Je!  Ukiuliza LLM "pendekeza lugha nzuri ya programu ya kujifunza kwa mashine"  Jibu la LLM litakuwa: "Mojawapo ya lugha zinazopendekezwa zaidi za ujifunzaji wa mashine ni Python. Chatu ni mtu wa kiwango cha juu…”  Je, iwapo ungependa shirika lako litoe maelezo mahususi ya shirika lililothibitishwa, yaani, kuboresha jibu kwa taarifa halisi ya shirika?  Wacha tuifanye wakati wa kuingiliana na LLM  Kwa nini?  LLM maarufu kama OpenAI's chatGPT, Gemini ya Google hufunzwa kuhusu data inayopatikana kwa umma. Mara nyingi hawana habari maalum ya shirika. Kuna nyakati fulani ambapo mashirika yangependa kutegemea LLMs. Hata hivyo, ingependa kuboresha jibu mahususi kwa shirika fulani au   wakati hakuna data ya msingi inayopatikana. kuongeza kanusho  Mchakato wa kufanya hivi unajulikana kama Kuweka Majibu ya LLM kwa kutumia misingi ya Maarifa.  Jinsi gani?  Wakati, naweza tu kuzungumza juu yake.  Kama mhandisi kuangalia vijisehemu vya msimbo hunipa ujasiri.  Kuzitekeleza huinua ujasiri wangu na pia hunipa furaha. Kushiriki kunanipa kuridhika 😄  Msimbo? Kwa nini isiwe hivyo! → Chatu? Bila shaka!!   Sakinisha maktaba zinazohitajika   pip install openai faiss-cpu numpy python-dotenv    : Ili kuingiliana na miundo ya GPT ya OpenAI na upachikaji. openai    : Maktaba ya Facebook AI kwa utafutaji bora wa mfanano, unaotumika kuhifadhi na kutafuta upachikaji. faiss-cpu    : Kwa shughuli za nambari, pamoja na kushughulikia upachikaji kama vidhibiti. numpy    : Kupakia anuwai za mazingira (kwa mfano, vitufe vya API) kutoka kwa faili ya   kwa usalama. python-dotenv .env   Weka vigezo vya Mazingira  Nenda kwa  https://platform.openai.com/settings/organization/api-keys  Bofya kwenye "Unda ufunguo mpya wa siri" kama inavyoonekana kwenye picha hapa chini.  Toa maelezo, Unaweza kutumia Akaunti ya Huduma. Toa jina la "Kitambulisho cha Akaunti ya Huduma" na uchague mradi.  Nakili ufunguo wa siri kwenye ubao wa kunakili  Unda faili   katika saraka ya mradi wako. Ongeza kitufe chako cha OpenAI API kwenye faili hii. .env   OPENAI_API_KEY=your_openai_api_key_here  Faili hii huweka ufunguo wako wa API salama na kutengwa na msimbo.   Anzisha vigezo vya mteja na upakiaji wa mazingira    hupakia faili   , na   hurejesha ufunguo wa API. Usanidi huu huweka ufunguo wako wa API salama. 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"))   Bainisha Msingi wa Data/Maarifa ya Msingi  Kamusi hii ina maelezo ya msingi ya mada. Kwa kweli, hii inaweza kuwa hifadhidata kubwa zaidi au hifadhidata.   # 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." }   Tengeneza Upachikaji wa Maandishi  Chaguo la kukokotoa kutengeneza upachikaji wa maandishi fulani kwa kutumia modeli ya upachikaji ya OpenAI. Chaguo hili la kukokotoa huita API ya OpenAI kupata upachikaji wa maandishi, ambayo hurejeshwa kama safu ya NumPy.   # 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)   Fahirisi ya FAISS na upachikaji kwa Data ya Kutuliza  Unda faharasa ya FAISS, muundo ulioboreshwa kwa utafutaji wa haraka wa kufanana, na ujaze na upachikaji wa data msingi.   # 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"))    : Ukubwa wa kila upachikaji, unaohitajika ili kuanzisha faharasa ya FAISS. dimension    : Huunda faharasa ya FAISS inayotumia umbali wa Euclidean (L2) kwa mfanano. index = faiss.IndexFlatL2(dimension)  Kwa kila ingizo katika   , msimbo huu hutoa upachikaji na kuuongeza kwenye faharasa ya FAISS. grounding_data   Kitendaji cha utafutaji wa Vekta  Kazi hutafuta faharasa ya FAISS kwa data ya msingi inayofanana zaidi kwa hoja.   # 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    : Hubadilisha maandishi ya hoja kuwa vekta ya kupachika. Query Embedding    : Hutafuta faharasa kwa vekta iliyo karibu zaidi na swali. FAISS Search    : Ikiwa umbali wa vekta wa karibu zaidi (D) uko chini ya kizingiti, hurejesha maelezo ya msingi. Vinginevyo, inaonyesha kuwa hakuna msingi wa kuaminika uliopatikana. Threshold Check   Swali kwa LLM  Tunauliza LLM kwa kutumia chatgpt api ya OpenAI na modeli ya gpt-4.   # 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   Jibu lililoimarishwa  Huongeza maelezo ya msingi ikiwa yanapatikana, au  Huongeza kanusho ikiwa hakuna taarifa muhimu ya msingi inayopatikana.   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.)"   Fafanua kazi kuu  Kazi kuu inachanganya kila kitu, hukuruhusu kuingiza mada, kuuliza LLM, na angalia ikiwa jibu linalingana na data ya msingi.   # 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()  Matokeo  Tekeleza hati  Omba kijisehemu hiki ukitumia   python groundin_llm.py  Jibu:   Maelezo  Ukigundua jibu, ingawa jibu kutoka kwa LLM lilikuwa "Mojawapo ya lugha zinazopendekezwa zaidi za upangaji kwa ujifunzaji wa mashine...", jibu la msingi lilikuwa "Java ni nzuri, ina nguvu nyingi za msimbo wa kujifunza wa Mashine, ina seti nyingi za maktaba zilizopo”.  Hii inawezekana kwa kutumia maktaba ya Meta ya FAISS kwa utaftaji wa vekta kulingana na kufanana.    : Mchakato  Kwanza rudisha majibu ya LLM.  Angalia ikiwa msingi wetu wa maarifa una habari yoyote muhimu kwa kutumia utaftaji wa vekta.  Ikiwa ipo rudisha jibu kutoka kwa "msingi wa maarifa"  Ikiwa sivyo, rudisha jibu la LLM kama lilivyo.   Hii ndio nambari:  https://github.com/sundeep110/groundingLLMs   Furaha ya Kutuliza!!

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