readme

.gitattributes

@@ -2,5 +2,4 @@
 *.gitignore filter=lfs diff=lfs merge=lfs -text
 *.yaml filter=lfs diff=lfs merge=lfs -text
 /Dockerfile filter=lfs diff=lfs merge=lfs -text
-*.py filter=lfs diff=lfs merge=lfs -text
 *.sqlite3 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,3 +1,121 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fdbf6cc3d9716db1fa57e7c2337fe353e09749488ca12353aa99d9f999dabf3f
-size 3739
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack import Pipeline
+from haystack_integrations.components.retrievers.chroma import ChromaEmbeddingRetriever
+from haystack_integrations.document_stores.chroma import ChromaDocumentStore
+from haystack.components.generators import OpenAIGenerator
+from haystack.components.builders import PromptBuilder
+import haystack.logging
+
+
+import streamlit as st
+
+from dotenv import load_dotenv
+from haystack import component
+import logging
+
+haystack.logging.configure_logging(use_json=True)
+
+logging.basicConfig(
+    format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING
+)
+logging.getLogger("haystack").setLevel(logging.INFO)
+
+load_dotenv()
+
+
+@component
+class ListToString:
+    @component.output_types(text=str)
+    def run(self, input_list: list[str]):
+        print(input_list[0])
+        return {"text": input_list[0]}
+
+
+@st.cache_resource
+def retrieval_pipeline(path):
+    document_store = ChromaDocumentStore(persist_path=path)
+    retriever = ChromaEmbeddingRetriever(document_store, top_k=5)
+
+    template = """Transform this query into a imaginary response that the
+    user could expect based on your knowledge. Use 1-3 sentences. Replace
+    entities or names that you invent with <axz>. The result should be in
+    German.
+    Query: {{
+    query}}"""
+
+    prompt_builder = PromptBuilder(template=template)
+
+    generator = OpenAIGenerator()
+
+    # Create a pipeline
+    basic_rag_pipeline = Pipeline()
+
+    # Add components to your pipeline
+    basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
+    basic_rag_pipeline.add_component("generator", generator)
+    basic_rag_pipeline.add_component("list_to_string", ListToString())
+    basic_rag_pipeline.add_component("retriever", retriever)
+    basic_rag_pipeline.add_component(
+        "text_embedder",
+        SentenceTransformersTextEmbedder(model="intfloat/multilingual-e5-small"),
+    )
+
+    basic_rag_pipeline.connect("prompt_builder", "generator")
+    basic_rag_pipeline.connect("generator.replies", "list_to_string.input_list")
+    basic_rag_pipeline.connect("list_to_string.text", "text_embedder.text")
+    basic_rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+
+    return basic_rag_pipeline
+
+
+def generation_pipeline():
+    template = """
+        Given the following information, answer the question.
+
+        Context:
+        {% for document in documents %}
+            {{ document.content }}
+        {% endfor %}
+
+        Bleibe chronologisch. Erkläre Konzepte und Begriffe wenn nötig.
+
+        Question: {{question}}
+        Answer:
+        """
+
+    prompt_builder = PromptBuilder(template=template)
+
+    generator = OpenAIGenerator(model="gpt-4")
+
+    # Create a pipeline
+    basic_rag_pipeline = Pipeline()
+
+    basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
+    basic_rag_pipeline.add_component("llm", generator)
+
+    basic_rag_pipeline.connect("prompt_builder", "llm")
+
+    return basic_rag_pipeline
+
+
+retrieval_pipe = retrieval_pipeline("chatbot/chromadb")
+generation_pipe = generation_pipeline()
+
+prompt = st.chat_input("Say something")
+
+if prompt:
+    response = retrieval_pipe.run({"prompt_builder": {"query": prompt}})
+
+    st.markdown("### Sources")
+    st.write(response["retriever"]["documents"])
+
+    answer = generation_pipe.run(
+        {
+            "prompt_builder": {
+                "question": prompt,
+                "documents": response["retriever"]["documents"],
+            }
+        }
+    )
+    st.markdown("### Answer")
+    st.write(answer["llm"]["replies"][0])

requirements.txt

@@ -1,3 +1,7 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5ef2686aa78fbb22db80cbec6bf2bccb66bc2c7bfdee6f32c8180d71ca27a2d9
-size 82
+streamlit
+haystack-ai
+bitsandbytes
+accelerate
+pypdf
+cryptography
+pre-commit