app.py

from fastapi import FastAPI
from langchain.vectorstores.chroma import Chroma
from langchain.prompts import ChatPromptTemplate
from langchain_community.llms import LlamaCpp
from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
from get_embedding_function import get_embedding_function
from populate_database import populate_database

CHROMA_PATH = "chroma"

PROMPT_TEMPLATE = """
Answer the question based only on the following context:

{context}

---

Answer the question based on the above context: {question}
"""

populate_database()

embedding_function = get_embedding_function()
db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)

callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
model = LlamaCpp(
    model_path="mistral-7b-instruct-v0.2.Q4_K_M.gguf",
    temperature=0.1,
    max_tokens=2000,
    top_p=1,
    callback_manager=callback_manager,
    verbose=True,  # Verbose is required to pass to the callback manager
)

app = FastAPI()

@app.get("/query")
async def getAnswer():
    query_text = "How many kids you have?"
    results = db.similarity_search_with_score(query_text, k=5)

    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
    prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
    prompt = prompt_template.format(context=context_text, question=query_text)

    response_text = model.invoke(prompt)
    sources = [doc.metadata.get("id", None) for doc, _score in results]
    formatted_response = f"Response: {response_text}\nSources: {sources}"
    return formatted_response