Fix secret management breaking change (#296)

* Fix secret management breaking change * Update content/blog/customizing-rag-to-summarize-hacker-news-posts-with-haystack2/index.md Co-authored-by: Tuana Çelik <[email protected]> * Update index.md --------- Co-authored-by: Tuana Çelik <[email protected]>
deepset-ai · Feb 6, 2024 · 2443ef1 · 2443ef1
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diff --git a/content/blog/astradb-haystack-integration/index.md b/content/blog/astradb-haystack-integration/index.md
@@ -48,8 +48,9 @@ Remember earlier when I mentioned you were going to need your credentials? I hop
 
 ```python
 from getpass import getpass
+import os
 
-OPENAI_API_KEY = getpass("Enter your openAI key:")
+os.environ["OPENAI_API_KEY"] = getpass("Enter your openAI key:")
 ASTRA_DB_ID = getpass("Enter your Astra database ID:")
 ASTRA_DB_APPLICATION_TOKEN = getpass("Enter your Astra application token (e.g.AstraCS:xxx ):")
 ASTRA_DB_REGION = getpass("Enter your AstraDB Region: ")
@@ -140,7 +141,7 @@ rag_pipeline.add_component(
 )
 rag_pipeline.add_component(instance=AstraRetriever(document_store=document_store), name="retriever")
 rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
-rag_pipeline.add_component(instance=OpenAIGenerator(api_key=OPENAI_API_KEY), name="llm")
+rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
 rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")
 rag_pipeline.connect("embedder", "retriever")
 rag_pipeline.connect("retriever", "prompt_builder.documents")

diff --git a/...ent/blog/customizing-rag-to-summarize-hacker-news-posts-with-haystack2/index.md b/...ent/blog/customizing-rag-to-summarize-hacker-news-posts-with-haystack2/index.md
@@ -117,8 +117,9 @@ First, we initialize all of the components we will need for the pipeline:
 ```python
 from haystack import Pipeline  
 from haystack.components.builders.prompt_builder import PromptBuilder  
-from haystack.components.generators import OpenAIGenerator  
-  
+from haystack.components.generators import OpenAIGenerator
+from haystack.utils import Secret
+
 prompt_template = """  
 You will be provided a few of the latest posts in HackerNews, followed by their URL.  
 For each post, provide a brief summary followed by the URL the full post can be found at.  
@@ -131,7 +132,7 @@ Posts:
 """  
   
 prompt_builder = PromptBuilder(template=prompt_template)  
-llm = OpenAIGenerator(mode="gpt-4", api_key='YOUR_API_KEY')  
+llm = OpenAIGenerator(mode="gpt-4", api_key=Secret.from_token('YOUR_API_KEY'))  
 fetcher = HackernewsNewestFetcher()
 ```
 Next, we add the components to a Pipeline:

diff --git a/content/blog/mixtral-8x7b-healthcare-chatbot/index.md b/content/blog/mixtral-8x7b-healthcare-chatbot/index.md
@@ -89,11 +89,12 @@ So now our flow is as follows:
 First, initialize the LLMs and warm them up.
 ```python
 from haystack.components.generators import HuggingFaceTGIGenerator
+from haystack.utils import Secret
 
-keyword_llm = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1", token=huggingface_token)
+keyword_llm = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1", token=Secret.from_token(huggingface_token))
 keyword_llm.warm_up()
 
-llm = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1", token=huggingface_token)
+llm = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1", token=Secret.from_token(huggingface_token))
 llm.warm_up()
 ```
 

diff --git a/content/blog/using-jina-embeddings-haystack/index.md b/content/blog/using-jina-embeddings-haystack/index.md
@@ -55,10 +55,11 @@ pip install jina-haystack chroma-haystack pypdf
 Then let's input our credentials. Or you can set them as environment variables instead if you're feeling fancy.
 
 ```python
-import getpass
+from getpass import getpass
+import os
 
-jina_api_key  = getpass.getpass("JINA api key:")
-hf_token = getpass.getpass("Enter your HuggingFace api token:")
+jina_api_key  = getpass("JINA api key:")
+os.environ["HF_API_TOKEN"] = getpass("Enter your HuggingFace api token: ")
 ```
 
 ## Building the indexing pipeline
@@ -147,7 +148,7 @@ question: {{question}}
 """
 
 text_embedder = JinaTextEmbedder(api_key=jina_api_key, model="jina-embeddings-v2-base-en")
-generator = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1", token=hf_token)
+generator = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1")
 generator.warm_up()
 
 prompt_builder = PromptBuilder(template=prompt)

diff --git a/content/overview/quick-start.md b/content/overview/quick-start.md
@@ -49,6 +49,7 @@ Then, index your data to the DocumentStore, build a RAG pipeline, and ask a ques
 import os
 
 from haystack import Pipeline, Document
+from haystack.utils import Secret
 from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.components.generators import OpenAIGenerator
@@ -75,7 +76,7 @@ Answer:
 
 retriever = InMemoryBM25Retriever(document_store=document_store)
 prompt_builder = PromptBuilder(template=prompt_template)
-llm = OpenAIGenerator(api_key=api_key)
+llm = OpenAIGenerator(api_key=Secret.from_token(api_key))
 
 rag_pipeline = Pipeline()
 rag_pipeline.add_component("retriever", retriever)