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Add Langchain Agent wrapper with OpenAI Function / Self-ask agent support (#82)

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* update Param() type hint in MVP

* update default embedding endpoint

* update Langchain agent wrapper

* update langchain agent
This commit is contained in:
Tuan Anh Nguyen Dang (Tadashi_Cin)
2023-11-20 16:26:08 +07:00
committed by GitHub
parent 0a3fc4b228
commit 8bb7ad91e0
9 changed files with 137 additions and 35 deletions
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11
knowledgehub/pipelines/qa.py
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@@ -49,14 +49,14 @@ class QuestionAnsweringPipeline(BaseComponent):
request_timeout=60,
)
vector_store: _[BaseVectorStore] = _(InMemoryVectorStore)
doc_store: _[BaseDocumentStore] = _(InMemoryDocumentStore)
vector_store: BaseVectorStore = _(InMemoryVectorStore)
doc_store: BaseDocumentStore = _(InMemoryDocumentStore)
rerankers: Sequence[BaseRerankingPipeline] = []
embedding: AzureOpenAIEmbeddings = AzureOpenAIEmbeddings.withx(
model="text-embedding-ada-002",
deployment="dummy-q2-text-embedding",
azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
azure_endpoint="https://bleh-dummy.openai.azure.com/",
openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
)
@@ -137,8 +137,9 @@ class AgentQAPipeline(QuestionAnsweringPipeline):
component=self.retrieving_pipeline,
)
if search_tool not in self.agent.plugins:
self.agent.plugins.append(search_tool)
self.agent.add_tools([search_tool])
def run(self, question: str, use_citation: bool = False) -> Document:
answer = self.agent(question, use_citation=use_citation)
kwargs = {"use_citation": use_citation} if use_citation else {}
answer = self.agent(question, **kwargs)
return answer