Langchain qa generation reddit

Langchain qa generation reddit. For example, here we show how to run GPT4All or LLaMA2 locally (e. The code works fine as long as the query does not include one of those in its results. for element_type, element_data in all_elements: New to LLMs and just found out about langChain yesterday. This is important because often times you may not have data to evaluate your question-answer system over, so this is a cheap and lightweight way to generate it! from langchain. This example uses Neo4j database, which is a native graph database. What are the cases in which you would use one method over another? Based on this blog , it seems like RetrievalQA is more efficient and would make. py available in the repo): import faiss from langchain import HuggingFacePipeline, LLMChain from transformers import GPT2LMHeadModel, TextGenerationPipeline, AutoTokenizer from langchain. Splited the text. Mar 10, 2024 · Content generation: Langchain’s advanced language models can generate high-quality, contextually relevant content for websites, blogs, and other digital platforms. 1. 10 votes, 35 comments. sort(key=lambda x: x[1]["top"]) # Now, all_elements contains your text and tables, sorted by their vertical position on the page. We are an unofficial community. json file to load ES modules: This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. vectorstores import Chroma from langchain. However, the content returned from a chain like RetrievalQA doesn’t contain contextual information for all the instructions in the prompt, likely causing the LLM to fallback on its training data for code generation for some instructions. Display enterprise applications accessible to a user. qa_generation. adapter. r/singularity. cypher. Tools I looked at include Langchain as well as a tool I built that leverages Langchain to scale data extraction and loading. However, in the official documentation ( Here) they do not mention the word "fine-tuning". Below we show how to easily go from a YouTube url to audio of the video to text to chat ! We wil use the OpenAIWhisperParser , which will use the OpenAI Whisper API to transcribe audio to text, and the OpenAIWhisperParserLocal for local support and running on Langchain for QA. Use an LLM ( GPT-3. I was able to get a QA bot with GPT 3. It enables applications that: Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc. evaluation. Suppose we want to summarize a blog post. load_dotenv() Introduction. Dec 22, 2023 · そのクラスは、langchain\chains\qa_with_sources\retrieval. 5-turbo) to auto-generate question-answer pairs from these docs. LangChain is a vast library for GenAI orchestration, it supports numerous LLMs, vector stores, document loaders and agents. QA Generation. Set up . You signed out in another tab or window. document_loaders import TextLoader. Everything between the ``` must be valid array. , on your laptop) using local embeddings and a local LLM. g. HugeGraph QA Chain. They've also started wrapping API endpoints with LLM interfaces. LangChain is an open-source framework and developer toolkit that helps developers. output_schema=MedicalBilling, llm=ChatOpenAI(. This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database. Aug 8, 2023 · After initializing the cache, you can use the LangChain Chat Models with gptcache. Jul 3, 2023 · inputs ( Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. Find advice on looking your best for any occasion and discuss the latest trends in men's fashion and how to incorporate them into your wardrobe. This allows us to pass in a list of Messages to the prompt using the “chat_history” input key, and these messages will be inserted after the system message and before the human message containing the latest question. throwawayrandomvowel. Hi, I'm trying to make kids stories generator with text and image on langchain. chains import GraphCypherQAChain from langchain. Training a model from scratch requires a ton of data in order to be effective. LangChain is a framework for developing applications powered by language models. [docs] def extract_cypher(text: str) -> str: """Extract Cypher code from a text. to. So basically what I need to do is build a chatbot that is able to identify user intents and. In the first step, use Document Loaders (at least 100 are available), provided by LangChain to convert anything from a simple Word document to an AWS S3 directory into Documents. Upload PDF, app decodes, chunks, and stores embeddings for QA Langchain tries to be a horizontal layer which works with everything underneath so langchain obfuscate lot of stuff. •. 1 upvote. for table in tables: first_cell = table[0][0] all_elements. Mar 23, 2023 · The main way of doing this is through a process commonly referred to as "Retrieval Augmented Generation". ai course with Harrison Chase. With the data added to the vectorstore, we can initialize the chain. A lot of pages and youTube videos are referring to using "VectorstoreIndexCreator" as fine-tuning. Here's an explanation of each step in the RunnableSequence. Works fine with langchain. As a reminder, the chatbot will be primarily in Spanish, and I'm using a tech stack that includes Langchain, LLaMA 2 7B/13B (llama-cpp-python Jun 17, 2023 · Reverse-engineering Reddit's public source code using GPT-4 and Activeloop with Langchain code understanding Understanding and analyzing the inner workings of Reddit's version 1 codebase. This key is used as the main input for whatever question a user may ask. The primary supported way to do this is with LCEL. Memory is a class that gets called at the start and at the end of every chain. As we enter into 2024, a reminder for people who haven't watched the AlphaGo documentary yet. OpenAI is an AI research and deployment company. There's a lot of overlap between them and I don't know if any of them actually adds a value to LLM workflows in a way that's maintainable and robust. These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through We’ll use the following packages: %pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai chromadb bs4. loader = PyPDFDirectoryLoader ("pdfs") docs = loader. chains import VectorDBQAWithSourcesChain import pickle import argparse parser = argparse. 5, Langchain, and using indexes for retrieval augmented generation. quote: "We’ve mentioned that these guys can reach speeds of up to 70 mph, but did you know they can go from " 22 more characters. input_keys except for inputs that will be set by the chain’s memory. To get started, use the following steps: 1. MembersOnline. Submit a PR with notes. pydantic_v1 import Field Effects of Chunk Sizes on Retrieval Augmented Generation (RAG) Applications. synthetic_data_generator = create_openai_data_generator(. Its take 2 sec to get the answer from chain I test this by adding time on evey step. Creating the Data Generator. from langchain. llm, retriever=vectorstore. If you want to replace it completely, you can override the default prompt template: Feb 12, 2024 · Langchain (Framework to develop apps powered by language models) Streamlit (To create machine learning and data science web apps) Vector database (FAISS — Facebook AI Similarity Search) Large Language Model (OpenAI GPT) Retrieval-Augmented Generation (RAG) This open-source project leverages cutting-edge tools and methods to enable seamless interaction with PDF documents. At the start, memory loads variables and passes them along in the chain. We can supply the specification to get_openapi_chain directly in order to query the API with OpenAI functions: pip install langchain langchain-openai. Training GPT took 330 years of compute time, parallel processing, over six months. First, it might be helpful to view the existing prompt template that is used by your chain: print ( chain. Imagine writing a piece of software that could understand, assist, and even generate code, similar to how a seasoned developer would. In this process, rather than just passing a user question directly to a language model, the system "retrieves" any documents that could be relevant in answering the question, and then passes those documents (along with the original question from langchain. combine_documents_chain. . as_retriever(), chain_type_kwargs={"prompt": prompt} Seeking Advice on Automating Responses Evaluation for My Chilean Law QA-Chatbot. Here are the 4 key steps that take place: Load a vector database with encoded documents. 277 upvotes · 58 comments. May 3, 2023 · The LangChain orchestrator provides these relevant records to the LLM along with the query and relevant prompt to carry out the required activity. I use them, fine-tuned my own models, my costs are way down compared to openai and my results aren't worse, plus when you submit a support ticket they respond within a day or two, not 4. Jan 4, 2024 · Generation: The relevant passages that are retrieved are fed into a generative model along with the original query to generate a response. You can find your customer ID by clicking on your name, on the top-right of the Vectara console window. # Set env var OPENAI_API_KEY or load from a . document_loaders import Docx2txtLoader SQL. I would love some gurus here to help and explain if adding more words to a text chunk "waters down" the semantic information in the embedding. Generate a question-answering chain with a specified set of UI-chosen configurations. , MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). neum. You may have to keep an eye on how to optimise for server resources, architectural patterns etc, this is something I have spent a lot of time doing, to arrive at templ1 = """You are a smart assistant designed to help high school teachers come up with reading comprehension questions. output_parsers import BaseLLMOutputParser from langchain_core. The aim of this project was to demonstrate how LLM can be made use of to achieve various functionalities using LangChain and RAG (Retrieval-Augmented Generation). The work-around right now is that I need to edit the langchain in my node_modules directly, so the prompt is now in “Working with LangChain and LangSmith on the Elastic AI Assistant had a significant positive impact on the overall pace and quality of the development and shipping experience. But from what I see, LangChain use English in the prompt that's used in the QARetrieval Module. If you built a specialized workflow, and now you want something similar, but with an LLM from Hugging Face instead of OpenAI, LangChain makes that change as simple as a few variables. ” Oct 10, 2023 · Next, we import the necessary libraries to set up the Neo4j DB QA Chain. No issue with text, it's pretty straight forward. chat_models import ChatOpenAI from langchain. There are many different types of memory - please see memory docs for the full catalog. In your project root directory, create a file named . I highly recommend it! I'm using the RetrievalQA chain with retriever that's created based on a vectorstore index. """LLM Chain for generating examples for question answering. Should contain all inputs specified in Chain. ConversationalRetrievalChain from database with long entries. An example of how machine learning can overcome all perceived odds. env and store your OpenAI API key in it as the value for OPENAI_API_KEY: OPENAI_API_KEY="Your OpenAI API key". dev. from_chain_type(. I tested a csv upload and Q&A to web gpt-4 and worked like a charm. So far, I've been able to just build my own little libraries to use in some LLM applications The Power of Rsync: Efficient File Synchronization for Linux. This notebook shows how to use the QAGenerationChain to come up with question-answer pairs over a specific document. ArgumentParser Text generation (the basic GPT function) Text embeddings (for search, and for similarity, and for q&a) Whisper (via serverless inference, and via API) Langchain and GPT-Index/LLama Index Pinecone for vector db I don't know much, but I know infinitely more than when I started and I sure could've saved myself back then a lot of time. """ from __future__ import annotations from typing import Any from langchain_core. Sign up for a Vectara account if you don’t already have one. chains. The traditional way to do ReAct agent is through prompt engineering. Document loaders deal with the specifics of accessing and converting data from a variety of different Spent several hours fighting with LangChain, debugging its internals, etc. ) Reason: rely on a language model to reason (about how to answer based on Help with conversational_qa_chain - Streamlit Messages. Relying Exclusively on Documentation: The task demands the LLM to write code performing several actions. Current Approach : Considering using foundation models to generate API endpoints. Neo4j Cypher graph QA. Full code I'm using (which is an edit of the qa. LangChain is an open source framework that enables combining large language models (LLM) with other external components to develop LLM-powered applications. They enable use cases such as: Welcome to r/mensfashion, your go-to subreddit for all things related to men's fashion and attire. In the end, I realized that LangChain hadn't done anything but get in the way, while I had implemented everything useful myself. LangChain gives you one standard interface for many use cases. It manages templates, composes components into chains and supports monitoring and observability. ai. XKCD for comics. Jul 24, 2023 · In this article, I’m going share on how I performed Question-Answering (QA) like a chatbot using Llama-2–7b-chat model with LangChain framework and FAISS library over the documents which I Integrate Microsoft Graph API with LangChain to enable my chatbot to: List users in groups. Introduction to LangChain. At the end of the day, out of the 100's of features langchain provides im probably using 20-25% of them at best and this works well for me, so yes I would use it in production as well. Hello, everyone I created a python flask api for document chatbot. chains import RetrievalQA import os from langchain. As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of updating code, better documentation, or project to feature. 2. LangChain Neo4j Integration. Run the following command to create a new Node project: npm init -y. I'd like to build a simple Q&A app over a list of docs (let's say 100 pages). 4. I would like to make an LLM model to generate good questions from it and validate the user's answer. I'm new here and so excited to get started on my journey of building LLM apps with LangChain. I'm trying to use the Mistral 7B model for question-answer generation using the QAGeneration Chain def llm_question_generation_pipeline(chunks): documents = [Document(page_content=chunk) for chunk in chunks] inputs ( Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. # RetrievalQA. I've been meaning to create a 32B local model of code-llama2 to help me with coding questions mostly. The document and the chatbot is supposed to support Indonesian. In summary, an embedding is a vector representation RetrievalQA does not identify correct context from document. We can create this in a few lines of code. The prompts and text generation performance could be checked without modifying the complete chain and passing all the metadata filters every time. OpenAI makes ChatGPT, GPT-4, and DALL·E 3. Aug 7, 2023 · Retrieval Augmented Generation(RAG) We use LangChain’s document loaders for this purpose. Then, using Document Transformers and Text Embedding Models, you transform your documents into embeddings. langchain_models. It worked well for the first few documents indexed, but I realized that as you add more documents, the answer quality (out of the box) is a lot lower, probably because the answer could be from a variety of docs. 20. With the schema and the prompt ready, the next step is to create the data generator. sourceId: 1, quote: "Built for speed, the cheetah can accelerate from zero to 45 in just 2. I want to make a streamlit app which has RAG and Memory. The LLM processes the request from the LangChain orchestrator and returns the result. Args: text: Text to extract Cypher code from. To give you a sneak preview, either pipeline can be wrapped in a single object: load_summarize_chain. env file: # import dotenv. *?)```" # Find all matches in the input You will need a Vectara account to use Vectara with LangChain. ago. In each step of the workflow, the higher the number of tokens you consume, the greater the cost will be. if the user is seeking information then perform semantic search to generate a response. 5. Sort of a personal KB (phind-33B, if you have better suggestions please let me know). I am currently using a Chroma database to store a large number of embeddings with varied lengths. embeddings. OpenAI's mission is to ensure that artificial general intelligence benefits all of humanity. First set environment variables and install packages: %pip install --upgrade --quiet langchain-openai tiktoken chromadb langchain. prompt. You can run a local docker container by running the executing the following script: docker run \. A book would be out dated before it went to print. With OpenAI Functions, because the model is fine-tuned for function picking, the performance is much better. This is a string of all previous chats (human & AI) concatenated together. At this point gptcache will cache the answer, the only difference from the original example is to change chat=ChatOpenAI(temperature=0) to chat = LangChainChat(chat=ChatOpenAI(temperature=0)), which will be commented in the code block. Two RAG use cases which we cover Document-based question generation. youtube. Tommie takes on the role of a person moving to a new town who is looking for a job, and Eve takes on the role of a Step 1 - The Setup: Store your documents as embeddings. if the user is seeking to perform some action (say, schedule an appointment) then collate all the information and push it to a database for appointments. LangChain has integrations with many open-source LLMs that can be run locally. We will pass the prompt in via the chain_type_kwargs argument. I've come across many LLM frameworks: Langchain, LlamaIndex, LMQL, guidance, Marvin, Instructor, etc. """ # The pattern to find Cypher code enclosed in triple backticks pattern = r"```(. chains import RetrievalQA. This object knows how to communicate with the underlying language model to get synthetic data. Ask the user to input a set of documents of interest. Revolutionary_Cry626. paper-qa uses the process shown below: embed docs into vectors; embed query into vector; search for top k passages in docs; create summary of each passage relevant to query; score and select only relevant summaries; put summaries into prompt; generate In it, we leverage a time-weighted Memory object backed by a LangChain retriever. In this case, LangChain offers a higher-level constructor method. Exploring Langchain offers a glimpse into the future of language technology and the exciting possibilities that await as AI-powered systems become more intuitive, efficient, and Q&A with thousands of documents. It allows a language model to interact with its environment and However, via langchain you can use open-source models or embeddings (see details below). Did a small write up exploring how to build scalable solutions that can enable RAG with thousands of documents. The next key is chatHistory. just to combine two of its features. The script below creates two instances of Generative Agents, Tommie and Eve, and runs a simulation of their interaction with their observations. We’ll use a prompt that includes a MessagesPlaceholder variable under the name “chat_history”. text_splitter import CharacterTextSplitter from langchain. 12. 15K subscribers in the LangChain community. Some of these embeddings exceed the maximum token length supported by GPT-3. Reload to refresh your session. env file like so: import getpass. load () 2. Members Online [N] EMNLP 2023 Anonymity Hypocrisy Eventually I rewrote the entire code without the LLM chains by breaking up the code into Query/Retriever Classes, Prompt Creator functions and Text Generation Classes. llms import OpenAI from langchain. Wanted to build a bot to chat with pdf. I have a lot of data about a specific topic in a vector store. --name=graph \. I'm back with an update and a new query regarding the QA-chatbot I'm developing for Chilean law, based on the LLM model. See here for setup instructions for these LLMs. Hi there, I've got a question. If you just want to get started as quickly as possible, this is the recommended way to do it: chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff") query = "What did the president say about Justice Breyer" chain({"input_documents": docs, "question": query}, return_only_outputs=True) {'output_text': ' The president Using custom prompt for RetrievalQA. -itd \. At the end, it saves any returned variables. This way the code was modular. The more advanced RAG features seem to be released in the Python version of Langchain first. Langchain is a year old and has been in a constant state of development with new things added daily since then. On the other hand, LangChain is a framework for developing applications powered by language models. My current setup involves using dense retrieval (specifically a combination of parent retriever that retrieves n chunks before and m chunks after the retrieved chunk, with n=1 and m=2, alongside with I was looking into some of the langchain metrics like Accuracy, correctness , cot_qa but have not been able to actually get any of them working, cause all the chains require a model that generates answers for the question instead of just evaluating correct answer with model answer. graphs import Jan 16, 2024 · 2. Sort by: Add a Comment. Show groups a user belongs to. With a small dataset, you will have a very limited LLM. LCEL is great for constructing your own chains, but it’s also nice to have chains that you can use off-the-shelf. So, technically you can fine tune a model just for tool Feb 16, 2024 · cd youtube-video-rag. Project High-Level Overview. base. graph_qa. #. We couldn’t have achieved the product experience delivered to our customers without LangChain, and we couldn’t have done it at the same pace without LangSmith. from __future__ import annotations import json from typing import Any, Dict, List, Optional from langchain_core The process of bringing the appropriate information and inserting it into the model prompt is known as Retrieval Augmented Generation (RAG). This is how it looks: _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. • 2 mo. Given a piece of text, you must come up with 10 question and answer pairs that can be used to test a student's reading comprehension abilities. There are two types of off-the-shelf chains that LangChain supports: Chains that are built with LCEL. Jumping on a bandwagon here. # dotenv. I would also like to know what the typical embedding time Vs text chunk length 4 days ago · Source code for langchain. Jdonavan. We need to set environment variable OPENAI_API_KEY, which can be done directly or loaded from a . 5 months and counting on my openai support tickets. r/OpenAI. I recently completed the Deeplearning. Jul 28, 2023 · Embark on an enlightening journey through the world of document-based question-answering chatbots using langchain! With a keen focus on detailed explanations and code walk-throughs, you’ll gain a deep understanding of each component - from creating a vector database to response generation. LLM Chat/RP Comparison/Test: Mistral 7B Base + Instruct. document_loaders import TextLoader from langchain. 3. And at what rate. The part is making me confused, what to feed into the model to generate relevant questions. You switched accounts on another tab or window. But things gets harder when I want to generate no dommesques images. qa. Embedding with open ai and for Q&A Langchain Faiss is used. openai import OpenAIEmbeddings from langchain. generate_chain. qa_chain = RetrievalQA. This repo and series is provided by DataIndependent and run by Greg Kamradt. }, {. Once you have completed your sign up you will have a Vectara customer ID. • 10 mo. You should first learn what an embedding is. 4 days ago · Source code for langchain. The broad and deep Neo4j integration allows for vector search, cypher generation and database The Hugging Face Model Hub hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together. llm_chain. Powered by Langchain, Chainlit, Chroma, and OpenAI, our application offers advanced natural language processing and retrieval augmented generation (RAG) capabilities. つまり、 _get_docs () が呼ばれたとき QA retriever chain with filter for specific docs Hi I’m trying to set up a simple qa chain to get answers from our company’s internal documents, problem is when I try to add search_kwargs with filter by source, to allow user to select specific documents to get answers from, it doesn’t seem to work consistently. 5 seconds and reach top speeds" 52 more characters. All of default guides say I should chunk the data and put it in VectorDB and then for every question, fetch top 3-5 similar doc chunks and pass it to LLM with the initial question to have this retrieval augmented in context learning. LangChain has a number of components designed to help build Q&A applications, and RAG applications more generally. pyで定義されています。. There are tons of free and open source models you can experiment with and fine-tune with your data. Building chat or QA applications on YouTube videos is a topic of high interest. Tried to do the same locally with csv loader, chroma and langchain and results (Q&A on the same dataset and GPT model - gpt4) were poor. I'm building a document QA bot. like Cohere Reranking, Hyde, Query-expansion etc. Nov 14, 2023 · Here’s a high-level diagram to illustrate how they work: High Level RAG Architecture. I did read around that this could be a good setup. Note: Here we focus on Q&A for unstructured data. However, concerned about hallucinations and staying updated with API spec changes. Many times, we used langchain, set 'verbose' variable to true and directly took the resulting prompt in directly call to openai which provided better control and quality. language_models import BaseLanguageModel from langchain_core. append(("table", first_cell)) # Sort all elements by their vertical starting coordinate (top) all_elements. But the output is rather disappointing (lots of hallucinations, perhaps coming from pre-training datasets, no information coming from the linked web page): Based on the provided Hey everyone, I'm working on improving my RAG (Retrieval-Augmented Generation) application with a focus on processing Czech language documents. そのクラス中の _get_docs () がLLMに渡すためのテキストチャンクを取得する関数であり、これをカスタマイズすることで、目的を達成できそうです。. I already have the setup for the RAG in langserve, but I'm struggling working out how to keep a good chat history integrated against the nextjs frontend and the langserve server. Encode the query Guidance programs allow you to interleave generation, prompting, and logical control into a single continuous flow matching how the language model actually processes the text. Gave up on LangChain and had a cleaner, more extensible implementation in two hours. I thought of using langchain + code-llama2 + chromadb. Set “type":"module" in the package. One_King2724. Defaults to None. template) This will print out the prompt, which will comes from here. Returns: Cypher code extracted from the text. result = qa_chain ( {"query": question}) After unsuccessful experiments I even use the original web source and the question from the example in the documentation. from() call above: The first input passed is an object containing a question key. Firstly, thank you so much for helping me with this. One of the most common types of databases that we can build Q&A systems for are SQL databases. Watched lots and lots of youtube videos, researched langchain documentation, so I’ve written the code like that (don't worry, it works :)): Loaded pdfs. The LangChain orchestrator gets the result from the LLM and sends it to the end-user through the Amazon Lex chatbot. Install the dependencies needed for Neo4j: LangChain 提供了一种标准的链接口、许多与其他工具的集成。LangChain 提供了用于常见应用程序的端到端的链调用。 代理（agents）: 代理涉及 LLM 做出行动决策、执行该行动、查看一个观察结果，并重复该过程直到完成。LangChain 提供了一个标准的代理接口，一系列 The pipeline for QA over code follows the steps we do for document question answering, with some differences: In particular, we can employ a splitting strategy that does a few things: Keeps each top-level function and class in the code is loaded into separate documents. From personal experience, the agent's performance degrades when you give it more than three tools at a time. Apr 15, 2023 · Here, we introduce a simple tool for evaluating QA chains ( see the code here) called auto-evaluator. You will need to have a running HugeGraph instance. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e. Initialize the chain. I have included a You signed in with another tab or window. return_only_outputs ( bool) – Whether to return only outputs in the response. nk tv op uk rv ci fo wx eb sc