Cleanup

app.py

@@ -14,7 +14,11 @@ from chain import get_chain
 
 STORE_DIR = "store"
 YOUTUBE_EMBED_TEMPLATE = """
-<iframe width="354" height="200" src="{source}" title="YouTube video player" frameborder="0" 
+<iframe width="354"
+    height="200"
+    src="{source}&cc_load_policy=1"
+    title="YouTube video player"
+    frameborder="0"
     allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
 </iframe>"""
 
@@ -80,8 +84,11 @@ def chat(inp, history, agent):
     return history, history, source_html
 
 
-block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
-with block:
+with gr.Blocks(css=".gradio-container {background-color: lightgray}") as demo:
+
+    state = gr.State()
+    agent_state = gr.State()
+
     gr.Markdown("<h3><center>ToKBot🤖 - Ask ToKCast Questions</center></h3>")
     openai_api_key_textbox = gr.Textbox(
         placeholder="Paste your OpenAI API key (sk-...)",
@@ -90,13 +97,8 @@ with block:
         type="password",
     )
 
+    sources = gr.HTML()
     chatbot = gr.Chatbot()
-    gr.Markdown("<h3>Excerpts</h3>")
-    sources = gr.HTML(
-        """<div style="min-height:200px;display:flex;align-items:center;justify-content:center;">
-            <h3 style="text-align:center;color:#555;font-size:2rem;">No videos</h3>
-        </div>"""
-    )
     with gr.Row():
         message = gr.Textbox(
             label="What's your question?",
@@ -107,24 +109,21 @@ with block:
 
     gr.Examples(
         examples=[
-            "What is a beginning of infinity?",
-            "How do memes differ from genes in how they replicate?",
+            'What does "the beginning of infinity" refer to?',
+            "How do memes differ from genes in their replication?",
             "What is the nature of knowledge and how does it grow?",
         ],
         inputs=message,
     )
 
     gr.HTML(
-        """A GPT-3/LangChain bot that answers questions about the TokCast podcast provides relevant video excerpts"""
+        """<p>A GPT-3/LangChain bot that answers questions about the TokCast podcast provides relevant video excerpts</p>"""
     )
 
     gr.HTML(
         "<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
     )
 
-    state = gr.State()
-    agent_state = gr.State()
-
     submit.click(
         chat,
         inputs=[message, state, agent_state],
@@ -142,4 +141,4 @@ with block:
         outputs=[agent_state],
     )
 
-block.launch()
+demo.launch()

chain.py

@@ -6,9 +6,8 @@ from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import FewShotPromptTemplate
-
-# from langchain.prompts.example_selector import SemanticSimilarityExampleSelector
 from langchain.vectorstores import FAISS
+
 from pydantic import BaseModel
 
 
@@ -33,20 +32,18 @@ class CustomChain(Chain, BaseModel):
             new_question = self.key_word_extractor.run(
                 question=question, chat_history=chat_history_str
             )
-
         else:
             new_question = question
-        docs = self.vstore.similarity_search(new_question, k=3)
+        docs = self.vstore.similarity_search(new_question, k=4)
         new_inputs = inputs.copy()
         new_inputs["question"] = new_question
         new_inputs["chat_history"] = chat_history_str
         answer, _ = self.chain.combine_docs(docs, **new_inputs)
-        sources = ""
+        sources = []
         if "SOURCES:" in answer:
             answer, sources = answer.split("SOURCES:")
-        sources = sources.split(", ")
-        answer = answer.strip()
-        return {"answer": answer, "sources": sources}
+            sources = sources.split(", ")
+        return {"answer": answer.strip(), "sources": sources}
 
 
 def get_chain(vectorstore: FAISS) -> Chain:
@@ -68,10 +65,6 @@ def get_chain(vectorstore: FAISS) -> Chain:
     {chat_history}
     Follow Up Input: {question}
     Standalone question:"""
-    # example_selector = SemanticSimilarityExampleSelector(
-    #     vectorstore=vectorstore,
-    #     k=4,
-    # )
 
     examples = [
         {
@@ -121,13 +114,9 @@ ANSWER:"""
         chain_type="stuff",
         prompt=PROMPT,
         document_prompt=EXAMPLE_PROMPT,
-        verbose=True,
     )
     return CustomChain(
-        chain=doc_chain,
-        vstore=vectorstore,
-        key_word_extractor=key_word_extractor,
-        verbose=True,
+        chain=doc_chain, vstore=vectorstore, key_word_extractor=key_word_extractor
     )