Add docuemnts evaluator, refactor validators

buster/busterbot.py

@@ -19,12 +19,8 @@ class BusterConfig:
 
     validator_cfg: dict = field(
         default_factory=lambda: {
-            "unknown_prompts": [
-                "I Don't know how to answer your question.",
-            ],
-            "unknown_threshold": 0.85,
-            "embedding_model": "text-embedding-ada-002",
             "use_reranking": True,
+            "validate_documents": False,
         }
     )
     tokenizer_cfg: dict = field(

buster/completers/base.py

@@ -137,6 +137,11 @@ def postprocess(self):
                 answer=self.answer_text, matched_documents=self.matched_documents
             )
 
+        if self.validator.validate_documents:
+            self.matched_documents = self.validator.check_documents_relevance(
+                answer=self.answer_text, matched_documents=self.matched_documents
+            )
+
         # access the property so it gets set if not computed alerady
         self.answer_relevant
 

buster/examples/cfg.py

@@ -4,19 +4,18 @@
 from buster.formatters.prompts import PromptFormatter
 from buster.retriever import DeepLakeRetriever, Retriever
 from buster.tokenizers import GPTTokenizer
-from buster.validators import QuestionAnswerValidator, Validator
+from buster.validators import Validator
 
 buster_cfg = BusterConfig(
     validator_cfg={
-        "unknown_response_templates": [
-            "I'm sorry, but I am an AI language model trained to assist with questions related to AI. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?",
-        ],
-        "unknown_threshold": 0.85,
-        "embedding_model": "text-embedding-ada-002",
-        "use_reranking": True,
-        "invalid_question_response": "This question does not seem relevant to my current knowledge.",
-        "check_question_prompt": """You are an chatbot answering questions on artificial intelligence.
-
+        "question_validator_cfg": {
+            "invalid_question_response": "This question does not seem relevant to my current knowledge.",
+            "completion_kwargs": {
+                "model": "gpt-3.5-turbo",
+                "stream": False,
+                "temperature": 0,
+            },
+            "check_question_prompt": """You are a chatbot answering questions on artificial intelligence.
 Your job is to determine wether or not a question is valid, and should be answered.
 More general questions are not considered valid, even if you might know the response.
 A user will submit a question. Respond 'true' if it is valid, respond 'false' if it is invalid.
@@ -30,11 +29,22 @@
 false
 
 A user will submit a question. Respond 'true' if it is valid, respond 'false' if it is invalid.""",
-        "completion_kwargs": {
-            "model": "gpt-3.5-turbo",
-            "stream": False,
-            "temperature": 0,
         },
+        "answer_validator_cfg": {
+            "unknown_response_templates": [
+                "I'm sorry, but I am an AI language model trained to assist with questions related to AI. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?",
+            ],
+            "unknown_threshold": 0.85,
+        },
+        "documents_validator_cfg": {
+            "completion_kwargs": {
+                "model": "gpt-3.5-turbo",
+                "stream": False,
+                "temperature": 0,
+            },
+        },
+        "use_reranking": True,
+        "validate_documents": True,
     },
     retriever_cfg={
         "path": "deeplake_store",
@@ -98,6 +108,6 @@ def setup_buster(buster_cfg: BusterConfig):
         prompt_formatter=PromptFormatter(tokenizer=tokenizer, **buster_cfg.prompt_formatter_cfg),
         **buster_cfg.documents_answerer_cfg,
     )
-    validator: Validator = QuestionAnswerValidator(**buster_cfg.validator_cfg)
+    validator: Validator = Validator(**buster_cfg.validator_cfg)
     buster: Buster = Buster(retriever=retriever, document_answerer=document_answerer, validator=validator)
     return buster

buster/llm_utils/embeddings.py

@@ -1,4 +1,5 @@
 import logging
+from functools import lru_cache
 
 import numpy as np
 import pandas as pd
@@ -11,7 +12,8 @@
 client = OpenAI()
 
 
-def get_openai_embedding(text: str, model: str = "text-embedding-ada-002"):
+@lru_cache
+def get_openai_embedding(text: str, model: str = "text-embedding-ada-002") -> np.array:
     try:
         text = text.replace("\n", " ")
         response = client.embeddings.create(

buster/validators/__init__.py

@@ -1,4 +1,3 @@
 from .base import Validator
-from .question_answer_validator import QuestionAnswerValidator
 
-__all__ = [Validator, QuestionAnswerValidator]
+__all__ = [Validator]

buster/validators/base.py

@@ -1,44 +1,53 @@
 import logging
-from abc import ABC, abstractmethod
-from functools import lru_cache
 
 import pandas as pd
 
 from buster.llm_utils import cosine_similarity, get_openai_embedding
+from buster.validators.validators import (
+    AnswerValidator,
+    DocumentsValidator,
+    QuestionValidator,
+)
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
 
 
-class Validator(ABC):
+class Validator:
     def __init__(
         self,
-        embedding_model: str,
-        unknown_threshold: float,
         use_reranking: bool,
-        invalid_question_response: str = "This question is not relevant to my internal knowledge base.",
+        validate_documents: bool,
+        question_validator_cfg=None,
+        answer_validator_cfg=None,
+        documents_validator_cfg=None,
     ):
-        self.embedding_model = embedding_model
-        self.unknown_threshold = unknown_threshold
+        self.question_validator = (
+            QuestionValidator(**question_validator_cfg) if question_validator_cfg is not None else QuestionValidator()
+        )
+        self.answer_validator = (
+            AnswerValidator(**answer_validator_cfg) if answer_validator_cfg is not None else AnswerValidator()
+        )
+        self.documents_validator = (
+            DocumentsValidator(**documents_validator_cfg)
+            if documents_validator_cfg is not None
+            else DocumentsValidator()
+        )
         self.use_reranking = use_reranking
-        self.invalid_question_response = invalid_question_response
-
-    @staticmethod
-    @lru_cache
-    def get_embedding(text: str, model: str):
-        """Currently supports OpenAI embeddings, override to add your own."""
-        logger.info("generating embedding")
-        return get_openai_embedding(text, model)
+        self.validate_documents = validate_documents
 
-    @abstractmethod
     def check_question_relevance(self, question: str) -> tuple[bool, str]:
-        ...
+        return self.question_validator.check_question_relevance(question)
 
-    @abstractmethod
     def check_answer_relevance(self, answer: str) -> bool:
-        ...
+        return self.answer_validator.check_answer_relevance(answer)
 
-    def rerank_docs(self, answer: str, matched_documents: pd.DataFrame) -> pd.DataFrame:
+    def check_documents_relevance(self, answer: str, matched_documents: pd.DataFrame) -> pd.DataFrame:
+        return self.documents_validator.check_documents_relevance(answer, matched_documents)
+
+    def rerank_docs(
+        self, answer: str, matched_documents: pd.DataFrame, embedding_fn=get_openai_embedding
+    ) -> pd.DataFrame:
         """Here we re-rank matched documents according to the answer provided by the llm.
 
         This score could be used to determine wether a document was actually relevant to generation.
@@ -48,10 +57,8 @@ def rerank_docs(self, answer: str, matched_documents: pd.DataFrame) -> pd.DataFr
             return matched_documents
         logger.info("Reranking documents based on answer similarity...")
 
-        answer_embedding = self.get_embedding(
-            answer,
-            model=self.embedding_model,
-        )
+        answer_embedding = embedding_fn(answer)
+
         col = "similarity_to_answer"
         matched_documents[col] = matched_documents.embedding.apply(lambda x: cosine_similarity(x, answer_embedding))