Remove unused files

BuildingAChainlitApp.md

@@ -1,214 +0,0 @@
-# Building a Chainlit App
-
-What if we want to take our Week 1 Day 2 assignment - [Pythonic RAG](https://github.com/AI-Maker-Space/AIE4/tree/main/Week%201/Day%202) - and bring it out of the notebook?
-
-Well - we'll cover exactly that here!
-
-## Anatomy of a Chainlit Application
-
-[Chainlit](https://docs.chainlit.io/get-started/overview) is a Python package similar to Streamlit that lets users write a backend and a front end in a single (or multiple) Python file(s). It is mainly used for prototyping LLM-based Chat Style Applications - though it is used in production in some settings with 1,000,000s of MAUs (Monthly Active Users).
-
-The primary method of customizing and interacting with the Chainlit UI is through a few critical [decorators](https://blog.hubspot.com/website/decorators-in-python).
-
-> NOTE: Simply put, the decorators (in Chainlit) are just ways we can "plug-in" to the functionality in Chainlit. 
-
-We'll be concerning ourselves with three main scopes:
-
-1. On application start - when we start the Chainlit application with a command like `chainlit run app.py`
-2. On chat start - when a chat session starts (a user opens the web browser to the address hosting the application)
-3. On message - when the users sends a message through the input text box in the Chainlit UI
-
-Let's dig into each scope and see what we're doing!
-
-## On Application Start:
-
-The first thing you'll notice is that we have the traditional "wall of imports" this is to ensure we have everything we need to run our application. 
-
-```python
-import os
-from typing import List
-from chainlit.types import AskFileResponse
-from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader
-from aimakerspace.openai_utils.prompts import (
-    UserRolePrompt,
-    SystemRolePrompt,
-    AssistantRolePrompt,
-)
-from aimakerspace.openai_utils.embedding import EmbeddingModel
-from aimakerspace.vectordatabase import VectorDatabase
-from aimakerspace.openai_utils.chatmodel import ChatOpenAI
-import chainlit as cl
-```
-
-Next up, we have some prompt templates. As all sessions will use the same prompt templates without modification, and we don't need these templates to be specific per template - we can set them up here - at the application scope. 
-
-```python
-system_template = """\
-Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer."""
-system_role_prompt = SystemRolePrompt(system_template)
-
-user_prompt_template = """\
-Context:
-{context}
-
-Question:
-{question}
-"""
-user_role_prompt = UserRolePrompt(user_prompt_template)
-```
-
-> NOTE: You'll notice that these are the exact same prompt templates we used from the Pythonic RAG Notebook in Week 1 Day 2!
-
-Following that - we can create the Python Class definition for our RAG pipeline - or *chain*, as we'll refer to it in the rest of this walkthrough. 
-
-Let's look at the definition first:
-
-```python
-class RetrievalAugmentedQAPipeline:
-    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
-        self.llm = llm
-        self.vector_db_retriever = vector_db_retriever
-
-    async def arun_pipeline(self, user_query: str):
-        ### RETRIEVAL
-        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
-
-        context_prompt = ""
-        for context in context_list:
-            context_prompt += context[0] + "\n"
-
-        ### AUGMENTED
-        formatted_system_prompt = system_role_prompt.create_message()
-
-        formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)
-
-
-        ### GENERATION
-        async def generate_response():
-            async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
-                yield chunk
-
-        return {"response": generate_response(), "context": context_list}
-```
-
-Notice a few things:
-
-1. We have modified this `RetrievalAugmentedQAPipeline` from the initial notebook to support streaming. 
-2. In essence, our pipeline is *chaining* a few events together:
-    1. We take our user query, and chain it into our Vector Database to collect related chunks
-    2. We take those contexts and our user's questions and chain them into the prompt templates
-    3. We take that prompt template and chain it into our LLM call
-    4. We chain the response of the LLM call to the user
-3. We are using a lot of `async` again!
-
-Now, we're going to create a helper function for processing uploaded text files.
-
-First, we'll instantiate a shared `CharacterTextSplitter`.
-
-```python
-text_splitter = CharacterTextSplitter()
-```
-
-Now we can define our helper.
-
-```python
-def process_text_file(file: AskFileResponse):
-    import tempfile
-
-    with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as temp_file:
-        temp_file_path = temp_file.name
-
-    with open(temp_file_path, "wb") as f:
-        f.write(file.content)
-
-    text_loader = TextFileLoader(temp_file_path)
-    documents = text_loader.load_documents()
-    texts = text_splitter.split_texts(documents)
-    return texts
-```
-
-Simply put, this downloads the file as a temp file, we load it in with `TextFileLoader` and then split it with our `TextSplitter`, and returns that list of strings!
-
-#### QUESTION #1:
-
-Why do we want to support streaming? What about streaming is important, or useful?
-
-## On Chat Start:
-
-The next scope is where "the magic happens". On Chat Start is when a user begins a chat session. This will happen whenever a user opens a new chat window, or refreshes an existing chat window.
-
-You'll see that our code is set-up to immediately show the user a chat box requesting them to upload a file. 
-
-```python
-while files == None:
-        files = await cl.AskFileMessage(
-            content="Please upload a Text File file to begin!",
-            accept=["text/plain"],
-            max_size_mb=2,
-            timeout=180,
-        ).send()
-```
-
-Once we've obtained the text file - we'll use our processing helper function to process our text!
-
-After we have processed our text file - we'll need to create a `VectorDatabase` and populate it with our processed chunks and their related embeddings!
-
-```python
-vector_db = VectorDatabase()
-vector_db = await vector_db.abuild_from_list(texts)
-```
-
-Once we have that piece completed - we can create the chain we'll be using to respond to user queries!
-
-```python
-retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
-        vector_db_retriever=vector_db,
-        llm=chat_openai
-    )
-```
-
-Now, we'll save that into our user session!
-
-> NOTE: Chainlit has some great documentation about [User Session](https://docs.chainlit.io/concepts/user-session). 
-
-### QUESTION #2: 
-
-Why are we using User Session here? What about Python makes us need to use this? Why not just store everything in a global variable?
-
-## On Message
-
-First, we load our chain from the user session:
-
-```python
-chain = cl.user_session.get("chain")
-```
-
-Then, we run the chain on the content of the message - and stream it to the front end - that's it!
-
-```python
-msg = cl.Message(content="")
-result = await chain.arun_pipeline(message.content)
-
-async for stream_resp in result["response"]:
-    await msg.stream_token(stream_resp)
-```
-
-## 🎉
-
-With that - you've created a Chainlit application that moves our Pythonic RAG notebook to a Chainlit application!
-
-## 🚧 CHALLENGE MODE 🚧
-
-For an extra challenge - modify the behaviour of your applciation by integrating changes you made to your Pythonic RAG notebook (using new retrieval methods, etc.)
-
-If you're still looking for a challenge, or didn't make any modifications to your Pythonic RAG notebook:
-
-1) Allow users to upload PDFs (this will require you to build a PDF parser as well)
-2) Modify the VectorStore to leverage [Qdrant](https://python-client.qdrant.tech/)
-
-> NOTE: The motivation for these challenges is simple - the beginning of the course is extremely information dense, and people come from all kinds of different technical backgrounds. In order to ensure that all learners are able to engage with the content confidently and comfortably, we want to focus on the basic units of technical competency required. This leads to a situation where some learners, who came in with more robust technical skills, find the introductory material to be too simple - and these open-ended challenges help us do this! 
-
-
-
-
-

aimakerspace/openai_utils/chatmodel.py

@@ -1,45 +0,0 @@
-from openai import OpenAI, AsyncOpenAI
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-
-
-class ChatOpenAI:
-    def __init__(self, model_name: str = "gpt-4o-mini"):
-        self.model_name = model_name
-        self.openai_api_key = os.getenv("OPENAI_API_KEY")
-        if self.openai_api_key is None:
-            raise ValueError("OPENAI_API_KEY is not set")
-
-    def run(self, messages, text_only: bool = True, **kwargs):
-        if not isinstance(messages, list):
-            raise ValueError("messages must be a list")
-
-        client = OpenAI()
-        response = client.chat.completions.create(
-            model=self.model_name, messages=messages, **kwargs
-        )
-
-        if text_only:
-            return response.choices[0].message.content
-
-        return response
-    
-    async def astream(self, messages, **kwargs):
-        if not isinstance(messages, list):
-            raise ValueError("messages must be a list")
-        
-        client = AsyncOpenAI()
-
-        stream = await client.chat.completions.create(
-            model=self.model_name,
-            messages=messages,
-            stream=True,
-            **kwargs
-        )
-
-        async for chunk in stream:
-            content = chunk.choices[0].delta.content
-            if content is not None:
-                yield content

aimakerspace/openai_utils/embedding.py

@@ -1,59 +0,0 @@
-from dotenv import load_dotenv
-from openai import AsyncOpenAI, OpenAI
-import openai
-from typing import List
-import os
-import asyncio
-
-
-class EmbeddingModel:
-    def __init__(self, embeddings_model_name: str = "text-embedding-3-small"):
-        load_dotenv()
-        self.openai_api_key = os.getenv("OPENAI_API_KEY")
-        self.async_client = AsyncOpenAI()
-        self.client = OpenAI()
-
-        if self.openai_api_key is None:
-            raise ValueError(
-                "OPENAI_API_KEY environment variable is not set. Please set it to your OpenAI API key."
-            )
-        openai.api_key = self.openai_api_key
-        self.embeddings_model_name = embeddings_model_name
-
-    async def async_get_embeddings(self, list_of_text: List[str]) -> List[List[float]]:
-        embedding_response = await self.async_client.embeddings.create(
-            input=list_of_text, model=self.embeddings_model_name
-        )
-
-        return [embeddings.embedding for embeddings in embedding_response.data]
-
-    async def async_get_embedding(self, text: str) -> List[float]:
-        embedding = await self.async_client.embeddings.create(
-            input=text, model=self.embeddings_model_name
-        )
-
-        return embedding.data[0].embedding
-
-    def get_embeddings(self, list_of_text: List[str]) -> List[List[float]]:
-        embedding_response = self.client.embeddings.create(
-            input=list_of_text, model=self.embeddings_model_name
-        )
-
-        return [embeddings.embedding for embeddings in embedding_response.data]
-
-    def get_embedding(self, text: str) -> List[float]:
-        embedding = self.client.embeddings.create(
-            input=text, model=self.embeddings_model_name
-        )
-
-        return embedding.data[0].embedding
-
-
-if __name__ == "__main__":
-    embedding_model = EmbeddingModel()
-    print(asyncio.run(embedding_model.async_get_embedding("Hello, world!")))
-    print(
-        asyncio.run(
-            embedding_model.async_get_embeddings(["Hello, world!", "Goodbye, world!"])
-        )
-    )

aimakerspace/openai_utils/prompts.py

@@ -1,78 +0,0 @@
-import re
-
-
-class BasePrompt:
-    def __init__(self, prompt):
-        """
-        Initializes the BasePrompt object with a prompt template.
-
-        :param prompt: A string that can contain placeholders within curly braces
-        """
-        self.prompt = prompt
-        self._pattern = re.compile(r"\{([^}]+)\}")
-
-    def format_prompt(self, **kwargs):
-        """
-        Formats the prompt string using the keyword arguments provided.
-
-        :param kwargs: The values to substitute into the prompt string
-        :return: The formatted prompt string
-        """
-        matches = self._pattern.findall(self.prompt)
-        return self.prompt.format(**{match: kwargs.get(match, "") for match in matches})
-
-    def get_input_variables(self):
-        """
-        Gets the list of input variable names from the prompt string.
-
-        :return: List of input variable names
-        """
-        return self._pattern.findall(self.prompt)
-
-
-class RolePrompt(BasePrompt):
-    def __init__(self, prompt, role: str):
-        """
-        Initializes the RolePrompt object with a prompt template and a role.
-
-        :param prompt: A string that can contain placeholders within curly braces
-        :param role: The role for the message ('system', 'user', or 'assistant')
-        """
-        super().__init__(prompt)
-        self.role = role
-
-    def create_message(self, format=True, **kwargs):
-        """
-        Creates a message dictionary with a role and a formatted message.
-
-        :param kwargs: The values to substitute into the prompt string
-        :return: Dictionary containing the role and the formatted message
-        """
-        if format:
-            return {"role": self.role, "content": self.format_prompt(**kwargs)}
-        
-        return {"role": self.role, "content": self.prompt}
-
-
-class SystemRolePrompt(RolePrompt):
-    def __init__(self, prompt: str):
-        super().__init__(prompt, "system")
-
-
-class UserRolePrompt(RolePrompt):
-    def __init__(self, prompt: str):
-        super().__init__(prompt, "user")
-
-
-class AssistantRolePrompt(RolePrompt):
-    def __init__(self, prompt: str):
-        super().__init__(prompt, "assistant")
-
-
-if __name__ == "__main__":
-    prompt = BasePrompt("Hello {name}, you are {age} years old")
-    print(prompt.format_prompt(name="John", age=30))
-
-    prompt = SystemRolePrompt("Hello {name}, you are {age} years old")
-    print(prompt.create_message(name="John", age=30))
-    print(prompt.get_input_variables())

aimakerspace/text_utils.py

@@ -1,136 +0,0 @@
-import os
-from typing import List
-import PyPDF2
-
-
-class TextFileLoader:
-    def __init__(self, path: str, encoding: str = "utf-8"):
-        self.documents = []
-        self.path = path
-        self.encoding = encoding
-
-    def load(self):
-        if os.path.isdir(self.path):
-            self.load_directory()
-        elif os.path.isfile(self.path) and self.path.endswith(".txt"):
-            self.load_file()
-        else:
-            raise ValueError(
-                "Provided path is neither a valid directory nor a .txt file."
-            )
-
-    def load_file(self):
-        with open(self.path, "r", encoding=self.encoding) as f:
-            self.documents.append(f.read())
-
-    def load_directory(self):
-        for root, _, files in os.walk(self.path):
-            for file in files:
-                if file.endswith(".txt"):
-                    with open(
-                        os.path.join(root, file), "r", encoding=self.encoding
-                    ) as f:
-                        self.documents.append(f.read())
-
-    def load_documents(self):
-        self.load()
-        return self.documents
-
-
-class CharacterTextSplitter:
-    def __init__(
-        self,
-        chunk_size: int = 1000,
-        chunk_overlap: int = 200,
-    ):
-        assert (
-            chunk_size > chunk_overlap
-        ), "Chunk size must be greater than chunk overlap"
-
-        self.chunk_size = chunk_size
-        self.chunk_overlap = chunk_overlap
-
-    def split(self, text: str) -> List[str]:
-        chunks = []
-        for i in range(0, len(text), self.chunk_size - self.chunk_overlap):
-            chunks.append(text[i : i + self.chunk_size])
-        return chunks
-
-    def split_texts(self, texts: List[str]) -> List[str]:
-        chunks = []
-        for text in texts:
-            chunks.extend(self.split(text))
-        return chunks
-
-
-class PDFLoader:
-    def __init__(self, path: str):
-        self.documents = []
-        self.path = path
-        print(f"PDFLoader initialized with path: {self.path}")
-
-    def load(self):
-        print(f"Loading PDF from path: {self.path}")
-        print(f"Path exists: {os.path.exists(self.path)}")
-        print(f"Is file: {os.path.isfile(self.path)}")
-        print(f"Is directory: {os.path.isdir(self.path)}")
-        print(f"File permissions: {oct(os.stat(self.path).st_mode)[-3:]}")
-        
-        try:
-            # Try to open the file first to verify access
-            with open(self.path, 'rb') as test_file:
-                pass
-            
-            # If we can open it, proceed with loading
-            self.load_file()
-            
-        except IOError as e:
-            raise ValueError(f"Cannot access file at '{self.path}': {str(e)}")
-        except Exception as e:
-            raise ValueError(f"Error processing file at '{self.path}': {str(e)}")
-
-    def load_file(self):
-        with open(self.path, 'rb') as file:
-            # Create PDF reader object
-            pdf_reader = PyPDF2.PdfReader(file)
-            
-            # Extract text from each page
-            text = ""
-            for page in pdf_reader.pages:
-                text += page.extract_text() + "\n"
-            
-            self.documents.append(text)
-
-    def load_directory(self):
-        for root, _, files in os.walk(self.path):
-            for file in files:
-                if file.lower().endswith('.pdf'):
-                    file_path = os.path.join(root, file)
-                    with open(file_path, 'rb') as f:
-                        pdf_reader = PyPDF2.PdfReader(f)
-                        
-                        # Extract text from each page
-                        text = ""
-                        for page in pdf_reader.pages:
-                            text += page.extract_text() + "\n"
-                        
-                        self.documents.append(text)
-
-    def load_documents(self):
-        self.load()
-        return self.documents
-
-
-if __name__ == "__main__":
-    loader = TextFileLoader("data/KingLear.txt")
-    loader.load()
-    splitter = CharacterTextSplitter()
-    chunks = splitter.split_texts(loader.documents)
-    print(len(chunks))
-    print(chunks[0])
-    print("--------")
-    print(chunks[1])
-    print("--------")
-    print(chunks[-2])
-    print("--------")
-    print(chunks[-1])

aimakerspace/vectordatabase.py

@@ -1,81 +0,0 @@
-import numpy as np
-from collections import defaultdict
-from typing import List, Tuple, Callable
-from aimakerspace.openai_utils.embedding import EmbeddingModel
-import asyncio
-
-
-def cosine_similarity(vector_a: np.array, vector_b: np.array) -> float:
-    """Computes the cosine similarity between two vectors."""
-    dot_product = np.dot(vector_a, vector_b)
-    norm_a = np.linalg.norm(vector_a)
-    norm_b = np.linalg.norm(vector_b)
-    return dot_product / (norm_a * norm_b)
-
-
-class VectorDatabase:
-    def __init__(self, embedding_model: EmbeddingModel = None):
-        self.vectors = defaultdict(np.array)
-        self.embedding_model = embedding_model or EmbeddingModel()
-
-    def insert(self, key: str, vector: np.array) -> None:
-        self.vectors[key] = vector
-
-    def search(
-        self,
-        query_vector: np.array,
-        k: int,
-        distance_measure: Callable = cosine_similarity,
-    ) -> List[Tuple[str, float]]:
-        scores = [
-            (key, distance_measure(query_vector, vector))
-            for key, vector in self.vectors.items()
-        ]
-        return sorted(scores, key=lambda x: x[1], reverse=True)[:k]
-
-    def search_by_text(
-        self,
-        query_text: str,
-        k: int,
-        distance_measure: Callable = cosine_similarity,
-        return_as_text: bool = False,
-    ) -> List[Tuple[str, float]]:
-        query_vector = self.embedding_model.get_embedding(query_text)
-        results = self.search(query_vector, k, distance_measure)
-        return [result[0] for result in results] if return_as_text else results
-
-    def retrieve_from_key(self, key: str) -> np.array:
-        return self.vectors.get(key, None)
-
-    async def abuild_from_list(self, list_of_text: List[str]) -> "VectorDatabase":
-        embeddings = await self.embedding_model.async_get_embeddings(list_of_text)
-        for text, embedding in zip(list_of_text, embeddings):
-            self.insert(text, np.array(embedding))
-        return self
-
-
-if __name__ == "__main__":
-    list_of_text = [
-        "I like to eat broccoli and bananas.",
-        "I ate a banana and spinach smoothie for breakfast.",
-        "Chinchillas and kittens are cute.",
-        "My sister adopted a kitten yesterday.",
-        "Look at this cute hamster munching on a piece of broccoli.",
-    ]
-
-    vector_db = VectorDatabase()
-    vector_db = asyncio.run(vector_db.abuild_from_list(list_of_text))
-    k = 2
-
-    searched_vector = vector_db.search_by_text("I think fruit is awesome!", k=k)
-    print(f"Closest {k} vector(s):", searched_vector)
-
-    retrieved_vector = vector_db.retrieve_from_key(
-        "I like to eat broccoli and bananas."
-    )
-    print("Retrieved vector:", retrieved_vector)
-
-    relevant_texts = vector_db.search_by_text(
-        "I think fruit is awesome!", k=k, return_as_text=True
-    )
-    print(f"Closest {k} text(s):", relevant_texts)