jsdio/voice_assistant
Template
1
0
Fork 0
mirror of https://github.com/ijaric/voice_assistant.git synced 2025-12-18 05:16:18 +00:00
Code Issues Packages Projects Releases Wiki Activity

build: wip: agent service

Browse Source
This commit is contained in:
Artem Litvinov
2023-10-15 06:40:05 +01:00
parent ee8183b4c3
commit 08350809ab
3 changed files with 214 additions and 24 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
src/assistant/lib/agent/services.py
View File

@@ -67,28 +67,24 @@ class AgentService:
llm = langchain.chat_models.ChatOpenAI(temperature=self.settings.openai.agent_temperature, openai_api_key=self.settings.openai.api_key.get_secret_value())
chat_history = langchain.memory.ChatMessageHistory()
# chat_history = []
# chat_history = langchain.memory.ChatMessageHistory()
chat_history = []
chat_history_name = f"{chat_history=}".partition("=")[0]
request_chat_history = models.RequestChatHistory(session_id=session_id)
chat_history_source = await self.chat_repository.get_messages_by_sid(request_chat_history)
chat_history.append(langchain.schema.HumanMessage(content="Hi there!"))
for entry in chat_history_source:
# chat_history.append(langchain.schema.messages.HumanMessage(content=first_question))
# chat_history.append(langchain.schema.messages.AIMessage(content=first_result["output"]))
print("ENTRY: ", entry)
if entry.content["role"] == "user":
chat_history.append(langchain.schema.messages.HumanMessage(content=entry.content["content"]))
chat_history.append(langchain.schema.HumanMessage(content=entry.content["content"]))
elif entry.content["role"] == "agent":
chat_history.append(langchain.schema.messages.AIMessage(content=entry.content["content"]))
chat_history.append(langchain.schema.AIMessage(content=entry.content["content"]))
# chat_history = [entry.model_dump() for entry in chat_history_source]
memory_buffer = langchain.memory.ConversationBufferMemory(memory_key=chat_history_name,chat_memory=chat_history)
# memory = langchain.memory.ConversationBufferMemory(memory_key=chat_history_name,chat_memory=chat_history)
print("CHAT HISTORY:", chat_history)
# chat_history_name = f"{chat_history=}".partition("=")[0]
prompt = langchain.prompts.ChatPromptTemplate.from_messages(
[
(
@@ -118,7 +114,10 @@ class AgentService:
| langchain.agents.output_parsers.OpenAIFunctionsAgentOutputParser()
)
agent_executor = langchain.agents.AgentExecutor(agent=agent, tools=tools, verbose=True, memory=memory_buffer)
print("AGENT:", agent)
agent_executor = langchain.agents.AgentExecutor(agent=agent, tools=tools, verbose=True)
print("CH:", type(chat_history), chat_history)
response = await agent_executor.ainvoke({"input": request.text, "chat_history": chat_history})
print("AI RESPONSE:", response)
user_request = models.RequestChatMessage(