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| from haystack import Pipeline | |
| from haystack.utils import Secret | |
| from haystack_integrations.components.retrievers.chroma import ChromaQueryTextRetriever | |
| # from haystack_integrations.components.generators.llama_cpp import LlamaCppGenerator | |
| from haystack.components.readers import ExtractiveReader | |
| # from haystack.components.generators import GPTGenerator | |
| from haystack.components.builders.prompt_builder import PromptBuilder | |
| from haystack.components.builders.answer_builder import AnswerBuilder | |
| from haystack.components.generators import OpenAIGenerator | |
| from haystack.document_stores.in_memory import InMemoryDocumentStore | |
| from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder | |
| from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever | |
| from dataloader import DataLoader | |
| from dotenv import load_dotenv | |
| import os | |
| load_dotenv() # Load variables from .env file | |
| chroma_store_loader = DataLoader() | |
| class Inferncer: | |
| def __init__(self): | |
| self.chroma_store = chroma_store_loader.chroma_store | |
| self.InMemory_store = chroma_store_loader.InMemory_store | |
| def OpenAI(self,query): | |
| template = """ | |
| Utilize the provided context related to Aditya Sugandhi to answer the question. If the answer is not explicitly available in the given information, generate a response using the Language Model (LLM). Optimize the process for clarity and efficiency. | |
| Context: | |
| {% for context in answers %} | |
| {{ context }} | |
| {% endfor %} | |
| Question: {{question}} | |
| Answer: | |
| """ | |
| api_key = os.environ.get("OPENAI_API_KEY") | |
| #ExtractiveReader to extract answers from the relevant context | |
| api_key = Secret.from_token(api_key) | |
| prompt_builder = PromptBuilder(template=template) | |
| retriever = ChromaQueryTextRetriever(document_store = self.chroma_store) | |
| #ExtractiveReader to extract answers from the relevant context | |
| api_key = Secret.from_token("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") | |
| llm = OpenAIGenerator(model="gpt-3.5-turbo-0125",api_key=api_key) | |
| reader = ExtractiveReader(model="deepset/roberta-base-squad2-distilled") | |
| extractive_qa_pipeline = Pipeline() | |
| extractive_qa_pipeline.add_component("retriever", retriever) | |
| extractive_qa_pipeline.add_component("reader",reader) | |
| extractive_qa_pipeline.add_component(instance=prompt_builder, name="prompt_builder") | |
| extractive_qa_pipeline.add_component("llm", llm) | |
| # extractive_qa_pipeline.connect("retriever.documents", "reader.documents") | |
| extractive_qa_pipeline.connect("retriever.documents", "reader.documents") | |
| extractive_qa_pipeline.connect("reader.answers", "prompt_builder.answers") | |
| extractive_qa_pipeline.connect("prompt_builder", "llm") | |
| # Define the input data for the pipeline components | |
| input_data = { | |
| "retriever": {"query": query, "top_k": 2}, | |
| "reader": {"query": query, "top_k": 2}, | |
| "prompt_builder": {"question": query}, | |
| # "reader": {"query": query} | |
| # Use 'max_tokens' instead of 'max_new_tokens' | |
| } | |
| # Run the pipeline with the updated input data | |
| results = extractive_qa_pipeline.run(input_data) | |
| return results | |
| # def LlamaCpp(self,query): | |
| # template = """ | |
| # ` Answer the question using the provided context based on Aditya. | |
| # Context: | |
| # {% for doc in documents %} | |
| # {{ doc.content }} | |
| # {% endfor %} | |
| # Question: {{question}} | |
| # Answer: | |
| # """ | |
| # self.InMemory_store = chroma_store_loader.InMemory_dataloader() | |
| # prompt_builder = PromptBuilder(template=template) | |
| # retriever = InMemoryEmbeddingRetriever(document_store = self.InMemory_store) | |
| # #ExtractiveReader to extract answers from the relevant context | |
| # llm = LlamaCppGenerator( | |
| # model_path="openchat-3.5-1210.Q3_K_S.ggml", | |
| # n_ctx=30000, | |
| # n_batch=256, | |
| # model_kwargs={"n_gpu_layers": 2, "main_gpu": 1}, | |
| # generation_kwargs={"max_tokens": 250, "temperature": 0.7}, | |
| # ) | |
| # llm.warm_up() | |
| # # reader = ExtractiveReader(model="deepset/roberta-base-squad2-distilled",) | |
| # extractive_qa_pipeline = Pipeline() | |
| # text_embedder = SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2") | |
| # extractive_qa_pipeline.add_component('text_embedder', text_embedder) | |
| # extractive_qa_pipeline.add_component("retriever", retriever) | |
| # # extractive_qa_pipeline.add_component("reader",reader) | |
| # extractive_qa_pipeline.add_component(instance=prompt_builder, name="prompt_builder") | |
| # extractive_qa_pipeline.add_component("llm", llm) | |
| # # extractive_qa_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder") | |
| # # extractive_qa_pipeline.connect("retriever.documents", "reader") | |
| # extractive_qa_pipeline.connect("text_embedder.embedding", "retriever.query_embedding") | |
| # extractive_qa_pipeline.connect("retriever.documents", "prompt_builder.documents") | |
| # extractive_qa_pipeline.connect("prompt_builder", "llm") | |
| # # extractive_qa_pipeline.connect("llm.replies", "answer_builder.replies") | |
| # # extractive_qa_pipeline.connect("retriever", "answer_builder.documents") | |
| # # Define the input data for the pipeline components | |
| # input_data = { | |
| # "text_embedder": {"text": query}, | |
| # # "retriever": {"query": query, "top_k": 3}, | |
| # # "reader": {"query": query}, | |
| # "prompt_builder": {"question": query}, | |
| # # "answer_builder": {"query": query}, | |
| # # Use 'max_tokens' instead of 'max_new_tokens' | |
| # } | |
| # # Run the pipeline with the updated input data | |
| # results = extractive_qa_pipeline.run(input_data) | |
| # return results | |
| # #{ | |
| # "error": "Cannot connect 'text_embedder' with 'retriever': no matching connections available.\n'text_embedder':\n - embedding: List[float]\n'retriever':\n - query: str (available)\n - _: Optional[Dict[str, Any]] (available)\n - top_k: Optional[int] (available)" | |
| # } | |