Update interaction.py

interaction.py

@@ -2,66 +2,21 @@ from gtts import gTTS
 import base64
 import os
 
-from haystack import Document
-from haystack.document_stores.in_memory import InMemoryDocumentStore
-from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
-from haystack.components.builders import PromptBuilder
-from haystack.components.generators.hugging_face_local import HuggingFaceLocalGenerator
-from haystack.pipeline import Pipeline
+from initialize import init_doc_store, define_components, define_pipeline
 
-def init_doc_store(path, files):
-    docs = []
-    for file in files:
-        with open(path + '/' + file, 'r') as f:
-            content = f.read()
-            docs.append(Document(content=content, meta={'name':file}))
-
-    document_store = InMemoryDocumentStore()
-    document_store.write_documents(docs)
-    return document_store
-
-def define_components(document_store):
-    retriever = InMemoryBM25Retriever(document_store, top_k=3)
-    
-    template = """
-    Given the following information, answer the question.
-    
-    Context:
-    {% for document in documents %}
-        {{ document.content }}
-    {% endfor %}
-    
-    Question: {{question}}
-    Answer:
-    """
-    prompt_builder = PromptBuilder(template=template)
-    
-    generator = HuggingFaceLocalGenerator(model="gpt2",
-                                          task="text-generation",
-                                        #   device='cuda',
-                                          generation_kwargs={
-                                            "max_new_tokens": 100,
-                                            "temperature": 0.9,
-                                            })
-    generator.warm_up()
-    return retreiver, prompt_builder, generator
-
-def define_pipeline(retreiver, prompt_builder, generator):
-    basic_rag_pipeline = Pipeline()
-    
-    basic_rag_pipeline.add_component("retriever", retriever)
-    basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
-    basic_rag_pipeline.add_component("llm", generator)
-    
-    basic_rag_pipeline.connect("retriever", "prompt_builder.documents")
-    basic_rag_pipeline.connect("prompt_builder", "llm")
-
-    return basic_rag_pipeline
+def init_pipeline():
+    path = 'RAG Files\'
+    files = os.listdir(path)
+    document_Store = init_doc_store(path, files)
+    retreiver, prompt_builder, generator = define_components(document_Store)
+    pipeline = define_pipeline(retreiver, prompt_builder, generator)
+    return pipeline
 
 def generate_response(question, pipeline):
 	response = pipeline.run({'retriever':{"query":question}, 'prompt_builder':{'question':question}})
     response = response['llm']['replies'][0]
     return response
+
 def audio_response(response):
 	audio_stream="response_audio.mp3"
 	tts = gTTS(response)