Ima?  Sichus huk LLM tapunki “huk hatun programacion simita yuyaychay makina yachaypaq” .  LLMs kutichiyqa kayhina kanman: “Huknin aswan yuyaychasqa programacion simikuna makina yachaypaq Python. Python nisqaqa hatun patayuqmi...”  ¿Imataq kanman sichus organizacionniyki huk chiqaqchasqa organizacionpa específico willayta qunanta munanki chaymanta kutichiyta chiqa organizacion willakuywan aswan allinchayta munanki?  LLMwan tinkuchkaspa chayta ruwasun  Imanasqa?  OpenAI kaqpa chatGPT kaqhina riqsisqa LLMkuna, Google kaqpa Gemini kaqhina llapa runapaq willakuykunapi yachachisqa kanku. Sapa kutim mana organizacionmanta willakuyniyuqchu kanku. Kanmi wakin pachakuna maypi organizacionkuna LLMkunapi hapipakuyta munankuman. Ichaqa, huk organizacionpaq específica kutichiyta aswan allinta ruwayta munanman utaq   munanman mayk'aq mana allpamanta willaykuna kanchu. mana ruway atiykunata yapayta  Chayta ruwanapaq ruwayqa riqsisqam Grounding of LLMpa kutichiyninwan Yachaypa sapinkunata servichikuspa.  Imayna?  Mientras, chaymanta rimaylla atiyman.  Ingeniero hina wakin código fragmentokunata qhawayqa confianzata quwan.  Chaykunata hunt’asqayqa confianzaytan hoqarin, kusikuytapas qowallantaqmi. Compartir quwan satisfacción 😄  ¿Código? Imanasqa mana! → ¿Pitonchu? Riki!!   Munasqa bibliotecakunata churay   pip install openai faiss-cpu numpy python-dotenv    : OpenAI kaqpa GPT modelonkunawan chaymanta churasqakunawan tinkinapaq. openai    : Facebook AI kaqpa bibliotecan allin rikch'anapaq maskanapaq, llamk'achisqa waqaychaypaq chaymanta maskanapaq churasqakuna. faiss-cpu    : Yupay llamk'anakunapaq, chaymanta churasqakunata vector hina hap'inapaq. numpy    : Pachamama tikraqkunata (kayhina, API llavekuna)   willañiqimanta amachasqa karganapaq. python-dotenv .env   Pachamamapa tikraqninkunata churay    nisqaman riy https://platform.openai.com/settings/organization/api-keys  “Mosoq pakasqa llaveta ruway” nisqapi ñit’iy uraypi kaq siq’ipi rikusqanchis hina.  Detalles quy, Huk Servicio Cuenta llamk'achiyta atikunki. “ID de cuenta de servicio” nisqapaq sutita quy hinaspa huk proyectota akllay.  Pakasqa llaveta portapapeles nisqaman copiay    willañiqita llamk'apuy qillqana mayt'uykipi ruray. OpenAI API llaveykita kay willañiqiman yapay. .env   OPENAI_API_KEY=your_openai_api_key_here  Kay willañiqiqa API llaveykita waqaychasqa chaymanta t'aqasqa codigomanta waqaychan.   Cliente chaymanta carga muyuriq tikraqkunata qallariy      willañiqita karga, chaymanta   API llaveta kutichin. Kay churayqa API llaveykita waqaychasqa waqaychan. load_dotenv() .env os.getenv("OPENAI_API_KEY")   import os from openai import OpenAI from dotenv import load_dotenv import faiss import numpy as np # Load environment variables load_dotenv() client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))   Allpamanta Willayta/Yachaykunapa sapinmanta sut’inchana  Kay simi pirwapiqa temakunapaq allpamanta willakuymi kachkan. Chiqamanta, kayqa aswan hatun willay huñu utaq willay tantana kanman.   # Grounding data grounding_data = { "Python": "Python is dynamically typed, which can be a double-edged sword. While it makes coding faster and more flexible, it can lead to runtime errors that might have been caught at compile-time in statically-typed languages.", "LLMs": "Large Language Models (LLMs) are neural networks trained on large text datasets.", "Data Science": "Data Science involves using algorithms, data analysis, and machine learning to understand and interpret data.", "Java": "Java is great, it powers most of the machine learning code, and has a rich set of libraries available." }   Texto Embeddings nisqakunata paqarichiy  OpenAI kaqpa churana rikch'ayninta llamk'achispa huk qusqa qillqapaq churaykunata paqarichinanpaq llamk'ana. Kay ruwana OpenAI API waqyan huk qillqa yaykuypaq churayta chaskinanpaq, chaymanta huk NumPy matriz hina kutichisqa   # Function to generate embedding for a text def get_embedding(text): response = client.embeddings.create( model="text-embedding-ada-002", input=text ) return np.array(response.data[0].embedding)   FAISS Índice chaymanta embeddings nisqakuna Allpaman churanapaq Willakuykunapaq  Huk FAISS indice ruway, huk estructura allinchasqa utqaylla rikch'akuy maskanakunapaq, chaymanta hunt'achiy allpapi willaypa churasqakunawan.   # Create FAISS index and populate it with grounding data embeddings dimension = len(get_embedding("test")) # Dimension of embeddings index = faiss.IndexFlatL2(dimension) # L2 distance index for similarity search grounding_embeddings = [] grounding_keys = list(grounding_data.keys()) for key, text in grounding_data.items(): embedding = get_embedding(text) grounding_embeddings.append(embedding) index.add(np.array([embedding]).astype("float32"))    : Sapa churasqapa sayaynin, FAISS indiceta qallarichinapaq necesitakun. dimension    : Huk FAISS indis nisqatam ruran, chaymi rikch'akuyninpaq euclídeo karu (L2) nisqawan llamk'achin. index = faiss.IndexFlatL2(dimension)  Sapa yaykusqapaq   kaqpi, kay codigo huk churayta paqarichimun chaymanta FAISS indisman yapan. grounding_data   Vector maskana ruway  Ruwayqa FAISS indispi maskan aswan rikch'aq allpamanta willakuy yaykuypaq huk tapuyman.   # Function to perform vector search on FAISS def vector_search(query_text, threshold=0.8): query_embedding = get_embedding(query_text).astype("float32").reshape(1, -1) D, I = index.search(query_embedding, 1) # Search for the closest vector if I[0][0] != -1 and D[0][0] <= threshold: return grounding_data[grounding_keys[I[0][0]]] else: return None # No similar grounding information available    : Tapuy qillqata huk churay vectorman tikran. Query Embedding    : Tapuyman aswan qaylla vector nisqapaq indis nisqapi maskan. FAISS Search    : Aswan qaylla vectorpa karun (D) umbralmanta uraypi kaptinqa, allpamanta willayta kutichin. Mana chayqa, mana confiable allpachakuy tarikusqanmantam qawarichin. Threshold Check   LLM nisqamanta tapukuy  LLM tapuyku OpenAI kaqpa chatgpt api chaymanta gpt-4 modelonwan.   # Query the LLM def query_llm(prompt): response = client.chat.completions.create( model="gpt-4", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] ) return response.choices[0].message.content   Aswan allin kutichiy  Allpamanta willayta yapan sichus kan, utaq  Huk mana ruway atiyta yapan sichus mana tupaq allpamanta willakuy tarikunchu.   def enhance_response(topic, llm_response): grounding_info = vector_search(llm_response) if grounding_info: # Check if the LLM's response aligns well with grounding information return f"{llm_response}\n\n(Verified Information: {grounding_info})" else: # Add a disclaimer when no grounding data is available return f"{llm_response}\n\n(Disclaimer: This information could not be verified against known data and may contain inaccuracies.)"   Hatun ruwayta sut’inchana  Hatun ruwayqa tukuy imata huñun, huk tema yaykuchiyta, LLM tapuyta, chaymanta kutichiy allpapi willakuywan tupachisqa kasqanmanta qhawayta atikun.   # Main function to execute the grounding check def main(): topic = input("Enter a topic: ") llm_response = query_llm(f"What can you tell me about {topic}?") grounding_info = vector_search(llm_response, threshold=0.8) print(f"LLM Response: {llm_response}") print(f"Grounding Information: {grounding_info}") if grounding_info != "No grounding information available": print("Response is grounded and reliable.") else: print("Potential hallucination detected. Using grounded information instead.") print(f"Grounded Answer: {grounding_info}") if __name__ == "__main__": main()  Tukusqa  Qillqasqata hunt’achiy  Kay phatmata waqyay llamk'achispa   python groundin_llm.py  Chay kutichiyqa:   Willay  Sichus kutichiyta reparanki, LLM kaqmanta kutichiy “Huk aswan yuyaychasqa programacion simikuna makina yachaypaq...” kaptinpas, allpapi kutichiyqa karqan “Java hatunmi, aswan Maquina yachay código kallpachan, huk qhapaq huñuyuqmi bibliotecakuna kasqanmanta”.  Kayqa atikunmi Metapa FAISS bibliotecanwan vector maskanapaq rikchakuyninpi hapipakuspa.    : Ruway  Ñawpaqtaqa LLMs kutichiyta kutichiy.  Qhaway sichus huk yachayniyku ima willakuypas tupaqnin vector maskanawan.  Sichus kan chayqa kutichiy kutichiyta "yachay wasimanta” .  Mana kaptinqa LLM kutichiyta kutichiy imayna kasqanman hina.   Kaypim kachkan chay codigo:  https://github.com/sundeep110/groundingLLMs   Kusisqa Allpachay!!

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