Delete rag/runnable.py

rag/runnable.py

@@ -1,129 +0,0 @@
-import os
-import random
-from datetime import datetime
-from operator import itemgetter
-from typing import Sequence
-
-import langsmith
-from langchain.memory import ConversationBufferWindowMemory
-from langchain_community.document_transformers import LongContextReorder
-from langchain_core.documents import Document
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import Runnable, RunnableLambda
-from langchain_openai import ChatOpenAI
-from zoneinfo import ZoneInfo
-
-from rag.retrievers import RetrieversConfig
-
-from .prompt_template import generate_prompt_template
-
-# Helpers
-
-
-def get_datetime() -> str:
-    """Get the current date and time."""
-    return datetime.now(ZoneInfo("America/Vancouver")).strftime("%A, %Y-%b-%d %H:%M:%S")
-
-
-def reorder_documents(docs: list[Document]) -> Sequence[Document]:
-    """Reorder documents to mitigate performance degradation with long contexts."""
-
-    return LongContextReorder().transform_documents(docs)
-
-
-def randomize_documents(documents: list[Document]) -> list[Document]:
-    """Randomize documents to vary model recommendations."""
-    random.shuffle(documents)
-    return documents
-
-
-class DocumentFormatter:
-    def __init__(self, prefix: str):
-        self.prefix = prefix
-
-    def __call__(self, docs: list[Document]) -> str:
-        """Format the Documents to markdown.
-        Args:
-            docs (list[Documents]): List of Langchain documents
-        Returns:
-            docs (str):
-        """
-        return "\n---\n".join(
-            [
-                f"- {self.prefix} {i+1}:\n\n\t" + d.page_content
-                for i, d in enumerate(docs)
-            ]
-        )
-
-
-def create_langsmith_client():
-    """Create a Langsmith client."""
-    os.environ["LANGCHAIN_TRACING_V2"] = "true"
-    os.environ["LANGCHAIN_PROJECT"] = "talltree-ai-assistant"
-    os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
-    langsmith_api_key = os.getenv("LANGCHAIN_API_KEY")
-    if not langsmith_api_key:
-        raise EnvironmentError("Missing environment variable: LANGCHAIN_API_KEY")
-    return langsmith.Client()
-
-
-# Set up Runnable and Memory
-
-
-def get_runnable(
-    model: str = "gpt-4o-mini", temperature: float = 0.1
-) -> tuple[Runnable, ConversationBufferWindowMemory]:
-    """Set up runnable and chat memory
-
-    Args:
-        model_name (str, optional): LLM model. Defaults to "gpt-4o".
-        temperature (float, optional): Model temperature. Defaults to 0.1.
-
-    Returns:
-        Runnable, Memory: Chain and Memory
-    """
-
-    # Set up Langsmith to trace the chain
-    create_langsmith_client()
-
-    # LLM and prompt template
-    llm = ChatOpenAI(
-        model=model,
-        temperature=temperature,
-    )
-
-    prompt = generate_prompt_template()
-
-    # Set retrievers with Hybrid search
-
-    retrievers_config = RetrieversConfig()
-
-    # Practitioners data
-    practitioners_data_retriever = retrievers_config.get_practitioners_retriever(k=10)
-
-    # Tall Tree documents with contact information for locations and services
-    documents_retriever = retrievers_config.get_documents_retriever(k=10)
-
-    # Set conversation history window memory. It only uses the last k interactions
-    memory = ConversationBufferWindowMemory(
-        memory_key="history",
-        return_messages=True,
-        k=6,
-    )
-
-    # Set up runnable using LCEL
-    setup = {
-        "practitioners_db": itemgetter("message")
-        | practitioners_data_retriever
-        | DocumentFormatter("Practitioner #"),
-        "tall_tree_db": itemgetter("message")
-        | documents_retriever
-        | DocumentFormatter("No."),
-        "timestamp": lambda _: get_datetime(),
-        "history": RunnableLambda(memory.load_memory_variables) | itemgetter("history"),
-        "message": itemgetter("message"),
-    }
-
-    chain = setup | prompt | llm | StrOutputParser()
-
-    return chain, memory