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chain.py

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+"""This module contains functions for loading a ConversationalRetrievalChain"""
+
+import logging
+
+import wandb
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from prompts import load_chat_prompt
+
+
+logger = logging.getLogger(__name__)
+
+
+def load_vector_store(wandb_run: wandb.run, openai_api_key: str) -> Chroma:
+    """Load a vector store from a Weights & Biases artifact
+    Args:
+        run (wandb.run): An active Weights & Biases run
+        openai_api_key (str): The OpenAI API key to use for embedding
+    Returns:
+        Chroma: A chroma vector store object
+    """
+    # load vector store artifact
+    vector_store_artifact_dir = wandb_run.use_artifact(
+        wandb_run.config.vector_store_artifact, type="search_index"
+    ).download()
+    embedding_fn = OpenAIEmbeddings(openai_api_key=openai_api_key)
+    # load vector store
+    vector_store = Chroma(
+        embedding_function=embedding_fn, persist_directory=vector_store_artifact_dir
+    )
+
+    return vector_store
+
+
+def load_chain(wandb_run: wandb.run, vector_store: Chroma, openai_api_key: str):
+    """Load a ConversationalQA chain from a config and a vector store
+    Args:
+        wandb_run (wandb.run): An active Weights & Biases run
+        vector_store (Chroma): A Chroma vector store object
+        openai_api_key (str): The OpenAI API key to use for embedding
+    Returns:
+        ConversationalRetrievalChain: A ConversationalRetrievalChain object
+    """
+    retriever = vector_store.as_retriever()
+    llm = ChatOpenAI(
+        openai_api_key=openai_api_key,
+        model_name=wandb_run.config.model_name,
+        temperature=wandb_run.config.chat_temperature,
+        max_retries=wandb_run.config.max_fallback_retries,
+    )
+    chat_prompt_dir = wandb_run.use_artifact(
+        wandb_run.config.chat_prompt_artifact, type="prompt"
+    ).download()
+    qa_prompt = load_chat_prompt(f"{chat_prompt_dir}/prompt.json")
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        chain_type="stuff",
+        retriever=retriever,
+        combine_docs_chain_kwargs={"prompt": qa_prompt},
+        return_source_documents=True,
+    )
+    return qa_chain
+
+
+def get_answer(
+    chain: ConversationalRetrievalChain,
+    question: str,
+    chat_history: list[tuple[str, str]],
+):
+    """Get an answer from a ConversationalRetrievalChain
+    Args:
+        chain (ConversationalRetrievalChain): A ConversationalRetrievalChain object
+        question (str): The question to ask
+        chat_history (list[tuple[str, str]]): A list of tuples of (question, answer)
+    Returns:
+        str: The answer to the question
+    """
+    result = chain(
+        inputs={"question": question, "chat_history": chat_history},
+        return_only_outputs=True,
+    )
+    response = f"Answer:\t{result['answer']}"
+    return response

config.py

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+"""Configuration for the LLM Apps Course"""
+from types import SimpleNamespace
+
+TEAM = None
+PROJECT = "llmapps"
+JOB_TYPE = "production"
+
+default_config = SimpleNamespace(
+    project=PROJECT,
+    entity=TEAM,
+    job_type=JOB_TYPE,
+    vector_store_artifact="demiroz/llmapps/vector_store:v1",
+    chat_prompt_artifact="demiroz/llmapps/chat_prompt:v0",
+    chat_temperature=0.3,
+    max_fallback_retries=1,
+    model_name="gpt-3.5-turbo",
+    eval_model="gpt-3.5-turbo",
+    eval_artifact="demiroz/llmapps/vector_store:v1",
+)