chore: import upstream snapshot with attribution

examples/eval-rag-full/retrieve.py

@@ -0,0 +1,92 @@
+import logging
+import os
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+from langchain_chroma import Chroma
+from langchain_core.documents import Document
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+# Constants
+EXAMPLE_DIR: Path = Path(__file__).resolve().parent
+CHROMA_PATH: str = str(EXAMPLE_DIR / "db")
+OPENAI_AI_MODEL: str = "gpt-4.1-mini"
+OPENAI_API_KEY: str | None = os.getenv("OPENAI_API_KEY")
+OPENAI_AI_EMBEDDING_MODEL: str = "text-embedding-3-large"
+
+# Initialize embeddings and load the Chroma database
+embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
+    model=OPENAI_AI_EMBEDDING_MODEL, openai_api_key=OPENAI_API_KEY
+)
+db_chroma: Chroma = Chroma(
+    collection_name="rag_collection",
+    persist_directory=CHROMA_PATH,
+    embedding_function=embeddings,
+)
+
+# Prompt template for generating answers
+PROMPT_TEMPLATE: str = """
+Answer the question based only on the following context:
+{context}
+Answer the question based on the above context: {question}.
+Provide a detailed answer.
+Don't justify your answers.
+Don't give information not mentioned in the CONTEXT INFORMATION.
+Do not say "according to the context" or "mentioned in the context" or similar.
+"""
+
+
+def call_api(
+    prompt: str, options: Dict[str, Any], context: Dict[str, Any]
+) -> Dict[str, str]:
+    """
+    Process a prompt using RAG and return the response.
+
+    Args:
+        prompt: The user's question or prompt
+        options: Configuration options including topK
+        context: Additional context for the request
+
+    Returns:
+        Dict containing the model's response
+
+    Raises:
+        Exception: If there's an error during processing
+    """
+    try:
+        k: int = options.get("config", {}).get("topK", 5)
+        docs_chroma: List[Tuple[Document, float]] = (
+            db_chroma.similarity_search_with_score(
+                prompt,
+                k=k,
+            )
+        )
+        context_text: str = "\n\n".join(
+            [doc.page_content for doc, _score in docs_chroma]
+        )
+
+        # Generate prompt using the template
+        prompt_template: ChatPromptTemplate = ChatPromptTemplate.from_template(
+            PROMPT_TEMPLATE
+        )
+        final_prompt: str = prompt_template.format(
+            context=context_text, question=prompt
+        )
+
+        # Fetch from OpenAI API
+        chat: ChatOpenAI = ChatOpenAI(
+            model_name=OPENAI_AI_MODEL, temperature=0, openai_api_key=OPENAI_API_KEY
+        )
+        message: HumanMessage = HumanMessage(content=final_prompt)
+        response: AIMessage = chat.invoke([message])
+
+        result: Dict[str, str] = {
+            "output": response.content,
+        }
+
+        return result
+    except Exception as e:
+        logging.error(f"Error in call_api: {str(e)}")
+        raise