Adding base app and knowledge text file

app.py

@@ -0,0 +1,183 @@
+from haystack.components.generators import OpenAIGenerator
+from haystack.utils import Secret
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.routers import ConditionalRouter
+from haystack_integrations.document_stores.chroma import ChromaDocumentStore
+from haystack import Pipeline
+from haystack.components.writers import DocumentWriter
+from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder
+from haystack_integrations.components.retrievers.chroma import ChromaEmbeddingRetriever
+from haystack.components.preprocessors import DocumentSplitter
+from haystack.components.converters.txt import TextFileToDocument
+from haystack.components.preprocessors import DocumentCleaner
+
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.retrievers import InMemoryEmbeddingRetriever
+
+import gradio as gr
+
+embedding_model = "dunzhang/stella_en_400M_v5"
+
+
+########################
+####### Indexing #######
+########################
+
+# In memory version for now
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+converter = TextFileToDocument()
+
+cleaner = DocumentCleaner()
+
+splitter = DocumentSplitter(split_by="word", split_length=200, split_overlap=100)
+
+embedder = SentenceTransformersDocumentEmbedder(model=embedding_model,
+                                                trust_remote_code=True)
+
+writer = DocumentWriter(document_store=document_store)
+
+indexing = Pipeline()
+
+indexing.add_component("converter", converter)
+indexing.add_component("cleaner", cleaner)
+indexing.add_component("splitter", splitter)
+indexing.add_component("embedder", embedder)
+indexing.add_component("writer", writer)
+
+indexing.connect("converter", "cleaner")
+indexing.connect("cleaner", "splitter")
+indexing.connect("splitter", "embedder")
+indexing.connect("embedder", "writer")
+
+indexing.run({"sources": ["knowledge-plain.txt"]})
+
+
+##################################
+####### Answering pipeline #######
+##################################
+
+no_answer_message = (
+    "I'm not allowed to answer this questions. Please ask something related to "
+    "APIs access in accordance DSA’s transparency and data-sharing provisions. "
+    "Is there anything else I can do for you? "
+)
+
+relevance_prompt_template = """
+Classify whether this user is asking for something related to social media APIs,
+the Digital Services Act (DSA), or any topic related to online platforms’ compliance
+with legal and data-sharing frameworks.
+
+Relevant topics include social media API access, data transparency, compliance
+with DSA provisions, and online platform regulations.
+
+Here is their message:
+
+{{query}}
+
+Here are the two previous messages. ONLY refer to these if the above message refers previous ones.
+
+{% for message in user_history[-2:] %}
+  * {{message["content"]}}
+
+{% endfor %}
+
+If the request is related to these topics, respond “YES”. If it is off-topic (e.g., unrelated to APIs, the DSA, or legal frameworks), respond “NO”."""
+
+routes = [
+    {
+        "condition": "{{'YES' in replies[0]}}",
+        "output": "{{query}}",
+        "output_name": "query",
+        "output_type": str,
+    },
+    {
+        "condition": "{{'NO' in replies[0]}}",
+        "output": no_answer_message,
+        "output_name": "no_answer",
+        "output_type": str,
+    }
+]
+
+query_prompt_template = """Conversation history:
+{{conv_history}}
+
+Here is what the user has requested:
+
+{{query}}
+
+Reply to the question with a short paragraph according to the following documents:
+
+{% for document in documents %}
+  * {{document.content}}
+
+{% endfor %}
+
+Do not mention the documents in your answer, present it as your own knowledge.
+"""
+
+prompt_builder = PromptBuilder(template=relevance_prompt_template)
+
+llm = OpenAIGenerator(
+    api_key=Secret.from_env_var("OPENAI_API_KEY"),
+    model="gpt-4o-mini",
+    generation_kwargs = {"max_tokens": 8192}
+)
+
+router = ConditionalRouter(routes=routes)
+
+embedder = SentenceTransformersTextEmbedder(model=embedding_model)
+
+# Again: in memory for now
+retriever = InMemoryEmbeddingRetriever(document_store)
+
+prompt_builder2 = PromptBuilder(template=query_prompt_template)
+
+llm2 = OpenAIGenerator(
+    api_key=Secret.from_env_var("OPENAI_API_KEY"),
+    model="gpt-4o-mini",
+    generation_kwargs = {"max_tokens": 8192}
+)
+
+answer_query = Pipeline()
+
+answer_query.add_component("prompt_builder", prompt_builder)
+answer_query.add_component("llm", llm)
+answer_query.add_component("router", router)
+answer_query.add_component("embedder", embedder)
+answer_query.add_component("retriever", retriever)
+answer_query.add_component("prompt_builder2", prompt_builder2)
+answer_query.add_component("llm2", llm2)
+
+answer_query.connect("prompt_builder", "llm")
+answer_query.connect("llm", "router")
+answer_query.connect("router.query", "embedder")
+answer_query.connect("embedder", "retriever")
+answer_query.connect("retriever", "prompt_builder2")
+answer_query.connect("prompt_builder2", "llm2")
+
+answer_query.warm_up()
+
+
+##########################
+####### Gradio app #######
+##########################
+
+def chat(message, history):
+    """
+    Chat function for Gradio. Uses the pipeline to produce next answer.
+    """
+    conv_history = "\n\n".join([f"{message["role"]}: {message["content"]}" for message in history[-2:]])
+    user_history = [message for message in history if message["role"] == "user"]
+    results = answer_query.run({"user_history": user_history, "query": message,
+                                "conv_history": conv_history})
+    if "llm2" in results:
+        answer = results["llm2"]["replies"][0]
+    elif "router" in results and "no_answer" in results["router"]:
+        answer = results["router"]["no_answer"]
+    else:
+        answer = "Sorry, a mistake occured"
+    return answer
+
+if __name__ == "__main__":
+    gr.ChatInterface(chat, type="messages").launch()