add everyting for docs

docs/snippets/modules/model_io/models/chat/how_to/chat_model_caching.mdx

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+```python
+import langchain
+from langchain.chat_models import ChatOpenAI
+
+llm = ChatOpenAI()
+```
+
+## In Memory Cache
+
+
+```python
+from langchain.cache import InMemoryCache
+langchain.llm_cache = InMemoryCache()
+
+# The first time, it is not yet in cache, so it should take longer
+llm.predict("Tell me a joke")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    CPU times: user 35.9 ms, sys: 28.6 ms, total: 64.6 ms
+    Wall time: 4.83 s
+    
+
+    "\n\nWhy couldn't the bicycle stand up by itself? It was...two tired!"
+```
+
+</CodeOutputBlock>
+
+
+```python
+# The second time it is, so it goes faster
+llm.predict("Tell me a joke")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    CPU times: user 238 µs, sys: 143 µs, total: 381 µs
+    Wall time: 1.76 ms
+
+
+    '\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
+```
+
+</CodeOutputBlock>
+
+## SQLite Cache
+
+
+```bash
+rm .langchain.db
+```
+
+
+```python
+# We can do the same thing with a SQLite cache
+from langchain.cache import SQLiteCache
+langchain.llm_cache = SQLiteCache(database_path=".langchain.db")
+```
+
+
+```python
+# The first time, it is not yet in cache, so it should take longer
+llm.predict("Tell me a joke")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    CPU times: user 17 ms, sys: 9.76 ms, total: 26.7 ms
+    Wall time: 825 ms
+
+
+    '\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
+```
+
+</CodeOutputBlock>
+
+
+```python
+# The second time it is, so it goes faster
+llm.predict("Tell me a joke")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    CPU times: user 2.46 ms, sys: 1.23 ms, total: 3.7 ms
+    Wall time: 2.67 ms
+
+
+    '\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
+```
+
+</CodeOutputBlock>

docs/snippets/modules/model_io/models/chat/how_to/llm_chain.mdx

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+```python
+chain = LLMChain(llm=chat, prompt=chat_prompt)
+```
+
+
+```python
+chain.run(input_language="English", output_language="French", text="I love programming.")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    "J'adore la programmation."
+```
+
+</CodeOutputBlock>

docs/snippets/modules/model_io/models/chat/how_to/prompts.mdx

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+You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.
+
+For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
+
+
+```python
+from langchain import PromptTemplate
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    AIMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+template="You are a helpful assistant that translates {input_language} to {output_language}."
+system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+human_template="{text}"
+human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+```
+
+
+```python
+chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+
+# get a chat completion from the formatted messages
+chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    AIMessage(content="J'adore la programmation.", additional_kwargs={})
+```
+
+</CodeOutputBlock>
+
+If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:
+
+
+```python
+prompt=PromptTemplate(
+    template="You are a helpful assistant that translates {input_language} to {output_language}.",
+    input_variables=["input_language", "output_language"],
+)
+system_message_prompt = SystemMessagePromptTemplate(prompt=prompt)
+```
+

docs/snippets/modules/model_io/models/chat/how_to/streaming.mdx

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+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import (
+    HumanMessage,
+)
+
+
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
+resp = chat([HumanMessage(content="Write me a song about sparkling water.")])
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    Verse 1:
+    Bubbles rising to the top
+    A refreshing drink that never stops
+    Clear and crisp, it's pure delight
+    A taste that's sure to excite
+    
+    Chorus:
+    Sparkling water, oh so fine
+    A drink that's always on my mind
+    With every sip, I feel alive
+    Sparkling water, you're my vibe
+    
+    Verse 2:
+    No sugar, no calories, just pure bliss
+    A drink that's hard to resist
+    It's the perfect way to quench my thirst
+    A drink that always comes first
+    
+    Chorus:
+    Sparkling water, oh so fine
+    A drink that's always on my mind
+    With every sip, I feel alive
+    Sparkling water, you're my vibe
+    
+    Bridge:
+    From the mountains to the sea
+    Sparkling water, you're the key
+    To a healthy life, a happy soul
+    A drink that makes me feel whole
+    
+    Chorus:
+    Sparkling water, oh so fine
+    A drink that's always on my mind
+    With every sip, I feel alive
+    Sparkling water, you're my vibe
+    
+    Outro:
+    Sparkling water, you're the one
+    A drink that's always so much fun
+    I'll never let you go, my friend
+    Sparkling
+```
+
+</CodeOutputBlock>