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Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.

Home Page: https://learn.deeplearning.ai/langchain-chat-with-your-data/lesson/1/introduction

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langchain chat-with-your-dat chatbot-development language-models llms document-loading conversational-ai embeddings question-answering document-splitting

langchain-chat-with-your-data's Introduction

🚀 LangChain: Chat with Your Data

📚 Welcome to the "LangChain: Chat with Your Data" course! Learn directly from the LangChain creator, Harrison Chase, and discover the power of LangChain in building chatbots that interact with information from your own documents and data.

LangChain: 🔗GitHub, 📚Documentation

Course Summary

📖 A short course on LangChain: Chat With Your Data! Explore two main topics: Retrieval Augmented Generation (RAG) and building a chatbot. Unlock the potential of Large Language Models (LLMs) to retrieve contextual documents and create chatbots that respond using your own data.

You'll learn about:

📥 Document Loading: Access over 80 unique loaders provided by LangChain to handle various data sources, including audio and video.

✂️ Document Splitting: Discover best practices and considerations for splitting data effectively.

🧮 Vector Stores and Embeddings: Dive into embeddings and explore vector store integrations within LangChain.

🔄 Retrieval: Grasp advanced techniques for accessing and indexing data in the vector store to retrieve relevant information beyond semantic queries.

🤔 Question Answering: Build a one-pass question-answering solution.

💬 Chat: Track and select pertinent information from conversations and data sources to build your own chatbot using LangChain.

💡 Start building practical applications that allow you to interact with data using LangChain and LLMs.

Key Points

🔑 Learn directly from the LangChain creator, Harrison Chase.
📊 Apply LLMs to your proprietary data and develop personalized assistants and specialized chatbots.
💡 Expand your utilization of LLMs through agents, chained calls, and memories.

About the Instructors

🌟Harrison Chase is Co-Founder and CEO at LangChain.

🌟Andrew Ng is Renowned AI researcher, co-founder of Coursera, and the founder of DeepLearning.AI. With a wealth of knowledge and expertise in the field, Andrew has played a pivotal role in popularizing AI education.

🔗 Reference: "LangChain: Chat with Your Data" course. To enroll in the course or for further information, visit deeplearning.ai.

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yuezhaobj helenhwl lifebringer systematicmethods paulhendi bot-benedict jinmc prototypingstill italoalberto ambertoken arichanen ananthmucharla vadsam lezamora tamphill gunterzhang480 ymazari ycai4591679 vinayvaddiparthi vennergd tainangao saleemmeo gabrielnarruda smach eloliveira multinucliated yanndd1 conroy9068 amishamuni atoxcode harvinder6766 evandrocleto liborseda devopsotrator alankarmisra minduz88 gdbsd shaswata09 rsilveira7 rohanrony kernelmode1986 ctfchan ak2911 cmlw2023 mikeg27 samsondada ibingzhaoi arunmunagala7 tarrywang manas95826 rogerhao sanjay-140114 arjun-234 esaatech danni3-256 swatakit mubin17 write2bapun kartikmehta09 sayantanr yanistig zhangxinming1991 xy-liao baptiste-pasquier mahmoodbabalola judejinjin javenli yanste vamshigvk sandy1811 davidleon123 fasemoreakinyemi aniljainbaba private-gpt sergejhorvat harrywang

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pip install chromadb is failing with error. Using 3.12.1 python kernel

Collecting chromadb
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�[31mERROR: Cannot install chromadb==0.1.0, chromadb==0.2.0, chromadb==0.3.0, chromadb==0.3.1, chromadb==0.3.2, chromadb==0.3.25, chromadb==0.3.26, chromadb==0.3.27, chromadb==0.3.29, chromadb==0.4.0, chromadb==0.4.1, chromadb==0.4.10, chromadb==0.4.11, chromadb==0.4.12, chromadb==0.4.13, chromadb==0.4.14, chromadb==0.4.15, chromadb==0.4.16, chromadb==0.4.17, chromadb==0.4.18, chromadb==0.4.19, chromadb==0.4.2, chromadb==0.4.20, chromadb==0.4.21, chromadb==0.4.22, chromadb==0.4.3, chromadb==0.4.4, chromadb==0.4.5, chromadb==0.4.6, chromadb==0.4.7, chromadb==0.4.8 and chromadb==0.4.9 because these package versions have conflicting dependencies.�[0m�[31m
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The conflict is caused by:
chromadb 0.4.22 depends on onnxruntime>=1.14.1
chromadb 0.4.21 depends on onnxruntime>=1.14.1
chromadb 0.4.20 depends on onnxruntime>=1.14.1
chromadb 0.4.19 depends on onnxruntime>=1.14.1
chromadb 0.4.18 depends on onnxruntime>=1.14.1
chromadb 0.4.17 depends on onnxruntime>=1.14.1
chromadb 0.4.16 depends on onnxruntime>=1.14.1
chromadb 0.4.15 depends on onnxruntime>=1.14.1
chromadb 0.4.14 depends on onnxruntime>=1.14.1
chromadb 0.4.13 depends on onnxruntime>=1.14.1
chromadb 0.4.12 depends on onnxruntime>=1.14.1
chromadb 0.4.11 depends on onnxruntime>=1.14.1
chromadb 0.4.10 depends on onnxruntime>=1.14.1
chromadb 0.4.9 depends on onnxruntime>=1.14.1
chromadb 0.4.8 depends on onnxruntime>=1.14.1
chromadb 0.4.7 depends on onnxruntime>=1.14.1
chromadb 0.4.6 depends on onnxruntime>=1.14.1
chromadb 0.4.5 depends on onnxruntime>=1.14.1
chromadb 0.4.4 depends on onnxruntime>=1.14.1
chromadb 0.4.3 depends on onnxruntime>=1.14.1
chromadb 0.4.2 depends on onnxruntime>=1.14.1
chromadb 0.4.1 depends on onnxruntime>=1.14.1
chromadb 0.4.0 depends on onnxruntime>=1.14.1
chromadb 0.3.29 depends on onnxruntime>=1.14.1
chromadb 0.3.27 depends on onnxruntime>=1.14.1
chromadb 0.3.26 depends on onnxruntime>=1.14.1
chromadb 0.3.25 depends on onnxruntime>=1.14.1
chromadb 0.3.2 depends on numpy~=1.21.6
chromadb 0.3.1 depends on numpy~=1.21.6
chromadb 0.3.0 depends on numpy~=1.21.6
chromadb 0.2.0 depends on numpy~=1.21.6
chromadb 0.1.0 depends on numpy~=1.21.6

To fix this you could try to:

loosen the range of package versions you've specified
remove package versions to allow pip attempt to solve the dependency conflict

�[31mERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts�[0m�[31m
�[0mNote: you may need to restart the kernel to use updated packages.

InvalidRequestError: The model `text-davinci-003` has been deprecated

InvalidRequestError                       Traceback (most recent call last)
Cell In[43], line 2
      1 question = "what did they say about matlab?"
----> 2 compressed_docs = compression_retriever.get_relevant_documents(question)
      3 pretty_print_docs(compressed_docs)

Points an error to this line of code(L4-Retrieval)

InvalidRequestError: The model text-davinci-003 has been deprecated, learn more here: https://platform.openai.com/docs/deprecations

I have use gpt-3.5-turbo-instruct model as per instruction in the notebook

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