hybrid + emb_only

app.py

@@ -23,8 +23,9 @@ def run(pipe,q):
     result = pipe.run({
         "query_rephrase_prompt_builder":{"query":q},
         "prompt_builder": { "query": q},
+        "prompt_builder_emb_only": { "query": q},
         "memory_joiner": {"values": [ChatMessage.from_user(q)]},
-    },include_outputs_from=["query_rephrase_llm","llm",'ranker','bm25_retriever','embedding_retriever'])
+    },include_outputs_from=["query_rephrase_llm","llm",'ranker','bm25_retriever','embedding_retriever','llm_emb_only'])
 
     query_rephrase = result['query_rephrase_llm']['replies'][0]
     print('query_rephrase:',query_rephrase)
@@ -36,22 +37,33 @@ def run(pipe,q):
         
     reply = result['llm']['replies'][0]
     docs = result['ranker']['documents']
-    print('reply:',reply.content)
     print('ranker')
     log_docs(docs)
+    print('reply:',reply.content)
+
+    def create_response(reply,docs) :
+        html = '<div class="ref-title">参考記事</div><div class="ref">'
+        for doc in get_unique_docs(docs) :
+            title = doc.meta['title']
+            link = doc.meta['link']
+            row = f'<div><a class="link" href="{link}" target="_blank">{title}</a></div>'
+            html += row
+        html += '</div>'
+        return {'reply':reply.content,'sources':html}
+
+    hybrid_response = create_response(reply,docs)
+
+    reply_emb_only = result['llm_emb_only']['replies'][0]
+    print('reply_emb_only :',reply_emb_only.content)
     
-    html = '<div class="ref-title">参考記事</div><div class="ref">'
-    for doc in get_unique_docs(docs) :
-        title = doc.meta['title']
-        link = doc.meta['link']
-        row = f'<div><a class="link" href="{link}" target="_blank">{title}</a></div>'
-        html += row
-    html += '</div>'
-    return {'reply':reply.content,'sources':html}
+    emb_only_response = create_response(reply_emb_only,result['embedding_retriever']['documents'])
+    
+    return {'hybrid':hybrid_response,'emb_only' :emb_only_response}
 
 def rag(q,history):
     result = run(pipe,q)
-    return result['reply'] + result['sources']
+    return '<h3>ハイブリッド検索</h3>' + result['hybrid']['reply'] + result['hybrid']['sources'] + \
+        '<h3>ベクトル検索のみ</h3>'+ result['emb_only']['reply'] + result['emb_only']['sources']
 
 app = gr.ChatInterface(
     rag, 

pipe.py

@@ -92,6 +92,8 @@ document_joiner = DocumentJoiner()
 ranker = TransformersSimilarityRanker(model=ranker_model,meta_fields_to_embed=['title'],model_kwargs={'trust_remote_code':True})
 prompt_builder = ChatPromptBuilder(template=messages,variables=["query", "documents", "memories"], required_variables=["query", "documents", "memories"])
 gemini = GoogleAIGeminiChatGenerator(model=gemini_model)
+prompt_builder_emb_only = ChatPromptBuilder(template=messages,variables=["query", "documents", "memories"], required_variables=["query", "documents", "memories"])
+gemini_emb_only = GoogleAIGeminiChatGenerator(model=gemini_model)
 
 memory_store = InMemoryChatMessageStore()
 memory_joiner = ListJoiner(List[ChatMessage])
@@ -113,6 +115,8 @@ pipe.add_component("llm", gemini)
 pipe.add_component("memory_retriever", memory_retriever)
 pipe.add_component("memory_joiner", memory_joiner)
 pipe.add_component("memory_writer", memory_writer)
+pipe.add_component("prompt_builder_emb_only", prompt_builder_emb_only)
+pipe.add_component("llm_emb_only", gemini_emb_only)
 
 pipe.connect("memory_retriever", "query_rephrase_prompt_builder.memories")
 pipe.connect("query_rephrase_prompt_builder.prompt", "query_rephrase_llm")
@@ -129,4 +133,7 @@ pipe.connect("ranker.documents", "prompt_builder.documents")
 pipe.connect("memory_retriever", "prompt_builder.memories")
 pipe.connect("prompt_builder.prompt", "llm.messages")
 pipe.connect("llm.replies", "memory_joiner")
-pipe.connect("memory_joiner", "memory_writer")
+pipe.connect("memory_joiner", "memory_writer")
+pipe.connect("embedding_retriever.documents", "prompt_builder_emb_only.documents")
+pipe.connect("memory_retriever", "prompt_builder_emb_only.memories")
+pipe.connect("prompt_builder_emb_only.prompt", "llm_emb_only.messages")