LangChain is a framework designed for developing applications powered by language models. It provides a set of tools and abstractions that make it easier to build complex applications that leverage the capabilities of large language models (LLMs) like OpenAI's GPT-4. LangChain is especially useful for tasks that require integrating language models with other systems, managing interactions, and handling the flow of data.
1. Model_Prompt_Parser.ipynb :- In this notebook we will see the models used in langchain, what are prompt templates, and what is parsing in langchain.
2. Memory.ipynb :- In this notebook we will see the different ways to store the history of conversation in langchain.
3. Chains.ipynb :- In this notebook we will see what are chains and its types and how to use it.
4. QnA.ipynb :- In this notebook we will pass a document and ask questions to the langauge model via Chains, we will also see some methods to perform this task.
5. Evaluation.ipynb :- In this notebook we will see how can we evaluate our code using LangChain.
6. Agents.ipynb :- In this notebook we will see what are agents in langchain and how to use it.