Update app.py

app.py

@@ -163,13 +163,27 @@ def search(prompt: str):
     from haystack.components.rankers import TransformersSimilarityRanker
     from haystack.components.joiners import DocumentJoiner
     from haystack.components.generators import OpenAIGenerator
-    from haystack.utils import Secret
+    from haystack.utils import Secret    
+    from haystack.components.builders import PromptBuilder
     
     start_time = time.time()
     
     # Querying
 
-
+    
+    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 = OpenAIGenerator(
         api_key=Secret.from_env_var("OCTOAI_TOKEN"),
         api_base_url="https://text.octoai.run/v1",
@@ -183,19 +197,22 @@ def search(prompt: str):
      )
     querying.add_component("retriever", QdrantHybridRetriever(document_store=document_store))
     querying.add_component("document_joiner", DocumentJoiner())
-    querying.add_component("ranker", TransformersSimilarityRanker(model="BAAI/bge-m3"))
+    querying.add_component("ranker", TransformersSimilarityRanker(model="BAAI/bge-m3"))    
+    querying.add_component("prompt_builder", prompt_builder)
     querying.add_component("llm", generator)
     
     querying.connect("sparse_text_embedder.sparse_embedding", "retriever.query_sparse_embedding")
     querying.connect("dense_text_embedder.embedding", "retriever.query_embedding")
     querying.connect("retriever", "document_joiner")
     querying.connect("document_joiner", "ranker")
-    querying.connect("ranker", "llm")
+    querying.connect("ranker", "prompt_builder")
+    querying.connect("prompt_builder", "llm")
     
     results = querying.run(
         {"dense_text_embedder": {"text": prompt},
          "sparse_text_embedder": {"text": prompt},
          "ranker": {"query": prompt},
+         "prompt_builder": {"query": prompt},
          "llm": {"query": prompt},
         }
     )