Merge pull request #2273 from Agenta-AI/docs/AGE-1294-cookbook-tracin…

…g-langchain

Added Cookbook observability Langchain

docs/docs/tutorials/cookbooks/observability_langchain.mdx

@@ -0,0 +1,129 @@
+---
+title: "Observability LangChain"
+---
+
+```mdx-code-block
+import Image from "@theme/IdealImage";
+
+```
+
+:::note
+This guide is also available as a [Jupyter Notebook](https://github.com/Agenta-AI/agenta/blob/main/cookbook/observability_langchain.ipynb).
+:::
+
+# LLM Observability for LangChain with Agenta
+
+## Introduction
+
+This guide shows you how to set up tracing for a RAG application in Langchain using Agenta, the open-source LLMOps platform.
+
+Tracing allows us to debug effectively complex LLM applications. It allows us to view exact prompts sent and contexts retrieved.
+
+We'll build a simple Q&A RAG application that answers questions about Agenta's documentation. Then, we'll instrument the calls (i.e., trace them) and view the trace in Agenta.
+
+<Image
+  style={{ display: "block", margin: "10 auto" }}
+  img={require("/images/cookbooks/langchain-tracing.png")}
+  alt="Illustration of observability"
+  loading="lazy"
+/>
+
+## Setup
+
+First, let's install the langchain, Agenta, and instrumentation dependencies
+
+```python
+pip install --quiet agenta langchain langchain_community langchain langchain-chroma langchain-openai opentelemetry-instrumentation-langchain
+```
+
+```python
+import os
+import getpass
+
+os.environ["OPENAI_API_KEY"] = getpass.getpass(prompt="OpenAI API key:")
+# Create an Agenta API key under https://cloud.agenta.ai/settings?tab=apiKeys
+os.environ["AGENTA_API_KEY"] = getpass.getpass(prompt="Agenta API key:")
+os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" # Change for self-hosted
+
+```
+
+## Setup tracing
+
+```python
+import agenta as ag
+from opentelemetry.instrumentation.langchain import LangchainInstrumentor
+
+ag.init()
+
+LangchainInstrumentor().instrument()
+
+```
+
+## The Q&A RAG Application
+
+This Langchain RAG application:
+
+1. Ingests a page from our documentation (chunks and embeds it)
+2. Runs the user's query and retrieves relevant context from the page
+3. Creates a prompt based on the question and retrieved context, then returns an answer
+
+```python
+from langchain_openai import ChatOpenAI
+
+
+import bs4
+from langchain import hub
+from langchain_chroma import Chroma
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import OpenAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.prompts import ChatPromptTemplate
+
+prompt = """
+You are an assistant for question-answering tasks.
+Use the following pieces of retrieved context to answer the question.
+If you don't know the answer, just say that you don't know.
+Use three sentences maximum and keep the answer concise and to the point.
+
+Question: {question}
+
+Context: {context}
+
+Answer:
+"""
+
+prompt_template = ChatPromptTemplate([
+    ("human", prompt),
+])
+
+llm = ChatOpenAI(model="gpt-4o-mini")
+
+loader = WebBaseLoader(
+    web_paths=("https://docs.agenta.ai/prompt-management/prompt-management-sdk",),
+    bs_kwargs=dict(
+        parse_only=bs4.SoupStrainer('article')  # Only parse the core
+    ),
+)
+docs = loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+splits = text_splitter.split_documents(docs)
+vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
+
+# Retrieve and generate using the relevant snippets of the blog.
+retriever = vectorstore.as_retriever()
+
+
+rag_chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | prompt_template
+    | llm
+    | StrOutputParser()
+)
+
+rag_chain.invoke("How can I save a new version of a prompt in Agenta?")
+
+
+```

docs/sidebars.ts

@@ -58,6 +58,11 @@ const sidebars: SidebarsConfig = {
 
   ],
   guidesSidebar: [
+    {
+      label: "Cookbooks",
+      ...CATEGORY_UTILITIES,
+      items: [{ type: "autogenerated", dirName: "tutorials/cookbooks" }],
+    },
     {
       label: "Custom Workflows",
       ...CATEGORY_UTILITIES,