Fix: ask chatbot

3 - PB/MVP/src/backend/adapter/out/ask_chatbot/ask_chatbot_langchain.py

@@ -1,3 +1,6 @@
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chains.base import Chain
+
 from domain.chat.message_response import MessageResponse
 from domain.chat.message import Message
 from domain.chat.chat_id import ChatId
@@ -8,10 +11,25 @@
 
 from datetime import datetime, timezone
 
+from adapter.out.upload_documents.langchain_embedding_model import LangchainEmbeddingModel
+from adapter.out.persistence.postgres.chat_history_manager import ChatHistoryManager
+
+
 class AskChatbotLangchain(AskChatbotPort):
+    def __init__(self, chain: Chain, chatHistoryManager: ChatHistoryManager):
+        self.chain = chain
+        self.chatHistoryManager = chatHistoryManager
     def askChatbot(self, message: Message, chatId: ChatId) -> MessageResponse:
+        embeddingModel = LangchainEmbeddingModel()
+        if chatId is not None:
+            self.chain.memory = self.chatHistoryManager.getChatHistory(chatId)
+        answer = self.chain.run(message.content)
+        print(answer, flush=True)
         return MessageResponse(
             True,
-            Message(content="I'm a chatbot, this is my response.", timestamp=datetime.now(timezone.utc), relevantDocuments=[DocumentId("DocumentoRilevante.pdf")], sender=MessageSender.CHATBOT),
+            Message(content=answer,
+                    timestamp=datetime.now(timezone.utc),
+                    relevantDocuments=[DocumentId("DocumentoRilevante.pdf")],
+                    sender=MessageSender.CHATBOT),
             chatId
         )

3 - PB/MVP/src/backend/adapter/out/configuration_manager.py

@@ -1,5 +1,9 @@
 import os
 
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_openai import OpenAI
+
 from application.port.out.documents_uploader_port import DocumentsUploaderPort
 from application.port.out.embeddings_uploader_port import EmbeddingsUploaderPort
 from application.port.out.delete_documents_port import DeleteDocumentsPort
@@ -32,6 +36,7 @@
 from adapter.out.upload_documents.documents_uploader_AWSS3 import DocumentsUploaderAWSS3
 from adapter.out.get_documents.get_documents_content_awss3 import GetDocumentsContentAWSS3
 from adapter.out.ask_chatbot.ask_chatbot_langchain import AskChatbotLangchain
+from adapter.out.persistence.postgres.chat_history_manager import ChatHistoryManager
 
 
 class ConfigurationException(Exception):
@@ -153,5 +158,34 @@ def getGetDocumentsContentPort(self) -> GetDocumentsContentPort:
         return configuredDocumentStore
 
     def getAskChatbotPort(self) -> AskChatbotPort:
-        #TODO
-        return AskChatbotLangchain()
+        configuration = self.postgresConfigurationORM.getConfigurationChoices(os.environ.get('USER_ID'))
+        if configuration.vectorStore == PostgresVectorStoreType.PINECONE:
+            configuredVectorStore = VectorStorePineconeManager()
+        elif configuration.vectorStore == PostgresVectorStoreType.CHROMA_DB:
+            configuredVectorStore = VectorStoreChromaDBManager()
+        else:
+            raise ConfigurationException('Vector store non configurato.')
+        if configuration.embeddingModel == PostgresEmbeddingModelType.HUGGINGFACE:
+            configuredEmbeddingModel = HuggingFaceEmbeddingModel()
+        elif configuration.embeddingModel == PostgresEmbeddingModelType.OPENAI:
+            configuredEmbeddingModel = OpenAIEmbeddingModel()
+        else:
+            raise ConfigurationException('Embeddings model non configurato.')
+        if configuration.LLMModel == PostgresLLMModelType.HUGGINGFACE:
+            with open('/run/secrets/openai_key', 'r') as file:
+                openai_key = file.read()
+            configuredLLMModel = OpenAI(openai_api_key= openai_key, model_name="gpt-3.5-turbo-instruct", temperature=0.3)
+        elif configuration.LLMModel == PostgresLLMModelType.OPENAI:
+            with open('/run/secrets/huggingface_key', 'r') as file:
+                hugging_face = file.read()
+            configuredLLMModel = HuggingFaceEndpoint(repo_id="google/flan-5-large", temperature=0.3, token=hugging_face)
+        else:
+            raise ConfigurationException('LLM model non configurato.')
+
+
+        chain = ConversationalRetrievalChain.from_llm(
+            llm=configuredLLMModel,
+            retriever=configuredVectorStore.getRetriever(configuredEmbeddingModel),
+            return_source_documents=True
+        )
+        return AskChatbotLangchain(chain=chain, chatHistoryManager=ChatHistoryManager())

3 - PB/MVP/src/backend/adapter/out/persistence/postgres/chat_history_manager.py

@@ -0,0 +1,14 @@
+from typing import List
+
+from langchain_community.chat_message_histories import PostgresChatMessageHistory
+from langchain_core.messages import BaseMessage
+import os
+
+from domain.chat.chat_id import ChatId
+from langchain.memory import ConversationBufferMemory
+
+class ChatHistoryManager:
+    def getChatHistory(self, chatId:ChatId)-> PostgresChatMessageHistory:
+        history = PostgresChatMessageHistory(session_id=str(chatId.id),
+                                             connection_string=os.environ.get('DATABASE_URL'))
+        return history

3 - PB/MVP/src/backend/adapter/out/persistence/vector_store/vector_store_chromaDB_manager.py

@@ -1,17 +1,22 @@
 import os
 from typing import List
 import chromadb
+from langchain_core.retrievers import BaseRetriever
+
 from adapter.out.persistence.vector_store.vector_store_manager import VectorStoreManager
 from adapter.out.persistence.vector_store.vector_store_document_operation_response import VectorStoreDocumentOperationResponse
 from adapter.out.persistence.vector_store.vector_store_document_status_response import VectorStoreDocumentStatusResponse
 from langchain_core.documents.base import Document as LangchainCoreDocument
+from langchain_community.vectorstores import Chroma
+from adapter.out.upload_documents.langchain_embedding_model import LangchainEmbeddingModel
+
 
 class VectorStoreChromaDBManager(VectorStoreManager):
     def __init__(self):
-        cromadb = chromadb.PersistentClient(path=os.environ.get("CHROMA_DB_PATH"))
+        self.chromadb = chromadb.PersistentClient(path=os.environ.get("CHROMA_DB_PATH"))
         with open('/run/secrets/chromadb_collection', 'r') as file:
             chromadbCollection = file.read()
-        self.collection = cromadb.get_or_create_collection(chromadbCollection)
+        self.collection = self.chromadb.get_or_create_collection(chromadbCollection)
 
     def getDocumentsStatus(self, documentsIds: List[str]) -> List[VectorStoreDocumentStatusResponse]:
         vectorStoreDocumentStatusResponses = []
@@ -106,4 +111,7 @@ def uploadEmbeddings(self, documentsIds: List[str], documentsChunks: List[List[L
                 vectorStoreDocumentOperationResponses.append(VectorStoreDocumentOperationResponse(documentId, False, "Errore nel caricamento degli embeddings."))
                 continue
         return vectorStoreDocumentOperationResponses
-
+
+
+    def getRetriever(self, embeddingModel : LangchainEmbeddingModel) -> BaseRetriever:
+        return Chroma(client=self.chromadb, collection_name = self.collection.name, embedding_function=embeddingModel.getEmbedQueryFunction()).as_retriever()

3 - PB/MVP/src/backend/adapter/out/persistence/vector_store/vector_store_manager.py

@@ -1,10 +1,15 @@
 from typing import List
 
+from langchain_core.retrievers import BaseRetriever
+
 from adapter.out.persistence.vector_store.vector_store_document_operation_response import VectorStoreDocumentOperationResponse
 from adapter.out.persistence.vector_store.vector_store_document_status_response import VectorStoreDocumentStatusResponse
 
 from langchain_core.documents.base import Document as LangchainCoreDocument
 
+from adapter.out.upload_documents.langchain_embedding_model import LangchainEmbeddingModel
+
+
 class VectorStoreManager:
     def getDocumentsStatus(self, documentsIds: List[str]) -> List[VectorStoreDocumentStatusResponse]:
         pass
@@ -19,4 +24,7 @@ def enableDocuments(self, documentsIds: List[str]) -> List[VectorStoreDocumentOp
         pass
 
     def uploadEmbeddings(self, documentsIds: List[str], documentsChunks: List[List[LangchainCoreDocument]], documentsEmbeddings: List[List[List[float]]]) -> List[VectorStoreDocumentOperationResponse]:
+        pass
+
+    def getRetriever(self, embeddingModel : LangchainEmbeddingModel) -> BaseRetriever:
         pass

3 - PB/MVP/src/backend/adapter/out/persistence/vector_store/vector_store_pinecone_manager.py

@@ -1,10 +1,15 @@
 from typing import List
+
+from langchain_core.retrievers import BaseRetriever
 from pinecone import Pinecone
 from pinecone import PineconeApiException
 from adapter.out.persistence.vector_store.vector_store_manager import VectorStoreManager
 from adapter.out.persistence.vector_store.vector_store_document_operation_response import VectorStoreDocumentOperationResponse
 from adapter.out.persistence.vector_store.vector_store_document_status_response import VectorStoreDocumentStatusResponse
 from langchain_core.documents.base import Document as LangchainCoreDocument
+from langchain_community.vectorstores import Pinecone as PineconeLangchain
+from adapter.out.upload_documents.langchain_embedding_model import LangchainEmbeddingModel
+
 
 class VectorStorePineconeManager(VectorStoreManager):
     def __init__(self):
@@ -180,4 +185,6 @@ def uploadEmbeddings(self, documentsId: List[str], documentsChunks: List[List[La
                     vectorStoreDocumentOperationResponses.append(VectorStoreDocumentOperationResponse(documentId, False, f"Errore nel caricamento degli embeddings: {e}"))
 
         return vectorStoreDocumentOperationResponses
-
+
+    def getRetriever(self, embeddingModel : LangchainEmbeddingModel) -> BaseRetriever:
+        return PineconeLangchain(self.index, embeddingModel.getEmbedQueryFunction(), "text").as_retriever()

3 - PB/MVP/src/backend/adapter/out/upload_documents/huggingface_embedding_model.py

@@ -16,4 +16,6 @@ def embedDocument(self, documentChunks: List[str]) -> List[List[float]]:
         try:
             return self.model.embed_documents(documentChunks)
         except Exception as e:
-            return []
+            return []
+    def getEmbedQueryFunction(self):
+        return self.model.embed_query

3 - PB/MVP/src/backend/adapter/out/upload_documents/langchain_embedding_model.py

@@ -2,4 +2,6 @@
 
 class LangchainEmbeddingModel:
     def embedDocument(self, documentChunks: List[str]) -> List[List[float]]:
+        pass
+    def getEmbedQueryFunction(self):
         pass

3 - PB/MVP/src/backend/adapter/out/upload_documents/openai_embedding_model.py

@@ -1,6 +1,6 @@
 from typing import List
 
-from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_openai import OpenAIEmbeddings
 from adapter.out.upload_documents.langchain_embedding_model import LangchainEmbeddingModel
 
 
@@ -17,3 +17,5 @@ def embedDocument(self, documentChunks: List[str]) -> List[List[float]]:
             return self.model.embed_documents(documentChunks)
         except Exception as e:
             return []
+    def getEmbedQueryFunction(self):
+        return self.model.embed_query

3 - PB/MVP/src/backend/domain/chat/chat_filter.py

@@ -1,4 +1,4 @@
-
+from dataclasses import dataclass
+@dataclass
 class ChatFilter:
-    def __init__(self, searchFilter:str):
-        self.searchFilter = searchFilter
+    searchFilter:str

3 - PB/MVP/src/backend/requirements.txt

@@ -9,13 +9,17 @@ InstructorEmbedding
 langchain
 langchain_community
 langchain_core
+langchain-openai
 openai
 pypdf
 PyPDF2
 pytest
 python-dotenv
 pinecone-client
-psycopg2-binary
+#psycopg2-binary
+#psycopg
+#psycopg-c
+#pytest-mock
 # sentence-transformers
 tiktoken
 # torch