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-2 votes
0 answers
67 views

I am developing a NL2SQL model, and for this I am using defog/sqlcoder-7b. While working with this model, I am facing issues where it is not able to generate complex SQL queries, especially queries ...
1 vote
0 answers
36 views

I'm using FastMCP in python to implement a MCP server. Currently I run into a problem when it comes to streaming of the generated tokens from the LLM. I don't want to wait for the completed response ...
0 votes
0 answers
84 views

I’m using the Gemini File Search API, but quite often the model responds with an error or warning about "recitation", and the answer gets cut off or completely withheld. I’m not entirely sure what ...
-1 votes
0 answers
119 views

I trying out LangChain for my project,but most of the documentation and blogs use classic version LangChain 1.1.3 and I am confused about what the replacement is for agent creation APIs. What worked ...
Tooling
0 votes
0 replies
62 views

I'm trying to use metadata in RAG systems using LangChain. I see a lot of tutorials using SelfQueryRetriever, but it appears that this was deprecated in recent versions. Is this correct? I couldn't ...
Advice
2 votes
2 replies
78 views

I’m building a tool that generates new mathematics exam problems using an internal database of past problems. My current setup uses a RAG pipeline, Pinecone as the vector database, and GPT-5 as the ...
Best practices
1 vote
2 replies
142 views

I'm building a voice-based calling system where users can create AI agents that make outbound phone calls. The agent uses Deepgram for real-time transcription and ElevenLabs/Cartesia for speech ...
Advice
0 votes
1 replies
59 views

I have a large set of phrases obtained via Azure Fast Transcription, and I need to group them into coherent semantic chunks (to use later in a RAG pipeline). Initially, I tried grouping phrases based ...
0 votes
0 answers
26 views

I'm using LlamaIndex 0.14.7. I would like to embed document text without concatenating metadata, because I put a long text in metadata. Here's my code: table_vec_store: SimpleVectorStore = ...
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0 votes
0 answers
60 views

This is my embedding code, which I run once only: embeddings = OpenAIEmbeddings(model="text-embedding-3-large") vector_store = MongoDBAtlasVectorSearch.from_connection_string( ...
1 vote
1 answer
153 views

I’m trying to evaluate my Retrieval-Augmented Generation (RAG) pipeline using Ragas. . Here’s a complete version of my code: """# RAG Evaluation""" from datasets import ...
0 votes
1 answer
77 views

My objective is to do keyword filtering in Chroma. I have a field called keywords with a list of strings and I want to filter with it, but chroma won't let me add lists as a field. I checked my Chroma ...
1 vote
0 answers
54 views

I built a RAG chatbot using LangChain + ChromaDB + OpenAI embeddings. The pipeline works, but sometimes the chatbot doesn’t return the most relevant PDF content, even though it exists in the vector DB....
1 vote
0 answers
75 views

I built a RAG chatbot in python,langchain, and FAISS for the vectorstore. And the data is stored as JSON. The chatbot sometimes refuses to answer when a question is rephrased. Here are two ...
0 votes
0 answers
31 views

Question: I'm building a memory-augmented AI system using RAG with persistent vector storage, but facing memory leaks and context contamination between sessions. Problem: Vector embeddings aren't ...

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