scalable backends ▪ All things .NET ▪ Pragmatic end-to-end architectures ▪ Developer productivity ▪ Software quality [email protected] @phoenixhawk https://www.thinktecture.com Sebastian Gingter Developer Consultant @ Thinktecture AG "Talk to your Data": Improving RAG solutions based on real-world experiences
semantic search ▪ Problems and possible strategies ▪ Pragmatic approaches for your own data ▪ No deep-dive into ▪ LLMs ▪ LangChain What to expect (and what not): "Talk to your Data": Improving RAG solutions based on real-world experiences
bit of theory ) ▪ Indexing ▪ Retrieval ▪ Not good enough? – Indexing II ▪ HyDE & alternative indexing methods ▪ Conclusion Agenda "Talk to your Data": Improving RAG solutions based on real-world experiences
Improving RAG solutions based on real-world experiences Cleanup & Split Text Embedding Question Text Embedding Save Query Relevant Text Question LLM Vector DB Embedding model Embedding model Indexing / Embedding QA
words and variants ▪ Classic SQL: WHERE ‘content’ LIKE ‘%searchterm%’ ▪ We can search only for things where we know that its somewhere in the text ▪ New: Semantic search ▪ Compares for the same contextual meaning ▪ “Das Rudel rollt das runde Gerät auf dem Rasen herum” ▪ “The pack enjoys rolling a round thing on the green grass” ▪ “Die Hunde spielen auf der Wiese mit dem Ball” ▪ “The dogs play with the ball on the meadow” Semantic Search "Talk to your Data": Improving RAG solutions based on real-world experiences
on numbers ▪ Computing is “applied mathematics” ▪ AI also only calculates on numbers Semantic Search "Talk to your Data": Improving RAG solutions based on real-world experiences
Tokens ▪ We need a numeric representation of meaning ▪ Embeddings Semantic Search "Talk to your Data": Improving RAG solutions based on real-world experiences
larger elements ▪ Tokens are parts of text ▪ Words ▪ Syllables ▪ Punctuation ▪ … ▪ Tokens are translated to token IDs ▪ Example: https://platform.openai.com/tokenizer Tokens "Talk to your Data": Improving RAG solutions based on real-world experiences
based on real-world experiences ▪ Topologic: Value of a high dimensional space is “embedded” into a lower dimensional space ▪ Natural / human language is very complex (high dimensional) ▪ Task: Map high complexity to lower complexity / dimensions ▪ Injective function ▪ Similar to hash, or a lossy compression
a numeric representation of its meaning ▪ Representation is a Vector in an n-dimensional space ▪ n floating point values ▪ OpenAI ▪ “text-embedding-ada-002” uses 1536 dimensions ▪ “text-embedding-3-small” 512 and 1536 ▪ “text-embedding-3-large” 256, 1024 and 3072 ▪ Huggingface models have a very wide range of dimensions Embeddings "Talk to your Data": Improving RAG solutions based on real-world experiences https://huggingface.co/spaces/mteb/leaderboard & https://openai.com/blog/new-embedding-models-and-api-updates
a different meaning, individual to the model ▪ Vectors from different models are incompatible with each other ▪ they live in different vector spaces ▪ Some embedding models are multi-language, but not all ▪ In an LLM, also the first step is to embed the input into a lower dimensional space Embeddings "Talk to your Data": Improving RAG solutions based on real-world experiences
et al., Google, 2013 "Talk to your Data": Improving RAG solutions based on real-world experiences Man Woman Brother Sister https://arxiv.org/abs/1301.3781
on real-world experiences ▪ Task: Create a vector from an input ▪ Extract meaning / semantics ▪ Embedding models usually are very shallow & fast Word2Vec is only two layers ▪ Similar to the first step of an LLM ▪ Convert text to values for input layer ▪ This comparison is very simplified, but one could say: ▪ The embedding model ‘maps’ the meaning into the model’s ‘brain’
case ▪ e.g. ▪ intfloat/multilingual-e5-large-instruct ~ 50 % ▪ T-Systems-onsite/german-roberta-sentence-transformer-v2 < 70 % ▪ danielheinz/e5-base-sts-en-de > 80 % ▪ Maybe fine-tuning of the embedding model might be an option ▪ As of now: Treat embedding models as exchangeable commodities! Important "Talk to your Data": Improving RAG solutions based on real-world experiences
▪ Embeds the high-dimensional natural language meaning into a lower dimensional-space (the model’s ‘brain’) ▪ No magic, just applied mathematics ▪ Math. representation: Vector of n dimensions ▪ Technical representation: array of floating point numbers Recap Embeddings "Talk to your Data": Improving RAG solutions based on real-world experiences
▪ LangChain has very strong support for loading data ▪ Support for cleanup ▪ Support for splitting Loading "Talk to your Data": Improving RAG solutions based on real-world experiences https://python.langchain.com/docs/integrations/document_loaders
/ not concise enough Splitting (Text Segmentation) "Talk to your Data": Improving RAG solutions based on real-world experiences ▪ by size (text length) ▪ by character (\n\n) ▪ by paragraph, sentence, words (until small enough) ▪ by size (tokens) ▪ overlapping chunks (token-wise)
solutions based on real-world experiences Splitted (smaller) parts Embedding- Model Embedding 𝑎 𝑏 𝑐 … Vector- Database Document Metadata: Reference to original document
on real-world experiences Embedding- Model Embedding 𝑎 𝑏 𝑐 … Vector- Database “What is the name of the teacher?” Query Doc. 1: 0.86 Doc. 2: 0.84 Doc. 3: 0.79 Weighted result … (Answer generation)
It’s just as good as your embeddings ▪ All chunks need to be sized correctly and distinguishable enough ▪ Garbage in, garbage out Not good enough? "Talk to your Data": Improving RAG solutions based on real-world experiences
Embedddings) "Talk to your Data": Improving RAG solutions based on real-world experiences LLM, e.g. GPT-3.5-turbo Embedding 𝑎 𝑏 𝑐 … Vector- Database Doc. 3: 0.86 Doc. 2: 0.81 Doc. 1: 0.81 Weighted result Hypothetical Document Embedding- Model Write a company policy that contains all information which will answer the given question: {QUERY} “What should I do, if I missed the last train?” Query https://arxiv.org/abs/2212.10496
be transformed through an LLM (slow & expensive) ▪ A lot of requests will probably be very similar to each other ▪ Each time a different hypothetical document is generated, even for an extremely similar request ▪ Leads to very different results each time ▪ Idea: Alternative indexing ▪ Transform the document, not the query What else? "Talk to your Data": Improving RAG solutions based on real-world experiences
Data": Improving RAG solutions based on real-world experiences LLM, e.g. GPT-3.5-turbo Transformed document Write 3 questions, which are answered by the following document. Chunk of Document Embedding- Model Embedding 𝑎 𝑏 𝑐 … Vector- Database Metadata: content of original chunk
RAG solutions based on real-world experiences Embedding- Model Embedding 𝑎 𝑏 𝑐 … Vector- Database Doc. 3: 0.89 Doc. 1: 0.86 Doc. 2: 0.76 Weighted result Original document from metadata “What should I do, if I missed the last train?” Query
real-world experiences Cleanup & Split Text Embedding Question Text Embedding Save Query Relevant Text Question LLM Vector DB Embedding model Embedding model Indexing / Embedding QA Retrieval-augmented generation (RAG) Indexing & (Semantic) search
HyQE or alternative indexing ▪ How many questions? ▪ With or without summary ▪ Other approaches ▪ Only generate summary ▪ Extract “Intent” from user input and search by that ▪ Transform document and query to a common search embedding ▪ HyKSS: Hybrid Keyword and Semantic Search https://www.deg.byu.edu/papers/HyKSS.pdf ▪ Always evaluate approaches with your own data & queries ▪ The actual / final approach is more involved as it seems on the first glance Recap: Not good enough? "Talk to your Data": Improving RAG solutions based on real-world experiences
AI business use-case ▪ Quality of results depend heavily on data quality and preparation pipeline ▪ RAG pattern can produce breathtaking good results without the need for user training Conclusion "Talk to your Data": Improving RAG solutions based on real-world experiences
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