gpt4all works now.

agent/backend/gpt4all_service.py

@@ -13,15 +13,13 @@
 from agent.data_model.internal_model import RetrievalResults
 from agent.data_model.request_data_model import (
     Filtering,
-    LLMBackend,
-    LLMProvider,
     RAGRequest,
     SearchRequest,
 )
 from agent.utils.utility import (
     convert_qdrant_result_to_retrieval_results,
-    generate_prompt,
     generate_collection_gpt4all,
+    generate_prompt,
 )
 from agent.utils.vdb import init_vdb
 
@@ -140,6 +138,7 @@ def search(self, search: SearchRequest, filtering: Filtering) -> list[RetrievalR
         Args:
         ----
             search (SearchRequest): The search request.
+            filtering (Filtering): The filtering parameters.
 
         Returns:
         -------
@@ -151,24 +150,27 @@ def search(self, search: SearchRequest, filtering: Filtering) -> list[RetrievalR
 
         return convert_qdrant_result_to_retrieval_results(docs)
 
-    def rag(self, rag_request: RAGRequest) -> tuple:
+    def rag(self, rag_request: RAGRequest, search: SearchRequest, filtering: Filtering) -> tuple:
         """RAG takes a Rag Request Object and performs a semantic search and then a generation.
 
         Args:
         ----
             rag_request (RAGRequest): The RAG Request Object.
+            search (SearchRequest): The search request.
+            filtering (Filtering): The filtering parameters.
 
         Returns:
         -------
             Tuple[str, str, List[RetrievalResults]]: The answer, the prompt and the metadata.
         """
-        documents = self.search(rag_request.search)
-        if rag_request.search.amount == 0:
+        documents = self.search(search=search, filtering=filtering)
+        if search.amount == 0:
             msg = "No documents found."
             raise ValueError(msg)
-        text = "\n".join([doc.document for doc in documents]) if rag_request.search.amount > 1 else documents[0].document
+        text = "\n".join([doc.document for doc in documents]) if search.amount > 1 else documents[0].document
 
-        prompt = generate_prompt(prompt_name="gpt4all-completion.j2", text=text, query=rag_request.search.query)
+        # TODO: Add the history to the prompt
+        prompt = generate_prompt(prompt_name="gpt4all-completion.j2", text=text, query=search.query, language=rag_request.language)
 
         answer = self.generate(prompt)
 
@@ -180,28 +182,17 @@ def rag(self, rag_request: RAGRequest) -> tuple:
 
     gpt4all_service.embed_documents(directory="tests/resources/")
 
-    docs = gpt4all_service.search(
-        SearchRequest(
-            query="Was ist Attention?",
-            amount=3,
-            filtering=Filtering(threshold=0.0, collection_name="gpt4all"),
-            llm_backend=LLMBackend(token="gpt4all", provider=LLMProvider.GPT4ALL),
-        )
-    )
+    docs = gpt4all_service.search(SearchRequest(query="Was ist Attention?", amount=3), Filtering(threshold=0.0, collection_name="gpt4all"))
 
     logger.info(f"Documents: {docs}")
 
     answer, prompt, meta_data = gpt4all_service.rag(
-        RAGRequest(
-            search=SearchRequest(
-                query="Was ist Attention?",
-                amount=3,
-                filtering=Filtering(threshold=0.0, collection_name="gpt4all"),
-                llm_backend=LLMBackend(token="gpt4all", provider=LLMProvider.GPT4ALL),
-            ),
-            documents=docs,
-            query="Was ist das?",
-        )
+        RAGRequest(language="detect", history={}),
+        SearchRequest(
+            query="Was ist Attention?",
+            amount=3,
+        ),
+        Filtering(threshold=0.0, collection_name="gpt4all"),
     )
 
     logger.info(f"Answer: {answer}")