Create app.py

app.py

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+from langchain.docstore.document import Document
+from langchain.vectorstores import FAISS
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.memory.simple import SimpleMemory
+
+from langchain.chains import ConversationChain, LLMChain, SequentialChain
+from langchain.memory import ConversationBufferMemory
+
+from langchain.prompts import ChatPromptTemplate, PromptTemplate
+from langchain.document_loaders import UnstructuredFileLoader
+
+from langchain.chat_models import ChatOpenAI
+from langchain.llms import OpenAI
+from langchain.memory import ConversationSummaryMemory
+
+from langchain.callbacks import PromptLayerCallbackHandler
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    AIMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+from langchain.schema import AIMessage, HumanMessage, SystemMessage
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.callbacks.base import BaseCallbackHandler
+import gradio as gr
+
+from threading import Thread
+from queue import Queue, Empty
+from threading import Thread
+from collections.abc import Generator
+from langchain.llms import OpenAI
+from langchain.callbacks.base import BaseCallbackHandler
+
+import itertools
+import time
+import os
+import getpass
+import json
+import sys
+from typing import Any, Dict, List, Union
+
+import promptlayer
+import openai
+import gradio as gr
+
+from pydantic import BaseModel, Field, validator
+
+#Load the FAISS Model ( vector )
+openai.api_key = os.environ["OPENAI_API_KEY"]
+db = FAISS.load_local("db", OpenAIEmbeddings())
+
+#API Keys 
+promptlayer.api_key = os.environ["PROMPTLAYER"]
+
+from langchain.callbacks import PromptLayerCallbackHandler
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    AIMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.memory import ConversationSummaryMemory
+
+# Defined a QueueCallback, which takes as a Queue object during initialization. Each new token is pushed to the queue.
+class QueueCallback(BaseCallbackHandler):
+    """Callback handler for streaming LLM responses to a queue."""
+
+    def __init__(self, q):
+        self.q = q
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        self.q.put(token)
+
+    def on_llm_end(self, *args, **kwargs: Any) -> None:
+        return self.q.empty()
+
+class DDSAgent:
+
+  def __init__(self, name, db, prompt_template='', model_name='gpt-4', verbose=False, temp=0.2):
+    self.db = db
+    self.verbose = verbose
+    self.llm = ChatOpenAI(
+      model_name="gpt-4",
+      temperature=temp
+      )
+
+    #The zero shot prompt provided at creation
+    self.prompt_template = prompt_template
+
+    #The LLM used for conversation summarization
+    self.summary_llm = ChatOpenAI(
+        model_name=model_name,
+        max_tokens=25,
+        callbacks=[PromptLayerCallbackHandler(pl_tags=["froebel"])],
+        streaming=False,
+        )
+
+    #Reviews convesation history and summarizes it to keep the token count down.
+    self.memory = ConversationSummaryMemory(llm=self.summary_llm,
+                                            max_token_limit=200,
+                                            memory_key="memory",
+                                            input_key="input")
+
+  def chain(self, prompt: PromptTemplate, llm: ChatOpenAI) -> LLMChain:
+        return LLMChain(
+            llm=llm,
+            prompt=prompt,
+            verbose=self.verbose,
+            memory=self.memory
+        )
+
+  def lookup(self, input, num_docs=5):
+    docs = self.db.similarity_search(input, k=num_docs)
+    docs_to_string = ""
+    for doc in docs:
+      docs_to_string += str(doc.page_content)
+    return docs_to_string
+
+  def stream(self, input) -> Generator:
+
+  # Create a Queue
+    q = Queue()
+    job_done = object()
+
+    #RAG
+    docs = self.lookup(input,5)
+
+    llm = ChatOpenAI(
+        model_name='gpt-4',
+        callbacks=[QueueCallback(q),
+                  PromptLayerCallbackHandler(pl_tags=["froebel"])],
+        streaming=True,
+        )
+
+    prompt = PromptTemplate(
+          input_variables=['input','docs','history'],
+          template=self.prompt_template
+          # partial_variables={"format_instructions": self.parser.get_format_instructions()}
+        )
+
+    # Create a funciton to call - this will run in a thread
+    def task():
+        resp = self.chain(prompt,llm).run(
+            {'input':input,
+             'docs':docs,
+             'history':self.memory})
+        q.put(job_done)
+
+    # Create a thread and start the function
+    t = Thread(target=task)
+    t.start()
+
+    content = ""
+
+    # Get each new token from the queue and yield for our generator
+    while True:
+        try:
+            next_token = q.get(True, timeout=1)
+            if next_token is job_done:
+                break
+            content += next_token
+            yield next_token, content
+        except Empty:
+            continue
+
+
+
+
+agent_prompt = """
+            Roleplay
+            You are a UBD ( Understanding by Design ) coach.
+            Educators come to you to develop UBD based learning experiences 
+            and curriculum. 
+            
+            This is the conversation up until now:
+            {history}
+
+            The teacher says:
+            {input}
+
+            As a result, following standards were matched:
+            {docs}
+
+            Respond to the teacher message.
+
+            You have three objectives: 
+            
+            a) to help them through the design process
+            b) to help simplify the process for the educator
+            c) to help build confidence and understand in the ubd process
+
+            Take it step by step and keep.
+            Keep focused on the current task at hand.
+            Close with a single guiding step in the form of a question. 
+            Be encouraging. 
+
+            Do not start with "AI:" or any self identifying text.
+
+        """
+
+dds = DDSAgent('agent', db, prompt_template=agent_prompt)
+
+def ask_agent(input, history):
+    for next_token, content in dds.stream(input):
+        yield(content)
+
+gr.ChatInterface(ask_agent,
+                 title="UBD Coach",
+                  description="""
+                  Using the Understanding By Design framework? I can help. (/◕ヮ◕)/
+                  """,
+                  theme="monochrome",
+                  retry_btn=None,
+                  undo_btn=None,
+                  clear_btn=None
+                 ).queue().launch(debug=True)