Metadata-Version: 2.4
Name: gildea
Version: 0.4.1
Summary: Python client and MCP server for the Gildea AI market intelligence API
Project-URL: Homepage, https://gildea.ai
Project-URL: Documentation, https://docs.gildea.ai
Author: Holly Jones
License-Expression: MIT
Keywords: ai,api-client,competitive-intelligence,market-intelligence,mcp
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Requires-Dist: httpx>=0.27
Provides-Extra: dev
Requires-Dist: mcp[server]>=1.3; extra == 'dev'
Requires-Dist: pytest-asyncio>=0.24; extra == 'dev'
Requires-Dist: pytest-httpx>=0.35; extra == 'dev'
Requires-Dist: pytest>=8; extra == 'dev'
Requires-Dist: ruff>=0.15; extra == 'dev'
Provides-Extra: mcp
Requires-Dist: mcp[server]>=1.3; extra == 'mcp'
Description-Content-Type: text/markdown

# Gildea

Python client and MCP server for the [Gildea](https://gildea.ai) AI market intelligence API.

Gildea tracks 500+ expert sources on the AI economy, decomposes each one into verified reasoning chains (thesis, arguments, claims, evidence), and serves them through a REST API. This package gives you a Python client and an MCP server so AI assistants can use the data directly.

Hybrid retrieval — Cohere `embed-english-v3.0` dense + Pinecone DeepImpact sparse, fused via RRF, with Cohere cross-encoder reranking — for high precision on verified, citable text units. See [How search works](https://docs.gildea.ai/concepts/search).

## Install

```bash
# Python client only
pip install gildea

# With MCP server
pip install gildea[mcp]
```

## Quick start

The differentiating call is `search()`. Every hit is a verified atomic fact carrying a verdict, a similarity score, and an evidence-backed citation:

```python
from gildea_sdk import Gildea

client = Gildea(api_key="gld_your_key_here")

results = client.search(query="data center power constraints")

for hit in results["data"][:3]:
    print(f"\n{hit['unit']['text']}")
    print(f"  ↳ {hit['citation']['signal_title']} ({hit['citation']['registrable_domain']})")
    print(f"  ↳ verdict: {hit['verification']['final_verdict']}, "
          f"score: {hit['verification']['primary_score']:.2f}")
```

```
Spending on data center construction has surpassed a $42B annualized pace, a more than 300% increase…
  ↳ America's $1T AI Gamble (apricitas.io)
  ↳ verdict: pass, score: 0.94

Major technology companies are projected to spend approximately $650 billion in 2026 on AI data centers…
  ↳ Nebius Plans to Raise $3.75 Billion in Debt After Meta Deal (bloomberg.com)
  ↳ verdict: pass, score: 0.92
```

## Drill into a source

Pass any `signal_id` from a search result to get the full verified decomposition — thesis, supporting arguments, evidence-backed claims:

```python
signal_id = results["data"][0]["citation"]["signal_id"]
signal = client.signals.get(signal_id, include="evidence")

for claim in signal["decomposition"].get("claims", []):
    print(f"{claim['unit']['text']}  [{claim['verification']['final_verdict']}]")
```

## Entity intelligence

Trend direction, scale, and notability across the full corpus:

```python
nvidia = client.entities.get("NVIDIA")
print(f"{nvidia['display_name']}: {nvidia['direction']} ({nvidia['scale']} scale, {nvidia['notability']} notability)")
# NVIDIA: Declining (Large scale, High notability)
```

## Cross-source consensus

Find verified text units that semantically match a known one — useful for "find more like this" and corroborating a claim across sources:

```python
unit_id = results["data"][0]["unit"]["unit_id"]
similar = client.search(similar_to=unit_id, limit=5)
```

## Embed your own content

`/v1/embed` returns 1024-dim Cohere `embed-english-v3.0` vectors in the same space as Gildea's stored unit embeddings. Pair with `include="embeddings"` on signal detail to compute cosine similarity client-side:

```python
import numpy as np

# Embed user content (memo, draft, query) in Gildea's vector space
user_vec = np.array(client.embed("Infrastructure spending will slow in H2.")["embedding"])

# Fetch a signal with per-unit embeddings
signal = client.signals.get(signal_id, include="embeddings")

# Find related units locally — no extra API calls
def iter_units(decomp):
    for arg in decomp.get("arguments", []):
        yield from arg.get("sentences", [])
    yield from decomp.get("claims", [])
    if "thesis" in decomp:
        yield from decomp["thesis"].get("sentences", [])
    if "summary" in decomp:
        yield from decomp["summary"].get("sentences", [])

for u in iter_units(signal["decomposition"]):
    if "embedding" in u:
        sim = float(np.dot(user_vec, np.array(u["embedding"])))  # both unit-normalized
        if sim > 0.7:
            print(f"[{sim:.3f}] {u['unit']['text']}")
```

See [docs.gildea.ai/concepts/embeddings](https://docs.gildea.ai/concepts/embeddings) for the full local-similarity pattern.

## MCP server

Use Gildea as a tool inside Claude. The server is a thin proxy over the REST API — same auth, same data.

### Claude Desktop

Add to your `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "gildea": {
      "command": "uvx",
      "args": ["--from", "gildea[mcp]", "gildea-mcp"],
      "env": {
        "GILDEA_API_KEY": "gld_your_key_here"
      }
    }
  }
}
```

Restart Claude Desktop. The Gildea tools (`search_text_units`, `get_signal_detail`, `get_entity_profile`, etc.) appear in the tool list automatically.

### Claude Code

```bash
claude mcp add gildea -- uvx --from gildea[mcp] gildea-mcp
```

Then set `GILDEA_API_KEY` in your environment.

### Other MCP clients

The server speaks standard MCP and works with any compliant client (Cursor, VS Code via Cline/Continue, ChatGPT Desktop, etc.). Each client has its own config syntax — see your client's MCP documentation.

### Available tools

| Tool | What it does |
|------|---|
| `search_text_units` | Hybrid search across verified text units, or vector similarity via `similar_to` |
| `list_signals` | Browse signals by entity, theme, date, content type |
| `get_signal_detail` | Full verified decomposition: thesis, arguments, claims, evidence |
| `get_entity_profile` | Entity trend analytics, co-occurrence, theme distribution |
| `list_entities` | Discover entities by trend direction, notability, scale |
| `get_themes` | Theme overview across value chain and market force axes |
| `get_theme_detail` | Single theme trend analytics and cross-theme relationships |

## API key

Get yours at [gildea.ai](https://gildea.ai). Free tier: 5 requests/minute, 200 requests/month, full API + MCP access — no feature gates.

## Documentation

Full API docs at [docs.gildea.ai](https://docs.gildea.ai).

## License

MIT
