Update app.py

app.py

@@ -7,237 +7,7 @@ import time
 from langsmith import traceable
 import random
 
-##### Begin Library Code
 
-from transformers import pipeline
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
-from pydantic import BaseModel
-from typing import List, Optional
-from tqdm import tqdm
-import re
-import os
-
-
-class TeapotAISettings(BaseModel):
-    """
-    Pydantic settings model for TeapotAI configuration.
-    
-    Attributes:
-        use_rag (bool): Whether to use RAG (Retrieve and Generate).
-        rag_num_results (int): Number of top documents to retrieve based on similarity.
-        rag_similarity_threshold (float): Similarity threshold for document relevance.
-        verbose (bool): Whether to print verbose updates.
-        log_level (str): The log level for the application (e.g., "info", "debug").
-    """
-    use_rag: bool = True  # Whether to use RAG (Retrieve and Generate)
-    rag_num_results: int = 3  # Number of top documents to retrieve based on similarity
-    rag_similarity_threshold: float = 0.5  # Similarity threshold for document relevance
-    verbose: bool = True  # Whether to print verbose updates
-    log_level: str = "info"  # Log level setting (e.g., 'info', 'debug')
-
-
-class TeapotAI:
-    """
-    TeapotAI class that interacts with a language model for text generation and retrieval tasks.
-    
-    Attributes:
-        model (str): The model identifier.
-        model_revision (Optional[str]): The revision/version of the model.
-        api_key (Optional[str]): API key for accessing the model (if required).
-        settings (TeapotAISettings): Configuration settings for the AI instance.
-        generator (callable): The pipeline for text generation.
-        embedding_model (callable): The pipeline for feature extraction (document embeddings).
-        documents (List[str]): List of documents for retrieval.
-        document_embeddings (np.ndarray): Embeddings for the provided documents.
-    """
-    
-    def __init__(self, model_revision: Optional[str] = None, api_key: Optional[str] = None,
-                 documents: List[str] = [], settings: TeapotAISettings = TeapotAISettings()):
-        """
-        Initializes the TeapotAI class with optional model_revision and api_key.
-
-        Parameters:
-            model_revision (Optional[str]): The revision/version of the model to use.
-            api_key (Optional[str]): The API key for accessing the model if needed.
-            documents (List[str]): A list of documents for retrieval. Defaults to an empty list.
-            settings (TeapotAISettings): The settings configuration (defaults to TeapotAISettings()).
-        """
-        self.model = "teapotai/teapotllm"
-        self.model_revision = model_revision
-        self.api_key = api_key
-        self.settings = settings
-        
-        if self.settings.verbose:
-            print(""" _____                      _         _    ___        __o__    _;; 
-|_   _|__  __ _ _ __   ___ | |_      / \  |_ _|   __ /-___-\__/ /
-  | |/ _ \/ _` | '_ \ / _ \| __|    / _ \  | |   (  |       |__/
-  | |  __/ (_| | |_) | (_) | |_    / ___ \ | |    \_|~~~~~~~|
-  |_|\___|\__,_| .__/ \___/ \__/  /_/   \_\___|      \_____/
-               |_|   """)
-        
-        if self.settings.verbose:
-            print(f"Loading Model: {self.model} Revision: {self.model_revision or 'Latest'}")
-        
-        self.generator = pipeline("text2text-generation", model=self.model, revision=self.model_revision) if model_revision else pipeline("text2text-generation", model=self.model)
-
-        self.documents = documents
-        
-        if self.settings.use_rag and self.documents:
-            self.embedding_model = pipeline("feature-extraction", model="teapotai/teapotembedding")
-            self.document_embeddings = self._generate_document_embeddings(self.documents)
-
-    def _generate_document_embeddings(self, documents: List[str]) -> np.ndarray:
-        """
-        Generate embeddings for the provided documents using the embedding model.
-
-        Parameters:
-            documents (List[str]): A list of document strings to generate embeddings for.
-
-        Returns:
-            np.ndarray: A NumPy array of document embeddings.
-        """
-        embeddings = []
-        
-        if self.settings.verbose:
-            print("Generating embeddings for documents...")
-            for doc in tqdm(documents, desc="Document Embedding", unit="doc"):
-                embeddings.append(self.embedding_model(doc)[0][0])
-        else:
-            for doc in documents:
-                embeddings.append(self.embedding_model(doc)[0][0])
-                
-        return np.array(embeddings)
-
-    def rag(self, query: str) -> List[str]:
-        """
-        Perform RAG (Retrieve and Generate) by finding the most relevant documents based on cosine similarity.
-
-        Parameters:
-            query (str): The query string to find relevant documents for.
-
-        Returns:
-            List[str]: A list of the top N most relevant documents.
-        """
-        if not self.settings.use_rag or not self.documents:
-            return []
-
-        query_embedding = self.embedding_model(query)[0][0]
-        similarities = cosine_similarity([query_embedding], self.document_embeddings)[0]
-
-        filtered_indices = [i for i, similarity in enumerate(similarities) if similarity >= self.settings.rag_similarity_threshold]
-        top_n_indices = sorted(filtered_indices, key=lambda i: similarities[i], reverse=True)[:self.settings.rag_num_results]
-
-        return [self.documents[i] for i in top_n_indices]
-
-    def generate(self, input_text: str) -> str:
-        """
-        Generate text based on the input string using the teapotllm model.
-
-        Parameters:
-            input_text (str): The text prompt to generate a response for.
-
-        Returns:
-            str: The generated output from the model.
-        """
-        
-        
-        result = self.generator(input_text, max_length=512)[0].get("generated_text")
-        
-
-        if self.settings.log_level == "debug":
-            print(input_text)
-            print(result)
-        
-        return result
-
-    def query(self, query: str, context: str = "") -> str:
-        """
-        Handle a query and context, using RAG if no context is provided, and return a generated response.
-
-        Parameters:
-            query (str): The query string to be answered.
-            context (str): The context to guide the response. Defaults to an empty string.
-
-        Returns:
-            str: The generated response based on the input query and context.
-        """
-        if self.settings.use_rag and not context:
-            context = "\n".join(self.rag(query))  # Perform RAG if no context is provided
-        
-        input_text = f"Context: {context}\nQuery: {query}"
-        return self.generate(input_text)
-
-    def chat(self, conversation_history: List[dict]) -> str:
-        """
-        Engage in a chat by taking a list of previous messages and generating a response.
-
-        Parameters:
-            conversation_history (List[dict]): A list of previous messages, each containing 'content'.
-
-        Returns:
-            str: The generated response based on the conversation history.
-        """
-        chat_history = "".join([message['content'] + "\n" for message in conversation_history])
-
-        if self.settings.use_rag:
-            context_documents = self.rag(chat_history)  # Perform RAG on the conversation history
-            context = "\n".join(context_documents)
-            chat_history = f"Context: {context}\n" + chat_history
-
-        return self.generate(chat_history + "\n" + "agent:")
-
-    def extract(self, class_annotation: BaseModel, query: str = "", context: str = "") -> BaseModel:
-        """
-        Extract fields from a Pydantic class annotation by querying and processing each field.
-
-        Parameters:
-            class_annotation (BaseModel): The Pydantic class to extract fields from.
-            query (str): The query string to guide the extraction. Defaults to an empty string.
-            context (str): Optional context for the query.
-
-        Returns:
-            BaseModel: An instance of the provided Pydantic class with extracted field values.
-        """
-        if self.settings.use_rag:
-            context_documents = self.rag(query)
-            context = "\n".join(context_documents) + context
-        
-        output = {}
-        for field_name, field in class_annotation.__fields__.items():
-            type_annotation = field.annotation
-            description = field.description
-            description_annotation = f"({description})" if description else ""
-
-            result = self.query(f"Extract the field {field_name} {description_annotation} to a {type_annotation}", context=context)
-
-            # Process result based on field type
-            if type_annotation == bool:
-                parsed_result = (
-                    True if re.search(r'\b(yes|true)\b', result, re.IGNORECASE)
-                    else (False if re.search(r'\b(no|false)\b', result, re.IGNORECASE) else None)
-                )
-            elif type_annotation in [int, float]:
-                parsed_result = re.sub(r'[^0-9.]', '', result)
-                if parsed_result:
-                    try:
-                        parsed_result = type_annotation(parsed_result)
-                    except Exception:
-                        parsed_result = None
-                else:
-                    parsed_result = None
-            elif type_annotation == str:
-                parsed_result = result.strip()
-            else:
-                raise ValueError(f"Unsupported type annotation: {type_annotation}")
-
-            output[field_name] = parsed_result
-        
-        return class_annotation(**output)
-        
-##### End Library Code
 def log_time(func):
     def wrapper(*args, **kwargs):
         start_time = time.time()