fix: Move check for default PromptTemplates in PromptTemplate itself

haystack/nodes/prompt/prompt_node.py

@@ -2,7 +2,6 @@
 import copy
 import logging
 from typing import Dict, List, Optional, Tuple, Union, Any
-import warnings
 
 import torch
 
@@ -11,7 +10,6 @@
 from haystack.telemetry import send_event
 from haystack.nodes.prompt.prompt_model import PromptModel
 from haystack.nodes.prompt.prompt_template import PromptTemplate
-from haystack.nodes.prompt.legacy_default_templates import LEGACY_DEFAULT_TEMPLATES
 
 logger = logging.getLogger(__name__)
 
@@ -216,14 +214,6 @@ def get_prompt_template(self, prompt_template: Union[str, PromptTemplate, None]
         if isinstance(prompt_template, PromptTemplate):
             return prompt_template
 
-        if prompt_template in LEGACY_DEFAULT_TEMPLATES:
-            warnings.warn(
-                f"You're using a legacy prompt template '{prompt_template}', "
-                "we strongly suggest you use prompts from the official Haystack PromptHub: "
-                "https://prompthub.deepset.ai/"
-            )
-            return LEGACY_DEFAULT_TEMPLATES[prompt_template]
-
         # If it's the name of a template that was used already
         if prompt_template in self._prompt_templates_cache:
             return self._prompt_templates_cache[prompt_template]

haystack/nodes/prompt/prompt_template.py

@@ -4,6 +4,7 @@
 import os
 import ast
 import json
+import warnings
 from pathlib import Path
 from abc import ABC
 from uuid import uuid4
@@ -46,6 +47,112 @@
 PROMPTHUB_MAX_RETRIES = int(os.environ.get(HAYSTACK_REMOTE_API_MAX_RETRIES, 5))
 
 
+#############################################################################
+# This templates were hardcoded in the prompt_template module. When adding
+# support for PromptHub integration we decided to remove them with the PR
+# that added the integration: https://github.com/deepset-ai/haystack/pull/4879/
+#
+# That PR also changed the PromptNode API forcing the user to change how
+# they use the node.
+#
+# After some discussion we deemed the change to be too breaking for existing
+# use cases and which steps would have been necessary to migrate to the
+# new API in case someone was using an harcoded template we decided to
+# bring them back.
+#
+# So for the time being this must live here, no new template must be added
+# to this dictionary.
+#############################################################################
+
+
+LEGACY_DEFAULT_TEMPLATES: Dict[str, Dict[str, Any]] = {
+    # DO NOT ADD ANY NEW TEMPLATE IN HERE!
+    "question-answering": {
+        "prompt": "Given the context please answer the question. Context: {join(documents)}; Question: "
+        "{query}; Answer:",
+        "output_parser": AnswerParser(),
+    },
+    "question-answering-per-document": {
+        "prompt": "Given the context please answer the question. Context: {documents}; Question: " "{query}; Answer:",
+        "output_parser": AnswerParser(),
+    },
+    "question-answering-with-references": {
+        "prompt": "Create a concise and informative answer (no more than 50 words) for a given question "
+        "based solely on the given documents. You must only use information from the given documents. "
+        "Use an unbiased and journalistic tone. Do not repeat text. Cite the documents using Document[number] notation. "
+        "If multiple documents contain the answer, cite those documents like ‘as stated in Document[number], Document[number], etc.’. "
+        "If the documents do not contain the answer to the question, say that ‘answering is not possible given the available information.’\n"
+        "{join(documents, delimiter=new_line, pattern=new_line+'Document[$idx]: $content', str_replace={new_line: ' ', '[': '(', ']': ')'})} \n Question: {query}; Answer: ",
+        "output_parser": AnswerParser(reference_pattern=r"Document\[(\d+)\]"),
+    },
+    "question-answering-with-document-scores": {
+        "prompt": "Answer the following question using the paragraphs below as sources. "
+        "An answer should be short, a few words at most.\n"
+        "Paragraphs:\n{documents}\n"
+        "Question: {query}\n\n"
+        "Instructions: Consider all the paragraphs above and their corresponding scores to generate "
+        "the answer. While a single paragraph may have a high score, it's important to consider all "
+        "paragraphs for the same answer candidate to answer accurately.\n\n"
+        "After having considered all possibilities, the final answer is:\n"
+    },
+    "question-generation": {"prompt": "Given the context please generate a question. Context: {documents}; Question:"},
+    "conditioned-question-generation": {
+        "prompt": "Please come up with a question for the given context and the answer. "
+        "Context: {documents}; Answer: {answers}; Question:"
+    },
+    "summarization": {"prompt": "Summarize this document: {documents} Summary:"},
+    "question-answering-check": {
+        "prompt": "Does the following context contain the answer to the question? "
+        "Context: {documents}; Question: {query}; Please answer yes or no! Answer:",
+        "output_parser": AnswerParser(),
+    },
+    "sentiment-analysis": {
+        "prompt": "Please give a sentiment for this context. Answer with positive, "
+        "negative or neutral. Context: {documents}; Answer:"
+    },
+    "multiple-choice-question-answering": {
+        "prompt": "Question:{query} ; Choose the most suitable option to answer the above question. "
+        "Options: {options}; Answer:",
+        "output_parser": AnswerParser(),
+    },
+    "topic-classification": {"prompt": "Categories: {options}; What category best describes: {documents}; Answer:"},
+    "language-detection": {
+        "prompt": "Detect the language in the following context and answer with the "
+        "name of the language. Context: {documents}; Answer:"
+    },
+    "translation": {
+        "prompt": "Translate the following context to {target_language}. Context: {documents}; Translation:"
+    },
+    "zero-shot-react": {
+        "prompt": "You are a helpful and knowledgeable agent. To achieve your goal of answering complex questions "
+        "correctly, you have access to the following tools:\n\n"
+        "{tool_names_with_descriptions}\n\n"
+        "To answer questions, you'll need to go through multiple steps involving step-by-step thinking and "
+        "selecting appropriate tools and their inputs; tools will respond with observations. When you are ready "
+        "for a final answer, respond with the `Final Answer:`\n\n"
+        "Use the following format:\n\n"
+        "Question: the question to be answered\n"
+        "Thought: Reason if you have the final answer. If yes, answer the question. If not, find out the missing information needed to answer it.\n"
+        "Tool: pick one of {tool_names} \n"
+        "Tool Input: the input for the tool\n"
+        "Observation: the tool will respond with the result\n"
+        "...\n"
+        "Final Answer: the final answer to the question, make it short (1-5 words)\n\n"
+        "Thought, Tool, Tool Input, and Observation steps can be repeated multiple times, but sometimes we can find an answer in the first pass\n"
+        "---\n\n"
+        "Question: {query}\n"
+        "Thought: Let's think step-by-step, I first need to {transcript}"
+    },
+    "conversational-agent": {
+        "prompt": "The following is a conversation between a human and an AI.\n{history}\nHuman: {query}\nAI:"
+    },
+    "conversational-summary": {
+        "prompt": "Condense the following chat transcript by shortening and summarizing the content without losing important information:\n{chat_transcript}\nCondensed Transcript:"
+    },
+    # DO NOT ADD ANY NEW TEMPLATE IN HERE!
+}
+
+
 class PromptNotFoundError(Exception):
     ...
 
@@ -217,32 +324,37 @@ def __init__(self, prompt: str, output_parser: Optional[Union[BaseOutputParser,
         super().__init__()
         name, prompt_text = "", ""
 
-        try:
-            # if it looks like a prompt template name
-            if re.fullmatch(r"[-a-zA-Z0-9_/]+", prompt):
-                name = prompt
-                prompt_text = self._fetch_from_prompthub(prompt)
-
-            # if it's a path to a YAML file
-            elif Path(prompt).exists():
-                with open(prompt, "r", encoding="utf-8") as yaml_file:
-                    prompt_template_parsed = yaml.safe_load(yaml_file.read())
-                    if not isinstance(prompt_template_parsed, dict):
-                        raise ValueError("The prompt loaded is not a prompt YAML file.")
-                    name = prompt_template_parsed["name"]
-                    prompt_text = prompt_template_parsed["prompt_text"]
-
-            # Otherwise it's a on-the-fly prompt text
-            else:
-                prompt_text = prompt
-                name = "custom-at-query-time"
-
-        except OSError as exc:
-            logger.info(
-                "There was an error checking whether this prompt is a file (%s). Haystack will assume it's not.",
-                str(exc),
+        if prompt in LEGACY_DEFAULT_TEMPLATES:
+            warnings.warn(
+                f"You're using a legacy prompt template '{prompt}', "
+                "we strongly suggest you use prompts from the official Haystack PromptHub: "
+                "https://prompthub.deepset.ai/"
             )
-            # In case of errors, let's directly assume this is a text prompt
+            name = prompt
+            prompt_text = LEGACY_DEFAULT_TEMPLATES[prompt]["prompt"]
+            output_parser = LEGACY_DEFAULT_TEMPLATES[prompt].get("output_parser")
+
+        # if it looks like a prompt template name
+        elif re.fullmatch(r"[-a-zA-Z0-9_/]+", prompt):
+            name = prompt
+            try:
+                prompt_text = self._fetch_from_prompthub(prompt)
+            except HTTPError as http_error:
+                if http_error.response.status_code != 404:
+                    raise http_error
+                raise PromptNotFoundError(f"Prompt template named '{name}' not available in the Prompt Hub.")
+
+        # if it's a path to a YAML file
+        elif len(prompt) < 255 and Path(prompt).exists():
+            with open(prompt, "r", encoding="utf-8") as yaml_file:
+                prompt_template_parsed = yaml.safe_load(yaml_file.read())
+                if not isinstance(prompt_template_parsed, dict):
+                    raise ValueError("The prompt loaded is not a prompt YAML file.")
+                name = prompt_template_parsed["name"]
+                prompt_text = prompt_template_parsed["prompt_text"]
+
+        # Otherwise it's a on-the-fly prompt text
+        else:
             prompt_text = prompt
             name = "custom-at-query-time"
 
@@ -296,8 +408,6 @@ def output_variable(self) -> Optional[str]:
     def _fetch_from_prompthub(self, name) -> str:
         """
         Looks for the given prompt in the PromptHub if the prompt is not in the local cache.
-
-        Raises PromptNotFoundError if the prompt is not present in the hub.
         """
         try:
             prompt_data: prompthub.Prompt = prompthub.fetch(name, timeout=PROMPTHUB_TIMEOUT)

test/prompt/test_prompt_node.py

@@ -8,8 +8,8 @@
 
 from haystack import Document, Pipeline, BaseComponent, MultiLabel
 from haystack.nodes.prompt import PromptTemplate, PromptNode, PromptModel
+from haystack.nodes.prompt.prompt_template import LEGACY_DEFAULT_TEMPLATES
 from haystack.nodes.prompt.invocation_layer import HFLocalInvocationLayer, DefaultTokenStreamingHandler
-from haystack.nodes.prompt.legacy_default_templates import LEGACY_DEFAULT_TEMPLATES
 
 
 @pytest.fixture
@@ -103,8 +103,8 @@ def test_get_prompt_template_no_default_template(mock_model):
 def test_get_prompt_template_from_legacy_default_template(mock_model):
     node = PromptNode()
     template = node.get_prompt_template("question-answering")
-    assert template.name == "custom-at-query-time"
-    assert template.prompt_text == LEGACY_DEFAULT_TEMPLATES["question-answering"].prompt_text
+    assert template.name == "question-answering"
+    assert template.prompt_text == LEGACY_DEFAULT_TEMPLATES["question-answering"]["prompt"]
 
 
 @pytest.mark.unit

test/prompt/test_prompt_template.py

@@ -7,12 +7,13 @@
 
 from haystack.nodes.prompt import PromptTemplate
 from haystack.nodes.prompt.prompt_node import PromptNode
-from haystack.nodes.prompt.prompt_template import PromptTemplateValidationError
+from haystack.nodes.prompt.prompt_template import PromptTemplateValidationError, LEGACY_DEFAULT_TEMPLATES
 from haystack.nodes.prompt.shapers import AnswerParser
 from haystack.pipelines.base import Pipeline
 from haystack.schema import Answer, Document
 
 
+@pytest.fixture
 def mock_prompthub():
     with patch("haystack.nodes.prompt.prompt_template.PromptTemplate._fetch_from_prompthub") as mock_prompthub:
         mock_prompthub.side_effect = [
@@ -28,6 +29,14 @@ def test_prompt_templates_from_hub():
         mock_prompthub.fetch.assert_called_with("deepset/question-answering", timeout=30)
 
 
+@pytest.mark.unit
+def test_prompt_templates_from_legacy_set(mock_prompthub):
+    p = PromptTemplate("question-answering")
+    assert p.name == "question-answering"
+    assert p.prompt_text == LEGACY_DEFAULT_TEMPLATES["question-answering"]["prompt"]
+    mock_prompthub.assert_not_called()
+
+
 @pytest.mark.unit
 def test_prompt_templates_from_file(tmp_path):
     path = tmp_path / "test-prompt.yml"