Dh gte

Which yields more precise retrieval. While larger chunks are more susceptible to noise. Popular strategies include using small chunks but retrieving a bit of the surrounding chunks around it (since it may have relevant info) or store multiple embeddings per document (ex. summary embedding per document).1chunk_sizes = [100, 300, 500, 700, 900]2for chunk_size in chunk_sizes:3 experiment_name = f"chunk-size-{chunk_size}"4 run_experiment(...)5rag-based-llm-applications-chart-2chunk-size-plotIt appears that larger chunk sizes do help but tapers off (too much context might be too noisy). Larger chunk sizes aren’t always better.Note: If we were to use larger chunk sizes (ours is based on characters), keep in mind that most open source embedding models have a maximum sequence length of 512 sub-word tokens. This means that if our chunk contains more than 512 sub-word tokens (4 chars ≈ 1 token), the embedding wouldn't account for it anyway (unless we fine-tune our embedding model to have longer sequence lengths).1CHUNK_SIZE = 7002CHUNK_OVERLAP = 503LinkNumber of chunksNext, we'll experiment with the number of chunks to use. More chunks will allow us to add more context but too many could potentially introduce a lot of noise.Note: The chunk_size we chose multiplied by the num_chunks needs to fit inside our LLM's context length. We're experimenting with the chunk size and number of chunks as if they were independent variables but they are heavily related. Especially since all of our LLMs have a finite maximum context length. So ideally, we would tune for a combination if chunk_size * num_chunks.1num_chunks_list = [1, 3, 5, 7, 9]2for num_chunks in num_chunks_list:3 experiment_name = f"num-chunks-{num_chunks}"4 run_experiment(...)5rag-based-llm-applications-chart-3num-chunks-plotIncreasing our number of chunks improves our retrieval and quality scores. We had to stop testing at num_chunks of 9 because we started to hit maximum context length often. This is a compelling reason to invest in extending context size via RoPE scaling (rotary position embeddings), etc.Sanity check: Our retrieval score (in general) should increase as we increase the number of chunks.LinkEmbedding modelsSo far, we've used thenlper/gte-base as our embedding model because it's a relatively small (0.22 GB) and performant option. But now, let's explore other popular options such as thenlper/gte-large (0.67 GB), the current leader on the MTEB leaderboard, BAAI/bge-large-en (1.34 GB), and OpenAI's text-embedding-ada-002.1embedding_model_names = ["thenlper/gte-base", "thenlper/gte-large", "BAAI/bge-large-en", "text-embedding-ada-002"]2for embedding_model_name in embedding_model_names:3 experiment_name = f"{embedding_model_name.split('/')[-1]}"4 run_experiment(...)5rag-based-llm-applications-chart-4embeddings-plotThis is an interesting outcome because the #1 (BAAI/bge-large-en) on the current leaderboard isn't necessarily the best for our specific task. Using the smaller thenlper/gte-large produced the best retrieval and quality scores in our experiments.1EMBEDDING_MODEL_NAME = "thenlper/gte-large"LinkOSS vs. closed LLMsWe're now going to use the best configurations from above to evaluate different choices for the main LLM.Note:We've been using a specific LLM so far to decide on the configuration so that specific LLM's performance here will be a

(****)– TRUCK DRIVER (*)– V-RALLY 4 (***)– WRC 5 (*)– WRC 6 (*)– WRC 7 (*)– WRC 8 (***)– WRC 9 (***)(****)– WRECKFEST (***)(*) Compatible only in “SEQUENTIAL (+/-)” mode with the Thrustmaster T-GT, T300 RS, T300 GTE, T150 Force Feedback and T150 Ferrari Force Feedback racing wheels = shifter connected to the wheel’s DIN connector.(**)Compatible only in “H (7+1)” mode with the Thrustmaster T-GT, T500 RS, Ferrari F1® Wheel Integral T500, T300 RS, T300 GTE, T150 Force Feedback and T150 Ferrari Force Feedback racing wheels = shifter connected to the console’s USB port.(***) Compatible in “SEQUENTIAL (+/-)” and in “H (7+1)” mode with the Thrustmaster T-GT, T300 RS, T300 GTE, T150 Force Feedback and T150 Ferrari Force Feedback racing wheels = shifter connected to the wheel’s DIN connector.(****) Compatible in “SEQUENTIAL (+/-)” and in “H (7+1)” mode with the Thrustmaster T-GT, T500 RS, Ferrari F1® Wheel Integral T500, T300 RS, T300 GTE, T150 Force Feedback and T150 Ferrari Force Feedback racing wheels = shifter connected to the console’s USB port.The TH8A gearbox is compatible with the following PS5™ games (#):– DiRT® 5 – PS5™(***)– MONSTER TRUCK CHAMPIONSHIP – PS5™(***)– WRC 9 – PS5™(***)– WRECKFEST – PS5™ (***)(***) Compatible in “SEQUENTIAL (+/-)” and in “H (7+1)” mode with the Thrustmaster T-GT, T300 RS, T300 GTE, T150 Force Feedback and T150 Ferrari Force Feedback racing wheels = shifter connected to the wheel’s DIN connector.(#) PS5 games compatibility has been tested and endorsed by Thrustmaster. At stage of publication, PS5 compatibility of the full range has not been yet tested nor endorsed by Sony Interactive Entertainment LLC.The TH8A Shifter is compatible with the following Xbox One games:– ASSETTO CORSA™ (**)– ASSETTO CORSA COMPETIZIONE (*)– CarX Drift Racing Online (**)– Dakar 18 (**)– DIRT RALLY™ (**)– DIRT RALLY 2.0™ (**)– DiRT® 3 – Xbox 360 backward compatibility (*)– DiRT® SHOWDOWN – Xbox 360 backward compatibility (*)– DiRT® 4 (**)– DiRT® 5 (**)– FIA Truck Racing Championship (**)– FORZA HORIZON™ – Xbox 360 backward compatibility (**)– FORZA HORIZON® 2 (**)– FORZA HORIZON® 3 (**)– FORZA HORIZON® 4 (**)– FORZA MOTORSPORT® 5 (**)– FORZA MOTORSPORT ® 6 (**)– FORZA MOTORSPORT ® 7 (**)– F1® 2014 – Xbox 360 backward compatibility (*)– F1® 2015 (*)– F1® 2016 (*)– F1® 2017 (*)– F1® 2018 (**)– F1® 2021 (*)– GRAVEL (**)– GRID™ (**)– GRID™ 2 – Xbox 360 backward compatibility (**)– GRID™ AUTOSPORT – Xbox 360 backward compatibility (**)– MONSTER TRUCK CHAMPIONSHIP (**)– MudRunner: A Spintires game (**)– Nascar Heat Evolution (**)– Nascar Heat 2 (**)– Nascar Heat 3 (**)– Nascar Heat 4 (**)– Nascar Heat 5 (**)– NEED FOR SPEED™ PAYBACK (*)– NEED FOR SPEED™ HEAT (*) with the TX & the TMX– NEED FOR SPEED™ HEAT

Gates in special applications can be realised. In the field of sensor technology, FEIG ELECTRONIC offers various solutions, from radar detectors for automated door opening to induction loop detectors for various applications. The company continuously focuses on research and development in order to maintain its technological leadership. GfA Elektromaten GfA Elektromaten produces drive and control solutions for industrial doors. With an impressive sales volume of over 200,000 units per year, the company is undoubtedly one of the leading market players in Europe. GfA Elektromaten's products are widely used and enjoy an excellent reputation in the industry. The door operators are manufactured at the Düsseldorf site, which, like the company processes, is certified in accordance with DIN EN ISO 9001:2015. As expected, the name GfA Elektromaten vouches for quality and reliability in the world of drive and control solutions for industrial doors. GfA Elektromaten offers a wide range of drive solutions for all types of industrial doors, including roller doors, sectional doors, high-speed doors and sliding doors. The comprehensive product range ensures that the specific requirements of customers in various industries can be met. From robust operators for heavy industrial doors to efficient solutions for fast opening doors - GfA Elektromaten is the trusted partner for versatile door operator solutions. GTE Force measurement devices for doors from GTE are one of the standard tools for specialists in the installation and maintenance of gates and doors. The company, based in Viersen, Germany, is a pioneer in the field of force gauges and offers various solutions for accurate measurement of forces on doors and gates. However, GTE also offers other products, such as control technology and fire protection systems. The KMG force gauge enables precise and reliable measurement of the forces acting on doors and gates. This is crucial for accurate adjustment and maintenance, which ultimately contributes to efficient and safe operation. The latest version, KMG-Lite BT, relies on Bluetooth technology to further enhance usage and connectivity. These devices transmit measurement data via Bluetooth to the app on the installer's smartphone or tablet. In the field of industrial doors, GTE also supplies a

Free. This option is only available with a few check cashing applications, while most other check cashing applications charge between 1% to 5% for check cashing services.Additionally, some check cashing applications offer free check cashing services for their registered members. For example, GTE Financial Deposit2Go check cashing service offers free check cashing services for its GTE members. Therefore, if you have a GTE account, you can cash your checks for free, which can help you save money on the usual 1% to 5% check cashing fee.Can You Use A Check Cashing App Without A Bank Account?Many check cashing applications allow you to use their service without a bank account. These applications have a deposit feature that allows you to deposit the money from your check directly into the check-cashing application’s account. While some check cashing applications are created by a bank and may require you to have an account with them before accessing the service, it is not always necessary to have a bank account with other check cashing applications.Typically, the check cashing app’s account is linked to a bank account or a prepaid debit or credit card for withdrawal purposes. However, it is not necessary to use a bank account for the withdrawal process to be completed. Once the account is linked to a prepaid credit card, you can withdraw from the check cashing application easily, making it not necessary to have a bank account before using a check cashing app.

Not always be worth the effort.synthetic data is not exactly like the types of questions that users ask (might be worth creating a dataset of more realistic queries or prompt tuning for more synthetic data that is more representative of user queries).Fine-tuning the entire embedding model on our small embedding dataset might be causing overfitting.Our experiment's evaluation is on a small dataset so slightly tuning embeddings via MNR may not increase retrieval recall much/if at all.LinkEmbedding layerTo help mitigate the overfitting, we can avoid retraining the entire embedding model and freeze all layers except for the embedding layer (word/subtoken embedding only, not the positional or token type layers).BertEmbeddings( (word_embeddings): Embedding(30522, 1024, padding_idx=0) (position_embeddings): Embedding(512, 1024) (token_type_embeddings): Embedding(2, 1024) (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False))1# Reinitialize base embedding model2embedding_model = SentenceTransformer(EMBEDDING_MODEL_NAME) # gte-large34# Unfreeze embedding layers5for param in embedding_model._modules["0"]._modules["auto_model"]._modules["embeddings"].parameters(): param.requires_grad = True67# Freeze Bert encoder layers8for param in embedding_model._modules["0"]._modules["auto_model"]._modules["encoder"].parameters(): param.requires_grad = FalseNow we can run the exact same training workflow as we did with full parameter fine-tuning:1# Training setup2num_epochs = 23batch_size = 44train_dataloader = DataLoader(train_dataset, batch_size=batch_size)5loss = MultipleNegativesRankingLoss(embedding_model)6warmup_steps = int(0.1 * num_epochs * len(train_dataloader)) # not used78# Train9experiment_name = "gte-large-fine-tuned-el"10gte_large_ft_path = str(Path(EFS_DIR, experiment_name))11embedding_model.fit(12 train_objectives=[(train_dataloader, loss)],13 epochs=num_epochs,14 warmup_steps=0,15 optimizer_params={"lr": 1e-5},16 weight_decay=0,17 output_path=gte_large_ft_path,18 show_progress_bar=True,19 evaluator=evaluator,20 callback=val_callback)EPOCH: 0, VAL SCORE:0.7938EPOCH: 1, VAL SCORE:0.79651sql_dump_fp = Path(EFS_DIR, "sql_dumps", f"{experiment_name}_{CHUNK_SIZE}_{CHUNK_OVERLAP}.sql")2run_experiment(sql_dump_fp, **kwargs)gte-large-fine-tuned-el retrieval score: 0.7344632768361582 quality score: 3.5819209039548023ft-embeddingMuch better validation scores and overall better performance but it's not worth the effort compared to using our base gte-large embedding model. This again can be improved with larger/higher quality datasets and perhaps even a larger testing dataset to capture small improvements in our retrieval scores.Note: even though the retrieval scores are the same, the quality scores differ due to the order in which the new embedding models determine the top k relevant chunks and if different relevant sources were introduced.1experiment_name = "gte-large-fine-tuned-el"2EMBEDDING_MODEL_PATH = str(Path(EFS_DIR, experiment_name)) # can pass this in directly for embedding_model_name3SQL_DUMP_FP = Path(EFS_DIR, "sql_dumps", f"{experiment_name}_{CHUNK_SIZE}_{CHUNK_OVERLAP}.sql")LinkPrompt engineeringThere's too much we can do when it comes to engineering the prompt (x-of-thought, multimodal, self-refine, query decomposition, etc.) so we're going to try out just a few interesting ideas. We're going to allow the LLM to ignore anything not relevant. The idea here is to show how quickly we can go from prompt engineering to evaluation report.rag-based-llm-applications-prompt-engineering1# Prompt2generation_system_content = "Answer the query using the context provided. Be succinct. Contexts are organized in a list of dictionaries [{'text': }, {'text': }, ...]. Feel free to ignore any contexts in the list that don't seem relevant to the query. "34# Evaluate5experiment_name = "prompt-ignore-contexts"6run_experiment(7 experiment_name=experiment_name,8 generation_system_content=generation_system_content, # new prompt9 **kwargs)prompt-ignore-contexts retrieval score: 0.7288135593220338 quality score: 3.519774011299435It seems this specific prompt engineering effort didn't help improve the quality of our system. As we mentioned earlier,

The better the gyroscopes and camera, the more accurate it will be. Still, expect the app to be able to place the target somewhere in the field of view at low to medium power at best – it’s far from perfectly accurate.The StarSense Explorer DX 102 is 102mm f/6.5 – large enough to show you a fair amount of things and with just long enough of a focal ratio to stave off the worst of chromatic aberration, though it will show a fair amount of it on bright objects like the Moon and planets. The scope’s alt-azimuth mount is fairly easy to use and has slow-motion cable adjustments and gears on both the altitude (up-down) and azimuth (left-right) axis. The Explorer DX 102 includes two 1.25” barreled Kellner eyepieces, a 25mm providing 26x and a 10mm providing 65x – enough to get you started, but you’ll probably want more eyepieces later on. The included prism star diagonal is acceptable in quality, and the included red dot is barely even needed most of the time if you use the StarSense Explorer app. Sky-Watcher StarTravel 102 AZ-GTe Refractor TelescopeBest Between $600-$700FEATURES: f/4.9 aperture, 500mm focal length and two 1.25” Plossl oculars (5mm with 20x magnification and 10mm with 50x magnification) BENEFITSLightweight and portable package fits in a backpackWide field of view great for deep-sky objectsGoTo mount easily aims itself at targets and tracks as the night goes onThe StarTravel 102 AZ-GTe features the extremely lightweight and portable AZ-GTe mount, which can be used as a GoTo mount controlled with your smartphone when powered on, automatically slewing to and tracking any target you choose. When powered off, you can loosen the clutches to aim the scope manually – which is great when traveling without a reliable power source. When dismantled, the entire StarTravel