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Asaad Almutareb
commited on
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•
096b9ec
1
Parent(s):
383c59f
Revert "added stuff"
Browse filesThis reverts commit 8a93d3866170273593eb3e236a3a87d9dbbcc5a7.
revert changes from old containers
- app.py +13 -171
- lc-embeddings.py +0 -0
app.py
CHANGED
@@ -3,162 +3,9 @@ from langchain.chains import RetrievalQAWithSourcesChain
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# gradio
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import gradio as gr
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#import random
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import time
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#boto3 for S3 access
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import boto3
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from botocore import UNSIGNED
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from botocore.client import Config
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# access .env file
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import os
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from dotenv import load_dotenv
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#from bs4 import BeautifulSoup
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# HF libraries
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from langchain.llms import HuggingFaceHub
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from langchain.embeddings import HuggingFaceHubEmbeddings
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# vectorestore
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from langchain.vectorstores import Chroma
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#from langchain.vectorstores import FAISS
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# retrieval chain
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from langchain.chains import RetrievalQA
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# from langchain.chains import RetrievalQAWithSourcesChain
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# prompt template
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from langchain.prompts import PromptTemplate
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from langchain.memory import ConversationBufferMemory
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# logging
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import logging
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#import zipfile
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# improve results with retriever
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# from langchain.retrievers import ContextualCompressionRetriever
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# from langchain.retrievers.document_compressors import LLMChainExtractor
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# from langchain.retrievers.document_compressors import EmbeddingsFilter
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# from langchain.retrievers.multi_query import MultiQueryRetriever
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# from langchain.retrievers import BM25Retriever, EnsembleRetriever
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# reorder retrived documents
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# from langchain.document_transformers import LongContextReorder
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# github issues
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#from langchain.document_loaders import GitHubIssuesLoader
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# debugging
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from langchain.globals import set_verbose
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# caching
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from langchain.globals import set_llm_cache
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#from langchain.cache import InMemoryCache
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# We can do the same thing with a SQLite cache
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from langchain.cache import SQLiteCache
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#set_llm_cache(InMemoryCache())
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set_verbose(True)
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# load .env variables
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config = load_dotenv(".env")
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HUGGINGFACEHUB_API_TOKEN=os.getenv('HUGGINGFACEHUB_API_TOKEN')
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AWS_S3_LOCATION=os.getenv('AWS_S3_LOCATION')
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AWS_S3_FILE=os.getenv('AWS_S3_FILE')
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VS_DESTINATION=os.getenv('VS_DESTINATION')
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# initialize Model config
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# HuggingFaceH4/zephyr-7b-beta
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# mistralai/Mistral-7B-Instruct-v0.1
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model_id = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={
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"temperature":0.1,
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"max_new_tokens":1024,
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"repetition_penalty":1.2,
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# "streaming": True,
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# "return_full_text":True
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})
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#model_name = "sentence-transformers/multi-qa-mpnet-base-dot-v1"
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model_name = "sentence-transformers/all-mpnet-base-v2"
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embeddings = HuggingFaceHubEmbeddings(repo_id=model_name)
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# remove old vectorstore
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if os.path.exists(VS_DESTINATION):
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os.remove(VS_DESTINATION)
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# remove old sqlite cache
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if os.path.exists('.langchain.sqlite'):
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os.remove('.langchain.sqlite')
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set_llm_cache(SQLiteCache(database_path=".langchain.sqlite"))
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# retrieve vectorsrore
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s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
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## Chroma DB
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s3.download_file(AWS_S3_LOCATION, AWS_S3_FILE, VS_DESTINATION)
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# use the cached embeddings instead of embeddings to speed up re-retrival
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db = Chroma(persist_directory="./vectorstore", embedding_function=embeddings)
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db.get()
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## FAISS DB
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# s3.download_file('rad-rag-demos', 'vectorstores/faiss_db_ray.zip', './chroma_db/faiss_db_ray.zip')
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# with zipfile.ZipFile('./chroma_db/faiss_db_ray.zip', 'r') as zip_ref:
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# zip_ref.extractall('./chroma_db/')
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# FAISS_INDEX_PATH='./chroma_db/faiss_db_ray'
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# db = FAISS.load_local(FAISS_INDEX_PATH, embeddings)
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# initialize the bm25 retriever and chroma/faiss retriever
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# bm25_retriever = BM25Retriever.
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# bm25_retriever.k = 2
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# Retrieve more documents with higher diversity useful if your dataset has many similar documents
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retriever = db.as_retriever(search_type="mmr")#, search_kwargs={'k': 3, 'lambda_mult': 0.25})
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# Above a certain threshold
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# retriever = db.as_retriever(
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# search_type="similarity_score_threshold",
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# search_kwargs={'score_threshold': 0.6}
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# )
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# # asks LLM to create 3 alternatives baed on user query
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# multi_retriever = MultiQueryRetriever.from_llm(retriever=retriever, llm=model_id)
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# # asks LLM to extract relevant parts from retrieved documents
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# compressor = LLMChainExtractor.from_llm(model_id)
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# compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=multi_retriever)
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global qa
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You are the friendly documentation AI buddy Arti, who helps the Human in using RAY, the open-source unified framework for scaling AI and Python applications.
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Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question :
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------
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<ctx>
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{context}
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</ctx>
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------
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<hs>
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{history}
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</hs>
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------
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{question}
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Answer:
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"""
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prompt = PromptTemplate(
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input_variables=["history", "context", "question"],
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template=template,
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)
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memory = ConversationBufferMemory(memory_key="history", input_key="question")
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# logging for the chain
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logging.basicConfig()
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logging.getLogger("langchain.retrievers").setLevel(logging.INFO)
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logging.getLogger("langchain.chains").setLevel(logging.INFO)
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qa = RetrievalQA.from_chain_type(llm=model_id, retriever=retriever, return_source_documents=True, verbose=True, chain_type_kwargs={
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"verbose": True,
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"memory": memory,
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"prompt": prompt
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}
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)
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# qa = RetrievalQAWithSourcesChain.from_chain_type(llm=model_id, retriever=retriever, return_source_documents=True, verbose=True, chain_type_kwargs={
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# "verbose": True,
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# "memory": memory,
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# "prompt": prompt,
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# "document_variable_name": "context"
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# }
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# )
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#####
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@@ -174,29 +21,24 @@ def create_gradio_interface(qa:RetrievalQAWithSourcesChain):
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history = history + [(text, None)]
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return history, ""
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def bot(history):
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history[-1][1] += character
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time.sleep(0.01)
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yield history
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# history[-1][1] = print_this #response['answer']
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# return history
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def infer(question, history):
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query = question
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result = qa({"query": query, "history": history, "question": question})
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return result
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css="""
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#col-container {
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"""
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title = """
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<div style="text-align: center;max-width: 1920px;">
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# gradio
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import gradio as gr
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global qa
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from qa import qa
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#####
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history = history + [(text, None)]
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return history, ""
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def bot(history):
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response = infer(history[-1][0], history)
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sources = [doc.metadata.get("source") for doc in response['source_documents']]
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src_list = '\n'.join(sources)
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print_this = response['answer'] + "\n\n\n Sources: \n\n\n" + src_list
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history[-1][1] = print_this #response['answer']
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return history
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def infer(question, history):
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query = question
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result = qa({"query": query, "history": history, "question": question})
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return result
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css="""
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#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
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"""
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title = """
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<div style="text-align: center;max-width: 1920px;">
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lc-embeddings.py
DELETED
File without changes
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