From 046fbebf5646f8fd95210603b03b18b94e464d13 Mon Sep 17 00:00:00 2001 From: Alex Date: Tue, 14 Feb 2023 13:06:28 +0000 Subject: [PATCH 1/3] Enable other llm's --- application/app.py | 38 +++++++++++++++++++++++++++------- scripts/parser/open_ai_func.py | 6 ++++++ 2 files changed, 36 insertions(+), 8 deletions(-) diff --git a/application/app.py b/application/app.py index aa9089ed..ce648152 100644 --- a/application/app.py +++ b/application/app.py @@ -5,10 +5,21 @@ import datetime from flask import Flask, request, render_template # os.environ["LANGCHAIN_HANDLER"] = "langchain" import faiss -from langchain import OpenAI -from langchain.chains import VectorDBQAWithSourcesChain +from langchain import FAISS +from langchain import OpenAI, VectorDBQA, HuggingFaceHub, Cohere +from langchain.chains.question_answering import load_qa_chain from langchain.prompts import PromptTemplate import requests +from langchain.embeddings import OpenAIEmbeddings + +# from manifest import Manifest +# from langchain.llms.manifest import ManifestWrapper + +# manifest = Manifest( +# client_name = "huggingface", +# client_connection = "http://127.0.0.1:5000" +# ) + # Redirect PosixPath to WindowsPath on Windows import platform @@ -58,22 +69,33 @@ def api_answer(): else: vectorstore = "" + # loading the index and the store and the prompt template - index = faiss.read_index(f"{vectorstore}docs.index") + docsearch = FAISS.load_local(vectorstore, OpenAIEmbeddings(openai_api_key=api_key)) - with open(f"{vectorstore}faiss_store.pkl", "rb") as f: - store = pickle.load(f) - store.index = index + #docsearch = FAISS(OpenAIEmbeddings(openai_api_key="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"), index, store) # create a prompt template c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template) # create a chain with the prompt template and the store - chain = VectorDBQAWithSourcesChain.from_llm(llm=OpenAI(openai_api_key=api_key, temperature=0), vectorstore=store, combine_prompt=c_prompt) + #llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 2048}) + llm = OpenAI(openai_api_key=api_key, temperature=0) + #llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key) + # llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key) + + qa_chain = load_qa_chain(llm = llm, chain_type="map_reduce", + combine_prompt=c_prompt) + + + chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=2) + # fetch the answer - result = chain({"question": question}) + result = chain({"query": question}) + print(result) # some formatting for the frontend + result['answer'] = result['result'] result['answer'] = result['answer'].replace("\\n", "
") result['answer'] = result['answer'].replace("SOURCES:", "") # mock result diff --git a/scripts/parser/open_ai_func.py b/scripts/parser/open_ai_func.py index 00c57be9..2fe05170 100644 --- a/scripts/parser/open_ai_func.py +++ b/scripts/parser/open_ai_func.py @@ -3,6 +3,7 @@ import pickle import tiktoken from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings +#from langchain.embeddings import HuggingFaceEmbeddings def num_tokens_from_string(string: str, encoding_name: str) -> int: @@ -22,6 +23,11 @@ def call_openai_api(docs): #docs = docs[:n] c1 = 0 store = FAISS.from_documents(docs_test, OpenAIEmbeddings()) + + # Uncomment for MPNet embeddings + # model_name = "sentence-transformers/all-mpnet-base-v2" + # hf = HuggingFaceEmbeddings(model_name=model_name) + # store = FAISS.from_documents(docs_test, hf) for i in tqdm(docs, desc="Embedding 🦖", unit="docs", total=len(docs), bar_format='{l_bar}{bar}| Time Left: {remaining}'): try: import time From 3d0642dca31bc5e555010f30cd798b3560814dd9 Mon Sep 17 00:00:00 2001 From: Alex Date: Tue, 14 Feb 2023 13:44:47 +0000 Subject: [PATCH 2/3] remove key... --- application/app.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/application/app.py b/application/app.py index ce648152..41549568 100644 --- a/application/app.py +++ b/application/app.py @@ -73,8 +73,6 @@ def api_answer(): # loading the index and the store and the prompt template docsearch = FAISS.load_local(vectorstore, OpenAIEmbeddings(openai_api_key=api_key)) - - #docsearch = FAISS(OpenAIEmbeddings(openai_api_key="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"), index, store) # create a prompt template c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template) # create a chain with the prompt template and the store From 4d1ff8238d03af410a340a4cdf7f6692b8997c90 Mon Sep 17 00:00:00 2001 From: Alex Date: Wed, 15 Feb 2023 18:40:23 +0000 Subject: [PATCH 3/3] switching between llms --- application/app.py | 95 ++++++++++++++++++++----------- application/index.faiss | Bin 0 -> 8534061 bytes application/index.pkl | Bin 0 -> 2982020 bytes application/static/src/chat.js | 1 + application/templates/index.html | 12 ++-- scripts/parser/open_ai_func.py | 20 ++----- 6 files changed, 76 insertions(+), 52 deletions(-) create mode 100644 application/index.faiss create mode 100644 application/index.pkl diff --git a/application/app.py b/application/app.py index e2bbf962..8a66ff9d 100644 --- a/application/app.py +++ b/application/app.py @@ -1,56 +1,68 @@ import os -import pickle -import dotenv -import datetime -from flask import Flask, request, render_template -# os.environ["LANGCHAIN_HANDLER"] = "langchain" -import faiss +import dotenv +import requests +from flask import Flask, request, render_template from langchain import FAISS from langchain import OpenAI, VectorDBQA, HuggingFaceHub, Cohere from langchain.chains.question_answering import load_qa_chain +from langchain.embeddings import OpenAIEmbeddings, HuggingFaceHubEmbeddings, CohereEmbeddings, HuggingFaceInstructEmbeddings from langchain.prompts import PromptTemplate -import requests -from langchain.embeddings import OpenAIEmbeddings -# from manifest import Manifest -# from langchain.llms.manifest import ManifestWrapper +# os.environ["LANGCHAIN_HANDLER"] = "langchain" -# manifest = Manifest( -# client_name = "huggingface", -# client_connection = "http://127.0.0.1:5000" -# ) +if os.getenv("LLM_NAME") is not None: + llm_choice = os.getenv("LLM_NAME") +else: + llm_choice = "openai" +if os.getenv("EMBEDDINGS_NAME") is not None: + embeddings_choice = os.getenv("EMBEDDINGS_NAME") +else: + embeddings_choice = "openai_text-embedding-ada-002" + + + +if llm_choice == "manifest": + from manifest import Manifest + from langchain.llms.manifest import ManifestWrapper + + manifest = Manifest( + client_name="huggingface", + client_connection="http://127.0.0.1:5000" + ) # Redirect PosixPath to WindowsPath on Windows import platform + if platform.system() == "Windows": import pathlib + temp = pathlib.PosixPath pathlib.PosixPath = pathlib.WindowsPath # loading the .env file dotenv.load_dotenv() - with open("combine_prompt.txt", "r") as f: template = f.read() -# check if OPENAI_API_KEY is set -if os.getenv("OPENAI_API_KEY") is not None: +if os.getenv("API_KEY") is not None: api_key_set = True - else: api_key_set = False - - +if os.getenv("EMBEDDINGS_KEY") is not None: + embeddings_key_set = True +else: + embeddings_key_set = False app = Flask(__name__) @app.route("/") def home(): - return render_template("index.html", api_key_set=api_key_set) + return render_template("index.html", api_key_set=api_key_set, llm_choice=llm_choice, + embeddings_choice=embeddings_choice) @app.route("/api/answer", methods=["POST"]) @@ -60,7 +72,14 @@ def api_answer(): if not api_key_set: api_key = data["api_key"] else: - api_key = os.getenv("OPENAI_API_KEY") + api_key = os.getenv("API_KEY") + if not embeddings_key_set: + embeddings_key = data["embeddings_key"] + else: + embeddings_key = os.getenv("EMBEDDINGS_KEY") + + print(embeddings_key) + print(api_key) # check if the vectorstore is set if "active_docs" in data: @@ -70,24 +89,32 @@ def api_answer(): else: vectorstore = "" - # loading the index and the store and the prompt template - docsearch = FAISS.load_local(vectorstore, OpenAIEmbeddings(openai_api_key=api_key)) + # Note if you have used other embeddings than OpenAI, you need to change the embeddings + if embeddings_choice == "openai_text-embedding-ada-002": + docsearch = FAISS.load_local(vectorstore, OpenAIEmbeddings(openai_api_key=embeddings_key)) + elif embeddings_choice == "huggingface_sentence-transformers/all-mpnet-base-v2": + docsearch = FAISS.load_local(vectorstore, HuggingFaceHubEmbeddings()) + elif embeddings_choice == "huggingface_hkunlp/instructor-large": + docsearch = FAISS.load_local(vectorstore, HuggingFaceInstructEmbeddings()) + elif embeddings_choice == "cohere_medium": + docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key)) # create a prompt template c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template) - # create a chain with the prompt template and the store + if llm_choice == "openai": + llm = OpenAI(openai_api_key=api_key, temperature=0) + elif llm_choice == "manifest": + llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 2048}) + elif llm_choice == "huggingface": + llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key) + elif llm_choice == "cohere": + llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key) - #llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 2048}) - llm = OpenAI(openai_api_key=api_key, temperature=0) - #llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key) - # llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key) - - qa_chain = load_qa_chain(llm = llm, chain_type="map_reduce", + qa_chain = load_qa_chain(llm=llm, chain_type="map_reduce", combine_prompt=c_prompt) - chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=2) # fetch the answer @@ -105,6 +132,7 @@ def api_answer(): # } return result + @app.route("/api/docs_check", methods=["POST"]) def check_docs(): # check if docs exist in a vectorstore folder @@ -128,7 +156,8 @@ def check_docs(): with open(vectorstore + "faiss_store.pkl", "wb") as f: f.write(r.content) - return {"status": 'loaded'} + return {"status": 'loaded'} + # handling CORS @app.after_request diff --git a/application/index.faiss b/application/index.faiss new file mode 100644 index 0000000000000000000000000000000000000000..f7f30d3375cd8cc8b0567ad197e055b304329ce5 GIT binary patch literal 8534061 zcmY&gcU+Ed)Nd$Fm9kUX5{XFN*SX0^_Q)oZk)6GNNTftcqCq7iB@&UkuX7ub$WA0e zNJjSF-gEW7|GYk*KGpNoeP7pg&iStAN#A9?J8I}^Xw1>k_`iRq|L?P=rucSCtGWlz 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