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app.py

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+"""## RetrievalQA with LLaMA 2-70B on Together API"""
+# import libraries
+import os
+import together
+import logging
+from typing import Any, Dict, List, Mapping, Optional
+from pydantic import Extra, Field, root_validator
+from langchain.llms.base import LLM
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import TextLoader
+from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders import DirectoryLoader
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.chains.question_answering import load_qa_chain
+import gradio as gr
+
+# set your API key
+os.environ["TOGETHER_API_KEY"] = "6216ce36aadcb06c35436e7d6bbbc18b354d8140f6e805db485d70ecff4481d0"
+together.api_key = os.environ["TOGETHER_API_KEY"]
+
+# set llama model
+together.Models.start("togethercomputer/llama-2-70b-chat")
+
+
+class TogetherLLM(LLM):
+    """Together large language models."""
+
+    model: str = "togethercomputer/llama-2-70b-chat"
+    """model endpoint to use"""
+
+    together_api_key: str = os.environ["TOGETHER_API_KEY"]
+    """Together API key"""
+
+    temperature: float = 0.7
+    """What sampling temperature to use."""
+
+    max_tokens: int = 512
+    """The maximum number of tokens to generate in the completion."""
+
+    class Config:
+        extra = Extra.forbid
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of LLM."""
+        return "together"
+
+    def _call(
+        self,
+        prompt: str,
+        **kwargs: Any,
+    ) -> str:
+        """Call to Together endpoint."""
+        together.api_key = self.together_api_key
+        output = together.Complete.create(prompt,
+                                          model=self.model,
+                                          max_tokens=self.max_tokens,
+                                          temperature=self.temperature,
+                                          )
+        text = output['output']['choices'][0]['text']
+        return text
+
+# Load and process the text files
+loader = TextLoader('data.txt')
+# loader = DirectoryLoader('./folder/', glob="./*.pdf", loader_cls=PyPDFLoader)
+documents = loader.load()
+
+# Make a chain
+llm = TogetherLLM(
+    model= "togethercomputer/llama-2-70b-chat",
+    temperature = 0.1,
+    max_tokens = 1024
+)
+
+# chain
+chain = load_qa_chain(llm=llm, chain_type="stuff")
+query1= "what is this story about?"
+chain.run(input_documents=documents, question=query1)
+
+
+# gradio
+description = "This is a chatbot application based on the llama2 70B model. Simply type an input to get started with chatting."
+examples = [["what is your contact number?"], ["where you are currently working?"]]
+
+
+def greet(query1, history):
+  return chain.run(input_documents=documents, question="answer as if person responding. do not ask question back. \n Question: "+query1)
+
+gr.ChatInterface(greet,title = "Chat with my Bot", description=description,examples=examples).launch(debug = True)