Add the main app

main.py

@@ -0,0 +1,254 @@
+from fastapi import FastAPI, HTTPException, Request, Form
+from fastapi.responses import JSONResponse, HTMLResponse
+from pydantic import BaseModel
+from fastapi.templating import Jinja2Templates
+from fastapi.responses import RedirectResponse
+
+from dotenv import load_dotenv
+
+import os
+
+from langchain_community.vectorstores import Chroma
+from langchain_community.document_loaders import WebBaseLoader
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+
+from langchain.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+import chromadb
+
+
+app = FastAPI()
+load_dotenv()
+
+templates = Jinja2Templates(directory="templates")
+
+
+CHUNK_SIZE = 300
+CHUNK_OVERLAP = 75
+OPENAI_API_KEY = os.environ['OPENAI_API_KEY']
+
+
+class Wiki(BaseModel):
+    wiki_url : str
+
+
+def load_wiki_article(url: str):
+    try:
+        loader = WebBaseLoader(
+            web_path=url
+        )
+        docs = loader.load()
+        print("Wiki Loaded Successfuly")
+    except Exception as e:
+        raise Exception(e)
+
+    return docs
+
+def wiki_text_splitter(docs):
+
+    text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+    chunk_size=CHUNK_SIZE, 
+    chunk_overlap=CHUNK_OVERLAP)
+
+    # Make splits
+    splits = text_splitter.split_documents(docs)
+
+    return splits
+
+async def create_db(splits):
+
+    chroma_instance = Chroma(persist_directory='db/')
+    delete_db_documents(chroma_instance)
+
+    vectorstore = Chroma.from_documents(documents=splits, 
+                                    embedding=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY),
+                                    persist_directory='db/')
+    #collection_metadata={"hnsw:M": 2024,"hnsw:ef": 500}
+
+async def get_retriever():
+    # load chroma db
+    chroma_instance = Chroma(persist_directory='db/', embedding_function=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY))
+    retriever = chroma_instance.as_retriever()
+
+    return retriever
+
+
+def delete_db_documents(chroma_instance):
+
+    chroma_db =  chromadb.PersistentClient('db/')
+    chroma_db.reset()
+
+    # ids = chroma_instance.get()['ids']
+    # if len(ids) > 0:
+
+    #     print("count before", chroma_instance._collection.count())
+    #     chroma_instance._collection.delete(ids=ids)
+    # print("count after", chroma_instance._collection.count())
+
+
+@app.get("/", response_class=HTMLResponse)
+def read_form(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/send", response_class=HTMLResponse)
+async def read_wiki_url(request: Request, wiki: Wiki):
+
+    url = wiki.wiki_url
+    error_message = ""
+    docs = load_wiki_article(url)
+    splits = wiki_text_splitter(docs)
+    await create_db(splits)
+    #print('Finished execution')
+
+
+    return templates.TemplateResponse("chat.html", {"request": request})
+
+    # try:
+
+    #     # docs = load_wiki_article(url)
+    #     # splits = wiki_text_splitter(docs)
+    #     # await create_db(splits)
+    #     print('Finished execution')
+    #     #return templates.TemplateResponse("chat.html", {"request": request})
+    #     return RedirectResponse(url="/chat")
+
+    # except Exception as e:
+    #     rerror_message = str(e)
+    #     print(error_message)
+    #     return templates.TemplateResponse("index.html", {"request": request, "error_message": error_message})
+
+
+@app.get("/chat", response_class=HTMLResponse)
+async def chat(request: Request):
+    # Add any necessary context variables to the context dictionary
+    print("Chat endpoint reached")
+    context = {"request": request}
+    return templates.TemplateResponse("chat.html", context)
+
+
+
+@app.post("/chat", response_class=HTMLResponse)
+async def ask_respond(request: Request):
+
+    request_body = await request.json()
+
+    # Access the value of the "question" field from the request body
+    user_question = request_body.get("question")
+
+    retriever = await get_retriever()
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.7) 
+
+    template = """Answer the question based only on the following context:
+    {context} and when you dont know the answer say I dont have  information about AND MENTION WHAT THE USER AKED YOU ABOUT
+    but you can be kind for example when people are greeting you
+
+    Question: {question}
+    """
+    # template = """Answer the question based only on the following context:
+    # {context}
+    # Refer to the {history} of the conversation -if available- to better understand the question
+
+    # Question: {question}
+    # """
+    prompt = ChatPromptTemplate.from_template(template)
+
+    rag_chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    print("human_question:", user_question)
+    answer = rag_chain.invoke(user_question)
+
+    print("ai_answer:", answer)
+
+    return JSONResponse(content={"answer": answer})
+
+
+
+
+
+# def x():
+#     from langchain.chains import create_retrieval_chain
+#     from langchain.chains.combine_documents import create_stuff_documents_chain
+
+#     qa_system_prompt = """You are an assistant for question-answering tasks. \
+#     Use the following pieces of retrieved context to answer the question. \
+#     If you don't know the answer, just say that you don't know. \
+#     Use three sentences maximum and keep the answer concise.\
+
+#     {context}"""
+#     qa_prompt = ChatPromptTemplate.from_messages(
+#         [
+#             ("system", qa_system_prompt),
+#             MessagesPlaceholder("chat_history"),
+#             ("human", "{input}"),
+#         ]
+#     )
+#     question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+
+#     rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
+
+
+
+
+#%%
+# from langchain_openai import OpenAIEmbeddings
+# from langchain_community.vectorstores import Chroma
+# from langchain_openai import ChatOpenAI
+# from langchain.prompts import ChatPromptTemplate
+# import os
+# OPENAI_API_KEY = os.environ['OPENAI_API_KEY']
+# # Initialize Chroma with the saved database
+# chroma_instance = Chroma(persist_directory='.', embedding_function=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY))
+
+
+# # %%
+# ids = chroma_instance.get()['ids']
+# print("count before", chroma_instance._collection.count())
+
+
+# #chroma_instance._collection.delete(ids=ids)
+# print("count after", chroma_instance._collection.count())
+# #  %%
+# import chromadb
+
+# chroma_db =  chromadb.PersistentClient('.')
+# chroma_db.reset()
+
+# chroma_db.delete_collection('langchain')
+
+
+
+#%%
+
+# # Prompt
+# template = """Answer the question based only on the following context:
+# {context} and when you dont know the answer say 'Sorry I can't answer that'
+
+# Question: {question}
+# """
+
+# prompt = ChatPromptTemplate.from_template(template)
+# prompt
+
+# retriever = chroma_instance.as_retriever()
+# llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) 
+
+# from langchain_core.output_parsers import StrOutputParser
+# from langchain_core.runnables import RunnablePassthrough
+
+# rag_chain = (
+#     {"context": retriever, "question": RunnablePassthrough()}
+#     | prompt
+#     | llm
+#     | StrOutputParser()
+# )
+
+# rag_chain.invoke("who founded vodafone?")
+# %%