diff --git a/.gitignore b/.gitignore
index 22b85a8..ea175e5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -159,4 +159,7 @@ cython_debug/
 .vscode
 
 # MacOS
-.DS_Store
\ No newline at end of file
+.DS_Store
+
+# Ruff
+.ruff_cache
diff --git a/course/module-1/crawlers/linkedin.py b/course/module-1/crawlers/linkedin.py
index 8fb8c62..dd4547d 100644
--- a/course/module-1/crawlers/linkedin.py
+++ b/course/module-1/crawlers/linkedin.py
@@ -37,8 +37,10 @@ def extract(self, link: str, **kwargs):
 
         self.driver.get(link)
         time.sleep(5)
-        button = self.driver.find_element(By.CSS_SELECTOR,
-                                          ".app-aware-link.profile-creator-shared-content-view__footer-action")
+        button = self.driver.find_element(
+            By.CSS_SELECTOR,
+            ".app-aware-link.profile-creator-shared-content-view__footer-action"
+        )
         button.click()
 
         # Scrolling and scraping posts
@@ -57,12 +59,7 @@ def extract(self, link: str, **kwargs):
         self.driver.close()
 
         self.model.bulk_insert(
-            [
-                PostDocument(
-                    platform="linkedin", content=post, author_id=kwargs.get("user")
-                )
-                for post in posts
-            ]
+            [PostDocument(platform="linkedin", content=post, author_id=kwargs.get("user")) for post in posts]
         )
 
         logger.info(f"Finished scrapping data for profile: {link}")
@@ -98,9 +95,7 @@ def _get_page_content(self, url: str) -> BeautifulSoup:
         time.sleep(5)
         return BeautifulSoup(self.driver.page_source, "html.parser")
 
-    def _extract_posts(
-        self, post_elements: List[Tag], post_images: Dict[str, str]
-    ) -> Dict[str, Dict[str, str]]:
+    def _extract_posts(self, post_elements: List[Tag], post_images: Dict[str, str]) -> Dict[str, Dict[str, str]]:
         """
         Extracts post texts and combines them with their respective images.
 
@@ -139,16 +134,8 @@ def login(self):
         """Log in to LinkedIn."""
         self.driver.get("https://www.linkedin.com/login")
         if not settings.LINKEDIN_USERNAME and not settings.LINKEDIN_PASSWORD:
-            raise ImproperlyConfigured(
-                "LinkedIn scraper requires an valid account to perform extraction"
-            )
+            raise ImproperlyConfigured("LinkedIn scraper requires an valid account to perform extraction")
 
-        self.driver.find_element(By.ID, "username").send_keys(
-            settings.LINKEDIN_USERNAME
-        )
-        self.driver.find_element(By.ID, "password").send_keys(
-            settings.LINKEDIN_PASSWORD
-        )
-        self.driver.find_element(
-            By.CSS_SELECTOR, ".login__form_action_container button"
-        ).click()
+        self.driver.find_element(By.ID, "username").send_keys(settings.LINKEDIN_USERNAME)
+        self.driver.find_element(By.ID, "password").send_keys(settings.LINKEDIN_PASSWORD)
+        self.driver.find_element(By.CSS_SELECTOR, ".login__form_action_container button").click()
diff --git a/course/module-2/docker-compose.yml b/course/module-2/docker-compose.yml
index c08c0e5..a2723e2 100644
--- a/course/module-2/docker-compose.yml
+++ b/course/module-2/docker-compose.yml
@@ -42,20 +42,3 @@ services:
       - ~/rabbitmq/data/:/var/lib/rabbitmq/
       - ~/rabbitmq/log/:/var/log/rabbitmq
     restart: always
-
-  # cdc:
-  #   build:
-  #     context: .
-  #     dockerfile: .docker/Dockerfile.cdc
-  #   container_name: scrabble_cdc
-    # depends_on:
-      # - mq
-      # # - mongo1
-      # # - mongo2
-      # # - mongo3
-    # restart: always
-    # environment:
-    #   - MONGO_DATABASE_HOST=mongodb://mongo1:30001,mongo2:30002,mongo3:30003/?replicaSet=my-replica-set
-    #   - MONGO_DATABASE_NAME=scrabble
-
-
diff --git a/course/module-3/.env.example b/course/module-3/.env.example
index b2202fc..6c0264c 100644
--- a/course/module-3/.env.example
+++ b/course/module-3/.env.example
@@ -1,3 +1,4 @@
+# Ingestion embedding model config
 EMBEDDING_MODEL_ID="sentence-transformers/all-MiniLM-L6-v2"
 EMBEDDING_MODEL_MAX_INPUT_LENGTH=256
 EMBEDDING_SIZE=384
@@ -19,3 +20,5 @@ RABBITMQ_DEFAULT_PASSWORD="guest"
 RABBITMQ_HOST="localhost"
 RABBITMQ_PORT= 5673
 
+# Retrieval config
+OPENAI_API_KEY="str"
diff --git a/course/module-3/Makefile b/course/module-3/Makefile
index 4da985a..7fd9c5d 100644
--- a/course/module-3/Makefile
+++ b/course/module-3/Makefile
@@ -14,4 +14,7 @@ local-insert-data-mongo: #Insert data to mongodb
 	poetry run python insert_data_mongo.py
 
 local-bytewax: # Run bytewax pipeline
-	poetry run python -m bytewax.run data_flow/bytewax_pipeline     
\ No newline at end of file
+	poetry run python -m bytewax.run data_flow/bytewax_pipeline 
+
+local-test-retriever: # Test retriever
+	poetry run python retriever.py
\ No newline at end of file
diff --git a/course/module-3/data_logic/embedding_data_handlers.py b/course/module-3/data_logic/embedding_data_handlers.py
index 6646361..33fdfe1 100644
--- a/course/module-3/data_logic/embedding_data_handlers.py
+++ b/course/module-3/data_logic/embedding_data_handlers.py
@@ -27,6 +27,7 @@ def embedd(self, data_model: PostChunkModel) -> PostEmbeddedChunkModel:
             entry_id=data_model.entry_id,
             platform=data_model.platform,
             chunk_id=data_model.chunk_id,
+            chunk_content=data_model.chunk_content,
             embedded_content=embedd_text(data_model.chunk_content),
             author_id=data_model.author_id,
             type=data_model.type,
@@ -39,6 +40,7 @@ def embedd(self, data_model: ArticleChunkModel) -> ArticleEmbeddedChunkModel:
             entry_id=data_model.entry_id,
             platform=data_model.platform,
             link=data_model.link,
+            chunk_content=data_model.chunk_content,
             chunk_id=data_model.chunk_id,
             embedded_content=embedd_text(data_model.chunk_content),
             author_id=data_model.author_id,
@@ -53,6 +55,7 @@ def embedd(self, data_model: RepositoryChunkModel) -> RepositoryEmbeddedChunkMod
             name=data_model.name,
             link=data_model.link,
             chunk_id=data_model.chunk_id,
+            chunk_content=data_model.chunk_content,
             embedded_content=embedd_text(data_model.chunk_content),
             owner_id=data_model.owner_id,
             type=data_model.type,
diff --git a/course/module-3/insert_data_mongo.py b/course/module-3/insert_data_mongo.py
index ff81165..c81a61e 100644
--- a/course/module-3/insert_data_mongo.py
+++ b/course/module-3/insert_data_mongo.py
@@ -1,7 +1,7 @@
 from db.documents import ArticleDocument, PostDocument, RepositoryDocument
 
 
-def insert_post_into_collection():
+def insert_post_into_collection_1():
     post = {
         "first post": """Excited to share some big news! 🎉 Our team has been working tirelessly on developing a groundbreaking solution designed to revolutionize the tech industry, and today, we're finally ready to unveil it to the world. Introducing InnovateX, our latest product that leverages cutting-edge AI to streamline operations and enhance productivity for businesses globally. 🚀
 
@@ -24,7 +24,106 @@ def insert_post_into_collection():
     print("Post inserted into collection")
 
 
-def insert_article_into_collection():
+def insert_post_into_collection_2():
+    post = {
+        'first post': """Join me in acknowledging the transformative impact of OpenAI's groundbreaking work! From revolutionizing natural language processing to advancing robotics and healthcare, OpenAI's research has far-reaching implications across diverse fields. By fostering collaboration and knowledge sharing, OpenAI is accelerating the pace of innovation and driving progress in AI-driven technologies. As we witness the remarkable achievements of OpenAI, let's continue to support and amplify their efforts in shaping a future where AI serves as a powerful force for innovation and societal benefit. #OpenAI #AI #Innovation #Technology"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_3():
+    post = {
+        'first post': """Excited to delve deeper into the fascinating realm of Retrieval Augmented Generation (RAG)! This cutting-edge approach combines the strengths of retrieval-based models with generative models, promising groundbreaking advancements in natural language processing (NLP). By enabling systems to retrieve and generate information dynamically, RAG holds immense potential for revolutionizing various applications, from question answering systems to content creation tools. I'm eager to explore how RAG can enhance user experiences, streamline information retrieval processes, and drive innovation across industries. Join me on this journey as we uncover the transformative capabilities of RAG in shaping the future of AI-driven technologies. #RAG #AI #NLP #Innovation"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_4():
+    post = {
+        'first post': """Just wrapped up an intensive exploration of Retrieval Augmented Generation (RAG), and I'm truly astounded by its potential to redefine the landscape of artificial intelligence! By seamlessly integrating retrieval and generation models, RAG introduces a novel paradigm that empowers AI systems to dynamically retrieve and generate information in real-time. This innovative approach opens up new avenues for enhancing human-computer interactions, enabling more intuitive and contextually relevant responses. From personalized virtual assistants to AI-driven content creation platforms, the applications of RAG are boundless. I'm excited to witness the transformative impact of RAG across various domains and industries. #ArtificialIntelligence #RAG #Innovation #FutureTech"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_5():
+    post = {
+        'first post': """Are you intrigued by the convergence of retrieval and generation models in natural language processing? Join me on a journey of discovery as we explore the fascinating world of Retrieval Augmented Generation (RAG). By seamlessly integrating the strengths of both approaches, RAG offers a powerful framework for enhancing information retrieval, question answering, and content generation tasks. Its ability to dynamically retrieve and generate contextually relevant responses marks a significant leap forward in AI capabilities. Let's embark on this exploration together and unlock the full potential of RAG in shaping the future of AI-driven technologies. #RAG #AI #NLP #Technology"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe4200-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_6():
+    post = {
+        'first post': """Just completed an in-depth study of Retrieval Augmented Generation (RAG), and I'm incredibly excited about its transformative potential in natural language processing (NLP). By leveraging the complementary strengths of retrieval-based and generative models, RAG enables AI systems to dynamically retrieve and generate information, leading to more contextually relevant and coherent responses. Whether it's enhancing search engines, improving chatbots, or revolutionizing content creation tools, RAG offers a versatile framework with countless applications. I can't wait to see how this innovative approach will reshape the way we interact with information and AI systems in the years to come. #RAG #NLP #AI #Innovation"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_7():
+    post = {
+        'first post': """Just completed an in-depth study of Retrieval Augmented Generation (RAG), and I'm incredibly excited about its transformative potential in natural language processing (NLP). By leveraging the complementary strengths of retrieval-based and generative models, RAG enables AI systems to dynamically retrieve and generate information, leading to more contextually relevant and coherent responses. Whether it's enhancing search engines, improving chatbots, or revolutionizing content creation tools, RAG offers a versatile framework with countless applications. I can't wait to see how this innovative approach will reshape the way we interact with information and AI systems in the years to come. #RAG #NLP #AI #Innovation"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_8():
+    post = {
+        'first post': """Just attended an enlightening webinar on Retrieval Augmented Generation (RAG) and its potential to revolutionize content creation in the digital age. By seamlessly integrating retrieval-based and generative models, RAG offers a unique solution to the challenges of generating high-quality and contextually relevant content. Its ability to dynamically retrieve information from large knowledge bases while generating coherent and engaging narratives opens up exciting possibilities for marketers, writers, and content creators. Whether it's crafting personalized product descriptions or generating compelling blog posts, RAG promises to elevate the art of content creation to new heights. #RAG #ContentCreation #AI #Innovation"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_9():
+    post = {
+        'first post': """Python classes are a fundamental concept in object-oriented programming, and mastering them is essential for building robust and scalable applications. Whether you're a beginner eager to learn the basics or an experienced developer looking to deepen your understanding, delving into Python classes is a rewarding journey. From encapsulation and inheritance to polymorphism and abstraction, classes empower you to organize your code effectively and create reusable components. Join me in exploring the intricacies of Python classes as we unlock the full potential of object-oriented programming in Python. Let's level up our coding skills together! #Python #Programming #Classes #OOP"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_10():
+    post = {
+        'first post': """Let's dive into the world of Python classes! As the building blocks of object-oriented programming in Python, classes play a pivotal role in structuring and organizing code for scalability and maintainability. Whether you're developing web applications, data analysis tools, or machine learning models, understanding how to leverage classes effectively is essential. From defining attributes and methods to implementing inheritance and polymorphism, mastering Python classes empowers you to create elegant and efficient solutions to complex problems. Join me in exploring the depths of Python classes as we elevate our coding skills to new heights! #Python #OOP #Coding #Programming"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe4200-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_article_into_collection_1():
     content = {"test": "Test data for a article content"}
     ArticleDocument(
         platform="medium",
@@ -36,6 +135,20 @@ def insert_article_into_collection():
     print("Article inserted into collection")
 
 
+def insert_article_into_collection_2():
+    content = {"test": """Retrieval Augmented Generation (RAG) represents a cutting-edge approach in the field of natural language processing (NLP) that merges the strengths of retrieval-based models and generative models. The key concept behind RAG is its ability to dynamically retrieve relevant information from a large corpus or knowledge base and seamlessly incorporate it into generative responses.
+    In traditional generative models, outputs are generated based solely on the internal knowledge of the model. This can lead to issues with outdated information or hallucinations, where the model creates content that seems plausible but is factually incorrect. RAG mitigates these risks by integrating retrieval components that can access up-to-date and contextually appropriate information in real-time. This hybrid approach enhances accuracy and contextual relevance, making it ideal for applications like question answering, chatbots, and content creation tools.
+    RAG's impact is transformative, enabling AI systems to provide more reliable and accurate responses while adapting to changing information landscapes. This flexibility opens the door to a range of new possibilities, from personalized virtual assistants to intelligent document summarization. As RAG continues to evolve, it holds great promise for revolutionizing how we interact with AI systems and extract valuable insights from large volumes of data."""}
+    ArticleDocument(
+        platform="medium",
+        link="/htttps/alex/paul",
+        content=content,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+
+    print("Article inserted into collection")
+
+
 def insert_repo_into_collection():
     content = {"test": "Test data for a repo content into db"}
     RepositoryDocument(
@@ -49,6 +162,16 @@ def insert_repo_into_collection():
 
 
 if __name__ == "__main__":
-    insert_post_into_collection()
-    insert_article_into_collection()
+    insert_post_into_collection_1()
+    insert_post_into_collection_2()
+    insert_post_into_collection_3()
+    insert_post_into_collection_4()
+    insert_post_into_collection_5()
+    insert_post_into_collection_6()
+    insert_post_into_collection_7()
+    insert_post_into_collection_8()
+    insert_post_into_collection_9()
+    insert_post_into_collection_10()
+    insert_article_into_collection_1()
+    insert_article_into_collection_2()
     insert_repo_into_collection()
diff --git a/course/module-3/llm_components/__init__.py b/course/module-3/llm_components/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/course/module-3/llm_components/chain.py b/course/module-3/llm_components/chain.py
new file mode 100644
index 0000000..ea0fcf1
--- /dev/null
+++ b/course/module-3/llm_components/chain.py
@@ -0,0 +1,10 @@
+from langchain.chains.llm import LLMChain
+from langchain.prompts import PromptTemplate
+
+
+class GeneralChain:
+    @staticmethod
+    def get_chain(llm, template: PromptTemplate, output_key: str, verbose=True):
+        return LLMChain(
+            llm=llm, prompt=template, output_key=output_key, verbose=verbose
+        )
diff --git a/course/module-3/llm_components/prompt_templates.py b/course/module-3/llm_components/prompt_templates.py
new file mode 100644
index 0000000..534634c
--- /dev/null
+++ b/course/module-3/llm_components/prompt_templates.py
@@ -0,0 +1,64 @@
+from abc import ABC, abstractmethod
+
+from langchain.prompts import PromptTemplate
+from pydantic import BaseModel
+
+
+class BasePromptTemplate(ABC, BaseModel):
+    @abstractmethod
+    def create_template(self) -> PromptTemplate:
+        pass
+
+
+class QueryExpansionTemplate(BasePromptTemplate):
+    prompt: str = """You are an AI language model assistant. Your task is to generate Five
+    different versions of the given user question to retrieve relevant documents from a vector
+    database. By generating multiple perspectives on the user question, your goal is to help
+    the user overcome some of the limitations of the distance-based similarity search.
+    Provide these alternative questions seperated by newlines.
+    Original question: {question}"""
+
+    question: str
+
+    def create_template(self) -> PromptTemplate:
+        template = PromptTemplate(
+            template=self.prompt, input_variables=["question"], verbose=True
+        )
+        template.format(question=self.question)
+        return template
+
+
+class SelfQueryTemplate(BasePromptTemplate):
+    prompt: str = """You are an AI language model assistant. Your task is to extract information from a user question.
+    The required information that needs to be extracted is the user id. 
+    Your response should consists of only the extracted id (e.g. 1345256), nothing else.
+    User question: {question}"""
+
+    question: str
+
+    def create_template(self) -> PromptTemplate:
+        template = PromptTemplate(
+            template=self.prompt, input_variables=["question"], verbose=True
+        )
+        template.format(question=self.question)
+        return template
+
+
+class RerankingTemplate(BasePromptTemplate):
+    prompt: str = """You are an AI language model assistant. Your task is to rerank passages related to a query
+    based on their relevance. The most relevant passages should be put at the beginning and at the end. 
+    You should only pick at max 5 passages.
+    The following are passages related to this query: {question}.
+    Passages: {passages}
+    """
+
+    question: str
+
+    passages: str
+
+    def create_template(self) -> PromptTemplate:
+        template = PromptTemplate(
+            template=self.prompt, input_variables=["question", "passages"], verbose=True
+        )
+        template.format(question=self.question, passages=self.passages)
+        return template
diff --git a/course/module-3/models/embedded_chunk.py b/course/module-3/models/embedded_chunk.py
index 5d4dc28..94e5f9c 100644
--- a/course/module-3/models/embedded_chunk.py
+++ b/course/module-3/models/embedded_chunk.py
@@ -9,6 +9,7 @@ class PostEmbeddedChunkModel(DBDataModel):
     entry_id: str
     platform: str
     chunk_id: str
+    chunk_content: str
     embedded_content: np.ndarray
     author_id: str
     type: str
@@ -20,6 +21,7 @@ def save(self) -> Tuple[str, np.ndarray, dict]:
         data = {
             "id": self.entry_id,
             "platform": self.platform,
+            "content": self.chunk_content,
             "owner_id": self.author_id,
             "type": self.type,
         }
@@ -32,6 +34,7 @@ class ArticleEmbeddedChunkModel(DBDataModel):
     platform: str
     link: str
     chunk_id: str
+    chunk_content: str
     embedded_content: np.ndarray
     author_id: str
     type: str
@@ -43,6 +46,7 @@ def save(self) -> Tuple[str, np.ndarray, dict]:
         data = {
             "id": self.entry_id,
             "platform": self.platform,
+            "content": self.chunk_content,
             "link": self.link,
             "author_id": self.author_id,
             "type": self.type,
@@ -56,6 +60,7 @@ class RepositoryEmbeddedChunkModel(DBDataModel):
     name: str
     link: str
     chunk_id: str
+    chunk_content: str
     embedded_content: np.ndarray
     owner_id: str
     type: str
@@ -67,6 +72,7 @@ def save(self) -> Tuple[str, np.ndarray, dict]:
         data = {
             "id": self.entry_id,
             "name": self.name,
+            "content": self.chunk_content,
             "link": self.link,
             "owner_id": self.owner_id,
             "type": self.type,
diff --git a/course/module-3/poetry.lock b/course/module-3/poetry.lock
index a7af53d..50e7b29 100644
--- a/course/module-3/poetry.lock
+++ b/course/module-3/poetry.lock
@@ -1,88 +1,88 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
-version = "3.9.3"
+version = "3.9.5"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"},
-    {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc"},
-    {file = "aiohttp-3.9.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:82e6aa28dd46374f72093eda8bcd142f7771ee1eb9d1e223ff0fa7177a96b4a5"},
-    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f56455b0c2c7cc3b0c584815264461d07b177f903a04481dfc33e08a89f0c26b"},
-    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bca77a198bb6e69795ef2f09a5f4c12758487f83f33d63acde5f0d4919815768"},
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 ]
 
 [package.dependencies]
@@ -205,6 +205,20 @@ charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
+[[package]]
+name = "bson"
+version = "0.5.10"
+description = "BSON codec for Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "bson-0.5.10.tar.gz", hash = "sha256:d6511b2ab051139a9123c184de1a04227262173ad593429d21e443d6462d6590"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.4.0"
+six = ">=1.9.0"
+
 [[package]]
 name = "bytewax"
 version = "0.18.2"
@@ -429,6 +443,17 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
+[[package]]
+name = "distro"
+version = "1.9.0"
+description = "Distro - an OS platform information API"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
 [[package]]
 name = "dnspython"
 version = "2.6.1"
@@ -465,13 +490,13 @@ dev = ["coverage", "coveralls", "pytest"]
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
 ]
 
 [package.extras]
@@ -479,13 +504,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.13.3"
+version = "3.13.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -698,135 +723,135 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "grpcio"
-version = "1.62.1"
+version = "1.62.2"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.7"
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+    {file = "grpcio_tools-1.62.2-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:7d8b4e00c3d7237b92260fc18a561cd81f1da82e8be100db1b7d816250defc66"},
+    {file = "grpcio_tools-1.62.2-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fe08d2038f2b7c53259b5c49e0ad08c8e0ce2b548d8185993e7ef67e8592cca"},
+    {file = "grpcio_tools-1.62.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:19216e1fb26dbe23d12a810517e1b3fbb8d4f98b1a3fbebeec9d93a79f092de4"},
+    {file = "grpcio_tools-1.62.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:b8574469ecc4ff41d6bb95f44e0297cdb0d95bade388552a9a444db9cd7485cd"},
+    {file = "grpcio_tools-1.62.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:4f6f32d39283ea834a493fccf0ebe9cfddee7577bdcc27736ad4be1732a36399"},
+    {file = "grpcio_tools-1.62.2-cp39-cp39-win32.whl", hash = "sha256:76eb459bdf3fb666e01883270beee18f3f11ed44488486b61cd210b4e0e17cc1"},
+    {file = "grpcio_tools-1.62.2-cp39-cp39-win_amd64.whl", hash = "sha256:217c2ee6a7ce519a55958b8622e21804f6fdb774db08c322f4c9536c35fdce7c"},
 ]
 
 [package.dependencies]
-grpcio = ">=1.62.1"
+grpcio = ">=1.62.2"
 protobuf = ">=4.21.6,<5.0dev"
 setuptools = "*"
 
@@ -1035,18 +1060,19 @@ files = [
 
 [[package]]
 name = "jsonpickle"
-version = "3.0.3"
-description = "Python library for serializing any arbitrary object graph into JSON"
+version = "3.0.4"
+description = "Serialize any Python object to JSON"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jsonpickle-3.0.3-py3-none-any.whl", hash = "sha256:e8d6dcc58f6722bea0321cd328fbda81c582461185688a535df02be0f699afb4"},
-    {file = "jsonpickle-3.0.3.tar.gz", hash = "sha256:5691f44495327858ab3a95b9c440a79b41e35421be1a6e09a47b6c9b9421fd06"},
+    {file = "jsonpickle-3.0.4-py3-none-any.whl", hash = "sha256:04ae7567a14269579e3af66b76bda284587458d7e8a204951ca8f71a3309952e"},
+    {file = "jsonpickle-3.0.4.tar.gz", hash = "sha256:a1b14c8d6221cd8f394f2a97e735ea1d7edc927fbd135b26f2f8700657c8c62b"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "sphinx"]
-testing = ["ecdsa", "feedparser", "gmpy2", "numpy", "pandas", "pymongo", "pytest (>=3.5,!=3.7.3)", "pytest-checkdocs (>=1.2.3)", "pytest-cov", "pytest-enabler (>=1.0.1)", "pytest-ruff", "scikit-learn", "simplejson", "sqlalchemy", "ujson"]
+docs = ["furo", "rst.linker (>=1.9)", "sphinx"]
+packaging = ["build", "twine"]
+testing = ["bson", "ecdsa", "feedparser", "gmpy2", "numpy", "pandas", "pymongo", "pytest (>=3.5,!=3.7.3)", "pytest-benchmark", "pytest-benchmark[histogram]", "pytest-checkdocs (>=1.2.3)", "pytest-cov", "pytest-enabler (>=1.0.1)", "pytest-ruff (>=0.2.1)", "scikit-learn", "scipy", "scipy (>=1.9.3)", "simplejson", "sqlalchemy", "ujson"]
 
 [[package]]
 name = "jsonpointer"
@@ -1061,13 +1087,13 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.1.14"
+version = "0.1.16"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain-0.1.14-py3-none-any.whl", hash = "sha256:94f9b5df2421faaf762d4f43b9d65c270c2f701934580d281e4c6226deef7234"},
-    {file = "langchain-0.1.14.tar.gz", hash = "sha256:124c6244cf3957616b98f2df07dc2992fc40dff6ed1a62d8ee8a40f1e0260a40"},
+    {file = "langchain-0.1.16-py3-none-any.whl", hash = "sha256:bc074cc5e51fad79b9ead1572fc3161918d0f614a6c8f0460543d505ad249ac7"},
+    {file = "langchain-0.1.16.tar.gz", hash = "sha256:b6bce78f8c071baa898884accfff15c3d81da2f0dd86c20e2f4c80b41463f49f"},
 ]
 
 [package.dependencies]
@@ -1075,8 +1101,8 @@ 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 = ">=0.0.30,<0.1"
-langchain-core = ">=0.1.37,<0.2.0"
+langchain-community = ">=0.0.32,<0.1"
+langchain-core = ">=0.1.42,<0.2.0"
 langchain-text-splitters = ">=0.0.1,<0.1"
 langsmith = ">=0.1.17,<0.2.0"
 numpy = ">=1,<2"
@@ -1102,19 +1128,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.31"
+version = "0.0.34"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.0.31-py3-none-any.whl", hash = "sha256:905c01b978a1cef7fdcddd2d9241dedc9987db6f23ba1b58d974e38b1cdf2775"},
-    {file = "langchain_community-0.0.31.tar.gz", hash = "sha256:9a970bc2bb59bb4c204b696d8c62c2534f6ddb31005005cc1b7d7f934e58a5fc"},
+    {file = "langchain_community-0.0.34-py3-none-any.whl", hash = "sha256:bc13b21a44bbfca01bff8b35c10a26d71485b57c1d284f499b577ba6e1a5d84a"},
+    {file = "langchain_community-0.0.34.tar.gz", hash = "sha256:96e9a807d9b4777820df5a970996f6bf3ad5632137bf0f4d863bd832bdeb2b0f"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.37,<0.2.0"
+langchain-core = ">=0.1.45,<0.2.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@@ -1124,17 +1150,17 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
 
 [[package]]
 name = "langchain-core"
-version = "0.1.40"
+version = "0.1.45"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.1.40-py3-none-any.whl", hash = "sha256:618dbb7ab44d8b263b91e384db1ff07d0db256ae5bdafa0123a115b6a75a13f1"},
-    {file = "langchain_core-0.1.40.tar.gz", hash = "sha256:34c06fc0e6d3534b738c63f85403446b4be71161665b7e091f9bb19c914ec100"},
+    {file = "langchain_core-0.1.45-py3-none-any.whl", hash = "sha256:91eff20de0bcf5f025e1d8c4582cb597a9c17527965eb03b314486e7c834e7df"},
+    {file = "langchain_core-0.1.45.tar.gz", hash = "sha256:526532c1af279a9e2debe7a4e143ba6e980cf90b5ab2e0991c2230ee04c693e2"},
 ]
 
 [package.dependencies]
@@ -1148,6 +1174,22 @@ tenacity = ">=8.1.0,<9.0.0"
 [package.extras]
 extended-testing = ["jinja2 (>=3,<4)"]
 
+[[package]]
+name = "langchain-openai"
+version = "0.1.3"
+description = "An integration package connecting OpenAI and LangChain"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_openai-0.1.3-py3-none-any.whl", hash = "sha256:fa1f27815649291447e5370cb08e2f5a84e5c7c6121d0c055a6e296bd16d1e47"},
+    {file = "langchain_openai-0.1.3.tar.gz", hash = "sha256:7f6e377d6bf88d6c2b1969fe5eecc1326271757512739e2f17c855cd7af53345"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.1.42,<0.2.0"
+openai = ">=1.10.0,<2.0.0"
+tiktoken = ">=0.5.2,<1"
+
 [[package]]
 name = "langchain-text-splitters"
 version = "0.0.1"
@@ -1181,13 +1223,13 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.1.40"
+version = "0.1.49"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.40-py3-none-any.whl", hash = "sha256:aa47d0f5a1eabd5c05ac6ce2cd3e28ccfc554d366e856a27b7c3c17c443881cb"},
-    {file = "langsmith-0.1.40.tar.gz", hash = "sha256:50fdf313741cf94e978de06025fd180b56acf1d1a4549b0fd5453ef23d5461ef"},
+    {file = "langsmith-0.1.49-py3-none-any.whl", hash = "sha256:cf0db7474c0dfb22015c22bf97f62e850898c3c6af9564dd111c2df225acc1c8"},
+    {file = "langsmith-0.1.49.tar.gz", hash = "sha256:5aee8537763f9d62b3368d79d7bfef881e2bfaa28639011d8d7328770cbd6419"},
 ]
 
 [package.dependencies]
@@ -1733,64 +1775,87 @@ files = [
     {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
 ]
 
+[[package]]
+name = "openai"
+version = "1.23.2"
+description = "The official Python library for the openai API"
+optional = false
+python-versions = ">=3.7.1"
+files = [
+    {file = "openai-1.23.2-py3-none-any.whl", hash = "sha256:293a36effde29946eb221040c89c46a4850f2f2e30b37ef09ff6d75226d71b42"},
+    {file = "openai-1.23.2.tar.gz", hash = "sha256:b84aa3005357ceb38f22a269e0e22ee58ce103897f447032d021906f18178a8e"},
+]
+
+[package.dependencies]
+anyio = ">=3.5.0,<5"
+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+tqdm = ">4"
+typing-extensions = ">=4.7,<5"
+
+[package.extras]
+datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
+
 [[package]]
 name = "orjson"
-version = "3.10.0"
+version = "3.10.1"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "orjson-3.10.0-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:47af5d4b850a2d1328660661f0881b67fdbe712aea905dadd413bdea6f792c33"},
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-    {file = "orjson-3.10.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5dcb32e949eae80fb335e63b90e5808b4b0f64e31476b3777707416b41682db5"},
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 torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
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 ]
 
 [package.dependencies]
 joblib = ">=1.2.0"
-numpy = ">=1.19.5,<2.0"
+numpy = ">=1.19.5"
 scipy = ">=1.6.0"
 threadpoolctl = ">=2.0.0"
 
@@ -2778,13 +2833,13 @@ test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "po
 
 [[package]]
 name = "sentence-transformers"
-version = "2.6.1"
+version = "2.7.0"
 description = "Multilingual text embeddings"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2795,22 +2850,25 @@ scikit-learn = "*"
 scipy = "*"
 torch = ">=1.11.0"
 tqdm = "*"
-transformers = ">=4.32.0,<5.0.0"
+transformers = ">=4.34.0,<5.0.0"
+
+[package.extras]
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 [[package]]
 name = "setuptools"
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+version = "69.5.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
-huggingface_hub = ">=0.16.4,<1.0"
+huggingface-hub = ">=0.16.4,<1.0"
 
 [package.extras]
 dev = ["tokenizers[testing]"]
-docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
-testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
 [[package]]
 name = "torch"
@@ -3193,13 +3293,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.39.3"
+version = "4.40.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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+    {file = "transformers-4.40.0.tar.gz", hash = "sha256:fdb01dfe6a492bd34e3fa2aefffa470b1d8a2341db47a932f83ed33839d96b03"},
 ]
 
 [package.dependencies]
@@ -3211,21 +3311,21 @@ pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.4.1"
-tokenizers = ">=0.14,<0.19"
+tokenizers = ">=0.19,<0.20"
 tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.21.0)"]
 agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
-all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
 deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
 docs-specific = ["hf-doc-builder"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -3246,16 +3346,16 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.19)"]
+tokenizers = ["tokenizers (>=0.19,<0.20)"]
 torch = ["accelerate (>=0.21.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -3647,4 +3747,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10, <3.12"
-content-hash = "16cce05a646294e28bea061733b3ca9c549c1f8fc750b9bde27bd2e6c1b32d4f"
+content-hash = "5fbbde1409f32472af9dd6348c5c39e8f4bcb003ffaf3e1820b41dba8d4cfd9e"
diff --git a/course/module-3/pyproject.toml b/course/module-3/pyproject.toml
index f288b85..e17aa39 100644
--- a/course/module-3/pyproject.toml
+++ b/course/module-3/pyproject.toml
@@ -1,8 +1,10 @@
 [tool.poetry]
+name = "rag-system"
 description = ""
+version = "0.1.0"
 authors = ["vlad_adu <vladadumitracesei02@gmail.com>"]
 readme = "README.md"
-package-mode = false
+
 
 [tool.poetry.dependencies]
 python = ">=3.10, <3.12"
@@ -10,13 +12,15 @@ pydantic = "^2.6.3"
 pydantic-settings = "^2.1.0"
 bytewax = "^0.18.2"
 pika = "^1.3.2"
-pymongo = "^4.6.2"
 qdrant-client = "^1.8.0"
 unstructured = "^0.12.6"
 langchain = "^0.1.13"
 sentence-transformers = "^2.6.1"
 instructorembedding = "^1.0.1"
 numpy = "^1.26.4"
+langchain-openai = "^0.1.3"
+bson = "^0.5.10"
+pymongo = "^4.6.3"
 
 
 [build-system]
diff --git a/course/module-3/rag/query_expanison.py b/course/module-3/rag/query_expanison.py
new file mode 100644
index 0000000..18d387c
--- /dev/null
+++ b/course/module-3/rag/query_expanison.py
@@ -0,0 +1,22 @@
+from langchain_openai import ChatOpenAI
+
+from llm_components.chain import GeneralChain
+from llm_components.prompt_templates import QueryExpansionTemplate
+from settings import settings
+
+
+class QueryExpansion:
+    @staticmethod
+    def generate_response(query: str) -> list[str]:
+        prompt = QueryExpansionTemplate(question=query).create_template()
+        model = ChatOpenAI(model=settings.OPENAI_MODEL_ID, temperature=0)
+
+        chain = GeneralChain().get_chain(
+            llm=model, output_key="expanded_queries", template=prompt
+        )
+
+        response = chain.invoke({"question": query})
+        result = response["expanded_queries"]
+        queries = result.strip().split("\n")
+        cleaned_queries = [item.strip() for item in queries if item.strip()]
+        return cleaned_queries
diff --git a/course/module-3/rag/reranking.py b/course/module-3/rag/reranking.py
new file mode 100644
index 0000000..93f880c
--- /dev/null
+++ b/course/module-3/rag/reranking.py
@@ -0,0 +1,19 @@
+from langchain_openai import ChatOpenAI
+
+from llm_components.chain import GeneralChain
+from llm_components.prompt_templates import RerankingTemplate
+
+
+class Reranker:
+    @staticmethod
+    def generate_response(query: str, passages: str) -> list[str]:
+        prompt = RerankingTemplate(question=query, passages=passages).create_template()
+        model = ChatOpenAI(model="gpt-4-1106-preview")
+        chain = GeneralChain().get_chain(
+            llm=model, output_key="rerank", template=prompt
+        )
+
+        response = chain.invoke({"question": query, "passages": passages})
+        result = response["rerank"]
+
+        return result
diff --git a/course/module-3/rag/retriever.py b/course/module-3/rag/retriever.py
new file mode 100644
index 0000000..c231ef9
--- /dev/null
+++ b/course/module-3/rag/retriever.py
@@ -0,0 +1,109 @@
+import concurrent.futures
+
+import utils
+from qdrant_client import QdrantClient, models
+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
+
+
+class VectorRetriever:
+    """
+    Class for retrieving vectors from a Vector store in a RAG system using query expansion and Multitenancy search.
+    """
+
+    def __init__(self, query: str):
+        self._client = QdrantClient(
+            host=settings.QDRANT_DATABASE_HOST, port=settings.QDRANT_DATABASE_PORT
+        )
+        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
+    ):
+        vectors = [
+            self._client.search(
+                collection_name="vector_posts",
+                query_filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key="author_id",
+                            match=models.MatchValue(
+                                value=metadata_filter_value,
+                            ),
+                        )
+                    ]
+                ),
+                query_vector=self._embedder.encode(generated_query).tolist(),
+                limit=k,
+            ),
+            self._client.search(
+                collection_name="vector_articles",
+                query_filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key="author_id",
+                            match=models.MatchValue(
+                                value=metadata_filter_value,
+                            ),
+                        )
+                    ]
+                ),
+                query_vector=self._embedder.encode(generated_query).tolist(),
+                limit=k,
+            ),
+            self._client.search(
+                collection_name="vector_repositories",
+                query_filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key="owner_id",
+                            match=models.MatchValue(
+                                value=metadata_filter_value,
+                            ),
+                        )
+                    ]
+                ),
+                query_vector=self._embedder.encode(generated_query).tolist(),
+                limit=k,
+            ),
+        ]
+
+        return utils.flatten(vectors)
+
+    def retrieve_top_k(self, k: int) -> list:
+        generated_queries = self._query_expander.generate_response(self.query)
+        metadata_filter_value = self._metadata_extractor.generate_response(self.query)
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+            search_tasks = [
+                executor.submit(
+                    self._search_single_query, query, metadata_filter_value, k
+                )
+                for query in generated_queries
+            ]
+
+            hits = [
+                task.result() for task in concurrent.futures.as_completed(search_tasks)
+            ]
+            hits = utils.flatten(hits)
+
+            return hits
+
+    def rerank(self, hits: list) -> list[str]:
+        content_list = [hit.payload["content"] for hit in hits]
+        passages = "\n".join(content_list)
+        rerank_hits = self._reranker.generate_response(
+            query=self.query, passages=passages
+        )
+
+        return rerank_hits
+
+    def set_query(self, query: str):
+        self.query = query
diff --git a/course/module-3/rag/self_query.py b/course/module-3/rag/self_query.py
new file mode 100644
index 0000000..9aebf88
--- /dev/null
+++ b/course/module-3/rag/self_query.py
@@ -0,0 +1,19 @@
+from langchain_openai import ChatOpenAI
+
+from llm_components.chain import GeneralChain
+from llm_components.prompt_templates import SelfQueryTemplate
+
+
+class SelfQuery:
+    @staticmethod
+    def generate_response(query) -> str:
+        prompt = SelfQueryTemplate(question=query).create_template()
+        model = ChatOpenAI(model="gpt-4-1106-preview", temperature=0)
+
+        chain = GeneralChain().get_chain(
+            llm=model, output_key="metadata_filter_value", template=prompt
+        )
+
+        response = chain.invoke({"question": query})
+        result = response["metadata_filter_value"]
+        return result
diff --git a/course/module-3/retriever.py b/course/module-3/retriever.py
new file mode 100644
index 0000000..030ce03
--- /dev/null
+++ b/course/module-3/retriever.py
@@ -0,0 +1,18 @@
+from dotenv import load_dotenv
+
+from rag.retriever import VectorRetriever
+
+if __name__ == "__main__":
+    load_dotenv()
+    query = """
+        Hello my id is: dbe92510-c33f-4ff7-9908-ee6356fe251f
+        Could you please draft a LinkedIn post discussing RAG systems?
+        I'm particularly interested in highlighting how RAG systems streamline project management processes and provide a clear visual representation of task statuses. 
+        Additionally, if you could touch on any recent advancements or best practices in utilizing RAG systems for efficient project tracking, that would be fantastic. 
+        Looking forward to sharing valuable insights with my professional network! 
+        """
+    retriever = VectorRetriever(query=query)
+    hits = retriever.retrieve_top_k(5)
+    print(hits)
+    result = retriever.rerank(hits=hits)
+    print(result)
diff --git a/course/module-3/settings.py b/course/module-3/settings.py
index cbfc8a1..05d3613 100644
--- a/course/module-3/settings.py
+++ b/course/module-3/settings.py
@@ -12,13 +12,18 @@ class AppSettings(BaseSettings):
     EMBEDDING_SIZE: int = 384
     EMBEDDING_MODEL_DEVICE: str = "cpu"
 
+    OPENAI_MODEL_ID: str = "gpt-4-1106-preview"
+    OPENAI_API_KEY: str = "None"
+
     # MongoDB configs
-    MONGO_DATABASE_HOST: str = "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"
+    MONGO_DATABASE_HOST: str = "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"  # mongodb://127.0.0.1:27017/?replicaSet=rs0
     MONGO_DATABASE_NAME: str = "scrabble"
 
     # QdrantDB config
     QDRANT_DATABASE_HOST: str = "localhost"
     QDRANT_DATABASE_PORT: int = 6333
+    QDRANT_DATABASE_URL: str = "http://localhost:6333"
+
     CLEANED_DATA_OUTPUT_COLLECTION_NAME: str = "cleaned_posts"
     QDRANT_APIKEY: Optional[str] = None
 
diff --git a/course/module-3/utils/__init__.py b/course/module-3/utils/__init__.py
index e69de29..9f029a9 100644
--- a/course/module-3/utils/__init__.py
+++ b/course/module-3/utils/__init__.py
@@ -0,0 +1,4 @@
+def flatten(nested_list: list) -> list:
+    """Flatten a list of lists into a single list."""
+
+    return [item for sublist in nested_list for item in sublist]