openai-cookbook/examples/data/parsed_pdf_docs_with_embeddings.csv

1	content	embeddings
2	Overview Retrieval-Augmented Generationenhances the capabilities of languagemodels by combining them with aretrieval system. This allows the modelto leverage external knowledge sourcesto generate more accurate andcontextually relevant responses. Example use cases - Provide answers with up-to-date information - Generate contextual responses What we’ll cover ● Technical patterns ● Best practices ● Common pitfalls ● Resources	[-0.013741373, 0.029359376, 0.054372873, 0.022729442, -0.027704978, 0.0035402863, -0.0035649787, 0.03622389, -0.02171705, 0.03780421, 0.010364674, -0.05861998, 0.010574561, -0.033705253, 0.0028211174, -0.006549684, -0.017346477, -0.0056947065, -0.038470905, 0.014136453, 0.015432809, -0.0097535355, -0.0064756065, 0.01128447, -0.009099184, -0.02118616, -0.021815818, 0.0776332, 0.010457271, -0.01970461, 0.022223245, -0.030840926, -0.033384252, -0.0044816877, 0.036767125, 0.021951627, 0.049804762, 0.012691942, -0.00024210219, 0.035754733, 0.02871737, -0.015420463, -0.0032408899, 0.059657067, 0.019235453, 0.04076731, -0.013445063, -0.028421061, -0.007932464, 0.07200331, -0.034371953, -0.020321922, -0.008932509, -0.03614981, -0.028988987, -0.03612512, -0.024643108, -0.0047070067, 0.0044076103, 0.016729165, 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3	What is RAG Retrieve information to Augment the model’s knowledge and Generate the output “What is yourreturn policy?” ask result search LLM return information Total refunds: 0-14 days % of value vouchers: 14-30 days $5 discount on next order: > 30 days “You can get a full refund upto 14 days after thepurchase, then up to 30 daysyou would get a voucher forhalf the value of your order” KnowledgeBase / Externalsources RAG stands for "Retrieve information to Augment the model’s knowledge and Generate the output." This process involves using a language model (LLM) to enhance its responses by accessing external information sources. Here's how it works: . **User Query**: A user asks a question, such as "What is your return policy?" . **LLM Processing**: The language model receives the question and initiates a search for relevant information. . **Information Retrieval**: The LLM accesses a knowledge base or external sources to find the necessary details. In this example, the information retrieved includes: - Total refunds available from 0 to 14 days. - 50% value vouchers for returns between 14 to 30 days. - A $5 discount on the next order for returns after 30 days. . **Response Generation**: The LLM uses the retrieved information to generate a coherent response for the user. For instance, it might say, "You can get a full refund up to 14 days after the purchase, then up to 30 days you would get a voucher for half the value of your order." This method allows the model to provide accurate and up-to-date answers by leveraging external data sources.	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4	When to use RAG Good for ✅ Not good for ❌ ● ● Introducing new information to the model ● Teaching the model a specific format, style, to update its knowledge Reducing hallucinations by controlling content /!\ Hallucinations can still happen with RAG or language ➔ Use fine-tuning or custom models instead ● Reducing token usage ➔ Consider fine-tuning depending on the use case **Good for:** - **Introducing new information to the model:** RAG (Retrieval-Augmented Generation) is effective for updating a model's knowledge by incorporating new data. - **Reducing hallucinations by controlling content:** While RAG can help minimize hallucinations, it's important to note that they can still occur. **Not good for:** - **Teaching the model a specific format, style, or language:** For these tasks, it's better to use fine-tuning or custom models. - **Reducing token usage:** If token usage is a concern, consider fine-tuning based on the specific use case.	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5	Technical patterns Data preparation Input processing Retrieval Answer Generation ● Chunking ● ● Embeddings Augmentingcontent ● Inputaugmentation ● NER ● Search ● Context window ● Multi-stepretrieval ● Optimisation ● Safety checks ● Embeddings ● Re-ranking	[-0.0034501953, 0.03871357, 0.07771268, 0.004146183, 0.017548408, 0.0052377535, 0.010808629, 0.04906416, -0.03121832, 0.042830013, 0.06591063, -0.009910389, -0.017239079, -0.009981772, -0.008560054, -0.01406252, -0.009981772, 0.0214388, -0.012694339, 0.047160603, 0.041711673, -0.034906458, -0.008500568, 0.004657764, 0.027173262, 0.023306664, -0.00617466, 0.08389924, 0.012111375, -0.020522714, 0.007507149, -0.025840774, -0.01093355, 0.00840539, 0.050349057, 0.0055262614, -0.025293501, 0.011605743, -0.0063471696, 0.017441332, 0.035453733, -0.02833919, -0.015157065, 0.03186077, 0.0075666355, 0.0048094536, -0.036262743, -0.0069836713, -0.03709555, 0.07085987, -0.021581568, 0.019249711, -0.0027214903, 0.009880646, -0.0009398808, 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6	Technical patterns Data preparation chunk documents into multiplepieces for easier consumption content embeddings .983, 0.123, 0.289… .876, 0.145, 0.179… .983, 0.123, 0.289… Augment contentusing LLMs Ex: parse text only, ask gpt-4 to rephrase &summarize each part, generate bullet points… BEST PRACTICES Pre-process content for LLMconsumption:Add summary, headers for eachpart, etc. + curate relevant data sources KnowledgeBase COMMON PITFALLS ➔ Having too much low-quality content ➔ Having too large documents	[-0.0024594103, 0.023041151, 0.053115055, -0.00151447, 0.034315445, -0.022247573, 0.010993804, 0.026051277, -0.013572935, 0.044960346, 0.04624649, -0.009228774, -0.021755006, -0.02040045, 0.013066687, 0.033084027, 0.03291984, 0.0006396519, -0.009851323, 0.039514754, 0.04657487, 0.0011065636, 0.007285875, 0.018621739, 0.0010509789, 0.01017286, 0.004498087, 0.04750527, -0.0002646688, -0.028842486, 0.031332683, -0.017294547, -0.009570834, -0.009071427, 0.035820507, 0.04925662, 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7	Technical patterns Data preparation: chunking Why chunking? If your system doesn’t requireentire documents to providerelevant answers, you canchunk them into multiple piecesfor easier consumption (reducedcost & latency). Other approaches: graphs ormap-reduce Things to consider ● Overlap: ○ ○ Should chunks be independent or overlap oneanother? If they overlap, by how much? ● Size of chunks: ○ What is the optimal chunk size for my use case? ○ Do I want to include a lot in the context window orjust the minimum? ● Where to chunk: ○ ○ Should I chunk every N tokens or use specificseparators?Is there a logical way to split the context that wouldhelp the retrieval process? ● What to return: ○ ○ Should I return chunks across multiple documentsor top chunks within the same doc? Should chunks be linked together with metadata toindicate common properties?	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8	Technical patterns Data preparation: embeddings What to embed? Depending on your use caseyou might not want just toembed the text in thedocuments but metadata as well- anything that will make it easierto surface this specific chunk ordocument when performing asearch Examples Embedding Q&A posts in a forum You might want to embed the title of the posts,the text of the original question and the content ofthe top answers. Additionally, if the posts are tagged by topic orwith keywords, you can embed those too. Embedding product specs In additional to embedding the text contained indocuments describing the products, you mightwant to add metadata that you have on theproduct such as the color, size, etc. in yourembeddings.	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9	Technical patterns Data preparation: augmenting content What does “Augmentingcontent” mean? Augmenting content refers tomodifications of the original contentto make it more digestible for asystem relying on RAG. Themodifications could be a change informat, wording, or addingdescriptive content such assummaries or keywords. Example approaches Make it a guide* Reformat the content to look more likea step-by-step guide with clearheadings and bullet-points, as thisformat is more easily understandableby an LLM. Add descriptive metadata* Consider adding keywords or text thatusers might search for when thinkingof a specific product or service. Multimodality Leverage modelssuch as Whisper orGPT-4V totransform audio orvisual content intotext. 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10	Technical patterns Input processing Process input according to task Q&A HyDE: Ask LLM to hypothetically answer thequestion & use the answer to search the KB embeddings .983, 0.123, 0.289… .876, 0.145, 0.179… Content search Prompt LLM to rephrase input & optionally addmore context query SELECT * from items… DB search NER: Find relevant entities to be used for akeyword search or to construct a search query keywords red summer BEST PRACTICES Consider how to transform theinput to match content in thedatabase Consider using metadata toaugment the user input COMMON PITFALLS ➔ Comparing directly the inputto the database withoutconsidering the taskspecificities	[-0.015815059, 0.031102022, 0.057312217, 0.030851873, 0.056867506, -0.02355582, 0.016370948, 0.061147854, -0.0052878996, 0.054810714, 0.031379968, -0.006034876, -0.012479722, -0.008532905, -0.01268818, -0.004864034, -0.003229371, 0.032047037, -0.0067227897, 0.017871851, 0.07304389, -0.031880267, -0.015231375, 0.038884476, 0.01953952, 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11	Technical patterns Input processing: input augmentation What is input augmentation? Example approaches Augmenting the input means turningit into something different, eitherrephrasing it, splitting it in severalinputs or expanding it. This helps boost performance asthe LLM might understand betterthe user intent. 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12	Technical patterns Input processing: NER Why use NER? Using NER (Named EntityRecognition) allows to extractrelevant entities from the input, thatcan then be used for moredeterministic search queries.This can be useful when the scopeis very constrained. Example Searching for movies If you have a structured database containingmetadata on movies, you can extract genre,actors or directors names, etc. from the userquery and use this to search the database Note: You can use exact values or embeddings afterhaving extracted the relevant entities	[-0.021743642, 0.03672044, 0.010429188, -0.0009951688, 0.035992824, -0.004405119, 0.0012847, 0.070287876, -0.017632604, 0.04654328, 0.013169881, -0.003713882, -0.023453545, -0.0180813, 0.0026270032, 0.023198878, 0.00022453828, 0.043560047, -0.022289356, 0.023247387, 0.09434777, 0.009883475, -0.0100229345, -0.012745437, 0.04152272, 0.023417164, 0.015170829, 0.11195611, 0.039461136, -0.020057995, -0.023975004, -0.00409285, 0.009816776, -0.00037650426, 0.04164399, -0.03262153, -0.022325737, 0.017329428, -0.0050751334, 0.018711902, 0.015789304, -0.012169407, -0.027552458, 0.067668445, -0.036308125, -0.014479592, -0.029225979, -0.020094376, -0.012539279, 0.028086044, -0.012563533, -0.021852786, 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13	Technical patterns Retrieval re-ranking INPUT embeddings .983, 0.123, 0.289… .876, 0.145, 0.179… query SELECT * from items… keywords red summer Semanticsearch RESULTS RESULTS vector DB relational /nosql db FINAL RESULT Used togenerate output BEST PRACTICES Use a combination of semanticsearch and deterministic querieswhere possible + Cache output where possible COMMON PITFALLS ➔ The wrong elements could becompared when looking attext similarity, that is whyre-ranking is important	[0.0054991697, 0.038819227, 0.05731342, -0.0016716415, 0.022912102, 0.0010638472, 0.008026532, 0.030169148, -0.017233828, 0.022805965, 0.024782747, -0.016039798, -0.0065472624, -0.02649419, 0.008822552, 0.0076683233, 0.011237145, 0.0013888886, -0.029665003, 0.031601984, 0.037386395, -0.05317412, 0.0070116073, 0.013194027, 0.03375124, 0.017711438, 0.022514092, 0.07445438, 0.05298838, -0.026242116, -0.0104344925, -0.033618566, -0.0010870645, -0.011243779, 0.013432833, 0.009883911, -0.00898839, 0.0055157533, 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14	Technical patterns Retrieval: search How to search? Semantic search Keyword search Search query There are many differentapproaches to search depending onthe use case and the existingsystem. Using embeddings, youcan perform semanticsearches. You cancompare embeddingswith what is in yourdatabase and find themost similar. If you have extractedspecific entities orkeywords to search for,you can search for thesein your database. Based on the extractedentities you have or theuser input as is, you canconstruct search queries(SQL, cypher…) and usethese queries to searchyour database. You can use a hybrid approach and combine several of these. You can perform multiple searches in parallel or in sequence, orsearch for keywords with their embeddings for example.	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15	Technical patterns Retrieval: multi-step retrieval What is multi-step retrieval? In some cases, there might beseveral actions to be performed toget the required information togenerate an answer. Things to consider ● Framework to be used: ○ When there are multiple steps to perform,consider whether you want to handle thisyourself or use a framework to make it easier ● Cost & Latency: ○ ○ Performing multiple steps at the retrievalstage can increase latency and costsignificantly Consider performing actions in parallel toreduce latency ● Chain of Thought: ○ ○ Guide the assistant with the chain of thoughtapproach: break down instructions intoseveral steps, with clear guidelines onwhether to continue, stop or do somethingelse.This is more appropriate when tasks need tobe performed sequentially - for example: “ifthis didn’t work, then do this”	[-0.012983024, 0.0087274425, 0.04976932, 0.008695637, 0.013142052, 0.020978939, 0.01994844, 0.028294215, -0.028930325, 0.045494653, 0.05177943, -0.0466651, -0.0014097809, -0.054247543, -0.00607804, 0.0022725062, 0.0045450125, -0.022721883, -0.018701661, 0.0070544705, 0.042644877, -0.007665137, -0.023815993, 0.028981214, 0.002911798, -0.02499916, 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16	Technical patterns Retrieval: re-ranking What is re-ranking? Example approaches Re-ranking means re-ordering theresults of the retrieval process tosurface more relevant results. This is particularly important whendoing semantic searches. Rule-based re-ranking You can use metadata to rank results by relevance. Forexample, you can look at the recency of the documents, attags, specific keywords in the title, etc. Re-ranking algorithms There are several existing algorithms/approaches you can usebased on your use case: BERT-based re-rankers,cross-encoder re-ranking, TF-IDF algorithms…	[-0.024321754, 0.031516537, 0.06409896, -0.013820279, 0.033018477, 0.019585794, -0.0059169205, 0.03263088, -0.032970026, 0.032873128, 0.003373311, -0.023582896, -0.01621854, 0.005075107, 0.039171588, -0.006516486, 0.0036155595, -0.0016715149, -0.044597957, 0.02834308, 0.025969043, -0.047286917, -0.0045330757, 0.039341163, 0.01572193, -0.0024194573, 0.030208394, 0.03539251, 0.0412307, 0.021899268, 0.006107691, -0.021632794, -0.021487447, -0.017974842, 0.022650238, -0.018374551, -0.034617316, 0.0012733189, 0.005099332, 0.0046118065, 0.011367513, 0.027640559, -0.04227237, 0.023328535, -0.016533462, 0.009835291, 0.012160877, -0.05460282, 0.006443811, 0.050145447, -0.0333334, -0.009163051, -0.027882807, -0.00019190626, 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17	Technical patterns Answer Generation FINAL RESULT Piece of contentretrieved LLM Prompt includingthe content User sees thefinal result BEST PRACTICES Evaluate performance after eachexperimentation to assess if it’sworth exploring other paths + Implement guardrails if applicable COMMON PITFALLS ➔ Going for fine-tuning withouttrying other approaches ➔ Not paying attention to theway the model is prompted	[0.03357087, 0.00030311814, 0.05451445, 0.0072308844, -0.0047810976, -0.01034949, 0.0026446998, 0.02390931, 0.013085914, 0.05913121, 0.062341537, -0.035252474, -0.029856062, -0.011939367, -0.0021402196, -0.022288857, 0.012978902, -0.023939883, -0.012504997, 0.013062983, 0.025759071, -0.0123674115, 0.018971518, 0.009462828, -0.00032031632, -0.004490639, 0.005381123, 0.03745384, 0.013147063, -0.00052406715, 0.036934074, -0.027364235, -0.025468612, -0.023557702, 0.032867655, 0.02829676, 0.03020767, 0.0314918, -0.006439768, -0.001299419, 0.0033937767, 0.0049454356, -0.0023504195, 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18	Technical patterns Answer Generation: context window How to manage context? Depending on your use case, there areseveral things to consider whenincluding retrieved content into thecontext window to generate an answer. Things to consider ● Context window max size: ○ ○ There is a maximum size, so putting toomuch content is not ideal In conversation use cases, theconversation will be part of the contextas well and will add to that size ● Cost & Latency vs Accuracy: ○ More context results in increased latency and additional costs since therewill be more input tokens Less context might also result indecreased accuracy ○ ● “Lost in the middle” problem: ○ When there is too much context, LLMstend to forget the text “in the middle” ofthe content and might look over someimportant information.	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19	Technical patterns Answer Generation: optimisation How to optimise? There are a few differentmethods to consider whenoptimising a RAG application. Try them from left to right, anditerate with several of theseapproaches if needed. Prompt Engineering Few-shot examples Fine-tuning At each point of theprocess, experiment withdifferent prompts to getthe expected input formator generate a relevantoutput. Try guiding the model ifthe process to get to thefinal outcome containsseveral steps. If the model doesn’tbehave as expected,provide examples of whatyou want e.g. provideexample user inputs andthe expected processingformat. If giving a few examplesisn’t enough, considerfine-tuning a model withmore examples for eachstep of the process: youcan fine-tune to get aspecific input processingor output format.	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20	Technical patterns Answer Generation: safety checks Why include safety checks? Just because you provide the modelwith (supposedly) relevant contextdoesn’t mean the answer willsystematically be truthful or on-point. Depending on the use case, youmight want to double-check. Example evaluation framework: RAGAS	[-0.01279862, 0.037135735, 0.0381074, 0.033583093, -0.02348691, 0.01367919, -0.01756584, -0.008760147, 0.02968126, 0.0422066, 0.021801682, -0.05356291, -0.007188786, 0.025840156, -0.010779384, -0.016654907, 0.01191805, -0.0026549925, -0.030744016, 0.034979858, 0.012851758, 0.0012942851, -0.0040346775, -0.015607333, -0.025642786, -0.02335027, 0.024974769, 0.016715635, 0.0325507, -0.013117447, 0.027722752, -0.035921156, -0.048856415, -0.030121544, 0.0024215656, 0.016578995, 0.019235885, 0.019372525, -0.011804184, 0.014552168, 0.015341643, 0.0035640285, 0.0051961183, 0.05037464, 0.008342636, 0.018613415, 0.006782661, -0.051285572, -0.024185292, 0.0043686866, -0.031153936, -0.010544059, 0.026659995, -0.023669098, -0.010885659, -0.0083654085, -0.059666164, 0.00885124, 0.046821997, 0.016563812, -0.009921587, -0.04928152, -0.04867423, 0.015409963, -0.023365453, -0.024868492, -0.049524434, 0.0022260945, -0.013944878, 0.05331999, 0.03194341, 0.0033381928, 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22	**Overview** Retrieval-Augmented Generation (RAG) enhances language models by integrating them with a retrieval system. This combination allows the model to access external knowledge sources, resulting in more accurate and contextually relevant responses. **Example Use Cases:** - Providing answers with up-to-date information - Generating contextual responses **What We’ll Cover:** - Technical patterns - Best practices - Common pitfalls - Resources	[-0.02409391, 0.03011419, 0.037144244, 0.027736755, -0.021742037, -0.0082251625, -0.018188665, 0.018252574, -0.011721016, 0.032875083, 0.0043969788, -0.05112766, 0.012909734, -0.0214864, -0.0014419598, -0.02009317, -0.030472085, -0.01021275, -0.041541222, 0.014929277, 0.011567633, -0.018866107, 0.0013405033, -0.00081804255, -0.0339232, -0.009893202, 0.008071779, 0.08159974, 0.0038569428, -0.028887128, 0.022739027, -0.03269614, -0.012877779, -0.010136059, 0.044762265, 0.027787883, 0.042078063, -0.0036140864, 0.0073815556, 0.025257064, 0.012270639, -0.011395077, -0.0006902234, 0.056240425, 0.025129244, 0.04238483, -0.012775524, -0.03177584, 0.012174774, 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23	**Technical Patterns** This image outlines four key technical patterns involved in data processing and answer generation: . **Data Preparation** - **Chunking**: Breaking down data into smaller, manageable pieces. - **Embeddings**: Converting data into numerical formats that can be easily processed by machine learning models. - **Augmenting Content**: Enhancing data with additional information to improve its quality or usefulness. . **Input Processing** - **Input Augmentation**: Adding extra data or features to the input to improve model performance. - **NER (Named Entity Recognition)**: Identifying and classifying key entities in the text, such as names, dates, and locations. - **Embeddings**: Similar to data preparation, embeddings are used here to represent input data in a format suitable for processing. . **Retrieval** - **Search**: Locating relevant information from a dataset. - **Multi-step Retrieval**: Using multiple steps or methods to refine the search process and improve accuracy. - **Re-ranking**: Adjusting the order of retrieved results based on relevance or other criteria. . **Answer Generation** - **Context Window**: Using a specific portion of data to generate relevant answers. - **Optimisation**: Improving the efficiency and accuracy of the answer generation process. - **Safety Checks**: Ensuring that the generated answers are safe and appropriate for use.	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24	**Technical Patterns: Data Preparation** This presentation focuses on the process of preparing data for easier consumption by large language models (LLMs). . **Content Chunking**: - Documents are divided into smaller, manageable pieces. This makes it easier for LLMs to process the information. . **Embeddings**: - Each chunk of content is converted into embeddings, which are numerical representations (e.g., 0.983, 0.123, 0.289) that capture the semantic meaning of the text. These embeddings are then stored in a knowledge base. . **Augmenting Content**: - Content can be enhanced using LLMs. For example, GPT-4 can be used to rephrase, summarize, and generate bullet points from the text. . **Best Practices**: - Pre-process content for LLM consumption by adding summaries and headers for each part. - Curate relevant data sources to ensure quality and relevance. . **Common Pitfalls**: - Avoid having too much low-quality content. - Ensure documents are not too large, as this can hinder processing efficiency. 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25	**Technical Patterns: Data Preparation - Chunking** **Why Chunking?** Chunking is a technique used when your system doesn't need entire documents to provide relevant answers. By breaking documents into smaller pieces, you can make data easier to process, which reduces cost and latency. This approach is beneficial for systems that need to handle large volumes of data efficiently. Other methods for data preparation include using graphs or map-reduce. **Things to Consider** . **Overlap:** - Should chunks be independent or overlap with one another? - If they overlap, by how much should they do so? . **Size of Chunks:** - What is the optimal chunk size for your specific use case? - Do you want to include a lot of information in the context window, or just the minimum necessary? . **Where to Chunk:** - Should you chunk every N tokens or use specific separators? - Is there a logical way to split the context that would aid the retrieval process? . **What to Return:** - Should you return chunks across multiple documents or focus on top chunks within the same document? - Should chunks be linked together with metadata to indicate common properties? These considerations help in designing an efficient chunking strategy that aligns with your system's requirements and goals.	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