Venkat.py

from fastapi import FastAPI

# from transformers import pipeline
from txtai.embeddings import Embeddings
from txtai.pipeline import Extractor
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain import HuggingFaceHub
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from txtai.embeddings import Embeddings
from txtai.pipeline import Extractor

import pandas as pd
import sqlite3
import os

# NOTE - we configure docs_url to serve the interactive Docs at the root path
# of the app. This way, we can use the docs as a landing page for the app on Spaces.
app = FastAPI(docs_url="/")
# app = FastAPI()

# pipe = pipeline("text2text-generation", model="google/flan-t5-small")


# @app.get("/generate")
# def generate(text: str):
#     """
#     Using the text2text-generation pipeline from `transformers`, generate text
#     from the given input text. The model used is `google/flan-t5-small`, which
#     can be found [here](https://huggingface.co/google/flan-t5-small).
#     """
#     output = pipe(text)
#     return {"output": output[0]["generated_text"]}


def load_embeddings(
    domain: str = "",
    db_present: bool = True,
    path: str = "sentence-transformers/all-MiniLM-L6-v2",
    index_name: str = "index",
):
    # Create embeddings model with content support
    embeddings = Embeddings({"path": path, "content": True})

    # if Vector DB is not present
    if not db_present:
        return embeddings
    else:
        if domain == "":
            embeddings.load(index_name)  # change this later
        else:
            print(3)
            embeddings.load(f"{index_name}/{domain}")
        return embeddings


def _check_if_db_exists(db_path: str) -> bool:
    return os.path.exists(db_path)


def _text_splitter(doc):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=50,
        length_function=len,
    )
    return text_splitter.transform_documents(doc)


def _load_docs(path: str):
    load_doc = WebBaseLoader(path).load()
    doc = _text_splitter(load_doc)
    return doc


def _stream(dataset, limit, index: int = 0):
    for row in dataset:
        yield (index, row.page_content, None)
        index += 1

        if index >= limit:
            break


def _max_index_id(path):
    db = sqlite3.connect(path)

    table = "sections"
    df = pd.read_sql_query(f"select * from {table}", db)
    return {"max_index": df["indexid"].max()}


def _upsert_docs(doc, embeddings, vector_doc_path: str, db_present: bool):
    print(vector_doc_path)
    if db_present:
        print(1)
        max_index = _max_index_id(f"{vector_doc_path}/documents")
        print(max_index)
        embeddings.upsert(_stream(doc, 500, max_index["max_index"]))
        print("Embeddings done!!")
        embeddings.save(vector_doc_path)
        print("Embeddings done - 1!!")
    else:
        print(2)
        embeddings.index(_stream(doc, 500, 0))
        embeddings.save(vector_doc_path)
        max_index = _max_index_id(f"{vector_doc_path}/documents")
        print(max_index)
    # check
    # max_index = _max_index_id(f"{vector_doc_path}/documents")
    # print(max_index)
    return max_index




@app.get("/index/{domain}/")
def get_domain_file_path(domain: str, file_path: str):
    print(domain, file_path)
    print(os.getcwd())
    bool_value = _check_if_db_exists(db_path=f"{os.getcwd()}/index/{domain}/documents")
    print(bool_value)
    if bool_value:
        embeddings = load_embeddings(domain=domain, db_present=bool_value)
        print(embeddings)
        doc = _load_docs(file_path)
        max_index = _upsert_docs(
            doc=doc,
            embeddings=embeddings,
            vector_doc_path=f"{os.getcwd()}/index/{domain}",
            db_present=bool_value,
        )
        # print("-------")
    else:
        embeddings = load_embeddings(domain=domain, db_present=bool_value)
        doc = _load_docs(file_path)
        max_index = _upsert_docs(
            doc=doc,
            embeddings=embeddings,
            vector_doc_path=f"{os.getcwd()}/index/{domain}",
            db_present=bool_value,
        )
    # print("Final - output : ", max_index)
    return "Executed Successfully!!"


def _check_if_db_exists(db_path: str) -> bool:
    return os.path.exists(db_path)


def _load_embeddings_from_db(
    db_present: bool,
    domain: str,
    path: str = "sentence-transformers/all-MiniLM-L6-v2",
):
    # Create embeddings model with content support
    embeddings = Embeddings({"path": path, "content": True})
    # if Vector DB is not present
    if not db_present:
        return embeddings
    else:
        if domain == "":
            embeddings.load("index")  # change this later
        else:
            print(3)
            embeddings.load(f"{os.getcwd()}/index/{domain}")
        return embeddings


def _prompt(question):
    return f"""Answer the following question using only the context below. Say 'Could not find answer within the context' when the question can't be answered.
            Question: {question}
            Context: """


def _search(query, extractor, question=None):
    # Default question to query if empty
    if not question:
        question = query

    # template = f"""Answer the following question using only the context below. Say 'no answer' when the question can't be answered.
    #         Question: {question}
    #         Context: """

    # prompt = PromptTemplate(template=template, input_variables=["question"])
    # llm_chain = LLMChain(prompt=prompt, llm=extractor)

    # return {"question": question, "answer": llm_chain.run(question)}
    return extractor([("answer", query, _prompt(question), False)])[0][1]


@app.get("/rag")
def rag(domain: str, question: str):
    db_exists = _check_if_db_exists(db_path=f"{os.getcwd()}/index/{domain}/documents")
    print(db_exists)
    # if db_exists:
    embeddings = _load_embeddings_from_db(db_exists, domain)
    extractor = Extractor(similarity=embeddings, path="google/flan-t5-base")
    # llm = HuggingFaceHub(
    #     repo_id="google/flan-t5-xxl",
    #     model_kwargs={"temperature": 1, "max_length": 1000000},
    # )
    # else:
    answer = _search(question, extractor)
    return {"question": question, "answer": answer}



'''
load embedding and models for extractor during start up
'''

# Create extractor instance
#extractor = Extractor(path="google/flan-t5-large")
#extractor = Extractor(embeddings, "TheBloke/Llama-2-7B-GGUF")
#extractor = Extractor(embeddings, "google/flan-t5-xl")