From 84fd1e5a2d90ae463bb15925cb737a7504e15bea Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 01:16:17 +0300
Subject: [PATCH 01/19] prepare for module-4

---
 course/module-3/cleaned_articles.json         |   1 +
 course/module-3/db/qdrant.py                  |  30 +-
 course/module-3/finetune_data/__init__.py     |   0
 .../finetune_data/example_content.json        |  10 +
 course/module-3/finetune_data/exceptions.py   |  14 +
 course/module-3/finetune_data/file_handler.py |  18 +
 .../module-3/finetune_data/generate_data.py   | 121 ++++
 .../finetune_data/llm_communicator.py         |  36 ++
 course/module-3/insert_data_mongo.py          |   9 +-
 course/module-3/poetry.lock                   | 584 +++++++++++++++++-
 course/module-3/pyproject.toml                |   3 +
 course/module-3/settings.py                   |   7 +
 12 files changed, 814 insertions(+), 19 deletions(-)
 create mode 100644 course/module-3/cleaned_articles.json
 create mode 100644 course/module-3/finetune_data/__init__.py
 create mode 100644 course/module-3/finetune_data/example_content.json
 create mode 100644 course/module-3/finetune_data/exceptions.py
 create mode 100644 course/module-3/finetune_data/file_handler.py
 create mode 100644 course/module-3/finetune_data/generate_data.py
 create mode 100644 course/module-3/finetune_data/llm_communicator.py

diff --git a/course/module-3/cleaned_articles.json b/course/module-3/cleaned_articles.json
new file mode 100644
index 0000000..da5625a
--- /dev/null
+++ b/course/module-3/cleaned_articles.json
@@ -0,0 +1 @@
+[{"instruction": "Write a detailed explanation about the benefits of using Change Data Capture (CDC) in building production-ready AI systems, emphasizing real-time data integration, efficient data pipelines, minimized system impact, and event-driven architectures.", "content": "Change Data Capture (CDC), commonly known as CDC, is an efficient way to track changes in a database. The purpose of CDC is to capture insertions, updates, and deletions applied to a database and to make this change data available in a format easily consumable by downstream applications. Why do we need CDC pattern? Real-time Data Syncing: CDC facilitates near-real-time data integration and syncing. Efficient Data Pipelines: It allows incremental data loading, which is more efficient than bulk load operations. Minimized System Impact: CDC minimizes the impact on the source system by reducing the need for performance-intensive queries. Event-Driven Architectures: It enables event-driven architectures by streaming database events."}, {"instruction": "Write a post highlighting the significance of integrating MLOps best practices in the development of real-world LLM systems, focusing on key components such as experiment trackers, model registries, prompt monitoring, and versioning.", "content": "The articles text starting from 'Along the 4 microservices' until 'Level: intermediate' line."}]
\ No newline at end of file
diff --git a/course/module-3/db/qdrant.py b/course/module-3/db/qdrant.py
index 2b4dd71..2c8f894 100644
--- a/course/module-3/db/qdrant.py
+++ b/course/module-3/db/qdrant.py
@@ -1,7 +1,8 @@
-import logger_utils
 from qdrant_client import QdrantClient
 from qdrant_client.http.exceptions import UnexpectedResponse
 from qdrant_client.http.models import Batch, Distance, VectorParams
+
+import logger_utils
 from settings import settings
 
 logger = logger_utils.get_logger(__name__)
@@ -13,10 +14,18 @@ class QdrantDatabaseConnector:
     def __init__(self):
         if self._instance is None:
             try:
-                self._instance = QdrantClient(
-                    host=settings.QDRANT_DATABASE_HOST,
-                    port=settings.QDRANT_DATABASE_PORT,
-                )
+                if settings.QDRANT_CLOUD:
+                    print("aici")
+                    self._instance = QdrantClient(
+                        url=settings.QDRANT_CLOUD_URL,
+                        api_key=settings.QDRANT_APIKEY,
+                    )
+                else:
+                    self._instance = QdrantClient(
+                        host=settings.QDRANT_DATABASE_HOST,
+                        port=settings.QDRANT_DATABASE_PORT,
+                    )
+
             except UnexpectedResponse:
                 logger.exception(
                     "Couldn't connect to the database.",
@@ -30,16 +39,12 @@ def get_collection(self, collection_name: str):
         return self._instance.get_collection(collection_name=collection_name)
 
     def create_non_vector_collection(self, collection_name: str):
-        self._instance.create_collection(
-            collection_name=collection_name, vectors_config={}
-        )
+        self._instance.create_collection(collection_name=collection_name, vectors_config={})
 
     def create_vector_collection(self, collection_name: str):
         self._instance.create_collection(
             collection_name=collection_name,
-            vectors_config=VectorParams(
-                size=settings.EMBEDDING_SIZE, distance=Distance.COSINE
-            ),
+            vectors_config=VectorParams(size=settings.EMBEDDING_SIZE, distance=Distance.COSINE),
         )
 
     def write_data(self, collection_name: str, points: Batch):
@@ -50,6 +55,9 @@ def write_data(self, collection_name: str, points: Batch):
 
             raise
 
+    def scroll(self, collection_name: str, limit: int):
+        return self._instance.scroll(collection_name=collection_name, limit=limit)
+
     def close(self):
         if self._instance:
             self._instance.close()
diff --git a/course/module-3/finetune_data/__init__.py b/course/module-3/finetune_data/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/course/module-3/finetune_data/example_content.json b/course/module-3/finetune_data/example_content.json
new file mode 100644
index 0000000..1d3230b
--- /dev/null
+++ b/course/module-3/finetune_data/example_content.json
@@ -0,0 +1,10 @@
+[
+    {
+      "instruction": "Write a post presenting a list of essential ML monitoring metrics, aiming to educate followers on effective monitoring practices.",
+      "content": "I compiled a list of the top 10 metrics your ML monitoring system should track.\n.\n#𝟏 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐦𝐞𝐭𝐫𝐢𝐜𝐬\nUsed to monitor the health of your overall software system.\n➝ latency\n➝ throughput\n➝ CPU/GPU utilization\n➝ memory utilization\n➝ the number of requests your model receives in the last X minutes/hours/days\n➝ the number of successful requests\n#𝟐 𝐌𝐋-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐦𝐞𝐭𝐫𝐢𝐜𝐬\nUsed to monitor the performance of your ML system.\n➝ accuracy (extremely useful when you have feedback or natural labels from the user: click rate, upvote, downvote, purchases, bookmarks, views, etc.)\n➝ predictions (as predictions are low dimensional various statistics are easy to compute + the distribution of the prediction represents a proxy for the input distribution)\n➝ features (feature validation + two-sample tests for drift detection)\n➝ raw inputs (these are harder to monitor due to their scattered nature within the infrastructure)\n-----\n💡 My goal is to make\nhashtag\n#\nml\neasy and intuitive. Follow me for daily insights about\nhashtag\n#\ndata\n,\nhashtag\n#\nml\n, and\nhashtag\n#\nmlops\n."
+    },
+    {
+      "instruction": "Explain the benefits of using the walrus operator in Python for writing cleaner code.",
+      "content": "Here is some 𝗣𝘆𝘁𝗵𝗼𝗻 𝘀𝘆𝗻𝘁𝗮𝘅 𝘀𝘂𝗴𝗮𝗿 that will help you 𝘄𝗿𝗶𝘁𝗲 𝗰𝗹𝗲𝗮𝗻𝗲𝗿 𝗰𝗼𝗱𝗲 ↓ \n\nI am talking about the 𝘸𝘢𝘭𝘳𝘶𝘴 𝘰𝘱𝘦𝘳𝘢𝘵𝘰𝘳 denoted by the `:=` symbol. \n\nIt was introduced in Python 3.8, but I rarely see it used. \n\nThus, as a \"clean code\" freak, I wanted to dedicate a post to it. \n\n𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝘄𝗮𝗹𝗿𝘂𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿 𝗱𝗼? \n\nIt's an assignment expression that allows you to assign and return a value in the same expression. \n\n𝗪𝗵𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝘁? \n\n𝘊𝘰𝘯𝘤𝘪𝘴𝘦𝘯𝘦𝘴𝘴: It reduces the number of lines needed for variable assignment and checking, making code more concise. \n\n𝘙𝘦𝘢𝘥𝘢𝘣𝘪𝘭𝘪𝘵𝘺: It can enhance readability by keeping related logic close, although this depends on the context and the reader's familiarity with exotic Python syntax. \n\n𝙃𝙚𝙧𝙚 𝙖𝙧𝙚 𝙨𝙤𝙢𝙚 𝙚𝙭𝙖𝙢𝙥𝙡𝙚𝙨 \n\n↓↓↓ \n\n1. Using the walrus operator, you can directly assign the result of the 𝘭𝘦𝘯() function inside an if statement. \n\n2. Avoid calling the same function twice in a while loop. The benefit is less code and makes everything more readable. \n\n3. Another use case arises in list comprehensions where a value computed in a filtering condition is also needed in the expression body. Before the 𝘸𝘢𝘭𝘳𝘶𝘴 𝘰𝘱𝘦𝘳𝘢𝘵𝘰𝘳, if you had to apply a function to an item from a list and filter it based on some criteria, you had to refactor it to a standard for loop. \n\n. \n\nWhen writing clean code, the detail matters. \n\nThe details make the difference between a codebase that can be read like a book or one with 10 WTFs / seconds. \n\n. \n\nWhat do you think? Does the walrus operator make the Python code more readable and concise? \n\nhashtag\n#machinelearning \nhashtag\n#mlops \nhashtag\n#datascience \n"
+    }
+  ]
\ No newline at end of file
diff --git a/course/module-3/finetune_data/exceptions.py b/course/module-3/finetune_data/exceptions.py
new file mode 100644
index 0000000..0edca09
--- /dev/null
+++ b/course/module-3/finetune_data/exceptions.py
@@ -0,0 +1,14 @@
+class DatasetError(Exception):
+    pass
+
+
+class FileNotFoundError(DatasetError):
+    pass
+
+
+class JSONDecodeError(DatasetError):
+    pass
+
+
+class APICommunicationError(DatasetError):
+    pass
diff --git a/course/module-3/finetune_data/file_handler.py b/course/module-3/finetune_data/file_handler.py
new file mode 100644
index 0000000..1c01929
--- /dev/null
+++ b/course/module-3/finetune_data/file_handler.py
@@ -0,0 +1,18 @@
+import json
+
+from finetune_data.exceptions import JSONDecodeError
+
+
+class FileHandler:
+    def read_json(self, filename: str) -> list:
+        try:
+            with open(filename, "r") as file:
+                return json.load(file)
+        except FileNotFoundError:
+            raise FileNotFoundError(f"The file '{filename}' does not exist.")
+        except json.JSONDecodeError:
+            raise JSONDecodeError(f"The file '{filename}' is not properly formatted as JSON.")
+
+    def write_json(self, filename: str, data: list):
+        with open(filename, "w") as file:
+            json.dump(data, file, indent=4)
diff --git a/course/module-3/finetune_data/generate_data.py b/course/module-3/finetune_data/generate_data.py
new file mode 100644
index 0000000..fc346d2
--- /dev/null
+++ b/course/module-3/finetune_data/generate_data.py
@@ -0,0 +1,121 @@
+import json
+import logging
+
+from comet_ml import Artifact, Experiment
+
+from db.qdrant import connection as client
+from finetune_data.file_handler import FileHandler
+from finetune_data.llm_communicator import GptCommunicator
+from settings import settings
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+data_type = "articles"
+USER_PROMPT = (
+    f"In the following rows I will give you 2 json objects as example, each of them having an instruction which "
+    f"describes what to write about and the corresponding {data_type} content that follows this instruction. Afterwards I "
+    f"will give you batches of other contents of {data_type} and please generate me one instruction for each of them. The {data_type} text "
+    f"for which you have to generate the instructions is under Content number x lines. Please structure them in json format,"
+    f"ready to be loaded in json,a list of objects only with fields called instruction ( the generated part ) and content ( the provided part ). "
+    f"Please do not add any extra characters!\n"
+)
+
+
+class DataFormatter:
+
+    @classmethod
+    def format_data(cls, data_points: list, is_example: bool) -> str:
+        text = ""
+        for index, data_point in enumerate(data_points):
+            if not is_example:
+                text += f"Content number {index + 1}\n"
+            text += str(data_point) + "\n"
+        return text
+
+    @classmethod
+    def format_batch(cls, context_msg: str, data_points: list) -> str:
+        delimiter_msg = context_msg
+        delimiter_msg += cls.format_data(data_points, False)
+        return delimiter_msg
+
+    @classmethod
+    def format_initial_prompt(cls, example_content: list, inference_posts: list):
+        initial_prompt = (
+            USER_PROMPT  # Assuming USER_PROMPT is defined somewhere as a global constant or class attribute
+        )
+        initial_prompt += f"You must generate exactly a list of {len(inference_posts)} json objects, using the contents provided under CONTENTS FOR GENERATION, the EXAMPLE JSONS are just for example purpose.\n"
+        initial_prompt += "EXAMPLE JSONS:\n"
+        initial_prompt += cls.format_data(example_content, True)
+        initial_prompt += cls.format_batch("CONTENTS FOR GENERATION: \n", inference_posts)
+        return initial_prompt
+
+
+class DatasetGenerator:
+    def __init__(self, file_handler: FileHandler, api_communicator: GptCommunicator, data_formatter: DataFormatter):
+        self.file_handler = file_handler
+        self.api_communicator = api_communicator
+        self.data_formatter = data_formatter
+
+    def generate_training_data(self, example_file: str, collection_name: str, batch_size: int = 1):
+        example_content = self.file_handler.read_json(example_file)
+        all_contents = self.fetch_all_cleaned_content(collection_name)
+        response = []
+        for i in range(0, len(all_contents), batch_size):
+            batch = all_contents[i : i + batch_size]
+            initial_prompt = data_formatter.format_initial_prompt(example_content, batch)
+            response += self.api_communicator.send_prompt(initial_prompt)
+
+        self.push_to_comet(response, collection_name)
+
+    def push_to_comet(self, data: list, collection_name: str):
+        try:
+            logging.info(f"Starting to push data to Comet: {collection_name}")
+
+            # Assuming the settings module has been properly configured with the required attributes
+            experiment = Experiment(
+                api_key=settings.COMET_API_KEY, project_name=settings.COMET_PROJECT, workspace=settings.COMET_WORKSPACE
+            )
+
+            file_name = f"{collection_name}.json"
+            logging.info(f"Writing data to file: {file_name}")
+
+            with open(file_name, "w") as f:
+                json.dump(data, f)
+
+            logging.info("Data written to file successfully")
+
+            artifact = Artifact(collection_name)
+            artifact.add(file_name)
+            logging.info(f"Artifact created and file added: {file_name}")
+
+            experiment.log_artifact(artifact)
+            experiment.end()
+            logging.info("Data pushed to Comet successfully and experiment ended")
+
+        except Exception as e:
+            logging.error(f"Failed to push data to Comet: {e}", exc_info=True)
+
+    def fetch_all_cleaned_content(self, collection_name: str) -> list:
+        all_cleaned_contents = []
+
+        scroll_response = client.scroll(collection_name=collection_name, limit=10000)
+        points = scroll_response[0]
+
+        for point in points:
+            cleaned_content = point.payload["cleaned_content"]
+            if cleaned_content:
+                all_cleaned_contents.append(cleaned_content)
+
+        return all_cleaned_contents
+
+
+if __name__ == "__main__":
+    example_file = (
+        "/Users/vesaalexandru/Workspaces/decodeML/llm-twin-course/course/module-3/finetune_data/example_content.json"
+    )
+    collection_name = "cleaned_posts"
+    file_handler = FileHandler()
+    api_communicator = GptCommunicator()
+    data_formatter = DataFormatter()
+    dataset_generator = DatasetGenerator(file_handler, api_communicator, data_formatter)
+    dataset_generator.generate_training_data(example_file=example_file, collection_name=collection_name, batch_size=1)
diff --git a/course/module-3/finetune_data/llm_communicator.py b/course/module-3/finetune_data/llm_communicator.py
new file mode 100644
index 0000000..c1d8b3d
--- /dev/null
+++ b/course/module-3/finetune_data/llm_communicator.py
@@ -0,0 +1,36 @@
+import json
+
+from openai import OpenAI
+
+from finetune_data.exceptions import APICommunicationError
+from settings import settings
+
+MAX_LENGTH = 16384
+SYSTEM_PROMPT = "You are a technical writer handing someone's account to post about AI and MLOps."
+
+
+class GptCommunicator:
+    def __init__(self, gpt_model: str = "gpt-3.5-turbo"):
+        self.api_key = settings.OPENAI_API_KEY
+        self.gpt_model = gpt_model
+
+    def send_prompt(self, prompt: str) -> list:
+        try:
+            client = OpenAI(api_key=self.api_key)
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt[:MAX_LENGTH]},
+                ],
+                model=self.gpt_model,
+            )
+            response = chat_completion.choices[0].message.content
+            return json.loads(self.clean_response(response))
+        except Exception as e:
+            raise APICommunicationError(f"An error occurred while communicating with API: {e}")
+
+    @staticmethod
+    def clean_response(response: str) -> str:
+        start_index = response.find("[")
+        end_index = response.rfind("]")
+        return response[start_index : end_index + 1]
diff --git a/course/module-3/insert_data_mongo.py b/course/module-3/insert_data_mongo.py
index 03eb85a..4f52966 100644
--- a/course/module-3/insert_data_mongo.py
+++ b/course/module-3/insert_data_mongo.py
@@ -2,6 +2,7 @@
 from pathlib import Path
 
 import gdown
+
 import logger_utils
 from db.documents import ArticleDocument, PostDocument, RepositoryDocument
 
@@ -66,9 +67,7 @@ def insert_posts(file_name: str, author_id: str) -> None:
         posts: dict[str, dict] = json.load(file)
 
     for post_content in posts.values():
-        PostDocument(
-            platform="linkedin", content=post_content, author_id=author_id
-        ).save()
+        PostDocument(platform="linkedin", content=post_content, author_id=author_id).save()
 
     logger.info("Posts inserted into collection", num=len(posts), author_id=author_id)
 
@@ -85,9 +84,7 @@ def insert_articles(file_name: str, author_id: str) -> None:
             author_id=author_id,
         ).save()
 
-    logger.info(
-        "Articles inserted into collection", num=len(articles), author_id=author_id
-    )
+    logger.info("Articles inserted into collection", num=len(articles), author_id=author_id)
 
 
 def insert_repositories(file_name: str, author_id: str) -> None:
diff --git a/course/module-3/poetry.lock b/course/module-3/poetry.lock
index 1b61a23..54d0e43 100644
--- a/course/module-3/poetry.lock
+++ b/course/module-3/poetry.lock
@@ -399,6 +399,48 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "comet-ml"
+version = "3.41.0"
+description = "Supercharging Machine Learning"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "comet_ml-3.41.0-py3-none-any.whl", hash = "sha256:bdbb4ab0097567eed9d89a9e14372d7623993a64b330adc6c74a0ea9de379167"},
+]
+
+[package.dependencies]
+dulwich = {version = ">=0.20.6,<0.20.33 || >0.20.33", markers = "python_version >= \"3.0\""}
+everett = {version = ">=1.0.1,<3.2.0", extras = ["ini"]}
+jsonschema = ">=2.6.0,<3.1.0 || >3.1.0"
+psutil = ">=5.6.3"
+python-box = "<7.0.0"
+requests = ">=2.18.4"
+requests-toolbelt = ">=0.8.0"
+rich = {version = ">=13.3.2", markers = "python_version >= \"3.7.0\""}
+semantic-version = ">=2.8.0"
+sentry-sdk = ">=1.1.0"
+simplejson = "*"
+six = "*"
+urllib3 = ">=1.21.1"
+websocket-client = ">=0.55.0,<1.4.0"
+wrapt = ">=1.11.2"
+wurlitzer = ">=1.0.2"
+
+[[package]]
+name = "configobj"
+version = "5.0.8"
+description = "Config file reading, writing and validation."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "configobj-5.0.8-py2.py3-none-any.whl", hash = "sha256:a7a8c6ab7daade85c3f329931a807c8aee750a2494363934f8ea84d8a54c87ea"},
+    {file = "configobj-5.0.8.tar.gz", hash = "sha256:6f704434a07dc4f4dc7c9a745172c1cad449feb548febd9f7fe362629c627a97"},
+]
+
+[package.dependencies]
+six = "*"
+
 [[package]]
 name = "dataclasses-json"
 version = "0.6.4"
@@ -460,6 +502,71 @@ idna = ["idna (>=3.6)"]
 trio = ["trio (>=0.23)"]
 wmi = ["wmi (>=1.5.1)"]
 
+[[package]]
+name = "dulwich"
+version = "0.22.1"
+description = "Python Git Library"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "dulwich-0.22.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:892914dc2e80403d16e59a3b440044f6092fde5ffd4ec1fdf36d6ff20a8e624d"},
+    {file = "dulwich-0.22.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b03092399f0f5d3e112405b890128afdb9e1f203eafb812f5d9105b0f5fac9d4"},
+    {file = "dulwich-0.22.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a2c790517ed884bc1b1590806222ab44989eeb7e7f14dfa915561c7ee3b9ac73"},
+    {file = "dulwich-0.22.1-cp310-cp310-win32.whl", hash = "sha256:524d3497a86f79959c9c1d729715d60d8171ed3f71621d45afb4faee5a47e8a1"},
+    {file = "dulwich-0.22.1-cp310-cp310-win_amd64.whl", hash = "sha256:d3146843b972f744aed551e8ac9fac5714baa864393e480586d467b7b4488426"},
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+
+[package.extras]
+fastimport = ["fastimport"]
+https = ["urllib3 (>=1.24.1)"]
+paramiko = ["paramiko"]
+pgp = ["gpg"]
+
 [[package]]
 name = "emoji"
 version = "2.10.1"
@@ -474,6 +581,24 @@ files = [
 [package.extras]
 dev = ["coverage", "coveralls", "pytest"]
 
+[[package]]
+name = "everett"
+version = "3.1.0"
+description = "Configuration library for Python applications"
+optional = false
+python-versions = "*"
+files = [
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+]
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+[package.dependencies]
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+
+[package.extras]
+ini = ["configobj"]
+yaml = ["PyYAML"]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.1"
@@ -1000,6 +1125,17 @@ files = [
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 ]
 
+[[package]]
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+version = "1.3.5"
+description = "Install packages from within code"
+optional = false
+python-versions = ">=2.7, >=3.5"
+files = [
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+]
+
 [[package]]
 name = "instructorembedding"
 version = "1.0.1"
@@ -1091,6 +1227,41 @@ files = [
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 ]
 
+[[package]]
+name = "jsonschema"
+version = "4.22.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
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+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rpds-py = ">=0.7.1"
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2023.12.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
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+
 [[package]]
 name = "langchain"
 version = "0.1.16"
@@ -2012,6 +2183,17 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
+[[package]]
+name = "pip"
+version = "24.0"
+description = "The PyPA recommended tool for installing Python packages."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
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 version = "2.8.2"
@@ -2051,6 +2233,34 @@ files = [
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 ]
 
+[[package]]
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+description = "Cross-platform lib for process and system monitoring in Python."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
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+
+[package.extras]
+test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+
 [[package]]
 name = "pydantic"
 version = "2.7.0"
@@ -2288,6 +2498,41 @@ files = [
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 ]
 
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+version = "6.1.0"
+description = "Advanced Python dictionaries with dot notation access"
+optional = false
+python-versions = ">=3.7"
+files = [
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+tomli = ["tomli", "tomli-w"]
+yaml = ["ruamel.yaml (>=0.17)"]
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 version = "2.8.2"
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 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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+    {file = "simplejson-3.19.2-py3-none-any.whl", hash = "sha256:bcedf4cae0d47839fee7de344f96b5694ca53c786f28b5f773d4f0b265a159eb"},
+    {file = "simplejson-3.19.2.tar.gz", hash = "sha256:9eb442a2442ce417801c912df68e1f6ccfcd41577ae7274953ab3ad24ef7d82c"},
+]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3645,6 +4198,22 @@ brotli = ["brotli (==1.0.9)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotl
 secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
 socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
+[[package]]
+name = "websocket-client"
+version = "1.3.3"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "websocket-client-1.3.3.tar.gz", hash = "sha256:d58c5f284d6a9bf8379dab423259fe8f85b70d5fa5d2916d5791a84594b122b1"},
+    {file = "websocket_client-1.3.3-py3-none-any.whl", hash = "sha256:5d55652dc1d0b3c734f044337d929aaf83f4f9138816ec680c1aefefb4dc4877"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
 [[package]]
 name = "wrapt"
 version = "1.16.0"
@@ -3724,6 +4293,17 @@ files = [
     {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
 ]
 
+[[package]]
+name = "wurlitzer"
+version = "3.1.0"
+description = "Capture C-level output in context managers"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "wurlitzer-3.1.0-py3-none-any.whl", hash = "sha256:a20994e53681fea8fb9d16dc05ff5f9f9701f09723d894456a6d6b29cfb3188d"},
+    {file = "wurlitzer-3.1.0.tar.gz", hash = "sha256:b31d3b18ab6b8394b3adb8b21841ad6d4b6bb195b9b642953872dbbab4c29b2a"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -3830,4 +4410,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10, <3.12"
-content-hash = "83fa709f8e76ab0bccacf1218949fff2030a6e70c595ddb2a1bcecd20961cfa7"
+content-hash = "4dfbb782dd8c15cdd86b9384af79b2f660c743e65e5d16883d8aa6f5aadb2191"
diff --git a/course/module-3/pyproject.toml b/course/module-3/pyproject.toml
index e2aed86..db34828 100644
--- a/course/module-3/pyproject.toml
+++ b/course/module-3/pyproject.toml
@@ -28,6 +28,9 @@ gdown = "^5.1.0"
 pymongo = "^4.7.1"
 structlog = "^24.1.0"
 rich = "^13.7.1"
+pip = "^24.0"
+install = "^1.3.5"
+comet-ml = "^3.41.0"
 
 
 [build-system]
diff --git a/course/module-3/settings.py b/course/module-3/settings.py
index d6c7c39..fa14599 100644
--- a/course/module-3/settings.py
+++ b/course/module-3/settings.py
@@ -22,6 +22,8 @@ class AppSettings(BaseSettings):
     QDRANT_DATABASE_PORT: int = 6333
     QDRANT_DATABASE_URL: str = "http://localhost:6333"
 
+    QDRANT_CLOUD_URL: str = "https://9f7d031c-7c76-4dbb-9c68-481a898d23e9.europe-west3-0.gcp.cloud.qdrant.io"
+    QDRANT_CLOUD: bool = True
     QDRANT_APIKEY: str | None = None
 
     # MQ config
@@ -30,5 +32,10 @@ class AppSettings(BaseSettings):
     RABBITMQ_HOST: str = "localhost"
     RABBITMQ_PORT: int = 5673
 
+    # CometML config
+    COMET_API_KEY: str | None = None
+    COMET_WORKSPACE: str | None = None
+    COMET_PROJECT: str | None = None
+
 
 settings = AppSettings()

From 40e42320a1ceade7892e5eec767b5fe7293e11cc Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 01:27:43 +0300
Subject: [PATCH 02/19] module-4 integrated in module-3. Need to be tested

---
 course/module-3/Makefile                                   | 6 +++++-
 course/module-3/cleaned_articles.json                      | 1 -
 course/module-3/{finetune_data => finetuning}/__init__.py  | 0
 .../{finetune_data => finetuning}/example_content.json     | 0
 .../module-3/{finetune_data => finetuning}/exceptions.py   | 0
 .../module-3/{finetune_data => finetuning}/file_handler.py | 0
 .../{finetune_data => finetuning}/generate_data.py         | 7 ++++---
 .../llm_communication.py}                                  | 0
 8 files changed, 9 insertions(+), 5 deletions(-)
 delete mode 100644 course/module-3/cleaned_articles.json
 rename course/module-3/{finetune_data => finetuning}/__init__.py (100%)
 rename course/module-3/{finetune_data => finetuning}/example_content.json (100%)
 rename course/module-3/{finetune_data => finetuning}/exceptions.py (100%)
 rename course/module-3/{finetune_data => finetuning}/file_handler.py (100%)
 rename course/module-3/{finetune_data => finetuning}/generate_data.py (94%)
 rename course/module-3/{finetune_data/llm_communicator.py => finetuning/llm_communication.py} (100%)

diff --git a/course/module-3/Makefile b/course/module-3/Makefile
index 349e9d4..db834c6 100644
--- a/course/module-3/Makefile
+++ b/course/module-3/Makefile
@@ -16,5 +16,9 @@ local-insert-data-mongo: #Insert data to mongodb
 local-bytewax: # Run bytewax pipeline
 	RUST_BACKTRACE=full poetry run python -m bytewax.run data_flow/bytewax_pipeline 
 
+generate-dataset: # Generate dataset for finetuning and version it in CometML
+	poetry run python finetuning/generate_data.py
+
 local-test-retriever: # Test retriever
-	poetry run python retriever.py
\ No newline at end of file
+	poetry run python retriever.py
+
diff --git a/course/module-3/cleaned_articles.json b/course/module-3/cleaned_articles.json
deleted file mode 100644
index da5625a..0000000
--- a/course/module-3/cleaned_articles.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"instruction": "Write a detailed explanation about the benefits of using Change Data Capture (CDC) in building production-ready AI systems, emphasizing real-time data integration, efficient data pipelines, minimized system impact, and event-driven architectures.", "content": "Change Data Capture (CDC), commonly known as CDC, is an efficient way to track changes in a database. The purpose of CDC is to capture insertions, updates, and deletions applied to a database and to make this change data available in a format easily consumable by downstream applications. Why do we need CDC pattern? Real-time Data Syncing: CDC facilitates near-real-time data integration and syncing. Efficient Data Pipelines: It allows incremental data loading, which is more efficient than bulk load operations. Minimized System Impact: CDC minimizes the impact on the source system by reducing the need for performance-intensive queries. Event-Driven Architectures: It enables event-driven architectures by streaming database events."}, {"instruction": "Write a post highlighting the significance of integrating MLOps best practices in the development of real-world LLM systems, focusing on key components such as experiment trackers, model registries, prompt monitoring, and versioning.", "content": "The articles text starting from 'Along the 4 microservices' until 'Level: intermediate' line."}]
\ No newline at end of file
diff --git a/course/module-3/finetune_data/__init__.py b/course/module-3/finetuning/__init__.py
similarity index 100%
rename from course/module-3/finetune_data/__init__.py
rename to course/module-3/finetuning/__init__.py
diff --git a/course/module-3/finetune_data/example_content.json b/course/module-3/finetuning/example_content.json
similarity index 100%
rename from course/module-3/finetune_data/example_content.json
rename to course/module-3/finetuning/example_content.json
diff --git a/course/module-3/finetune_data/exceptions.py b/course/module-3/finetuning/exceptions.py
similarity index 100%
rename from course/module-3/finetune_data/exceptions.py
rename to course/module-3/finetuning/exceptions.py
diff --git a/course/module-3/finetune_data/file_handler.py b/course/module-3/finetuning/file_handler.py
similarity index 100%
rename from course/module-3/finetune_data/file_handler.py
rename to course/module-3/finetuning/file_handler.py
diff --git a/course/module-3/finetune_data/generate_data.py b/course/module-3/finetuning/generate_data.py
similarity index 94%
rename from course/module-3/finetune_data/generate_data.py
rename to course/module-3/finetuning/generate_data.py
index fc346d2..cf3b19f 100644
--- a/course/module-3/finetune_data/generate_data.py
+++ b/course/module-3/finetuning/generate_data.py
@@ -5,7 +5,7 @@
 
 from db.qdrant import connection as client
 from finetune_data.file_handler import FileHandler
-from finetune_data.llm_communicator import GptCommunicator
+from finetune_data.llm_communication import GptCommunicator
 from settings import settings
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
@@ -113,9 +113,10 @@ def fetch_all_cleaned_content(self, collection_name: str) -> list:
     example_file = (
         "/Users/vesaalexandru/Workspaces/decodeML/llm-twin-course/course/module-3/finetune_data/example_content.json"
     )
-    collection_name = "cleaned_posts"
+    collection_names = ["cleaned_articles", "cleaned_posts"]
     file_handler = FileHandler()
     api_communicator = GptCommunicator()
     data_formatter = DataFormatter()
     dataset_generator = DatasetGenerator(file_handler, api_communicator, data_formatter)
-    dataset_generator.generate_training_data(example_file=example_file, collection_name=collection_name, batch_size=1)
+    for collection in collection_names:
+        dataset_generator.generate_training_data(example_file=example_file, collection_name=collection, batch_size=1)
diff --git a/course/module-3/finetune_data/llm_communicator.py b/course/module-3/finetuning/llm_communication.py
similarity index 100%
rename from course/module-3/finetune_data/llm_communicator.py
rename to course/module-3/finetuning/llm_communication.py

From 1f3613cac0f7b7cdfe783a548eefb50c8e646f5c Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 02:07:54 +0300
Subject: [PATCH 03/19] added readme file

---
 course/module-3/README.md    | 205 ++++++++++++++++++++++++++++++++++-
 course/module-3/db/qdrant.py |   1 -
 2 files changed, 204 insertions(+), 2 deletions(-)

diff --git a/course/module-3/README.md b/course/module-3/README.md
index 2fd9f95..f78e22b 100644
--- a/course/module-3/README.md
+++ b/course/module-3/README.md
@@ -1 +1,204 @@
-TBD
\ No newline at end of file
+# Module 3: RAG
+TBD
+
+
+# Module 4: Generate Data for LLM finetuning task
+
+# Introduction
+This Python module automates the generation of datasets specifically formatted for training and fine-tuning Large Language Models (LLMs). It interfaces with Qdrant, sends structured prompts to LLMs, and manages data with Comet ML for experiment tracking and artifact logging.
+
+### Why is fine-tuning important?
+1. **Model Customization**: Tailors the LLM's responses to specific domains or tasks.
+2. **Improved Accuracy**: Enhances the model's understanding of nuanced language used in specialized fields.
+3. **Efficiency**: Reduces the need for extensive post-processing by producing more relevant outputs directly.
+4. **Adaptability**: Allows models to continuously learn from new data, staying relevant as language and contexts evolve.
+
+
+# Module Structure
+### File Handling
+- `file_handler.py`: Manages file I/O operations, enabling reading and writing of JSON formatted data.
+
+### LLM Communication
+- `llm_communication.py`: Handles communication with OpenAI's LLMs, sending prompts and processing responses.
+
+### Data Generation
+- `generate_data.py`: Orchestrates the generation of training data by integrating file handling, LLM communication, and data formatting.
+
+
+# Installation and Setup
+To prepare your environment for this module, follow these steps:
+- `poetry init`
+- `poetry install`
+
+# Docker Settings
+### Host Configuration
+To ensure that your Docker containers can communicate with each other you need to update your `/etc/hosts` file. 
+Add the following entries to map the hostnames to your local machine:
+
+```plaintext
+# Docker MongoDB Hosts Configuration
+127.0.0.1       mongo1
+127.0.0.1       mongo2
+127.0.0.1       mongo3
+```
+
+# CometML Integration
+
+## Overview
+[CometML](https://www.comet.ml/) is a cloud-based platform that provides tools for tracking, comparing, explaining, and optimizing experiments and models in machine learning. CometML helps data scientists and teams to better manage and collaborate on machine learning experiments.
+
+## Why Use CometML?
+- **Experiment Tracking**: CometML automatically tracks your code, experiments, and results, allowing you to compare between different runs and configurations visually.
+- **Model Optimization**: It offers tools to compare different models side by side, analyze hyperparameters, and track model performance across various metrics.
+- **Collaboration and Sharing**: Share findings and models with colleagues or the ML community, enhancing team collaboration and knowledge transfer.
+- **Reproducibility**: By logging every detail of the experiment setup, CometML ensures experiments are reproducible, making it easier to debug and iterate.
+
+## CometML Variables
+When integrating CometML into your projects, you'll need to set up several environment variables to manage the authentication and configuration:
+
+- `COMET_API_KEY`: Your unique API key that authenticates your interactions with the CometML API.
+- `COMET_PROJECT`: The project name under which your experiments will be logged.
+- `COMET_WORKSPACE`: The workspace name that organizes various projects and experiments.
+
+## Obtaining CometML Variables
+
+To access and set up the necessary CometML variables for your project, follow these steps:
+
+1. **Create an Account or Log In**:
+   - Visit [CometML's website](https://www.comet.com/) and log in if you already have an account, or sign up if you're a new user.
+
+2. **Create a New Project**:
+   - Once logged in, navigate to your dashboard. Here, you can create a new project by clicking on "New Project" and entering the relevant details for your project.
+
+3. **Access API Key**:
+   - After creating your project, you will need to obtain your API key. Navigate to your account settings by clicking on your profile at the top right corner. Select 'API Keys' from the menu, and you'll see an option to generate or copy your existing API key.
+
+4. **Set Environment Variables**:
+   - Add the obtained `COMET_API_KEY` to your environment variables, along with the `COMET_PROJECT` and `COMET_WORKSPACE` names you have set up.
+
+
+
+# Qdrant Integration
+
+## Overview
+[Qdrant](https://qdrant.tech/) is an open-source vector database designed for storing and searching large volumes of high-dimensional vector data. It is particularly suited for tasks that require searching through large datasets to find items similar to a query item, such as in recommendation systems or image retrieval applications.
+
+## Why Use Qdrant?
+Qdrant is a robust vector database optimized for handling high-dimensional vector data, making it ideal for machine learning and AI applications. Here are the key reasons for using Qdrant:
+
+- **Efficient Searching**: Utilizes advanced indexing mechanisms to deliver fast and accurate search capabilities across high-dimensional datasets.
+- **Scalability**: Built to accommodate large-scale data sets, which is critical for enterprise-level deployments.
+- **Flexibility**: Supports a variety of distance metrics and filtering options that allow for precise customization of search results.
+- **Integration with ML Pipelines**: Seamlessly integrates into machine learning pipelines, enabling essential functions like nearest neighbor searches.
+
+## Setting Up Qdrant
+
+Qdrant can be utilized via its Docker implementation or through its managed cloud service(https://cloud.qdrant.io/login). This flexibility allows you to choose an environment that best suits your project's needs.
+
+### Environment Variables
+To configure your environment for Qdrant, set the following variables:
+
+## Docker Variables
+- `QDRANT_HOST`: The hostname or IP address where your Qdrant server is running.
+- `QDRANT_PORT`: The port on which Qdrant listens, typically `6333` for Docker setups.
+
+## Qdrant Cloud Variables
+- `QDRANT_CLOUD_URL`: The URL for accessing Qdrant Cloud services.
+- `QDRANT_APIKEY`: The API key for authenticating with Qdrant Cloud.
+
+Additionally, you can control the connection mode (Cloud or Docker) using a setting in your configuration file. More details can be found in db/qdrant.py :
+
+```python
+QDRANT_CLOUD: True  # Set to False to use Docker setup
+```
+
+
+
+# Usage
+
+The project includes a `Makefile` for easy management of common tasks. Here are the main commands you can use:
+
+- `make help`: Displays help for each make command.
+- `make local-start-infra`: Build and start mongodb, mq and qdrant.
+- `make local-start-cdc`: Start cdc system
+- `make insert-data-mongo`: Insert data to mongodb
+- `make local-bytewax`: Run bytewax pipeline
+- `make generate-dataset`: Generate dataset for finetuning and version it in CometML
+
+
+
+
+
+
+**Before running the commands, ensure your environment variables are correctly set up in your `.env` file to guarantee that everything works properly.**
+
+## Environment Configuration
+
+Ensure your `.env` file includes the following configurations:
+
+```plaintext
+MONGO_DATABASE_HOST="mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"
+MONGO_DATABASE_NAME="scrabble"
+
+# QdrantDB config
+QDRANT_DATABASE_HOST="localhost"
+QDRANT_DATABASE_PORT=6333
+QDRANT_APIKEY= your-key
+QDRANT_CLOUD_URL=your-url
+QDRANT_CLOUD=False
+
+# MQ config
+RABBITMQ_DEFAULT_USERNAME=guest
+RABBITMQ_DEFAULT_PASSWORD=guest
+RABBITMQ_HOST=localhost
+RABBITMQ_PORT= 5673
+
+COMET_API_KEY="your-key"
+COMET_WORKSPACE="alexandruvesa"
+COMET_PROJECT='decodingml'
+
+OPENAI_API_KEY = "your-key"
+```
+
+
+
+# Meet your teachers!
+The course is created under the [Decoding ML](https://decodingml.substack.com/) umbrella by:
+
+<table>
+  <tr>
+    <td><a href="https://github.com/iusztinpaul" target="_blank"><img src="https://github.com/iusztinpaul.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Paul Iusztin</strong><br />
+      <i>Senior ML & MLOps Engineer</i>
+    </td>
+  </tr>
+  <tr>
+    <td><a href="https://github.com/alexandruvesa" target="_blank"><img src="https://github.com/alexandruvesa.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Alexandru Vesa</strong><br />
+      <i>Senior AI Engineer</i>
+    </td>
+  </tr>
+  <tr>
+    <td><a href="https://github.com/Joywalker" target="_blank"><img src="https://github.com/Joywalker.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Răzvanț Alexandru</strong><br />
+      <i>Senior ML Engineer</i>
+    </td>
+  </tr>
+</table>
+
+# License
+
+This course is an open-source project released under the MIT license. Thus, as long you distribute our LICENSE and acknowledge our work, you can safely clone or fork this project and use it as a source of inspiration for whatever you want (e.g., university projects, college degree projects, personal projects, etc.).
+
+# 🏆 Contribution
+
+A big "Thank you 🙏" to all our contributors! This course is possible only because of their efforts.
+
+<p align="center">
+    <a href="https://github.com/decodingml/llm-twin-course/graphs/contributors">
+      <img src="https://contrib.rocks/image?repo=decodingml/llm-twin-course" />
+    </a>
+</p>
diff --git a/course/module-3/db/qdrant.py b/course/module-3/db/qdrant.py
index 2c8f894..67a66d8 100644
--- a/course/module-3/db/qdrant.py
+++ b/course/module-3/db/qdrant.py
@@ -15,7 +15,6 @@ def __init__(self):
         if self._instance is None:
             try:
                 if settings.QDRANT_CLOUD:
-                    print("aici")
                     self._instance = QdrantClient(
                         url=settings.QDRANT_CLOUD_URL,
                         api_key=settings.QDRANT_APIKEY,

From 411382c38a17bb1c28ed695a66b0a347375989d5 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 02:12:10 +0300
Subject: [PATCH 04/19] added to readme

---
 course/module-3/README.md | 32 +++++++++++++++++++++++++++-----
 1 file changed, 27 insertions(+), 5 deletions(-)

diff --git a/course/module-3/README.md b/course/module-3/README.md
index f78e22b..21d5255 100644
--- a/course/module-3/README.md
+++ b/course/module-3/README.md
@@ -1,3 +1,9 @@
+# Introduction
+This module is composed from 2 modules:
+- RAG module
+- Finetuning dataset preparation module
+
+
 # Module 3: RAG
 TBD
 
@@ -122,14 +128,10 @@ The project includes a `Makefile` for easy management of common tasks. Here are
 - `make local-start-infra`: Build and start mongodb, mq and qdrant.
 - `make local-start-cdc`: Start cdc system
 - `make insert-data-mongo`: Insert data to mongodb
-- `make local-bytewax`: Run bytewax pipeline
+- `make local-bytewax`: Run bytewax pipeline and send data to Qdrant
 - `make generate-dataset`: Generate dataset for finetuning and version it in CometML
 
 
-
-
-
-
 **Before running the commands, ensure your environment variables are correctly set up in your `.env` file to guarantee that everything works properly.**
 
 ## Environment Configuration
@@ -160,6 +162,26 @@ COMET_PROJECT='decodingml'
 OPENAI_API_KEY = "your-key"
 ```
 
+# Supplementary Tools
+
+This section covers additional tools and scripts included in the project that assist with specific tasks, such as data insertion.
+
+## Script: `insert_data_mongo.py`
+
+### Overview
+The `insert_data_mongo.py` script is designed to manage the automated downloading and insertion of various types of documents (articles, posts, repositories) into a MongoDB database. It facilitates the initial population of the database with structured data for further processing or analysis.
+
+### Features
+- **Dataset Downloading**: Automatically downloads JSON formatted data files from Google Drive based on predefined file IDs.
+- **Dynamic Data Insertion**: Inserts different types of documents (articles, posts, repositories) into the MongoDB database, associating each entry with its respective author.
+- **Logging**: Provides logging for each step of the process to track the number and type of documents inserted.
+
+### How It Works
+1. **Download Data**: The script first checks if the specified `output_dir` directory exists and contains any files. If not, it creates the directory and downloads the data files from Google Drive.
+2. **Insert Data**: Based on the type specified in the downloaded files, it inserts posts, articles, or repositories into the MongoDB database.
+3. **Logging**: After each insertion, the script logs the number of items inserted and their associated author ID to help monitor the process.
+
+
 
 
 # Meet your teachers!

From d9a34ff86d4cd25d247382612ce459fc80f9dc53 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 02:37:57 +0300
Subject: [PATCH 05/19] updated

---
 course/module-3/README.md                       |  4 ++++
 course/module-3/finetuning/file_handler.py      |  2 +-
 course/module-3/finetuning/generate_data.py     | 16 ++++++++++------
 course/module-3/finetuning/llm_communication.py |  2 +-
 4 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/course/module-3/README.md b/course/module-3/README.md
index 21d5255..3d0f33d 100644
--- a/course/module-3/README.md
+++ b/course/module-3/README.md
@@ -21,6 +21,10 @@ This Python module automates the generation of datasets specifically formatted f
 
 
 # Module Structure
+
+### Example Content
+- `example_content.json`: Prompt examples used to guide the LLM in dataset generation
+
 ### File Handling
 - `file_handler.py`: Manages file I/O operations, enabling reading and writing of JSON formatted data.
 
diff --git a/course/module-3/finetuning/file_handler.py b/course/module-3/finetuning/file_handler.py
index 1c01929..37ee5d1 100644
--- a/course/module-3/finetuning/file_handler.py
+++ b/course/module-3/finetuning/file_handler.py
@@ -1,6 +1,6 @@
 import json
 
-from finetune_data.exceptions import JSONDecodeError
+from finetuning.exceptions import JSONDecodeError
 
 
 class FileHandler:
diff --git a/course/module-3/finetuning/generate_data.py b/course/module-3/finetuning/generate_data.py
index cf3b19f..d6f979c 100644
--- a/course/module-3/finetuning/generate_data.py
+++ b/course/module-3/finetuning/generate_data.py
@@ -1,16 +1,20 @@
 import json
 import logging
+import sys
+from pathlib import Path
+
+sys.path.append(str(Path(__file__).resolve().parent.parent))
 
 from comet_ml import Artifact, Experiment
 
 from db.qdrant import connection as client
-from finetune_data.file_handler import FileHandler
-from finetune_data.llm_communication import GptCommunicator
+from finetuning.file_handler import FileHandler
+from finetuning.llm_communication import GptCommunicator
 from settings import settings
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
-data_type = "articles"
+data_type = "posts"
 USER_PROMPT = (
     f"In the following rows I will give you 2 json objects as example, each of them having an instruction which "
     f"describes what to write about and the corresponding {data_type} content that follows this instruction. Afterwards I "
@@ -110,9 +114,9 @@ def fetch_all_cleaned_content(self, collection_name: str) -> list:
 
 
 if __name__ == "__main__":
-    example_file = (
-        "/Users/vesaalexandru/Workspaces/decodeML/llm-twin-course/course/module-3/finetune_data/example_content.json"
-    )
+    filename = "example_content.json"
+    relative_directory = "finetuning"
+    example_file = Path(relative_directory) / filename
     collection_names = ["cleaned_articles", "cleaned_posts"]
     file_handler = FileHandler()
     api_communicator = GptCommunicator()
diff --git a/course/module-3/finetuning/llm_communication.py b/course/module-3/finetuning/llm_communication.py
index c1d8b3d..79c0413 100644
--- a/course/module-3/finetuning/llm_communication.py
+++ b/course/module-3/finetuning/llm_communication.py
@@ -2,7 +2,7 @@
 
 from openai import OpenAI
 
-from finetune_data.exceptions import APICommunicationError
+from finetuning.exceptions import APICommunicationError
 from settings import settings
 
 MAX_LENGTH = 16384

From c0b0e46b047898270c6ed26f6aa3424027a3790b Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 19:51:24 +0300
Subject: [PATCH 06/19] Readme restructured

---
 course/module-3/finetuning/qdrant.py | 67 ++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 course/module-3/finetuning/qdrant.py

diff --git a/course/module-3/finetuning/qdrant.py b/course/module-3/finetuning/qdrant.py
new file mode 100644
index 0000000..67a66d8
--- /dev/null
+++ b/course/module-3/finetuning/qdrant.py
@@ -0,0 +1,67 @@
+from qdrant_client import QdrantClient
+from qdrant_client.http.exceptions import UnexpectedResponse
+from qdrant_client.http.models import Batch, Distance, VectorParams
+
+import logger_utils
+from settings import settings
+
+logger = logger_utils.get_logger(__name__)
+
+
+class QdrantDatabaseConnector:
+    _instance: QdrantClient = None
+
+    def __init__(self):
+        if self._instance is None:
+            try:
+                if settings.QDRANT_CLOUD:
+                    self._instance = QdrantClient(
+                        url=settings.QDRANT_CLOUD_URL,
+                        api_key=settings.QDRANT_APIKEY,
+                    )
+                else:
+                    self._instance = QdrantClient(
+                        host=settings.QDRANT_DATABASE_HOST,
+                        port=settings.QDRANT_DATABASE_PORT,
+                    )
+
+            except UnexpectedResponse:
+                logger.exception(
+                    "Couldn't connect to the database.",
+                    host=settings.QDRANT_DATABASE_HOST,
+                    port=settings.QDRANT_DATABASE_PORT,
+                )
+
+                raise
+
+    def get_collection(self, collection_name: str):
+        return self._instance.get_collection(collection_name=collection_name)
+
+    def create_non_vector_collection(self, collection_name: str):
+        self._instance.create_collection(collection_name=collection_name, vectors_config={})
+
+    def create_vector_collection(self, collection_name: str):
+        self._instance.create_collection(
+            collection_name=collection_name,
+            vectors_config=VectorParams(size=settings.EMBEDDING_SIZE, distance=Distance.COSINE),
+        )
+
+    def write_data(self, collection_name: str, points: Batch):
+        try:
+            self._instance.upsert(collection_name=collection_name, points=points)
+        except Exception:
+            logger.exception("An error occurred while inserting data.")
+
+            raise
+
+    def scroll(self, collection_name: str, limit: int):
+        return self._instance.scroll(collection_name=collection_name, limit=limit)
+
+    def close(self):
+        if self._instance:
+            self._instance.close()
+
+            logger.info("Connected to database has been closed.")
+
+
+connection = QdrantDatabaseConnector()

From b5f09b32cbe32a7795f5252b1c46d845441bfcc5 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 20:02:05 +0300
Subject: [PATCH 07/19] Readme for module3+module4 added

---
 course/module-3/README.md | 144 +++++++++++++++++++++++++-------------
 1 file changed, 96 insertions(+), 48 deletions(-)

diff --git a/course/module-3/README.md b/course/module-3/README.md
index 3d0f33d..1e8fc2f 100644
--- a/course/module-3/README.md
+++ b/course/module-3/README.md
@@ -3,40 +3,8 @@ This module is composed from 2 modules:
 - RAG module
 - Finetuning dataset preparation module
 
-
-# Module 3: RAG
-TBD
-
-
-# Module 4: Generate Data for LLM finetuning task
-
-# Introduction
-This Python module automates the generation of datasets specifically formatted for training and fine-tuning Large Language Models (LLMs). It interfaces with Qdrant, sends structured prompts to LLMs, and manages data with Comet ML for experiment tracking and artifact logging.
-
-### Why is fine-tuning important?
-1. **Model Customization**: Tailors the LLM's responses to specific domains or tasks.
-2. **Improved Accuracy**: Enhances the model's understanding of nuanced language used in specialized fields.
-3. **Efficiency**: Reduces the need for extensive post-processing by producing more relevant outputs directly.
-4. **Adaptability**: Allows models to continuously learn from new data, staying relevant as language and contexts evolve.
-
-
-# Module Structure
-
-### Example Content
-- `example_content.json`: Prompt examples used to guide the LLM in dataset generation
-
-### File Handling
-- `file_handler.py`: Manages file I/O operations, enabling reading and writing of JSON formatted data.
-
-### LLM Communication
-- `llm_communication.py`: Handles communication with OpenAI's LLMs, sending prompts and processing responses.
-
-### Data Generation
-- `generate_data.py`: Orchestrates the generation of training data by integrating file handling, LLM communication, and data formatting.
-
-
 # Installation and Setup
-To prepare your environment for this module, follow these steps:
+To prepare your environment for these modules, follow these steps:
 - `poetry init`
 - `poetry install`
 
@@ -123,20 +91,7 @@ QDRANT_CLOUD: True  # Set to False to use Docker setup
 ```
 
 
-
-# Usage
-
-The project includes a `Makefile` for easy management of common tasks. Here are the main commands you can use:
-
-- `make help`: Displays help for each make command.
-- `make local-start-infra`: Build and start mongodb, mq and qdrant.
-- `make local-start-cdc`: Start cdc system
-- `make insert-data-mongo`: Insert data to mongodb
-- `make local-bytewax`: Run bytewax pipeline and send data to Qdrant
-- `make generate-dataset`: Generate dataset for finetuning and version it in CometML
-
-
-**Before running the commands, ensure your environment variables are correctly set up in your `.env` file to guarantee that everything works properly.**
+**Before running any commands, ensure your environment variables are correctly set up in your `.env` file to guarantee that everything works properly.**
 
 ## Environment Configuration
 
@@ -167,7 +122,7 @@ OPENAI_API_KEY = "your-key"
 ```
 
 # Supplementary Tools
-
+You need a real dataset to run and test the modules. 
 This section covers additional tools and scripts included in the project that assist with specific tasks, such as data insertion.
 
 ## Script: `insert_data_mongo.py`
@@ -185,6 +140,99 @@ The `insert_data_mongo.py` script is designed to manage the automated downloadin
 2. **Insert Data**: Based on the type specified in the downloaded files, it inserts posts, articles, or repositories into the MongoDB database.
 3. **Logging**: After each insertion, the script logs the number of items inserted and their associated author ID to help monitor the process.
 
+# Module 3: RAG
+
+# Introduction
+
+A production RAG system is split into 3 main components:
+
+    - ingestion: clean, chunk, embed, and load your data to a vector DB
+    - retrieval: query your vector DB for context
+    - generation: attach the retrieved context to your prompt and pass it to an LLM
+
+The ingestion component sits in the feature pipeline, while the retrieval and generation components are implemented inside the inference pipeline.
+
+You can also use the retrieval and generation components in your training pipeline to fine-tune your LLM further on domain-specific prompts.
+
+You can apply advanced techniques to optimize your RAG system for ingestion, retrieval and generation.
+
+That being said, there are 3 main types of advanced RAG techniques:
+
+    - Pre-retrieval optimization [ingestion]: tweak how you create the chunks
+    - Retrieval optimization [retrieval]: improve the queries to your vector DB
+    - Post-retrieval optimization [retrieval]: process the retrieved chunks to filter out the noise
+
+# RAG Module Structure
+### Query Expansion
+- `query_expansion.py`: Handles the expansion of a given query into multiple variations using language model-based templates. It integrates the `ChatOpenAI` class from `langchain_openai` and a custom `QueryExpansionTemplate` to generate expanded queries suitable for further processing.
+
+### Reranking
+- `reranking.py`: Manages the reranking of retrieved documents based on relevance to the original query. It uses a `RerankingTemplate` and the `ChatOpenAI` model to reorder the documents in order of relevance, making use of language model outputs.
+
+### Retriever
+- `retriever.py`: Performs vector-based retrieval of documents from a vector database using query expansion and reranking strategies. It utilizes the `QueryExpansion` and `Reranker` classes, as well as `QdrantClient` for database interactions and `SentenceTransformer` for generating query vectors.
+
+### Self Query
+- `self_query.py`: Generates metadata attributes related to a query, such as author ID, using a self-query mechanism. It employs a `SelfQueryTemplate` and the `ChatOpenAI` model to extract required metadata from the query context.
+
+# Usage
+After you have everything setup, environemnt variables, [CometML](https://www.comet.com/site/?cache=20258256) account and [Qdrant](https://qdrant.tech/) account, it's time to insert data in your environment.
+
+The workflow is straightforward:
+
+- Start all the services: MongoDB, Qdrant, RabbitMQ
+- Start the CDC system (for more details you can check https://medium.com/decodingml/the-3nd-out-of-11-lessons-of-the-llm-twin-free-course-ba82752dad5a)
+- Insert data to mongodb by running  `make insert-data-mongo`
+- Insert data into Qdrant VectorDB by running the [Bytewax](https://bytewax.io/) pipelines
+- Go to retriver.py (outside of rag folder) and write your own query
+
+The project includes a `Makefile` for easy management of common tasks. Here are the main commands you can use:
+
+- `make help`: Displays help for each make command.
+- `make local-start-infra`: Build and start mongodb, mq and qdrant.
+- `make local-start-cdc`: Start cdc system
+- `make insert-data-mongo`: Insert data to mongodb
+- `make local-bytewax`: Run bytewax pipeline and send data to Qdrant
+- `make local-test-retriever:-`: Test RAG retrieval
+
+
+# Module 4: Generate Data for LLM finetuning task
+
+# Introduction
+This Python module automates the generation of datasets specifically formatted for training and fine-tuning Large Language Models (LLMs). It interfaces with Qdrant, sends structured prompts to LLMs, and manages data with Comet ML for experiment tracking and artifact logging.
+
+### Why is fine-tuning important?
+1. **Model Customization**: Tailors the LLM's responses to specific domains or tasks.
+2. **Improved Accuracy**: Enhances the model's understanding of nuanced language used in specialized fields.
+3. **Efficiency**: Reduces the need for extensive post-processing by producing more relevant outputs directly.
+4. **Adaptability**: Allows models to continuously learn from new data, staying relevant as language and contexts evolve.
+
+
+# Module Structure
+
+### Example Content
+- `example_content.json`: Prompt examples used to guide the LLM in dataset generation
+
+### File Handling
+- `file_handler.py`: Manages file I/O operations, enabling reading and writing of JSON formatted data.
+
+### LLM Communication
+- `llm_communication.py`: Handles communication with OpenAI's LLMs, sending prompts and processing responses.
+
+### Data Generation
+- `generate_data.py`: Orchestrates the generation of training data by integrating file handling, LLM communication, and data formatting.
+
+
+# Usage
+
+The project includes a `Makefile` for easy management of common tasks. Here are the main commands you can use:
+
+- `make help`: Displays help for each make command.
+- `make local-start-infra`: Build and start mongodb, mq and qdrant.
+- `make local-start-cdc`: Start cdc system
+- `make insert-data-mongo`: Insert data to mongodb
+- `make local-bytewax`: Run bytewax pipeline and send data to Qdrant
+- `make generate-dataset`: Generate dataset for finetuning and version it in CometML
 
 
 

From 28eae7c75f53e8d5e8e0c03fb74cc635b6488c03 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 20:08:44 +0300
Subject: [PATCH 08/19] Qdrant client was important directly from db/qdrant in
 rag module

---
 course/module-3/rag/retriever.py | 37 +++++++++++---------------------
 1 file changed, 12 insertions(+), 25 deletions(-)

diff --git a/course/module-3/rag/retriever.py b/course/module-3/rag/retriever.py
index 2d74180..1a967e3 100644
--- a/course/module-3/rag/retriever.py
+++ b/course/module-3/rag/retriever.py
@@ -1,16 +1,16 @@
 import concurrent.futures
 
+from qdrant_client import models
+from sentence_transformers.SentenceTransformer import SentenceTransformer
+
+import logger_utils
 import utils
-from qdrant_client import QdrantClient, models
+from db.qdrant import connection as client
 from rag.query_expanison import QueryExpansion
 from rag.reranking import Reranker
 from rag.self_query import SelfQuery
-from sentence_transformers.SentenceTransformer import SentenceTransformer
 from settings import settings
 
-import logger_utils
-
-
 logger = logger_utils.get_logger(__name__)
 
 
@@ -20,18 +20,14 @@ class VectorRetriever:
     """
 
     def __init__(self, query: str):
-        self._client = QdrantClient(
-            host=settings.QDRANT_DATABASE_HOST, port=settings.QDRANT_DATABASE_PORT
-        )
+        self._client = client
         self.query = query
         self._embedder = SentenceTransformer(settings.EMBEDDING_MODEL_ID)
         self._query_expander = QueryExpansion()
         self._metadata_extractor = SelfQuery()
         self._reranker = Reranker()
 
-    def _search_single_query(
-        self, generated_query: str, metadata_filter_value: str, k: int
-    ):
+    def _search_single_query(self, generated_query: str, metadata_filter_value: str, k: int):
         assert k > 3, "k should be greater than 3"
 
         query_vector = self._embedder.encode(generated_query).tolist()
@@ -86,9 +82,7 @@ def _search_single_query(
         return utils.flatten(vectors)
 
     def retrieve_top_k(self, k: int, to_expand_to_n_queries: int) -> list:
-        generated_queries = self._query_expander.generate_response(
-            self.query, to_expand_to_n=to_expand_to_n_queries
-        )
+        generated_queries = self._query_expander.generate_response(self.query, to_expand_to_n=to_expand_to_n_queries)
         logger.info(
             "Successfully generated queries for search.",
             num_queries=len(generated_queries),
@@ -102,15 +96,10 @@ def retrieve_top_k(self, k: int, to_expand_to_n_queries: int) -> list:
 
         with concurrent.futures.ThreadPoolExecutor() as executor:
             search_tasks = [
-                executor.submit(
-                    self._search_single_query, query, author_id, k
-                )
-                for query in generated_queries
+                executor.submit(self._search_single_query, query, author_id, k) for query in generated_queries
             ]
 
-            hits = [
-                task.result() for task in concurrent.futures.as_completed(search_tasks)
-            ]
+            hits = [task.result() for task in concurrent.futures.as_completed(search_tasks)]
             hits = utils.flatten(hits)
 
         logger.info("All documents retrieved successfully.", num_documents=len(hits))
@@ -119,10 +108,8 @@ def retrieve_top_k(self, k: int, to_expand_to_n_queries: int) -> list:
 
     def rerank(self, hits: list, keep_top_k: int) -> list[str]:
         content_list = [hit.payload["content"] for hit in hits]
-        rerank_hits = self._reranker.generate_response(
-            query=self.query, passages=content_list, keep_top_k=keep_top_k
-        )
-        
+        rerank_hits = self._reranker.generate_response(query=self.query, passages=content_list, keep_top_k=keep_top_k)
+
         logger.info("Documents reranked successfully.", num_documents=len(rerank_hits))
 
         return rerank_hits

From b5e4a53ba93d5c757f0d7144ada26c22623b257e Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Tue, 7 May 2024 20:15:38 +0300
Subject: [PATCH 09/19] temporary fixed articles data into mongo

---
 course/module-3/insert_data_mongo.py | 42 +++++++++++++++++++++-------
 1 file changed, 32 insertions(+), 10 deletions(-)

diff --git a/course/module-3/insert_data_mongo.py b/course/module-3/insert_data_mongo.py
index 4f52966..de4bf76 100644
--- a/course/module-3/insert_data_mongo.py
+++ b/course/module-3/insert_data_mongo.py
@@ -1,4 +1,5 @@
 import json
+import re
 from pathlib import Path
 
 import gdown
@@ -9,6 +10,13 @@
 logger = logger_utils.get_logger(__name__)
 
 
+def fix_json_content(content: str) -> str:
+    """
+    Fixes common JSON escape errors in the provided content.
+    """
+    return re.sub(r'\\(?!["\\bfnrt/])', r"\\\\", content)
+
+
 def download_dataset(output_dir: Path = Path("data")) -> list:
     files = [
         {
@@ -67,22 +75,36 @@ def insert_posts(file_name: str, author_id: str) -> None:
         posts: dict[str, dict] = json.load(file)
 
     for post_content in posts.values():
-        PostDocument(platform="linkedin", content=post_content, author_id=author_id).save()
-
+        try:
+            PostDocument(platform="linkedin", content=post_content, author_id=author_id).save()
+        except:
+            continue
     logger.info("Posts inserted into collection", num=len(posts), author_id=author_id)
 
 
 def insert_articles(file_name: str, author_id: str) -> None:
-    with open(file_name, "r") as file:
-        articles: list[dict] = json.load(file)
+    file_name = (
+        "/Users/vesaalexandru/Workspaces/decodeML/llm-twin-course/course/module-3/dataset/articles_paul_iusztin.json"
+    )
+    try:
+        with open(file_name, "r") as file:
+            articles: list[dict] = json.load(file)
+    except:
+        with open(file_name, "r") as file:
+            content = file.read()
+            corrected_content = fix_json_content(content)
+            articles: list[dict] = json.loads(corrected_content)
 
     for article_content in articles:
-        ArticleDocument(
-            platform="medium",
-            link="/htttps/alex/paul",
-            content=article_content,
-            author_id=author_id,
-        ).save()
+        try:
+            ArticleDocument(
+                platform="medium",
+                link="/htttps/alex/paul",
+                content=article_content,
+                author_id=author_id,
+            ).save()
+        except:
+            continue
 
     logger.info("Articles inserted into collection", num=len(articles), author_id=author_id)
 

From 96fa75aceb2d8e26fe8837c0b79aad9d451f0666 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Wed, 8 May 2024 09:34:46 +0300
Subject: [PATCH 10/19] deleted unnecesary modules

---
 course/module-3/finetuning/qdrant.py | 67 ----------------------------
 1 file changed, 67 deletions(-)
 delete mode 100644 course/module-3/finetuning/qdrant.py

diff --git a/course/module-3/finetuning/qdrant.py b/course/module-3/finetuning/qdrant.py
deleted file mode 100644
index 67a66d8..0000000
--- a/course/module-3/finetuning/qdrant.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from qdrant_client import QdrantClient
-from qdrant_client.http.exceptions import UnexpectedResponse
-from qdrant_client.http.models import Batch, Distance, VectorParams
-
-import logger_utils
-from settings import settings
-
-logger = logger_utils.get_logger(__name__)
-
-
-class QdrantDatabaseConnector:
-    _instance: QdrantClient = None
-
-    def __init__(self):
-        if self._instance is None:
-            try:
-                if settings.QDRANT_CLOUD:
-                    self._instance = QdrantClient(
-                        url=settings.QDRANT_CLOUD_URL,
-                        api_key=settings.QDRANT_APIKEY,
-                    )
-                else:
-                    self._instance = QdrantClient(
-                        host=settings.QDRANT_DATABASE_HOST,
-                        port=settings.QDRANT_DATABASE_PORT,
-                    )
-
-            except UnexpectedResponse:
-                logger.exception(
-                    "Couldn't connect to the database.",
-                    host=settings.QDRANT_DATABASE_HOST,
-                    port=settings.QDRANT_DATABASE_PORT,
-                )
-
-                raise
-
-    def get_collection(self, collection_name: str):
-        return self._instance.get_collection(collection_name=collection_name)
-
-    def create_non_vector_collection(self, collection_name: str):
-        self._instance.create_collection(collection_name=collection_name, vectors_config={})
-
-    def create_vector_collection(self, collection_name: str):
-        self._instance.create_collection(
-            collection_name=collection_name,
-            vectors_config=VectorParams(size=settings.EMBEDDING_SIZE, distance=Distance.COSINE),
-        )
-
-    def write_data(self, collection_name: str, points: Batch):
-        try:
-            self._instance.upsert(collection_name=collection_name, points=points)
-        except Exception:
-            logger.exception("An error occurred while inserting data.")
-
-            raise
-
-    def scroll(self, collection_name: str, limit: int):
-        return self._instance.scroll(collection_name=collection_name, limit=limit)
-
-    def close(self):
-        if self._instance:
-            self._instance.close()
-
-            logger.info("Connected to database has been closed.")
-
-
-connection = QdrantDatabaseConnector()

From e659467d0db12c78e1b42fd4b340cc3b641eb29f Mon Sep 17 00:00:00 2001
From: Anca <ancamuscalagiu1@gmail.com>
Date: Wed, 8 May 2024 09:56:58 +0300
Subject: [PATCH 11/19] skip batch if error

---
 course/module-3/finetuning/llm_communication.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/course/module-3/finetuning/llm_communication.py b/course/module-3/finetuning/llm_communication.py
index 79c0413..53a856e 100644
--- a/course/module-3/finetuning/llm_communication.py
+++ b/course/module-3/finetuning/llm_communication.py
@@ -1,4 +1,5 @@
 import json
+import logging
 
 from openai import OpenAI
 
@@ -8,6 +9,8 @@
 MAX_LENGTH = 16384
 SYSTEM_PROMPT = "You are a technical writer handing someone's account to post about AI and MLOps."
 
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
 
 class GptCommunicator:
     def __init__(self, gpt_model: str = "gpt-3.5-turbo"):
@@ -17,6 +20,7 @@ def __init__(self, gpt_model: str = "gpt-3.5-turbo"):
     def send_prompt(self, prompt: str) -> list:
         try:
             client = OpenAI(api_key=self.api_key)
+            logging.info("Sending batch to LLM")
             chat_completion = client.chat.completions.create(
                 messages=[
                     {"role": "system", "content": SYSTEM_PROMPT},
@@ -27,7 +31,7 @@ def send_prompt(self, prompt: str) -> list:
             response = chat_completion.choices[0].message.content
             return json.loads(self.clean_response(response))
         except Exception as e:
-            raise APICommunicationError(f"An error occurred while communicating with API: {e}")
+            logging.error(f"Skipping batch! An error occurred while communicating with API: {e}")
 
     @staticmethod
     def clean_response(response: str) -> str:

From 6137933bec8df21f8075151563d1ee2f9432ae18 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Wed, 8 May 2024 11:17:23 +0300
Subject: [PATCH 12/19] comments done

---
 course/module-3/.env.example                |   4 +-
 course/module-3/db/qdrant.py                |   2 +-
 course/module-3/finetuning/file_handler.py  |   4 +-
 course/module-3/finetuning/generate_data.py |  36 +-
 course/module-3/insert_data_mongo.py        |  12 +-
 course/module-3/poetry.lock                 | 983 ++++++++++----------
 course/module-3/pyproject.toml              |   8 +
 course/module-3/settings.py                 |   4 +-
 8 files changed, 562 insertions(+), 491 deletions(-)

diff --git a/course/module-3/.env.example b/course/module-3/.env.example
index 666b839..163b6f4 100644
--- a/course/module-3/.env.example
+++ b/course/module-3/.env.example
@@ -11,8 +11,8 @@ MONGO_DATABASE_NAME="scrabble"
 # QdrantDB config
 QDRANT_DATABASE_HOST="localhost"
 QDRANT_DATABASE_PORT=6333
-QDRANT_APIKEY=
-
+QDRANT_APIKEY=str
+USE_QDRANT_CLOUD=False
 # MQ config
 RABBITMQ_DEFAULT_USERNAME="guest"
 RABBITMQ_DEFAULT_PASSWORD="guest"
diff --git a/course/module-3/db/qdrant.py b/course/module-3/db/qdrant.py
index 67a66d8..a589c7e 100644
--- a/course/module-3/db/qdrant.py
+++ b/course/module-3/db/qdrant.py
@@ -14,7 +14,7 @@ class QdrantDatabaseConnector:
     def __init__(self):
         if self._instance is None:
             try:
-                if settings.QDRANT_CLOUD:
+                if settings.USE_QDRANT_CLOUD:
                     self._instance = QdrantClient(
                         url=settings.QDRANT_CLOUD_URL,
                         api_key=settings.QDRANT_APIKEY,
diff --git a/course/module-3/finetuning/file_handler.py b/course/module-3/finetuning/file_handler.py
index 37ee5d1..79e7665 100644
--- a/course/module-3/finetuning/file_handler.py
+++ b/course/module-3/finetuning/file_handler.py
@@ -11,7 +11,9 @@ def read_json(self, filename: str) -> list:
         except FileNotFoundError:
             raise FileNotFoundError(f"The file '{filename}' does not exist.")
         except json.JSONDecodeError:
-            raise JSONDecodeError(f"The file '{filename}' is not properly formatted as JSON.")
+            raise JSONDecodeError(
+                f"The file '{filename}' is not properly formatted as JSON."
+            )
 
     def write_json(self, filename: str, data: list):
         with open(filename, "w") as file:
diff --git a/course/module-3/finetuning/generate_data.py b/course/module-3/finetuning/generate_data.py
index d6f979c..e1724fe 100644
--- a/course/module-3/finetuning/generate_data.py
+++ b/course/module-3/finetuning/generate_data.py
@@ -12,7 +12,9 @@
 from finetuning.llm_communication import GptCommunicator
 from settings import settings
 
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
 
 data_type = "posts"
 USER_PROMPT = (
@@ -26,7 +28,6 @@
 
 
 class DataFormatter:
-
     @classmethod
     def format_data(cls, data_points: list, is_example: bool) -> str:
         text = ""
@@ -43,30 +44,37 @@ def format_batch(cls, context_msg: str, data_points: list) -> str:
         return delimiter_msg
 
     @classmethod
-    def format_initial_prompt(cls, example_content: list, inference_posts: list):
-        initial_prompt = (
-            USER_PROMPT  # Assuming USER_PROMPT is defined somewhere as a global constant or class attribute
-        )
+    def format_prompt(cls, example_content: list, inference_posts: list):
+        initial_prompt = USER_PROMPT  # Assuming USER_PROMPT is defined somewhere as a global constant or class attribute
         initial_prompt += f"You must generate exactly a list of {len(inference_posts)} json objects, using the contents provided under CONTENTS FOR GENERATION, the EXAMPLE JSONS are just for example purpose.\n"
         initial_prompt += "EXAMPLE JSONS:\n"
         initial_prompt += cls.format_data(example_content, True)
-        initial_prompt += cls.format_batch("CONTENTS FOR GENERATION: \n", inference_posts)
+        initial_prompt += cls.format_batch(
+            "CONTENTS FOR GENERATION: \n", inference_posts
+        )
         return initial_prompt
 
 
 class DatasetGenerator:
-    def __init__(self, file_handler: FileHandler, api_communicator: GptCommunicator, data_formatter: DataFormatter):
+    def __init__(
+        self,
+        file_handler: FileHandler,
+        api_communicator: GptCommunicator,
+        data_formatter: DataFormatter,
+    ):
         self.file_handler = file_handler
         self.api_communicator = api_communicator
         self.data_formatter = data_formatter
 
-    def generate_training_data(self, example_file: str, collection_name: str, batch_size: int = 1):
+    def generate_training_data(
+        self, example_file: str, collection_name: str, batch_size: int = 1
+    ):
         example_content = self.file_handler.read_json(example_file)
         all_contents = self.fetch_all_cleaned_content(collection_name)
         response = []
         for i in range(0, len(all_contents), batch_size):
             batch = all_contents[i : i + batch_size]
-            initial_prompt = data_formatter.format_initial_prompt(example_content, batch)
+            initial_prompt = data_formatter.format_prompt(example_content, batch)
             response += self.api_communicator.send_prompt(initial_prompt)
 
         self.push_to_comet(response, collection_name)
@@ -77,7 +85,9 @@ def push_to_comet(self, data: list, collection_name: str):
 
             # Assuming the settings module has been properly configured with the required attributes
             experiment = Experiment(
-                api_key=settings.COMET_API_KEY, project_name=settings.COMET_PROJECT, workspace=settings.COMET_WORKSPACE
+                api_key=settings.COMET_API_KEY,
+                project_name=settings.COMET_PROJECT,
+                workspace=settings.COMET_WORKSPACE,
             )
 
             file_name = f"{collection_name}.json"
@@ -123,4 +133,6 @@ def fetch_all_cleaned_content(self, collection_name: str) -> list:
     data_formatter = DataFormatter()
     dataset_generator = DatasetGenerator(file_handler, api_communicator, data_formatter)
     for collection in collection_names:
-        dataset_generator.generate_training_data(example_file=example_file, collection_name=collection, batch_size=1)
+        dataset_generator.generate_training_data(
+            example_file=example_file, collection_name=collection, batch_size=1
+        )
diff --git a/course/module-3/insert_data_mongo.py b/course/module-3/insert_data_mongo.py
index de4bf76..30368bf 100644
--- a/course/module-3/insert_data_mongo.py
+++ b/course/module-3/insert_data_mongo.py
@@ -76,16 +76,16 @@ def insert_posts(file_name: str, author_id: str) -> None:
 
     for post_content in posts.values():
         try:
-            PostDocument(platform="linkedin", content=post_content, author_id=author_id).save()
+            PostDocument(
+                platform="linkedin", content=post_content, author_id=author_id
+            ).save()
         except:
             continue
     logger.info("Posts inserted into collection", num=len(posts), author_id=author_id)
 
 
 def insert_articles(file_name: str, author_id: str) -> None:
-    file_name = (
-        "/Users/vesaalexandru/Workspaces/decodeML/llm-twin-course/course/module-3/dataset/articles_paul_iusztin.json"
-    )
+    file_name = "/Users/vesaalexandru/Workspaces/decodeML/llm-twin-course/course/module-3/dataset/articles_paul_iusztin.json"
     try:
         with open(file_name, "r") as file:
             articles: list[dict] = json.load(file)
@@ -106,7 +106,9 @@ def insert_articles(file_name: str, author_id: str) -> None:
         except:
             continue
 
-    logger.info("Articles inserted into collection", num=len(articles), author_id=author_id)
+    logger.info(
+        "Articles inserted into collection", num=len(articles), author_id=author_id
+    )
 
 
 def insert_repositories(file_name: str, author_id: str) -> None:
diff --git a/course/module-3/poetry.lock b/course/module-3/poetry.lock
index 54d0e43..b205cf9 100644
--- a/course/module-3/poetry.lock
+++ b/course/module-3/poetry.lock
@@ -615,13 +615,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.13.4"
+version = "3.14.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.13.4-py3-none-any.whl", hash = "sha256:404e5e9253aa60ad457cae1be07c0f0ca90a63931200a47d9b6a6af84fd7b45f"},
-    {file = "filelock-3.13.4.tar.gz", hash = "sha256:d13f466618bfde72bd2c18255e269f72542c6e70e7bac83a0232d6b1cc5c8cf4"},
+    {file = "filelock-3.14.0-py3-none-any.whl", hash = "sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f"},
+    {file = "filelock-3.14.0.tar.gz", hash = "sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a"},
 ]
 
 [package.extras]
@@ -854,136 +854,120 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "grpcio"
-version = "1.62.2"
+version = "1.63.0"
 description = "HTTP/2-based RPC framework"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-grpcio = ">=1.62.2"
-protobuf = ">=4.21.6,<5.0dev"
+grpcio = ">=1.63.0"
+protobuf = ">=5.26.1,<6.0dev"
 setuptools = "*"
 
 [[package]]
@@ -1071,13 +1055,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.22.2"
+version = "0.23.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1090,16 +1074,16 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
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 hf-transfer = ["hf-transfer (>=0.1.4)"]
 inference = ["aiohttp", "minijinja (>=1.0)"]
 quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
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 torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
@@ -1147,15 +1131,29 @@ files = [
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 ]
 
+[[package]]
+name = "intel-openmp"
+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1264,13 +1262,13 @@ referencing = ">=0.31.0"
 
 [[package]]
 name = "langchain"
-version = "0.1.16"
+version = "0.1.17"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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+    {file = "langchain-0.1.17-py3-none-any.whl", hash = "sha256:f6c5b5fdb529545e6cafbb4ba099031508e621ba1ed7985cf078a597ade3458b"},
+    {file = "langchain-0.1.17.tar.gz", hash = "sha256:709b80afa00ae634dfc7042f3e4c20309267b21ffeacc7d7494d58bcae1862f7"},
 ]
 
 [package.dependencies]
@@ -1278,8 +1276,8 @@ aiohttp = ">=3.8.3,<4.0.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langchain-community = ">=0.0.32,<0.1"
-langchain-core = ">=0.1.42,<0.2.0"
+langchain-community = ">=0.0.36,<0.1"
+langchain-core = ">=0.1.48,<0.2.0"
 langchain-text-splitters = ">=0.0.1,<0.1"
 langsmith = ">=0.1.17,<0.2.0"
 numpy = ">=1,<2"
@@ -1305,19 +1303,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.34"
+version = "0.0.37"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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+    {file = "langchain_community-0.0.37-py3-none-any.whl", hash = "sha256:52e8557602dc099c2e236ec8a0599a956e2f08cfeb61e501815f5ec2d8545747"},
+    {file = "langchain_community-0.0.37.tar.gz", hash = "sha256:db2b5829bb20bc5b04c126b69143dbc31a880e949e94110c236b2c176906889f"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.45,<0.2.0"
+langchain-core = ">=0.1.51,<0.2.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@@ -1327,17 +1325,17 @@ tenacity = ">=8.1.0,<9.0.0"
 
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 [[package]]
 name = "langchain-core"
-version = "0.1.45"
+version = "0.1.52"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 [package.dependencies]
@@ -1353,18 +1351,18 @@ extended-testing = ["jinja2 (>=3,<4)"]
 
 [[package]]
 name = "langchain-openai"
-version = "0.1.3"
+version = "0.1.6"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 [package.dependencies]
-langchain-core = ">=0.1.42,<0.2.0"
-openai = ">=1.10.0,<2.0.0"
+langchain-core = ">=0.1.46,<0.2.0"
+openai = ">=1.24.0,<2.0.0"
 tiktoken = ">=0.5.2,<1"
 
 [[package]]
@@ -1400,13 +1398,13 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.1.49"
+version = "0.1.55"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -1631,6 +1629,24 @@ files = [
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 ]
 
+[[package]]
+name = "mkl"
+version = "2021.4.0"
+description = "Intel® oneAPI Math Kernel Library"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+intel-openmp = "==2021.*"
+tbb = "==2021.*"
+
 [[package]]
 name = "mpmath"
 version = "1.3.0"
@@ -1957,12 +1973,13 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.19.3"
+version = "2.20.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [[package]]
@@ -1989,13 +2006,13 @@ files = [
 
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-version = "1.23.2"
+version = "1.26.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -2012,62 +2029,57 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
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 python-versions = ">=3.8"
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 urllib3 = ">=1.26.14,<3"
 
 [package.extras]
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+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
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-    {file = "torch-2.2.2-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:dff696de90d6f6d1e8200e9892861fd4677306d0ef604cb18f2134186f719f82"},
-    {file = "torch-2.2.2-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:3a4dd910663fd7a124c056c878a52c2b0be4a5a424188058fe97109d4436ee42"},
+    {file = "torch-2.3.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:d8ea5a465dbfd8501f33c937d1f693176c9aef9d1c1b0ca1d44ed7b0a18c52ac"},
+    {file = "torch-2.3.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:09c81c5859a5b819956c6925a405ef1cdda393c9d8a01ce3851453f699d3358c"},
+    {file = "torch-2.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:1bf023aa20902586f614f7682fedfa463e773e26c58820b74158a72470259459"},
+    {file = "torch-2.3.0-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:758ef938de87a2653bba74b91f703458c15569f1562bf4b6c63c62d9c5a0c1f5"},
+    {file = "torch-2.3.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:493d54ee2f9df100b5ce1d18c96dbb8d14908721f76351e908c9d2622773a788"},
+    {file = "torch-2.3.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:bce43af735c3da16cc14c7de2be7ad038e2fbf75654c2e274e575c6c05772ace"},
+    {file = "torch-2.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:729804e97b7cf19ae9ab4181f91f5e612af07956f35c8b2c8e9d9f3596a8e877"},
+    {file = "torch-2.3.0-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:d24e328226d8e2af7cf80fcb1d2f1d108e0de32777fab4aaa2b37b9765d8be73"},
+    {file = "torch-2.3.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:b0de2bdc0486ea7b14fc47ff805172df44e421a7318b7c4d92ef589a75d27410"},
+    {file = "torch-2.3.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:a306c87a3eead1ed47457822c01dfbd459fe2920f2d38cbdf90de18f23f72542"},
+    {file = "torch-2.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:f9b98bf1a3c8af2d4c41f0bf1433920900896c446d1ddc128290ff146d1eb4bd"},
+    {file = "torch-2.3.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:dca986214267b34065a79000cee54232e62b41dff1ec2cab9abc3fc8b3dee0ad"},
+    {file = "torch-2.3.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:20572f426965dd8a04e92a473d7e445fa579e09943cc0354f3e6fef6130ce061"},
+    {file = "torch-2.3.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:e65ba85ae292909cde0dde6369826d51165a3fc8823dc1854cd9432d7f79b932"},
+    {file = "torch-2.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:5515503a193781fd1b3f5c474e89c9dfa2faaa782b2795cc4a7ab7e67de923f6"},
+    {file = "torch-2.3.0-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:6ae9f64b09516baa4ef890af0672dc981c20b1f0d829ce115d4420a247e88fba"},
+    {file = "torch-2.3.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:cd0dc498b961ab19cb3f8dbf0c6c50e244f2f37dbfa05754ab44ea057c944ef9"},
+    {file = "torch-2.3.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:e05f836559251e4096f3786ee99f4a8cbe67bc7fbedba8ad5e799681e47c5e80"},
+    {file = "torch-2.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:4fb27b35dbb32303c2927da86e27b54a92209ddfb7234afb1949ea2b3effffea"},
+    {file = "torch-2.3.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:760f8bedff506ce9e6e103498f9b1e9e15809e008368594c3a66bf74a8a51380"},
 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = "*"
 jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
 networkx = "*"
 nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
@@ -3897,10 +3944,10 @@ nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linu
 nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
+triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -3929,13 +3976,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.40.0"
+version = "4.40.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.40.0-py3-none-any.whl", hash = "sha256:92797ec3368ed4476a053529a4039a12ad09167d9e371981dda4afb4bdf590ac"},
-    {file = "transformers-4.40.0.tar.gz", hash = "sha256:fdb01dfe6a492bd34e3fa2aefffa470b1d8a2341db47a932f83ed33839d96b03"},
+    {file = "transformers-4.40.2-py3-none-any.whl", hash = "sha256:71cb94301ec211a2e1d4b8c8d18dcfaa902dfa00a089dceca167a8aa265d6f2d"},
+    {file = "transformers-4.40.2.tar.gz", hash = "sha256:657b6054a2097671398d976ad46e60836e7e15f9ea9551631a96e33cb9240649"},
 ]
 
 [package.dependencies]
@@ -3997,17 +4044,17 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
-version = "2.2.0"
+version = "2.3.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 files = [
-    {file = "triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5"},
-    {file = "triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0"},
-    {file = "triton-2.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5"},
-    {file = "triton-2.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab"},
-    {file = "triton-2.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace"},
-    {file = "triton-2.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df"},
+    {file = "triton-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ce4b8ff70c48e47274c66f269cce8861cf1dc347ceeb7a67414ca151b1822d8"},
+    {file = "triton-2.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c3d9607f85103afdb279938fc1dd2a66e4f5999a58eb48a346bd42738f986dd"},
+    {file = "triton-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:218d742e67480d9581bafb73ed598416cc8a56f6316152e5562ee65e33de01c0"},
+    {file = "triton-2.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:381ec6b3dac06922d3e4099cfc943ef032893b25415de295e82b1a82b0359d2c"},
+    {file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"},
+    {file = "triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8f636e0341ac348899a47a057c3daea99ea7db31528a225a3ba4ded28ccc65"},
 ]
 
 [package.dependencies]
@@ -4410,4 +4457,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10, <3.12"
-content-hash = "4dfbb782dd8c15cdd86b9384af79b2f660c743e65e5d16883d8aa6f5aadb2191"
+content-hash = "df0ee9555cbcdd286b85f470ed3044630043ad4b6799907b158d1cd30ffaf518"
diff --git a/course/module-3/pyproject.toml b/course/module-3/pyproject.toml
index db34828..c240aad 100644
--- a/course/module-3/pyproject.toml
+++ b/course/module-3/pyproject.toml
@@ -10,6 +10,13 @@ authors = [
 package-mode = false
 readme = "README.md"
 
+[tool.ruff]
+line-length = 88
+select = [
+    "F401",
+    "F403",
+]
+
 
 [tool.poetry.dependencies]
 python = ">=3.10, <3.12"
@@ -31,6 +38,7 @@ rich = "^13.7.1"
 pip = "^24.0"
 install = "^1.3.5"
 comet-ml = "^3.41.0"
+ruff = "^0.4.3"
 
 
 [build-system]
diff --git a/course/module-3/settings.py b/course/module-3/settings.py
index fa14599..00cf117 100644
--- a/course/module-3/settings.py
+++ b/course/module-3/settings.py
@@ -22,8 +22,8 @@ class AppSettings(BaseSettings):
     QDRANT_DATABASE_PORT: int = 6333
     QDRANT_DATABASE_URL: str = "http://localhost:6333"
 
-    QDRANT_CLOUD_URL: str = "https://9f7d031c-7c76-4dbb-9c68-481a898d23e9.europe-west3-0.gcp.cloud.qdrant.io"
-    QDRANT_CLOUD: bool = True
+    QDRANT_CLOUD_URL: str = "str"
+    USE_QDRANT_CLOUD: bool = True
     QDRANT_APIKEY: str | None = None
 
     # MQ config

From c940c3f87ea9792f3c239a149f0a6381be581e18 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Wed, 8 May 2024 11:50:35 +0300
Subject: [PATCH 13/19] end-to-end testing. Still problem to articles. Try and
 except added. TODO: generalise generation for articles

---
 course/module-3/finetuning/generate_data.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/course/module-3/finetuning/generate_data.py b/course/module-3/finetuning/generate_data.py
index e1724fe..f88d91e 100644
--- a/course/module-3/finetuning/generate_data.py
+++ b/course/module-3/finetuning/generate_data.py
@@ -73,9 +73,12 @@ def generate_training_data(
         all_contents = self.fetch_all_cleaned_content(collection_name)
         response = []
         for i in range(0, len(all_contents), batch_size):
-            batch = all_contents[i : i + batch_size]
-            initial_prompt = data_formatter.format_prompt(example_content, batch)
-            response += self.api_communicator.send_prompt(initial_prompt)
+            try:
+                batch = all_contents[i : i + batch_size]
+                initial_prompt = data_formatter.format_prompt(example_content, batch)
+                response += self.api_communicator.send_prompt(initial_prompt)
+            except:
+                continue
 
         self.push_to_comet(response, collection_name)
 

From 94ced14bbbf9ec12d49b74dbdbd2f2cff00259f3 Mon Sep 17 00:00:00 2001
From: Anca <ancamuscalagiu1@gmail.com>
Date: Wed, 8 May 2024 21:36:30 +0300
Subject: [PATCH 14/19] fixed articles bug, removed try catch

---
 course/module-3/finetuning/generate_data.py   | 54 ++++++++-----------
 .../module-3/finetuning/llm_communication.py  |  1 +
 2 files changed, 24 insertions(+), 31 deletions(-)

diff --git a/course/module-3/finetuning/generate_data.py b/course/module-3/finetuning/generate_data.py
index f88d91e..605017b 100644
--- a/course/module-3/finetuning/generate_data.py
+++ b/course/module-3/finetuning/generate_data.py
@@ -18,67 +18,62 @@
 
 data_type = "posts"
 USER_PROMPT = (
-    f"In the following rows I will give you 2 json objects as example, each of them having an instruction which "
-    f"describes what to write about and the corresponding {data_type} content that follows this instruction. Afterwards I "
-    f"will give you batches of other contents of {data_type} and please generate me one instruction for each of them. The {data_type} text "
-    f"for which you have to generate the instructions is under Content number x lines. Please structure them in json format,"
-    f"ready to be loaded in json,a list of objects only with fields called instruction ( the generated part ) and content ( the provided part ). "
-    f"Please do not add any extra characters!\n"
+    f"I will give you batches of contents of {data_type}. Please generate me exactly 1 instruction for each of them. The {data_type} text "
+    f"for which you have to generate the instructions is under Content number x lines. Please structure the answer in json format,"
+    f"ready to be loaded by json.loads(), a list of objects only with fields called instruction and content. For the content field, copy the number of the content only!."
+    f"Please do not add any extra characters and make sure it is a list with objects in valid json format!\n"
 )
 
 
 class DataFormatter:
     @classmethod
-    def format_data(cls, data_points: list, is_example: bool) -> str:
+    def format_data(cls, data_points: list, is_example: bool, start_index: int) -> str:
         text = ""
         for index, data_point in enumerate(data_points):
             if not is_example:
-                text += f"Content number {index + 1}\n"
+                text += f"Content number {start_index + index }\n"
             text += str(data_point) + "\n"
         return text
 
     @classmethod
-    def format_batch(cls, context_msg: str, data_points: list) -> str:
+    def format_batch(cls, context_msg: str, data_points: list, start_index: int) -> str:
         delimiter_msg = context_msg
-        delimiter_msg += cls.format_data(data_points, False)
+        delimiter_msg += cls.format_data(data_points, False, start_index)
         return delimiter_msg
 
     @classmethod
-    def format_prompt(cls, example_content: list, inference_posts: list):
+    def format_prompt(cls, inference_posts: list, start_index : int):
         initial_prompt = USER_PROMPT  # Assuming USER_PROMPT is defined somewhere as a global constant or class attribute
-        initial_prompt += f"You must generate exactly a list of {len(inference_posts)} json objects, using the contents provided under CONTENTS FOR GENERATION, the EXAMPLE JSONS are just for example purpose.\n"
-        initial_prompt += "EXAMPLE JSONS:\n"
-        initial_prompt += cls.format_data(example_content, True)
+        initial_prompt += f"You must generate exactly a list of {len(inference_posts)} json objects, using the contents provided under CONTENTS FOR GENERATION\n"
         initial_prompt += cls.format_batch(
-            "CONTENTS FOR GENERATION: \n", inference_posts
+            "\nCONTENTS FOR GENERATION: \n", inference_posts, start_index
         )
         return initial_prompt
 
 
 class DatasetGenerator:
     def __init__(
-        self,
-        file_handler: FileHandler,
-        api_communicator: GptCommunicator,
-        data_formatter: DataFormatter,
+            self,
+            file_handler: FileHandler,
+            api_communicator: GptCommunicator,
+            data_formatter: DataFormatter,
     ):
         self.file_handler = file_handler
         self.api_communicator = api_communicator
         self.data_formatter = data_formatter
 
     def generate_training_data(
-        self, example_file: str, collection_name: str, batch_size: int = 1
+            self, collection_name: str, batch_size: int = 1
     ):
-        example_content = self.file_handler.read_json(example_file)
         all_contents = self.fetch_all_cleaned_content(collection_name)
         response = []
         for i in range(0, len(all_contents), batch_size):
-            try:
-                batch = all_contents[i : i + batch_size]
-                initial_prompt = data_formatter.format_prompt(example_content, batch)
-                response += self.api_communicator.send_prompt(initial_prompt)
-            except:
-                continue
+            batch = all_contents[i: i + batch_size]
+            initial_prompt = data_formatter.format_prompt(batch, i)
+            response += self.api_communicator.send_prompt(initial_prompt)
+            for j in range(i, i + batch_size):
+                response[j]["content"] = all_contents[j]
+
 
         self.push_to_comet(response, collection_name)
 
@@ -127,9 +122,6 @@ def fetch_all_cleaned_content(self, collection_name: str) -> list:
 
 
 if __name__ == "__main__":
-    filename = "example_content.json"
-    relative_directory = "finetuning"
-    example_file = Path(relative_directory) / filename
     collection_names = ["cleaned_articles", "cleaned_posts"]
     file_handler = FileHandler()
     api_communicator = GptCommunicator()
@@ -137,5 +129,5 @@ def fetch_all_cleaned_content(self, collection_name: str) -> list:
     dataset_generator = DatasetGenerator(file_handler, api_communicator, data_formatter)
     for collection in collection_names:
         dataset_generator.generate_training_data(
-            example_file=example_file, collection_name=collection, batch_size=1
+            collection_name=collection, batch_size=1
         )
diff --git a/course/module-3/finetuning/llm_communication.py b/course/module-3/finetuning/llm_communication.py
index 53a856e..8723d79 100644
--- a/course/module-3/finetuning/llm_communication.py
+++ b/course/module-3/finetuning/llm_communication.py
@@ -32,6 +32,7 @@ def send_prompt(self, prompt: str) -> list:
             return json.loads(self.clean_response(response))
         except Exception as e:
             logging.error(f"Skipping batch! An error occurred while communicating with API: {e}")
+            return []
 
     @staticmethod
     def clean_response(response: str) -> str:

From 7f1675c477070e10aac250d0d7be474ec8725907 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Thu, 9 May 2024 08:09:08 +0300
Subject: [PATCH 15/19] everything its working

---
 course/module-3/db/mongo.py                   |  3 ++-
 .../module-3/finetuning/example_content.json  | 10 ----------
 course/module-3/finetuning/generate_data.py   | 19 ++++++++-----------
 3 files changed, 10 insertions(+), 22 deletions(-)
 delete mode 100644 course/module-3/finetuning/example_content.json

diff --git a/course/module-3/db/mongo.py b/course/module-3/db/mongo.py
index 3cb6c0e..a49526a 100644
--- a/course/module-3/db/mongo.py
+++ b/course/module-3/db/mongo.py
@@ -1,6 +1,7 @@
-import logger_utils
 from pymongo import MongoClient
 from pymongo.errors import ConnectionFailure
+
+import logger_utils
 from settings import settings
 
 logger = logger_utils.get_logger(__name__)
diff --git a/course/module-3/finetuning/example_content.json b/course/module-3/finetuning/example_content.json
deleted file mode 100644
index 1d3230b..0000000
--- a/course/module-3/finetuning/example_content.json
+++ /dev/null
@@ -1,10 +0,0 @@
-[
-    {
-      "instruction": "Write a post presenting a list of essential ML monitoring metrics, aiming to educate followers on effective monitoring practices.",
-      "content": "I compiled a list of the top 10 metrics your ML monitoring system should track.\n.\n#𝟏 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐦𝐞𝐭𝐫𝐢𝐜𝐬\nUsed to monitor the health of your overall software system.\n➝ latency\n➝ throughput\n➝ CPU/GPU utilization\n➝ memory utilization\n➝ the number of requests your model receives in the last X minutes/hours/days\n➝ the number of successful requests\n#𝟐 𝐌𝐋-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐦𝐞𝐭𝐫𝐢𝐜𝐬\nUsed to monitor the performance of your ML system.\n➝ accuracy (extremely useful when you have feedback or natural labels from the user: click rate, upvote, downvote, purchases, bookmarks, views, etc.)\n➝ predictions (as predictions are low dimensional various statistics are easy to compute + the distribution of the prediction represents a proxy for the input distribution)\n➝ features (feature validation + two-sample tests for drift detection)\n➝ raw inputs (these are harder to monitor due to their scattered nature within the infrastructure)\n-----\n💡 My goal is to make\nhashtag\n#\nml\neasy and intuitive. Follow me for daily insights about\nhashtag\n#\ndata\n,\nhashtag\n#\nml\n, and\nhashtag\n#\nmlops\n."
-    },
-    {
-      "instruction": "Explain the benefits of using the walrus operator in Python for writing cleaner code.",
-      "content": "Here is some 𝗣𝘆𝘁𝗵𝗼𝗻 𝘀𝘆𝗻𝘁𝗮𝘅 𝘀𝘂𝗴𝗮𝗿 that will help you 𝘄𝗿𝗶𝘁𝗲 𝗰𝗹𝗲𝗮𝗻𝗲𝗿 𝗰𝗼𝗱𝗲 ↓ \n\nI am talking about the 𝘸𝘢𝘭𝘳𝘶𝘴 𝘰𝘱𝘦𝘳𝘢𝘵𝘰𝘳 denoted by the `:=` symbol. \n\nIt was introduced in Python 3.8, but I rarely see it used. \n\nThus, as a \"clean code\" freak, I wanted to dedicate a post to it. \n\n𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝘄𝗮𝗹𝗿𝘂𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿 𝗱𝗼? \n\nIt's an assignment expression that allows you to assign and return a value in the same expression. \n\n𝗪𝗵𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝘁? \n\n𝘊𝘰𝘯𝘤𝘪𝘴𝘦𝘯𝘦𝘴𝘴: It reduces the number of lines needed for variable assignment and checking, making code more concise. \n\n𝘙𝘦𝘢𝘥𝘢𝘣𝘪𝘭𝘪𝘵𝘺: It can enhance readability by keeping related logic close, although this depends on the context and the reader's familiarity with exotic Python syntax. \n\n𝙃𝙚𝙧𝙚 𝙖𝙧𝙚 𝙨𝙤𝙢𝙚 𝙚𝙭𝙖𝙢𝙥𝙡𝙚𝙨 \n\n↓↓↓ \n\n1. Using the walrus operator, you can directly assign the result of the 𝘭𝘦𝘯() function inside an if statement. \n\n2. Avoid calling the same function twice in a while loop. The benefit is less code and makes everything more readable. \n\n3. Another use case arises in list comprehensions where a value computed in a filtering condition is also needed in the expression body. Before the 𝘸𝘢𝘭𝘳𝘶𝘴 𝘰𝘱𝘦𝘳𝘢𝘵𝘰𝘳, if you had to apply a function to an item from a list and filter it based on some criteria, you had to refactor it to a standard for loop. \n\n. \n\nWhen writing clean code, the detail matters. \n\nThe details make the difference between a codebase that can be read like a book or one with 10 WTFs / seconds. \n\n. \n\nWhat do you think? Does the walrus operator make the Python code more readable and concise? \n\nhashtag\n#machinelearning \nhashtag\n#mlops \nhashtag\n#datascience \n"
-    }
-  ]
\ No newline at end of file
diff --git a/course/module-3/finetuning/generate_data.py b/course/module-3/finetuning/generate_data.py
index 605017b..e00ed18 100644
--- a/course/module-3/finetuning/generate_data.py
+++ b/course/module-3/finetuning/generate_data.py
@@ -42,8 +42,8 @@ def format_batch(cls, context_msg: str, data_points: list, start_index: int) ->
         return delimiter_msg
 
     @classmethod
-    def format_prompt(cls, inference_posts: list, start_index : int):
-        initial_prompt = USER_PROMPT  # Assuming USER_PROMPT is defined somewhere as a global constant or class attribute
+    def format_prompt(cls, inference_posts: list, start_index: int):
+        initial_prompt = USER_PROMPT
         initial_prompt += f"You must generate exactly a list of {len(inference_posts)} json objects, using the contents provided under CONTENTS FOR GENERATION\n"
         initial_prompt += cls.format_batch(
             "\nCONTENTS FOR GENERATION: \n", inference_posts, start_index
@@ -53,28 +53,25 @@ def format_prompt(cls, inference_posts: list, start_index : int):
 
 class DatasetGenerator:
     def __init__(
-            self,
-            file_handler: FileHandler,
-            api_communicator: GptCommunicator,
-            data_formatter: DataFormatter,
+        self,
+        file_handler: FileHandler,
+        api_communicator: GptCommunicator,
+        data_formatter: DataFormatter,
     ):
         self.file_handler = file_handler
         self.api_communicator = api_communicator
         self.data_formatter = data_formatter
 
-    def generate_training_data(
-            self, collection_name: str, batch_size: int = 1
-    ):
+    def generate_training_data(self, collection_name: str, batch_size: int = 1):
         all_contents = self.fetch_all_cleaned_content(collection_name)
         response = []
         for i in range(0, len(all_contents), batch_size):
-            batch = all_contents[i: i + batch_size]
+            batch = all_contents[i : i + batch_size]
             initial_prompt = data_formatter.format_prompt(batch, i)
             response += self.api_communicator.send_prompt(initial_prompt)
             for j in range(i, i + batch_size):
                 response[j]["content"] = all_contents[j]
 
-
         self.push_to_comet(response, collection_name)
 
     def push_to_comet(self, data: list, collection_name: str):

From 2a67ab1cfb85304c51b03c6a3afbcdfbf98dc4c7 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Thu, 9 May 2024 08:39:10 +0300
Subject: [PATCH 16/19] Readme modified

---
 course/module-3/README.md | 54 ++++++++++++++++++++++-----------------
 1 file changed, 31 insertions(+), 23 deletions(-)

diff --git a/course/module-3/README.md b/course/module-3/README.md
index 1e8fc2f..eb89bac 100644
--- a/course/module-3/README.md
+++ b/course/module-3/README.md
@@ -1,13 +1,40 @@
 # Introduction
-This module is composed from 2 modules:
-- RAG module
-- Finetuning dataset preparation module
+This module is composed from 2 components:
+- RAG component
+- Finetuning dataset preparation component
+
+
+A production RAG system is split into 3 main components:
+
+    - ingestion: clean, chunk, embed, and load your data to a vector DB
+    - retrieval: query your vector DB for context
+    - generation: attach the retrieved context to your prompt and pass it to an LLM
+
+The ingestion component sits in the feature pipeline, while the retrieval and generation components are implemented inside the inference pipeline.
+
+You can also use the retrieval and generation components in your training pipeline to fine-tune your LLM further on domain-specific prompts.
+
+You can apply advanced techniques to optimize your RAG system for ingestion, retrieval and generation.
+
+That being said, there are 3 main types of advanced RAG techniques:
+
+    - Pre-retrieval optimization [ingestion]: tweak how you create the chunks
+    - Retrieval optimization [retrieval]: improve the queries to your vector DB
+    - Post-retrieval optimization [retrieval]: process the retrieved chunks to filter out the noise
+
+You can learn more about RAG from Decoding ML LLM Twin Course: 
+- Lesson 4: [SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG — in Real-Time!](https://medium.com/decodingml/sota-python-streaming-pipelines-for-fine-tuning-llms-and-rag-in-real-time-82eb07795b87)
+- Lesson 5: [The 4 Advanced RAG Algorithms You Must Know to Implement](https://medium.com/decodingml/the-4-advanced-rag-algorithms-you-must-know-to-implement-5d0c7f1199d2)
+
+![Advanced RAG architecture](https://miro.medium.com/v2/resize:fit:720/format:webp/1*ui2cQRlRDVnKrXPXk7COLA.png "Advanced RAG architecture")
 
 # Installation and Setup
-To prepare your environment for these modules, follow these steps:
+To prepare your environment for these components, follow these steps:
 - `poetry init`
 - `poetry install`
 
+
+
 # Docker Settings
 ### Host Configuration
 To ensure that your Docker containers can communicate with each other you need to update your `/etc/hosts` file. 
@@ -142,25 +169,6 @@ The `insert_data_mongo.py` script is designed to manage the automated downloadin
 
 # Module 3: RAG
 
-# Introduction
-
-A production RAG system is split into 3 main components:
-
-    - ingestion: clean, chunk, embed, and load your data to a vector DB
-    - retrieval: query your vector DB for context
-    - generation: attach the retrieved context to your prompt and pass it to an LLM
-
-The ingestion component sits in the feature pipeline, while the retrieval and generation components are implemented inside the inference pipeline.
-
-You can also use the retrieval and generation components in your training pipeline to fine-tune your LLM further on domain-specific prompts.
-
-You can apply advanced techniques to optimize your RAG system for ingestion, retrieval and generation.
-
-That being said, there are 3 main types of advanced RAG techniques:
-
-    - Pre-retrieval optimization [ingestion]: tweak how you create the chunks
-    - Retrieval optimization [retrieval]: improve the queries to your vector DB
-    - Post-retrieval optimization [retrieval]: process the retrieved chunks to filter out the noise
 
 # RAG Module Structure
 ### Query Expansion

From c3af8bb6aea9ea6ebdbcfc84c9b9d668770eafc5 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Thu, 9 May 2024 08:42:59 +0300
Subject: [PATCH 17/19] test readme

---
 course/module-3/README.md | 62 ++++++++++++++++++++++-----------------
 1 file changed, 35 insertions(+), 27 deletions(-)

diff --git a/course/module-3/README.md b/course/module-3/README.md
index eb89bac..ea61d42 100644
--- a/course/module-3/README.md
+++ b/course/module-3/README.md
@@ -3,7 +3,7 @@ This module is composed from 2 components:
 - RAG component
 - Finetuning dataset preparation component
 
-
+# RAG component
 A production RAG system is split into 3 main components:
 
     - ingestion: clean, chunk, embed, and load your data to a vector DB
@@ -28,14 +28,30 @@ You can learn more about RAG from Decoding ML LLM Twin Course:
 
 ![Advanced RAG architecture](https://miro.medium.com/v2/resize:fit:720/format:webp/1*ui2cQRlRDVnKrXPXk7COLA.png "Advanced RAG architecture")
 
-# Installation and Setup
+# TEST
+
+# Finetuning dataset preparation component
+The finetuning dataset preparation module automates the generation of datasets specifically formatted for training and fine-tuning Large Language Models (LLMs). It interfaces with Qdrant, sends structured prompts to LLMs, and manages data with Comet ML for experiment tracking and artifact logging.
+
+### Why is fine-tuning important?
+1. **Model Customization**: Tailors the LLM's responses to specific domains or tasks.
+2. **Improved Accuracy**: Enhances the model's understanding of nuanced language used in specialized fields.
+3. **Efficiency**: Reduces the need for extensive post-processing by producing more relevant outputs directly.
+4. **Adaptability**: Allows models to continuously learn from new data, staying relevant as language and contexts evolve.
+
+TBD add about lesson
+![Finetuning Dataset Preparation Flow](https://cdn-images-1.medium.com/max/800/1*gufpoEo92ZtuGQlVx6HVTw.png "Finetuning Dataset Preparation Flow")
+
+
+# Dependencies
+## Installation and Setup
 To prepare your environment for these components, follow these steps:
 - `poetry init`
 - `poetry install`
 
 
 
-# Docker Settings
+## Docker Settings
 ### Host Configuration
 To ensure that your Docker containers can communicate with each other you need to update your `/etc/hosts` file. 
 Add the following entries to map the hostnames to your local machine:
@@ -47,10 +63,12 @@ Add the following entries to map the hostnames to your local machine:
 127.0.0.1       mongo3
 ```
 
+For the Windows users check this article: https://medium.com/workleap/the-only-local-mongodb-replica-set-with-docker-compose-guide-youll-ever-need-2f0b74dd8384
+
 # CometML Integration
 
 ## Overview
-[CometML](https://www.comet.ml/) is a cloud-based platform that provides tools for tracking, comparing, explaining, and optimizing experiments and models in machine learning. CometML helps data scientists and teams to better manage and collaborate on machine learning experiments.
+[CometML](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) is a cloud-based platform that provides tools for tracking, comparing, explaining, and optimizing experiments and models in machine learning. CometML helps data scientists and teams to better manage and collaborate on machine learning experiments.
 
 ## Why Use CometML?
 - **Experiment Tracking**: CometML automatically tracks your code, experiments, and results, allowing you to compare between different runs and configurations visually.
@@ -70,7 +88,7 @@ When integrating CometML into your projects, you'll need to set up several envir
 To access and set up the necessary CometML variables for your project, follow these steps:
 
 1. **Create an Account or Log In**:
-   - Visit [CometML's website](https://www.comet.com/) and log in if you already have an account, or sign up if you're a new user.
+   - Visit [CometML's website](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) and log in if you already have an account, or sign up if you're a new user.
 
 2. **Create a New Project**:
    - Once logged in, navigate to your dashboard. Here, you can create a new project by clicking on "New Project" and entering the relevant details for your project.
@@ -86,7 +104,7 @@ To access and set up the necessary CometML variables for your project, follow th
 # Qdrant Integration
 
 ## Overview
-[Qdrant](https://qdrant.tech/) is an open-source vector database designed for storing and searching large volumes of high-dimensional vector data. It is particularly suited for tasks that require searching through large datasets to find items similar to a query item, such as in recommendation systems or image retrieval applications.
+[Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course) is an open-source vector database designed for storing and searching large volumes of high-dimensional vector data. It is particularly suited for tasks that require searching through large datasets to find items similar to a query item, such as in recommendation systems or image retrieval applications.
 
 ## Why Use Qdrant?
 Qdrant is a robust vector database optimized for handling high-dimensional vector data, making it ideal for machine learning and AI applications. Here are the key reasons for using Qdrant:
@@ -98,7 +116,7 @@ Qdrant is a robust vector database optimized for handling high-dimensional vecto
 
 ## Setting Up Qdrant
 
-Qdrant can be utilized via its Docker implementation or through its managed cloud service(https://cloud.qdrant.io/login). This flexibility allows you to choose an environment that best suits your project's needs.
+[Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course) can be utilized via its Docker implementation or through its managed cloud service(https://cloud.qdrant.io/login). This flexibility allows you to choose an environment that best suits your project's needs.
 
 ### Environment Variables
 To configure your environment for Qdrant, set the following variables:
@@ -111,6 +129,8 @@ To configure your environment for Qdrant, set the following variables:
 - `QDRANT_CLOUD_URL`: The URL for accessing Qdrant Cloud services.
 - `QDRANT_APIKEY`: The API key for authenticating with Qdrant Cloud.
 
+Please check this article to learn how to obtain these variables (https://qdrant.tech/documentation/cloud/quickstart-cloud/)
+
 Additionally, you can control the connection mode (Cloud or Docker) using a setting in your configuration file. More details can be found in db/qdrant.py :
 
 ```python
@@ -160,14 +180,14 @@ The `insert_data_mongo.py` script is designed to manage the automated downloadin
 ### Features
 - **Dataset Downloading**: Automatically downloads JSON formatted data files from Google Drive based on predefined file IDs.
 - **Dynamic Data Insertion**: Inserts different types of documents (articles, posts, repositories) into the MongoDB database, associating each entry with its respective author.
-- **Logging**: Provides logging for each step of the process to track the number and type of documents inserted.
+
 
 ### How It Works
 1. **Download Data**: The script first checks if the specified `output_dir` directory exists and contains any files. If not, it creates the directory and downloads the data files from Google Drive.
 2. **Insert Data**: Based on the type specified in the downloaded files, it inserts posts, articles, or repositories into the MongoDB database.
 3. **Logging**: After each insertion, the script logs the number of items inserted and their associated author ID to help monitor the process.
 
-# Module 3: RAG
+# RAG Component
 
 
 # RAG Module Structure
@@ -183,15 +203,15 @@ The `insert_data_mongo.py` script is designed to manage the automated downloadin
 ### Self Query
 - `self_query.py`: Generates metadata attributes related to a query, such as author ID, using a self-query mechanism. It employs a `SelfQueryTemplate` and the `ChatOpenAI` model to extract required metadata from the query context.
 
-# Usage
-After you have everything setup, environemnt variables, [CometML](https://www.comet.com/site/?cache=20258256) account and [Qdrant](https://qdrant.tech/) account, it's time to insert data in your environment.
+### Usage
+After you have everything setup, environemnt variables, [CometML](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) account and [Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course) account, it's time to insert data in your environment.
 
 The workflow is straightforward:
 
 - Start all the services: MongoDB, Qdrant, RabbitMQ
 - Start the CDC system (for more details you can check https://medium.com/decodingml/the-3nd-out-of-11-lessons-of-the-llm-twin-free-course-ba82752dad5a)
 - Insert data to mongodb by running  `make insert-data-mongo`
-- Insert data into Qdrant VectorDB by running the [Bytewax](https://bytewax.io/) pipelines
+- Insert data into Qdrant VectorDB by running the [Bytewax](https://bytewax.io?utm_source=github&utm_medium=decodingml&utm_campaign=2024_q1) pipelines
 - Go to retriver.py (outside of rag folder) and write your own query
 
 The project includes a `Makefile` for easy management of common tasks. Here are the main commands you can use:
@@ -204,22 +224,10 @@ The project includes a `Makefile` for easy management of common tasks. Here are
 - `make local-test-retriever:-`: Test RAG retrieval
 
 
-# Module 4: Generate Data for LLM finetuning task
-
-# Introduction
-This Python module automates the generation of datasets specifically formatted for training and fine-tuning Large Language Models (LLMs). It interfaces with Qdrant, sends structured prompts to LLMs, and manages data with Comet ML for experiment tracking and artifact logging.
-
-### Why is fine-tuning important?
-1. **Model Customization**: Tailors the LLM's responses to specific domains or tasks.
-2. **Improved Accuracy**: Enhances the model's understanding of nuanced language used in specialized fields.
-3. **Efficiency**: Reduces the need for extensive post-processing by producing more relevant outputs directly.
-4. **Adaptability**: Allows models to continuously learn from new data, staying relevant as language and contexts evolve.
-
+# Generate Data for LLM finetuning task component
 
-# Module Structure
+# Component Structure
 
-### Example Content
-- `example_content.json`: Prompt examples used to guide the LLM in dataset generation
 
 ### File Handling
 - `file_handler.py`: Manages file I/O operations, enabling reading and writing of JSON formatted data.
@@ -231,7 +239,7 @@ This Python module automates the generation of datasets specifically formatted f
 - `generate_data.py`: Orchestrates the generation of training data by integrating file handling, LLM communication, and data formatting.
 
 
-# Usage
+### Usage
 
 The project includes a `Makefile` for easy management of common tasks. Here are the main commands you can use:
 

From 958728cac2cbbb1c82b87f6b816a5bc2e836848f Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Thu, 9 May 2024 08:43:19 +0300
Subject: [PATCH 18/19] test readme

---
 course/module-3/README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/course/module-3/README.md b/course/module-3/README.md
index ea61d42..5059eb3 100644
--- a/course/module-3/README.md
+++ b/course/module-3/README.md
@@ -28,7 +28,6 @@ You can learn more about RAG from Decoding ML LLM Twin Course:
 
 ![Advanced RAG architecture](https://miro.medium.com/v2/resize:fit:720/format:webp/1*ui2cQRlRDVnKrXPXk7COLA.png "Advanced RAG architecture")
 
-# TEST
 
 # Finetuning dataset preparation component
 The finetuning dataset preparation module automates the generation of datasets specifically formatted for training and fine-tuning Large Language Models (LLMs). It interfaces with Qdrant, sends structured prompts to LLMs, and manages data with Comet ML for experiment tracking and artifact logging.

From a86fa654f3ad757a9c2bc42a6e63c5f33e386c15 Mon Sep 17 00:00:00 2001
From: Vesa Alexandru <vesaalexandru95@gmail.com>
Date: Fri, 10 May 2024 09:46:22 +0300
Subject: [PATCH 19/19] fixes

---
 course/module-3/Makefile                    | 2 +-
 course/module-3/README.md                   | 6 +++---
 course/module-3/finetuning/generate_data.py | 5 +----
 3 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/course/module-3/Makefile b/course/module-3/Makefile
index db834c6..99ac753 100644
--- a/course/module-3/Makefile
+++ b/course/module-3/Makefile
@@ -17,7 +17,7 @@ local-bytewax: # Run bytewax pipeline
 	RUST_BACKTRACE=full poetry run python -m bytewax.run data_flow/bytewax_pipeline 
 
 generate-dataset: # Generate dataset for finetuning and version it in CometML
-	poetry run python finetuning/generate_data.py
+	python finetuning/generate_data.py
 
 local-test-retriever: # Test retriever
 	poetry run python retriever.py
diff --git a/course/module-3/README.md b/course/module-3/README.md
index 5059eb3..630ef05 100644
--- a/course/module-3/README.md
+++ b/course/module-3/README.md
@@ -128,12 +128,12 @@ To configure your environment for Qdrant, set the following variables:
 - `QDRANT_CLOUD_URL`: The URL for accessing Qdrant Cloud services.
 - `QDRANT_APIKEY`: The API key for authenticating with Qdrant Cloud.
 
-Please check this article to learn how to obtain these variables (https://qdrant.tech/documentation/cloud/quickstart-cloud/)
+Please check this article to learn how to obtain these variables (https://qdrant.tech/documentation/cloud/quickstart-cloud/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course)
 
 Additionally, you can control the connection mode (Cloud or Docker) using a setting in your configuration file. More details can be found in db/qdrant.py :
 
 ```python
-QDRANT_CLOUD: True  # Set to False to use Docker setup
+USE_QDRANT_CLOUD: True  # Set to False to use Docker setup
 ```
 
 
@@ -152,7 +152,7 @@ QDRANT_DATABASE_HOST="localhost"
 QDRANT_DATABASE_PORT=6333
 QDRANT_APIKEY= your-key
 QDRANT_CLOUD_URL=your-url
-QDRANT_CLOUD=False
+USE_QDRANT_CLOUD=False
 
 # MQ config
 RABBITMQ_DEFAULT_USERNAME=guest
diff --git a/course/module-3/finetuning/generate_data.py b/course/module-3/finetuning/generate_data.py
index e00ed18..0818eb6 100644
--- a/course/module-3/finetuning/generate_data.py
+++ b/course/module-3/finetuning/generate_data.py
@@ -1,10 +1,7 @@
 import json
 import logging
-import sys
-from pathlib import Path
-
-sys.path.append(str(Path(__file__).resolve().parent.parent))
 
+# sys.path.append(str(Path(__file__).resolve().parent.parent))
 from comet_ml import Artifact, Experiment
 
 from db.qdrant import connection as client