fixes

app.py

@@ -100,13 +100,13 @@ def get_docs():
             documents.extend(text_splitter.split_documents(load))
     return documents
 
-def set_openai_api_key(api_key, agent):
+def set_openai_api_key(api_key, model_selector, agent):
     if api_key:
         os.environ["OPENAI_API_KEY"] = api_key
-        documents = get_docs
+        documents = get_docs()
         embeddings = OpenAIEmbeddings()
-        vectorstore = CachedChroma.from_documents_with_cache(".persisted_data", documents, embeddings, collection_name="pycbc")
-        qa_chain = get_new_chain1(vectorstore)
+        vectorstore = CachedChroma.from_documents_with_cache(".persisted_data", documents, embedding=embeddings, collection_name="pycbc")
+        qa_chain = get_new_chain1(vectorstore, model_selector)
         os.environ["OPENAI_API_KEY"] = ""
         return qa_chain
 
@@ -125,7 +125,6 @@ def chat(inp, history, agent):
     print(history)
     return history, history
 
-
 block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
 
 with block:
@@ -138,6 +137,7 @@ with block:
             lines=1,
             type="password",
         )
+        model_selector = gr.Dropdown(["gpt-3.5-turbo", "gpt-4"], label="Model")
 
     chatbot = gr.Chatbot()
 
@@ -151,16 +151,14 @@ with block:
 
     gr.Examples(
         examples=[
-            "What are agents?",
-            "How do I summarize a long document?",
-            "What types of memory exist?",
+            "What is pycbc?",
         ],
         inputs=message,
     )
 
     gr.HTML(
         """
-    This simple application is an implementation of ChatGPT but over an external dataset (in this case, the LangChain documentation)."""
+    This simple application is an implementation of ChatGPT but over an external dataset (in this case, the pycbc source code)."""
     )
 
     gr.HTML(
@@ -175,7 +173,7 @@ with block:
 
     openai_api_key_textbox.change(
         set_openai_api_key,
-        inputs=[openai_api_key_textbox, agent_state],
+        inputs=[openai_api_key_textbox, model_selector, agent_state],
         outputs=[agent_state],
     )
 

chain.py

@@ -20,6 +20,7 @@ import langchain
 # logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
 from langchain.vectorstores import Chroma
 from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
 from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
@@ -62,7 +63,7 @@ class CustomChain(Chain, BaseModel):
         return {"answer": answer}
 
 
-def get_new_chain1(vectorstore) -> Chain:
+def get_new_chain1(vectorstore, model_selector) -> Chain:
 
     _eg_template = """## Example:
 
@@ -91,7 +92,7 @@ def get_new_chain1(vectorstore) -> Chain:
         example_prompt=_eg_prompt,
         input_variables=["question", "chat_history"],
     )
-    llm = OpenAI(temperature=0, model_name="text-davinci-003")
+    llm = ChatOpenAI(temperature=0, model_name=model_selector)
     key_word_extractor = LLMChain(llm=llm, prompt=prompt)
 
     EXAMPLE_PROMPT = PromptTemplate(
@@ -111,7 +112,7 @@ Question: {question}
 Answer in Markdown:"""
     PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
     doc_chain = load_qa_chain(
-        OpenAI(temperature=0, model_name="text-davinci-003", max_tokens=-1),
+        ChatOpenAI(temperature=0, model_name=model_selector, max_tokens=-1),
         chain_type="stuff",
         prompt=PROMPT,
         document_prompt=EXAMPLE_PROMPT,