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| import requests | |
| import json | |
| import ast | |
| def prompt_generator(question: str, db_knn: dict) -> tuple[str, str, str]: | |
| context = "" | |
| references = "" | |
| for i in range(len(db_knn['matches'])): | |
| data = db_knn['matches'][i]['metadata']['data'] | |
| context += (data + "\n") | |
| data = ast.literal_eval(data) | |
| line_number = "" | |
| if data['type'] == "function" or data['type'] == "class": | |
| line_number = f"#L{data['lineno'][0]}-L{data['lineno'][1]}" | |
| references += ("<https://github.com/fury-gl/fury/blob/" + data['path'] + line_number + ">").replace("fury-0.10.0", "v0.10.0") | |
| if data.get("function_name"): | |
| references += f"\tFunction Name: {data.get('function_name')}" | |
| elif data.get("class_name"): | |
| references += f"\tClass Name: {data.get('class_name')}" | |
| elif data['type'] == 'rst': | |
| references += f"\tDocumentation: {data['path'].split("/")[-1]}" | |
| elif data['type'] == 'documentation_examples': | |
| references += f"\tDocumentation: {data['path'].split("/")[-1]}" | |
| references += "\n" | |
| prompt = f""" | |
| You are a senior developer. Answer the users question based on the context provided. | |
| Question: {question} | |
| Context: {context} | |
| """ | |
| return prompt, context, references | |
| def groq_llm_output(question: str, db_knn: dict, llm: str, stream: bool) -> tuple[str, str]: | |
| """ | |
| Returns output from the LLM using the given user-question and retrived context | |
| """ | |
| URL_LLM = 'https://robinroy03-fury-bot.hf.space' | |
| prompt, context, references = prompt_generator(question, db_knn) | |
| obj = { | |
| 'model': llm, | |
| 'prompt': prompt, | |
| 'stream': stream | |
| } | |
| response = requests.post(URL_LLM + "/api/groq/generate", json=obj) | |
| response_json = json.loads(response.text) | |
| return (response_json['choices'][0]['message']['content'], references) | |
| def google_llm_output(question: str, db_knn: dict, llm: str, stream: bool) -> tuple[str, str]: | |
| URL_LLM = 'https://robinroy03-fury-bot.hf.space' | |
| prompt, context, references = prompt_generator(question, db_knn) | |
| obj = { | |
| 'model': llm, | |
| 'prompt': prompt, | |
| 'stream': stream | |
| } | |
| response = requests.post(URL_LLM + "/api/google/generate", json=obj) | |
| response_json = json.loads(response.text) | |
| return (response_json['candidates'][0]['content']['parts'][0]['text'], references) | |
| def embedding_output(message: str) -> list: | |
| """ | |
| Returns embeddings for the given message | |
| rtype: list of embeddings. Length depends on the model. | |
| """ | |
| URL_EMBEDDING = 'https://robinroy03-fury-embeddings-endpoint.hf.space' | |
| response = requests.post(URL_EMBEDDING + "/embedding", json={"text": message}) | |
| response_json = json.loads(response.text) | |
| return response_json['output'] | |
| def db_output(embedding: list, knn: int) -> dict: | |
| """ | |
| Returns the KNN results. | |
| rtype: JSON | |
| """ | |
| URL_DB = 'https://robinroy03-fury-db-endpoint.hf.space' | |
| response = requests.post(URL_DB + "/query", json={"embeddings": embedding, "knn": knn}) | |
| response_json = json.loads(response.text) | |
| return response_json | |
| def ollama_llm_output(question: str, db_knn: dict, llm: str, stream: bool) -> tuple[str, str]: | |
| URL_LLM = 'https://robinroy03-ollama-server-backend.hf.space' | |
| # URL_LLM = "http://localhost:11434" | |
| prompt, context, references = prompt_generator(question, db_knn) | |
| obj = { | |
| "model": llm, | |
| "prompt": question, | |
| "stream": stream | |
| } | |
| try: | |
| response = requests.post(URL_LLM + "/api/generate", json=obj) | |
| except Exception as e: | |
| print(e) | |
| return {"error": e} | |
| response_json = json.loads(response.text) | |
| return response_json, references |