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Update SQL templates (#12464)

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Lance Martin 2023-10-27 16:34:37 -07:00 committed by GitHub
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14 changed files with 204 additions and 28 deletions
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16
templates/sql-llama2/README.md
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@ -8,8 +8,14 @@ But, it can be adapted to any API that support LLaMA2, including [Fireworks](htt
See related templates `sql-ollama` and `sql-llamacpp` for private, local chat with SQL.
## Installation
```bash
# from inside your LangServe instance
poe add sql-llama2
```
## Set up SQL DB
This template includes an example DB of 2023 NBA rosters.
You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb).
## LLM
This template will use a `Replicate` [hosted version](https://replicate.com/meta/llama-2-13b-chat/versions/f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d) of LLaMA2.
Be sure that `REPLICATE_API_TOKEN` is set in your environment.

0
templates/sql-llama2/nba_roster.db
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65
templates/sql-llama2/sql_llama2.ipynb Normal file
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@ -0,0 +1,65 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "22f3f9f9-80ee-4da1-ba12-105a0ce74203",
"metadata": {},
"source": [
"## Run Template\n",
"\n",
"In `server.py`, set -\n",
"```\n",
"add_routes(app, chain, path=\"/sql_llama2\")\n",
"```\n",
"\n",
"This template includes an example DB of 2023 NBA rosters.\n",
"\n",
"We can ask questions related to NBA players. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "4545c603-77ec-4c15-b9c0-a70529eebed0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\" Sure thing! Here's the natural language response based on the given SQL query and response:\\n\\nKlay Thompson plays for the Golden State Warriors.\""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langserve.client import RemoteRunnable\n",
"sql_app = RemoteRunnable('http://0.0.0.0:8001/sql_llama2')\n",
"sql_app.invoke({\"question\": \"What team is Klay Thompson on?\"})"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

2
templates/sql-llama2/sql_llama2/__init__.py
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@ -1,3 +1,3 @@
from llama2.chain import chain
from sql_llama2.chain import chain
__all__ = ["chain"]

10
templates/sql-llama2/sql_llama2/chain.py
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@ -2,6 +2,7 @@ from pathlib import Path
from langchain.llms import Replicate
from langchain.prompts import ChatPromptTemplate
from langchain.pydantic_v1 import BaseModel
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
from langchain.utilities import SQLDatabase
@ -14,13 +15,11 @@ llm = Replicate(
model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},
)
db_path = Path(__file__).parent / "nba_roster.db"
rel = db_path.relative_to(Path.cwd())
db_string = f"sqlite:///{rel}"
db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0)
def get_schema(_):
return db.get_table_info()
@ -28,7 +27,6 @@ def get_schema(_):
def run_query(query):
return db.run(query)
template_query = """Based on the table schema below, write a SQL query that would answer the user's question:
{schema}
@ -66,8 +64,12 @@ prompt_response = ChatPromptTemplate.from_messages(
]
)
# Supply the input types to the prompt
class InputType(BaseModel):
question: str
chain = (
RunnablePassthrough.assign(query=sql_response)
RunnablePassthrough.assign(query=sql_response).with_types(input_type=InputType)
| RunnablePassthrough.assign(
schema=get_schema,
response=lambda x: db.run(x["query"]),

7
templates/sql-llamacpp/README.md
View File

@ -27,10 +27,3 @@ You can select other files and specify their download path (browse [here](https:
This template includes an example DB of 2023 NBA rosters.
You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb).
## Installation
```bash
# from inside your LangServe instance
poe add sql/llama2-ollama
```

0
templates/sql-llamacpp/nba_roster.db
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54
templates/sql-llamacpp/sql-llamacpp.ipynb Normal file
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@ -0,0 +1,54 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a0314df0-da99-4086-a96f-b14df05b3362",
"metadata": {},
"source": [
"## Run Template\n",
"\n",
"In `server.py`, set -\n",
"```\n",
"add_routes(app, chain, path=\"/sql_llamacpp\")\n",
"```\n",
"\n",
"This template includes an example DB of 2023 NBA rosters.\n",
"\n",
"We can ask questions related to NBA players. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff5869c6-2065-48f3-bb43-52a515968276",
"metadata": {},
"outputs": [],
"source": [
"from langserve.client import RemoteRunnable\n",
"sql_app = RemoteRunnable('http://0.0.0.0:8001/sql_llamacpp')\n",
"sql_app.invoke({\"question\": \"What team is Klay Thompson on?\"})"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

2
templates/sql-llamacpp/sql_llamacpp/__init__.py
View File

@ -1,3 +1,3 @@
from llamacpp.chain import chain
from sql_llamacpp.chain import chain
__all__ = ["chain"]

6
templates/sql-llamacpp/sql_llamacpp/chain.py
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@ -6,6 +6,7 @@ import requests
from langchain.llms import LlamaCpp
from langchain.memory import ConversationBufferMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.pydantic_v1 import BaseModel
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
from langchain.utilities import SQLDatabase
@ -113,9 +114,12 @@ prompt_response = ChatPromptTemplate.from_messages(
("human", template),
]
)
# Supply the input types to the prompt
class InputType(BaseModel):
question: str
chain = (
RunnablePassthrough.assign(query=sql_response_memory)
RunnablePassthrough.assign(query=sql_response_memory).with_types(input_type=InputType)
| RunnablePassthrough.assign(
schema=get_schema,
response=lambda x: db.run(x["query"]),

9
templates/sql-ollama/README.md
View File

@ -15,11 +15,4 @@ Also follow instructions to download your LLM of interest:
This template includes an example DB of 2023 NBA rosters.
You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb).
## Installation
```bash
# from inside your LangServe instance
poe add sql-ollama
```
You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb).

0
templates/sql-ollama/nba_roster.db
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54
templates/sql-ollama/sql-ollama.ipynb Normal file
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@ -0,0 +1,54 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "d55f5fd9-21eb-433d-9259-0a588d9197c0",
"metadata": {},
"source": [
"## Run Template\n",
"\n",
"In `server.py`, set -\n",
"```\n",
"add_routes(app, chain, path=\"/sql_ollama\")\n",
"```\n",
"\n",
"This template includes an example DB of 2023 NBA rosters.\n",
"\n",
"We can ask questions related to NBA players. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50c27e82-92d8-4fa1-8bc4-b6544e59773d",
"metadata": {},
"outputs": [],
"source": [
"from langserve.client import RemoteRunnable\n",
"sql_app = RemoteRunnable('http://0.0.0.0:8001/sql_ollama')\n",
"sql_app.invoke({\"question\": \"What team is Klay Thompson on?\"})"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

7
templates/sql-ollama/sql_ollama/chain.py
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@ -3,6 +3,7 @@ from pathlib import Path
from langchain.chat_models import ChatOllama
from langchain.memory import ConversationBufferMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.pydantic_v1 import BaseModel
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
from langchain.utilities import SQLDatabase
@ -82,8 +83,12 @@ prompt_response = ChatPromptTemplate.from_messages(
]
)
# Supply the input types to the prompt
class InputType(BaseModel):
question: str
chain = (
RunnablePassthrough.assign(query=sql_response_memory)
RunnablePassthrough.assign(query=sql_response_memory).with_types(input_type=InputType)
| RunnablePassthrough.assign(
schema=get_schema,
response=lambda x: db.run(x["query"]),