Update app.py

this is a test for embeddings tab

app.py

@@ -6,6 +6,13 @@ from langchain.chains import ConversationChain
 from langchain.llms import OpenAI
 from threading import Lock
 
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores.faiss import FAISS
+from langchain.docstore.document import Document
+from langchain.chains.question_answering import load_qa_chain
+
+
 
 def load_chain():
     """Logic for loading the chain you want to use should go here."""
@@ -14,22 +21,36 @@ def load_chain():
     return chain
 
 
-def set_openai_api_key(api_key: str):
+def set_openai_api_key(api_key):
     """Set the api key and return chain.
     If no api_key, then None is returned.
     """
-    if api_key:
+    if api_key and api_key.startswith("sk-") and len(api_key) > 50:
         os.environ["OPENAI_API_KEY"] = api_key
-        chain = load_chain()
+        print("\n\n ++++++++++++++ Setting OpenAI API key ++++++++++++++ \n\n")
+        print(str(datetime.datetime.now()) + ": Before OpenAI, OPENAI_API_KEY length: " + str(
+            len(os.environ["OPENAI_API_KEY"])))
+        llm = OpenAI(temperature=0, max_tokens=MAX_TOKENS)
+        print(str(datetime.datetime.now()) + ": After OpenAI, OPENAI_API_KEY length: " + str(
+            len(os.environ["OPENAI_API_KEY"])))
+        chain, express_chain, memory = load_chain(TOOLS_DEFAULT_LIST, llm)
+
+        # Pertains to question answering functionality
+        embeddings = OpenAIEmbeddings()
+        qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+
+        print(str(datetime.datetime.now()) + ": After load_chain, OPENAI_API_KEY length: " + str(
+            len(os.environ["OPENAI_API_KEY"])))
         os.environ["OPENAI_API_KEY"] = ""
-        return chain
+        return chain, express_chain, llm, embeddings, qa_chain, memory
+    return None, None, None, None, None, None
 
 class ChatWrapper:
 
     def __init__(self):
         self.lock = Lock()
     def __call__(
-        self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain: Optional[ConversationChain]
+        self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain: Optional[ConversationChain], use_embeddings, monologue:bool
     ):
         """Execute the chat functionality."""
         self.lock.acquire()
@@ -42,6 +63,20 @@ class ChatWrapper:
             # Set OpenAI key
             import openai
             openai.api_key = api_key
+            if not monologue:
+                if use_embeddings:
+                    if inp and inp.strip() != "":
+                        if docsearch:
+                            docs = docsearch.similarity_search(inp)
+                            output = str(qa_chain.run(input_documents=docs, question=inp))
+                        else:
+                            output, hidden_text = "Please supply some text in the the Embeddings tab.", None
+                    else:
+                        output, hidden_text = "What's on your mind?", None
+                else:
+                    output, hidden_text = run_chain(chain, inp, capture_hidden_text=trace_chain)
+            else:
+                output, hidden_text = inp, None
             # Run chain and append input.
             output = chain.run(input=inp)
             history.append((inp, output))
@@ -51,11 +86,29 @@ class ChatWrapper:
             self.lock.release()
         return history, history
 
+# Pertains to question answering functionality
+def update_embeddings(embeddings_text, embeddings, qa_chain):
+    if embeddings_text:
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        texts = text_splitter.split_text(embeddings_text)
+
+        docsearch = FAISS.from_texts(texts, embeddings)
+        print("Embeddings updated")
+        return docsearch
+
+
+# Pertains to question answering functionality
+def update_use_embeddings(widget, state):
+    if widget:
+        state = widget
+        return state
+
 chat = ChatWrapper()
 
 block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
 
-with block:
+
+with gt.Tab("Chat"):
     with gr.Row():
         gr.Markdown("<h3><center>LangChain Demo</center></h3>")
 
@@ -85,7 +138,20 @@ with block:
         inputs=message,
     )
 
-    
+
+with gr.Tab("Embeddings"):
+        embeddings_text_box = gr.Textbox(label="Enter text for embeddings and hit Create:",
+                                         lines=20)
+
+        with gr.Row():
+            use_embeddings_cb = gr.Checkbox(label="Use embeddings", value=False)
+            use_embeddings_cb.change(update_use_embeddings, inputs=[use_embeddings_cb, use_embeddings_state],
+                                     outputs=[use_embeddings_state])
+
+            embeddings_text_submit = gr.Button(value="Create", variant="secondary").style(full_width=False)
+            embeddings_text_submit.click(update_embeddings,
+                                         inputs=[embeddings_text_box, embeddings_state, qa_chain_state],
+                                         outputs=[docsearch_state])
 
     state = gr.State()
     agent_state = gr.State()