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custom prompts

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This commit is contained in:
Alex
2023-11-22 23:55:41 +00:00
parent e9a9cbbd07
commit b2770f67a1
6 changed files with 244 additions and 127 deletions
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48
application/api/answer/routes.py
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@@ -25,6 +25,7 @@ mongo = MongoClient(settings.MONGO_URI)
db = mongo["docsgpt"]
conversations_collection = db["conversations"]
vectors_collection = db["vectors"]
prompts_collection = db["prompts"]
answer = Blueprint('answer', __name__)
if settings.LLM_NAME == "gpt4":
@@ -43,10 +44,10 @@ with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") a
chat_reduce_template = f.read()
with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
chat_reduce_creative = f.read()
chat_combine_creative = f.read()
with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
chat_reduce_strict = f.read()
chat_combine_strict = f.read()
api_key_set = settings.API_KEY is not None
embeddings_key_set = settings.EMBEDDINGS_KEY is not None
@@ -90,23 +91,6 @@ def get_vectorstore(data):
return vectorstore
# def get_docsearch(vectorstore, embeddings_key):
# if settings.EMBEDDINGS_NAME == "openai_text-embedding-ada-002":
# if is_azure_configured():
# os.environ["OPENAI_API_TYPE"] = "azure"
# openai_embeddings = OpenAIEmbeddings(model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME)
# else:
# openai_embeddings = OpenAIEmbeddings(openai_api_key=embeddings_key)
# docsearch = FAISS.load_local(vectorstore, openai_embeddings)
# elif settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
# docsearch = FAISS.load_local(vectorstore, HuggingFaceHubEmbeddings())
# elif settings.EMBEDDINGS_NAME == "huggingface_hkunlp/instructor-large":
# docsearch = FAISS.load_local(vectorstore, HuggingFaceInstructEmbeddings())
# elif settings.EMBEDDINGS_NAME == "cohere_medium":
# docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key))
# return docsearch
def is_azure_configured():
return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
@@ -115,13 +99,16 @@ def complete_stream(question, docsearch, chat_history, api_key, prompt_id, conve
llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
if prompt_id == 'default':
prompt = chat_reduce_template
prompt = chat_combine_template
elif prompt_id == 'creative':
prompt = chat_reduce_creative
prompt = chat_combine_creative
elif prompt_id == 'strict':
prompt = chat_reduce_strict
prompt = chat_combine_strict
else:
prompt = chat_reduce_template
prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
import sys
print(prompt_id, file=sys.stderr)
print(prompt, file=sys.stderr)
docs = docsearch.search(question, k=2)
@@ -253,6 +240,19 @@ def api_answer():
embeddings_key = data["embeddings_key"]
else:
embeddings_key = settings.EMBEDDINGS_KEY
if 'prompt_id' in data:
prompt_id = data["prompt_id"]
else:
prompt_id = 'default'
if prompt_id == 'default':
prompt = chat_combine_template
elif prompt_id == 'creative':
prompt = chat_combine_creative
elif prompt_id == 'strict':
prompt = chat_combine_strict
else:
prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
# use try and except to check for exception
try:
@@ -270,7 +270,7 @@ def api_answer():
docs = docsearch.search(question, k=2)
# join all page_content together with a newline
docs_together = "\n".join([doc.page_content for doc in docs])
p_chat_combine = chat_combine_template.replace("{summaries}", docs_together)
p_chat_combine = prompt.replace("{summaries}", docs_together)
messages_combine = [{"role": "system", "content": p_chat_combine}]
source_log_docs = []
for doc in docs: