all_output.txt

First, let's outline the core classes, functions, and methods that will be necessary:

1. SitemapLoader: This class will be responsible for loading all pages from a provided sitemap link.
2. DocumentChunker: This class will be responsible for chunking the documents and embedding them into vectors using the OpenAI library.
3. PineconeDatabase: This class will be responsible for storing vectors in a Pinecone vector database with a vector dimension size of 1024.
4. WebBaseLoader: This class will be responsible for loading documents from a provided GitHub repo.
5. BM25Encoder: This class will be responsible for creating a PineconeHybridSearchRetriever.
6. RetreivalQA: This class will be responsible for creating a langchain retrievalqachain for each retriever type.
7. ConversationAgent: This class will be responsible for providing relevant information based on the user's query.
8. CustomResponseSchema: This class will be responsible for defining the custom schema for the agent's responses.
9. StreamlitApp: This class will be responsible for creating a Streamlit conversational app frontend for user interaction.

Now, let's create the code for each of these classes and functions. We'll start with the "entrypoint" file, which will be the main.py file.

main.py
```python
from sitemap_loader import SitemapLoader
from document_chunker import DocumentChunker
from pinecone_database import PineconeDatabase
from web_base_loader import WebBaseLoader
from bm25_encoder import BM25Encoder
from retreival_qa import RetreivalQA
from conversation_agent import ConversationAgent
from custom_response_schema import CustomResponseSchema
from streamlit_app import StreamlitApp

def main():
    # Load all pages from a provided sitemap link
    sitemap_loader = SitemapLoader("sitemap_link")
    pages = sitemap_loader.load_sitemap()

    # Chunk the documents and embed them into vectors
    document_chunker = DocumentChunker()
    vectors = document_chunker.chunk_and_embed(pages)

    # Store vectors in a Pinecone vector database
    pinecone_database = PineconeDatabase()
    pinecone_database.store_vectors(vectors)

    # Load documents from a provided GitHub repo
    web_base_loader = WebBaseLoader("repo_url")
    documents = web_base_loader.load_documents()

    # Create a PineconeHybridSearchRetriever
    bm25_encoder = BM25Encoder()
    retriever = bm25_encoder.create_retriever()

    # Create a langchain retrievalqachain for each retriever type
    retreival_qa = RetreivalQA()
    qa_chain = retreival_qa.create_chain(retriever)

    # Provide relevant information based on the user's query
    conversation_agent = ConversationAgent()
    response = conversation_agent.get_response("user_query")

    # Define the custom schema for the agent's responses
    custom_response_schema = CustomResponseSchema()
    schema = custom_response_schema.define_schema()

    # Create a Streamlit conversational app frontend for user interaction
    streamlit_app = StreamlitApp()
    streamlit_app.run()

if __name__ == "__main__":
    main()
```

Now, let's create the code for each of the classes that are imported by the main.py file. We'll start with the SitemapLoader class.

sitemap_loader.py
```python
class SitemapLoader:
    def __init__(self, sitemap_link):
        self.sitemap_link = sitemap_link

    def load_sitemap(self):
        # Code to load all pages from the provided sitemap link
        pass
```

document_chunker.py
```python
class DocumentChunker:
    def chunk_and_embed(self, pages):
        # Code to chunk the documents and embed them into vectors
        pass
```

pinecone_database.py
```python
class PineconeDatabase:
    def store_vectors(self, vectors):
        # Code to store vectors in a Pinecone vector database
        pass
```

web_base_loader.py
```python
class WebBaseLoader:
    def __init__(self, repo_url):
        self.repo_url = repo_url

    def load_documents(self):
        # Code to load documents from the provided GitHub repo
        pass
```

bm25_encoder.py
```python
class BM25Encoder:
    def create_retriever(self):
        # Code to create a PineconeHybridSearchRetriever
        pass
```

retreival_qa.py
```python
class RetreivalQA:
    def create_chain(self, retriever):
        # Code to create a langchain retrievalqachain for each retriever type
        pass
```

conversation_agent.py
```python
class ConversationAgent:
    def get_response(self, user_query):
        # Code to provide relevant information based on the user's query
        pass
```

custom_response_schema.py
```python
from pydantic import BaseModel, Field

class CustomResponseSchema(BaseModel):
    def define_schema(self):
        # Code to define the custom schema for the agent's responses
        pass
```

streamlit_app.py
```python
class StreamlitApp:
    def run(self):
        # Code to create a Streamlit conversational app frontend for user interaction
        pass
```

Please note that the actual implementation of these classes and functions will depend on the specific requirements and constraints of your project. The code provided here is just a skeleton to give you an idea of how to structure your project.