added local path for vectordb errors

app.py

@@ -11,6 +11,11 @@ from langchain_core.prompts import PromptTemplate
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.runnable import RunnablePassthrough
 from langchain.schema.runnable.config import RunnableConfig
+from pathlib import Path
+
+DATA_DIR = "./data" 
+VECTORSTORE_DIR = os.path.join(DATA_DIR, "vectorstore") 
+VECTORSTORE_PATH = os.path.join(VECTORSTORE_DIR, "index.faiss")
 
 # GLOBAL SCOPE - ENTIRE APPLICATION HAS ACCESS TO VALUES SET IN THIS SCOPE #
 # ---- ENV VARIABLES ---- # 
@@ -58,9 +63,13 @@ hf_embeddings = HuggingFaceEndpointEmbeddings(
     huggingfacehub_api_token=HF_TOKEN,
 )
 
-if os.path.exists("./data/vectorstore"):
+vectordb = os.path.join("./data", "vectorstore") 
+vectordbfile = os.path.join(VECTORSTORE_DIR, "index.faiss")
+
+
+if os.path.exists(vectordbfile):
     vectorstore = FAISS.load_local(
-        "./data/vectorstore", 
+        vectordb, 
         hf_embeddings, 
         allow_dangerous_deserialization=True # this is necessary to load the vectorstore from disk as it's stored as a `.pkl` file.
     )
@@ -68,18 +77,19 @@ if os.path.exists("./data/vectorstore"):
     print("Loaded Vectorstore")
 else:
     print("Indexing Files")
-    os.makedirs("./data/vectorstore", exist_ok=True)
+    os.makedirs(vectordb, exist_ok=True)
     for i in range(0, len(split_documents), 32):
         if i == 0:
             vectorstore = FAISS.from_documents(split_documents[i:i+32], hf_embeddings)
             continue
         vectorstore.add_documents(split_documents[i:i+32])
-    vectorstore.save_local("./data/vectorstore")
+    vectorstore.save_local(vectordb)
+    hf_retriever = vectorstore.as_retriever()
     
     ### 4. INDEX FILES
     ### NOTE: REMEMBER TO BATCH THE DOCUMENTS WITH MAXIMUM BATCH SIZE = 32
 
-hf_retriever = vectorstore.as_retriever()
+
 
 # -- AUGMENTED -- #
 """

solution_app.py

@@ -1,155 +0,0 @@
-import os
-import chainlit as cl
-from dotenv import load_dotenv
-from operator import itemgetter
-from langchain_huggingface import HuggingFaceEndpoint
-from langchain_community.document_loaders import TextLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_huggingface import HuggingFaceEndpointEmbeddings
-from langchain_core.prompts import PromptTemplate
-from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.schema.runnable.config import RunnableConfig
-
-# GLOBAL SCOPE - ENTIRE APPLICATION HAS ACCESS TO VALUES SET IN THIS SCOPE #
-# ---- ENV VARIABLES ---- # 
-"""
-This function will load our environment file (.env) if it is present.
-
-NOTE: Make sure that .env is in your .gitignore file - it is by default, but please ensure it remains there.
-"""
-load_dotenv()
-
-"""
-We will load our environment variables here.
-"""
-HF_LLM_ENDPOINT = os.environ["HF_LLM_ENDPOINT"]
-HF_EMBED_ENDPOINT = os.environ["HF_EMBED_ENDPOINT"]
-HF_TOKEN = os.environ["HF_TOKEN"]
-
-# ---- GLOBAL DECLARATIONS ---- #
-
-# -- RETRIEVAL -- #
-"""
-1. Load Documents from Text File
-2. Split Documents into Chunks
-3. Load HuggingFace Embeddings (remember to use the URL we set above)
-4. Index Files if they do not exist, otherwise load the vectorstore
-"""
-document_loader = TextLoader("./data/paul_graham_essays.txt")
-documents = document_loader.load()
-
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=30)
-split_documents = text_splitter.split_documents(documents)
-
-hf_embeddings = HuggingFaceEndpointEmbeddings(
-    model=HF_EMBED_ENDPOINT,
-    task="feature-extraction",
-    huggingfacehub_api_token=HF_TOKEN,
-)
-
-if os.path.exists("./data/vectorstore"):
-    vectorstore = FAISS.load_local(
-        "./data/vectorstore", 
-        hf_embeddings, 
-        allow_dangerous_deserialization=True # this is necessary to load the vectorstore from disk as it's stored as a `.pkl` file.
-    )
-    hf_retriever = vectorstore.as_retriever()
-    print("Loaded Vectorstore")
-else:
-    print("Indexing Files")
-    os.makedirs("./data/vectorstore", exist_ok=True)
-    for i in range(0, len(split_documents), 32):
-        if i == 0:
-            vectorstore = FAISS.from_documents(split_documents[i:i+32], hf_embeddings)
-            continue
-        vectorstore.add_documents(split_documents[i:i+32])
-    vectorstore.save_local("./data/vectorstore")
-
-hf_retriever = vectorstore.as_retriever()
-
-# -- AUGMENTED -- #
-"""
-1. Define a String Template
-2. Create a Prompt Template from the String Template
-"""
-RAG_PROMPT_TEMPLATE = """\
-<|start_header_id|>system<|end_header_id|>
-You are a helpful assistant. You answer user questions based on provided context. If you can't answer the question with the provided context, say you don't know.<|eot_id|>
-
-<|start_header_id|>user<|end_header_id|>
-User Query:
-{query}
-
-Context:
-{context}<|eot_id|>
-
-<|start_header_id|>assistant<|end_header_id|>
-"""
-
-rag_prompt = PromptTemplate.from_template(RAG_PROMPT_TEMPLATE)
-
-# -- GENERATION -- #
-"""
-1. Create a HuggingFaceEndpoint for the LLM
-"""
-hf_llm = HuggingFaceEndpoint(
-    endpoint_url=HF_LLM_ENDPOINT,
-    max_new_tokens=512,
-    top_k=10,
-    top_p=0.95,
-    temperature=0.3,
-    repetition_penalty=1.15,
-    huggingfacehub_api_token=HF_TOKEN,
-)
-
-@cl.author_rename
-def rename(original_author: str):
-    """
-    This function can be used to rename the 'author' of a message. 
-
-    In this case, we're overriding the 'Assistant' author to be 'Paul Graham Essay Bot'.
-    """
-    rename_dict = {
-        "Assistant" : "Paul Graham Essay Bot"
-    }
-    return rename_dict.get(original_author, original_author)
-
-@cl.on_chat_start
-async def start_chat():
-    """
-    This function will be called at the start of every user session. 
-
-    We will build our LCEL RAG chain here, and store it in the user session. 
-
-    The user session is a dictionary that is unique to each user session, and is stored in the memory of the server.
-    """
-
-    lcel_rag_chain = (
-        {"context": itemgetter("query") | hf_retriever, "query": itemgetter("query")}
-        | rag_prompt | hf_llm
-    )
-
-    cl.user_session.set("lcel_rag_chain", lcel_rag_chain)
-
-@cl.on_message  
-async def main(message: cl.Message):
-    """
-    This function will be called every time a message is recieved from a session.
-
-    We will use the LCEL RAG chain to generate a response to the user query.
-
-    The LCEL RAG chain is stored in the user session, and is unique to each user session - this is why we can access it here.
-    """
-    lcel_rag_chain = cl.user_session.get("lcel_rag_chain")
-
-    msg = cl.Message(content="")
-
-    for chunk in await cl.make_async(lcel_rag_chain.stream)(
-        {"query": message.content},
-        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
-    ):
-        await msg.stream_token(chunk)
-
-    await msg.send()