| import gradio as gr | |
| from haystack.components.generators import HuggingFaceTGIGenerator | |
| generator = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
| generator.warm_up() | |
| from haystack.components.fetchers.link_content import LinkContentFetcher | |
| from haystack.components.converters import HTMLToDocument | |
| from haystack.components.preprocessors import DocumentSplitter | |
| from haystack.components.rankers import TransformersSimilarityRanker | |
| from haystack.components.generators import GPTGenerator | |
| from haystack.components.builders.prompt_builder import PromptBuilder | |
| from haystack import Pipeline | |
| fetcher = LinkContentFetcher() | |
| converter = HTMLToDocument() | |
| document_splitter = DocumentSplitter(split_by="word", split_length=50) | |
| similarity_ranker = TransformersSimilarityRanker(top_k=3) | |
| prompt_template = """ | |
| According to these documents: | |
| {% for doc in documents %} | |
| {{ doc.content }} | |
| {% endfor %} | |
| Answer the given question: {{question}} | |
| Answer: | |
| """ | |
| prompt_builder = PromptBuilder(template=prompt_template) | |
| pipeline = Pipeline() | |
| pipeline.add_component("fetcher", fetcher) | |
| pipeline.add_component("converter", converter) | |
| pipeline.add_component("splitter", document_splitter) | |
| pipeline.add_component("ranker", similarity_ranker) | |
| pipeline.add_component("prompt_builder", prompt_builder) | |
| pipeline.add_component("llm", generator) | |
| pipeline.connect("fetcher.streams", "converter.sources") | |
| pipeline.connect("converter.documents", "splitter.documents") | |
| pipeline.connect("splitter.documents", "ranker.documents") | |
| pipeline.connect("ranker.documents", "prompt_builder.documents") | |
| pipeline.connect("prompt_builder.prompt", "llm.prompt") | |
| def respond(prompt, use_rag): | |
| if use_rag: | |
| result = pipeline.run({"prompt_builder": {"question": prompt}, | |
| "ranker": {"query": prompt}, | |
| "fetcher": {"urls": ["https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent"]}, | |
| "llm":{"generation_kwargs": {"max_new_tokens": 350}}}) | |
| return result['llm']['replies'][0] | |
| else: | |
| result = generator.run(prompt, generation_kwargs={"max_new_tokens": 350}) | |
| return result["replies"][0] | |
| iface = gr.Interface(fn=respond, inputs=["text", "checkbox"], outputs="text") | |
| iface.launch() |