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refactor: processing of blackbox chroma query and chat

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ACBBZ
2024-05-28 07:06:16 +00:00
parent 76971c87f5
commit 347eea2f9c
4 changed files with 44 additions and 49 deletions
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5
requirements.txt
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@ -5,3 +5,8 @@ uvicorn==0.29.0
SpeechRecognition==3.10.3
PyYAML==6.0.1
injector==0.21.0
chromadb==0.5.0
langchain==0.1.17
langchain-community==0.0.36
sentence-transformers==2.7.0
openai

69
src/blackbox/chroma_chat.py
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@ -31,64 +31,47 @@ class ChromaChat(Blackbox):
@logging_time(logger=logger)
def processing(self, question: str, context: list, settings: dict) -> str:
# chroma_chat settings
# {
# "chroma_embedding_model": "bge-large-zh-v1.5",
# "chroma_host": "10.6.82.192",
# "chroma_port": "8000",
# "chroma_collection_id": "123",
# "chroma_n_results": 3,
# "model_name": "Qwen1.5-14B-Chat",
# "context": [],
# "template": "",
# "temperature": 0.8,
# "top_p": 0.8,
# "n": 1,
# "max_tokens": 1024,
# "frequency_penalty": 0.5,
# "presence_penalty": 0.8,
# "stop": 100,
# "model_url": "http://120.196.116.194:48892/v1/chat/completions",
# "model_key": "YOUR_API_KEY"
# }
if settings is None:
settings = {}
# # chat setting
user_model_name = settings.get("model_name")
user_context = context
user_question = question
user_template = settings.get("template")
user_temperature = settings.get("temperature")
user_top_p = settings.get("top_p")
user_n = settings.get("n")
user_max_tokens = settings.get("max_tokens")
user_stop = settings.get("stop")
user_frequency_penalty = settings.get("frequency_penalty")
user_presence_penalty = settings.get("presence_penalty")
# # chroma_query settings
chroma_embedding_model = settings.get("chroma_embedding_model")
chroma_host = settings.get("chroma_host")
chroma_port = settings.get("chroma_port")
chroma_collection_id = settings.get("chroma_collection_id")
chroma_n_results = settings.get("chroma_n_results")
if context == None:
context = []
if user_context == None:
user_context = []
if user_question is None:
return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
chroma_settings_json={
"chroma_embedding_model": chroma_embedding_model,
"chroma_host": chroma_host,
"chroma_port": chroma_port,
"chroma_collection_id": chroma_collection_id,
"chroma_n_results": chroma_n_results
}
# chroma answer
chroma_result = self.chroma_query(user_question, chroma_settings_json)
chroma_result = self.chroma_query(user_question, settings)
# chat prompt
fast_question = f"问题: {user_question}。根据问题，总结以下内容和来源：{chroma_result}"
chat_settings_json = {
"model_name": user_model_name,
"context": user_context,
"template": user_template,
"temperature": user_temperature,
"top_p": user_top_p,
"n": user_n,
"max_tokens": user_max_tokens,
"stop": user_stop,
"frequency_penalty": user_frequency_penalty,
"presence_penalty": user_presence_penalty
}
fast_question = f"问题: {user_question}。- 根据知识库内的检索结果，以清晰简洁的表达方式回答问题。只从检索的内容中选取与问题相关信息。- 不要编造答案，如果答案不在经核实的资料中或无法从经核实的资料中得出，请回答“我无法回答您的问题。”检索内容：{chroma_result}"
# chat answer
response = self.chat(fast_question, chat_settings_json)
response = self.chat(fast_question, user_context, settings)
return response

19
src/blackbox/chroma_query.py
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@ -20,7 +20,7 @@ class ChromaQuery(Blackbox):
def __init__(self, *args, **kwargs) -> None:
# config = read_yaml(args[0])
# load chromadb and embedding model
self.embedding_model_1 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/model/Weight/BAAI/bge-small-en-v1.5", device = "cuda")
self.embedding_model_1 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/model/Weight/BAAI/bge-large-zh-v1.5", device = "cuda")
# self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/model/Weight/BAAI/bge-small-en-v1.5", device = "cuda")
self.client_1 = chromadb.HttpClient(host='10.6.82.192', port=8000)
# self.client_2 = chromadb.HttpClient(host='10.6.82.192', port=8000)
@ -51,7 +51,7 @@ class ChromaQuery(Blackbox):
return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
if chroma_embedding_model is None or chroma_embedding_model.isspace() or chroma_embedding_model == "":
chroma_embedding_model = "bge-small-en-v1.5"
chroma_embedding_model = "bge-large-zh-v1.5"
if chroma_host is None or chroma_host.isspace() or chroma_host == "":
chroma_host = "10.6.82.192"
@ -60,17 +60,22 @@ class ChromaQuery(Blackbox):
chroma_port = "8000"
if chroma_collection_id is None or chroma_collection_id.isspace() or chroma_collection_id == "":
chroma_collection_id = DEFAULT_COLLECTION_ID
chroma_collection_id = "g2e"
if chroma_n_results is None or chroma_n_results == "":
chroma_n_results = 3
# load client
# load client and embedding model from init
if re.search(r"10.6.82.192", chroma_host) and re.search(r"8000", chroma_port):
client = self.client_1
else:
client = chromadb.HttpClient(host=chroma_host, port=chroma_port)
if re.search(r"bge-small-en-v1.5", chroma_embedding_model):
if re.search(r"bge-large-zh-v1.5", chroma_embedding_model):
embedding_model = self.embedding_model_1
else:
chroma_embedding_model = "/model/Weight/BAAI/" + chroma_embedding_model
embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=chroma_embedding_model, device = "cuda")
# load collection
collection = client.get_collection(chroma_collection_id, embedding_function=embedding_model)
@ -81,7 +86,9 @@ class ChromaQuery(Blackbox):
n_results=3,
)
response = str(results["documents"] + results["metadatas"])
# response = str(results["documents"] + results["metadatas"])
response = str(results["documents"])
return response

0
test.pdf
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