Langchain csv question answering reddit. Be straight forward on answering questions.

Langchain csv question answering reddit. Hi, So I learning to build RAG system with LLaMa 2 and local embeddings. Jan 9, 2024 · A short tutorial on how to get an LLM to answer questins from your own data by hosting a local open source LLM through Ollama, LangChain and a Vector DB in just a few lines of code. Be straight forward on answering questions. See our how-to guide on question-answering over CSV data for more detail. From basic lookups like 'what books were published in the last two I want to ingest hundreds of csv files, all the column data is different except for them sharing a similar column related to state. Concise, although not missing any important information. I developed a simple agent which is able to answer simple queries like , how many rows in dataframe, list all transaction realated to xyz, etc. Note that querying data in CSVs can follow a similar approach. . We would like to show you a description here but the site won’t allow us. May 22, 2023 · This tutorial will look to show how we can use the OpenAI package and langchain, to look at a csv file and ask it questions about the file and the agent will send back a response. Aug 14, 2023 · We could have made some educated guesses, or tried to generate synthetic questions to ask. Execute SQL query: Execute the query. js (so the Javascript library) that uses a CSV with soccer info to answer questions. The data is mostly pertaining to demographics like economics, age, race, income, education, and health related outcomes. Would any know of a cheaper, free and fast language model that can run locally on CPU only? Hii, I am trying to develop a data analysis agent, and using langchain CSV agent with local llm mistral through Ollama. You are an experienced researcher, expert at interpreting and answering questions based on provided sources. For a more in depth explanation of what these chain types are, see here. Thank you! Hi I think this is due to the fact that you perform a search looking for similarities in your csv that you transformed into embeddings vectors and when you ask your question your chain get the most similar chunks (your 4 rows) of your csv and pass them to the llm model. Each row is a book and the columns are author (s), genres, publisher (s), release dates, ratings, and then one column is the brief summaries of the books. You’re right, pdf is just splitting them page by page, chunking, store the embeddings and then connect LLM for information retrieval. Specific questions, for example "How many goals did Haaland score?" This notebook walks through how to use LangChain for question answering over a list of documents. Like working with SQL databases, the key to working with CSV files is to give an LLM access to tools for querying and interacting with the data. I am using it at a personal level and feel that it can get quite expensive (10 to 40 cents a query). Answer the question: Model responds to user input using the query results. First we prepare the data. I am a beginner in this field. Then, the reply should be appended to the csv without the columns (again, specify this in the prompt) and eventually, you’ll have a csv to pull to the Dataframe to query I tried to use langchain with a huggingface LLM and found it was simpler to import huggingface. But we wanted to optimize instead for real questions, as we also wanted to do a bit of exploration here into what types of questions real users would want to ask. the model will never be able to ingest big chunks of data, you are limited to the max tokens, you should consider using Does anyone have a working CSV RAG application using LangChain and open-source embeddings and LLMs? I've been trying to get a working implementation for a while, but I'm running into the same problem with CSV files. com I'm new to Langchain and I made a chatbot using Next. Setup First, get required packages and set environment variables: You should probably split these into chunks, ask the LLM to provide topics and questions for each chunk and produce a CSV output, and also provide it with a meeting name and date for context and have it return it in the csv. I have tested the following using the Langchain question-answering tutorial, and paid for the OpenAI API usage fees. Deep_Lobster8003 Built a CSV Question and Answering using Langchain, OpenAI and Streamlit In this section we'll go over how to build Q&A systems over data stored in a CSV file (s). See full list on github. I have this big csv of data on books. I don’t think we’ve found a way to be able to chat with tabular data yet. As soon as I run a query, it's not able to retrieve more than four relevant chunks from the vectordb. Can someone suggest me how can I plot charts using agents. It covers four different types of chains: stuff, map_reduce, refine, map_rerank. So I am able to capture the location of the data observations and relate them to other data. Using the provided context, answer the user's question to the best of your ability using only the resources provided. The library has a document question and answering model listed as an example in their docs. js directly when using one of their models. I need a general way to ingest all these csv files I've been experimenting with it using a local version of our company's database, and I have this vision of developing a chatbot that can talk to our database and answer questions related to the information we have in our database. I’ve been trying to find a way to process hundreds of semi-related csv files and then use an llm to answer questions. I am trying to build an agent to answer questions on this csv. I have a few questions: I've read a few comments on this subreddit indicating that Langchain is not good for SQL. gaojib qcm dgmul dlnzt mznjun kpitmd uvac lkji rqmxtn rbkkzvci