From 353fdd7aba4873e9046e5b0f6a07694acb15c7ed Mon Sep 17 00:00:00 2001
From: David Oplatka <david.oplatka@vectara.com>
Date: Fri, 15 Nov 2024 14:09:27 -0800
Subject: [PATCH] Add Custom Prompt Parameter (#16976)
---
.../indices/managed/vectara/retriever.py | 7 +++++++
.../pyproject.toml | 2 +-
.../tests/test_indices_managed_vectara.py | 20 +++++++++++++++++++
3 files changed, 28 insertions(+), 1 deletion(-)
diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py
index 0018475f6c..705fcf6e87 100644
--- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py
+++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py
@@ -89,6 +89,9 @@ class VectaraRetriever(BaseRetriever):
summary_response_lang: language to use for summary generation.
summary_num_results: number of results to use for summary generation.
summary_prompt_name: name of the prompt to use for summary generation.
+ prompt_text: the custom prompt, using appropriate prompt variables and functions.
+ See (https://docs.vectara.com/docs/1.0/prompts/custom-prompts-with-metadata)
+ for more details.
citations_style: The style of the citations in the summary generation,
either "numeric", "html", "markdown", or "none".
This is a Vectara Scale only feature. Defaults to None.
@@ -118,6 +121,7 @@ class VectaraRetriever(BaseRetriever):
summary_response_lang: str = "eng",
summary_num_results: int = 7,
summary_prompt_name: str = "vectara-summary-ext-24-05-sml",
+ prompt_text: Optional[str] = None,
citations_style: Optional[str] = None,
citations_url_pattern: Optional[str] = None,
citations_text_pattern: Optional[str] = None,
@@ -132,6 +136,7 @@ class VectaraRetriever(BaseRetriever):
self._n_sentences_before = n_sentences_before
self._n_sentences_after = n_sentences_after
self._filter = filter
+ self._prompt_text = prompt_text
self._citations_style = citations_style.upper() if citations_style else None
self._citations_url_pattern = citations_url_pattern
self._citations_text_pattern = citations_text_pattern
@@ -286,6 +291,8 @@ class VectaraRetriever(BaseRetriever):
"summarizerPromptName": self._summary_prompt_name,
}
data["query"][0]["summary"] = [summary_config]
+ if self._prompt_text:
+ data["query"][0]["summary"][0]["promptText"] = self._prompt_text
if chat:
data["query"][0]["summary"][0]["chat"] = {
"store": True,
diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-vectara/pyproject.toml
index c15ff6ade8..590de33e61 100644
--- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/pyproject.toml
+++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/pyproject.toml
@@ -31,7 +31,7 @@ exclude = ["**/BUILD"]
license = "MIT"
name = "llama-index-indices-managed-vectara"
readme = "README.md"
-version = "0.2.4"
+version = "0.2.5"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/tests/test_indices_managed_vectara.py b/llama-index-integrations/indices/llama-index-indices-managed-vectara/tests/test_indices_managed_vectara.py
index 04b4ef3ec2..b15e8ae964 100644
--- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/tests/test_indices_managed_vectara.py
+++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/tests/test_indices_managed_vectara.py
@@ -214,6 +214,26 @@ def test_chain_rerank_retrieval(vectara1) -> None:
assert res[0].node.get_content() == docs[2].text
+def test_custom_prompt(vectara1) -> None:
+ docs = get_docs()
+
+ qe = vectara1.as_query_engine(
+ similarity_top_k=3,
+ n_sentences_before=0,
+ n_sentences_after=0,
+ reranker="mmr",
+ mmr_diversity_bias=0.2,
+ summary_enabled=True,
+ prompt_text='[\n {"role": "system", "content": "You are an expert in summarizing the future of Vectara\'s inegration with LlamaIndex. Your summaries are insightful, concise, and highlight key innovations and changes."},\n #foreach ($result in $vectaraQueryResults)\n {"role": "user", "content": "What are the key points in result number $vectaraIdxWord[$foreach.index] about Vectara\'s LlamaIndex integration?"},\n {"role": "assistant", "content": "In result number $vectaraIdxWord[$foreach.index], the key points are: ${result.getText()}"},\n #end\n {"role": "user", "content": "Can you generate a comprehensive summary on \'Vectara\'s LlamaIndex Integration\' incorporating all the key points discussed?"}\n]\n',
+ )
+
+ res = qe.query("How will Vectara's integration look in the future?")
+ assert "integration" in str(res).lower()
+ assert "llamaindex" in str(res).lower()
+ assert "vectara" in str(res).lower()
+ assert "first" in str(res).lower()
+
+
@pytest.fixture()
def vectara2():
try:
--
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