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25
README.md
25
README.md
@@ -8,6 +8,7 @@
|
||||
```
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+------------------+
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| Auth0 |
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| (RBAC roles) |
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+--------+---------+
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|
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v
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@@ -18,18 +19,22 @@
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+------------------+ | - Auth0 SSO |
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+----------+----------+
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|
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| AG-UI Protocol
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v
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+----------+----------+
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| web/agent/ |
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| (LangGraph) |
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| (PydanticAI) |
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| - Google Gemini |
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| - x-user-roles |
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+----------+----------+
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|
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| Streamable HTTP
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v
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+----------+----------+
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| mcp/ |
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| (FastMCP Server) |
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| - Semantic search |
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| - Role filtering |
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+----------+----------+
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|
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+--------------------+--------------------+
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@@ -65,8 +70,8 @@
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| Component | Description | Tech Stack |
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|-----------|-------------|------------|
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| **web/** | Frontend application with chat UI | Next.js, CopilotKit, Auth0 |
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| **web/agent/** | AI agent for answering cave questions | LangGraph, Google Gemini |
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| **mcp/** | MCP server exposing semantic search tools | FastMCP, Cohere |
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| **web/agent/** | AI agent for answering cave questions | PydanticAI, AG-UI, Google Gemini |
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| **mcp/** | MCP server exposing semantic search tools | FastMCP, Starlette, Cohere |
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| **poller/** | Document ingestion and processing pipeline | Python, Claude API, Cohere |
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## Data Flow
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@@ -80,9 +85,11 @@
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- Stored in PostgreSQL with pgvector
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2. **Search & Chat** (mcp + agent)
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- User authenticates via Auth0 (roles assigned)
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- User asks question via web UI
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- Agent calls MCP tools for semantic search
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- MCP queries pgvector for relevant documents
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- Web API extracts user roles from session, passes to agent
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- Agent creates MCP connection with `x-user-roles` header
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- MCP queries pgvector, filtering by user's roles
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- Agent synthesizes response with citations
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## Getting Started
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@@ -95,6 +102,13 @@ See individual component READMEs:
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Each component requires its own environment variables. See the respective READMEs for details.
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| Component | Key Variables |
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|-----------|---------------|
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| **web/** | `AUTH0_*`, `AGENT_URL` |
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| **web/agent/** | `GOOGLE_API_KEY`, `CAVE_MCP_URL` |
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| **mcp/** | `COHERE_API_KEY`, `DB_*` |
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| **poller/** | `ANTHROPIC_API_KEY`, `COHERE_API_KEY`, `AWS_*`, `DB_*` |
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**Never commit `.env` files** - they are gitignored.
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## CI/CD
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@@ -106,6 +120,7 @@ Gitea Actions workflows build and push Docker images on changes to `main`:
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| build-push-web | `web/**` (excluding agent) | `cavepediav2-web:latest` |
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| build-push-agent | `web/agent/**` | `cavepediav2-agent:latest` |
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| build-push-poller | `poller/**` | `cavepediav2-poller:latest` |
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| build-push-mcp | `mcp/**` | `cavepediav2-mcp:latest` |
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## License
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28
mcp/Dockerfile
Normal file
28
mcp/Dockerfile
Normal file
@@ -0,0 +1,28 @@
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FROM python:3.13-slim
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# Install uv
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
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WORKDIR /app
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ENV PYTHONPATH=/app
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# Copy dependency files
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COPY pyproject.toml uv.lock ./
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# Install dependencies
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RUN uv sync --frozen --no-dev
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# Copy application code
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COPY server.py ./
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# Create non-root user
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RUN useradd --create-home --shell /bin/bash mcp
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USER mcp
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EXPOSE 8021
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ENV PORT=8021
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ENV HOST="0.0.0.0"
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CMD ["uv", "run", "--frozen", "uvicorn", "server:app", "--host", "0.0.0.0", "--port", "8021"]
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@@ -10,4 +10,5 @@ dependencies = [
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"dotenv>=0.9.9",
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"fastmcp>=2.13.3",
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"psycopg[binary]>=3.3.2",
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"uvicorn>=0.38.0",
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]
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@@ -1,48 +0,0 @@
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from pgvector.psycopg import register_vector, Bit
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from psycopg.rows import dict_row
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from urllib.parse import unquote
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import anthropic
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import cohere
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import dotenv
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import datetime
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import json
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import minio
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import numpy as np
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import os
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import psycopg
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import time
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dotenv.load_dotenv('/home/paul/scripts-private/lech/cavepedia-v2/poller.env')
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COHERE_API_KEY = os.getenv('COHERE_API_KEY')
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co = cohere.ClientV2(COHERE_API_KEY)
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conn = psycopg.connect(
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host='127.0.0.1',
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port=4010,
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dbname='cavepediav2_db',
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user='cavepediav2_user',
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password='cavepediav2_pw',
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row_factory=dict_row,
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)
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def embed(text, input_type):
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resp = co.embed(
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texts=[text],
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model='embed-v4.0',
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input_type=input_type,
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embedding_types=['float'],
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)
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return resp.embeddings.float[0]
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def search():
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query = 'links trip with not more than 2 people'
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query_embedding = embed(query, 'search_query')
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rows = conn.execute('SELECT * FROM embeddings ORDER BY embedding <=> %s::vector LIMIT 5', (query_embedding,)).fetchall()
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for row in rows:
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print(row['bucket'])
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print(row['key'])
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if __name__ == '__main__':
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search()
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@@ -35,12 +35,18 @@ mcp = FastMCP("Cavepedia MCP")
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def get_user_roles() -> list[str]:
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"""Extract user roles from the X-User-Roles header."""
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headers = get_http_headers()
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print(f"DEBUG: All headers received: {dict(headers)}")
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roles_header = headers.get("x-user-roles", "")
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print(f"DEBUG: x-user-roles header value: '{roles_header}'")
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if roles_header:
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try:
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return json.loads(roles_header)
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except json.JSONDecodeError:
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roles = json.loads(roles_header)
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print(f"DEBUG: Parsed roles: {roles}")
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return roles
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except json.JSONDecodeError as e:
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print(f"DEBUG: JSON decode error: {e}")
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return []
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print("DEBUG: No roles header found, returning empty list")
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return []
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def embed(text, input_type):
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@@ -53,42 +59,44 @@ def embed(text, input_type):
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assert resp.embeddings.float_ is not None
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return resp.embeddings.float_[0]
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def search(query, roles: list[str]) -> list[dict]:
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def search(query, roles: list[str], limit: int = 3, max_content_length: int = 1500) -> list[dict]:
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query_embedding = embed(query, 'search_query')
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if not roles:
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# No roles = no results
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return []
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rows = conn.execute(
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'SELECT * FROM embeddings WHERE embedding IS NOT NULL AND role = ANY(%s) ORDER BY embedding <=> %s::vector LIMIT 5',
|
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(roles, query_embedding)
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'SELECT * FROM embeddings WHERE embedding IS NOT NULL AND role = ANY(%s) ORDER BY embedding <=> %s::vector LIMIT %s',
|
||||
(roles, query_embedding, limit)
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||||
).fetchall()
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||||
docs = []
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||||
for row in rows:
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docs.append({ 'key': row['key'], 'content': row['content']})
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||||
content = row['content'] or ''
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||||
if len(content) > max_content_length:
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content = content[:max_content_length] + '...[truncated, use get_document_page for full text]'
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docs.append({'key': row['key'], 'content': content})
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return docs
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@mcp.tool
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def get_cave_location(cave: str, state: str, county: str) -> list[dict]:
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"""Lookup cave location as coordinates. Returns up to 5 matches, ordered by most to least relevant."""
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"""Lookup cave location as coordinates."""
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roles = get_user_roles()
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return search(f'{cave} Location, latitude, Longitude. Located in {state} and {county} county.', roles)
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||||
|
||||
@mcp.tool
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||||
def general_caving_information(query: str) -> list[dict]:
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||||
"""General purpose endpoint for any topic related to caves. Returns up to 5 matches, ordered by most to least relevant."""
|
||||
"""General purpose search for any topic related to caves."""
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||||
roles = get_user_roles()
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||||
return search(query, roles)
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||||
|
||||
@mcp.tool
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||||
def get_document_page(document: str, page: int) -> dict:
|
||||
"""Lookup a specific page of a document by its path and page number. Document should be the path like 'nss/compasstape/issue_20.pdf'."""
|
||||
def get_document_page(key: str) -> dict:
|
||||
"""Fetch full content for a document page. Pass the exact 'key' value from search results."""
|
||||
roles = get_user_roles()
|
||||
if not roles:
|
||||
return {"error": "No roles assigned"}
|
||||
|
||||
key = f"{document}/page-{page}.pdf"
|
||||
row = conn.execute(
|
||||
'SELECT key, content FROM embeddings WHERE key = %s AND role = ANY(%s)',
|
||||
(key, roles)
|
||||
@@ -106,5 +114,14 @@ def get_user_info() -> dict:
|
||||
"roles": roles,
|
||||
}
|
||||
|
||||
from starlette.responses import JSONResponse
|
||||
from starlette.routing import Route
|
||||
|
||||
async def health(request):
|
||||
return JSONResponse({"status": "ok"})
|
||||
|
||||
app = mcp.http_app()
|
||||
app.routes.append(Route("/health", health))
|
||||
|
||||
if __name__ == "__main__":
|
||||
mcp.run(transport='http', host='::1', port=9031)
|
||||
|
||||
2
mcp/uv.lock
generated
2
mcp/uv.lock
generated
@@ -82,6 +82,7 @@ dependencies = [
|
||||
{ name = "dotenv" },
|
||||
{ name = "fastmcp" },
|
||||
{ name = "psycopg", extra = ["binary"] },
|
||||
{ name = "uvicorn" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
@@ -91,6 +92,7 @@ requires-dist = [
|
||||
{ name = "dotenv", specifier = ">=0.9.9" },
|
||||
{ name = "fastmcp", specifier = ">=2.13.3" },
|
||||
{ name = "psycopg", extras = ["binary"], specifier = ">=3.3.2" },
|
||||
{ name = "uvicorn", specifier = ">=0.38.0" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
@@ -137,7 +137,7 @@ def split_files():
|
||||
key = row["key"]
|
||||
|
||||
with conn.cursor() as cur:
|
||||
logger.info(f"SPLITTING bucket: {bucket}, key: {key}")
|
||||
logger.info(f"Splitting bucket: {bucket}, key: {key}")
|
||||
|
||||
##### get pdf #####
|
||||
s3.download_file(bucket, key, "/tmp/file.pdf")
|
||||
@@ -146,6 +146,10 @@ def split_files():
|
||||
with open("/tmp/file.pdf", "rb") as f:
|
||||
reader = PdfReader(f)
|
||||
|
||||
# Handle PDFs with permission restrictions (no password, but encrypted)
|
||||
if reader.is_encrypted:
|
||||
reader.decrypt("")
|
||||
|
||||
for i in range(len(reader.pages)):
|
||||
writer = PdfWriter()
|
||||
writer.add_page(reader.pages[i])
|
||||
@@ -351,6 +355,23 @@ def fix_pages():
|
||||
i -= 1
|
||||
|
||||
|
||||
def upload_file_list():
|
||||
"""Upload a list of all processed files to S3"""
|
||||
BUCKET_PUBLIC = "cavepediav2-public"
|
||||
|
||||
rows = conn.execute("SELECT key FROM metadata WHERE split = true ORDER BY key")
|
||||
files = [row["key"] for row in rows]
|
||||
|
||||
content = "\n".join(files)
|
||||
s3.put_object(
|
||||
Bucket=BUCKET_PUBLIC,
|
||||
Key="files.txt",
|
||||
Body=content.encode("utf-8"),
|
||||
ContentType="text/plain",
|
||||
)
|
||||
logger.info(f"Uploaded file list with {len(files)} files to s3://{BUCKET_PUBLIC}/files.txt")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
create_tables()
|
||||
|
||||
@@ -360,6 +381,7 @@ if __name__ == "__main__":
|
||||
check_batches()
|
||||
ocr_main()
|
||||
embeddings_main()
|
||||
upload_file_list()
|
||||
|
||||
logger.info("sleeping 5 minutes")
|
||||
time.sleep(5 * 60)
|
||||
|
||||
@@ -8,6 +8,7 @@ dependencies = [
|
||||
"anthropic>=0.52.0",
|
||||
"boto3>=1.42.4",
|
||||
"cohere>=5.15.0",
|
||||
"cryptography>=3.1",
|
||||
"pgvector>=0.4.1",
|
||||
"psycopg[binary]>=3.2.9",
|
||||
"pypdf>=5.5.0",
|
||||
|
||||
90
poller/uv.lock
generated
90
poller/uv.lock
generated
@@ -1,6 +1,6 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.13"
|
||||
requires-python = "==3.13.*"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
@@ -79,6 +79,29 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3", size = 159618, upload-time = "2025-04-26T02:12:27.662Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cffi"
|
||||
version = "2.0.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "pycparser", marker = "implementation_name != 'PyPy'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" }
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||||
wheels = [
|
||||
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|
||||
{ url = "https://files.pythonhosted.org/packages/41/89/4c0e345041186f82a31aee7b9d4219a910df672b9fef26f129f0cda07a29/jiter-0.10.0-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fa3402a2ff9815960e0372a47b75c76979d74402448509ccd49a275fa983ef8a", size = 392215, upload-time = "2025-05-18T19:04:34.827Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/55/58/ee607863e18d3f895feb802154a2177d7e823a7103f000df182e0f718b38/jiter-0.10.0-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:1956f934dca32d7bb647ea21d06d93ca40868b505c228556d3373cbd255ce853", size = 522762, upload-time = "2025-05-18T19:04:36.19Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/15/d0/9123fb41825490d16929e73c212de9a42913d68324a8ce3c8476cae7ac9d/jiter-0.10.0-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:fcedb049bdfc555e261d6f65a6abe1d5ad68825b7202ccb9692636c70fcced86", size = 513427, upload-time = "2025-05-18T19:04:37.544Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d8/b3/2bd02071c5a2430d0b70403a34411fc519c2f227da7b03da9ba6a956f931/jiter-0.10.0-cp314-cp314-win32.whl", hash = "sha256:ac509f7eccca54b2a29daeb516fb95b6f0bd0d0d8084efaf8ed5dfc7b9f0b357", size = 210127, upload-time = "2025-05-18T19:04:38.837Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/03/0c/5fe86614ea050c3ecd728ab4035534387cd41e7c1855ef6c031f1ca93e3f/jiter-0.10.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5ed975b83a2b8639356151cef5c0d597c68376fc4922b45d0eb384ac058cfa00", size = 318527, upload-time = "2025-05-18T19:04:40.612Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b3/4a/4175a563579e884192ba6e81725fc0448b042024419be8d83aa8a80a3f44/jiter-0.10.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3aa96f2abba33dc77f79b4cf791840230375f9534e5fac927ccceb58c5e604a5", size = 354213, upload-time = "2025-05-18T19:04:41.894Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -395,6 +446,7 @@ dependencies = [
|
||||
{ name = "anthropic" },
|
||||
{ name = "boto3" },
|
||||
{ name = "cohere" },
|
||||
{ name = "cryptography" },
|
||||
{ name = "pgvector" },
|
||||
{ name = "psycopg", extra = ["binary"] },
|
||||
{ name = "pypdf" },
|
||||
@@ -414,6 +466,7 @@ requires-dist = [
|
||||
{ name = "anthropic", specifier = ">=0.52.0" },
|
||||
{ name = "boto3", specifier = ">=1.42.4" },
|
||||
{ name = "cohere", specifier = ">=5.15.0" },
|
||||
{ name = "cryptography", specifier = ">=3.1" },
|
||||
{ name = "mypy", marker = "extra == 'dev'", specifier = ">=1.15.0" },
|
||||
{ name = "pgvector", specifier = ">=0.4.1" },
|
||||
{ name = "psycopg", extras = ["binary"], specifier = ">=3.2.9" },
|
||||
@@ -460,6 +513,15 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7b/1d/bf54cfec79377929da600c16114f0da77a5f1670f45e0c3af9fcd36879bc/psycopg_binary-3.2.9-cp313-cp313-win_amd64.whl", hash = "sha256:2290bc146a1b6a9730350f695e8b670e1d1feb8446597bed0bbe7c3c30e0abcb", size = 2928009, upload-time = "2025-05-13T16:08:53.67Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pycparser"
|
||||
version = "2.23"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/fe/cf/d2d3b9f5699fb1e4615c8e32ff220203e43b248e1dfcc6736ad9057731ca/pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2", size = 173734, upload-time = "2025-09-09T13:23:47.91Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a0/e3/59cd50310fc9b59512193629e1984c1f95e5c8ae6e5d8c69532ccc65a7fe/pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934", size = 118140, upload-time = "2025-09-09T13:23:46.651Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.11.5"
|
||||
|
||||
@@ -25,4 +25,4 @@ EXPOSE 8000
|
||||
ENV PORT=8000
|
||||
ENV HOST="0.0.0.0"
|
||||
|
||||
CMD ["uv", "run", "uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8000"]
|
||||
CMD ["uv", "run", "--frozen", "uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8000"]
|
||||
|
||||
@@ -5,8 +5,9 @@ description = "Cavepedia AI Agent with MCP tools"
|
||||
requires-python = ">=3.13"
|
||||
dependencies = [
|
||||
"uvicorn",
|
||||
"starlette",
|
||||
"pydantic-ai",
|
||||
"google-genai",
|
||||
"openai",
|
||||
"mcp",
|
||||
"ag-ui-protocol",
|
||||
"python-dotenv",
|
||||
|
||||
@@ -6,8 +6,8 @@ import os
|
||||
import logging
|
||||
import httpx
|
||||
|
||||
from pydantic_ai import Agent
|
||||
from pydantic_ai.models.google import GoogleModel
|
||||
from pydantic_ai import Agent, ModelMessage, RunContext
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
|
||||
# Set up logging based on environment
|
||||
log_level = logging.DEBUG if os.getenv("DEBUG") else logging.INFO
|
||||
@@ -17,51 +17,82 @@ logging.basicConfig(
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MCP_URL = "https://mcp.caving.dev/mcp"
|
||||
CAVE_MCP_URL = os.getenv("CAVE_MCP_URL", "https://mcp.caving.dev/mcp")
|
||||
|
||||
logger.info("Initializing Cavepedia agent...")
|
||||
|
||||
|
||||
def limit_history(ctx: RunContext[None], messages: list[ModelMessage]) -> list[ModelMessage]:
|
||||
"""Limit conversation history to manage token usage."""
|
||||
# Keep last 8 messages for context, but not unlimited
|
||||
return messages[-8:]
|
||||
|
||||
def check_mcp_available(url: str, timeout: float = 5.0) -> bool:
|
||||
"""Check if MCP server is reachable."""
|
||||
"""Check if MCP server is reachable via health endpoint."""
|
||||
try:
|
||||
# Just check if we can connect - don't need a full MCP handshake
|
||||
response = httpx.get(url, timeout=timeout, follow_redirects=True)
|
||||
# Any response (even 4xx/5xx) means server is reachable
|
||||
# 502 means upstream is down, so treat as unavailable
|
||||
if response.status_code == 502:
|
||||
logger.warning(f"MCP server returned 502 Bad Gateway")
|
||||
return False
|
||||
# Use the health endpoint instead of the MCP endpoint
|
||||
health_url = url.rsplit("/", 1)[0] + "/health"
|
||||
response = httpx.get(health_url, timeout=timeout, follow_redirects=True)
|
||||
if response.status_code == 200:
|
||||
return True
|
||||
logger.warning(f"MCP health check returned {response.status_code}")
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.warning(f"MCP server not reachable: {e}")
|
||||
return False
|
||||
|
||||
# Try to configure MCP if server is available
|
||||
toolsets = []
|
||||
if check_mcp_available(MCP_URL):
|
||||
# Check if MCP is available at startup
|
||||
MCP_AVAILABLE = check_mcp_available(CAVE_MCP_URL)
|
||||
logger.info(f"MCP server available: {MCP_AVAILABLE}")
|
||||
|
||||
AGENT_INSTRUCTIONS = """Caving assistant. Help with exploration, safety, surveying, locations, geology, equipment, history, conservation.
|
||||
|
||||
Rules:
|
||||
1. Cite sources when possible.
|
||||
2. Say when uncertain. Never hallucinate.
|
||||
3. Be safety-conscious.
|
||||
4. Can create ascii diagrams/maps.
|
||||
5. Be direct—no sycophantic phrases.
|
||||
6. Keep responses concise.
|
||||
7. Search ONCE, then answer with what you found. Do not search repeatedly for the same topic."""
|
||||
|
||||
|
||||
def create_agent(user_roles: list[str] | None = None):
|
||||
"""Create an agent with MCP tools configured for the given user roles."""
|
||||
toolsets = []
|
||||
|
||||
if MCP_AVAILABLE and user_roles:
|
||||
try:
|
||||
import json
|
||||
from pydantic_ai.mcp import MCPServerStreamableHTTP
|
||||
|
||||
roles_header = json.dumps(user_roles)
|
||||
logger.info(f"Creating MCP server with roles: {roles_header}")
|
||||
|
||||
mcp_server = MCPServerStreamableHTTP(
|
||||
url=MCP_URL,
|
||||
url=CAVE_MCP_URL,
|
||||
headers={"x-user-roles": roles_header},
|
||||
timeout=30.0,
|
||||
)
|
||||
toolsets.append(mcp_server)
|
||||
logger.info(f"MCP server configured: {MCP_URL}")
|
||||
logger.info(f"MCP server configured with roles: {user_roles}")
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not configure MCP server: {e}")
|
||||
else:
|
||||
elif not user_roles:
|
||||
logger.info("No user roles provided - MCP tools disabled")
|
||||
else:
|
||||
logger.info("MCP server unavailable - running without MCP tools")
|
||||
|
||||
# Create the agent with Google Gemini model
|
||||
agent = Agent(
|
||||
model=GoogleModel("gemini-2.5-pro"),
|
||||
return Agent(
|
||||
model="anthropic:claude-sonnet-4-5",
|
||||
toolsets=toolsets if toolsets else None,
|
||||
instructions="""You are a helpful caving assistant. Help users with all aspects of caving including cave exploration, safety, surveying techniques, cave locations, geology, equipment, history, conservation, and any other caving-related topics.
|
||||
instructions=AGENT_INSTRUCTIONS,
|
||||
history_processors=[limit_history],
|
||||
model_settings=ModelSettings(max_tokens=4096),
|
||||
)
|
||||
|
||||
IMPORTANT RULES:
|
||||
1. Always cite your sources at the end of each response when possible.
|
||||
2. If you're not certain about information, say so clearly. Do NOT make up information or hallucinate facts.
|
||||
3. Provide accurate, helpful, and safety-conscious information.""",
|
||||
)
|
||||
|
||||
logger.info(f"Agent initialized successfully (MCP: {'enabled' if toolsets else 'disabled'})")
|
||||
# Create a default agent for health checks etc
|
||||
agent = create_agent()
|
||||
|
||||
logger.info("Agent module initialized successfully")
|
||||
|
||||
@@ -4,8 +4,11 @@ Self-hosted PydanticAI agent server using AG-UI protocol.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import logging
|
||||
from dotenv import load_dotenv
|
||||
from pydantic_ai.usage import UsageLimits
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
|
||||
# Load environment variables BEFORE importing agent
|
||||
load_dotenv()
|
||||
@@ -19,18 +22,66 @@ logging.basicConfig(
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Validate required environment variables
|
||||
if not os.getenv("GOOGLE_API_KEY"):
|
||||
logger.error("GOOGLE_API_KEY environment variable is required")
|
||||
if not os.getenv("ANTHROPIC_API_KEY"):
|
||||
logger.error("ANTHROPIC_API_KEY environment variable is required")
|
||||
sys.exit(1)
|
||||
|
||||
import uvicorn
|
||||
from src.agent import agent
|
||||
from starlette.applications import Starlette
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import Response, JSONResponse
|
||||
from starlette.routing import Route
|
||||
|
||||
from pydantic_ai.ui.ag_ui import AGUIAdapter
|
||||
|
||||
from src.agent import create_agent
|
||||
|
||||
logger.info("Creating AG-UI app...")
|
||||
|
||||
# Convert PydanticAI agent to ASGI app with AG-UI protocol
|
||||
debug_mode = os.getenv("DEBUG", "").lower() in ("true", "1", "yes")
|
||||
app = agent.to_ag_ui(debug=debug_mode)
|
||||
|
||||
async def handle_agent_request(request: Request) -> Response:
|
||||
"""Handle incoming AG-UI requests with dynamic role-based MCP configuration."""
|
||||
# Debug: log all incoming headers
|
||||
logger.info(f"DEBUG: All request headers: {dict(request.headers)}")
|
||||
|
||||
# Extract user roles from request headers
|
||||
roles_header = request.headers.get("x-user-roles", "")
|
||||
logger.info(f"DEBUG: x-user-roles header value: '{roles_header}'")
|
||||
user_roles = []
|
||||
|
||||
if roles_header:
|
||||
try:
|
||||
user_roles = json.loads(roles_header)
|
||||
logger.info(f"Request received with roles: {user_roles}")
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"Failed to parse x-user-roles header: {e}")
|
||||
|
||||
# Create agent with the user's roles
|
||||
agent = create_agent(user_roles)
|
||||
|
||||
# Dispatch the request using AGUIAdapter with usage limits
|
||||
return await AGUIAdapter.dispatch_request(
|
||||
request,
|
||||
agent=agent,
|
||||
usage_limits=UsageLimits(
|
||||
request_limit=5, # Max 5 LLM requests per query
|
||||
tool_calls_limit=3, # Max 3 tool calls per query
|
||||
),
|
||||
model_settings=ModelSettings(max_tokens=4096),
|
||||
)
|
||||
|
||||
|
||||
async def health(request: Request) -> Response:
|
||||
"""Health check endpoint."""
|
||||
return JSONResponse({"status": "ok"})
|
||||
|
||||
|
||||
app = Starlette(
|
||||
routes=[
|
||||
Route("/", handle_agent_request, methods=["POST"]),
|
||||
Route("/health", health, methods=["GET"]),
|
||||
],
|
||||
)
|
||||
|
||||
logger.info("AG-UI app created successfully")
|
||||
|
||||
|
||||
10
web/agent/uv.lock
generated
10
web/agent/uv.lock
generated
@@ -1,6 +1,10 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.13"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.14'",
|
||||
"python_full_version < '3.14'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ag-ui-protocol"
|
||||
@@ -226,22 +230,24 @@ version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "ag-ui-protocol" },
|
||||
{ name = "google-genai" },
|
||||
{ name = "httpx" },
|
||||
{ name = "mcp" },
|
||||
{ name = "openai" },
|
||||
{ name = "pydantic-ai" },
|
||||
{ name = "python-dotenv" },
|
||||
{ name = "starlette" },
|
||||
{ name = "uvicorn" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "ag-ui-protocol" },
|
||||
{ name = "google-genai" },
|
||||
{ name = "httpx" },
|
||||
{ name = "mcp" },
|
||||
{ name = "openai" },
|
||||
{ name = "pydantic-ai" },
|
||||
{ name = "python-dotenv" },
|
||||
{ name = "starlette" },
|
||||
{ name = "uvicorn" },
|
||||
]
|
||||
|
||||
|
||||
@@ -6,18 +6,31 @@ import {
|
||||
|
||||
import { HttpAgent } from "@ag-ui/client";
|
||||
import { NextRequest } from "next/server";
|
||||
import { auth0 } from "@/lib/auth0";
|
||||
|
||||
const serviceAdapter = new ExperimentalEmptyAdapter();
|
||||
|
||||
const runtime = new CopilotRuntime({
|
||||
agents: {
|
||||
vpi_1000: new HttpAgent({
|
||||
url: process.env.AGENT_URL || "http://localhost:8000/",
|
||||
}),
|
||||
},
|
||||
});
|
||||
|
||||
export const POST = async (req: NextRequest) => {
|
||||
// Get user session and roles
|
||||
const session = await auth0.getSession();
|
||||
const userRoles = (session?.user?.roles as string[]) || [];
|
||||
|
||||
console.log("DEBUG: User roles from session:", userRoles);
|
||||
|
||||
// Create HttpAgent with user roles header
|
||||
const agent = new HttpAgent({
|
||||
url: process.env.AGENT_URL || "http://localhost:8000/",
|
||||
headers: {
|
||||
"x-user-roles": JSON.stringify(userRoles),
|
||||
},
|
||||
});
|
||||
|
||||
const runtime = new CopilotRuntime({
|
||||
agents: {
|
||||
vpi_1000: agent,
|
||||
},
|
||||
});
|
||||
|
||||
const { handleRequest } = copilotRuntimeNextJSAppRouterEndpoint({
|
||||
runtime,
|
||||
serviceAdapter,
|
||||
|
||||
@@ -114,7 +114,6 @@ export default function CopilotKitPage() {
|
||||
<div className="flex-1 flex justify-center py-8 px-2 overflow-hidden relative">
|
||||
<div className="h-full w-full max-w-5xl flex flex-col">
|
||||
<CopilotChat
|
||||
instructions={"You are a knowledgeable caving assistant. Help users with all aspects of caving including cave exploration, safety, surveying techniques, cave locations, geology, equipment, history, conservation, and any other caving-related topics. Provide accurate, helpful, and safety-conscious information. CRITICAL: Always cite sources at the end of each response."}
|
||||
labels={{
|
||||
title: "AI Cartwright",
|
||||
initial: "Hello! I'm here to help with anything related to caving. Ask me about caves, techniques, safety, equipment, or anything else caving-related!",
|
||||
|
||||
Reference in New Issue
Block a user