# OpenPipe Python Client
This client allows you automatically report your OpenAI calls to [OpenPipe](https://openpipe.ai/).
## Installation
`pip install openpipe`
## Usage
1. Create a project at https://app.openpipe.ai
2. Find your project's API key at https://app.openpipe.ai/settings
3. Configure the OpenPipe client as shown below.
```python
from openpipe import OpenAI
import os
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key="My API Key",
openpipe={
# Set the OpenPipe API key you got in step (2) above.
# If you have the `OPENPIPE_API_KEY` environment variable set we'll read from it by default
"api_key": "My OpenPipe API Key",
}
)
```
You can now use your new OpenAI client, which functions identically to the generic OpenAI client while also reporting calls to your OpenPipe instance.
## Special Features
### Tagging
OpenPipe has a concept of "tagging." This is very useful for grouping a certain set of completions together. When you're using a dataset for fine-tuning, you can select all the prompts that match a certain set of tags. Here's how you can use the tagging feature:
```python
completion = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "system", "content": "count to 10"}],
openpipe={
"tags": {"prompt_id": "counting"},
"log_request": True, # Enable/disable data collection. Defaults to True.
},
)
```
#### Should I Wait to Enable Logging?
We recommend keeping request logging turned on from the beginning. If you change your prompt you can just set a new `prompt_id` tag so you can select just the latest version when you're ready to create a dataset.
## Usage with langchain
> Assuming you have created a project and have the openpipe key.
```python
from openpipe.langchain_llm import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnableSequence
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"Classify user query into positive, negative or neutral.",
),
("human", "{query}"),
]
)
llm = ChatOpenAI(model="gpt-3.5-turbo")\
.with_tags(chain_name="classify", any_key="some")
# To provide the openpipe key explicitly
# llm = ChatOpenAI(model="gpt-3.5-turbo", openpipe_kwargs={"api_key": "My OpenPipe API Key"})\
# .with_tags(chain_name="classify", any_key="some")
chain: RunnableSequence = prompt | llm
res = chain.invoke(
{"query": "this is good"}
)
```
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"description": "# OpenPipe Python Client\n\nThis client allows you automatically report your OpenAI calls to [OpenPipe](https://openpipe.ai/).\n\n## Installation\n\n`pip install openpipe`\n\n## Usage\n\n1. Create a project at https://app.openpipe.ai\n2. Find your project's API key at https://app.openpipe.ai/settings\n3. Configure the OpenPipe client as shown below.\n\n```python\nfrom openpipe import OpenAI\nimport os\n\nclient = OpenAI(\n # defaults to os.environ.get(\"OPENAI_API_KEY\")\n api_key=\"My API Key\",\n openpipe={\n # Set the OpenPipe API key you got in step (2) above.\n # If you have the `OPENPIPE_API_KEY` environment variable set we'll read from it by default\n \"api_key\": \"My OpenPipe API Key\",\n }\n)\n```\n\nYou can now use your new OpenAI client, which functions identically to the generic OpenAI client while also reporting calls to your OpenPipe instance.\n\n## Special Features\n\n### Tagging\n\nOpenPipe has a concept of \"tagging.\" This is very useful for grouping a certain set of completions together. When you're using a dataset for fine-tuning, you can select all the prompts that match a certain set of tags. Here's how you can use the tagging feature:\n\n```python\ncompletion = client.chat.completions.create(\n model=\"gpt-3.5-turbo\",\n messages=[{\"role\": \"system\", \"content\": \"count to 10\"}],\n openpipe={\n \"tags\": {\"prompt_id\": \"counting\"},\n \"log_request\": True, # Enable/disable data collection. Defaults to True.\n },\n)\n```\n\n#### Should I Wait to Enable Logging?\n\nWe recommend keeping request logging turned on from the beginning. If you change your prompt you can just set a new `prompt_id` tag so you can select just the latest version when you're ready to create a dataset.\n\n## Usage with langchain\n\n> Assuming you have created a project and have the openpipe key.\n\n```python\nfrom openpipe.langchain_llm import ChatOpenAI\nfrom langchain.prompts import ChatPromptTemplate\nfrom langchain.schema.runnable import RunnableSequence\n\nprompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"Classify user query into positive, negative or neutral.\",\n ),\n (\"human\", \"{query}\"),\n ]\n)\nllm = ChatOpenAI(model=\"gpt-3.5-turbo\")\\\n .with_tags(chain_name=\"classify\", any_key=\"some\")\n\n# To provide the openpipe key explicitly\n# llm = ChatOpenAI(model=\"gpt-3.5-turbo\", openpipe_kwargs={\"api_key\": \"My OpenPipe API Key\"})\\\n# .with_tags(chain_name=\"classify\", any_key=\"some\")\n\nchain: RunnableSequence = prompt | llm\nres = chain.invoke(\n {\"query\": \"this is good\"}\n)\n```\n",
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