updated requirements file

requirements.txt

@@ -1,190 +1,132 @@
-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
-from tqdm.asyncio import tqdm_asyncio
-import asyncio
-from tqdm.asyncio import tqdm
-
-# 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,
-)
-
-async def add_documents_async(vectorstore, documents):
-    await vectorstore.aadd_documents(documents)
-
-async def process_batch(vectorstore, batch, is_first_batch, pbar):
-    if is_first_batch:
-        result = await FAISS.afrom_documents(batch, hf_embeddings)
-    else:
-        await add_documents_async(vectorstore, batch)
-        result = vectorstore
-    pbar.update(len(batch))
-    return result
-
-async def main():
-    print("Indexing Files")
-    
-    vectorstore = None
-    batch_size = 32
-    
-    batches = [split_documents[i:i+batch_size] for i in range(0, len(split_documents), batch_size)]
-    
-    async def process_all_batches():
-        nonlocal vectorstore
-        tasks = []
-        pbars = []
-        
-        for i, batch in enumerate(batches):
-            pbar = tqdm(total=len(batch), desc=f"Batch {i+1}/{len(batches)}", position=i)
-            pbars.append(pbar)
-            
-            if i == 0:
-                vectorstore = await process_batch(None, batch, True, pbar)
-            else:
-                tasks.append(process_batch(vectorstore, batch, False, pbar))
-        
-        if tasks:
-            await asyncio.gather(*tasks)
-        
-        for pbar in pbars:
-            pbar.close()
-    
-    await process_all_batches()
-    
-    hf_retriever = vectorstore.as_retriever()
-    print("\nIndexing complete. Vectorstore is ready for use.")
-    return hf_retriever
-
-async def run():
-    retriever = await main()
-    return retriever
-
-hf_retriever = asyncio.run(run())
-
-# -- 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()
+aiofiles==23.2.1
+aiohappyeyeballs==2.4.3
+aiohttp==3.10.8
+aiosignal==1.3.1
+annotated-types==0.7.0
+anyio==3.7.1
+async-timeout==4.0.3
+asyncer==0.0.2
+attrs==24.2.0
+bidict==0.23.1
+certifi==2024.8.30
+chainlit==0.7.700
+charset-normalizer==3.3.2
+click==8.1.7
+dataclasses-json==0.5.14
+Deprecated==1.2.14
+distro==1.9.0
+exceptiongroup==1.2.2
+faiss-cpu==1.8.0.post1
+fastapi==0.100.1
+fastapi-socketio==0.0.10
+filelock==3.16.1
+filetype==1.2.0
+frozenlist==1.4.1
+fsspec==2024.9.0
+googleapis-common-protos==1.65.0
+greenlet==3.1.1
+grpcio==1.66.2
+grpcio-tools==1.62.3
+h11==0.14.0
+h2==4.1.0
+hpack==4.0.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.25.1
+hyperframe==6.0.1
+idna==3.10
+importlib_metadata==8.4.0
+Jinja2==3.1.4
+jiter==0.5.0
+joblib==1.4.2
+jsonpatch==1.33
+jsonpointer==3.0.0
+langchain==0.3.0
+langchain-community==0.3.0
+langchain-core==0.3.1
+langchain-huggingface==0.1.0
+langchain-openai==0.2.0
+langchain-qdrant==0.1.4
+langchain-text-splitters==0.3.0
+langsmith==0.1.121
+Lazify==0.4.0
+MarkupSafe==2.1.5
+marshmallow==3.22.0
+mpmath==1.3.0
+multidict==6.1.0
+mypy-extensions==1.0.0
+nest-asyncio==1.6.0
+networkx==3.2.1
+numpy==1.26.4
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==9.1.0.70
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.20.5
+nvidia-nvjitlink-cu12==12.6.77
+nvidia-nvtx-cu12==12.1.105
+openai==1.51.0
+opentelemetry-api==1.27.0
+opentelemetry-exporter-otlp==1.27.0
+opentelemetry-exporter-otlp-proto-common==1.27.0
+opentelemetry-exporter-otlp-proto-grpc==1.27.0
+opentelemetry-exporter-otlp-proto-http==1.27.0
+opentelemetry-instrumentation==0.48b0
+opentelemetry-proto==1.27.0
+opentelemetry-sdk==1.27.0
+opentelemetry-semantic-conventions==0.48b0
+orjson==3.10.7
+packaging==23.2
+pillow==10.4.0
+portalocker==2.10.1
+protobuf==4.25.5
+pydantic==2.9.2
+pydantic-settings==2.5.2
+pydantic_core==2.23.4
+PyJWT==2.9.0
+PyMuPDF==1.24.10
+PyMuPDFb==1.24.10
+python-dotenv==1.0.1
+python-engineio==4.9.1
+python-graphql-client==0.4.3
+python-multipart==0.0.6
+python-socketio==5.11.4
+PyYAML==6.0.2
+qdrant-client==1.11.2
+regex==2024.9.11
+requests==2.32.3
+safetensors==0.4.5
+scikit-learn==1.5.2
+scipy==1.13.1
+sentence-transformers==3.1.1
+simple-websocket==1.0.0
+sniffio==1.3.1
+SQLAlchemy==2.0.35
+starlette==0.27.0
+sympy==1.13.3
+syncer==2.0.3
+tenacity==8.5.0
+threadpoolctl==3.5.0
+tiktoken==0.7.0
+tokenizers==0.20.0
+tomli==2.0.1
+torch==2.4.1
+tqdm==4.66.5
+transformers==4.45.1
+triton==3.0.0
+typing-inspect==0.9.0
+typing_extensions==4.12.2
+uptrace==1.26.0
+urllib3==2.2.3
+uvicorn==0.23.2
+watchfiles==0.20.0
+websockets==13.1
+wrapt==1.16.0
+wsproto==1.2.0
+yarl==1.13.1
+zipp==3.20.2