LangChain provides a few built-in handlers that you can use to get started. These are available in the `langchain/callbacks` module. The most basic handler is the `StdOutCallbackHandler`, which simply logs all events to `stdout`.
LangChain provides a few built-in handlers that you can use to get started. These are available in the `langchain_core/callbacks` module. The most basic handler is the `StdOutCallbackHandler`, which simply logs all events to `stdout`.
**Note**: when the `verbose` flag on the object is set to true, the `StdOutCallbackHandler` will be invoked even without being explicitly passed in.
```python
from langchain.callbacks import StdOutCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain_openai import OpenAI
from langchain.prompts import PromptTemplate
from langchain_core.prompts import PromptTemplate
handler = StdOutCallbackHandler()
llm = OpenAI()
@ -160,5 +160,5 @@ The `verbose` argument is available on most objects throughout the API (Chains,
### When do you want to use each of these?
- Constructor callbacks are most useful for use cases such as logging, monitoring, etc., which are _not specific to a single request_, but rather to the entire chain. For example, if you want to log all the requests made to an `LLMChain`, you would pass a handler to the constructor.
- Request callbacks are most useful for use cases such as streaming, where you want to stream the output of a single request to a specific websocket connection, or other similar use cases. For example, if you want to stream the output of a single request to a websocket, you would pass a handler to the `call()` method
- Request callbacks are most useful for use cases such as streaming, where you want to stream the output of a single request to a specific websocket connection, or other similar use cases. For example, if you want to stream the output of a single request to a websocket, you would pass a handler to the `invoke()` method
"One of the common types of databases that we can build Q&A systems for are graph databases. LangChain comes with a number of built-in chains and agents that are compatible with graph query language dialects like Cypher, SparQL, and others (e.g., Neo4j, MemGraph, Amazon Neptune, OntoText, Tigegraph). They enable use cases such as:\n",
"One of the common types of databases that we can build Q&A systems for are graph databases. LangChain comes with a number of built-in chains and agents that are compatible with graph query language dialects like Cypher, SparQL, and others (e.g., Neo4j, MemGraph, Amazon Neptune, Kùzu, OntoText, Tigergraph). They enable use cases such as:\n",
"\n",
"* Generating queries that will be run based on natural language questions,\n",
"* Creating chatbots that can answer questions based on database data,\n",
crissebasbol
3 months ago
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