GITBOOK-56: Muhammad's Jun 4 changes

docs/SUMMARY.md

@@ -16,12 +16,13 @@
 * [🧠 LLMs](core-components/llms.md)
 * [🔋 Generator](core-components/generator.md)
 * [📊 Evaluation](core-components/evaluation.md)
+* [⏰ Observability](core-components/observability.md)
 
 ## Advanced RAG 
 
 * [📚 Re-ranker Retrievers](advanced-rag/re-ranker-retrievers.md)
 * [🔀 Hybrid Retrievers](advanced-rag/hybrid-retrievers.md)
-* [🥶 Finetune Embeddings](advanced-rag/finetune-embeddings.md)
+* [📐 Finetune Embeddings](advanced-rag/finetune-embeddings.md)
 
 ## Use Cases
 

docs/advanced-rag/finetune-embeddings.md

@@ -1,4 +1,4 @@
-# 🥶 Finetune Embeddings
+# 📐 Finetune Embeddings
 
 Beyondllm lets you fine-tune embedding models on your own data to achieve more accurate and better results. \
 \

docs/core-components/observability.md

@@ -0,0 +1,55 @@
+# ⏰ Observability
+
+> Note: Beyondllm are currently only supports observability for OpenAI models as of now
+
+Observability is required to monitor and evaluate the performance and behaviour of your pipeline. Some key features that observability offer are: 
+
+* **Tracking metrics:** This includes things like response time, token usage and the kind of api call (embedding, llm, etc).
+* **Analyzing input and output:** Looking at the prompts users provide and the responses the LLM generates can provide valuable insights.
+
+Overall, LLM observability is a crucial practice for anyone developing or using large language models. It helps to ensure that these powerful tools are reliable, effective, and monitored. 
+
+Beyondllm offer observability layer with the help of [Phoenix](https://phoenix.arize.com/). We have integrated [phoenix](https://phoenix.arize.com/) within our library so you can run the dashboard with just a single command. 
+
+```python
+from beyondllm import observe
+```
+
+First you import the observe module from beyondllm 
+
+```python
+Observe = observe.Observer()
+```
+
+You then make an object of the observe.Observer()
+
+```
+Observe.run()
+```
+
+You then run the Observe object and Voila you have your dashboard running. Whatever api call you make will be reflected on your dashboard. 
+
+<figure><img src="../.gitbook/assets/Screenshot 2024-06-05 at 00.27.55.png" alt=""><figcaption><p>Dashboard </p></figcaption></figure>
+
+<figure><img src="../.gitbook/assets/Screenshot 2024-06-05 at 00.27.41.png" alt=""><figcaption></figcaption></figure>
+
+### Example Snippet
+
+```python
+from beyondllm import source,retrieve,generator, llms, embeddings
+from beyondllm.observe import Observer
+import os
+
+os.environ['OPENAI_API_KEY'] = 'sk-****'
+
+Observe = Observer()
+Observe.run()
+
+llm=llms.ChatOpenAIModel()
+embed_model = embeddings.OpenAIEmbeddings()
+
+data = source.fit("https://medium.aiplanet.com/introducing-beyondllm-094902a252e2",dtype="url",chunk_size=512,chunk_overlap=50)
+retriever = retrieve.auto_retriever(data,embed_model,type="normal",top_k=4)
+
+pipeline = generator.Generate(question="why use BeyondLLM?",retriever=retriever, llm=llm)
+```