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Fix typos (#12128)

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Fix typos
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omahs 2023-10-23 02:16:03 +02:00 committed by GitHub
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commit f3cc9bba5b
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11 changed files with 14 additions and 14 deletions
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2
docs/docs/expression_language/how_to/fallbacks.ipynb
View File

@ -107,7 +107,7 @@
"# Now let's try with fallbacks to Anthropic\n",
"with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n",
" try:\n",
" print(llm.invoke(\"Why did the the chicken cross the road?\"))\n",
" print(llm.invoke(\"Why did the chicken cross the road?\"))\n",
" except:\n",
" print(\"Hit error\")"
]

2
docs/docs/guides/fallbacks.ipynb
View File

@ -109,7 +109,7 @@
"# Now let's try with fallbacks to Anthropic\n",
"with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n",
" try:\n",
" print(llm.invoke(\"Why did the the chicken cross the road?\"))\n",
" print(llm.invoke(\"Why did the chicken cross the road?\"))\n",
" except:\n",
" print(\"Hit error\")"
]

6
docs/docs/guides/local_llms.ipynb
View File

@ -148,7 +148,7 @@
"\n",
"Inference speed is a challenge when running models locally (see above).\n",
"\n",
"To minimize latency, it is desiable to run models locally on GPU, which ships with many consumer laptops [e.g., Apple devices](https://www.apple.com/newsroom/2022/06/apple-unveils-m2-with-breakthrough-performance-and-capabilities/).\n",
"To minimize latency, it is desirable to run models locally on GPU, which ships with many consumer laptops [e.g., Apple devices](https://www.apple.com/newsroom/2022/06/apple-unveils-m2-with-breakthrough-performance-and-capabilities/).\n",
"\n",
"And even with GPU, the available GPU memory bandwidth (as noted above) is important.\n",
"\n",
@ -254,7 +254,7 @@
"\n",
"`f16_kv`: whether the model should use half-precision for the key/value cache\n",
"* Value: True\n",
"* Meaning: The model will use half-precision, which can be more memory efficient; Metal only support True."
"* Meaning: The model will use half-precision, which can be more memory efficient; Metal only supports True."
]
},
{
@ -291,7 +291,7 @@
"id": "f56f5168",
"metadata": {},
"source": [
"The console log will show the the below to indicate Metal was enabled properly from steps above:\n",
"The console log will show the below to indicate Metal was enabled properly from steps above:\n",
"```\n",
"ggml_metal_init: allocating\n",
"ggml_metal_init: using MPS\n",

2
docs/docs/integrations/document_loaders/bibtex.ipynb
View File

@ -49,7 +49,7 @@
"metadata": {},
"source": [
"`BibtexLoader` has these arguments:\n",
"- `file_path`: the path the the `.bib` bibtex file\n",
"- `file_path`: the path of the `.bib` bibtex file\n",
"- optional `max_docs`: default=None, i.e. not limit. Use it to limit number of retrieved documents.\n",
"- optional `max_content_chars`: default=4000. Use it to limit the number of characters in a single document.\n",
"- optional `load_extra_meta`: default=False. By default only the most important fields from the bibtex entries: `Published` (publication year), `Title`, `Authors`, `Summary`, `Journal`, `Keywords`, and `URL`. If True, it will also try to load return `entry_id`, `note`, `doi`, and `links` fields. \n",

2
docs/docs/integrations/tools/bearly.ipynb
View File

@ -55,7 +55,7 @@
"id": "ac5c88ce",
"metadata": {},
"source": [
"Let's add some files to the the sandbox"
"Let's add some files to the sandbox"
]
},
{

2
docs/docs/modules/data_connection/indexing.ipynb
View File

@ -66,7 +66,7 @@
"\n",
"The record manager relies on a time-based mechanism to determine what content can be cleaned up (when using `full` or `incremental` cleanup modes).\n",
"\n",
"If two tasks run back-to-back, and the first task finishes before the the clock time changes, then the second task may not be able to clean up content.\n",
"If two tasks run back-to-back, and the first task finishes before the clock time changes, then the second task may not be able to clean up content.\n",
"\n",
"This is unlikely to be an issue in actual settings for the following reasons:\n",
"\n",

2
docs/docs/modules/memory/agent_with_memory.ipynb
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@ -12,7 +12,7 @@
"- [Memory in LLMChain](/docs/modules/memory/how_to/adding_memory.html)\n",
"- [Custom Agents](/docs/modules/agents/how_to/custom_agent.html)\n",
"\n",
"In order to add a memory to an agent we are going to the the following steps:\n",
"In order to add a memory to an agent we are going to perform the following steps:\n",
"\n",
"1. We are going to create an `LLMChain` with memory.\n",
"2. We are going to use that `LLMChain` to create a custom Agent.\n",

2
docs/docs/use_cases/question_answering/vector_db_qa.mdx
View File

@ -65,7 +65,7 @@ qa.run(query)
</CodeOutputBlock>
The above way allows you to really simply change the chain_type, but it doesn't provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](/docs/modules/chains/additional/question_answering.html)) and then pass that directly to the the RetrievalQA chain with the `combine_documents_chain` parameter. For example:
The above way allows you to really simply change the chain_type, but it doesn't provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](/docs/modules/chains/additional/question_answering.html)) and then pass that directly to the RetrievalQA chain with the `combine_documents_chain` parameter. For example:
```python

2
libs/langchain/langchain/chains/sequential.py
View File

@ -52,7 +52,7 @@ class SequentialChain(Chain):
if set(input_variables).intersection(set(memory_keys)):
overlapping_keys = set(input_variables) & set(memory_keys)
raise ValueError(
f"The the input key(s) {''.join(overlapping_keys)} are found "
f"The input key(s) {''.join(overlapping_keys)} are found "
f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
)

4
libs/langchain/langchain/chat_models/azureml_endpoint.py
View File

@ -76,7 +76,7 @@ class LlamaContentFormatter(ContentFormatterBase):
return self.format_request_payload(prompt=prompt, model_kwargs=model_kwargs)
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
"""Formats the request according the the chosen api"""
"""Formats the request according to the chosen api"""
return str.encode(prompt)
def format_response_payload(self, output: bytes) -> str:
@ -118,7 +118,7 @@ class AzureMLChatOnlineEndpoint(SimpleChatModel):
@validator("http_client", always=True, allow_reuse=True)
@classmethod
def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient:
"""Validate that api key and python package exists in environment."""
"""Validate that api key and python package exist in environment."""
endpoint_key = get_from_dict_or_env(
values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY"
)

2
libs/langchain/langchain/llms/azureml_endpoint.py
View File

@ -180,7 +180,7 @@ class LlamaContentFormatter(ContentFormatterBase):
"""Content formatter for LLaMa"""
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
"""Formats the request according the the chosen api"""
"""Formats the request according to the chosen api"""
prompt = ContentFormatterBase.escape_special_characters(prompt)
request_payload = json.dumps(
{