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Fix: add missing properties (#530)
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@ -1,12 +1,14 @@
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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List, Optional
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from langchain.chat_models import ChatOpenAI
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings.openai import OpenAIEmbeddings
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from llm.models.FunctionCall import FunctionCall
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from llm.models.FunctionCall import FunctionCall
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from llm.models.OpenAiAnswer import OpenAiAnswer
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from llm.models.OpenAiAnswer import OpenAiAnswer
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from logger import get_logger
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from logger import get_logger
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from models.chat import ChatHistory
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from models.chat import ChatHistory
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from repository.chat.get_chat_history import get_chat_history
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from repository.chat.get_chat_history import get_chat_history
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from repository.chat.update_chat_history import update_chat_history
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from repository.chat.update_chat_history import update_chat_history
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from supabase import Client, create_client
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from vectorstore.supabase import CustomSupabaseVectorStore
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from vectorstore.supabase import CustomSupabaseVectorStore
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from .base import BaseBrainPicking
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from .base import BaseBrainPicking
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@ -61,6 +63,25 @@ class OpenAIFunctionsBrainPicking(BaseBrainPicking):
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def openai_client(self) -> ChatOpenAI:
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def openai_client(self) -> ChatOpenAI:
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return ChatOpenAI(openai_api_key=self.openai_api_key)
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return ChatOpenAI(openai_api_key=self.openai_api_key)
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@property
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def embeddings(self) -> OpenAIEmbeddings:
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return OpenAIEmbeddings(openai_api_key=self.openai_api_key)
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@property
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def supabase_client(self) -> Client:
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return create_client(
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self.brain_settings.supabase_url, self.brain_settings.supabase_service_key
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)
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@property
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def vector_store(self) -> CustomSupabaseVectorStore:
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return CustomSupabaseVectorStore(
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self.supabase_client,
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self.embeddings,
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table_name="vectors",
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brain_id=self.brain_id,
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)
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def _get_model_response(
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def _get_model_response(
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self,
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self,
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messages: List[Dict[str, str]],
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messages: List[Dict[str, str]],
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@ -103,14 +124,8 @@ class OpenAIFunctionsBrainPicking(BaseBrainPicking):
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Retrieve documents related to the question
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Retrieve documents related to the question
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"""
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"""
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logger.info("Getting context")
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logger.info("Getting context")
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vector_store = CustomSupabaseVectorStore(
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self.supabase_client,
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self.embeddings,
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table_name="vectors",
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brain_id=self.brain_id,
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)
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return vector_store.similarity_search(query=question)
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return self.vector_store.similarity_search(query=question)
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def _construct_prompt(
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def _construct_prompt(
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self, question: str, useContext: bool = False, useHistory: bool = False
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self, question: str, useContext: bool = False, useHistory: bool = False
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