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Fix the servor 500 error and show error message to client

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UnknownDev
2023-02-23 11:29:52 +00:00
parent 2210412ed2
commit fabe4d53d6
5 changed files with 163 additions and 102 deletions
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105
application/app.py
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@@ -9,7 +9,7 @@ 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
from error import bad_request
# os.environ["LANGCHAIN_HANDLER"] = "langchain"
if os.getenv("LLM_NAME") is not None:
@@ -74,6 +74,8 @@ def api_answer():
data = request.get_json()
question = data["question"]
history = data["history"]
print('-'*5)
print(data["embeddings_key"])
if not api_key_set:
api_key = data["api_key"]
else:
@@ -83,62 +85,69 @@ def api_answer():
else:
embeddings_key = os.getenv("EMBEDDINGS_KEY")
# use try and except to check for exception
try:
# check if the vectorstore is set
if "active_docs" in data:
vectorstore = "vectors/" + data["active_docs"]
if data['active_docs'] == "default":
# check if the vectorstore is set
if "active_docs" in data:
vectorstore = "vectors/" + data["active_docs"]
if data['active_docs'] == "default":
vectorstore = ""
else:
vectorstore = ""
else:
vectorstore = ""
# loading the index and the store and the prompt template
# 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))
# loading the index and the store and the prompt template
# 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
if history:
history = json.loads(history)
template_temp = template_hist.replace("{historyquestion}", history[0]).replace("{historyanswer}", history[1])
c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template_temp, template_format="jinja2")
else:
c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template, template_format="jinja2")
# create a prompt template
if history:
history = json.loads(history)
print(history)
template_temp = template_hist.replace("{historyquestion}", history[0]).replace("{historyanswer}", history[1])
c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template_temp, template_format="jinja2")
else:
c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template, template_format="jinja2")
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)
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)
qa_chain = load_qa_chain(llm=llm, chain_type="map_reduce",
combine_prompt=c_prompt)
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=4)
chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=4)
# fetch the answer
result = chain({"query": question})
print(result)
# fetch the answer
result = chain({"query": question})
print(result)
# some formatting for the frontend
result['answer'] = result['result']
result['answer'] = result['answer'].replace("\\n", "<br>")
result['answer'] = result['answer'].replace("SOURCES:", "")
# mock result
# result = {
# "answer": "The answer is 42",
# "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
# }
return result
# some formatting for the frontend
result['answer'] = result['result']
result['answer'] = result['answer'].replace("\\n", "<br>")
result['answer'] = result['answer'].replace("SOURCES:", "")
# mock result
# result = {
# "answer": "The answer is 42",
# "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
# }
return result
except Exception as e:
print(str(e))
return bad_request(500,str(e))
@app.route("/api/docs_check", methods=["POST"])