upd model file
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@ -22,17 +22,36 @@ mistral_api_key = "hXDC4RBJk1qy5pOlrgr01GtOlmyCBaNs"
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if not mistral_api_key:
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raise ValueError("Mistral API key not found in configuration.")
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###############################################################################
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# Jednoduché funkcie pre preklad (stub)
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# Simple functions for translation (stub)
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###############################################################################
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def translate_to_slovak(text: str) -> str:
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return text
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def translate_preserving_medicine_names(text: str) -> str:
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return text
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###############################################################################
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# Funkcia pre vyhodnotenie úplnosti odpovede
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# Function for generating detailed evaluation description via Mistral
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###############################################################################
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def generate_detailed_description(query: str, answer: str, rating: float) -> str:
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prompt = (
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f"Podrobne opíš, prečo odpoveď: '{answer}' na otázku: '{query}' dosiahla hodnotenie {rating} zo 10. "
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"Uveď relevantné aspekty, ktoré ovplyvnili toto hodnotenie, vrátane úplnosti, presnosti a kvality vysvetlenia."
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)
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try:
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description = llm_small.generate_text(prompt=prompt, max_tokens=150, temperature=0.5)
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return description.strip()
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except Exception as e:
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logger.error(f"Error generating detailed description: {e}")
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return "Nie je dostupný podrobný popis."
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###############################################################################
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# Function for evaluating the completeness of the answer
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###############################################################################
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def evaluate_complete_answer(query: str, answer: str) -> dict:
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evaluation_prompt = (
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@ -48,12 +67,13 @@ def evaluate_complete_answer(query: str, answer: str) -> dict:
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try:
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score = float(score_str.strip())
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except Exception as e:
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logger.error(f"Chyba pri parsovaní skóre: {e}")
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logger.error(f"Error parsing evaluation score: {e}")
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score = 0.0
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return {"rating": round(score, 2), "explanation": "Vyhodnotenie na základe požadovaných kritérií."}
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return {"rating": round(score, 2), "explanation": "Evaluation based on required criteria."}
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###############################################################################
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# Funkcia pre validáciu logiky odpovede
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# Function for validating the response logic
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###############################################################################
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def validate_answer_logic(query: str, answer: str) -> str:
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validation_prompt = (
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@ -66,14 +86,39 @@ def validate_answer_logic(query: str, answer: str) -> str:
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)
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try:
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validated_answer = llm_small.generate_text(prompt=validation_prompt, max_tokens=800, temperature=0.5)
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logger.info(f"Validovaná odpoveď: {validated_answer}")
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logger.info(f"Validated answer: {validated_answer}")
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return validated_answer
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except Exception as e:
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logger.error(f"Chyba pri validácii odpovede: {e}")
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logger.error(f"Error during answer validation: {e}")
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return answer
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###############################################################################
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# Funkcia pre vytvorenie dynamického promptu s informáciami z dokumentov
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# Function for logging the evaluation result to file
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###############################################################################
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def log_evaluation_to_file(model: str, search_type: str, rating: float, detailed_desc: str, answer: str):
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# Nahradenie medzier podčiarkovníkmi pre názov modelu
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safe_model = model.replace(" ", "_")
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file_name = f"{safe_model}_{search_type}.txt"
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timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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log_entry = (
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f"Timestamp: {timestamp}\n"
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f"Rating: {rating}/10\n"
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f"Detailed description:\n{detailed_desc}\n"
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f"Answer:\n{answer}\n"
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+ "=" * 80 + "\n\n"
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)
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try:
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with open(file_name, "a", encoding="utf-8") as f:
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f.write(log_entry)
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logger.info(f"Hodnotenie bolo zapísané do súboru {file_name}.")
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except Exception as e:
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logger.error(f"Error writing evaluation to file {file_name}: {e}")
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###############################################################################
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# Function for creating a dynamic prompt with information from documents
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###############################################################################
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def build_dynamic_prompt(query: str, documents: list) -> str:
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documents_str = "\n".join(documents)
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@ -90,8 +135,9 @@ def build_dynamic_prompt(query: str, documents: list) -> str:
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)
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return prompt
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###############################################################################
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# Funkcia na získanie používateľských dát z databázy prostredníctvom endpointu /api/get_user_data
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# Function to get user data from the database via endpoint /api/get_user_data
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###############################################################################
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def get_user_data_from_db(chat_id: str) -> str:
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try:
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@ -100,13 +146,14 @@ def get_user_data_from_db(chat_id: str) -> str:
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data = response.json()
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return data.get("user_data", "")
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else:
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logger.warning(f"Nepodarilo sa získať user_data, status: {response.status_code}")
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logger.warning(f"Nezískané user_data, status: {response.status_code}")
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except Exception as e:
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logger.error(f"Chyba pri získavaní user_data z DB: {e}", exc_info=True)
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logger.error(f"Error retrieving user_data from DB: {e}", exc_info=True)
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return ""
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###############################################################################
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# Trieda pre volanie Mistral LLM
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# Class for calling Mistral LLM
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###############################################################################
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class CustomMistralLLM:
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def __init__(self, api_key: str, endpoint_url: str, model_name: str):
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@ -131,54 +178,72 @@ class CustomMistralLLM:
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response = requests.post(self.endpoint_url, headers=headers, json=payload)
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response.raise_for_status()
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result = response.json()
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logger.info(f"Úplná odpoveď od modelu {self.model_name}: {result}")
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logger.info(f"Full response from model {self.model_name}: {result}")
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return result.get("choices", [{}])[0].get("message", {}).get("content", "No response")
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except HTTPError as e:
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if response.status_code == 429:
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logger.warning(f"Rate limit prekročený. Čakám {delay} sekúnd pred ďalšou skúškou.")
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logger.warning(f"Rate limit exceeded. Waiting {delay} seconds before retry.")
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time.sleep(delay)
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attempt += 1
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else:
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logger.error(f"HTTP chyba: {e}")
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logger.error(f"HTTP Error: {e}")
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raise e
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except Exception as ex:
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logger.error(f"Chyba: {str(ex)}")
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logger.error(f"Error: {str(ex)}")
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raise ex
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raise Exception("Dosiahnutý maximálny počet pokusov pre API request")
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raise Exception("Reached maximum number of retries for API request")
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###############################################################################
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# Funkcia pre kontrolu, či správa súvisí s témou medicíny a liekov
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# Initialisation of Embeddings and Elasticsearch
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###############################################################################
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def check_if_message_is_relevant(query: str) -> (bool, str):
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# Ak je dotaz rovnaký s textami pre doplňujúce informácie, preskočíme kontrolu
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missing_msgs = [
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"Prosím, uveďte vek pacienta.",
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"Má pacient nejaké chronické ochorenia alebo alergie?",
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"Ide o liek na predpis alebo voľnopredajný liek?"
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]
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if query.strip() in missing_msgs:
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return True, "Ano"
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logger.info("Loading HuggingFaceEmbeddings model...")
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
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prompt_relevance = (
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f"Pozri si nasledujúci dotaz užívateľa: '{query}'.\n"
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"Patrí tento dotaz logicky do témy medicíny a odporúčaní liekov? "
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"Ak áno, odpíš presne slovom 'Ano'. Ak nie, uveď dôvod, prečo sa dotaz netýka našej témy."
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index_name = "drug_docs"
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if config.get("useCloud", False):
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logger.info("Using cloud Elasticsearch.")
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cloud_id = "tt:dXMtZWFzdC0yLmF3cy5lbGFzdGljLWNsb3VkLmNvbTo0NDMkOGM3ODQ0ZWVhZTEyNGY3NmFjNjQyNDFhNjI4NmVhYzMkZTI3YjlkNTQ0ODdhNGViNmEyMTcxMjMxNmJhMWI0ZGU="
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vectorstore = ElasticsearchStore(
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es_cloud_id=cloud_id,
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index_name=index_name,
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embedding=embeddings,
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es_user="elastic",
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es_password="sSz2BEGv56JRNjGFwoQ191RJ"
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)
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response = llm_small.generate_text(prompt=prompt_relevance, max_tokens=200, temperature=0.3)
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response = response.strip()
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if response.lower() == "ano":
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return True, "Ano"
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else:
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return False, response
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else:
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logger.info("Using local Elasticsearch.")
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vectorstore = ElasticsearchStore(
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es_url="http://elasticsearch:9200",
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index_name=index_name,
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embedding=embeddings,
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)
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logger.info("Connected to Elasticsearch.")
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###############################################################################
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# Funkcia pre klasifikáciu dopytu: vyhľadávanie vs. upresnenie
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# Initialisation of LLM small & large
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###############################################################################
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llm_small = CustomMistralLLM(
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api_key=mistral_api_key,
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endpoint_url="https://api.mistral.ai/v1/chat/completions",
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model_name="mistral-small-latest"
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)
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llm_large = CustomMistralLLM(
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api_key=mistral_api_key,
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endpoint_url="https://api.mistral.ai/v1/chat/completions",
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model_name="mistral-large-latest"
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)
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###############################################################################
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# Request classification function: vyhladavanie vs. upresnenie
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###############################################################################
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def classify_query(query: str, chat_history: str = "") -> str:
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if not chat_history.strip():
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return "vyhladavanie"
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prompt = (
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"Si zdravotnícky expert, ktorý analyzuje otázky používateľov. "
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"Ty si zdravotnícky expert, ktorý analyzuje otázky používateľov. "
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"Analyzuj nasledujúci dopyt a urči, či ide o dopyt na vyhľadanie liekov alebo "
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"o upresnenie/doplnenie už poskytnutej odpovede.\n"
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"Ak dopyt obsahuje výrazy ako 'čo pit', 'aké lieky', 'odporuč liek', 'hľadám liek', "
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@ -196,15 +261,16 @@ def classify_query(query: str, chat_history: str = "") -> str:
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return "upresnenie"
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return "vyhladavanie"
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###############################################################################
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# Šablóna pre upresnenie dopytu
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# Template for upresnenie dopytu
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###############################################################################
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def build_upresnenie_prompt_no_history(chat_history: str, user_query: str) -> str:
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prompt = f"""
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Si zdravotnícky expert. Máš k dispozícii históriu chatu a novú upresňujúcu otázku.
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Ty si zdravotnícky expert. Máš k dispozícii históriu chatu a novú upresňujúcu otázku.
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Ak v histórii chatu už existuje jasná odpoveď na túto upresňujúcu otázku, napíš:
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"FOUND_IN_HISTORY: <ľudský vysvetľujúci text>"
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"FOUND_IN_HISTORY: <ľudský vysvetľajúci text>"
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Ak však v histórii chatu nie je dostatok informácií, napíš:
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"NO_ANSWER_IN_HISTORY: <krátky vyhľadávací dotaz do Elasticsearch>"
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@ -220,8 +286,9 @@ Upresňujúca otázka od používateľa:
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"""
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return prompt
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###############################################################################
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# Funkcia pre získanie posledného vyhľadávacieho dopytu z histórie
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# Function for retrieving the last vyhladavacieho dopytu z histórie
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###############################################################################
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def extract_last_vyhladavacie_query(chat_history: str) -> str:
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lines = chat_history.splitlines()
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@ -232,8 +299,9 @@ def extract_last_vyhladavacie_query(chat_history: str) -> str:
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break
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return last_query
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###############################################################################
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# Trieda pre agenta konverzácie (dátové ukladanie: vek, anamnéza, predpis, user_data, search_query)
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# Agent class for data storage: vek, anamneza, predpis, user_data, search_query
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###############################################################################
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class ConversationalAgent:
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def __init__(self):
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@ -291,13 +359,15 @@ class ConversationalAgent:
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def ask_follow_up(self, missing_info: dict) -> str:
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return " ".join(missing_info.values())
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###############################################################################
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# Hlavná funkcia process_query_with_mistral s aktualizovanou logikou
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# Main function process_query_with_mistral with updated logic and logging
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###############################################################################
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CHAT_HISTORY_ENDPOINT = "http://localhost:5000/api/chat_history_detail"
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def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10):
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logger.info("Spustenie spracovania dopytu.")
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logger.info("Processing query started.")
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chat_history = ""
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if chat_context:
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@ -319,17 +389,6 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
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except Exception as e:
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logger.error(f"Chyba pri načítaní histórie: {e}")
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# Kontrola relevancie správy
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is_relevant, relevance_response = check_if_message_is_relevant(query)
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if not is_relevant:
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logger.info("Dotaz sa netýka témy medicíny, vraciam vysvetlenie.")
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return {
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"best_answer": relevance_response,
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"model": "RelevanceCheck",
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"rating": 0,
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"explanation": "Dotaz sa netýka témy medicíny a odporúčaní liekov."
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}
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agent = ConversationalAgent()
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if chat_history:
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agent.load_memory_from_history(chat_history)
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@ -348,11 +407,12 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
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try:
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update_response = requests.post("http://localhost:5000/api/save_user_data", json=update_payload)
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if update_response.status_code == 200:
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logger.info("Používateľské dáta boli úspešne aktualizované cez endpoint /api/save_user_data (data question flag).")
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logger.info(
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"User data was successfully updated via endpoint /api/save_user_data (data question flag).")
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else:
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logger.warning(f"Neúspešná aktualizácia dát (data question flag): {update_response.text}")
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logger.warning(f"Failed to update data (data question flag): {update_response.text}")
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except Exception as e:
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logger.error(f"Chyba pri aktualizácii user_data cez endpoint (data question flag): {e}")
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logger.error(f"Error when updating user_data via endpoint (data question flag): {e}")
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if missing_info:
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logger.info(f"Chýbajúce informácie: {missing_info}")
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@ -363,30 +423,34 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
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try:
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update_response = requests.post("http://localhost:5000/api/save_user_data", json=update_payload)
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if update_response.status_code == 200:
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logger.info("Používateľské dáta boli úspešne aktualizované cez endpoint /api/save_user_data.")
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logger.info("User data was successfully updated via endpoint /api/save_user_data.")
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else:
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logger.warning(f"Neúspešná aktualizácia dát: {update_response.text}")
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logger.warning(f"Failed to update the data: {update_response.text}")
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except Exception as e:
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logger.error(f"Chyba pri aktualizácii user_data cez endpoint: {e}")
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logger.error(f"Error when updating user_data via endpoint: {e}")
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return {
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"best_answer": combined_missing_text,
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"model": "FollowUp (new chat)",
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"rating": 0,
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"explanation": "Pre pokračovanie je potrebné doplniť ďalšie údaje.",
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"explanation": "Additional data pre pokračovanie is required.",
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"patient_data": query
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}
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qtype = classify_query(query, chat_history)
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logger.info(f"Typ dopytu: {qtype}")
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logger.info(f"Časť histórie chatu: {chat_history[:200]}...")
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logger.info(f"Chat context (snippet): {chat_history[:200]}...")
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# Určenie typu vyhľadávania: "vector" pre upresnenie, inak "text"
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search_type = "vector" if qtype == "upresnenie" else "text"
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if qtype == "vyhladavanie":
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user_data_db = get_user_data_from_db(chat_id)
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if user_data_db:
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query = query + " Údaje človeka: " + user_data_db
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query = query + " Udaje cloveka: " + user_data_db
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agent.long_term_memory["search_query"] = query
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if qtype == "upresnenie":
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# Kombinácia pôvodného vyhľadávacieho dopytu a upresňujúcej otázky
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original_search = agent.long_term_memory.get("search_query")
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if not original_search:
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original_search = extract_last_vyhladavacie_query(chat_history)
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@ -395,21 +459,21 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
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combined_query = (original_search + " " + query).strip()
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user_data_db = get_user_data_from_db(chat_id)
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if user_data_db:
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combined_query += " Údaje človeka: " + user_data_db
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logger.info(f"Kombinovaný dopyt pre vyhľadávanie: {combined_query}")
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combined_query += " Udaje cloveka: " + user_data_db
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logger.info(f"Combined query for search: {combined_query}")
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upres_prompt = build_upresnenie_prompt_no_history(chat_history, combined_query)
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response_str = llm_small.generate_text(upres_prompt, max_tokens=1200, temperature=0.5)
|
||||
normalized = response_str.strip()
|
||||
logger.info(f"Odpoveď na prompt pre upresnenie: {normalized}")
|
||||
logger.info(f"Upresnenie prompt response: {normalized}")
|
||||
|
||||
if re.match(r"(?i)^found_in_history:\s*", normalized):
|
||||
logger.info("Nájdené FOUND_IN_HISTORY – vykonávam vyhľadávanie s kombinovaným dopytom.")
|
||||
logger.info("Zistený FOUND_IN_HISTORY – vykonávame vyhľadávanie s kombinovaným dopytom.")
|
||||
elif re.match(r"(?i)^no_answer_in_history:\s*", normalized):
|
||||
parts = re.split(r"(?i)^no_answer_in_history:\s*", normalized, maxsplit=1)
|
||||
if len(parts) >= 2:
|
||||
combined_query = parts[1].strip()
|
||||
logger.info(f"Upravený vyhľadávací dopyt z NO_ANSWER_IN_HISTORY: {combined_query}")
|
||||
logger.info(f"Upravený vyhľadávací dopyт z NO_ANSWER_IN_HISTORY: {combined_query}")
|
||||
|
||||
vector_results = vectorstore.similarity_search(combined_query, k=k)
|
||||
max_docs = 5
|
||||
@ -420,7 +484,7 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
|
||||
"best_answer": "Ľutujem, nenašli sa žiadne relevantné informácie.",
|
||||
"model": "Upresnenie-NoResults",
|
||||
"rating": 0,
|
||||
"explanation": "Žiadne výsledky z vyhľadávania."
|
||||
"explanation": "No results from search."
|
||||
}
|
||||
joined_docs = "\n".join(vector_docs)
|
||||
final_prompt = (
|
||||
@ -430,6 +494,7 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
|
||||
"Vygeneruj odporúčanie liekov alebo vysvetlenie, ak je to relevantné.\n"
|
||||
"Prosím, odpovedaj stručne a dostatočne, bez nadmernej dĺžky."
|
||||
)
|
||||
# Volanie oboch modelov pre upresnenie (vectorový dopyt)
|
||||
ans_small = llm_small.generate_text(final_prompt, max_tokens=1200, temperature=0.7)
|
||||
ans_large = llm_large.generate_text(final_prompt, max_tokens=1200, temperature=0.7)
|
||||
val_small = validate_answer_logic(combined_query, ans_small)
|
||||
@ -437,27 +502,25 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
|
||||
eval_small = evaluate_complete_answer(combined_query, val_small)
|
||||
eval_large = evaluate_complete_answer(combined_query, val_large)
|
||||
candidates = [
|
||||
{"summary": val_small, "eval": eval_small, "model": "Mistral Small"},
|
||||
{"summary": val_large, "eval": eval_large, "model": "Mistral Large"},
|
||||
{"model": "Mistral Small", "summary": val_small, "eval": eval_small},
|
||||
{"model": "Mistral Large", "summary": val_large, "eval": eval_large},
|
||||
]
|
||||
|
||||
# Pre každého kandidáta vygenerujeme detailný popis a zapíšeme výsledok do príslušného súboru
|
||||
for candidate in candidates:
|
||||
detailed_desc = generate_detailed_description(combined_query, candidate["summary"],
|
||||
candidate["eval"]["rating"])
|
||||
log_evaluation_to_file(candidate["model"], "vector", candidate["eval"]["rating"], detailed_desc,
|
||||
candidate["summary"])
|
||||
|
||||
best = max(candidates, key=lambda x: x["eval"]["rating"])
|
||||
logger.info(f"Odpoveď od modelu {best['model']} má rating: {best['eval']['rating']}/10")
|
||||
|
||||
evaluation_table = "=== Výsledky hodnotenia odpovedí ===\n"
|
||||
evaluation_table += "{:<15} | {:<6} | {:<60}\n".format("Model", "Rating", "Evaluovaný text")
|
||||
evaluation_table += "-" * 100 + "\n"
|
||||
for candidate in candidates:
|
||||
model_name = candidate["model"]
|
||||
rating = candidate["eval"]["rating"]
|
||||
evaluated_text = candidate["summary"].replace("\n", " ")
|
||||
evaluation_table += "{:<15} | {:<6} | {:<60}\n".format(model_name, rating, evaluated_text)
|
||||
evaluation_table += "=" * 100 + "\n"
|
||||
|
||||
final_answer = translate_preserving_medicine_names(best["summary"])
|
||||
memory_json = json.dumps(agent.long_term_memory)
|
||||
memory_block = f"[MEMORY]{memory_json}[/MEMORY]"
|
||||
final_answer_with_memory = final_answer + "\n\n"
|
||||
|
||||
return {
|
||||
"best_answer": final_answer_with_memory,
|
||||
"model": best["model"],
|
||||
@ -465,6 +528,7 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
|
||||
"explanation": best["eval"]["explanation"]
|
||||
}
|
||||
|
||||
# Vetva pre vyhľadávanie typu "vyhladavanie" (textový dopyt)
|
||||
vector_results = vectorstore.similarity_search(query, k=k)
|
||||
max_docs = 5
|
||||
max_len = 1000
|
||||
@ -474,7 +538,7 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
|
||||
"best_answer": "Ľutujem, nenašli sa žiadne relevantné informácie.",
|
||||
"model": "Vyhladavanie-NoDocs",
|
||||
"rating": 0,
|
||||
"explanation": "Žiadne výsledky"
|
||||
"explanation": "No results"
|
||||
}
|
||||
joined_docs = "\n".join(vector_docs)
|
||||
final_prompt = (
|
||||
@ -484,54 +548,34 @@ def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10
|
||||
"Vygeneruj odporúčanie liekov alebo vysvetlenie, ak je to relevantné.\n"
|
||||
"Prosím, odpovedaj stručne a dostatočne, bez nadmernej dĺžky."
|
||||
)
|
||||
answer = llm_small.generate_text(final_prompt, max_tokens=1200, temperature=0.7)
|
||||
# Volanie oboch modelov pre textový dopyt
|
||||
ans_small = llm_small.generate_text(final_prompt, max_tokens=1200, temperature=0.7)
|
||||
ans_large = llm_large.generate_text(final_prompt, max_tokens=1200, temperature=0.7)
|
||||
val_small = validate_answer_logic(query, ans_small)
|
||||
val_large = validate_answer_logic(query, ans_large)
|
||||
eval_small = evaluate_complete_answer(query, val_small)
|
||||
eval_large = evaluate_complete_answer(query, val_large)
|
||||
candidates = [
|
||||
{"model": "Mistral Small", "summary": val_small, "eval": eval_small},
|
||||
{"model": "Mistral Large", "summary": val_large, "eval": eval_large},
|
||||
]
|
||||
# Logovanie výsledkov do súborov pre textový dopyt
|
||||
for candidate in candidates:
|
||||
detailed_desc = generate_detailed_description(query, candidate["summary"], candidate["eval"]["rating"])
|
||||
log_evaluation_to_file(candidate["model"], "text", candidate["eval"]["rating"], detailed_desc,
|
||||
candidate["summary"])
|
||||
|
||||
best = max(candidates, key=lambda x: x["eval"]["rating"])
|
||||
logger.info(f"Odpoveď od modelu {best['model']} má rating: {best['eval']['rating']}/10")
|
||||
|
||||
final_answer = translate_preserving_medicine_names(best["summary"])
|
||||
memory_json = json.dumps(agent.long_term_memory)
|
||||
memory_block = f"[MEMORY]{memory_json}[/MEMORY]"
|
||||
answer_with_memory = answer + "\n\n"
|
||||
final_answer_with_memory = final_answer + "\n\n"
|
||||
return {
|
||||
"best_answer": answer_with_memory,
|
||||
"model": "Vyhladavanie-Final",
|
||||
"rating": 9,
|
||||
"explanation": "Vyhľadávacia cesta"
|
||||
"best_answer": final_answer_with_memory,
|
||||
"model": best["model"],
|
||||
"rating": best["eval"]["rating"],
|
||||
"explanation": best["eval"]["explanation"]
|
||||
}
|
||||
|
||||
###############################################################################
|
||||
# Inicializácia Embeddings a Elasticsearch
|
||||
###############################################################################
|
||||
logger.info("Načítavam model HuggingFaceEmbeddings...")
|
||||
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
|
||||
|
||||
index_name = "drug_docs"
|
||||
if config.get("useCloud", False):
|
||||
logger.info("Používam cloud Elasticsearch.")
|
||||
cloud_id = "tt:dXMtZWFzdC0yLmF3cy5lbGFzdGljLWNsb3VkLmNvbTo0NDMkOGM3ODQ0ZWVhZTEyNGY3NmFjNjQyNDFhNjI4NmVhYzMkZTI3YjlkNTQ0ODdhNGViNmEyMTcxMjMxNmJhMWI0ZGU="
|
||||
vectorstore = ElasticsearchStore(
|
||||
es_cloud_id=cloud_id,
|
||||
index_name=index_name,
|
||||
embedding=embeddings,
|
||||
es_user="elastic",
|
||||
es_password="sSz2BEGv56JRNjGFwoQ191RJ"
|
||||
)
|
||||
else:
|
||||
logger.info("Používam lokálny Elasticsearch.")
|
||||
vectorstore = ElasticsearchStore(
|
||||
es_url="http://elasticsearch:9200",
|
||||
index_name=index_name,
|
||||
embedding=embeddings,
|
||||
)
|
||||
|
||||
logger.info("Pripojenie k Elasticsearch bolo úspešné.")
|
||||
|
||||
###############################################################################
|
||||
# Inicializácia LLM small a large
|
||||
###############################################################################
|
||||
llm_small = CustomMistralLLM(
|
||||
api_key=mistral_api_key,
|
||||
endpoint_url="https://api.mistral.ai/v1/chat/completions",
|
||||
model_name="mistral-small-latest"
|
||||
)
|
||||
llm_large = CustomMistralLLM(
|
||||
api_key=mistral_api_key,
|
||||
endpoint_url="https://api.mistral.ai/v1/chat/completions",
|
||||
model_name="mistral-large-latest"
|
||||
)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user