blog: Langsmith blogpost

docs/blog/posts/langsmith.md

@@ -0,0 +1,161 @@
+---
+draft: False
+date: 2024-02-18
+tags:
+  - langsmith
+authors:
+  - jxnl
+---
+
+# Seamless Support with Langsmith
+
+Its a common misconception that LangChain's [LangSmith](https://www.langchain.com/langsmith) is only compatible with LangChain's models. In reality, LangSmith is a unified DevOps platform for developing, collaborating, testing, deploying, and monitoring LLM applications. In this blog we will explore how LangSmith can be used to enhance the OpenAI client alongside `instructor`.
+
+<!-- more -->
+
+## LangSmith
+
+In order to use langsmith, you first need to set your LangSmith API key.
+
+```
+export LANGCHAIN_API_KEY=<your-api-key>
+```
+
+Next, you will need to install the LangSmith SDK:
+
+```
+pip install -U langsmith
+pip install -U instructor
+```
+
+If you want to pull this example down from [instructor-hub](../../hub/index.md) you can use the following command:
+
+```bash
+instructor hub pull --slug batch_classification_langsmith --py > batch_classification_langsmith.py
+```
+
+In this example we'll use the `wrap_openai` function to wrap the OpenAI client with LangSmith. This will allow us to use LangSmith's observability and monitoring features with the OpenAI client. Then we'll use `instructor` to patch the client with the `TOOLS` mode. This will allow us to use `instructor` to add additional functionality to the client.
+
+```python
+import instructor
+import asyncio
+
+from langsmith import traceable
+from langsmith.wrappers import wrap_openai
+
+from openai import AsyncOpenAI
+from pydantic import BaseModel, Field, field_validator
+from typing import List
+from enum import Enum
+
+# Wrap the OpenAI client with LangSmith
+client = wrap_openai(AsyncOpenAI())
+
+# Patch the client with instructor
+client = instructor.patch(client, mode=instructor.Mode.TOOLS)
+
+# Rate limit the number of requests
+sem = asyncio.Semaphore(5)
+
+# Use an Enum to define the types of questions
+class QuestionType(Enum):
+    CONTACT = "CONTACT"
+    TIMELINE_QUERY = "TIMELINE_QUERY"
+    DOCUMENT_SEARCH = "DOCUMENT_SEARCH"
+    COMPARE_CONTRAST = "COMPARE_CONTRAST"
+    EMAIL = "EMAIL"
+    PHOTOS = "PHOTOS"
+    SUMMARY = "SUMMARY"
+
+
+# You can add more instructions and examples in the description
+# or you can put it in the prompt in `messages=[...]`
+class QuestionClassification(BaseModel):
+    """
+    Predict the type of question that is being asked.
+    Here are some tips on how to predict the question type:
+    CONTACT: Searches for some contact information.
+    TIMELINE_QUERY: "When did something happen?
+    DOCUMENT_SEARCH: "Find me a document"
+    COMPARE_CONTRAST: "Compare and contrast two things"
+    EMAIL: "Find me an email, search for an email"
+    PHOTOS: "Find me a photo, search for a photo"
+    SUMMARY: "Summarize a large amount of data"
+    """
+
+    # If you want only one classification, just change it to
+    #   `classification: QuestionType` rather than `classifications: List[QuestionType]``
+    chain_of_thought: str = Field(
+        ..., description="The chain of thought that led to the classification"
+    )
+    classification: List[QuestionType] = Field(
+        description=f"An accuracy and correct prediction predicted class of question. Only allowed types: {[t.value for t in QuestionType]}, should be used",
+    )
+
+    @field_validator("classification", mode="before")
+    def validate_classification(cls, v):
+        # sometimes the API returns a single value, just make sure it's a list
+        if not isinstance(v, list):
+            v = [v]
+        return v
+
+
+@traceable(name="classify-question")
+async def classify(data: str) -> QuestionClassification:
+    """
+    Perform multi-label classification on the input text.
+    Change the prompt to fit your use case.
+
+    Args:
+        data (str): The input text to classify.
+    """
+    async with sem:  # some simple rate limiting
+        return data, await client.chat.completions.create(
+            model="gpt-4-turbo-preview",
+            response_model=QuestionClassification,
+            max_retries=2,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Classify the following question: {data}",
+                },
+            ],
+        )
+
+
+async def main(questions: List[str]):
+    tasks = [classify(question) for question in questions]
+
+    for task in asyncio.as_completed(tasks):
+        question, label = await task
+        resp = {
+            "question": question,
+            "classification": [c.value for c in label.classification],
+            "chain_of_thought": label.chain_of_thought,
+        }
+        resps.append(resp)
+    return resps
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    questions = [
+        "What was that ai app that i saw on the news the other day?",
+        "Can you find the trainline booking email?",
+        "what did I do on Monday?",
+        "Tell me about todays meeting and how it relates to the email on Monday",
+    ]
+
+    resp = asyncio.run(main(questions))
+
+    for r in resp:
+        print("q:", r["question"])
+        #> q: what did I do on Monday?
+        print("c:", r["classification"])
+        #> c: ['SUMMARY']
+```
+
+If you follow what we've done is wrapped the client and proceeded to quickly use asyncio to classify a list of questions. This is a simple example of how you can use LangSmith to enhance the OpenAI client. You can use LangSmith to monitor and observe the client, and use `instructor` to add additional functionality to the client.
+
+To take a look at trace of this run check out this shareable [link](https://smith.langchain.com/public/eaae9f95-3779-4bbb-824d-97aa8a57a4e0/r).

docs/hub/batch_classification_langsmith.md

@@ -0,0 +1,151 @@
+# Seamless Support with Langsmith
+
+Its a common misconception that LangChain's [LangSmith](https://www.langchain.com/langsmith) is only compatible with LangChain's models. In reality, LangSmith is a unified DevOps platform for developing, collaborating, testing, deploying, and monitoring LLM applications. In this blog we will explore how LangSmith can be used to enhance the OpenAI client alongside `instructor`.
+
+If you want to try this example using `instructor hub`, you can pull it by running
+
+```bash
+pip install -U langsmith
+instructor hub pull --slug batch_classification_langsmith --py > langsmith_example.py
+```
+
+## LangSmith
+
+In order to use langsmith, you first need to set your LangSmith API key.
+
+```bash
+export LANGCHAIN_API_KEY=<your-api-key>
+```
+
+Next, you will need to install the LangSmith SDK:
+
+```bash
+pip install -U langsmith
+pip install -U instructor
+```
+
+In this example we'll use the `wrap_openai` function to wrap the OpenAI client with LangSmith. This will allow us to use LangSmith's observability and monitoring features with the OpenAI client. Then we'll use `instructor` to patch the client with the `TOOLS` mode. This will allow us to use `instructor` to add additional functionality to the client.
+
+```python
+import instructor
+import asyncio
+
+from langsmith import traceable
+from langsmith.wrappers import wrap_openai
+
+from openai import AsyncOpenAI
+from pydantic import BaseModel, Field, field_validator
+from typing import List
+from enum import Enum
+
+# Wrap the OpenAI client with LangSmith
+client = wrap_openai(AsyncOpenAI())
+
+# Patch the client with instructor
+client = instructor.patch(client, mode=instructor.Mode.TOOLS)
+
+# Rate limit the number of requests
+sem = asyncio.Semaphore(5)
+
+# Use an Enum to define the types of questions
+class QuestionType(Enum):
+    CONTACT = "CONTACT"
+    TIMELINE_QUERY = "TIMELINE_QUERY"
+    DOCUMENT_SEARCH = "DOCUMENT_SEARCH"
+    COMPARE_CONTRAST = "COMPARE_CONTRAST"
+    EMAIL = "EMAIL"
+    PHOTOS = "PHOTOS"
+    SUMMARY = "SUMMARY"
+
+
+# You can add more instructions and examples in the description
+# or you can put it in the prompt in `messages=[...]`
+class QuestionClassification(BaseModel):
+    """
+    Predict the type of question that is being asked.
+    Here are some tips on how to predict the question type:
+    CONTACT: Searches for some contact information.
+    TIMELINE_QUERY: "When did something happen?
+    DOCUMENT_SEARCH: "Find me a document"
+    COMPARE_CONTRAST: "Compare and contrast two things"
+    EMAIL: "Find me an email, search for an email"
+    PHOTOS: "Find me a photo, search for a photo"
+    SUMMARY: "Summarize a large amount of data"
+    """
+
+    # If you want only one classification, just change it to
+    #   `classification: QuestionType` rather than `classifications: List[QuestionType]``
+    chain_of_thought: str = Field(
+        ..., description="The chain of thought that led to the classification"
+    )
+    classification: List[QuestionType] = Field(
+        description=f"An accuracy and correct prediction predicted class of question. Only allowed types: {[t.value for t in QuestionType]}, should be used",
+    )
+
+    @field_validator("classification", mode="before")
+    def validate_classification(cls, v):
+        # sometimes the API returns a single value, just make sure it's a list
+        if not isinstance(v, list):
+            v = [v]
+        return v
+
+
+@traceable(name="classify-question")
+async def classify(data: str) -> QuestionClassification:
+    """
+    Perform multi-label classification on the input text.
+    Change the prompt to fit your use case.
+
+    Args:
+        data (str): The input text to classify.
+    """
+    async with sem:  # some simple rate limiting
+        return data, await client.chat.completions.create(
+            model="gpt-4-turbo-preview",
+            response_model=QuestionClassification,
+            max_retries=2,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Classify the following question: {data}",
+                },
+            ],
+        )
+
+
+async def main(questions: List[str]):
+    tasks = [classify(question) for question in questions]
+
+    for task in asyncio.as_completed(tasks):
+        question, label = await task
+        resp = {
+            "question": question,
+            "classification": [c.value for c in label.classification],
+            "chain_of_thought": label.chain_of_thought,
+        }
+        resps.append(resp)
+    return resps
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    questions = [
+        "What was that ai app that i saw on the news the other day?",
+        "Can you find the trainline booking email?",
+        "what did I do on Monday?",
+        "Tell me about todays meeting and how it relates to the email on Monday",
+    ]
+
+    resp = asyncio.run(main(questions))
+
+    for r in resp:
+        print("q:", r["question"])
+        #> q: what did I do on Monday?
+        print("c:", r["classification"])
+        #> c: ['SUMMARY']
+```
+
+If you follow what we've done is wrapped the client and proceeded to quickly use asyncio to classify a list of questions. This is a simple example of how you can use LangSmith to enhance the OpenAI client. You can use LangSmith to monitor and observe the client, and use `instructor` to add additional functionality to the client.
+
+To take a look at trace of this run check out this shareable [link](https://smith.langchain.com/public/eaae9f95-3779-4bbb-824d-97aa8a57a4e0/r).

mkdocs.yml

@@ -173,6 +173,7 @@ nav:
     - Using Llama CPP: 'hub/llama-cpp-python.md'
     - Using Together Compute: 'hub/together.md'
     - Using Anyscale: 'hub/anyscale.md'
+    - Batch Async Classification w/ Langsmith: 'hub/batch_classification_langsmith.md'
   - Tutorials:
     - Tutorials (Notebooks): 'tutorials/1-introduction.ipynb'
     - Tips and Tricks: 'tutorials/2-tips.ipynb'