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Procfile

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+web: chainlit run app.py --port $PORT --no-cache

app.json

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+{
+  "name": "ChEdBot",
+  "image": "heroku/python",
+  "env": {
+    "DIALOGUE_SHEET": {},
+    "PROMPT_ENGINEERING_SHEET": {},
+    "PERSONA_SHEET": {},
+    "AGENT": {},
+    "OPENAI_API_KEY": {},
+    "OPENAI_API_TYPE": {},
+    "OPENAI_API_BASE": {},
+    "OPENAI_API_VERSION": {}
+  }
+}

app.py

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+import os
+import pandas as pd
+import chainlit as cl
+from chainlit import user_session
+from chainlit.types import LLMSettings
+from langchain import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms import AzureOpenAI
+from langchain.document_loaders import DataFrameLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.vectorstores import Chroma
+from langchain.vectorstores.base import VectorStoreRetriever
+
+
+current_agent = os.environ["AGENT"]
+
+
+def load_dialogues():
+    df = pd.read_excel(os.environ["DIALOGUE_SHEET"], header=0, keep_default_na=False)
+    df = df[df["Agent"] == current_agent]
+    return df.astype(str)
+
+
+def load_persona():
+    df = pd.read_excel(os.environ["PERSONA_SHEET"], header=0, keep_default_na=False)
+    df = df[df["Agent"] == current_agent]
+    return df.astype(str)
+
+
+def load_prompt_engineering():
+    df = pd.read_excel(
+        os.environ["PROMPT_ENGINEERING_SHEET"], header=0, keep_default_na=False
+    )
+    df = df[df["Agent"] == current_agent]
+    return df.astype(str)
+
+
+def load_documents(df, page_content_column: str):
+    return DataFrameLoader(df, page_content_column).load()
+
+
+def init_embedding_function():
+    EMBEDDING_MODEL_FOLDER = ".embedding-model"
+    return HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-mpnet-base-v2",
+        encode_kwargs={"normalize_embeddings": True},
+        cache_folder=EMBEDDING_MODEL_FOLDER,
+    )
+
+
+def load_vectordb(init: bool = False):
+    vectordb = None
+    VECTORDB_FOLDER = ".vectordb"
+    if not init:
+        vectordb = Chroma(
+            embedding_function=init_embedding_function(),
+            persist_directory=VECTORDB_FOLDER,
+        )
+    if init or not vectordb.get()["ids"]:
+        vectordb = Chroma.from_documents(
+            documents=load_documents(load_dialogues(), page_content_column="Utterance"),
+            embedding=init_embedding_function(),
+            persist_directory=VECTORDB_FOLDER,
+        )
+        vectordb.persist()
+    return vectordb
+
+
+def get_retriever(context_state: str, vectordb):
+    return VectorStoreRetriever(
+        vectorstore=vectordb,
+        search_type="similarity",
+        search_kwargs={
+            "filter": {
+                "$or": [{"Context": {"$eq": ""}}, {"Context": {"$eq": context_state}}]
+            },
+            "k": 1,
+        },
+    )
+
+
+vectordb = load_vectordb()
+
+
+@cl.langchain_factory(use_async=True)
+def factory():
+    df_prompt_engineering = load_prompt_engineering()
+    user_session.set("context_state", "")
+
+    llm_settings = LLMSettings(
+        model_name="text-davinci-003",
+        temperature=df_prompt_engineering["Temperature"].values[0],
+    )
+    user_session.set("llm_settings", llm_settings)
+
+    llm = AzureOpenAI(
+        deployment_name="davinci003",
+        model_name=llm_settings.model_name,
+        temperature=llm_settings.temperature,
+        streaming=True,
+    )
+
+    utterance_prompt = PromptTemplate.from_template(
+        df_prompt_engineering["Utterance-Prompt"].values[0]
+    )
+
+    chat_memory = ConversationBufferWindowMemory(
+        memory_key="History",
+        input_key="Utterance",
+        k=df_prompt_engineering["History"].values[0],
+    )
+
+    utterance_chain = LLMChain(
+        prompt=utterance_prompt,
+        llm=llm,
+        verbose=False,
+        memory=chat_memory,
+    )
+
+    continuation_prompt = PromptTemplate.from_template(
+        df_prompt_engineering["Continuation-Prompt"].values[0]
+    )
+
+    continuation_chain = LLMChain(
+        prompt=continuation_prompt,
+        llm=llm,
+        verbose=False,
+        memory=chat_memory,
+    )
+
+    user_session.set("continuation_chain", continuation_chain)
+
+    return utterance_chain
+
+
+@cl.langchain_run
+async def run(agent, input_str):
+    global vectordb
+    if input_str == "/reload":
+        vectordb = load_vectordb(True)
+        await cl.Message(content="Data loaded").send()
+    else:
+        df_persona = load_persona()
+
+        retriever = get_retriever(user_session.get("context_state"), vectordb)
+
+        document = retriever.get_relevant_documents(query=input_str)
+
+        response = await agent.acall(
+            {
+                "Persona": df_persona.loc[
+                    df_persona["AI"] == document[0].metadata["AI"]
+                ]["Persona"].values[0],
+                "Utterance": input_str,
+                "Response": document[0].metadata["Response"],
+            },
+            callbacks=[cl.AsyncLangchainCallbackHandler()],
+        )
+        await cl.Message(
+            content=response["text"],
+            author=document[0].metadata["AI"],
+            llm_settings=user_session.get("llm_settings"),
+        ).send()
+        user_session.set("context_state", document[0].metadata["Contextualisation"])
+        continuation = document[0].metadata["Continuation"]
+
+        while continuation != "":
+            document_continuation = vectordb.get(where={"Intent": continuation})
+            continuation_chain = user_session.get("continuation_chain")
+            response = await continuation_chain.acall(
+                {
+                    "Persona": df_persona.loc[
+                        df_persona["AI"] == document_continuation["metadatas"][0]["AI"]
+                    ]["Persona"].values[0],
+                    "Utterance": "",
+                    "Response": document_continuation["metadatas"][0]["Response"],
+                },
+                callbacks=[cl.AsyncLangchainCallbackHandler()],
+            )
+            await cl.Message(
+                content=response["text"],
+                author=document_continuation["metadatas"][0]["AI"],
+                llm_settings=user_session.get("llm_settings"),
+            ).send()
+            user_session.set(
+                "context_state",
+                document_continuation["metadatas"][0]["Contextualisation"],
+            )
+            continuation = document_continuation["metadatas"][0]["Continuation"]

chainlit.md

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+# Welcome to ChEdBot 🦜!
+
+## Useful Links 🔗
+
+- **Documentation:** [ChEdBot Documentation]() 📚
+
+We can't wait to see what you create with ChEdBot!

requirements.txt

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+chainlit
+langchain
+sentence_transformers
+chromadb
+openpyxl

runtime.txt

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+python-3.10