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| import os | |
| import time | |
| import dspy | |
| from dsp.utils import deduplicate | |
| from dspy.retrieve.faiss_rm import FaissRM | |
| from langchain_community.document_loaders import PyPDFLoader | |
| from langchain_community.document_loaders import CSVLoader | |
| from langchain_text_splitters import RecursiveCharacterTextSplitter | |
| # os.environ["AZURE_OPENAI_API_KEY"] = "" | |
| class GenerateSearchQuery(dspy.Signature): | |
| """Write a simple search query that will help answer a complex question.""" | |
| context = dspy.InputField(desc="may contain relevant content") | |
| question = dspy.InputField() | |
| query = dspy.OutputField() | |
| class GenerateAnswer(dspy.Signature): | |
| """give me a answer for user question based on context""" | |
| context = dspy.InputField(desc="may contain relevant content") | |
| question = dspy.InputField() | |
| answer = dspy.OutputField() | |
| class DocQA(dspy.Module): | |
| def __init__(self, file_path,passages_per_hop=3, max_hops=2): | |
| super().__init__() | |
| self.cache = "cache.json" | |
| self.llm = dspy.AzureOpenAI(api_base="https://azureadople.openai.azure.com/", | |
| api_version="2023-09-15-preview", | |
| model="GPT-3") | |
| self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] | |
| self.retrieve = dspy.Retrieve(k=passages_per_hop) | |
| self.generate_answer = dspy.ChainOfThought(GenerateAnswer) | |
| self.max_hops = max_hops | |
| self.knowledge_base = self.create_knowledge_base(file_path) | |
| def load_documents(self, file_path): | |
| print("file_path", file_path) | |
| loader = CSVLoader(file_path) | |
| documents = loader.load() | |
| return documents | |
| def split_documents(self, documents): | |
| text_splitter = RecursiveCharacterTextSplitter( | |
| chunk_size=6000, | |
| chunk_overlap=0, | |
| length_function=len, | |
| is_separator_regex=False, | |
| ) | |
| docs = text_splitter.split_documents(documents) | |
| document_chunks = [page_content.page_content for page_content in docs] | |
| print("input context Ready") | |
| return document_chunks | |
| def create_knowledge_base(self, file_path): | |
| print("file_path", file_path) | |
| document = self.load_documents(file_path) | |
| split_documents = self.split_documents(document) | |
| knowledge_base = FaissRM(split_documents) | |
| return knowledge_base | |
| def run(self,question): | |
| dspy.settings.configure(lm=self.llm, rm=self.knowledge_base) | |
| passages = self.retrieve(question).passages | |
| context = deduplicate(passages) | |
| pred = self.generate_answer(context=context, question=question) | |
| return pred.answer | |