18 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
18 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! #semanticsearch #embeddings #ai
19 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! #semanticsearch #embeddings #ai
19 days ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
20 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! #semanticsearch #embeddings #ai
22 days ago
GPT-4 API integration complete. Our chatbot now handles 80% of support queries automatically! Implemented conversation memory with vector embeddings. The prompt engineering took longer than the code. Fallback to human agents works seamlessly. Support team can focus on complex issues now! #gpt4 #chatbot #ai #customersupport
22 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
23 days ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
24 days ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
26 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
26 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
26 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
27 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! #semanticsearch #embeddings #ai
28 days ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
30 days ago
GPT-4 API integration complete. Our chatbot now handles 80% of support queries automatically! Implemented conversation memory with vector embeddings. The prompt engineering took longer than the code. Fallback to human agents works seamlessly. Support team can focus on complex issues now! #gpt4 #chatbot #ai #customersupport
30 days ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
30 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
1 month ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! #semanticsearch #embeddings #ai
1 month ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
1 month ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! #semanticsearch #embeddings #ai
1 month ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
1 month ago
GPT-4 API integration complete. Our chatbot now handles 80% of support queries automatically! Implemented conversation memory with vector embeddings. The prompt engineering took longer than the code. Fallback to human agents works seamlessly. Support team can focus on complex issues now! #gpt4 #chatbot #ai #customersupport
1 month ago
GPT-4 API integration complete. Our chatbot now handles 80% of support queries automatically! Implemented conversation memory with vector embeddings. The prompt engineering took longer than the code. Fallback to human agents works seamlessly. Support team can focus on complex issues now! #gpt4 #chatbot #ai #customersupport
1 month ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
1 month ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
1 month ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
1 month ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
1 month ago
GPT-4 API integration complete. Our chatbot now handles 80% of support queries automatically! Implemented conversation memory with vector embeddings. The prompt engineering took longer than the code. Fallback to human agents works seamlessly. Support team can focus on complex issues now! #gpt4 #chatbot #ai #customersupport
1 month ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
1 month ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
