerWithHaystackAndGPT3():

The ragAnswerWithHaystackAndGPT3 function retrieves relevant documents for a given question using the retrieveDocumentsReader method. It then generates an answer for the query using GPT-3 with the retrieved documents via the generateAnswerWithGPT3 method. The final response is returned.

Function – generateAnswerWithGPT3():

The generateAnswerWithGPT3 function, given a list of retrieved documents and a question, communicates with OpenAI’s GPT-3 to generate an answer. It first processes the documents, filtering and extracting a hash value. Using a chat-style format, it prompts GPT-3 with the processed documents and captures its response. If an error occurs, an error message is printed, and “Not Available!” is returned.

Function – retrieveDocumentsReader():

The retrieveDocumentsReader function takes in a question and an optional parameter, top_k (defaulted to 9). It is called the retriever.retrieve method with the given parameters. The result of the retrieval will generate at max nine responses from the RAG engine, which will be fed to OpenAI.

React:

App.js (This is the main react script, that will create the interface & parse the data apart from the authentication)

// App.js
import React, { useState } from 'react';
import axios from 'axios';
import './App.css';

const App = () => {
  const [isLoggedIn, setIsLoggedIn] = useState(false);
  const [username, setUsername] = useState('');
  const [password, setPassword] = useState('');
  const [message, setMessage] = useState('');
  const [chatLog, setChatLog] = useState([{ sender: 'MuBot', message: 'Welcome to MuBot! Please explore the world of History from our brilliant collections! Do you want to proceed to see the catalog?'}]);

  const handleLogin = async (e) => {
    e.preventDefault();
    try {
      const response = await axios.post('http://localhost:5000/login', { username, password });
      if (response.status === 200) {
        setIsLoggedIn(true);
      }
    } catch (error) {
      console.error('Login error:', error);
    }
  };

  const sendMessage = async (username) => {
    if (message.trim() === '') return;

    // Create a new chat entry
    const newChatEntry = {
      sender: 'user',
      message: message.trim(),
    };

    // Clear the input field
    setMessage('');

    try {
      // Make API request to Python-based API
      const response = await axios.post('http://localhost:5000/chat', { message: newChatEntry.message }); // Replace with your API endpoint URL
      const responseData = response.data;

      // Print the response to the console for debugging
      console.log('API Response:', responseData);

      // Parse the nested JSON from the 'message' attribute
      const jsonData = JSON.parse(responseData.message);

      // Check if the data contains 'departments'
      if (jsonData.departments) {

        // Extract the 'departments' attribute from the parsed data
        const departments = jsonData.departments;

        // Extract the department names and create a single string with line breaks
        const botResponseText = departments.reduce((acc, department) => {return acc + department.departmentId + ' ' + department.displayName + '\n';}, '');

        // Update the chat log with the bot's response
        setChatLog((prevChatLog) => [...prevChatLog, { sender: 'user', message: message }, { sender: 'bot', message: botResponseText },]);
      }
      else if (jsonData.records)
      {
        // Data structure 2: Artwork information
        const records = jsonData.records;

        // Prepare chat entries
        const chatEntries = [];

        // Iterate through records and extract text, image, and wiki information
        records.forEach((record) => {
          const textInfo = Object.entries(record).map(([key, value]) => {
            if (key !== 'Image' && key !== 'Wiki') {
              return `${key}: ${value}`;
            }
            return null;
          }).filter((info) => info !== null).join('\n');

          const imageLink = record.Image;
          //const wikiLinks = JSON.parse(record.Wiki.replace(/'/g, '"'));
          //const wikiLinks = record.Wiki;
          const wikiLinks = record.Wiki.split(',').map(link => link.trim());

          console.log('Wiki:', wikiLinks);

          // Check if there is a valid image link
          const hasValidImage = imageLink && imageLink !== '[]';

          const imageElement = hasValidImage ? (
            <img src={imageLink} alt="Artwork" style={{ maxWidth: '100%' }} />
          ) : null;

          // Create JSX elements for rendering the wiki links (if available)
          const wikiElements = wikiLinks.map((link, index) => (
            <div key={index}>
              <a href={link} target="_blank" rel="noopener noreferrer">
                Wiki Link {index + 1}
              </a>
            </div>
          ));

          if (textInfo) {
            chatEntries.push({ sender: 'bot', message: textInfo });
          }

          if (imageElement) {
            chatEntries.push({ sender: 'bot', message: imageElement });
          }

          if (wikiElements.length > 0) {
            chatEntries.push({ sender: 'bot', message: wikiElements });
          }
        });

        // Update the chat log with the bot's response
        setChatLog((prevChatLog) => [...prevChatLog, { sender: 'user', message }, ...chatEntries, ]);
      }

    } catch (error) {
      console.error('Error sending message:', error);
    }
  };

  if (!isLoggedIn) {
    return (
      <div className="login-container">
        <h2>Welcome to the MuBot</h2>
        <form onSubmit={handleLogin} className="login-form">
          <input
            type="text"
            placeholder="Enter your name"
            value={username}
            onChange={(e) => setUsername(e.target.value)}
            required
          />
          <input
            type="password"
            placeholder="Enter your password"
            value={password}
            onChange={(e) => setPassword(e.target.value)}
            required
          />
          <button type="submit">Login</button>
        </form>
      </div>
    );
  }

  return (
    <div className="chat-container">
      <div className="chat-header">
        <h2>Hello, {username}</h2>
        <h3>Chat with MuBot</h3>
      </div>
      <div className="chat-log">
        {chatLog.map((chatEntry, index) => (
          <div
            key={index}
            className={`chat-entry ${chatEntry.sender === 'user' ? 'user' : 'bot'}`}
          >
            <span className="user-name">{chatEntry.sender === 'user' ? username : 'MuBot'}</span>
            <p className="chat-message">{chatEntry.message}</p>
          </div>
        ))}
      </div>
      <div className="chat-input">
        <input
          type="text"
          placeholder="Type your message..."
          value={message}
          onChange={(e) => setMessage(e.target.value)}
          onKeyPress={(e) => {
            if (e.key === 'Enter') {
              sendMessage();
            }
          }}
        />
        <button onClick={sendMessage}>Send</button>
      </div>
    </div>
  );
};

export default App;

Please find some of the important logic –

Function – handleLogin():

The handleLogin asynchronous function responds to an event by preventing its default action. It attempts to post a login request with a username and password to a local server endpoint. If the response is successful with a status of 200, it updates a state variable to indicate a successful login; otherwise, it logs any encountered errors.

Function – sendMessage():

The sendMessage asynchronous function is designed to handle the user’s chat interaction:

If the message is empty (after trimming spaces), the function exits without further action.
A chat entry object is created with the sender set as ‘user’ and the trimmed message.
The input field’s message is cleared, and an API request is made to a local server endpoint with the chat message.
If the API responds with a ‘departments’ attribute in its JSON, a bot response is crafted by iterating over department details.
If the API responds with ‘records’ indicating artwork information, the bot crafts responses for each record, extracting text, images, and wiki links, and generating JSX elements for rendering them.
After processing the API response, the chat log state is updated with the user’s original message and the bot’s responses.
Errors, if encountered, are logged to the console.

This function enables interactive chat with bot responses that vary based on the nature of the data received from the API.

DIRECTORY STRUCTURES:

Let us explore the directory structure starting from the parent to some of the important child folder should look like this –

So, finally, we’ve done it.

I know that this post is relatively bigger than my earlier post. But, I think, you can get all the details once you go through it.

You will get the complete codebase in the following GitHub link.

I’ll bring some more exciting topics in the coming days from the Python verse. Please share & subscribe to my post & let me know your feedback.

Till then, Happy Avenging! 🙂

Enable OpenAI chatbot with the selected YouTube video content using LangChain, FAISS & YouTube data-API.

Posted on May 28, 2023May 29, 2023 by SatyakiDe in ai, api, audio, BOT, chaining, cloud, code, computing, Data Science, design, faiss, function, human, IoT, json, machine-learning, mobile, Model, natural-language, objects, openai, Python, Real-time, Technology, video, voice, youtubedataapi

Today, I’m very excited to demonstrate an effortless & new way to extract the transcript from YouTube videos & then answer the questions based on the topics selected by the users. In this post, I plan to deal with the user inputs to consider the case first & then it can summarize the video content through useful advanced analytics with the help of the LangChain & OpenAI-based model.

In this post, I’ve directly subscribed to OpenAI & I’m not using OpenAI from Azure. However, I’ll explore that in the future as well.
Before I explain the process to invoke this new library, why not view the demo first & then discuss it?

Demo

Isn’t it very exciting? This will lead to a whole new ballgame, where one can get critical decision-making information from these human sources along with their traditional advanced analytical data.

How will it help?

Let’s say as per your historical data & analytics, the dashboard is recommending prod-A, prod-B & prod-C as the top three products for potential top-performing brands. Whereas, you are getting some alerts from the TV news on prod-B due to the recent incidents. So, in that case, you don’t want to continue with the prod-B investment. You may find a new product named prod-Z. That may reduce the risk of your investment.

What is LangChain?

LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model but will also be:

Data-aware: connect a language model to other sources of data
Agentic: allow a language model to interact with its environment

The LangChain framework works around these principles.

To know more about this, please click the following link.

As you can see, this is one of the critical components in our solution, which will bind the OpenAI bot & it will feed the necessary data to provide the correct response.

What is FAISS?

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that do not fit in RAM. It also has supporting code for evaluation and parameter tuning.

Faiss developed using C++ with complete wrappers for Python—some of the most beneficial algorithms available both on CPU & in GPU as well. Facebook AI Research develops it.

To know more about this, please click the following link.

FLOW OF EVENTS:

Let us look at the flow diagram as it captures the sequence of events that unfold as part of the process.

Here are the steps that will follow in sequence –

The application will first get the topic on which it needs to look from YouTube & find the top 5 videos using the YouTube data-API.

Once the application returns a list of websites from the above step, LangChain will drive the application will extract the transcripts from the video & then optimize the response size in smaller chunks to address the costly OpenAI calls. During this time, it will invoke FAISS to create document DBs.

Finally, it will send those chunks to OpenAI for the best response based on your supplied template that performs the final analysis with small data required for your query & gets the appropriate response with fewer costs.

CODE:

Why don’t we go through the code made accessible due to this new library for this particular use case?

clsConfigClient.py (This is the main calling Python script for the input parameters.)

	################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 15-May-2020 ####
	#### Modified On: 28-May-2023 ####
	#### ####
	#### Objective: This script is a config ####
	#### file, contains all the keys for ####
	#### personal OpenAI-based video content ####
	#### enable bot. ####
	#### ####
	################################################

	import os
	import platform as pl

	class clsConfigClient(object):
	Curr_Path = os.path.dirname(os.path.realpath(__file__))

	os_det = pl.system()
	if os_det == "Windows":
	sep = '\\'
	else:
	sep = '/'

	conf = {
	'APP_ID': 1,
	'ARCH_DIR': Curr_Path + sep + 'arch' + sep,
	'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,
	'LOG_PATH': Curr_Path + sep + 'log' + sep,
	'DATA_PATH': Curr_Path + sep + 'data' + sep,
	'MODEL_PATH': Curr_Path + sep + 'model' + sep,
	'TEMP_PATH': Curr_Path + sep + 'temp' + sep,
	'MODEL_DIR': 'model',
	'APP_DESC_1': 'LangChain Demo!',
	'DEBUG_IND': 'N',
	'INIT_PATH': Curr_Path,
	'FILE_NAME': 'Output.csv',
	'MODEL_NAME': 'gpt-3.5-turbo',
	'OPEN_AI_KEY': "sk-kfrjfijdrkidjkfjd9474nbfjfkfjfhfhf84i84hnfhjdbv6Bgvv",
	'YOUTUBE_KEY': "AIjfjfUYGe64hHJ-LOFO5u-mkso9pPOJGFU",
	'TITLE': "LangChain Demo!",
	'TEMP_VAL': 0.2,
	'PATH' : Curr_Path,
	'MAX_CNT' : 5,
	'OUT_DIR': 'data'
	}

view raw

clsConfigClient.py

hosted with ❤ by GitHub

Some of the key entries from the above scripts are as follows –

'MODEL_NAME': 'gpt-3.5-turbo',
'OPEN_AI_KEY': "sk-kfrjfijdrkidjkfjd9474nbfjfkfjfhfhf84i84hnfhjdbv6Bgvv",
'YOUTUBE_KEY': "AIjfjfUYGe64hHJ-LOFO5u-mkso9pPOJGFU",
'TEMP_VAL': 0.2,

From the above code snippet, one can understand that we need both the API keys for YouTube & OpenAI. And they have separate costs & usage, which I’ll share later in the post. Also, notice that the temperature sets to 0.2 ( range between 0 to 1). That means our AI bot will be consistent in response. And our application will use the GPT-3.5-turbo model for its analytic response.

clsTemplate.py (Contains all the templates for OpenAI.)

	################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 27-May-2023 ####
	#### Modified On: 28-May-2023 ####
	#### ####
	#### Objective: This script is a config ####
	#### file, contains all the template for ####
	#### OpenAI prompts to get the correct ####
	#### response. ####
	#### ####
	################################################

	# Template to use for the system message prompt
	templateVal_1 = """
	You are a helpful assistant that that can answer questions about youtube videos
	based on the video's transcript: {docs}

	Only use the factual information from the transcript to answer the question.

	If you feel like you don't have enough information to answer the question, say "I don't know".

	Your answers should be verbose and detailed.
	"""

view raw

clsTemplate.py

hosted with ❤ by GitHub

The above code is self-explanatory. Here, we’re keeping the correct instructions for our OpenAI to respond within these guidelines.

clsVideoContentScrapper.py (Main class to extract the transcript from the YouTube videos & then answer the questions based on the topics selected by the users.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 27-May-2023 ####
	#### Modified On 28-May-2023 ####
	#### ####
	#### Objective: This is the main calling ####
	#### python class that will invoke the ####
	#### LangChain of package to extract ####
	#### the transcript from the YouTube videos & ####
	#### then answer the questions based on the ####
	#### topics selected by the users. ####
	#### ####
	#####################################################

	from langchain.document_loaders import YoutubeLoader
	from langchain.text_splitter import RecursiveCharacterTextSplitter
	from langchain.embeddings.openai import OpenAIEmbeddings
	from langchain.vectorstores import FAISS
	from langchain.chat_models import ChatOpenAI
	from langchain.chains import LLMChain

	from langchain.prompts.chat import (
	ChatPromptTemplate,
	SystemMessagePromptTemplate,
	HumanMessagePromptTemplate,
	)

	from googleapiclient.discovery import build

	import clsTemplate as ct
	from clsConfigClient import clsConfigClient as cf

	import os

	###############################################
	### Global Section ###
	###############################################
	open_ai_Key = cf.conf['OPEN_AI_KEY']
	os.environ["OPENAI_API_KEY"] = open_ai_Key
	embeddings = OpenAIEmbeddings(openai_api_key=open_ai_Key)

	YouTube_Key = cf.conf['YOUTUBE_KEY']
	youtube = build('youtube', 'v3', developerKey=YouTube_Key)

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	###############################################
	### End of Global Section ###
	###############################################

	class clsVideoContentScrapper:
	def __init__(self):
	self.model_name = cf.conf['MODEL_NAME']
	self.temp_val = cf.conf['TEMP_VAL']
	self.max_cnt = int(cf.conf['MAX_CNT'])

	def createDBFromYoutubeVideoUrl(self, video_url):
	try:
	loader = YoutubeLoader.from_youtube_url(video_url)
	transcript = loader.load()

	text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
	docs = text_splitter.split_documents(transcript)

	db = FAISS.from_documents(docs, embeddings)
	return db

	except Exception as e:
	x = str(e)
	print('Error: ', x)
	return ''


	def getResponseFromQuery(self, db, query, k=4):
	try:
	"""
	gpt-3.5-turbo can handle up to 4097 tokens. Setting the chunksize to 1000 and k to 4 maximizes
	the number of tokens to analyze.
	"""

	mod_name = self.model_name
	temp_val = self.temp_val

	docs = db.similarity_search(query, k=k)
	docs_page_content = " ".join([d.page_content for d in docs])

	chat = ChatOpenAI(model_name=mod_name, temperature=temp_val)

	# Template to use for the system message prompt
	template = ct.templateVal_1

	system_message_prompt = SystemMessagePromptTemplate.from_template(template)

	# Human question prompt
	human_template = "Answer the following question: {question}"
	human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

	chat_prompt = ChatPromptTemplate.from_messages(
	[system_message_prompt, human_message_prompt]
	)

	chain = LLMChain(llm=chat, prompt=chat_prompt)

	response = chain.run(question=query, docs=docs_page_content)
	response = response.replace("\n", "")
	return response, docs

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	return '', ''

	def topFiveURLFromYouTube(self, service, **kwargs):
	try:
	video_urls = []
	channel_list = []
	results = service.search().list(**kwargs).execute()

	for item in results['items']:
	print("Title: ", item['snippet']['title'])
	print("Description: ", item['snippet']['description'])
	channel = item['snippet']['channelId']
	print("Channel Id: ", channel)

	# Fetch the channel name using the channel ID
	channel_response = service.channels().list(part='snippet',id=item['snippet']['channelId']).execute()
	channel_title = channel_response['items'][0]['snippet']['title']
	print("Channel Title: ", channel_title)
	channel_list.append(channel_title)

	print("Video Id: ", item['id']['videoId'])
	vidURL = "https://www.youtube.com/watch?v=" + item['id']['videoId']
	print("Video URL: " + vidURL)
	video_urls.append(vidURL)
	print("\n")

	return video_urls, channel_list

	except Exception as e:
	video_urls = []
	channel_list = []
	x = str(e)
	print('Error: ', x)

	return video_urls, channel_list

	def extractContentInText(self, topic, query):
	try:
	discussedTopic = []
	strKeyText = ''
	cnt = 0
	max_cnt = self.max_cnt

	urlList, channelList = self.topFiveURLFromYouTube(youtube, q=topic, part='id,snippet',maxResults=max_cnt,type='video')
	print('Returned List: ')
	print(urlList)
	print()

	for video_url in urlList:
	print('Processing Video: ')
	print(video_url)
	db = self.createDBFromYoutubeVideoUrl(video_url)

	response, docs = self.getResponseFromQuery(db, query)

	if len(response) > 0:
	strKeyText = 'As per the topic discussed in ' + channelList[cnt] + ', '
	discussedTopic.append(strKeyText + response)

	cnt += 1

	return discussedTopic
	except Exception as e:
	discussedTopic = []
	x = str(e)
	print('Error: ', x)

	return discussedTopic

view raw

clsVideoContentScrapper.py

hosted with ❤ by GitHub

Let us understand the key methods step by step in detail –

def topFiveURLFromYouTube(self, service, **kwargs):
    try:
        video_urls = []
        channel_list = []
        results = service.search().list(**kwargs).execute()

        for item in results['items']:
            print("Title: ", item['snippet']['title'])
            print("Description: ", item['snippet']['description'])
            channel = item['snippet']['channelId']
            print("Channel Id: ", channel)

            # Fetch the channel name using the channel ID
            channel_response = service.channels().list(part='snippet',id=item['snippet']['channelId']).execute()
            channel_title = channel_response['items'][0]['snippet']['title']
            print("Channel Title: ", channel_title)
            channel_list.append(channel_title)

            print("Video Id: ", item['id']['videoId'])
            vidURL = "https://www.youtube.com/watch?v=" + item['id']['videoId']
            print("Video URL: " + vidURL)
            video_urls.append(vidURL)
            print("\n")

        return video_urls, channel_list

    except Exception as e:
        video_urls = []
        channel_list = []
        x = str(e)
        print('Error: ', x)

        return video_urls, channel_list

The above code will fetch the most relevant YouTube URLs & bind them into a list along with the channel names & then share the lists with the main functions.

def createDBFromYoutubeVideoUrl(self, video_url):
    try:
        loader = YoutubeLoader.from_youtube_url(video_url)
        transcript = loader.load()

        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
        docs = text_splitter.split_documents(transcript)

        db = FAISS.from_documents(docs, embeddings)
        return db

    except Exception as e:
        x = str(e)
        print('Error: ', x)
        return ''

The provided Python code defines a function createDBFromYoutubeVideoUrl which appears to create a database of text documents from the transcript of a YouTube video. Here’s the explanation in simple English:

The function createDBFromYoutubeVideoUrl has defined with one argument: video_url.
The function uses a try-except block to handle any potential exceptions or errors that may occur.
Inside the try block, the following steps are going to perform:

First, it creates a YoutubeLoader object from the provided video_url. This object is likely responsible for interacting with the YouTube video specified by the URL.
The loader object then loads the transcript of the video. This object is the text version of everything spoken in the video.
It then creates a RecursiveCharacterTextSplitter object with a specified chunk_size of 1000 and chunk_overlap of 100. This object may split the transcript into smaller chunks (documents) of text for easier processing or analysis. Each piece will be around 1000 characters long, and there will overlap of 100 characters between consecutive chunks.
The split_documents method of the text_splitter object will split the transcript into smaller documents. These documents are stored in the docs variable.
The FAISS.from_documents method is then called with docs and embeddings as arguments to create a FAISS (Facebook AI Similarity Search) index. This index is a database used for efficient similarity search and clustering of high-dimensional vectors, which in this case, are the embeddings of the documents. The FAISS index is stored in the db variable.
Finally, the db variable is returned, representing the created database from the video transcript.

4. If an exception occurs during the execution of the try block, the code execution moves to the except block:

Here, it first converts the exception e to a string x.
Then it prints an error message.
Finally, it returns an empty string as an indication of the error.

def getResponseFromQuery(self, db, query, k=4):
      try:
          """
          gpt-3.5-turbo can handle up to 4097 tokens. Setting the chunksize to 1000 and k to 4 maximizes
          the number of tokens to analyze.
          """

          mod_name = self.model_name
          temp_val = self.temp_val

          docs = db.similarity_search(query, k=k)
          docs_page_content = " ".join([d.page_content for d in docs])

          chat = ChatOpenAI(model_name=mod_name, temperature=temp_val)

          # Template to use for the system message prompt
          template = ct.templateVal_1

          system_message_prompt = SystemMessagePromptTemplate.from_template(template)

          # Human question prompt
          human_template = "Answer the following question: {question}"
          human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

          chat_prompt = ChatPromptTemplate.from_messages(
              [system_message_prompt, human_message_prompt]
          )

          chain = LLMChain(llm=chat, prompt=chat_prompt)

          response = chain.run(question=query, docs=docs_page_content)
          response = response.replace("\n", "")
          return response, docs

      except Exception as e:
          x = str(e)
          print('Error: ', x)

          return '', ''

The Python function getResponseFromQuery is designed to search a given database (db) for a specific query and then generate a response using a language model (possibly GPT-3.5-turbo). The answer is based on the content found and the particular question. Here is a simple English summary:

The function getResponseFromQuery takes three parameters: db, query, and k. The k parameter is optional and defaults to 4 if not provided. db is the database to search, the query is the question or prompts to analyze, and k is the number of similar items to return.
The function initiates a try-except block for handling any errors that might occur.
Inside the try block:

The function retrieves the model name and temperature value from the instance of the class this function is a part of.
The function then searches the db database for documents similar to the query and saves these in docs.
It concatenates the content of the returned documents into a single string docs_page_content.
It creates a ChatOpenAI object with the model name and temperature value.
It creates a system message prompt from a predefined template.
It creates a human message prompt, which is the query.
It combines these two prompts to form a chat prompt.
An LLMChain object is then created using the ChatOpenAI object and the chat prompt.
This LLMChain object is used to generate a response to the query using the content of the documents found in the database. The answer is then formatted by replacing all newline characters with empty strings.
Finally, the function returns this response along with the original documents.

If any error occurs during these operations, the function goes to the except block where:

The error message is printed.
The function returns two empty strings to indicate an error occurred, and no response or documents could be produced.

def extractContentInText(self, topic, query):
    try:
        discussedTopic = []
        strKeyText = ''
        cnt = 0
        max_cnt = self.max_cnt

        urlList, channelList = self.topFiveURLFromYouTube(youtube, q=topic, part='id,snippet',maxResults=max_cnt,type='video')
        print('Returned List: ')
        print(urlList)
        print()

        for video_url in urlList:
            print('Processing Video: ')
            print(video_url)
            db = self.createDBFromYoutubeVideoUrl(video_url)

            response, docs = self.getResponseFromQuery(db, query)

            if len(response) > 0:
                strKeyText = 'As per the topic discussed in ' + channelList[cnt] + ', '
                discussedTopic.append(strKeyText + response)

            cnt += 1

        return discussedTopic
    except Exception as e:
        discussedTopic = []
        x = str(e)
        print('Error: ', x)

        return discussedTopic

This Python function, extractContentInText, is aimed to extract relevant content from the transcripts of top YouTube videos on a specific topic and generate responses to a given query. Here’s a simple English translation:

The function extractContentInText is defined with topic and query as parameters.
It begins with a try-except block to catch and handle any possible exceptions.
In the try block:

It initializes several variables: an empty list discussedTopic to store the extracted information, an empty string strKeyText to keep specific parts of the content, a counter cnt initialized at 0, and max_cnt retrieved from the self-object to specify the maximum number of YouTube videos to consider.
It calls the topFiveURLFromYouTube function (defined previously) to get the URLs of the top videos on the given topic from YouTube. It also retrieves the list of channel names associated with these videos.
It prints the returned list of URLs.
Then, it starts a loop over each URL in the urlList.
- For each URL, it prints the URL, then creates a database from the transcript of the YouTube video using the function createDBFromYoutubeVideoUrl.
- It then uses the getResponseFromQuery function to get a response to the query based on the content of the database.
- If the length of the response is greater than 0 (meaning there is a response), it forms a string strKeyText to indicate the channel that the topic was discussed on and then appends the answer to this string. This entire string is then added to the discussedTopic list.
- It increments the counter cnt by one after each iteration.
- Finally, it returns the discussedTopic list, which now contains relevant content extracted from the videos.

If any error occurs during these operations, the function goes into the except block:

It first resets discussedTopic to an empty list.
Then it converts the exception e to a string and prints the error message.
Lastly, it returns the empty discussedTopic list, indicating that no content could be extracted due to the error.

testLangChain.py (Main Python script to extract the transcript from the YouTube videos & then answer the questions based on the topics selected by the users.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 27-May-2023 ####
	#### Modified On 28-May-2023 ####
	#### ####
	#### Objective: This is the main calling ####
	#### python script that will invoke the ####
	#### clsVideoContentScrapper class to extract ####
	#### the transcript from the YouTube videos. ####
	#### ####
	#####################################################

	import clsL as cl
	from clsConfigClient import clsConfigClient as cf
	import datetime
	import textwrap

	import clsVideoContentScrapper as cvsc

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	######################################
	### Get your global values ####
	######################################
	debug_ind = 'Y'

	# Initiating Logging Instances
	clog = cl.clsL()

	data_path = cf.conf['DATA_PATH']
	data_file_name = cf.conf['FILE_NAME']

	cVCScrapper = cvsc.clsVideoContentScrapper()

	######################################
	#### Global Flag ########
	######################################

	def main():
	try:
	var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print(''120)
	print('Start Time: ' + str(var))
	print(''120)

	#query = "What are they saying about Microsoft?"
	print('Please share your topic!')
	inputTopic = input('User: ')
	print('Please ask your questions?')
	inputQry = input('User: ')
	print()

	retList = cVCScrapper.extractContentInText(inputTopic, inputQry)
	cnt = 0

	for discussedTopic in retList:
	finText = str(cnt + 1) + ') ' + discussedTopic
	print()
	print(textwrap.fill(finText, width=150))

	cnt += 1

	r1 = len(retList)

	if r1 > 0:
	print()
	print('Successfully Scrapped!')
	else:
	print()
	print('Failed to Scrappe!')

	print(''120)
	var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('End Time: ' + str(var1))

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	if __name__ == "__main__":
	main()

view raw

testLangChain.py

hosted with ❤ by GitHub

Please find the key snippet –

def main():
    try:
        var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        print('*'*120)
        print('Start Time: ' + str(var))
        print('*'*120)

        #query = "What are they saying about Microsoft?"
        print('Please share your topic!')
        inputTopic = input('User: ')
        print('Please ask your questions?')
        inputQry = input('User: ')
        print()

        retList = cVCScrapper.extractContentInText(inputTopic, inputQry)
        cnt = 0

        for discussedTopic in retList:
            finText = str(cnt + 1) + ') ' + discussedTopic
            print()
            print(textwrap.fill(finText, width=150))

            cnt += 1

        r1 = len(retList)

        if r1 > 0:
            print()
            print('Successfully Scrapped!')
        else:
            print()
            print('Failed to Scrappe!')

        print('*'*120)
        var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        print('End Time: ' + str(var1))

    except Exception as e:
        x = str(e)
        print('Error: ', x)

if __name__ == "__main__":
    main()

The above main application will capture the topics from the user & then will give the user a chance to ask specific questions on the topics, invoking the main class to extract the transcript from YouTube & then feed it as a source using ChainLang & finally deliver the response. If there is no response, then it will skip the overall options.

USAGE & COST FACTOR:

Please find the OpenAI usage –

Please find the YouTube API usage –

So, finally, we’ve done it.

I know that this post is relatively bigger than my earlier post. But, I think, you can get all the details once you go through it.

You will get the complete codebase in the following GitHub link.

I’ll bring some more exciting topics in the coming days from the Python verse. Please share & subscribe to my post & let me know your feedback.

Till then, Happy Avenging! 🙂

Demonstration of GPT-3 model tuning using Python for an upcoming PyPi-package

Posted on April 21, 2023April 21, 2023 by SatyakiDe in analytic function, api, Azure, cloud, code, Data Science, function, gpt3, json, machine-learning, natural-language, numpy, openai, Pandas, Performance, Python, Real-time, Technology

Today, I’m very excited to demonstrate an effortless & new way to fine-tune the GPT-3 model using Python with the help of my new build (unpublished) PyPi package. In this post, I plan to deal with the custom website link as a response from this website depending upon the user queries with the help of the OpenAI-based tuned model.

In this post, I’ve directly subscribed to OpenAI & I’m not using OpenAI from Azure. However, I’ll explore that in the future as well.

Before I explain the process to invoke this new library, why not view the demo first & then discuss it?

Demo

Isn’t it exciting? Finally, we can efficiently handle your custom website URL using OpenAI tuned model.

What is ChatGPT?

ChatGPT is an advanced artificial intelligence language model developed by OpenAI based on the GPT-4 architecture. As an AI model, it is designed to understand and generate human-like text-based on the input it receives. ChatGPT can engage in various tasks, such as answering questions, providing recommendations, creating content, and simulating conversation. While it is highly advanced and versatile, it’s important to note that ChatGPT’s knowledge is limited to the data it was trained on, with a cutoff date of September 2021.

When to tune GPT model?

Tuning a GPT or any AI model might be necessary for various reasons. Here are some common scenarios when you should consider adjusting or fine-tuning a GPT model:

Domain-specific knowledge: If you need your model to have a deeper understanding of a specific domain or industry, you can fine-tune it with domain-specific data to improve its performance.
New or updated data: If new or updated information is not part of the original training data, you should fine-tune the model to ensure it has the most accurate and up-to-date knowledge.
Customization: If you require the model to have a specific style, tone, or focus, you can fine-tune it with data that reflects those characteristics.
Ethical or safety considerations: To make the model safer and more aligned with human values, you should fine-tune it to reduce biased or harmful outputs.
Improve performance: If the base model’s performance is unsatisfactory for a particular task or application, you can fine-tune it on a dataset more relevant to the job, often leading to better results.

Remember that tuning or fine-tuning a GPT model requires access to appropriate data and computational resources and an understanding of the model’s architecture and training techniques. Additionally, monitoring and evaluating the model’s performance after fine-tuning is essential to ensure that the desired improvements have been achieved.

FLOW OF EVENTS:

Let us look at the flow diagram as it captures the sequence of events that unfold as part of the process.

The initial Python-based client interacts with the tuned OpenAI models. This process enables it to get a precise response with custom data in a very convenient way. So that anyone can understand.

SOURCE DATA:

Let us understand how to feed the source data as it will deal with your website URL link.

The first data that we are going to talk about is the one that contains the hyperlink. Let us explore the sample here.

From the above diagram, one can easily understand that the application will interpret a unique hash number associated with a specific URL. This data will be used to look up the URL after the OpenAI response from the tuned model as a result of any user query.

Now, let us understand the actual source data.

If we closely check, we’ll see the source file contains two columns – prompt & completion. And the website reference is put inside the curly braces as shown – “{Hash Code that represents your URL}.”

During the response, the newly created library replaces the hash value with the correct URL after the successful lookup & presents the complete answer.

CODE:

Why don’t we go through the code made accessible due to this new library for this particular use case?

clsConfigClient.py (This is the main calling Python script for the input parameters.)

	################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 15-May-2020 ####
	#### Modified On: 21-Feb-2023 ####
	#### ####
	#### Objective: This script is a config ####
	#### file, contains all the keys for ####
	#### OpenAI fine-tune projects. ####
	#### ####
	################################################

	import os
	import platform as pl

	class clsConfigClient(object):
	Curr_Path = os.path.dirname(os.path.realpath(__file__))

	os_det = pl.system()
	if os_det == "Windows":
	sep = '\\'
	else:
	sep = '/'

	conf = {
	'APP_ID': 1,
	'ARCH_DIR': Curr_Path + sep + 'arch' + sep,
	'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,
	'LOG_PATH': Curr_Path + sep + 'log' + sep,
	'DATA_PATH': Curr_Path + sep + 'data' + sep,
	'TEMP_PATH': Curr_Path + sep + 'temp' + sep,
	'MODEL_DIR': 'model',
	'APP_DESC_1': 'ChatGPT Training!',
	'DEBUG_IND': 'N',
	'INIT_PATH': Curr_Path,
	'FILE_NAME': '2023-4-14-WP.csv',
	'LKP_FILE_NAME': 'HyperDetails.csv',
	'TEMP_FILE_NAME': 'chatGPTData.jsonl',
	'TITLE': "GPT-3 Training!",
	'PATH' : Curr_Path,
	'OUT_DIR': 'data',
	'OPEN_API_KEY': 'sk-hdhrujfrkfjfjfjfhjfjfisososT&jsdgL6KIxx',
	'MODEL_CD':'davinci',
	'URL': 'https://api.openai.com/v1/fine-tunes/',
	'EPOCH': 10,
	'SUFFIX': 'py-saty',
	'EXIT_KEYWORD': 'bye'
	}

view raw

clsConfigClient.py

hosted with ❤ by GitHub

Some of the important entries that will require later are as follows –

'FILE_NAME': '2023-4-14-WP.csv',
'LKP_FILE_NAME': 'HyperDetails.csv',
'OPEN_API_KEY': 'sk-hdhrujfrkfjfjfjfhjfjfisososT&jsdgL6KIxx',
'MODEL_CD':'davinci',
'URL': 'https://api.openai.com/v1/fine-tunes/',
'EXIT_KEYWORD': 'bye'

We’ll discuss these entries later.

trainChatGPTModel.py (This is the main calling Python script that will invoke the newly created fine-tune GPT-3 enabler.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 12-Feb-2023 ####
	#### Modified On 16-Feb-2023 ####
	#### ####
	#### Objective: This is the main calling ####
	#### python script that will invoke the ####
	#### newly created fine-tune GPT-3 enabler. ####
	#### ####
	#####################################################

	import pandas as p
	import clsL as cl
	from clsConfigClient import clsConfigClient as cf
	import datetime

	import clsTrainModel3 as tm

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	######################################
	### Get your global values ####
	######################################
	debug_ind = 'Y'
	#tModel = tm.clsTrainModel()
	tModel = tm.clsTrainModel3()

	# Initiating Logging Instances
	clog = cl.clsL()

	data_path = cf.conf['DATA_PATH']
	data_file_name = cf.conf['FILE_NAME']
	######################################
	#### Global Flag ########
	######################################

	######################################
	### Wrapper functions to invoke ###
	### the desired class from newly ###
	### built class. ###
	######################################

	######################################
	### End of wrapper functions. ###
	######################################

	def main():
	try:
	var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print(''120)
	print('Start Time: ' + str(var))
	print(''120)

	FullFileName = data_path + data_file_name

	r1 = tModel.trainModel(FullFileName)

	if r1 == 0:
	print('Successfully Trained!')
	else:
	print('Failed to Train!')

	#clog.logr(OutPutFileName, debug_ind, df, subdir)

	print(''120)
	var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('End Time: ' + str(var1))

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	if __name__ == "__main__":
	main()

view raw

trainChatGPTModel.py

hosted with ❤ by GitHub

Following are the key snippet from the above script –

data_path = cf.conf['DATA_PATH']
data_file_name = cf.conf['FILE_NAME']

And, then –

tModel = tm.clsTrainModel3()
FullFileName = data_path + data_file_name
r1 = tModel.trainModel(FullFileName)

As one can see, the package needs only the source data file to fine-tune GPT-3 model.

checkFineTuneChatGPTModelStat.py (This is the main Python script that will check the status of the tuned process that will happen inside the OpenAI-cloud environment.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 12-Feb-2023 ####
	#### Modified On 16-Feb-2023 ####
	#### ####
	#### Objective: This is the main calling ####
	#### python script that will invoke the ####
	#### newly created fine-tune job status inside ####
	#### the OpenAI environment. ####
	#####################################################

	import clsL as cl
	from clsConfigClient import clsConfigClient as cf
	import datetime

	import clsTestModel3 as tm

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	######################################
	### Get your global values ####
	######################################
	debug_ind = 'Y'

	# Initiating Logging Instances
	clog = cl.clsL()
	tmodel = tm.clsTestModel3()

	url_part = cf.conf['URL']
	open_api_key = cf.conf['OPEN_API_KEY']
	######################################
	#### Global Flag ########
	######################################

	def main():
	try:
	var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print(''120)
	print('Start Time: ' + str(var))
	print(''120)

	# Example usage
	input_text = str(input("Please provide the fine tune Id (Start with ft-*): "))
	url = url_part + input_text
	print('URL: ', url)

	r1 = tmodel.checkStat(url, open_api_key)

	if r1 == 0:
	print('Successfully checked the status of tuned GPT-3 model.')
	else:
	print('Failed to check the status of the tuned GPT-3 model.')

	print(''120)
	var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('End Time: ' + str(var1))

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	if __name__ == "__main__":
	main()

view raw

checkFineTuneChatGPTModelStat.py

hosted with ❤ by GitHub

To check the status of the fine-tuned job inside the OpenAI environment, one needs to provide the fine tune id, which generally starts with -> “ft-*.” One would get this value after the train script’s successful run.

Some of the other key snippets are –

tmodel = tm.clsTestModel3()

url_part = cf.conf['URL']
open_api_key = cf.conf['OPEN_API_KEY']

And, then –

input_text = str(input("Please provide the fine tune Id (Start with ft-*): "))
url = url_part + input_text
print('URL: ', url)

r1 = tmodel.checkStat(url, open_api_key)

The above snippet is self-explanatory as one is passing the fine tune id along with the OpenAI API key.

testChatGPTModel.py (This is the main testing Python script that will invoke the newly created fine-tune GPT-3 enabler to get a response with custom data.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 12-Feb-2023 ####
	#### Modified On 19-Apr-2023 ####
	#### ####
	#### Objective: This is the main calling ####
	#### python script that will invoke the ####
	#### newly created class that will test the ####
	#### tuned model output. ####
	#####################################################

	import clsL as cl
	from clsConfigClient import clsConfigClient as cf
	import datetime
	import pandas as p
	import clsTestModel3 as tm

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	######################################
	### Get your global values ####
	######################################
	debug_ind = 'Y'

	# Initiating Logging Instances
	clog = cl.clsL()
	tmodel = tm.clsTestModel3()

	open_api_key = cf.conf['OPEN_API_KEY']
	lkpDataPath = cf.conf['DATA_PATH']
	lkpFileName = cf.conf['LKP_FILE_NAME']

	######################################
	#### Global Flag ########
	######################################

	def main():
	try:
	var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('' 120)
	print('Start Time: ' + str(var))
	print('' 120)

	LookUpFileName = lkpDataPath + lkpFileName

	r1 = tmodel.testModel(LookUpFileName, open_api_key)

	if r1 == 0:
	print('Successfully tested the tuned GPT-3 model.')
	else:
	print('Failed to test the tuned GPT-3 model.')

	var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('End Time: ' + str(var1))

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	if __name__ == "__main__":
	main()

view raw

testChatGPTModel.py

hosted with ❤ by GitHub

Some of the key entries from the above snippet are as follows –

tmodel = tm.clsTestModel3()

open_api_key = cf.conf['OPEN_API_KEY']
lkpDataPath = cf.conf['DATA_PATH']
lkpFileName = cf.conf['LKP_FILE_NAME']

And, then –

LookUpFileName = lkpDataPath + lkpFileName
r1 = tmodel.testModel(LookUpFileName, open_api_key)

In the above lines, the application gets the correct URL value from the look file we’ve prepared for this specific use case.

deleteChatGPTModel.py (This is the main Python script that will delete the old intended tuned model, which is no longer needed.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 12-Feb-2023 ####
	#### Modified On 21-Feb-2023 ####
	#### ####
	#### Objective: This is the main calling ####
	#### python script that will invoke the ####
	#### newly created delete model methods for ####
	#### OpenAI. ####
	#####################################################

	import clsL as cl
	from clsConfigClient import clsConfigClient as cf
	import datetime
	import clsTestModel3 as tm

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	######################################
	### Get your global values ####
	######################################
	debug_ind = 'Y'

	# Initiating Logging Instances
	clog = cl.clsL()
	tmodel = tm.clsTestModel3()

	open_api_key = cf.conf['OPEN_API_KEY']

	######################################
	#### Global Flag ########
	######################################

	def main():
	try:
	var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('' 120)
	print('Start Time: ' + str(var))
	print('' 120)

	r1 = tmodel.delOldModel(open_api_key)

	if r1 == 0:
	print('Successfully checked the status of tuned GPT-3 model.')
	else:
	print('Failed to check the status of the tuned GPT-3 model.')

	var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('End Time: ' + str(var1))

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	if __name__ == "__main__":
	main()

view raw

deleteChatGPTModel.py

hosted with ❤ by GitHub

Some of the key snippets from the above scripts are –

tmodel = tm.clsTestModel3()

open_api_key = cf.conf['OPEN_API_KEY']

And, then –

r1 = tmodel.delOldModel(open_api_key)

We’ve demonstrated that using a straightforward method, one can delete any old tuned model from OpenAI that is no longer required.

KEY FEATURES TO CONSIDER DURING TUNING:

Data quality: Ensure that the data used for fine-tuning is clean, relevant, and representative of the domain you want the model to understand. Check for biases, inconsistencies, and errors in the dataset.
Overfitting: Be cautious of overfitting, which occurs when the model performs exceptionally well on the training data but poorly on unseen data. You can address overfitting by using regularization techniques, early stopping, or cross-validation.
Model size and resource requirements: GPT models can be resource-intensive. Be mindful of the hardware limitations and computational resources available when selecting the model size and the time and cost associated with training.
Hyperparameter tuning: Select appropriate hyperparameters for your fine-tuning processes, such as learning rate, batch size, and the number of epochs. Experiment with different combinations to achieve the best results without overfitting.
Evaluation metrics: Choose suitable evaluation metrics to assess the performance of your fine-tuned model. Consider using multiple metrics to understand your model’s performance comprehensively.
Ethical considerations: Be aware of potential biases in your dataset and how the model’s predictions might impact users. Address ethical concerns during the fine-tuning process and consider using techniques such as data augmentation or adversarial training to mitigate these biases.
Monitoring and maintenance: Continuously monitor the model’s performance after deployment, and be prepared to re-tune or update it as needed. Regular maintenance ensures that the model remains relevant and accurate.
Documentation: Document your tuning process, including the data used, model architecture, hyperparameters, and evaluation metrics. This factor will facilitate easier collaboration, replication, and model maintenance.
Cost: OpenAI fine-tuning can be extremely expensive, even for a small volume of data. Hence, organization-wise, one needs to be extremely careful while using this feature.

COST FACTOR:

Before we discuss the actual spending, let us understand the tested data volume to train & tune the model.

So, we’re talking about a total size of 500 KB (at max). And, we did 10 epochs during the training as you can see from the config file mentioned above.

So, it is pretty expensive. Use it wisely.

So, finally, we’ve done it.

I know that this post is relatively bigger than my earlier post. But, I think, you can get all the details once you go through it.

You will get the complete codebase in the following GitHub link.

I’ll bring some more exciting topics in the coming days from the Python verse. Please share & subscribe to my post & let me know your feedback.

Till then, Happy Avenging! 🙂

Tuning your model using the python-based low-code machine-learning library PyCaret

Posted on March 31, 2023March 31, 2023 by SatyakiDe in ai, Azure, call, cloud, code, computing, Crossplatform, Data Science, features, function, json, Keras, machine-learning, Model, numpy, Pandas, Performance, Python, Technology, video

Today, I’ll discuss another important topic before I will share the excellent use case next month, as I still need some time to finish that one. We’ll see how we can leverage the brilliant capability of a low-code machine-learning library named PyCaret.

But before going through the details, why don’t we view the demo & then go through it?

Demo

Architecture:

Let us understand the flow of events –

As one can see, the initial training requests are triggered from the PyCaret-driven training models. And the application can successfully process & identify the best models out of the other combinations.

Python Packages:

Following are the python packages that are necessary to develop this use case –

pip install pandas
pip install pycaret

PyCaret is dependent on a combination of other popular python packages. So, you need to install them successfully to run this package.

CODE:

clsConfigClient.py (Main configuration file)

	################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 15-May-2020 ####
	#### Modified On: 31-Mar-2023 ####
	#### ####
	#### Objective: This script is a config ####
	#### file, contains all the keys for ####
	#### personal AI-driven voice assistant. ####
	#### ####
	################################################

	import os
	import platform as pl

	class clsConfigClient(object):
	Curr_Path = os.path.dirname(os.path.realpath(__file__))

	os_det = pl.system()
	if os_det == "Windows":
	sep = '\\'
	else:
	sep = '/'

	conf = {
	'APP_ID': 1,
	'ARCH_DIR': Curr_Path + sep + 'arch' + sep,
	'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,
	'LOG_PATH': Curr_Path + sep + 'log' + sep,
	'DATA_PATH': Curr_Path + sep + 'data' + sep,
	'MODEL_PATH': Curr_Path + sep + 'model' + sep,
	'TEMP_PATH': Curr_Path + sep + 'temp' + sep,
	'MODEL_DIR': 'model',
	'APP_DESC_1': 'PyCaret Training!',
	'DEBUG_IND': 'N',
	'INIT_PATH': Curr_Path,
	'FILE_NAME': 'Titanic.csv',
	'MODEL_NAME': 'PyCaret-ft-personal-2023-03-31-04-29-53',
	'TITLE': "PyCaret Training!",
	'PATH' : Curr_Path,
	'OUT_DIR': 'data'
	}

view raw

clsConfigClient.py

hosted with ❤ by GitHub

I’m skipping this section as it is self-explanatory.

clsTrainModel.py (This is the main class that contains the core logic of low-code machine-learning library to evaluate the best model for your solutions.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 31-Mar-2023 ####
	#### Modified On 31-Mar-2023 ####
	#### ####
	#### Objective: This is the main class that ####
	#### contains the core logic of low-code ####
	#### machine-learning library to evaluate the ####
	#### best model for your solutions. ####
	#### ####
	#####################################################

	import clsL as cl
	from clsConfigClient import clsConfigClient as cf
	import datetime

	# Import necessary libraries
	import pandas as p
	from pycaret.classification import *

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	######################################
	### Get your global values ####
	######################################
	debug_ind = 'Y'

	# Initiating Logging Instances
	clog = cl.clsL()
	###############################################
	### End of Global Section ###
	###############################################


	class clsTrainModel:
	def __init__(self):
	self.model_path = cf.conf['MODEL_PATH']
	self.model_name = cf.conf['MODEL_NAME']

	def trainModel(self, FullFileName):
	try:
	df = p.read_csv(FullFileName)
	row_count = int(df.shape[0])
	print('Number of rows: ', str(row_count))

	print(df)

	# Initialize the setup in PyCaret
	clf_setup = setup(
	data=df,
	target="Survived",
	train_size=0.8, # 80% for training, 20% for testing
	categorical_features=["Sex", "Embarked"],
	ordinal_features={"Pclass": ["1", "2", "3"]},
	ignore_features=["Name", "Ticket", "Cabin", "PassengerId"],
	#silent=True, # Set to False for interactive setup
	)

	# Compare various models
	best_model = compare_models()

	# Create a specific model (e.g., Random Forest)
	rf_model = create_model("rf")

	# Hyperparameter tuning
	tuned_rf_model = tune_model(rf_model)

	# Evaluate model performance
	plot_model(tuned_rf_model, plot="confusion_matrix")
	plot_model(tuned_rf_model, plot="auc")

	# Finalize the model (train on the complete dataset)
	final_rf_model = finalize_model(tuned_rf_model)

	# Make predictions on new data
	new_data = df.drop("Survived", axis=1)
	predictions = predict_model(final_rf_model, data=new_data)

	# Writing into the Model
	FullModelName = self.model_path + self.model_name

	print('Model Output @:: ', str(FullModelName))
	print()

	# Save the fine-tuned model
	save_model(final_rf_model, FullModelName)

	return 0

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	return 1

view raw

clsTrainModel.py

hosted with ❤ by GitHub

Let us understand the code in simple terms –

Import necessary libraries and load the Titanic dataset.
Initialize the PyCaret setup, specifying the target variable, train-test split, categorical and ordinal features, and features to ignore.
Compare various models to find the best-performing one.
Create a specific model (Random Forest in this case).
Perform hyper-parameter tuning on the Random Forest model.
Evaluate the model’s performance using a confusion matrix and AUC-ROC curve.
Finalize the model by training it on the complete dataset.
Make predictions on new data.
Save the trained model for future use.

trainPYCARETModel.py (This is the main calling python script that will invoke the training class of PyCaret package.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 31-Mar-2023 ####
	#### Modified On 31-Mar-2023 ####
	#### ####
	#### Objective: This is the main calling ####
	#### python script that will invoke the ####
	#### training class of Pycaret package. ####
	#### ####
	#####################################################

	import clsL as cl
	from clsConfigClient import clsConfigClient as cf
	import datetime

	import clsTrainModel as tm

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	######################################
	### Get your global values ####
	######################################
	debug_ind = 'Y'

	# Initiating Logging Instances
	clog = cl.clsL()

	data_path = cf.conf['DATA_PATH']
	data_file_name = cf.conf['FILE_NAME']

	tModel = tm.clsTrainModel()

	######################################
	#### Global Flag ########
	######################################

	def main():
	try:
	var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print(''120)
	print('Start Time: ' + str(var))
	print(''120)

	FullFileName = data_path + data_file_name

	r1 = tModel.trainModel(FullFileName)

	if r1 == 0:
	print('Successfully Trained!')
	else:
	print('Failed to Train!')

	print(''120)
	var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('End Time: ' + str(var1))

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	if __name__ == "__main__":
	main()

view raw

trainPYCARETModel.py

hosted with ❤ by GitHub

The above code is pretty self-explanatory as well.

testPYCARETModel.py (This is the main calling python script that will invoke the testing script for PyCaret package.)

	#####################################################
	#### Written By: SATYAKI DE ####
	#### Written On: 31-Mar-2023 ####
	#### Modified On 31-Mar-2023 ####
	#### ####
	#### Objective: This is the main calling ####
	#### python script that will invoke the ####
	#### testing script for PyCaret package. ####
	#### ####
	#####################################################

	import clsL as cl
	from clsConfigClient import clsConfigClient as cf
	import datetime

	from pycaret.classification import load_model, predict_model

	import pandas as p

	# Disbling Warning
	def warn(args, *kwargs):
	pass

	import warnings
	warnings.warn = warn

	######################################
	### Get your global values ####
	######################################
	debug_ind = 'Y'

	# Initiating Logging Instances
	clog = cl.clsL()

	model_path = cf.conf['MODEL_PATH']
	model_name = cf.conf['MODEL_NAME']

	######################################
	#### Global Flag ########
	######################################

	def main():
	try:
	var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print(''120)
	print('Start Time: ' + str(var))
	print(''120)

	FullFileName = model_path + model_name

	# Load the saved model
	loaded_model = load_model(FullFileName)

	# Prepare new data for testing (make sure it has the same columns as the original data)
	new_data = p.DataFrame({
	"Pclass": [3, 1],
	"Sex": ["male", "female"],
	"Age": [22, 38],
	"SibSp": [1, 1],
	"Parch": [0, 0],
	"Fare": [7.25, 71.2833],
	"Embarked": ["S", "C"]
	})

	# Make predictions using the loaded model
	predictions = predict_model(loaded_model, data=new_data)

	# Display the predictions
	print(predictions)

	print(''120)
	var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
	print('End Time: ' + str(var1))

	except Exception as e:
	x = str(e)
	print('Error: ', x)

	if __name__ == "__main__":
	main()

view raw

testPYCARETModel.py

hosted with ❤ by GitHub

In this code, the application uses the stored model & then forecasts based on the optimized PyCaret model tuning.

Conclusion:

The above code demonstrates an end-to-end binary classification pipeline using the PyCaret library for the Titanic dataset. The goal is to predict whether a passenger survived based on the available features. Here are some conclusions you can draw from the code and data:

Ease of use: The code showcases how PyCaret simplifies the machine learning process, from data preprocessing to model training, evaluation, and deployment. With just a few lines of code, you can perform tasks that would require much more effort using lower-level libraries.
Model selection: The compare_models() function provides a quick and easy way to compare various machine learning algorithms and identify the best-performing one based on the chosen evaluation metric (accuracy by default). This selection helps you select a suitable model for the given problem.
Hyper-parameter tuning: The tune_model() function automates the process of hyper-parameter tuning to improve model performance. We tuned a Random Forest model to optimize its predictive power in the example.
Model evaluation: PyCaret provides several built-in visualization tools for assessing model performance. In the example, we used a confusion matrix and AUC-ROC curve to evaluate the performance of the tuned Random Forest model.
Model deployment: The example demonstrates how to make predictions using the trained model and save the model for future use. This deployment showcases how PyCaret can streamline the process of deploying a machine-learning model in a production environment.

It is important to note that the conclusions drawn from the code and data are specific to the Titanic dataset and the chosen features. Adjust the feature engineering, preprocessing, and model selection steps for different datasets or problems accordingly. However, the general workflow and benefits provided by PyCaret would remain the same.

So, finally, we’ve done it.

I know that this post is relatively bigger than my earlier post. But, I think, you can get all the details once you go through it.

You will get the complete codebase in the following GitHub link.

I’ll bring some more exciting topics in the coming days from the Python verse. Please share & subscribe to my post & let me know your feedback.

Till then, Happy Avenging! 🙂

	The LLM Security Chr… on The LLM Security Chronicles…
	AGENTIC AI IN THE EN… on AGENTIC AI IN THE ENTERPRISE:…
	AGENTIC AI IN THE EN… on AGENTIC AI IN THE ENTERPRISE:…
	AGENTIC AI IN THE EN… on AGENTIC AI IN THE ENTERPRISE:…
	AGENTIC AI IN THE EN… on Agentic AI in the Enterprise:…

Category: machine-learning

Creating a local LLM Cluster Server using Apple Silicon GPU

Like this:

Enabling & Exploring Stable Defussion – Part 3

1. `CLASS INSTANTIATE(pipe, output_path, filename)`

2. `_warmup`

3. `generate(prompt, num_inference_steps=12, guidance_scale=3.0)`

4. `genImage(prompt)`

1. `CLASS INSTANTIATE (pipeline)`

2. `generate_frames(pipeline, init_image, prompt, duration_seconds=10)`

3. `genVideo(prompt, inputImage, targetVideo, fps)`

Like this:

Validating source data against RAG-response using Open AI, GloVe, FAISS using Python

Like this:

Enable OpenAI chatbot with the selected YouTube video content using LangChain, FAISS & YouTube data-API.

Like this:

Demonstration of GPT-3 model tuning using Python for an upcoming PyPi-package

Like this:

Tuning your model using the python-based low-code machine-learning library PyCaret

Like this:

Share this:

Like this:

1. CLASS INSTANTIATE(pipe, output_path, filename)

2. _warmup

3. generate(prompt, num_inference_steps=12, guidance_scale=3.0)

4. genImage(prompt)

1. CLASS INSTANTIATE (pipeline)

2. generate_frames(pipeline, init_image, prompt, duration_seconds=10)

3. genVideo(prompt, inputImage, targetVideo, fps)

Share this:

Like this:

1. CLASS INSTANTIATION:

2. getPrompt2Video(prompt)

1. setup_gpu(force_cpu=False)

2. CLASS INSTANTIATION (force_cpu=False)

3. getTorchType

4. getText2ImagePipe(model_id, torchType)

5. getImage2VideoPipe(model_id, torchType)

Share this:

Like this:

Share this:

Like this:

Share this:

Like this:

Share this:

Like this:

Share this:

Like this:

Share this:

Like this:

Share this:

Like this:

Share this:

Like this:

1. `CLASS INSTANTIATE(pipe, output_path, filename)`

2. `_warmup`

3. `generate(prompt, num_inference_steps=12, guidance_scale=3.0)`

4. `genImage(prompt)`

1. `CLASS INSTANTIATE (pipeline)`

2. `generate_frames(pipeline, init_image, prompt, duration_seconds=10)`

3. `genVideo(prompt, inputImage, targetVideo, fps)`

1. `CLASS INSTANTIATION:`

2. `getPrompt2Video(prompt)`

1. `setup_gpu(force_cpu=False)`

2. `CLASS INSTANTIATION (force_cpu=False)`

3. `getTorchType`

4. `getText2ImagePipe(model_id, torchType)`

5. `getImage2VideoPipe(model_id, torchType)`