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README.md fixes (#74)

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fixed casing on `graphiti`
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Daniel Chalef 2024-09-01 12:35:31 -07:00 • committed by GitHub
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README.md
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@ -72,13 +72,13 @@ Optional:
> The simplest way to install Neo4j is via [Neo4j Desktop](https://neo4j.com/download/). It provides a user-friendly interface to manage Neo4j instances and databases.
```bash
pip install Graphiti-core
pip install graphiti-core
```
or
```bash
poetry add Graphiti-core
poetry add graphiti-core
```
@ -89,15 +89,15 @@ poetry add Graphiti-core
> Graphiti uses OpenAI for LLM inference and embedding. Ensure that an `OPENAI_API_KEY` is set in your environment. Support for Anthropic and Groq LLM inferences is available, too.
```python
from Graphiti_core import Graphiti
from Graphiti_core.nodes import EpisodeType
from graphiti_core import Graphiti
from graphiti_core.nodes import EpisodeType
from datetime import datetime
# Initialize Graphiti
Graphiti = Graphiti("bolt://localhost:7687", "neo4j", "password")
graphiti = Graphiti("bolt://localhost:7687", "neo4j", "password")
# Initialize the graph database with Graphiti's indices. This only needs to be done once.
Graphiti.build_indices_and_constraints()
graphiti.build_indices_and_constraints()
# Add episodes
episodes = [
@ -106,7 +106,7 @@ episodes = [
"As AG, Harris was in office from January 3, 2011 – January 3, 2017",
]
for i, episode in enumerate(episodes):
await Graphiti.add_episode(
await graphiti.add_episode(
name=f"Freakonomics Radio {i}",
episode_body=episode,
source=EpisodeType.text,
@ -117,7 +117,7 @@ for i, episode in enumerate(episodes):
# Search the graph
# Execute a hybrid search combining semantic similarity and BM25 retrieval
# Results are combined and reranked using Reciprocal Rank Fusion
results = await Graphiti.search('Who was the California Attorney General?')
results = await graphiti.search('Who was the California Attorney General?')
[
EntityEdge(
│ uuid='3133258f738e487383f07b04e15d4ac0',
@ -144,10 +144,10 @@ results = await Graphiti.search('Who was the California Attorney General?')
# Rerank search results based on graph distance
# Provide a node UUID to prioritize results closer to that node in the graph.
# Results are weighted by their proximity, with distant edges receiving lower scores.
await client.search('Who was the California Attorney General?', center_node_uuid)
await graphiti.search('Who was the California Attorney General?', center_node_uuid)
# Close the connection
Graphiti.close()
graphiti.close()
```