Toy vectordb embedding example adopted to llama-cpp-python causes failure
Labels: name: area: agent, name: area: embeddings, name: area: vector store, name: auto:bug

### System Info

I filed an issue with llama-cpp here https://github.com/ggerganov/llama.cpp/issues/3689 

langchain 
```Name: langchain
Version: 0.0.208
Summary: Building applications with LLMs through composability
Home-page: https://www.github.com/hwchase17/langchain
Author:
Author-email:
License: MIT
Location: Work\SHARK\shark.venv\Lib\site-packages
Requires: aiohttp, dataclasses-json, langchainplus-sdk, numexpr, numpy, openapi-schema-pydantic, pydantic, PyYAML, requests, SQLAlchemy, tenacity
```

llama-cpp-python
```Name: llama_cpp_python
Version: 0.2.11
Summary: Python bindings for the llama.cpp library
Home-page:
Author:
Author-email: Andrei Betlen <abetlen@gmail.com>
License: MIT
Location: Work\SHARK\shark.venv\Lib\site-packages
Requires: diskcache, numpy, typing-extensions
Required-by:
```


### Who can help?

_No response_

### Information

- [X] The official example notebooks/scripts
- [X] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction


the toy code is adopted from https://learn.activeloop.ai/courses/take/langchain/multimedia/46317643-langchain-101-from-zero-to-hero 
It's the first toy vector db embedding example with "Napoleon"

Here is the code to reproduce the  error:
```
import os
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

from langchain.embeddings import LlamaCppEmbeddings
from langchain.vectorstores import DeepLake
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import LlamaCpp
from langchain.chains import RetrievalQA

from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler


from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

# instantiate the LLM and embeddings models
llm = LlamaCpp(model_path="llama-2-13b-chat.Q5_K_M.gguf",
               temperature=0,
               max_tokens=1000, # this was lowered from the original value of 2000, but did not fix it
               top_p=1,
               Verbose=True)
embeddings = LlamaCppEmbeddings(model_path="llama-2-13b-chat.Q5_K_M.gguf")

# create our documents
texts = [
    "Napoleon Bonaparte was born in 15 August 1769",
    "Louis XIV was born in 5 September 1638"
]
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.create_documents(texts)

# create Deep Lake dataset
# TODO: use your organization id here. (by default, org id is your username)
my_activeloop_org_id = "<SOME_ID>"
my_activeloop_dataset_name = "langchain_llama_00"
dataset_path = f"hub://{my_activeloop_org_id}/{my_activeloop_dataset_name}"
db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings)

# add documents to our Deep Lake dataset
db.add_documents(docs)

retrieval_qa = RetrievalQA.from_chain_type(
	llm=llm,
	chain_type="stuff",
	retriever=db.as_retriever())


from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType

tools = [
    Tool(
        name="Retrieval QA System",
        func=retrieval_qa.run,
        description="Useful for answering questions."
    ),
]

agent = initialize_agent(
	tools,
	llm,
	agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
	verbose=True
)

response = agent.run("When was Napoleone born?")
print(response)
```

in `agent.run(..)` line llama-cpp says it's running out of memory
```
ggml_allocr_alloc: not enough space in the buffer (needed 442368, largest block available 290848)
GGML_ASSERT: C:\Users\jason\AppData\Local\Temp\pip-install-4x0xr_93\llama-cpp-python_fec9a526add744f5b2436cab2e2c4c28\vendor\llama.cpp\ggml-alloc.c:173: !"not enough space in the buffer"
```

I don't know enough about how LlamaCppEmbeddings works to know if this is an error on my end, or a bug in llama-cpp.
Any guidance is appreciated.
Thank you


### Expected behavior

I expect it to work like the openai example!