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Merge branch 'main' into feature/streaming

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Alex
2023-05-31 22:15:53 +01:00
committed by GitHub
parent fae3f55010 0b94f1717f
commit 3b8039a580
6 changed files with 53 additions and 16 deletions
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44
application/app.py
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@@ -24,9 +24,11 @@ from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
AIMessagePromptTemplate,
)
from pymongo import MongoClient
from werkzeug.utils import secure_filename
from langchain.llms import GPT4All
from core.settings import settings
from error import bad_request
@@ -108,6 +110,7 @@ def run_async_chain(chain, question, chat_history):
result["answer"] = answer
return result
def get_vectorstore(data):
if "active_docs" in data:
if data["active_docs"].split("/")[0] == "local":
@@ -134,6 +137,7 @@ def get_docsearch(vectorstore, embeddings_key):
docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key))
return docsearch
@celery.task(bind=True)
def ingest(self, directory, formats, name_job, filename, user):
resp = ingest_worker(self, directory, formats, name_job, filename, user)
@@ -216,17 +220,26 @@ def api_answer():
# Note if you have used other embeddings than OpenAI, you need to change the embeddings
docsearch = get_docsearch(vectorstore, embeddings_key)
c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template,
template_format="jinja2")
q_prompt = PromptTemplate(input_variables=["context", "question"], template=template_quest,
template_format="jinja2")
if settings.LLM_NAME == "openai_chat":
llm = ChatOpenAI(openai_api_key=api_key) # optional parameter: model_name="gpt-4"
messages_combine = [
SystemMessagePromptTemplate.from_template(chat_combine_template),
HumanMessagePromptTemplate.from_template("{question}")
]
messages_combine = [SystemMessagePromptTemplate.from_template(chat_combine_template)]
if history:
tokens_current_history = 0
tokens_max_history = 1000
#count tokens in history
history.reverse()
for i in history:
if "prompt" in i and "response" in i:
tokens_batch = llm.get_num_tokens(i["prompt"]) + llm.get_num_tokens(i["response"])
if tokens_current_history + tokens_batch < tokens_max_history:
tokens_current_history += tokens_batch
messages_combine.append(HumanMessagePromptTemplate.from_template(i["prompt"]))
messages_combine.append(AIMessagePromptTemplate.from_template(i["response"]))
messages_combine.append(HumanMessagePromptTemplate.from_template("{question}"))
import sys
print(messages_combine, file=sys.stderr)
p_chat_combine = ChatPromptTemplate.from_messages(messages_combine)
elif settings.LLM_NAME == "openai":
llm = OpenAI(openai_api_key=api_key, temperature=0)
@@ -236,6 +249,8 @@ def api_answer():
llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key)
elif settings.LLM_NAME == "cohere":
llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
elif settings.LLM_NAME == "gpt4all":
llm = GPT4All(model=settings.MODEL_PATH)
else:
raise ValueError("unknown LLM model")
@@ -251,9 +266,22 @@ def api_answer():
# result = chain({"question": question, "chat_history": chat_history})
# generate async with async generate method
result = run_async_chain(chain, question, chat_history)
elif settings.LLM_NAME == "gpt4all":
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
chain = ConversationalRetrievalChain(
retriever=docsearch.as_retriever(k=2),
question_generator=question_generator,
combine_docs_chain=doc_chain,
)
chat_history = []
# result = chain({"question": question, "chat_history": chat_history})
# generate async with async generate method
result = run_async_chain(chain, question, chat_history)
else:
qa_chain = load_qa_chain(llm=llm, chain_type="map_reduce",
combine_prompt=c_prompt, question_prompt=q_prompt)
combine_prompt=chat_combine_template, question_prompt=q_prompt)
chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=3)
result = chain({"query": question})