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LangChain vs. LlamaIndex
📚 Resources: https://stackoverflow.com/questions/76990736/differences-between-langchain-llamaindex
LangChain
You can think of LangChain as a framework rather than a tool. It provides a lot of tools right out of the box that enable you to interact with LLMs. Key LangChain components include chains. Chains allow the chaining of components together, meaning you could use a PromptTemplate and a LLMChain to:
- Create a prompt
- Query a LLM
Here's a quick example:
...
prompt = PromptTemplate(template=template, input_variables=["questions"])
chain = LLMChain(
llm=llm,
prompt=prompt
)
chain.run(query)
LlamaIndex
LlamaIndex, (previously known as GPT Index), is a data framework specifically designed for LLM apps. Its primary focus is on ingesting, structuring, and accessing private or domain-specific data. It offers a set of tools that facilitate the integration of custom data into LLMs.
Based on my experience with LlamaIndex, it is an ideal solution if you're looking to work with vector embeddings. Using its many available plugins you could load (or ingest) data from many sources easily, and generate vector embeddings using an embedding model.
One key feature of LlamaIndex is that it is optimized for index querying. After the data is ingested, an index is created. This index represents your vectorized data and can be easily queried like so:
...
query_engine = index.as_query_engine()
response = query_engine.query("Stackoverflow is Awesome.")
LlamaIndex abstracts this but it is essentially taking your query "Stackoverflow is Awesome." and comparing it with the most relevant information from your vectorized data (or index) which is then provided as context to the LLM.
Python
📚 Resources:
