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Feat: add BedrockCV for vision/image2text inference via LiteLLM (#14705)

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## Summary

- `CvModel["Bedrock"]` was absent from `rag/llm/cv_model.py`, causing
`model_instance()` to return `None` when a Bedrock model was used as a
PDF parser — even after correct model resolution.
- This PR adds `BedrockCV`, enabling Bedrock vision models (e.g.
`amazon.nova-pro-v1:0`, `anthropic.claude-3-5-sonnet`) to be used as PDF
parsers.

## What problem does this PR solve?

When a Bedrock model is selected as the PDF parser in a knowledge base,
ingestion failed with:

```
'LiteLLMBase' object has no attribute 'describe_with_prompt'
```

The root cause: `LiteLLMBase` (the Bedrock chat implementation) was the
only registered handler for the Bedrock factory. It does not implement
`describe_with_prompt`. `CvModel` had no Bedrock entry, so
`model_instance()` returned `None` for `image2text` requests.

## Type of change

- [x] New Feature (non-breaking change which adds functionality)

## Changes

**`rag/llm/cv_model.py`**

Adds `BedrockCV(Base)` with `_FACTORY_NAME = "Bedrock"`:

- Uses `litellm.completion` with the `bedrock/` prefix (consistent with
`LiteLLMBase`)
- Parses AWS credentials from the JSON key assembled by `add_llm`
(`auth_mode`, `bedrock_ak`, `bedrock_sk`, `bedrock_region`,
`aws_role_arn`)
- Supports three auth modes: `access_key_secret`, `iam_role` (via STS
`assume_role`), and default credential chain (IRSA, instance profile)
- Implements `describe_with_prompt` and `describe`

## Test plan

- [ ] Configure a Bedrock vision model (e.g. `amazon.nova-pro-v1:0`)
with valid AWS credentials
- [ ] Select it as PDF parser in a knowledge base
- [ ] Verify ingestion of a PDF document completes without errors
- [ ] Verify `CvModel["Bedrock"]` resolves to `BedrockCV`

🤖 Generated with [Claude Code](https://claude.ai/claude-code)

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
VincentLambert
2026-05-11 04:29:58 +02:00
committed by GitHub
parent 3c4d1da98f
commit 08bb53bbb1
3 changed files with 76 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
pyproject.toml
View File

@@ -16,6 +16,7 @@ dependencies = [
"azure-storage-file-datalake==12.16.0",
"beartype>=0.20.0,<1.0.0",
"bio==1.7.1",
"boto3>=1.28.0",
"boxsdk>=10.1.0",
"captcha>=0.7.1",
"chardet>=5.2.0,<6.0.0",

61
rag/llm/cv_model.py
View File

@@ -1276,14 +1276,67 @@ class RAGconCV(GptV4):
_FACTORY_NAME = "RAGcon"
def __init__(self, key, model_name, lang="Chinese", base_url="", **kwargs):
if not base_url:
base_url = "https://connect.ragcon.com/v1"
# Initialize client
self.client = OpenAI(api_key=key, base_url=base_url)
self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
self.model_name = model_name
self.lang = lang
Base.__init__(self, **kwargs)
Base.__init__(self, **kwargs)
class BedrockCV(Base):
_FACTORY_NAME = "Bedrock"
def __init__(self, key, model_name, lang="Chinese", **kwargs):
self.model_name = f"bedrock/{model_name}"
self.lang = lang
self._parse_credentials(key)
Base.__init__(self, **kwargs)
def _parse_credentials(self, key):
bedrock_key = json.loads(key)
self.auth_mode = bedrock_key.get("auth_mode", "")
self.aws_region = bedrock_key.get("bedrock_region", "us-east-1")
self.aws_ak = bedrock_key.get("bedrock_ak", "")
self.aws_sk = bedrock_key.get("bedrock_sk", "")
self.aws_role_arn = bedrock_key.get("aws_role_arn", "")
def _get_aws_creds(self):
if self.auth_mode == "access_key_secret":
return {
"aws_region_name": self.aws_region,
"aws_access_key_id": self.aws_ak,
"aws_secret_access_key": self.aws_sk,
}
elif self.auth_mode == "iam_role":
import boto3
sts_client = boto3.client("sts", region_name=self.aws_region)
resp = sts_client.assume_role(RoleArn=self.aws_role_arn, RoleSessionName="BedrockCVSession")
creds = resp["Credentials"]
return {
"aws_region_name": self.aws_region,
"aws_access_key_id": creds["AccessKeyId"],
"aws_secret_access_key": creds["SecretAccessKey"],
"aws_session_token": creds["SessionToken"],
}
else:
return {"aws_region_name": self.aws_region}
def describe_with_prompt(self, image, prompt=None):
import litellm
b64 = self.image2base64(image)
messages = self.vision_llm_prompt(b64, prompt)
res = litellm.completion(
model=self.model_name,
messages=messages,
**self._get_aws_creds(),
)
return res.choices[0].message.content.strip(), total_token_count_from_response(res)
def describe(self, image):
return self.describe_with_prompt(image)

20
uv.lock generated
View File

@@ -1,5 +1,4 @@
version = 1
revision = 3
requires-python = ">=3.12, <3.15"
resolution-markers = [
"python_full_version >= '3.14' and sys_platform == 'darwin'",
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]
[[package]]
@@ -5740,6 +5745,8 @@ wheels = [
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[[package]]
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@@ -6562,6 +6573,7 @@ dependencies = [
{ name = "azure-storage-file-datalake" },
{ name = "beartype" },
{ name = "bio" },
{ name = "boto3" },
{ name = "boxsdk" },
{ name = "captcha" },
{ name = "chardet" },
@@ -6706,6 +6718,7 @@ requires-dist = [
{ name = "azure-storage-file-datalake", specifier = "==12.16.0" },
{ name = "beartype", specifier = ">=0.20.0,<1.0.0" },
{ name = "bio", specifier = "==1.7.1" },
{ name = "boto3", specifier = ">=1.28.0" },
{ name = "boxsdk", specifier = ">=10.1.0" },
{ name = "captcha", specifier = ">=0.7.1" },
{ name = "chardet", specifier = ">=5.2.0,<6.0.0" },
@@ -6735,7 +6748,7 @@ requires-dist = [
{ name = "google-cloud-storage", specifier = ">=2.19.0,<3.0.0" },
{ name = "google-genai", specifier = ">=1.41.0,<2.0.0" },
{ name = "google-search-results", specifier = "==2.4.2" },
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{ name = "grpcio-status", specifier = "==1.67.1" },
{ name = "html-text", specifier = "==0.6.2" },
@@ -8129,6 +8142,9 @@ dependencies = [
{ name = "typing-extensions", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
{ name = "wrapt", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
]
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[[package]]
name = "tensorflow-intel"