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feat: History API (client) (#204)

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This commit is contained in:
cachho
2023-07-10 20:15:20 +02:00
committed by GitHub
parent b3cf834186
commit 65414af098
4 changed files with 111 additions and 41 deletions
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40
embedchain/config/ChatConfig.py
View File

@@ -1,14 +1,46 @@
from embedchain.config.QueryConfig import QueryConfig
from string import Template
DEFAULT_PROMPT = """
You are a chatbot having a conversation with a human. You are given chat history and context.
You need to answer the query considering context, chat history and your knowledge base. If you don't know the answer or the answer is neither contained in the context nor in history, then simply say "I don't know".
$context
History: $history
Query: $query
Helpful Answer:
"""
DEFAULT_PROMPT_TEMPLATE = Template(DEFAULT_PROMPT)
class ChatConfig(QueryConfig):
"""
Config for the `chat` method, inherits from `QueryConfig`.
"""
def __init__(self, stream: bool = False):
def __init__(self, template: Template = None, stream: bool = False):
"""
Initializes the QueryConfig instance.
Initializes the ChatConfig instance.
:param template: Optional. The `Template` instance to use as a template for prompt.
:param stream: Optional. Control if response is streamed back to the user
:raises ValueError: If the template is not valid as template should contain $context and $query
:raises ValueError: If the template is not valid as template should contain $context and $query and $history
"""
super().__init__(stream=stream)
if template is None:
template = DEFAULT_PROMPT_TEMPLATE
# History is set as 0 to ensure that there is always a history, that way, there don't have to be two templates.
# Having two templates would make it complicated because the history is not user controlled.
super().__init__(template, history=[0], stream=stream)
def set_history(self, history):
"""
Chat history is not user provided and not set at initialization time
:param history: (string) history to set
"""
self.history = history
return

62
embedchain/config/QueryConfig.py
View File

@@ -13,30 +13,78 @@ DEFAULT_PROMPT = """
Helpful Answer:
"""
DEFAULT_PROMPT_WITH_HISTORY = """
Use the following pieces of context to answer the query at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
I will provide you with our conversation history.
$context
History: $history
Query: $query
Helpful Answer:
"""
DEFAULT_PROMPT_TEMPLATE = Template(DEFAULT_PROMPT)
DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE = Template(DEFAULT_PROMPT_WITH_HISTORY)
query_re = re.compile(r"\$\{*query\}*")
context_re = re.compile(r"\$\{*context\}*")
history_re = re.compile(r"\$\{*history\}*")
class QueryConfig(BaseConfig):
"""
Config for the `query` method.
"""
def __init__(self, template: Template = None, stream: bool = False):
def __init__(self, template: Template = None, history = None, stream: bool = False):
"""
Initializes the QueryConfig instance.
:param template: Optional. The `Template` instance to use as a template for prompt.
:param history: Optional. A list of strings to consider as history.
:param stream: Optional. Control if response is streamed back to the user
:raises ValueError: If the template is not valid as template should contain $context and $query
:raises ValueError: If the template is not valid as template should contain $context and $query (and optionally $history).
"""
if not history:
self.history = None
else:
if len(history) == 0:
self.history = None
else:
self.history = history
if template is None:
template = DEFAULT_PROMPT_TEMPLATE
if not (re.search(query_re, template.template) \
and re.search(context_re, template.template)):
raise ValueError("`template` should have `query` and `context` keys")
self.template = template
if self.history is None:
template = DEFAULT_PROMPT_TEMPLATE
else:
template = DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE
if self.validate_template(template):
self.template = template
else:
if self.history is None:
raise ValueError("`template` should have `query` and `context` keys")
else:
raise ValueError("`template` should have `query`, `context` and `history` keys")
if not isinstance(stream, bool):
raise ValueError("`stream` should be bool")
self.stream = stream
def validate_template(self, template: Template):
"""
validate the template
:param template: the template to validate
:return: Boolean, valid (true) or invalid (false)
"""
if self.history is None:
return (re.search(query_re, template.template) \
and re.search(context_re, template.template))
else:
return (re.search(query_re, template.template) \
and re.search(context_re, template.template)
and re.search(history_re, template.template))

47
embedchain/embedchain.py
View File

@@ -11,6 +11,7 @@ from langchain.memory import ConversationBufferMemory
from embedchain.config import InitConfig, AddConfig, QueryConfig, ChatConfig
from embedchain.config.QueryConfig import DEFAULT_PROMPT
from embedchain.data_formatter import DataFormatter
from string import Template
gpt4all_model = None
@@ -144,16 +145,19 @@ class EmbedChain:
content = ""
return content
def generate_prompt(self, input_query, context, template: Template = None):
def generate_prompt(self, input_query, context, config: QueryConfig):
"""
Generates a prompt based on the given query and context, ready to be passed to an LLM
:param input_query: The query to use.
:param context: Similar documents to the query used as context.
:param template: Optional. The `Template` instance to use as a template for prompt.
:param config: Optional. The `QueryConfig` instance to use as configuration options.
:return: The prompt
"""
prompt = template.substitute(context = context, query = input_query)
if not config.history:
prompt = config.template.substitute(context = context, query = input_query)
else:
prompt = config.template.substitute(context = context, query = input_query, history = config.history)
return prompt
def get_answer_from_llm(self, prompt, config: ChatConfig):
@@ -181,30 +185,12 @@ class EmbedChain:
if config is None:
config = QueryConfig()
context = self.retrieve_from_database(input_query)
prompt = self.generate_prompt(input_query, context, config.template)
prompt = self.generate_prompt(input_query, context, config)
logging.info(f"Prompt: {prompt}")
answer = self.get_answer_from_llm(prompt, config)
logging.info(f"Answer: {answer}")
return answer
def generate_chat_prompt(self, input_query, context, chat_history=''):
"""
Generates a prompt based on the given query, context and chat history
for chat interface. This is then passed to an LLM.
:param input_query: The query to use.
:param context: Similar documents to the query used as context.
:param chat_history: User and bot conversation that happened before.
:return: The prompt
"""
prefix_prompt = f"""You are a chatbot having a conversation with a human. You are given chat history and context. You need to answer the query considering context, chat history and your knowledge base. If you don't know the answer or the answer is neither contained in the context nor in history, then simply say "I don't know"."""
chat_history_prompt = f"""\n----\nChat History: {chat_history}\n----"""
suffix_prompt = f"""\n####\nContext: {context}\n####\nQuery: {input_query}\nHelpful Answer:"""
prompt = prefix_prompt
if chat_history:
prompt += chat_history_prompt
prompt += suffix_prompt
return prompt
def chat(self, input_query, config: ChatConfig = None):
"""
@@ -217,18 +203,19 @@ class EmbedChain:
:param config: Optional. The `ChatConfig` instance to use as configuration options.
:return: The answer to the query.
"""
if config is None:
config = ChatConfig()
context = self.retrieve_from_database(input_query)
global memory
chat_history = memory.load_memory_variables({})["history"]
prompt = self.generate_chat_prompt(
input_query,
context,
chat_history=chat_history,
)
if config is None:
config = ChatConfig()
if chat_history:
config.set_history(chat_history)
prompt = self.generate_prompt(input_query, context, config)
logging.info(f"Prompt: {prompt}")
answer = self.get_answer_from_llm(prompt, config)
memory.chat_memory.add_user_message(input_query)
if isinstance(answer, str):
@@ -264,7 +251,7 @@ class EmbedChain:
if config is None:
config = QueryConfig()
context = self.retrieve_from_database(input_query)
prompt = self.generate_prompt(input_query, context, config.template)
prompt = self.generate_prompt(input_query, context, config)
logging.info(f"Prompt: {prompt}")
return prompt