Feat: New updates on the SDK (#58)

* fea: New updated on the sdks * feat: new updates on the SDK - releasing version v1.0.0 * feat: updating the name of the package to portkey_ai * feat: adding the CI pipeline for publishing to NPM on release * fix: reverting the changes on previos commit * fix: adding the conventional-commit-check * fix: adding metadata in feedback routes * fix: updated feedbacks response, added streaming in post and added over ride function in utils * fix: feedbacks and test cases * fix: fixing the url on the prompt completions API * fix: adding completions method to prompt api * fix: lint issues * fix: renaming the post method * feat: adding the get_headers function to retrieve the headers * fix: linting error fixes * fix: fixing the last chunk in streaming mode * feat: changes * fix: fixing llama_index imports * fix: fixing on the LLMmetadata on llamaindex. Assingned a default value to context window * fix: removing headers when parsing the dict * fix: adding types into llama_index and langchain * fix: formating * fix: removed the unused methods on the integrations and removed headers from the generic response * fix: lint fixes * doc: Udpating the readme file with the latest changes * doc: URL updated
Portkey-AI · Dec 8, 2023 · 22192ff · 22192ff
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diff --git a/.github/workflows/verify-conventional-commits.yml b/.github/workflows/verify-conventional-commits.yml
@@ -0,0 +1,11 @@
+name: verify-conventional-commits
+
+on: [pull_request]
+
+jobs:
+  conventional-commits-checker:
+    runs-on: ubuntu-latest
+    steps:
+      - name: verify conventional commits
+        uses: taskmedia/[email protected]
+
diff --git a/Makefile b/Makefile
@@ -5,6 +5,7 @@ help: ## Show all Makefile targets
 .PHONY: format lint
 format: ## Run code formatter: black
 	black .
+	ruff check . --fix         
 lint: ## Run linters: mypy, black, ruff
 	mypy .
 	black . --check
@@ -24,7 +25,7 @@ build:
 
 upload:
 	python -m pip install twine
-	python -m twine upload dist/portkey-ai-*
+	python -m twine upload dist/portkey_ai-*
 	rm -rf dist
 
 dev: 

diff --git a/README.md b/README.md
@@ -35,148 +35,24 @@ $ export PORTKEY_API_KEY=PORTKEY_API_KEY
 #### Now, let's make a request with GPT-4
 
 ```py
-import portkey
-from portkey import Config, LLMOptions
+from portkey_ai import Portkey
 
-portkey.config = Config(
-    mode="single",
-    llms=LLMOptions(provider="openai", api_key="OPENAI_API_KEY")
+# Construct a client with a virtual key
+portkey = Portkey(
+    api_key="PORTKEY_API_KEY",
+    virtual_key="VIRTUAL_KEY"
 )
 
-r = portkey.ChatCompletions.create(
-    model="gpt-4", 
-    messages=[
-        {"role": "user","content": "Hello World!"}
-    ]
+completion = portkey.chat.completions.create(
+    messages = [{ "role": 'user', "content": 'Say this is a test' }],
+    model = 'gpt-3.5-turbo'
 )
+print(completion)
 ```
 
 Portkey fully adheres to the OpenAI SDK signature. This means that you can instantly switch to Portkey and start using Portkey's advanced production features right out of the box.
 
 
-## **🪜 Detailed Integration Guide**
-
-**4 Steps to Integrate the SDK**
-1. Get your virtual key for AI providers.
-2. Construct your LLM, add Portkey features, provider features, and prompt.
-3. Construct the Portkey client and set your usage mode.
-4. Now call Portkey regularly like you would call your OpenAI constructor.
-
-Let's dive in! If you are an advanced user and want to directly jump to various full-fledged examples, [click here](https://github.com/Portkey-AI/portkey-python-sdk/tree/main/examples).
-
----
-
-### **Step 1️⃣ : Get your Virtual Keys for AI providers**
-
-Navigate to the "Virtual Keys" page on [Portkey](https://app.portkey.ai/) and hit the "Add Key" button. Choose your AI provider and assign a unique name to your key. Your virtual key is ready!
-
-### **Step 2️⃣ : Construct your LLM, add Portkey features, provider features, and prompt**
-
-**Portkey Features**:
-You can find a comprehensive [list of Portkey features here](#📔-list-of-portkey-features). This includes settings for caching, retries, metadata, and more.
-
-**Provider Features**:
-Portkey is designed to be flexible. All the features you're familiar with from your LLM provider, like `top_p`, `top_k`, and `temperature`, can be used seamlessly. Check out the [complete list of provider features here](https://github.com/Portkey-AI/portkey-python-sdk/blob/af0814ebf4f1961b5dfed438918fe68b26ef5f1e/portkey/api_resources/utils.py#L137).
-
-**Setting the Prompt Input**:
-This param lets you override any prompt that is passed during the completion call - set a model-specific prompt here to optimise the model performance. You can set the input in two ways. For models like Claude and GPT3, use `prompt` = `(str)`, and for models like GPT3.5 & GPT4, use `messages` = `[array]`.
-
-
-Here's how you can combine everything:
-
-```python
-from portkey import LLMOptions
-
-# Portkey Config
-provider = "openai"
-virtual_key = "key_a"
-trace_id = "portkey_sdk_test"
-
-# Model Settings
-model = "gpt-4"
-temperature = 1
-
-# User Prompt
-messages = [{"role": "user", "content": "Who are you?"}]
-
-# Construct LLM
-llm = LLMOptions(provider=provider, virtual_key=virtual_key, trace_id=trace_id, model=model, temperature=temperature)
-```
-
-### **Step 3️⃣ : Construct the Portkey Client**
-
-Portkey client's config takes 3 params: `api_key`, `mode`, `llms`.
-
-* `api_key`: You can set your Portkey API key here or with `os.ennviron` as done above.
-* `mode`: There are **3** modes - Single, Fallback, Loadbalance.
-  * **Single** - This is the standard mode. Use it if you do not want Fallback OR Loadbalance features.
-  * **Fallback** - Set this mode if you want to enable the Fallback feature.
-  * **Loadbalance** - Set this mode if you want to enable the Loadbalance feature. 
-* `llms`: This is an array where we pass our LLMs constructed using the LLMOptions constructor.
-
-```py
-import portkey
-from portkey import Config
-
-portkey.config = Config(mode="single",llms=[llm])
-```
-
-### **Step 4️⃣ : Let's Call the Portkey Client!**
-
-The Portkey client can do `ChatCompletions` and `Completions`.
-
-Since our LLM is GPT4, we will use ChatCompletions:
-
-```py
-response = portkey.ChatCompletions.create(
-    messages=[{
-      "role": "user",
-      "content": "Who are you ?"
-    }]
-)
-print(response.choices[0].message)
-```
-
-You have integrated Portkey's Python SDK in just 4 steps!
-
----
-
-## **🔁 Demo: Implementing GPT4 to GPT3.5 Fallback Using the Portkey SDK**
-
-```py
-import os
-os.environ["PORTKEY_API_KEY"] = "PORTKEY_API_KEY" # Setting the Portkey API Key
-
-import portkey
-from portkey import Config, LLMOptions
-
-# Let's construct our LLMs.
-llm1 = LLMOptions(provider="openai", model="gpt-4", virtual_key="key_a"),
-llm2 = LLMOptions(provider="openai", model="gpt-3.5-turbo", virtual_key="key_a")
-
-# Now let's construct the Portkey client where we will set the fallback logic
-portkey.config = Config(mode="fallback",llms=[llm1,llm2])
-
-# And, that's it!
-response = portkey.ChatCompletions.create()
-print(response.choices[0].message)
-```
-
-## **📔 Full List of Portkey Config**
-
-| Feature             | Config Key              | Value(Type)                                      | Required    |
-|---------------------|-------------------------|--------------------------------------------------|-------------|
-| Provider Name       | `provider`        | `string`                                         | ✅ Required  |
-| Model Name        | `model`        | `string`                                         | ✅ Required |
-| Virtual Key OR API Key        | `virtual_key` or `api_key`        | `string`                                         | ✅ Required (can be set externally) |
-| Cache Type          | `cache_status`          | `simple`, `semantic`                             | ❔ Optional |
-| Force Cache Refresh | `cache_force_refresh`   | `True`, `False` (Boolean)                                 | ❔ Optional |
-| Cache Age           | `cache_age`             | `integer` (in seconds)                           | ❔ Optional |
-| Trace ID            | `trace_id`              | `string`                                         | ❔ Optional |
-| Retries         | `retry`           | `{dict}` with two required keys: `"attempts"` which expects integers in [0,5] and `"on_status_codes"` which expects array of status codes like [429,502] <br> `Example`: { "attempts": 5, "on_status_codes":[429,500] }                      | ❔ Optional |
-| Metadata            | `metadata`              | `json object` [More info](https://docs.portkey.ai/key-features/custom-metadata)          | ❔ Optional |
-
-
 ## **🤝 Supported Providers**
 
 || Provider  | Support Status  | Supported Endpoints |
@@ -190,7 +66,7 @@ print(response.choices[0].message)
 
 ---
 
-#### [📝 Full Documentation](https://docs.portkey.ai/) | [🛠️ Integration Requests](https://github.com/Portkey-AI/portkey-python-sdk/issues) | 
+#### [📝 Full Documentation](https://docs.portkey.ai/docs) | [🛠️ Integration Requests](https://github.com/Portkey-AI/portkey-python-sdk/issues) | 
 
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