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| from chatbot.retriever import HybridRetrieverReranker | |
| from litellm import completion | |
| import os | |
| import ast | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") | |
| DENSE_RETRIEVER_MODEL_NAME = "all-MiniLM-L6-v2" | |
| CROSS_ENCODER_MODEL_NAME = "cross-encoder/ms-marco-MiniLM-L-12-v2" | |
| LLM_CORE_MODEL_NAME = "groq/llama3-8b-8192" | |
| class QuestionAnsweringBot: | |
| def __init__(self, docs, enable_bm25=True, enable_dense=True, enable_rerank=True, top_k_bm25=60, top_n_dense=30, top_n_rerank=2) -> None: | |
| self.retriever = HybridRetrieverReranker(docs) | |
| self.enable_bm25 = enable_bm25 | |
| self.enable_dense = enable_dense | |
| self.enable_rerank = enable_rerank | |
| self.top_k_bm25=top_k_bm25 | |
| self.top_n_dense=top_n_dense | |
| self.top_n_rerank=top_n_rerank | |
| def __get_answer__(self, question: str) -> str: | |
| PROMPT = """\ | |
| You are an intelligent assistant designed to provide accurate and relevant answers based on the provided context. | |
| Rules: | |
| - Always analyze the provided context thoroughly before answering. | |
| - Respond with factual and concise information. | |
| - If context is ambiguous or insufficient or you can't find answer, say 'I don't know.' | |
| - Do not speculate or fabricate information beyond the provided context. | |
| - Follow user instructions on the response style(default style is detailed response if user didn't provide any specifications): | |
| - If the user asks for a detailed response, provide comprehensive explanations. | |
| - If the user requests brevity, give concise and to-the-point answers. | |
| - When applicable, summarize and synthesize information from the context to answer effectively. | |
| - Avoid using information outside the given context. | |
| """ | |
| context = self.retriever.hybrid_retrieve(question, | |
| enable_bm25=self.enable_bm25, | |
| enable_dense=self.enable_dense, | |
| enable_rerank=self.enable_rerank, | |
| top_k_bm25=self.top_k_bm25, | |
| top_n_dense=self.top_n_dense, | |
| top_n_rerank=self.top_n_rerank | |
| ) | |
| context_text = [doc['raw_text'] for doc in context] | |
| response = completion( | |
| model=LLM_CORE_MODEL_NAME, | |
| temperature=0.0, | |
| messages=[ | |
| {"role": "system", "content": PROMPT}, | |
| {"role": "user", "content": f"Context: {context_text}\nQuestion: {question}"} | |
| ], | |
| api_key=GROQ_API_KEY | |
| ) | |
| return response, context | |
| def form_response(self, question): | |
| llm_response, context = self.__get_answer__(question) | |
| metadata_raw = [doc['chapter_name'] for doc in context] | |
| metadata_cleaned = [ast.literal_eval(item) for item in metadata_raw] | |
| print('User:', question) | |
| print('System:', llm_response.choices[0].message.content) | |
| return f"**{llm_response.choices[0].message.content}**\n\nResources: {[chapter for doc in metadata_cleaned for chapter in doc]}" | |