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Training ` Chatgpt ` with your personal data using langchain step by step in detail ](https://youtu.be/j3xOMde2v9Y?si=179HsiMU-hEPuSs4) by [ NextGen Machines ](https://www.youtube.com/@MayankGupta-kb5yc)
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  * ⛓ [ Build Chat PDF app in Python with LangChain, OpenAI, Streamlit | Full project | Learn Coding ](https://www.youtube.com/watch?v=WYzFzZg4YZI) by [ Jutsupoint ](https://www.youtube.com/@JutsuPoint)
  * ⛓ [ Build Eminem Bot App with LangChain, Streamlit, OpenAI | Full Python Project | Tutorial | AI ChatBot ](https://www.youtube.com/watch?v=a2shHB4MRZ4) by [ Jutsupoint ](https://www.youtube.com/@JutsuPoint)

###  [ Prompt Engineering and LangChain
](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW)
by [ Venelin Valkov ](https://www.youtube.com/@venelin_valkov) ​

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️  Querying a SQL
DB  We can replicate our SQLDatabaseChain with Runnables.
](/docs/expression_language/cookbook/sql_db) ## [ 📄️  Agents  You can pass a
Runnable into an agent. Make sure you have langchainhub
](/docs/expression_language/cookbook/agent) ## [ 📄️  Code writing  Example of
how to use LCEL to write Python code.
](/docs/expression_language/cookbook/code_writing) ## [ 📄️  Routing by
semantic similarity  With LCEL you can easily add [custom routing
](/docs/expression_language/cookbook/embedding_router) ## [ 📄️  Adding memory
This shows how to add memory to an arbitrary chain. Right now, you can
](/docs/expression_language/cookbook/memory) ## [ 📄️  Adding moderation  This
shows how to add in moderation (or other safeguards) around your
](/docs/expression_language/cookbook/moderation) ## [ 📄️  Managing prompt size
Agents dynamically call tools. The results of those tool calls are added
](/docs/expression_language/cookbook/prompt_size) ## [ 📄️  Using tools  You
can use any Tools with Runnables easily.
](/docs/expression_language/cookbook/tools)

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on adding streaming support for retries/fallbacks, so
you can get the added reliability without any latency cost.

**Access intermediate results** For more complex chains it’s often very useful
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produced. This can be used to let end-users know something is happening, or
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available on every [ LangServe ](/docs/langserve) server.

**Input and output schemas** Input and output schemas give every LCEL chain
Pydantic and JSONSchema schemas inferred from the structure of your chain.
This can be used for validation of inputs and outputs, and is an integral part
of LangServe.

**Seamless LangSmith tracing integration** As your chains get more and more
complex, it becomes increasingly important to understand what exactly is
happening at every step. With LCEL, **all** steps are automatically logged to
[ LangSmith ](/docs/langsmith/) for maximum observability and debuggability.

**Seamless LangServe deployment integration** Any chain created with LCEL can
be easily deployed using [ LangServe ](/docs/langserve) .

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sources_chain ](https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.loading.load_qa_with_sources_chain.html#langchain.chains.qa_with_sources.loading.load_qa_with_sources_chain) |  |  Retriever  |  Does question answering over documents you pass in, and cites it sources. Use this when you want the answer response to have sources in the text response. Use this over RetrievalQAWithSources when you want to pass in the documents directly (rather than rely on a retriever to get them).   
[ RetrievalQA ](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html#langchain.chains.retrieval_qa.base.RetrievalQA) |  |  Retriever  |  This chain first does a retrieval step to fetch relevant documents, then passes those documents into an LLM to generate a response.   
[ MultiPromptChain ](https://api.python.langchain.com/en/latest/chains/langchain.chains.router.multi_prompt.MultiPromptChain.html#langchain.chains.router.multi_prompt.MultiPromptChain) |  |  |  This chain routes input between multiple prompts. Use this when you have multiple potential prompts you could use to respond and want to route to just one.   
[ MultiRetrievalQAChain ](https://api.python.langchain.com/en/latest/chains/langchain.chains.router.multi_retrieval_qa.MultiRetrievalQAChain.html#langchain.chains.router.multi_retrieval_qa.MultiRetrievalQAChain) |  |  Retriever  |  This chain routes input between multiple retrievers. Use this when you have multiple potential retrievers you could fetch relevant documents from and want to route to just one.   
[ EmbeddingRouterChain ](https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html#langchain.chains.router.embedding_router.EmbeddingRouterChain) |  |  |  This chain uses embedding similarity to route incoming queries.   
[ LLMRouterChain ](https://api.python.langchain.com/en/latest/chains/langchain.chains.router.llm_router.LLMRouterChain.html#langchain.chains.router.llm_router.LLMRouterChain) |  |  |  This chain uses an LLM to route between potential options.   
load_summarize_chain  |  |  |   
[ LLMRequestsChain ](https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html#langchain.chains.llm_requests.LLMRequestsChain) |  |  |  This chain constructs a URL from user input, gets data at that URL, and then summarizes the response. Compared to APIChain, this chain is not focused on a single API spec but is more general   
  
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> Copy

The following example calls RANDOM multiple times within a single statement
and uses the same seed for each of those calls. RANDOM returns the same value
within  each row, but different values for different rows:

>
>     SELECT random(4711), random(4711) FROM table(generator(rowCount => 3));
>     +----------------------+----------------------+
>     |         RANDOM(4711) |         RANDOM(4711) |
>     |----------------------+----------------------|
>     | -3581185414942383166 | -3581185414942383166 |
>     |  1570543588041465562 |  1570543588041465562 |
>     | -6684111782596764647 | -6684111782596764647 |
>     +----------------------+----------------------+
>  
>
> Copy

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[ preview  On December 20th, 2023  Snowpark Container Services  With this
release, we are pleased to announce the preview of Snowpark Container
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Services includes support for security, configuration, and operational best
practices.  ](/en/developer-guide/snowpark-container-services/overview)

[ preview  On December 11th, 2023  Snowflake Python API  ](/en/developer-
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