Documentation: Update planning-tasks.md (#250)

This commit is contained in:
Tazik Shahjahan
2023-12-04 11:55:28 -08:00
committed by GitHub
parent 7a9f57be7d
commit 8d3c255240
+7 -7
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@@ -41,7 +41,7 @@ class Query(BaseModel):
...,
description="Question asked using a question answering system",
)
dependancies: List[int] = Field(
dependencies: List[int] = Field(
default_factory=list,
description="List of sub questions that need to be answered before asking this question",
)
@@ -94,7 +94,7 @@ def query_planner(question: str) -> QueryPlan:
},
]
QueryPlan = client.chat.completions.create(
root = client.chat.completions.create(
model=PLANNING_MODEL,
temperature=0,
response_model=QueryPlan,
@@ -108,7 +108,7 @@ def query_planner(question: str) -> QueryPlan:
plan = query_planner(
"What is the difference in populations of Canada and the Jason's home country?"
)
plan.dict()
plan.model_dump()
```
!!! warning "No RAG"
@@ -116,19 +116,19 @@ plan.dict()
While we build the query plan in this example, we do not propose a method to actually answer the question. You can implement your own answer function that perhaps makes a retrival and calls openai for retrival augmented generation. That step would also make use of function calls but goes beyond the scope of this example.
```python
{'query_graph': [{'dependancies': [],
{'query_graph': [{'dependencies': [],
'id': 1,
'node_type': <QueryType.SINGLE_QUESTION: 'SINGLE'>,
'question': "Identify Jason's home country"},
{'dependancies': [],
{'dependencies': [],
'id': 2,
'node_type': <QueryType.SINGLE_QUESTION: 'SINGLE'>,
'question': 'Find the population of Canada'},
{'dependancies': [1],
{'dependencies': [1],
'id': 3,
'node_type': <QueryType.SINGLE_QUESTION: 'SINGLE'>,
'question': "Find the population of Jason's home country"},
{'dependancies': [2, 3],
{'dependencies': [2, 3],
'id': 4,
'node_type': <QueryType.SINGLE_QUESTION: 'SINGLE'>,
'question': 'Calculate the difference in populations between Canada and Jason's home country"}]}