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## Getting Started
The `Docker Compose` files have been tested on `Linux` and `MacOS`. There are currently
no plans for `Windows` support in the immediate future.
There are 4 `Docker Compose` files depending on the desired LM deployment:
- `VertexAI` through Google Cloud
- `Claude` through Anthropic's API
- `AzureAI` serverless endpoint
- Local LM deployment through `Ollama`
Docker Compose enables the following functions:
- Run the required components for full e2e `Graph RAG` knowledge pipeline
- Check processing logs
- Load test text corpus and begin knowledge extraction
- Verify extracted graph edges and number of edges
- Run a query against the vector and graph stores to generate a response
using the chosen LM
### Clone the Repo
```
git clone https://github.com/trustgraph-ai/trustgraph trustgraph
cd trustgraph
```
### Install requirements
```
python3 -m venv env
. env/bin/activate
pip3 install pulsar-client
pip3 install cassandra-driver
export PYTHON_PATH=.
```
### Docker Compose files
Depending on your desired LM deployment, you will choose from one of the
following `Docker Compose` files:
- `docker-compose-azure.yaml`: AzureAI endpoint. Set `AZURE_TOKEN` to the secret token and
`AZURE_ENDPOINT` to the URL endpoint address for the deployed model.
- `docker-compose-claude.yaml`: Anthropic's API. Set `CLAUDE_KEY` to your API key.
- `docker-compose-ollama.yaml`: Local LM (currently using [Gemma2](https://ollama.com/library/gemma2) deployed through Ollama. Set `OLLAMA_HOST` to the machine running Ollama (e.g. `localhost` for Ollama running locally on your machine)
- `docker-compose-vertexai.yaml`: VertexAI API. Requires a `private.json` authentication file to authenticate with your GCP project. Filed should stored be at path `vertexai/private.json`.
**NOTE**: All tokens, paths, and authentication files must be set **PRIOR** to launching a `Docker Compose` file.
#### AzureAI Serverless Model Deployment
```
export AZURE_ENDPOINT=https://ENDPOINT.HOST.GOES.HERE/
export AZURE_TOKEN=TOKEN-GOES-HERE
docker-compose -f docker-compose-azure.yaml up -d
```
#### Claude through Anthropic API
```
export CLAUDE_KEY=TOKEN-GOES-HERE
docker-compose -f docker-compose-claude.yaml up -d
```
#### Ollama Hosted Model Deployment
```
export OLLAMA_HOST=localhost # Set to hostname of Ollama host
docker-compose -f docker-compose-ollama.yaml up -d
```
#### VertexAI through GCP
```
mkdir -p vertexai
cp {whatever} vertexai/private.json
docker-compose -f docker-compose-vertexai.yaml up -d
```
If you're running `SELinux` on Linux you may need to set the permissions on the
VertexAI directory so that the key file can be mounted on a Docker container using
the following command:
```
chcon -Rt svirt_sandbox_file_t vertexai/
```
### Verify Docker Containers
On first running a `Docker Compose` file, it may take a while (depending on your network connection) to pull all the necessary components. Once all of the components have been pulled, check that the TrustGraph containers are running:
```
docker ps
```
Any containers that have exited unexpectedly can be found by checking the `STATUS` field
using the following:
```
docker ps -a
```
### Warm-Up
Before proceeding, allow the system to enter a stable a working state. In general
`30 seconds` should be enough time for Pulsar to stablize.
The system uses Cassandra for a Graph store. Cassandra can take `60-70 seconds`
to achieve a working state.
### Load a Text Corpus
Create a sources directory and get a test PDF file. To demonstrate the power of TrustGraph, we're using a PDF of the public [Roger's Commision Report](https://sma.nasa.gov/SignificantIncidents/assets/rogers_commission_report.pdf) from the NASA Challenger disaster. This PDF includes complex formatting, unique terms, complex concepts, unique concepts, and information not commonly found in public knowledge sources.
```
mkdir sources
curl -o sources/Challenger-Report-Vol1.pdf https://sma.nasa.gov/SignificantIncidents/assets/rogers_commission_report.pdf
```
Load the file for knowledge extraction:
```
scripts/loader -f sources/Challenger-Report-Vol1.pdf
```
`File loaded.` indicates the PDF has been sucessfully loaded to the processing queues and extraction will begin.
### Processing Logs
At this point, many processing services are running concurrently. You can check the status of these processes with the following logs:
`PDF Decoder`:
```
docker logs trustgraph-pdf-decoder-1
```
Output should look:
```
Decoding 1f7b7055...
Done.
```
`Chunker`:
```
docker logs trustgraph-chunker-1
```
The output should be similiar to the output of the `Decode`, except it should be a sequence of many entries.
`Vectorizer`:
```
docker logs trustgraph-vectorize-1
```
Similar output to above processes, except many entries instead.
`Language Model Inference`:
```
docker logs trustgraph-llm-1
```
Output should be a sequence of entries:
```
Handling prompt fa1b98ae-70ef-452b-bcbe-21a867c5e8e2...
Send response...
Done.
```
`Knowledge Graph Definitions`:
```
docker logs trustgraph-kg-extract-definitions-1
```
Output should be an array of JSON objects with keys `entity` and `definition`:
```
Indexing 1f7b7055-p11-c1...
[
{
"entity": "Orbiter",
"definition": "A spacecraft designed for spaceflight."
},
{
"entity": "flight deck",
"definition": "The top level of the crew compartment, typically where flight controls are located."
},
{
"entity": "middeck",
"definition": "The lower level of the crew compartment, used for sleeping, working, and storing equipment."
}
]
Done.
```
`Knowledge Graph Relationshps`:
```
docker logs trustgraph-kg-extract-relationships-1
```
Output should be an array of JSON objects with keys `subject`, `predicate`, `object`, and `object-entity`:
```
Indexing 1f7b7055-p11-c3...
[
{
"subject": "Space Shuttle",
"predicate": "carry",
"object": "16 tons of cargo",
"object-entity": false
},
{
"subject": "friction",
"predicate": "generated by",
"object": "atmosphere",
"object-entity": true
}
]
Done.
```
### Graph Parsing
To check that the knowledge graph is successfully parsing data:
```
scripts/graph-show
```
The output should be a set of semantic triples in [N-Triples](https://www.w3.org/TR/rdf12-n-triples/) format.
```
http://trustgraph.ai/e/enterprise http://trustgraph.ai/e/was-carried to altitude and released for a gliding approach and landing at the Mojave Desert test center.
http://trustgraph.ai/e/enterprise http://www.w3.org/2000/01/rdf-schema#label Enterprise.
http://trustgraph.ai/e/enterprise http://www.w3.org/2004/02/skos/core#definition A prototype space shuttle orbiter used for atmospheric flight testing.
```
### Number of Graph Edges
N-Triples format is not particularly human readable. It's more useful to know how many graph edges have successfully been extracted from the text corpus:
```
scripts/graph-show | wc -l
```
The Challenger report has a long introduction with quite a bit of adminstrative text commonly found in official reports. The first few hundred graph edges mostly capture this document formatting knowledge. To fully test the ability to extract complex knowledge, wait until at least `1000` graph edges have been extracted. The full extraction for this PDF will extract many thousand graph edges.
### RAG Test Script
```
tests/test-graph-rag
```
This script forms a LM prompt asking for 20 facts regarding the Challenger disaster. Depending on how many graph edges have been extracted, the response will be similar to:
```
Here are 20 facts from the provided knowledge graph about the Space Shuttle disaster:
1. **Space Shuttle Challenger was a Space Shuttle spacecraft.**
2. **The third Spacelab mission was carried by Orbiter Challenger.**
3. **Francis R. Scobee was the Commander of the Challenger crew.**
4. **Earth-to-orbit systems are designed to transport payloads and humans from Earth's surface into orbit.**
5. **The Space Shuttle program involved the Space Shuttle.**
6. **Orbiter Challenger flew on mission 41-B.**
7. **Orbiter Challenger was used on STS-7 and STS-8 missions.**
8. **Columbia completed the orbital test.**
9. **The Space Shuttle flew 24 successful missions.**
10. **One possibility for the Space Shuttle was a winged but unmanned recoverable liquid-fuel vehicle based on the Saturn 5 rocket.**
11. **A Commission was established to investigate the space shuttle Challenger accident.**
12. **Judit h Arlene Resnik was Mission Specialist Two.**
13. **Mission 51-L was originally scheduled for December 1985 but was delayed until January 1986.**
14. **The Corporation's Space Transportation Systems Division was responsible for the design and development of the Space Shuttle Orbiter.**
15. **Michael John Smith was the Pilot of the Challenger crew.**
16. **The Space Shuttle is composed of two recoverable Solid Rocket Boosters.**
17. **The Space Shuttle provides for the broadest possible spectrum of civil/military missions.**
18. **Mission 51-L consisted of placing one satellite in orbit, deploying and retrieving Spartan, and conducting six experiments.**
19. **The Space Shuttle became the focus of NASA's near-term future.**
20. **The Commission focused its attention on safety aspects of future flights.**
```
For any errors with the `RAG` proces, check the following log:
```
docker logs -f trustgraph-graph-rag-1
```
### More RAG Test Queries
If you want to try different RAG queries, modify the `query` in the [test script](https://github.com/trustgraph-ai/trustgraph/blob/master/tests/test-graph-rag).
### Shutting Down
When shutting down the pipeline, it's best to shut down all Docker containers and volumes. Run the `docker compose down` command that corresponds to your model deployment:
`AzureAI Endpoint`:
```
docker-compose -f docker-compose-azure.yaml down --volumes
```
`Anthropic API`:
```
docker-compose -f docker-compose-claude.yaml down --volumes
```
`Ollama`:
```
docker-compose -f docker-compose-ollama.yaml down --volumes
```
`VertexAI API`:
```
docker-compose -f docker-compose-vertexai.yaml down --volumes
```
To confirm all Docker containers have been shut down, check that the following list is empty:
```
docker ps
```
To confirm all Docker volumes have been removed, check that the following list is empty:
```
docker volume ls
```

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#!/usr/bin/env python3
from trustgraph.kg.extract_definitions import run
run()

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scripts/.ipynb_checkpoints/llm-azure-text-checkpoint
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#!/usr/bin/env python3
from trustgraph.llm.azure_text import run
run()

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from . extract import *

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#!/usr/bin/env python3
from . extract import run
if __name__ == '__main__':
run()

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"""
Simple decoder, accepts vector+text chunks input, applies entity analysis to
get entity definitions which are output as graph edges.
"""
import pulsar
from pulsar.schema import JsonSchema
from langchain_community.document_loaders import PyPDFLoader
import tempfile
import base64
import os
import argparse
import rdflib
import json
import urllib.parse
import time
from ... schema import VectorsChunk, Triple, Source, Value
from ... log_level import LogLevel
from ... llm_client import LlmClient
from ... prompts import to_definitions
from ... rdf import TRUSTGRAPH_ENTITIES, DEFINITION
DEFINITION_VALUE = Value(value=DEFINITION, is_uri=True)
default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650')
default_input_queue = 'vectors-chunk-load'
default_output_queue = 'graph-load'
default_subscriber = 'kg-extract-definitions'
class Processor:
def __init__(
self,
pulsar_host=default_pulsar_host,
input_queue=default_input_queue,
output_queue=default_output_queue,
subscriber=default_subscriber,
log_level=LogLevel.INFO,
):
self.client = None
self.client = pulsar.Client(
pulsar_host,
logger=pulsar.ConsoleLogger(log_level.to_pulsar())
)
self.consumer = self.client.subscribe(
input_queue, subscriber,
schema=JsonSchema(VectorsChunk),
)
self.producer = self.client.create_producer(
topic=output_queue,
schema=JsonSchema(Triple),
)
self.llm = LlmClient(pulsar_host=pulsar_host)
def to_uri(self, text):
part = text.replace(" ", "-").lower().encode("utf-8")
quoted = urllib.parse.quote(part)
uri = TRUSTGRAPH_ENTITIES + quoted
return uri
def get_definitions(self, chunk):
prompt = to_definitions(chunk)
resp = self.llm.request(prompt)
defs = json.loads(resp)
return defs
def emit_edge(self, s, p, o):
t = Triple(s=s, p=p, o=o)
self.producer.send(t)
def run(self):
while True:
msg = self.consumer.receive()
try:
v = msg.value()
print(f"Indexing {v.source.id}...", flush=True)
chunk = v.chunk.decode("utf-8")
g = rdflib.Graph()
try:
defs = self.get_definitions(chunk)
print(json.dumps(defs, indent=4), flush=True)
for defn in defs:
s = defn["entity"]
s_uri = self.to_uri(s)
o = defn["definition"]
s_value = Value(value=str(s_uri), is_uri=True)
o_value = Value(value=str(o), is_uri=False)
self.emit_edge(s_value, DEFINITION_VALUE, o_value)
except Exception as e:
print("Exception: ", e, flush=True)
print("Done.", flush=True)
# Acknowledge successful processing of the message
self.consumer.acknowledge(msg)
except Exception as e:
print("Exception: ", e, flush=True)
# Message failed to be processed
self.consumer.negative_acknowledge(msg)
def __del__(self):
if self.client:
self.client.close()
def run():
parser = argparse.ArgumentParser(
prog='pdf-decoder',
description=__doc__,
)
parser.add_argument(
'-p', '--pulsar-host',
default=default_pulsar_host,
help=f'Pulsar host (default: {default_pulsar_host})',
)
parser.add_argument(
'-i', '--input-queue',
default=default_input_queue,
help=f'Input queue (default: {default_input_queue})'
)
parser.add_argument(
'-s', '--subscriber',
default=default_subscriber,
help=f'Queue subscriber name (default: {default_subscriber})'
)
parser.add_argument(
'-o', '--output-queue',
default=default_output_queue,
help=f'Output queue (default: {default_output_queue})'
)
parser.add_argument(
'-l', '--log-level',
type=LogLevel,
default=LogLevel.INFO,
choices=list(LogLevel),
help=f'Output queue (default: info)'
)
args = parser.parse_args()
while True:
try:
p = Processor(
pulsar_host=args.pulsar_host,
input_queue=args.input_queue,
output_queue=args.output_queue,
subscriber=args.subscriber,
log_level=args.log_level,
)
p.run()
except Exception as e:
print("Exception:", e, flush=True)
print("Will retry...", flush=True)
time.sleep(10)

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from . extract import *

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#!/usr/bin/env python3
from . extract import run
if __name__ == '__main__':
run()

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"""
Simple decoder, accepts vector+text chunks input, applies entity
relationship analysis to get entity relationship edges which are output as
graph edges.
"""
import pulsar
from pulsar.schema import JsonSchema
from langchain_community.document_loaders import PyPDFLoader
import tempfile
import base64
import os
import argparse
import rdflib
import json
import urllib.parse
import time
from ... schema import VectorsChunk, Triple, VectorsAssociation, Source, Value
from ... log_level import LogLevel
from ... llm_client import LlmClient
from ... prompts import to_relationships
from ... rdf import RDF_LABEL, TRUSTGRAPH_ENTITIES
RDF_LABEL_VALUE = Value(value=RDF_LABEL, is_uri=True)
default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650')
default_input_queue = 'vectors-chunk-load'
default_output_queue = 'graph-load'
default_subscriber = 'kg-extract-relationships'
default_vector_queue='vectors-load'
class Processor:
def __init__(
self,
pulsar_host=default_pulsar_host,
input_queue=default_input_queue,
vector_queue=default_vector_queue,
output_queue=default_output_queue,
subscriber=default_subscriber,
log_level=LogLevel.INFO,
):
self.client = pulsar.Client(
pulsar_host,
logger=pulsar.ConsoleLogger(log_level.to_pulsar())
)
self.consumer = self.client.subscribe(
input_queue, subscriber,
schema=JsonSchema(VectorsChunk),
)
self.producer = self.client.create_producer(
topic=output_queue,
schema=JsonSchema(Triple),
)
self.vec_prod = self.client.create_producer(
topic=vector_queue,
schema=JsonSchema(VectorsAssociation),
)
self.llm = LlmClient(pulsar_host=pulsar_host)
def to_uri(self, text):
part = text.replace(" ", "-").lower().encode("utf-8")
quoted = urllib.parse.quote(part)
uri = TRUSTGRAPH_ENTITIES + quoted
return uri
def get_relationships(self, chunk):
prompt = to_relationships(chunk)
resp = self.llm.request(prompt)
rels = json.loads(resp)
return rels
def emit_edge(self, s, p, o):
t = Triple(s=s, p=p, o=o)
self.producer.send(t)
def emit_vec(self, ent, vec):
r = VectorsAssociation(entity=ent, vectors=vec)
self.vec_prod.send(r)
def run(self):
while True:
msg = self.consumer.receive()
try:
v = msg.value()
print(f"Indexing {v.source.id}...", flush=True)
chunk = v.chunk.decode("utf-8")
g = rdflib.Graph()
try:
rels = self.get_relationships(chunk)
print(json.dumps(rels, indent=4), flush=True)
for rel in rels:
s = rel["subject"]
p = rel["predicate"]
o = rel["object"]
s_uri = self.to_uri(s)
s_value = Value(value=str(s_uri), is_uri=True)
p_uri = self.to_uri(p)
p_value = Value(value=str(p_uri), is_uri=True)
if rel["object-entity"]:
o_uri = self.to_uri(o)
o_value = Value(value=str(o_uri), is_uri=True)
else:
o_value = Value(value=str(o), is_uri=False)
self.emit_edge(
s_value,
p_value,
o_value
)
# Label for s
self.emit_edge(
s_value,
RDF_LABEL_VALUE,
Value(value=str(s), is_uri=False)
)
# Label for p
self.emit_edge(
p_value,
RDF_LABEL_VALUE,
Value(value=str(p), is_uri=False)
)
if rel["object-entity"]:
# Label for o
self.emit_edge(
o_value,
RDF_LABEL_VALUE,
Value(value=str(o), is_uri=False)
)
self.emit_vec(s_value, v.vectors)
self.emit_vec(p_value, v.vectors)
if rel["object-entity"]:
self.emit_vec(o_value, v.vectors)
except Exception as e:
print("Exception: ", e, flush=True)
print("Done.", flush=True)
# Acknowledge successful processing of the message
self.consumer.acknowledge(msg)
except Exception as e:
print("Exception: ", e, flush=True)
# Message failed to be processed
self.consumer.negative_acknowledge(msg)
def __del__(self):
self.client.close()
def run():
parser = argparse.ArgumentParser(
prog='kg-extract-relationships',
description=__doc__,
)
parser.add_argument(
'-p', '--pulsar-host',
default=default_pulsar_host,
help=f'Pulsar host (default: {default_pulsar_host})',
)
parser.add_argument(
'-i', '--input-queue',
default=default_input_queue,
help=f'Input queue (default: {default_input_queue})'
)
parser.add_argument(
'-s', '--subscriber',
default=default_subscriber,
help=f'Queue subscriber name (default: {default_subscriber})'
)
parser.add_argument(
'-o', '--output-queue',
default=default_output_queue,
help=f'Output queue (default: {default_output_queue})'
)
parser.add_argument(
'-l', '--log-level',
type=LogLevel,
default=LogLevel.INFO,
choices=list(LogLevel),
help=f'Output queue (default: info)'
)
parser.add_argument(
'-c', '--vector-queue',
default=default_vector_queue,
help=f'Vector output queue (default: {default_vector_queue})'
)
args = parser.parse_args()
while True:
try:
p = Processor(
pulsar_host=args.pulsar_host,
input_queue=args.input_queue,
output_queue=args.output_queue,
vector_queue=args.vector_queue,
subscriber=args.subscriber,
log_level=args.log_level,
)
p.run()
except Exception as e:
print("Exception:", e, flush=True)
print("Will retry...", flush=True)
time.sleep(10)

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"""
Simple LLM service, performs text prompt completion using the Azure
serverless endpoint service. Input is prompt, output is response.
"""
import requests
import json
from ... schema import TextCompletionRequest, TextCompletionResponse
from ... log_level import LogLevel
from ... base import ConsumerProducer
default_input_queue = 'llm-complete-text'
default_output_queue = 'llm-complete-text-response'
default_subscriber = 'llm-azure-text'
class Processor(ConsumerProducer):
def __init__(
self,
pulsar_host=None,
input_queue=default_input_queue,
output_queue=default_output_queue,
subscriber=default_subscriber,
log_level=LogLevel.INFO,
endpoint=None,
token=None,
):
super(Processor, self).__init__(
pulsar_host=pulsar_host,
log_level=log_level,
input_queue=input_queue,
output_queue=output_queue,
subscriber=subscriber,
input_schema=TextCompletionRequest,
output_schema=TextCompletionResponse,
)
self.endpoint = endpoint
self.token = token
def build_prompt(self, system, content):
data = {
"messages": [
{
"role": "system", "content": system
},
{
"role": "user", "content": content
}
],
"max_tokens": 4192,
"temperature": 0.2,
"top_p": 1
}
body = json.dumps(data)
return body
def call_llm(self, body):
url = self.endpoint
# Replace this with the primary/secondary key, AMLToken, or
# Microsoft Entra ID token for the endpoint
api_key = self.token
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {api_key}'
}
resp = requests.post(url, data=body, headers=headers)
result = resp.json()
message_content = result['choices'][0]['message']['content']
return message_content
def handle(self, msg):
v = msg.value()
# Sender-produced ID
id = msg.properties()["id"]
print(f"Handling prompt {id}...", flush=True)
prompt = self.build_prompt(
"You are a helpful chatbot",
v.prompt
)
response = self.call_llm(prompt)
print("Send response...", flush=True)
r = TextCompletionResponse(response=response)
self.producer.send(r, properties={"id": id})
print("Done.", flush=True)
@staticmethod
def add_args(parser):
ConsumerProducer.add_args(
parser, default_input_queue, default_subscriber,
default_output_queue,
)
parser.add_argument(
'-e', '--endpoint',
help=f'LLM model endpoint'
)
parser.add_argument(
'-k', '--token',
help=f'LLM model token'
)
def run():
Processor.start("llm-azure-text", __doc__)

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"""
Simple LLM service, performs text prompt completion using Claude.
Input is prompt, output is response.
"""
import pulsar
from pulsar.schema import JsonSchema
import tempfile
import base64
import os
import argparse
import anthropic
import time
from ... schema import TextCompletionRequest, TextCompletionResponse
from ... log_level import LogLevel
default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650')
default_input_queue = 'llm-complete-text'
default_output_queue = 'llm-complete-text-response'
default_subscriber = 'llm-claude-text'
default_model = 'claude-3-5-sonnet-20240620'
class Processor:
def __init__(
self,
pulsar_host=default_pulsar_host,
input_queue=default_input_queue,
output_queue=default_output_queue,
subscriber=default_subscriber,
log_level=LogLevel.INFO,
model=default_model,
api_key,
):
self.client = None
self.client = pulsar.Client(
pulsar_host,
logger=pulsar.ConsoleLogger(log_level.to_pulsar())
)
self.consumer = self.client.subscribe(
input_queue, subscriber,
schema=JsonSchema(TextCompletionRequest),
)
self.producer = self.client.create_producer(
topic=output_queue,
schema=JsonSchema(TextCompletionResponse),
)
self.model = model
self.claude = anthropic.Anthropic(api_key=api_key)
print("Initialised", flush=True)
def run(self):
while True:
msg = self.consumer.receive()
try:
v = msg.value()
# Sender-produced ID
id = msg.properties()["id"]
print(f"Handling prompt {id}...", flush=True)
prompt = v.prompt
response = message = self.claude.messages.create(
model=self.model,
max_tokens=1000,
temperature=0.1,
system = "You are a helpful chatbot.",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
}
]
}
]
)
resp = response.content[0].text
print(resp, flush=True)
print("Send response...", flush=True)
r = TextCompletionResponse(response=resp)
self.producer.send(r, properties={"id": id})
print("Done.", flush=True)
# Acknowledge successful processing of the message
self.consumer.acknowledge(msg)
except Exception as e:
print("Exception:", e, flush=True)
# Message failed to be processed
self.consumer.negative_acknowledge(msg)
def __del__(self):
self.client.close()
def run():
parser = argparse.ArgumentParser(
prog='llm-ollama-text',
description=__doc__,
)
parser.add_argument(
'-p', '--pulsar-host',
default=default_pulsar_host,
help=f'Pulsar host (default: {default_pulsar_host})',
)
parser.add_argument(
'-i', '--input-queue',
default=default_input_queue,
help=f'Input queue (default: {default_input_queue})'
)
parser.add_argument(
'-s', '--subscriber',
default=default_subscriber,
help=f'Queue subscriber name (default: {default_subscriber})'
)
parser.add_argument(
'-o', '--output-queue',
default=default_output_queue,
help=f'Output queue (default: {default_output_queue})'
)
parser.add_argument(
'-l', '--log-level',
type=LogLevel,
default=LogLevel.INFO,
choices=list(LogLevel),
help=f'Output queue (default: info)'
)
parser.add_argument(
'-m', '--model',
default="claude-3-5-sonnet-20240620",
help=f'LLM model (default: claude-3-5-sonnet-20240620)'
)
parser.add_argument(
'-k', '--api-key',
help=f'Claude API key'
)
args = parser.parse_args()
while True:
try:
p = Processor(
pulsar_host=args.pulsar_host,
input_queue=args.input_queue,
output_queue=args.output_queue,
subscriber=args.subscriber,
log_level=args.log_level,
model=args.model,
api_key=args.api_key,
)
p.run()
except Exception as e:
print("Exception:", e, flush=True)
print("Will retry...", flush=True)
time.sleep(10)