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@@ -24,4 +24,4 @@ WORKDIR $HOME/app
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["uvicorn", "Index:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["uvicorn", "Venkat:app", "--host", "0.0.0.0", "--port", "7860"]

Venkat.py

@@ -0,0 +1,218 @@
+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(embeddings)
+    # 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("google/flan-t5-base")
+#extractor = Extractor(embeddings, "TheBloke/Llama-2-7B-GGUF")
+#extractor = Extractor(embeddings, "google/flan-t5-xl")