Delete Venkat.py

Venkat.py

@@ -1,218 +0,0 @@
-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")